The ecology of host tolerance: responses of galápagos mockingbirds to philornis downsi

Anthropogenic disturbances to natural environments can create "natural experiments" that provide insight into ecological and evolutionary processes. Islands are particularly important laboratories for studying evolutionary ecology because they are home to unique communities that are particularly sensitive to disturbance. For instance, introductions of parasites and pathogens into island ecosystems can have devastating consequences for native hosts; however, they also provide a way to study the mechanisms of host defense against a novel threat. In this dissertation, I investigate the effects of human disturbance on the evolutionary ecology of birds in the Galápagos Islands. I focus on two notable ways humans have altered Galápagos ecosystems: the introduction of a parasitic nest fly, Philornis downsi, and urbanization of the town of Puerto Ayora. The majority of my dissertation examines interactions between P. downsi and Galápagos mockingbirds, an endemic Galápagos passerine. Although previous work suggested that mockingbirds are tolerant to P. downsi, I find that in years in which rainfall and food are limited, mockingbirds are much more vulnerable. These results demonstrate that tolerance is labile in hosts and requires adequate resources. In addition, I document carry-over effects of P. downsi on subsequent reproductive investment of mockingbirds. I show that parasitism can have indirect costs for hosts, as the introduction of a novel stressor can cause hosts to adjust resource allocation. The remainder of my dissertation explores potential evolutionary responses of Galápagos birds to environmental change. I focus on DNA methylation, an epigenetic marker on DNA molecules that can affect gene expression and phenotype without altering DNA sequence. I test for effects of P. downsi on DNA methylation of mockingbird nestlings, and effects of urbanization on DNA methylation of Darwin's finches. While I did not observe effects of P. downsi on methylation of mockingbird nestlings, urbanization does appear to affect methylation profiles of Darwin's finches. In summary, my work shows that Galápagos birds may often be able to respond to recent anthropogenic disturbance; however, these responses may be mediated by environmental conditions. These results have implications for the survival of wildlife populations in a rapidly changing world.

THE ECOLOGY OF HOST TOLERANCE: RESPONSES OF GALÁPAGOS MOCKINGBIRDS TO PHILORNIS DOWNSI by Sabrina Ming-Ha-Louie McNew A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Biology The University of Utah August 2018 Copyright © Sabrina Ming-Ha-Louie McNew 2018 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Sabrina Ming-Ha-Louie McNew has been approved by the following supervisory committee members: Dale H. Clayton , Chair May 16, 2018 Sarah Elizabeth Bush , Member May 16, 2018 Frederick R. Adler , Member May 15, 2018 Wayne K. Potts , Member May 22, 2018 Jeb P. Owen , Member and by Denise Dearing the Department/College/School of and by David B. Kieda, Dean of The Graduate School. Date Approved Date Approved Date Approved Date Approved Date Approved , Chair/Dean of Biology ABSTRACT Anthropogenic disturbances to natural environments can create "natural experiments" that provide insight into ecological and evolutionary processes. Islands are particularly important laboratories for studying evolutionary ecology because they are home to unique communities that are particularly sensitive to disturbance. For instance, introductions of parasites and pathogens into island ecosystems can have devastating consequences for native hosts; however, they also provide a way to study the mechanisms of host defense against a novel threat. In this dissertation, I investigate the effects of human disturbance on the evolutionary ecology of birds in the Galápagos Islands. I focus on two notable ways humans have altered Galápagos ecosystems: the introduction of a parasitic nest fly, Philornis downsi, and urbanization of the town of Puerto Ayora. The majority of my dissertation examines interactions between P. downsi and Galápagos mockingbirds, an endemic Galápagos passerine. Although previous work suggested that mockingbirds are tolerant to P. downsi, I find that in years in which rainfall and food are limited, mockingbirds are much more vulnerable. These results demonstrate that tolerance is labile in hosts and requires adequate resources. In addition, I document carry-over effects of P. downsi on subsequent reproductive investment of mockingbirds. I show that parasitism can have indirect costs for hosts, as the introduction of a novel stressor can cause hosts to adjust resource allocation. The remainder of my dissertation explores potential evolutionary responses of Galápagos birds to environmental change. I focus on DNA methylation, an epigenetic marker on DNA molecules that can affect gene expression and phenotype without altering DNA sequence. I test for effects of P. downsi on DNA methylation of mockingbird nestlings, and effects of urbanization on DNA methylation of Darwin's finches. While I did not observe effects of P. downsi on methylation of mockingbird nestlings, urbanization does appear to affect methylation profiles of Darwin's finches. In summary, my work shows that Galápagos birds may often be able to respond to recent anthropogenic disturbance; however, these responses may be mediated by environmental conditions. These results have implications for the survival of wildlife populations in a rapidly changing world. iv TABLE OF CONTENTS ABSTRACT....................................................................................................................... iii ACKNOWLEDGMENTS ............................................................................................... viii Chapters 1. INTRODUCTION .......................................................................................................... 1 Insights from human disturbance on islands.............................................................. 1 The Galápagos Islands as a laboratory ...................................................................... 3 Ecological responses of birds to Philornis nest flies ................................................. 5 Evolutionary responses of Galápagos birds to anthropogenic disturbance ............... 8 Conclusions and synthesis ......................................................................................... 9 References................................................................................................................ 10 2. ALIEN INVASION: BIOLOGY OF PHILORNIS FLIES HIGHLIGHTING PHILORNIS DOWNSI, AN INTRODUCED PARASITE OF GALÁPAGOS BIRDS ... 15 Introduction.............................................................................................................. 17 Background .............................................................................................................. 17 Population ecology of Philornis .............................................................................. 21 Effects of Philornis on host fitness .......................................................................... 21 Host defenses against Philornis ............................................................................... 25 Effects of Philornis downsi on host population dynamics ...................................... 26 Why is Philornis downsi so virulent in the Galápagos? .......................................... 27 Management of Philornis downsi ............................................................................ 27 Acknowledgments ................................................................................................... 29 Literature cited ........................................................................................................ 29 Supplemental material ............................................................................................. 35 3. VARIABLE TOLERANCE TO AN INTRODUCED PARASITE: GALÁPAGOS MOCKINGBIRDS AND PHILORNIS DOWNSI ............................................................. 78 Abstract .................................................................................................................... 78 Introduction.............................................................................................................. 79 Methods ................................................................................................................... 82 Results...................................................................................................................... 90 Discussion ................................................................................................................ 94 Acknowledgments ................................................................................................... 98 References................................................................................................................ 98 Supplemental tables and figures ............................................................................ 109 4. INDIRECT COSTS OF PHILORNIS DOWNSI TO FUTURE REPRODUCTION IN GALÁPAGOS MOCKINGBIRDS ................................................................................ 136 Abstract .................................................................................................................. 136 Introduction............................................................................................................ 137 Methods ................................................................................................................. 139 Results.................................................................................................................... 145 Discussion .............................................................................................................. 148 Acknowledgments ................................................................................................. 151 References.............................................................................................................. 151 5. DO GALÁPAGOS MOCKINGBIRDS BIAS SEX RATIOS OF THEIR BROODS? ...................................................................................................................... 162 Abstract .................................................................................................................. 162 Introduction............................................................................................................ 162 Methods ................................................................................................................. 165 Results.................................................................................................................... 168 Discussion .............................................................................................................. 168 References.............................................................................................................. 170 6. DOES PHILORNIS DOWNSI AFFECT DNA METHYLATION IN GALÁPAGOS MOCKINGBIRD NESTLINGS? ................................................................................... 176 Abstract .................................................................................................................. 176 Introduction............................................................................................................ 177 Methods ................................................................................................................. 180 Results.................................................................................................................... 185 Discussion .............................................................................................................. 186 Acknowledgments ................................................................................................. 187 References.............................................................................................................. 188 7. EPIGENETIC VARIATION BETWEEN URBAN AND RURAL POPULATIONS OF DARWIN'S FINCHES ............................................................................................. 194 Abstract .................................................................................................................. 195 Background ............................................................................................................ 195 Methods ................................................................................................................. 197 Results .................................................................................................................... 200 Discussion .............................................................................................................. 203 Conclusions............................................................................................................ 206 References.............................................................................................................. 207 Supplemental ......................................................................................................... 209 vi Appendices A. DOES HAEMOPROTEUS COLUMBAE AFFECT HOMING BEHAVIOR IN FERAL PIGEONS (COLUMBA LIVIA)? ....................................................................... 241 B. GALÁPAGOS MOCKINGBIRDS TOLERATE INTRODUCED PARASITES THAT AFFECT DARWIN'S FINCHES ................................................................................... 254 C. DARWIN'S FINCHES COMBAT INTRODUCED NEST PARASITES WITH FUMIGATED COTTON ................................................................................................ 266 vii ACKNOWLEDGMENTS I am grateful to the many people who contributed to the work in this dissertation. First, I thank Dale Clayton and Sarah Bush, my advisors, for their guidance and support throughout the six years I've spent in their lab. I thank them for showing me the elegance of a well-designed experiment and teaching me to take pride in a perfectly-written paragraph. I thank the other members of my committee, Fred Adler, Franz Goller, Jeb Owen and Wayne Potts, for their invaluable perspectives and advice. I am particularly grateful to Fred, whose door and mind are always open to new discussions and to Jeb, who hosted and trained me in his lab at Washington State University. I am lucky to have been a part of the Biology community at the University of Utah during my PhD work. I thank the staff of the Biology Department, particularly Shannon Nielson, Karen Zundel, and Suman Singh, for everything they do to support graduate students and research in our department. I am grateful to the Animal Care Staff at the University of Utah for their assistance during my captive animal experiments. I remember the insight and patience of Jon Gale, whose presence is greatly missed in the department. I have been lucky to learn from and get to know many fantastic faculty in our department, including Denise Dearing, Mike Shapiro, Don Feener, and especially Lissy Coley and Tom Kursar. I thank them for their encouragement and feedback throughout my graduate work. In addition to the faculty and staff of the Biology Department I am grateful to the graduate students and postdocs for creating a supportive and intellectually rich community. I thank Jimmy Ruff, Monte Neate-Clegg and the members of R Club for their helpful comments and discussion. I also would not have survived my PhD without the support of my lab mates, Sarah Knutie, Emily DiBlasi, Andrew Bartlow, Jordan Herman, Graham Goodman, and Emiko Waight, who gave me the courage to continue on bad days and who celebrated with me on the good ones. In particular, I am grateful to Sarah Knutie, who has been my main role model, motivation, and collaborator. I also thank Scott Villa, for his leadership and companionship in the lab, especially during the interminable last year of my PhD. Outside of Utah, I have been lucky to collaborate with many talented scientists who have been incredibly generous with their time. I thank Michael Skinner, Daniel Beck, and Ingrid Saddler-Riggleman at Washington State University for their advice and expertise. I thank Christina Richards's lab for hosting me at University of South Florida, teaching me new techniques and continuing to collaborate at distance on analysis. I am grateful to my mentors, Chris Witt and Jordan Karubian, who gave me the opportunity to work in the Neotropics and have continually supported me in the pursuit of new adventures. None of the work in this dissertation would have been possible without our talented and hardworking field crew. For that, I am incredibly thankful to Graham Goodman, Ashley Saulsberry, Angela Hansen, Goberth Cabrera, Daniela Vargas, and especially Janai Yépez. Graham, Ashley, and Angela all took extended absences from Utah in order to spend months with me in the field. I thank them for their patience, ix companionship and hard work. Goberth, Daniela and Janai helped make me feel at home in the Galápagos and navigate the challenges of working abroad. I am grateful to Janai for his positivity, insight, dependability and help in all crises, minor and major. I hope the work in this dissertation honors the memory of Daniela Vargas, a talented biologist who I was lucky to work with and call my friend. Finally, I thank my friends and family for their patience and support during graduate school. I am grateful to have lived in a place as spectacular as Utah for the past six years and to have met such fun, adventurous people. My relationships with my friends kept my mind and body healthy during my PhD. In particular, I am grateful to the support of Tyler Bugden, who navigated the ups and downs of grad school by my side with unwavering encouragement and support. Lastly, I thank my parents, Roger McNew and Jane Yee, for giving me the opportunities to do what I love and joining me along the way. My parents taught me to write and to love nature, the true skills of a scientist. x CHAPTER 1 INTRODUCTION Insights from human disturbance on islands Humans are altering the natural world in profound and destructive ways. Our actions are driving a wave of global mass extinctions and understanding our impacts on ecosystems is crucial for the management and conservation of wildlife populations (Aguirre and Tabor 2008, Dirzo et al. 2014, Boivin et al. 2016). However, anthropogenic change also creates an opportunity to understand the evolutionary and ecological processes that govern life on earth. When broad-scale experimental manipulations are either logistically or ethically impossible, "natural experiments" created by human disturbance can help identify the effects of various biotic and abiotic factors on species and communities (Fukami and Wardle 2005). Anthropogenic disturbance can be particularly potent on islands, whose isolation creates hotspots of endemism and specialization (Steadman 1995, Whittaker and Fernández-Palacios 2007). By some estimations, prehistoric humans may have driven over 2000 species of Pacific island birds extinct, which is nearly one-tenth of extant bird species (Steadman 1995). Island populations continue to be threatened by human activities including habitat destruction, and in particular, the introduction of predators, parasites, and pathogens (van Riper et al. 1986, Causton et al. 2006, Wyatt et al. 2008). 2 Anthropogenic effects on island ecosystems thus present both challenges for conservation as well as opportunities to study how species respond to novel stressors. Introductions of parasites and pathogens onto islands can have devastating consequences for native host populations (van Riper III et al. 2002, Tompkins et al. 2003, Bunbury et al. 2008). Hosts with no prior exposure to a novel parasite often lack defenses against it (Prenter et al. 2004, Schmid-Hempel 2011, Lymbery et al. 2014). If hosts cannot rapidly adapt or acclimate to the new threat, the population may decline or go extinct. A classic example is the extinction of several species of endemic Hawaiian honeycreepers (Drepanidinae) following the introduction of avian malaria (Plasmodium relictum) to the Hawaiian archipelago (van Riper III et al. 2002, Atkinson and LaPointe 2009). While Plasmodium infection generally causes little mortality in native hosts, P. relictum is highly pathogenic in naïve honeycreepers and often results in host death (Atkinson et al. 1995, Atkinson and Samuel 2010, Dimitrov et al. 2015). However, hosts are not always defenseless against parasites and predators. Hosts may change their behavior to avoid parasitism or, once infected, they may resist or tolerate parasites. Resistance refers to defenses that reduce parasite burden, while tolerance refers to mechanisms that help the host compensate for, or repair parasite damage (Boots 2008, Medzhitov et al. 2012). In the case of the honeycreepers and introduced malaria, researchers first observed a shift in host distributions, as birds moved upslope into "refugia" to avoid the mosquito vector of P. relictum (Atkinson and LaPointe 2009). Over time, however, several populations of one species, the Hawaii amakihi (Hemignathus virens), appear to have evolved tolerance to P. relictum, allowing their recolonization of lower habitats (Woodworth et al. 2005). These populations sustain 3 chronic infections without experiencing significant mortality. Amakihi are now considered one of the primary reservoirs of avian malaria in Hawaii because, since they are infectious but do not experience high mortality, they can effectively maintain and transmit malaria infections to other undefended honeycreeper species (Atkinson and LaPointe 2009). Thus, studying the ecology of host defense has implications for understanding variation in host susceptibility as well as predicting the impacts of an invasive parasite on a host community. Conversely, investigating the evolutionary basis of host defense can provide information about how defenses arise and spread in a host population. For instance, recent studies find that molecular changes apart from DNA mutations, such as changes to gene expression and DNA methylation, can generate a rapid phenotypic response to a stressor (Bonneaud et al. 2011, Rubenstein et al. 2016). These "epigenetic" changes provide a mechanism of rapid adaptation that can be induced directly by the environment and thus may spread faster than selection on genetic variants. Integrating ecological and evolutionary approaches can provide insight into both the origins of host defense as well as their implications for host populations and island communities. The Galápagos Islands as a laboratory My dissertation work takes place in the Galápagos Islands, which are unique among major Pacific archipelagos in that human disturbance was limited until relatively recently (Steadman 1995). Unlike island chains, such as Hawaii, that were colonized by humans thousands of years ago, humans have only had permanent settlements in the Galápagos over the last 200 years or so (Walsh and Mena 2013). However, as tourism 4 and development have increased drastically in the past few decades so has disturbance to Galápagos ecosystems (Gardener and Grenier 2011). Because about 97% of the Galapagos land area is protected as an Ecuadorian National Park, human impacts have fallen disproportionally on a few areas, notably the four inhabited islands (González et al. 2008). More than 20,000 people currently live in Puerto Ayora on Santa Cruz Island, the population center of the Galápagos, and an estimated 170,000 tourists visit the archipelago each year (Gardener and Grenier 2011). This development has brought concomitant habitat fragmentation, urbanization, and damaging species introductions. Indeed, in inhabited areas of the Galápagos there are now more exotic than native species of plants (Guézou et al. 2010). This period of rapid development presents a unique opportunity to study the effects of anthropogenic change on native Galápagos species in real-time. In my dissertation, I focus on two notable ways in which humans have altered the Galápagos: the introduction of a parasitic nest fly, Philornis downsi, and urbanization of the town of Puerto Ayora. The majority of my dissertation examines the ecological responses of Galápagos mockingbirds (Mimus parvulus) to P. downsi. I first broadly review effects of Philornis parasites on hosts in mainland South America and in the Galápagos, with the goal of identifying why P. downsi appears so virulent in the Galápagos. Then, I describe variation in defense of mockingbirds against P. downsi and potential trade-offs resulting from parasitism. In the last two chapters, I consider potential evolutionary responses to disturbance. I investigate DNA methylation, an epigenetic change that may underlie rapid phenotypic change. I test whether either P. downsi or urbanization around Puerto Ayora affects DNA methylation of endemic Galápagos birds. 5 Ecological responses of birds to Philornis nest flies Philornis downsi (Muscidae) is potentially the most significant threat to land birds in the Galápagos Islands. Larvae of P. downsi were first found in Galápagos birds' nests in the mid-1990s (Fessl et al. 2001). Since then, P. downsi has been documented on nearly all the major islands and in the nests of most native land bird species (Causton et al. 2006, McNew and Clayton 2018). The adult flies are not parasitic; however, the larval flies feed on nestling birds and their mothers, often causing high nestling mortality (Fessl et al. 2006, Kleindorfer and Dudaniec 2016). P. downsi has been implicated in the decline of the critically endangered medium tree finch and mangrove finch (Camarhynchus pauper and C. heliobates) (O'Connor et al. 2010, Lawson et al. 2016). Chapter 2 of my dissertation reviews the genus Philornis, focusing on studies of P. downsi in the Galápagos (McNew and Clayton 2018). First, I discuss the current knowledge about the distribution, ecology and host use of Philornis flies. Philornis species are broadly distributed in the Americas and parasitize more than 150 host species. Most research has focused on one species, P. downsi, because of its threat to birds in the Galápagos. Second, I investigate the effects of Philornis on their hosts and defenses that hosts use against parasitism. I compile data from experimental and observational studies of Philornis in its native range and in the Galápagos, where it is introduced. Consistent with anecdotal observations, the cost of parasitism is higher in Galápagos hosts than in most hosts in mainland South America. Surprisingly, studies suggest that native hosts of Philornis are not more resistant to parasitism, but rather, they may be more tolerant, i.e., better able to withstand parasitism once infested. The prevalence of Philornis also tends to be lower in its native distribution, potentially due to the presence of enemies such as 6 parasitoid wasps. These patterns suggest that P. downsi appears so virulent in the Galápagos due to a combination of high prevalence and a lack of tolerance in most Galápagos hosts. Not all Galápagos hosts are defenseless against P. downsi: Galápagos mockingbirds (Mimus parvulus) can tolerate P. downsi with little cost to fledging success (Knutie et al. 2016). Mockingbirds are larger than most other hosts of P. downsi in the Galápagos, which may contribute to their ability to withstand parasitism (Chapter 2). However, behavioral data also suggest that increased provisioning of parasitized nestlings may allow them to recover energy lost to the parasite (Knutie et al. 2016). If adequate provisioning by parents is key to nestling tolerance, successful defense may depend on the parents' ability to accurately judge the nutritional needs of their offspring and the resource availability to adequately fulfill those needs. In Chapter 3, I test whether mockingbird tolerance to P. downsi varies with environmental conditions. We experimentally manipulated P. downsi abundance in mockingbird nests over four field seasons. The effects of P. downsi on nestling condition and fledging success varied dramatically over the study period. In years of higher precipitation, in which green vegetation was abundant, mockingbirds were tolerant to P. downsi. However, in years in which rainfall was limited, tolerance was lower. The estimated tolerance to P. downsi each year was correlated with mean provisioning rates. Thus, in years in which resources are scarce, mockingbirds may not be able to compensate for the costs of P. downsi. Philornis downsi has well-documented costs to current reproductive success of hosts in the Galápagos; however, its unknown if those costs result in trade-offs in other 7 life history traits. Mounting a defense against parasitism is costly, and so hosts that increase energy allocation to defense in response to a novel parasite or pathogen may pay a price elsewhere. For example, in an experimental study of trade-offs between immune activation and reproductive success, exposure of house sparrows (Passer domesticus) to a nonreplicating antigen caused an increased immune response but a corresponding reduction in female reproductive success (Bonneaud et al. 2003). Thus, the introduction of P. downsi may have created an additional environmental stressor that influences resource allocation of mockingbirds. In Chapters 4 and 5, I investigate resource allocation and trade-offs in adult mockingbirds in response to environmental conditions and P. downsi. In Chapter 4 I examine whether P. downsi exacts a cost from adult mockingbirds in either survival or future reproductive success. I found that parasitism diminished short-term reproductive investment: when sham-fumigated parents renested, their brood size was significantly smaller than that of fumigated parents. These results suggest that parasitism not only affects current reproductive success of mockingbirds but can also have carry-over effects for future reproduction. In Chapter 5 I test whether mockingbirds bias sex ratios of their nestlings in response to either the environment or parasitism by P. downsi. Sex ratio adjustment can be an adaptive way to increase inclusive fitness in different environmental conditions. However, I did not find any evidence of sex ratio bias in mockingbird broods. 8 Evolutionary responses of Galápagos birds to anthropogenic disturbance In Chapters 6 and 7 I explore genetic and epigenetic responses of Galápagos birds to novel stressors. Studies of the molecular basis of adaptation to environmental change generally focus on genetic mutations and variation. However, recent studies report that heritable changes in gene expression can affect an organism's phenotype without altering DNA sequences (Bossdorf et al. 2008, Herrera and Bazaga 2011, Duncan et al. 2014). DNA methylation, or the binding of CH3 groups to DNA nucleotides, is one such epigenetic change that can alter gene expression; DNA methylation is, in some cases, heritable (Angers et al. 2010, Jones 2012, Duncan et al. 2014). In addition, methylation changes can be induced by the environment and are thought to occur more frequently than DNA mutations, suggesting that they may be important for rapid adaptation to environmental change (Richards et al. 2012). In Chapter 6 I test for epigenetic effects of P. downsi on nestling mockingbirds. We compared methylation profiles of parasitized and nonparasitized nestlings from two years in which mockingbirds were tolerant to P. downsi and two years in which they were not tolerant. We used a recently-developed reduced representation bisulfite sequencing technique - epiGBS - to identify differential methylation between parasitized and nonparasitized nestlings and tolerant and nontolerant nestlings (van Gurp et al. 2016). We did not identify any methylation differences either between treatment groups or between tolerant and nontolerant nestlings. However, sequence coverage was low, which likely limited power to detect differences. 9 Finally, in Chapter 7, I explore potential effects of urbanization on two species of Darwin's finches, the medium ground finch (Geospiza fortis) and the small ground finch (G. fuliginosa). Previous work suggests that urbanization of Puerto Ayora over the past 50 years has altered the diet and potentially the morphology of finches there (Hendry et al. 2006, De León et al. 2011). We investigated whether these land use changes have morphological, genetic or epigenetic effects on G. fortis and G. fuliginosa. We compared "urban" finches captured at the Charles Darwin Research Station on the outskirts of Puerto Ayora to finches from a "rural" population at El Garrapatero, a relatively undisturbed site about 10 km from Puerto Ayora. Geospiza fortis were larger at the urban site than the rural site; however, there was no difference in morphology between urban and rural G. fuliginosa. We did not find copy number (genetic) variation between populations for either species. However, we did find DNA methylation differences between populations for both G. fortis and G. fuliginosa. Although we do not know the functional significance of many of the methylation changes, these results are consistent with a role of epigenetic changes in rapid responses to environmental change. Conclusions and synthesis In summary, my dissertation investigates the evolutionary ecology of how Galápagos birds respond to recent environmental change. My research suggests that bird populations in the Galápagos can respond quickly to recent anthropogenic disturbance, whether it is the introduction of invasive parasites or habitat change through urbanization. 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Sex-specific fitness effects of unpredictable early life conditions are associated with DNA methylation in the avian glucocorticoid receptor. Molecular Ecology 25:1714-1728. Schmid-Hempel, P. 2011. Evolutionary Parasitology. Oxford University Press, Oxford. Steadman, D. W. 1995. Prehistoric extinctions of Pacific island birds: biodiversity meets zooarcheology. Science 267:1123-1131. Tompkins, D. M., A. R. White, and M. Boots. 2003. Ecological replacement of native red squirrels by invasive greys driven by disease. Ecology Letters 6:189-196. Walsh, S. J., and C. F. Mena. 2013. Science and Conservation in the Galapagos Islands: Frameworks & Perspectives. Springer, New York. Whittaker, R. J., and J. M. Fernández-Palacios. 2007. Island Biogeography: Ecology, Evolution and Conservation. Oxford University Press, Oxford. 14 Woodworth, B. L., C. T. Atkinson, D. a Lapointe, P. J. Hart, C. S. Spiegel, E. J. Tweed, C. Henneman, J. Lebrun, T. Denette, R. Demots, K. L. Kozar, D. Triglia, D. Lease, A. Gregor, T. Smith, and D. Duffy. 2005. Host population persistence in the face of introduced vector-borne diseases: Hawaii amakihi and avian malaria. Proceedings of the National Academy of Sciences of the United States of America 102:1531-6. Wyatt, K. B., P. F. Campos, M. T. P. Gilbert, S.-O. Kolokotronis, W. H. Hynes, R. DeSalle, S. J. Ball, P. Daszak, R. D. E. MacPhee, and A. D. Greenwood. 2008. Historical mammal extinction on Christmas Island (Indian Ocean) correlates with introduced infectious disease. PloS ONE 3:e3602. CHAPTER 2 ALIEN INVASION: BIOLOGY OF PHILORNIS FLIES HIGHLIGHTING PHILORNIS DOWNSI, AN INTRODUCED PARASITE OF GALÁPAGOS BIRDS Printed with permission from: McNew, S.M. and D.H. Clayton. 2018. Alien invasion: biology of Philornis flies highlighting Philornis downsi, an introduced parasite of Galápagos birds. Annual Review of Entomology 63:369-87. 16 EN63CH19_Clayton ARI 20 November 2017 15:20 17 INTRODUCTION Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. The introduced parasitic nest fly Philornis downsi (Diptera: Muscidae) is one of the most significant threats to birds in the Galápagos Islands. The larvae of this fly feed on nestling and adult birds and cause high nestling mortality in many endemic Galápagos species (64). P. downsi has already been implicated in the decline of two critically endangered species, the mangrove finch (Camarhynchus heliobates) and the medium tree finch (C. pauper) (79, 100). Moreover, mathematical models suggest that P. downsi has the potential to drive even common species locally extinct (74). Why P. downsi is so virulent in the Galápagos is a pressing question in the fields of disease ecology and conservation biology. P. downsi is one of approximately 50 species in the genus Philornis, all but two of which are obligate parasites of nestling birds. The goal of this article is to review the current knowledge of P. downsi and its congeneric relatives. We begin with a background overview of the biology of Philornis. Then, we examine the effects of Philornis flies on native and nonnative hosts at both the individual and population levels. Finally, we evaluate mechanisms of host defense against Philornis and discuss management options for the control of P. downsi in the Galápagos. BACKGROUND In this section, we review the systematics, distribution, life cycle, and hosts of Philornis flies. We also discuss the arrival, hosts, and distribution of P. downsi in the Galápagos Islands. Systematics and Biogeography Philornis Meinert (1890) is a genus of New World muscid flies (27, 64, 115). Philornis includes species originally described in the genera Aricia Macquart (1853), Hylemyia Loew (82), and Mydaea Jannicke (1867) (2, 92). The genus Philornis was expanded with the description of several Philornis species from Trinidad in the 1960s and subsequent work in South America in the 1980s (22, 32, 33). Early work placed Philornis in the family Calliphoridae, which includes ecologically similar parasites of birds. However, subsequent taxonomic revisions transferred Philornis to the Muscidae (2, 24). Taxonomic relationships among Philornis species are largely based on the morphology of adult specimens (25). The latest phylogeny, based on the morphology of 41 Philornis species, identified three distinct clades: the aitkeni, falsificus, and angustifrons groups (25). P. downsi is sister to the angustifrons group, which includes most of the described species. New molecular data suggest cryptic species within the genus, which prompts calls for more extensive molecular phylogenetic analysis of the group (105). Populations of Philornis have been found in the United States (104), the Caribbean (34, 118), Mexico (54, 123), Costa Rica, (120), Panama (9), Peru (101, 114), Brazil (57, 85, 102), Argentina (10, 21, 28, 89, 92, 94, 106, 111), Venezuela (116), and mainland Ecuador (13). Specimens are rare in collections (25) and generally reared from pupae and larvae collected from birds' nests. The lack of historical specimens and scant information on population genetics make determining the provenance of Philornis populations difficult. Outside of the Galápagos, Philornis populations are assumed to be native. However, it is possible that humans had a role in establishing Philornis populations on other islands (see the sidebar Did Philornis Also Invade Puerto Rico?). Natural History Information on the life cycle exists for only approximately half of the described Philornis species (115). The larvae of these species of Philornis are obligate associates of nestling birds, and all but two are parasites (24). Philornis and the related genus Passeromyia (which comprises five 370 McNew · Clayton EN63CH19_Clayton ARI 20 November 2017 18 15:20 DID PHILORNIS ALSO INVADE PUERTO RICO? Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Philornis is a common parasite of Puerto Rico birds such as Pearly-eyed thrashers (Margarops fuscatus), Puerto Rican parrots (Amazona vittata), and sharp-shinned hawks (Accipiter striatus venator) (5, 6, 30). Although the specific identity of Philornis in Puerto Rico is unclear (6), it is associated with high nestling mortality in all three hosts (5, 6, 30). The high prevalence and virulence of Philornis in Puerto Rico are more similar to those of Philornis in the Galápagos than to those of native Philornis spp. in other parts of the world. Moreover, widespread parasitoids of Philornis, such as Conura annulifera and Brachymeria subrugosa, are not reported from Puerto Rico (12, 95). These data suggest that as in the Galápagos, Philornis may have invaded Puerto Rico relatively recently. Work on the systematics and biogeography of Philornis in the Caribbean is needed to test this hypothesis. described species) are the only known genera in the Muscidae whose larvae parasitize birds (24). The adult flies (Figure 1a), which are free-living, feed on decaying matter (17, 23) and lay their eggs in the nests of birds. The larvae (Figure 1b) are either free-living coprophages (P. aitkeni and P. rufoscutellaris) or hematophagous parasites of nestlings and occasionally adult birds (5, 23, 115). Most parasitic Philornis species have subcutaneous larvae that burrow under the host's skin, where they feed on blood and tissues (24, 39, 117) (Figure 2); however, two of the parasitic species (P. downsi and P. falsifica) have larvae that are nonsubcutaneous and attach externally to the host to feed. After completing the three larval instar stages, the larvae pupate in the host's nest (the subcutaneous larvae leave the host to pupate) (113, 117). Philornis species spin a characteristic frothy cocoon that encloses the puparium (24, 33, 46). The adult fly emerges from this puparium 5-20 days later (77, 107, 113). The natural history of P. downsi is particularly well studied because of the parasite's threat to Galápagos birds. The first-instar larvae may live and feed in the nares (nostrils) or developing feather quills of nestling birds (46, 68) (Figure 3). However, the second- and third-instar larvae a b Figure 1 Philornis downsi life cycle: (a) adult stage and (b, top to bottom) three larval stages, a third-instar larva in the process of pupating, and a fully formed black pupa. Panel a courtesy of Jody O'Connor, and panel b courtesy of Sabrina McNew. www.annualreviews.org • Biology of Philornis Flies 371 ARI 20 November 2017 Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. EN63CH19_Clayton 15:20 19 Figure 2 Twelve-day-old tropical mockingbird (Mimus gilvus) nestling in Tobago with a heavy infestation of approximately 70 subcutaneous Philornis trinitensis larvae. Photo courtesy of Jordan Herman, reprinted from Reference 69. Figure 3 Medium ground finch (Geospiza fortis) nestling in the Galápagos with extensive damage to the nares (nostrils) from first-instar Philornis downsi. Photo courtesy of Sarah Huber. 372 McNew · Clayton EN63CH19_Clayton ARI 20 November 2017 20 15:20 live in the nest material (99). In vitro, P. downsi require 3 days for the eggs to hatch, 9-10 days to complete the larval stages, and another 9-10 days as pupae (77). Estimates from field-based studies suggest that the larvae might develop in as few as 4-7 days under natural conditions (66, 77). Thus, estimates of total time to develop from a newly laid egg to an adult fly average between 17 and 23 days (77). Intensity: the number of individual parasites in an infested host Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Host Use Philornis flies are known to parasitize more than 150 species of birds (39, 115) (Supplemental Table 1). Most hosts are passerines (perching birds); however, Philornis larvae have also been found in the nests of hawks (Accipitriformes), hummingbirds (Apodiformes), motmots (Coraciiformes), cuckoos (Cuculiformes), doves (Columbiformes), falcons (Falconiformes), woodpeckers (Piciformes), parrots (Psittaciformes), and owls (Strigiformes) (29, 101, 102, 104, 115) (Supplemental Table 1). Host specificity is difficult to evaluate in Philornis because many species are known from just one or a few records. A network analysis based on published host-parasite associations concluded that Philornis includes both specialist and generalist species (84). Within generalist species, however, there may still be variation in host use. A survey of P. torquans in a bird community in Argentina found that although P. torquans larvae were found on nestlings of 20 different bird species, the majority of larvae were found on nestlings of just two species, Pitangus sulphuratus and Phacellodomus ruber (3). More research is needed to evaluate how and why Philornis flies choose certain hosts to parasitize. Variation in host use may provide clues as to how Philornis find hosts and/or the defenses that hosts use to combat Philornis. Introduced P. downsi has been found in the nests of nearly all Galápagos passerines as well as the dark-billed cuckoo (Coccyzus melacoryphus) and the introduced smooth-billed ani (Crotophaga ani ) (47, 64, 81). Larger host species tend to have more P. downsi per nest (36, 55, 63, 71). Intensity (the number of individual parasites in an infected host) may increase proportionally with the amount of host tissue when host species do not differ in their ability to resist infestation (e.g., 71). In some cases, however, intensity is not correlated with host size (19, 100). In a comparison of small, medium, and large tree finches (Camarhynchus parvulus, C. pauper, and C. psittacula), O'Connor et al. (100) found that intensity was highest in nests of the medium-sized tree finch (C. pauper). It is unclear whether P. downsi preferentially infests C. pauper or the other species are somehow more resistant to parasitism. In any case, the disproportionately heavier intensity of P. downsi in C. pauper may be partly responsible for the decline of this critically endangered host species. Supplemental Material History of Philornis downsi in the Galápagos Specimens of adult P. downsi were collected in the Galápagos in the 1960s; however, larvae were not observed in nests there until 1998 (16, 44). Historical data corroborate a late twentieth-century arrival and spread of P. downsi in the Galápagos: P. downsi causes characteristic deformities in finch nares that are not seen in museum specimens of birds collected before 1962 (68) (Figure 3). Specimen collecting has been highly restricted in the Galápagos, which limits finer-scale estimation of the timing of the P. downsi invasion using this method. P. downsi has been found on nearly all islands in the Galápagos (62, 122). In the first and only systematic census of its distribution in the Galápagos, completed in 2005, P. downsi was found on 11 out of 13 main islands surveyed (122). Only Genovesa and Española were free of the parasite. Although these are small, outlying islands, Española and Genovesa are visited regularly by cruise ships, which could provide an avenue for the introduction of flies. An up-to-date careful census of www.annualreviews.org • Biology of Philornis Flies 373 EN63CH19_Clayton ARI 20 November 2017 Abundance: the number of individual parasites per host, including parasite-free hosts Prevalence: the percent of parasitized individuals (or nests) in a host population 15:20 21 the islands is needed to further track the spread and abundance of P. downsi in the Galápagos. Such a census would include the remote islands Darwin and Wolf, which are approximately 150 km northeast of the main archipelago and have not been censused thoroughly for P. downsi. Microsatellite data revealed low levels of genetic variation in P. downsi in the Galápagos, which suggests that the founding population was small and/or the product of a small number of introductions (37). The most likely source of P. downsi in the Galápagos is mainland Ecuador, where P. downsi is known to be native (13). Most maritime and airplane traffic to the islands originates in mainland Ecuador, further suggesting it as the original source of colonization (8, 16). POPULATION ECOLOGY OF PHILORNIS Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Native Philornis spp. intensity is often positively correlated with rainfall and temperature (3, 6, 78, 86, 93). In some systems, prevalence (the proportion of hosts parasitized) and intensity increase over the breeding season (6, 106, 110), presumably as a result of the increase in available hosts as birds breed. One study evaluated whether Philornis intensity increases with habitat disturbance in the form of urbanization, but it did not find a significant association (80). Philornis downsi in the Galápagos P. downsi tends to be most prevalent on Galápagos islands with humid highland regions and less prevalent on arid islands (122). These patterns may reflect a higher abundance of food for adult flies on the more humid islands or better access to hosts, which are less likely to breed in dry conditions (75, 122). Within individual islands, there is some evidence that flies are more common in the humid highland regions than the arid lowlands. P. downsi intensity is higher in the humid highlands of Floreana than the more arid lowlands (96); however, this is not the case on Santa Cruz Island (36). Populations of P. downsi may also be affected by climatic variation among years. Annual rainfall in the Galápagos varies by several orders of magnitude (51). The intensity of P. downsi typically increases in years of high rainfall (36, 47), when conditions for breeding hosts and adult flies are better. Oddly, P. downsi intensities do not appear to decrease in dry years on Santa Cruz Island (45, 46). Fly populations may be insulated from effects of dry years on Santa Cruz because permanently humid highland regions serve as a reservoir for adult flies (45, 46). Monitoring P. downsi population dynamics is difficult, partly due to challenges in developing attractants for trapping adult flies (17). A better understanding of the environmental factors influencing P. downsi population growth is key to developing management strategies (7). Recent evidence suggests that P. downsi is increasing in overall prevalence and intensity (64, 66). Comparisons of finch nests on Floreana Island from 2004 to 2013 suggest that fly intensity has increased over time; in 2014, nests were infested earlier and nestling mortality was greater (66). The authors suggest that high abundance of P. downsi on Floreana may mean that competition among flies for host resources has selected for earlier infestation of nests by flies each year. Nests may be parasitized by several female flies (38); thus the fly eggs that are laid first have the most time to develop before the host dies. EFFECTS OF PHILORNIS ON HOST FITNESS In this section, we discuss the effects of Philornis on host survival and reproduction. Using data from published studies, we compare the effects of P. downsi to those of other Philornis species to identify factors associated with virulence. 374 McNew · Clayton EN63CH19_Clayton ARI 20 November 2017 22 15:20 Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Native Philornis spp. Native Philornis parasitism is generally associated with negative effects on the growth rates and mass of nestlings (4, 94, 106, 110) (Figure 3). In some cases, parasitism causes significant nestling mortality (5, 6, 29, 35, 69, 106, 109, 110, 113, 117, 124) (Supplemental Table 2). In other cases, however, Philornis parasitism is associated with little or no nestling mortality (3, 20, 69, 83, 90, 93, 94). Intensity of Philornis in its native range is often correlated with nestling mortality (3, 69, 112). Timing of parasitism also influences mortality; infestation of hosts earlier in the nestling period is associated with higher mortality (4, 109). Infestation by Philornis spp. often varies within broods, with some nestlings experiencing higher intensity and mortality, whereas other nestlings fare better. In Puerto Rico, Arendt (6) found that the intensity of Philornis is higher in older than younger siblings within a brood, yet third- and fourth-born chicks died earlier than older siblings. This difference is attributed to size variation within the brood; because of asynchronous hatching, later-born nestlings are often the smallest and least able to tolerate parasitism. Variation in the effects of Philornis parasitism occurs among host species in the same community. Knutie et al. (69) recently showed that P. trinitensis in Tobago causes high mortality in black-faced grassquit (Tiaris bicolor) nestlings but not tropical mockingbird (Mimus gilvus) nestlings. In the Galápagos, P. downsi causes high mortality in Darwin's finches (see below), which are sister taxa of grassquits but not Galápagos mockingbirds, which are congeners of tropical mockingbirds (71). Hence, in these studies, the effect of Philornis on nestling survival differed between finch and mockingbird species but not between native and naive hosts. These results indicate that differences in host biology influence the ability of hosts to tolerate parasitism, independent of the evolutionary duration of the host-parasite association. Closely related hosts are not necessarily affected similarly by Philornis. For example, in a study of sympatric congeneric flycatchers, the campo suiriri (Suiriri affinis) and chapada flycatcher (S. islerorum), apparent mortality due to Philornis was observed only in the chapada flycatcher (83). Supplemental Material Philornis downsi in the Galápagos P. downsi parasitism is associated with high morbidity and mortality in almost all Galápagos hosts (19, 40, 45, 47, 58, 73, 76, 96) (Supplemental Table 2). Parasitized nestlings often have lower hemoglobin levels than unparasitized nestlings (40, 45, 71). Parasitized nestlings are also often smaller, based on measurements of overall body mass or other characteristics, such as tarsus length (45, 71). However, in cases where mortality of parasitized chicks is high and occurs early, growth rates between surviving parasitized and unparasitized chicks may not differ significantly (58, 73). First-instar P. downsi larvae can also deform the nares and beak, which may have implications for later-life song (50, 68) (Figure 2). Effects of P. downsi on host fitness can be severe, sometimes leading to 100% mortality of parasitized nestlings (76, 98). Mortality often increases with higher parasite intensity (19, 40, 47, 58; but see 71, 99). P. downsi appears to have little or no effect on at least two species of birds in the Galápagos: the Galápagos mockingbird (Mimus parvulus) and the vegetarian finch (Platyspiza crassirostris) (55, 71). Notably, these two species are the largest-bodied hosts of P. downsi that have been monitored in the Galápagos. Although P. downsi generally increases in intensity with host size, larger-bodied species may be more tolerant of parasite damage (see the section titled Host Tolerance below). Effects of P. downsi on host fitness also appear to be mediated by the environment, particularly seasonal rainfall. Cimadom et al. (19) hypothesized that low reproductive success in small tree finches and warbler finches (Certhida olivacea) on Santa Cruz Island was the result of several interacting factors, including P. downsi, heavy rainfall, and habitat change. In contrast, higher www.annualreviews.org • Biology of Philornis Flies 375 Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. EN63CH19_Clayton ARI 20 November 2017 15:20 23 rainfall also leads to greater food abundance for breeding birds. Heimpel et al. (55) suggest that a short, intense rainy season increased food available to breeding vegetarian finches, which may have helped to mitigate potential negative effects of P. downsi on nestlings. Finch reproductive success can be extremely low in dry years due to the combination of P. downsi and food limitation (45, 46, 75). Koop et al. (75) found that neither prevalence nor intensity of P. downsi in medium ground finch nests was diminished in an exceptionally dry year on Santa Cruz Island. Fledging success in the few nests found that year did not differ significantly between fumigated and sham-fumigated nests. However, reproductive success for both treatments was extremely low, with just one chick in each treatment group fledging successfully. No evidence, to date, has suggested that P. downsi affects the condition or survival of adult hosts. In an experimental study, Knutie et al. (70) found that P. downsi did not affect corticosterone levels or condition of nesting female finches, suggesting that the mortality of nestlings associated with parasitism is not mediated by a stress response in mothers. It is not known whether Philornis' effects on host fitness are mediated by secondary infections. Aitken et al. (1) suggested that Philornis larvae might vector arboviruses, leading to disease in nestlings or their parents in addition to the direct negative effects of feeding larvae. However, pathogen transmission by dipteran larvae does not appear to be common, and this hypothesis has received little additional study. Philornis downsi Versus Other Philornis Species Supplemental Material 376 The effects of P. downsi on Galápagos hosts appear to be more severe than the effects of Philornis spp. on native hosts. To test this hypothesis explicitly, we compared data on the effects of introduced P. downsi in the Galápagos to data on the effects of native Philornis spp. in other regions of the world. We defined cost of parasitism as the difference in fledging success between parasitized and unparasitized nestlings. We used linear mixed effect models to evaluate the impact of parasite prevalence, intensity, and host mass on the cost of parasitism (see Supplemental Tables 3 and 4 for detailed methods). Our results show that the cost of P. downsi parasitism is indeed higher than that of native Philornis spp. [linear mixed model (LMM) estimates: P. downsi = 43.8%, native Philornis spp. = 23.8%; p = 0.03]. Prevalence of P. downsi in the Galápagos is higher than that of native Philornis spp. (LMM estimates: P. downsi = 96.7%, native Philornis spp. = 49.16%; p < 0.001). Surprisingly, however, mean intensity per nestling does not differ significantly between P. downsi and other Philornis species (LMM estimates: P. downsi = 19.5, native Philornis spp. = 13.8; p = 0.42). In every study of P. downsi in the Galápagos, prevalence has exceeded 85% (Figure 4). Despite the lack of variation in this parameter, prevalence is a significant predictor of the cost of parasitism in the Galápagos (Figure 4) (Supplemental Table 3b). In contrast, prevalence does not predict the cost of native Philornis spp. to hosts in other locations (Figure 4) (Supplemental Table 3c). There is a marginally significant negative correlation between the cost of P. downsi parasitism and host mass in the Galápagos but not in native Philornis spp. (Figure 5) (Supplemental Table 4b,c). In general, both the prevalence and effects of P. downsi in Galápagos hosts are higher than those of other Philornis species (Figure 4). However, there are two noteworthy exceptions: native Philornis spp. in European starlings in Argentina (Sturnus vulgaris) and Philornis spp. in several bird species in Puerto Rico (6, 61). In the case of Argentina, although the Philornis spp. are native, the host-parasite interaction is a relatively new one (starlings were first seen in Argentina in 1987) (61). It is important to note, however, that the fledging success of the few unparasitized chicks in the Argentina study was also low, so controlled experimental work is needed to rigorously test the effects of Philornis on this species. In Puerto Rico, both the prevalence and effects on endemic hosts are much more similar to those seen in the Galápagos than those seen in other native hosts McNew · Clayton EN63CH19_Clayton ARI 20 November 2017 24 15:20 Cost of parasitism (%) 80 60 40 20 Galápagos hosts Puerto Rico hosts 20 40 60 Prevalence (%) 80 100 Central or South America hosts Figure 4 Relationship between Philornis prevalence (percentage of nests infested) and the cost of parasitism (percentage reduction in fledging success of parasitized nestlings compared to unparasitized nestlings). Each point represents data for a single host species. Shown are hosts of P. downsi in the Galápagos ( gray points), of Philornis spp. in Puerto Rico (light blue points), and of Philornis spp. in Central or South America (red points). The regression line for Galápagos hosts (solid line) and the (nonsignificant) regression line for native hosts (dashed line) are also indicated. The cost of parasitism increases significantly with prevalence in Galápagos hosts but not native hosts [linear mixed model (LMM) estimates: Galápagos p < 0.0001 and native p = 0.441 (Supplemental Tables 3b and 3c, respectively)]. Readers are referred to Supplemental Table 3a for details of methods and analyses. Supplemental Material 80 Cost of parasitism (%) Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. 0 0 60 40 20 Galápagos hosts Puerto Rico hosts 0 0 50 100 150 Central or South America hosts Host mass (g) Figure 5 Relationship between host species' mass and the cost of parasitism. Each point represents data for a single host species. Shown are hosts of Philornis downsi in the Galápagos ( gray points), of Philornis spp. in Puerto Rico (light blue points), and of Philornis spp. in Central or South America (red points). The regression line for Galápagos hosts (solid line) and the (nonsignificant) regression line for native hosts (dashed line) are also indicated. Larger species of hosts in the Galápagos show a marginally significant decrease in the cost of parasitism, whereas larger native hosts show a marginally significant increase in the cost of parasitism [linear mixed model (LMM) estimates: Galápagos p = 0.056 and native p = 0.066 (Supplemental Tables 4b and 4c, respectively)]. Readers are referred to Supplemental Table 4a for details of methods and analyses. www.annualreviews.org • Biology of Philornis Flies 377 EN63CH19_Clayton ARI 20 November 2017 15:20 25 (see the sidebar titled Did Philornis Also Invade Puerto Rico?). As with Philornis in the Galápagos, Philornis may be a recent arrival to Puerto Rico. These two case studies further suggest that the virulence of Philornis is higher in new hosts. HOST DEFENSES AGAINST PHILORNIS Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Several studies have tested for defenses by hosts against Philornis parasitism. The first line of defense includes mechanisms for avoiding parasitism in the first place. Once parasitized, however, hosts can try to tolerate or resist the parasites (108). Tolerance and resistance differ in their mechanisms and implications for host-parasite coevolution (108). Avoidance Some native hosts appear to alter their nesting behavior in response to Philornis. White-throated magpie-jays in Costa Rica (Calocitta formosa) breed over an extended 7-month period that spans the dry and wet seasons (78). The magpie-jays initiate their first broods during the dry season, which is surprising given that arthropod food resources are scarce at this time of year. However, Philornis, which causes significant nestling mortality in magpie-jays, increases in prevalence during the wet season. Thus, magpie-jays may avoid Philornis by breeding earlier than would be predicted by arthropod food availability alone. Spatial positioning of nests, such as height and distance to other nests, might also influence encounter rates with flies. For example, Kleindorfer et al. (67) found that the trapping frequency of female P. downsi and larval intensity per nest increased with trap height and nest height. Intensity of P. downsi also increases with host density (63). Differences in nest site preferences may therefore help explain why some species are parasitized at higher intensities than others. Nestlings in highly parasitized nests sometimes crawl on top of other nestmates to avoid parasitism (97). Immune Resistance Tolerance: a defense mechanism that maintains host health by compensating for damage done by the parasite without decreasing parasite load Resistance: a defense mechanism that maintains host health by reducing parasite load 378 Nesting female medium ground finches (Geospiza fortis) mount an immune response when parasitized (76). The strength of the female's immune response is inversely proportional to the number of parasites in her nest; thus, the immune response may help to reduce fly intensity in the nest (76). However, as Koop et al. (76) point out, the inverse correlation of immune response and parasite intensity may actually reflect the inability of highly parasitized birds to mount strong immune responses. Moreover, the immune response was not correlated with nestling survival, because 100% of parasitized nestlings died; thus, even if it was a response to parasitism, it was not an effective defense strategy. There is no evidence that immune responses to P. downsi are mounted by nestlings of Darwin's finches or Galápagos mockingbirds during their brief time in the nest (71, 76). Similarly, related native hosts (black-faced grassquits and tropical mockingbirds) do not mount an immune response when parasitized by P. trinitensis (69). These results suggest that the high cost of P. downsi to Galápagos nestling survival is not due to Galápagos hosts lacking the immune defenses found in native hosts. Behavioral Resistance Antiparasite behavior, such as preening, is often the first line of defense against external parasites (52, 119). However, behavioral defenses are not common against P. downsi in Darwin's finches. O'Conner et al. (97) reported a single observation of a nestling finch preening off and consuming a fly larva. An adult female finch in that same study was observed picking at a nestling's nares McNew · Clayton Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. EN63CH19_Clayton ARI 20 November 2017 26 15:20 (nostrils). P. downsi feeds mainly at night and hides in the nest material during the day, which may help it to avoid host detection (97). Some studies of native Philornis spp. report hosts removing larvae from nestlings by preening (49, 112), but this behavior does not appear to be widespread. Behavioral responses of female finches may actually exacerbate the effects of P. downsi. Observational studies find that females spend less time brooding and more time standing when nests are heavily parasitized, presumably to avoid being fed on by larvae themselves (71, 76). This behavior may disrupt thermoregulation of the nest and contribute to nestling mortality. Another recent study suggested the possibility that Darwin's finches use self-medicating behavior to deter Philornis. Cimadom et al. (18) observed members of four different finch species (Geospiza fortis, G. fuliginosa, Certhida olivacea, and Camarhynchus parvulus) rubbing leaves of the endemic Galápagos guava or guayabillo tree (Psidium galapegium) on their feathers. This leaf has insect-repellent properties; thus, the behavior may help birds repel P. downsi. More work is needed to test this interesting hypothesis. Host Tolerance Behavioral data from Galápagos mockingbirds suggest that increased provisioning of nestlings by parents allows nestlings to tolerate P. downsi with no ultimate reduction in fitness (71). In an experimental study, Knutie et al. (71) found that parasitized mockingbird nestlings begged more than unparasitized chicks and that their parents responded to this cue by increasing provisioning. By contrast, there was no effect of P. downsi on the begging or provisioning rates of the medium ground finch. Thus, larger-bodied hosts, such as mockingbirds, may be better able to withstand fly larvae feeding on them at night. Smaller hosts, such as Darwin's finches, may simply be too weak in the morning to signal their parents that they need extra provisioning (98). Because the mechanism of tolerance is increased provisioning, host tolerance of P. downsi may be condition-dependent. Galápagos mockingbird tolerance ultimately could have consequences for other host species in the community. Because tolerant hosts survive parasitism without reducing parasite populations, they may serve as reservoir hosts that contribute to the size of P. downsi populations, thus exacerbating the risk to smaller, more vulnerable hosts (53, 71). Tolerance to Philornis may be widespread among native hosts. Clutch sizes in most Neotropical birds are smaller than those of temperate relatives, a difference that may be an adaptive response to greater parasite pressure in the Neotropics (87, 88, 103). Moss & Camin (91) showed that purple martins (Progne subis) hatch fewer eggs in a colony heavily infested with martin mites (Dermanyssus prognephilus) compared to a fumigated colony. Moreover, in large broods, parasitized martin nestlings weighed less than unparasitized nestlings, presumably because the parents were unable to provision all of the chicks. These experimental data indicate that the added burden of nest parasites may limit the number of chicks that parents can rear. Darwin's finches have large clutch sizes compared to most other Neotropical passerines (51), possibly due to the absence of native parasites and predators. However, the introduction of P. downsi together with introduced predators such as rats and cats may mean that finch parents can no longer provision four or five chicks at once. If so, then it is possible that P. downsi will contribute to the evolution of smaller clutch sizes in Darwin's finches over time. EFFECTS OF PHILORNIS DOWNSI ON HOST POPULATION DYNAMICS P. downsi is a significant threat to the survival of several finch species in the Galápagos, such as the critically endangered medium tree finch and the mangrove finch (48, 74, 79, 99, 100). The situation for the mangrove finch is especially dire, with fewer than 100 individuals of this species known to exist (79). Although rat control efforts in the past 5 years have significantly decreased the risk of www.annualreviews.org • Biology of Philornis Flies 379 EN63CH19_Clayton ARI 20 November 2017 Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Parasite burden: a general term encompassing the more precise measures of prevalence, intensity, and abundance; also known as parasite load 15:20 27 predation of nestlings, P. downsi remains a significant threat to the species (48, 79). The current management strategy developed by the Charles Darwin Research Station, Galápagos National Park, and San Diego Zoo includes hand-rearing chicks in the laboratory to avoid parasitism (26). Even common finch species may be vulnerable to local extinction due to P. downsi. A recent population viability model suggests that P. downsi has the potential to cause local extinction of medium ground finch populations on Santa Cruz Island within 100 years (74). Fortunately, however, the model suggests that even a modest decrease in P. downsi prevalence (30-40%) will reduce the threat considerably. Thus, reducing populations of P. downsi in the Galápagos should benefit host populations, even if P. downsi is not completely eradicated (see below). Paradoxically, populations of the medium ground finch appear to be stable or even increasing, despite the effect of P. downsi on reproductive success (41). In the lowlands of Santa Cruz Island, the prevalence of P. downsi in nests is close to 100%, with the number of successful fledglings per parasitized nest ranging from zero to approximately 1.7 (74). What explains the resilience of the population in the presence of P. downsi? The answer may lie in the fact that Darwin's finches are relatively long-lived species and have large clutches for tropical birds (51). A prolific finch can hatch 45 nestlings over the course of her lifetime (51)! As a result, species such as the medium ground finch may be able to maintain reproductive rates greater than 1, even if fledging success in any particular clutch is low. Kleindorfer et al. (65) suggest that Darwin's finch populations are evolving in response to selection by P. downsi. They report an apparent increase in hybridization between small and medium tree finches on Floreana Island, with fewer P. downsi in the nests of hybrids. Additional research is needed to explore the relationship between host hybridization and resistance and/or tolerance to P. downsi. WHY IS PHILORNIS DOWNSI SO VIRULENT IN THE GALÁPAGOS? It is commonly assumed that P. downsi is virulent in the Galápagos because naive hosts lack resistance and/or tolerance mechanisms (59). However, there is little evidence that native hosts are better defended against Philornis than Galápagos hosts. Although native hosts have occasionally been observed to remove larvae by preening (49, 112), mean intensities of Philornis from the nests of Galápagos and native hosts do not differ significantly across the studies we evaluated. These data suggest that once infested, native and nonnative hosts do not differ in their ability to reduce the parasite burden. Instead, some native hosts may be more tolerant to Philornis than Darwin's finches. Relatively low mortality from Philornis in native hosts indicates that some mechanism, such as reduced clutch size, may allow native hosts to compensate for effects of Philornis. However, not all native hosts are tolerant to Philornis; a few studies document similar effects of Philornis in native and nonnative hosts (69). Prevalence of native Philornis populations is generally lower than in the Galápagos, potentially due to native enemies of Philornis, such as parasitoid wasps and ants, that are largely absent from the Galápagos (12, 31, 69). These enemies may serve as top-down controls on Philornis populations that then limit the effects of Philornis on native host populations. In summary, the high prevalence and virulence of P. downsi in the Galápagos may be a combination of enemy release from native predators and parasitoids that suppress native Philornis populations and a lack of tolerance in most endemic Galápagos hosts. MANAGEMENT OF PHILORNIS DOWNSI The development of effective control strategies for P. downsi is a major conservation priority in the Galápagos because of the threat this introduced parasite poses to endemic birds (14) (see the sidebar 380 McNew · Clayton EN63CH19_Clayton ARI 20 November 2017 28 15:20 THE PHILORNIS WORKING GROUP Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. Researchers from around the world are working to develop control strategies for Philornis downsi in the Galápagos. Efforts are coordinated through the Philornis Working Group, a collaboration of scientists from 15 institutions in eight countries, led by the Charles Darwin Foundation and Galápagos National Park. Workshops were conducted by this working group in 2012 and 2015 on Santa Cruz Island in the Galápagos. The workshops helped coordinate P. downsi research and highlight priorities for controlling P. downsi (7). Further information can be found at http://www.darwinfoundation.org/en/science-research/invasive-species/philornis-downsi/. titled The Philornis Working Group). Although fumigation of nests with diluted permethrin has been used in several studies to experimentally reduce P. downsi abundance (45, 71, 76), broadscale manual fumigation of nests is impractical. Knutie et al. (72) found that finches will readily incorporate permethrin-treated cotton into their nests and that treated cotton significantly reduces the intensity of P. downsi. The Charles Darwin Foundation and Galápagos National Park are investigating whether this self-fumigation technique can be used as a stopgap method to improve the reproductive success of the critically endangered mangrove finch. Although permethrin is considered to have extremely low toxicity for birds, its use carries at least some risk of inadvertent negative health effects for wildlife (including other native insects) as well as the evolution of resistance in P. downsi to permethrin (11, 15, 43, 60). Trapping of P. downsi may also be an effective way to reduce fly populations in targeted areas (7, 14). Unfortunately, the efficacy of trapping has been limited by a lack of specific and attractive food baits (7). Two new volatile compounds derived from fermentative yeasts may improve trapping success in the future (17). Other possible long-term control methods include sterile insect technique (SIT) or biological control of P. downsi (7, 14). SIT is a method in which sterilized males are released en masse as a form of birth control (42). This approach has been successful at eradicating screwworm (Cochliomyia hominivorax), another parasitic fly from North and Central America (42). The introduction of a biological control enemy of P. downsi, such as a parasitoid wasp, is also under consideration. A study of Conura annulifera (Hymenoptera: Chalcididae) suggested that this parasitoid wasp decreases P. downsi fitness and is fairly host-specific (12). However, both SIT and biological control require the rearing of significant numbers of P. downsi in culture, which is proving difficult (77, 121). Even if biological control proves viable, species introductions for biological control must be very carefully considered and monitored given the sensitivity of the Galápagos ecosystem (56). SUMMARY POINTS 1. Philornis flies are widespread nest parasites of birds in the Americas. 2. Studies have explored the biology and effects of only approximately half of the 50 described species of Philornis. 3. P. downsi is an introduced parasite of land birds in the Galápagos Islands that poses a significant threat to populations of Darwin's finches and other endemic bird species. 4. The effects of P. downsi on Galápagos hosts are typically greater than those of native Philornis spp. on native hosts. www.annualreviews.org • Biology of Philornis Flies 381 EN63CH19_Clayton ARI 20 November 2017 15:20 29 5. The severity of effects of P. downsi on Galápagos birds may be the result of enemy release, that is, a lack of P. downsi parasitoids or predators in the Galápagos. 6. Control measures for P. downsi are urgently needed to help endemic, endangered bird species such as the mangrove finch and medium tree finch. DISCLOSURE STATEMENT Annu. Rev. Entomol. 2018.63:369-387. Downloaded from www.annualreviews.org Access provided by University of Utah - Marriot Library on 01/21/18. For personal use only. The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review. ACKNOWLEDGMENTS We thank James Ruff for discussions on the analyses and Sarah Knutie and Sarah Bush for their helpful comments on the manuscript. This research was supported by a National Science Foundation (NSF) grant DEB-0816877 to D.H.C., and an NSF Graduate Research Fellowship to S.M.M. LITERATURE CITED 1. Aitken THG, Downs W, Anderson C. 1958. Parasitic Philornis flies as possible sources of arbor virus infections (Diptera, Anthomyidae). Proc. Soc. Exp. Biol. Med. 99(3):635-37 2. Aldrich J. 1923. The genus Philornis-a bird-infesting group of Anthomyiidae. Ann. Entomol. Soc. Am. 16(4):304-9 3. Antoniazzi LR, Manzoli DE, Rohrmann D, Saravia MJ, Silvestri L, Beldomenico PM. 2011. Climate variability affects the impact of parasitic flies on Argentinean forest birds. J. Zool. 283(2):126-34 4. Arendt WJ. 1985. Philornis ectoparasitism of pearly-eyed thrashers. I. Impact on growth and development of nestlings. Auk 102(2):270-80 5. Arendt WJ. 1985. 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Breeding ecology and predictors of nest success in the critically endangered Ridgway's hawk Buteo ridgwayi. Bird Conserv. Int. 25:385-98 www.annualreviews.org • Biology of Philornis Flies 387 35 Supplemental Material: Annu. Rev. Entomol. 2018. 63:369-87 https://doi.org/10.1146/annurev-ento-020117-043103 Alien Invasion: Biology of Philornis Flies Highlighting Philornis downsi, an Introduced Parasite of Galápagos Birds McNew and Clayton Supplemental Table 1. Known hosts of Philornis species, listed in taxonomic order. Current taxonomic names of hosts (where different) are indicated in footnotes. Supplemental Table 2. Effects of Philornis flies on hosts (includes publications with original data only). Supplemental Table 3. Linear Mixed Effect Model summary of the effects of parasite (P. downsi vs. native Philornis) and prevalence on cost of parasitism. Models were run first with an interaction term and then separately for P. downsi vs. native Philornis data. Bold p values indicate statistical significance. Supplemental Table 4. Linear Mixed Effect Model summaries of the effects of location (P. downsi vs. native Philornis) and host mass on cost of parasitism. Intercept is fixed at the mean mass value. Models were run first with an interaction term and then separately for P. downsi vs. native Philornis data. Bold p values indicate statistical significance. Supplemental Table 5. Data used in analyses reported in Supplemental Tables 3 and 4. 36 Supplemental Table 1. Host Taxon Host Common Name Philornis species Reference Accipiter striatus Puerto Rican sharp- Philornis sp. (15) venator shinned hawk Accipitriformes Accipitridae Falconiformes Falconidae Falco femoralis Aplomado falcon Philornis sp. (27) Falco sparverius American kestrel P. falsificus (43) Red junglefowl P. angustifrons (70) Galliformes Phasianidae Gallus gallus Columbiformes Columbidae Columba leucocephala White-crowned pigeon P. pici (70) Columbina passerina Common ground dove Philornis sp. (72) Columbina talpacoti Ruddy Ground-dove P. angustifrons (17, 73) Leptotila rufaxilla Grey-fronted Dove P. angustifrons (17) Leptotila verreauxi White-tipped Dove P. angustifrons, P. (17, 57) sanguinis Zenaida auriculata Eared Dove P. torquans (2) Zenaida macroura Mourning dove P. pici (70) 2 37 Psittaciformes Psittacidae Amazona amazonica Orange-winged P. falsificus (17) P. deceptiva, (6) Amazon Amazona vittata Puerto Rican Amazon Philornis sp. Ara macao Scarlet Macaw Philornis sp. (56) Aratinga a. acuticaudata Blue-crowned Philornis sp. (3) Philornis sp. (70) Philornis sp. (70) P. pici (70) parakeet Brotogeris versicolurus White-winged parakeet Forpus xanthopterygius Blue-winged parrotlet Pionus sp. Cuculiformes Cuculidae Coccyzus melacoryphus Dark-billed cuckoo P. downsi (21) Crotophaga ani Smooth-billed ani P. downsi, P. (17, 23) falsificus, P. angustifrons Srigiformes Strigidae Glaucidium brasilianum Ferruginous pygmy P. mimicola (59) owl 3 38 Megascopes asio Eastern Screech owl P. mimicola (59) Otus choliba Tropical Screech-Owl P. falsificus, P. (17, 70, glaucinis 71) Apodiformes Trochilidae Chaetocerus berlepschi Esmeraldas woodstar Philornis sp. (8) Chlorestes notatus Blue-chinned sapphire Philornis sp. (70) Eupetomena macroura Swallow-tailed Philornis sp. (58) Rufous-breasted P. glaucinis, P. (17) hermit niger Sapphire sp. P. sanguinis, P. Hummingbird Glaucis hirsutus Hylocharis sp. (70) torquans Thalurania sp. Woodnymph sp. P. insularis (70) Green-backed trogon Philornis sp. (70) Turquoise-browed P. fasciventris (12) Philornis sp. (70) Trogoniformes Trogonidae Trogon viridis Coraciiformes Momotidae Eumomota superciliosa motmot Momotus momota Amazonian motmot Piciformes 4 39 Galbulidae Galbula ruficauda Rufous-tailed Jacamar P. aitkeni, P. (16, 17) downsi, P. rufoscutellaris Capitonidae Black-girdled barbet Philornis sp. (70) Toco tucan Philornis sp. (70) Leuconerpes candidus i White woodpecker Philornis sp. (70) Piculus rubiginosus Golden-olive P. angustifrons (17) P. angustifrons (17) Cinereous becard Philornis sp. (70) Elaenia chiriquensis Lesser elaenia Philornis sp. (70) Fluvicola nengeta Masked water tyrant Philornis sp. (70) Legatus leucophaius Piratic flycatcher P. downsi, P. (17) Capito dayi Ramphastidae Ramphastos toco Picidae Woodpecker Passeriformes Pipridae Manacus manacus Black-and-white Manakin Tityridae Platypsaris rufus ii Tyrannidae deceptivus 5 40 Machetornis rixosa Cattle tyrant Philornis sp. (13) Megarhynchus pitanga Boat-billed flycatcher Philornis sp. (70) Mionectes macconnelli McConnell's Philornis sp. (57) P. porteri (32) P. downsi (44) P. carinata (70) P. gagnei (70) Rusty-margined P. angustifrons, P. (57, 70) flycatcher diminuta, P. Flycatcher Myiarchus crinitus Great crested flycatcher Myiarchus magnirostris Galapagos flycatcher Myiarchus sp. Myiarchus tyrannulus Brown-cresed flycatcher Myiozetetes cayanensis frontalis, P. vulgaris, Philornis sp. Myiozetetes similis Social flycatcher Myiozetetes sp. Pipromorpha McConnell's macconelli iii flycatcher Pitangus sp. Kiskadee sp. Philornis sp. (70) P. carinata (70) P. glaucinis (70) P. torquans, (70) Philornis sp. Pitangus sulphuratus Great kiskadee P. angustifrons, P. (2, 17, 49) deceptivus, P. 6 41 downsi, P. sanguinis, P. torquans, P. trinitensis Polystictus superciliaris Grey-backed tachuri Philornis sp. (29) Pyrocephalus rubinus Vermilion flycatcher P. downsi (23) Satrapa icterophrys Yellow-browed tyrant Philornis sp. (47) Sublegatus modestus Southern scrub Philornis sp. (13) flycatcher Suiriri affinis Campo Suiriri Philornis sp. (45) Suiriri islerorum Chapada flycatcher Philornis sp. (45) Tyrannus melancholicus Tropical kingbird P. bella, P. downsi, (17, 46) P. trinitensis Furnariidae Anumbius annumbi Certhiaxis cinnamomea Firewood gatherer Yellow-chinned P. pici, P. seguyi, (25, 47, Philornis sp. 50) Philornis sp. (70) spinetail Coryphistera alaudina Lark-like bushrunner Philornis sp. (13) Dendrocolaptes Planalto woodcreeper Philornis sp. (11, 51) Rufous hornero P. torquans (2) platyrostris Furnarius rufus 7 42 Lepidocolaptes souleyetii Streak-headed P. downsi (9) P. gagnei, P. (70) woodcreeper Leptasthenura platensis Tufted tit-spinetail trinitensis Phacellodomus ruber Greater thornbird P. torquans, (2, 13) Philornis sp. Phacellodomus rufifrons Rufous-fronted Philornis sp. (70) thornbird Phacellodomus sibilatrix Little thornbird P. torquans (2) Phacellodomus Freckle-breasted P. gagnei, P. (70) striaticollis thornbird glaucinis, P. trinitensis, Philornis sp. Pseudoseisura lophotes Brown cacholote P. pici, P. seguyi, P. (49, 50) torquans Philornis sp. (13) Synallaxis frontalis Sooty-fronted spinetail Philornis sp. (13) Synallaxis infuscata Pinto's spinetail Philornis sp. (70) Synallaxis spixii Spix's spinetail Philornis sp. (70) Xiphocolaptes albicollis White-throated P. nielseni (17) Schoeniophylax Chotoy spinetail phyganophilus woodcreeper 8 43 Thamnophilidae Black-hooded antwren Philornis sp. (70) Taraba major Great antshrike P. torquans (2) Thamnophilus murinus Mouse-colored P. trinitensis, (57) antshrike Philornis sp. Chestnut-backed P. glaucinis (70) Philornis sp. (70) Beechey jay Philornis sp. (74) Progne chalybea Grey-breased Martin P. downsi (17) Tachycineta leucorrhoa White-rumped Philornis sp. (61) Palm chat P. pici (70) Fasciated wren P. downsi (9) Myrmotherula erythronotus iv Thamnophilus palliatus antshrike Thamnophilus torquatus Rufous-winged antshrike Corvidae Cyanocorax beecheii Hirundinidae swallow Dulidae Dulus dominicus Troglodytidae Campylorhunchus faciatus 9 44 Troglodytes aedon House wren P. torquans, P. (2, 8, 17, seguyi, P. downsi, P. 47, 60, 73, trinitensis, Philornis 76) sp. Polioptilidae Polioptila dumicola Masked gnatcatcher P. torquans, (2, 25) Philornis sp. Polioptila plumbea Tropical gnatcatcher P. downsi (8) Cocoa Thrush P. downsi, P. (17) Turdidae Turdus fumigatus trinitensis Turdus nudigenis Spectacled thrush P. downsi (17) Turdus rufiventris Rufous-bellied thrush Philornis sp. (70) European starling Philornis sp. (31) Pearly-eyed thrasher P. deceptiva, P. pici, (4-6) Sturnidae Sturnus vulgaris Mimidae Margarops fuscatus Philornis sp. Mimus gilvus Tropical mockingbird P. angustifrons, P. (1, 17, 34, downsi, P. querulus, 70, 73) P. trinitensis, P. vulgaris 10 45 Mimus parvulus Galapagos P. downsi (23, 36) P. mimicola, P. (42, 70, obscura P. porteri, 72) mockingbird Mimus polyglottos Northern mockingbird P. spermophilae, Philornis sp. Mimus saturninus Chalk-browed P. seguyi, P. (25, 47, mockingbird torquans, Philornis 49, 62) sp. Estrilidae Common waxbill P. angustrifrons (70) House sparrow P. angustifrons (70) Coryphospingus pileatus Grey pileated finch Philornis sp. (70) Zonotrichia capensis Rufous-collared Philornis sp. (47) Estrilda astrild Passeridae Passer domesticus Emberizidae Sparrow Icteridae Agelaioides badius Grayish baywing Philornis sp. (25) Agelasticus thilius Yellow-winged P. torquans (17, 49) blackbird 11 46 Cacicus cela Yellow-rumped P. deceptivus, P. Cacique downsi, P. (9, 17, 70) angustifrons, P. pici Cacicus haemorrhous Red-rumped cacique P. pici, P. torquans (70) Curaeus forbesi Forbes's blackbird Philornis sp. (70) Gnorimopsar chopi Chopi blackbird Philornis sp. (70) P. pici (70) Gymnomystax mexicanus Oriole blackbird Icterus cayanensis Epaulet oriole P. pici (70) Icterus dominicensis Hispaniolan oriole P. angustifrons (70) Icterus icterus Venezuelan troupial P. angustifrons (70) Icterus nigrogularis Yellow oriole P. angustifrons, P. (26, 73) deceptivus, P. downsi Molothrus badius Grayish baywing Philornis sp. (70) Molothrus bonariensis Shiny cowbird P. angustifrons, P. (17, 25, downsi, P. gagnei, 47, 70) P. glaucinis, P. mansoni, P. obscura, P. pici, P. trinitensis, Philornis sp. 12 47 Molothrus rufoaxillaris Screaming cowbird P. gagnei, P. (25, 70) glaucinis, P. masoni, Philornis sp. Psarocolius bifasciatus Olive oropendola Philornis sp. (70) Psarocolius decumanus Crested oropendola P. angustifrons (70) Quiscalus lugubris Carib grackle P. angustifrons, P. (69, 73) downsi Scaphidura oryzivora v Giant cowbird P. angustifrons, P. (17) deceptiva Zarhynchus wagleri vi Chestnut-headed Philornis sp. (70) Oropendola Parulidae Setophaga petechia Yellow warbler P. downsi (21) Setophaga pitiayumi Tropical parula Philornis sp. (8) Mangrove finch P. downsi (24) Camarhynchus pallidus Woodpecker finch P. downsi (19, 23) Camarhynchus parvulus Small tree finch P. downsi (10, 19, Thraupidae Camarhynchus heliobates 23) Camarhynchus pauper Medium tree finch P. downsi (55) Camarhynchus Large tree finch P. downsi (19, 21) psittacula 13 48 Certhida olivacea Warbler finch P. downsi (10, 19, 23) Coereba flaveola Bananaquit P. downsi (17) Geospiza fortis Medium ground finch P. downsi (19, 30, 36-39) Geospiza fuliginosa Small ground finch P. downsi (19, 20, 23, 52-54) Geospiza scandens Cactus finch P. downsi (22, 23) Gubernatrix cristata Yellow cardinal, shiny Philornis sp. (18) P. trinitensis (17) P. torquans, P. (2, 13, 64) cowbird Oryzoborus angolensis Chestnut-bellied seed finch Paroaria coronata Red-crested cardinal seguyi, Philornis sp. Paroaria dominicana Red-cowled cardinal P. pici (70) Piranga erythrocephala Red-headed tanager Philornis sp. (70) Platyspiza crassirostris Vegetarian finch P. downsi (28) Ramphocelus carbo Silver-beaked Tanager P. diminuta, P. (17, 57) downsi, P. glaucinis, Philornis sp. Saltator atricollis Black-throated saltator Philornis sp. (70) Saltator similis Green-winged saltator (70) Philornis sp. 14 49 Schistochlamys Black-faced tanager P. trinitensis (57) Saffron finch P. torquans, P. (2, 9, 47, melanopis Sicalis flaveola downsi, Philornis sp. 61, 65) Sporophila bouvreuil Copper seedeater P. trinitensis (70) Sporophila caerulescens Double-collared P. angustifrons (70) seedeater Sporophila intermedia Grey seedeater P. angustifrons (70) Sporophila lineola Lined seedeater P. trinitensis, P. (17) sanguinis Sporophila nigricollis Yellow-bellied seed P. torquans (49) P. downsi, P. (17, 57) eater Tachyphonus rufus White-lined Tanager angustifrons Tangara cayana Burnished-buff Philornis sp. (70) tanager Tangara cyanocephala Red-necked tanager Philornis sp. (70) Thraupis episcopus Blue-gray Tanager P. angustifrons, P. (17) trinitensis Thraupis palmarum Palm Tanager P. downsi (17, 73) Thraupis sayaca Sayaca tanager Philornis sp. (70) Tiaris bicolor Black faced Grassquit P. trinitensis (34) 15 50 Volatina jacarina Blue-black grassquit P. angustifrons, P. (57) trinitensis, Philornis sp. i Melanerpes candidus Pachyramphus rufus iii Mionectes macconnelli iv Formicivora erythronotos v Molothrus oryzivorus vi Psarocolius wagleri ii 16 51 Supplemental Table 2. Effects of Philornis flies on hosts (includes publications with original data only). Size x Host Host Latin Philornis Type Loc. N/I Effect of common name species vii viii ix Bay- Molothrus Phil. sp. O ARG N winged badius, (subcut.) Cowbirds, Molotrhus nestling Screaming rufoaxillaris survival xii Ref. parasitism name 263 C, No effect 81 W on (25) cowbirds xi Planalto Dendrocolapte woodcreep s platyrostris Phil. sp. O ARG N (subcut.) 50 C, Negative 18 W effect on er (51) nestling growth Brown Pseudoseisura P. pici cacholote lophotes and and P. and Anumbius seguyi Firewood- annumbi O ARG N 304 C, No effect 117 W on (50) nestling survival gatherer 12 passerine Phil. sp. O ARG N 142 W No effect (48) on species 17 52 nestling survival xiii Chalk- Mimus browed saturninus, effect on mockingbi Molothrus nestling rd, shiny bonariensis survival P. seguyi O ARG N 108 W Negative (62) cowbird Planalto Dendrocolapte Phil. sp. O ARG N woodcreep s platyrostris 35 C, No effect 15 W on er (11) nestling survival 34 species, P. mostly torquans O ARG N 715 C, Negative 284 W effect on passerines (2) nestling survival xiv Red- Paroaria crested coronata P. seguyi O ARG N 131 W Negative (64) effect on cardinal nestling growth and survival House Troglodytes wren aedon P. seguyi O ARG N 148 W Negative (60) effect on 18 53 nestling growth and survival Yellow Gubernatrix cardinal cristata Phil. sp. O ARG N 18 W Negative (18) effect on nestling survival xv European Sturnis starlings vulgaris Phil. sp. O ARG I xvi 69 C, Unknown 22 W if (31) parasitism affects survival xvii Chapada Suiriri flycatcher islerorum and effect on and S. affinis nestling Phil. sp. O BRA N 45 W Negative Campo survival in Suiriri one host House Troglodytes P. wren aedon carinatus O CR N 214 W No effect (45) (76) on nestling 19 54 survival xviii White- Calocitta throated formosa Phil. sp. O CR N 80 C, Negative 24 W effect on magpie- nestling jay growth xix Ridgway's Buteo ridgwayi P. pici O DR N 156 W hawk 6 of 55 (40) (75) nest failues attributed to Philornis Medium Geospiza fortis P. downsi E GAL I 30 W No effect ground on finch condition xx (35) , of finch mothers Small tree Camarhynchus finch, pauper, G. small fuliginosa ground P. downsi O/E GAL I 14 Nares W xxi deformed (33) by P. downsi xxii finch 20 55 Mangrove Camarhynchus finch heliobates P. downsi O GAL I 81 W Negative (24) effect on nestling survival xxiii Medium Geospiza fortis P. downsi E GAL I 43 W Negative ground effect on finch nestling (39) survival xxiv 12 finch P. downsi O GAL I 177 W species Negative (23) effect on nestling survival Small Geospiza P. downsi E GAL I 23 W Negative ground fuliginosa, effect on finch, Geospiza fortis nestling Medium growth ground and finch survival Small Geospiza ground fuliginosa P. downsi O GAL I 58 C, Negative 24 W effect on (22) (20) finch 21 56 nestling survival Small Geospiza P. downsi O GAL I 249 W Negative ground fuliginosa, effect on finch, Geospiza nestling Medium fortis, survival xxv ground Camarhynchus finch, parvulus, Small tree Camarhynchus finch, psittacula, Medium Camarhynchus tree finch, pallidus, (19) Woodpeck Certhida er finch, olivacea Warbler finch Medium Geospiza fortis P. downsi O GAL I ground 161 C, Negative 63 W effect on finch (30) nestling survival xxvi Small, Camarhynchus medium parvulus, P. downsi O GAL I 53 C, Negative 17 W effect on (55) 22 57 and large pauper, nestling tree psittacula survival xxvii finches Medium Geospiza fortis P. downsi E GAL I ground 142 C, Negative 24 W effect on finch (37) nestling survival Medium Geospiza fortis P. downsi E GAL N 13 W No effect ground on finch nestling (38) survival xxviii Warbler Certhida P. downsi O GAL I 191 W Negative finch and olivacea, effect on Small tree Camarhynchus nestling finch pauper survival (10) xxix Medium Geospiza P. downsi E GAL I 127 W Negative ground fortis, Mimus effect on finch, parvulus survival of Galapagos finches mockingbi but not (36) rd 23 58 mockingbi rds xxx Vegetarian Platyspiza finch P. downsi O GAL I crassirostris 25 C, Lower 11 W effects on (28) nestling survival vs. other Darwin's finches. xxxi Beechy Cyanocorax Phil. sp. O MEX N 40 C Jay beecheii (subcut. effect on xxxii nestling ) Negative (74) growth xxxiii Chestnut- Psarocolius Phil. sp. O PAN N 2,139 Negative headed wagleri, (subcut.) C effect on oropendol Cacicus cela, nestling a, Yellow- Molothrus survival rumped oryzivorus xxxiv (66) Cacique, 24 59 Giant Cowbird Scarlet Ara macao macaw Phil. sp. O PER N 256 C (subcut.) Negative (56) effect on nestling growth, no effect on survival Tumbes Tachycineta swallow stolzmanni Phil. sp. O PER N 37 W Two out (67) of three parasitized nests failed Pearly- Margarops P. eyed fuscatus deceptivu thrasher O PR N 681 C, Negative 272 W effect on (5) nestling s growth xxxv Pearly- Margarops P. eyed fuscatus deceptivu thrasher s E PR N 681 C, Negative 272 W effect on (4) nestling survival xxxvi 25 60 Pearly- Margarops eyed fuscatus P. pici E PR N 80 W Negative (41) effect on thrasher nestling survival xxxvii Sharp- Accipiter shinned striatus hawk venator Phil. sp. O PR N 75 C, Negative 30 W effect on (15) nestling survival xxxviii Pearly- Margarops eyed fuscatus Phil. spp. O PR N thrasher TOB N Negative C, effect on 1170 nestling W survival 74 W Negative Black- Tiaris bicolor, P. faced Mimus gilvus trinitensi effect on s survival of grassquit, E 2467 Tropical grassquits mockingbi but not rd mockingbi (6) (34) rds 26 61 Great Myiarchus crested crinitus P. porteri O USA N 32 C, No effect 11 W on flycatcher (32) nestling survival Ferrugino Glaucidium P. us pygmy- brasilianum mimicola owl, and parasitism Eastern Megascopes affects screech asio survival own O USA N 138 C, Unknown 38 W if (59) xxxix vii Abbreviation: Type of study; E, experimental; O, observational. Abbreviation: Location of study; ARG, Argentina, BRA, Brazil; CR, Costa Rica; DR, Dominican Republic; GAL, Galapagos; MEX, Mexico; PAN, Panama; PER, Peru; PR, Puerto Rico; TOB, Tobago; USA, United States. ix Abbreviation: Host parasite relationship; N, native; I, introduced. x Abbreviation: Sample size of study; C, chicks (nestlings); W whole nests. xi Also reported prevalence and mortality for other hosts: Chalk-browed mockingbird, Mimus saturninus, Firewood gatherer, Anumbius annumbi, Masked gnatcatcher, Polioptila dumicola, Shiny cowbird, Molothrus bonariensis. xii Parents preened larvae off nestlings. xiii Low prevalence (14.8% of nests); 2/21 parasitized nests failed. xiv Effects correlated with intensity. xv P = 0.07. xvi Introduced host but native parasite. xvii High prevalence of parasitism (73%) and >90% of parasitized nests failed. However, success of non-parasitized nestlings was equally low. xviii Trend towards negative effect of parasites on nestling growth. xix Effects correlated with parasite intensity. xx Mass, corticosterone levels, or hematocrit levels. xxi Plus museum specimens. xxii Examination of historical specimens suggests post-1960s arrival of P. downsi. xxiii Estimated nestling mortality due to P. downsi =14%. viii 27 62 xxiv Immune response in female finches was inversely correlated with parasite abundance; nestlings did not mount a significant immune response to P. downsi. xxv Pooled data show negative correlation between intensity and nestling survival. xxvi High prevalence (64%-98%), intensity per nestling correlated with mortality. xxvii P. downsi suspected main cause of mortality in 41% of nestlings. xxviii Survival did not differ between parasitized and non-parasitized treatments; however reproductive success was extremely low overall due to dry conditions. xxix P. downsi estimateded to cause death of 56% of small tree finches and 37% of warbler finches. xxx Mockingbirds increase provisioning to nestlings in response to P. downsi. xxxi Parasitism suspected cause of mortality in 20% of nestlings. xxxii Unidentified subcutaneous Phil. species. xxxiii Parasitism did not affect 1-year survival probability of fledglings. xxxiv Reported that brood parasite cowbird nestlings preen Philornis larvae off nestmates. xxxv Effects correlated with parasite intensity. xxxvi High prevalence of parasitism (98%) and high associated mortality (47% of parasitized nestlings). xxxvii Fledging success was higher in the parasite-reduced treatment compared to control; however treatment only reduced parasite intensity in 1 out of 2 years. xxxviii All years: mortality of parasitized nestlings =61%. xxxix Anecdotal observations that parasitized nestlings were "lethargic." 28 63 Supplemental Table 3: Relationship between Philornis prevalence (% nests infested), Philornis species (binary: P. downsi or other Philornis) and cost of parasitism (reduction in fledging success of parasitized nests compared to non-parasitized nests).* 3a: All studies Model = Cost of parasitism ~ prevalence*Philornis species; random effect = host species N= 23 observations, 21 host species. Fixed Effects Estimate Standard error t Pr(>|t|) Intercept 18.103 10.200 1.775 0.094 P. downsi 0.152 0.181 0.842 0.411 -254.410 119.413 -2.131 0.055 2.743 1.243 2.207 0.048 Prevalence P. downsi*Prevalence 3b: Studies of P. downsi only (Galapagos) Model = Cost of parasitism ~ prevalence; random effect = host species N= 9 observations, 8 host species. Fixed Effects Estimate Standard error Intercept -219.400 2.700 Prevalence t Pr(>|t|) 6.147 -25.690 <0.0001 0.001 1797.200 <0.0001 29 64 3c: Studies of native Philornis only (outside the Galapagos) Model = Cost of parasitism ~ prevalence; random effect = host species N= 14 observations, 13 host species. Fixed Effects Intercept Prevalence Estimate Standard error t Pr(>|t|) 18.536 10.074 1.840 0.091 0.144 0.181 0.796 0.441 * We used data from experimental or correlational studies reporting the prevalence and/or intensity and effects of Philornis on at least ten nesting pairs of a given host species. We excluded two studies in which fledging success of non-parasitized birds was essentially zero (31, 38). Data were extracted from published text and tables, or from figures using WebPlotDigitizer (63). We analyzed data using linear mixed-effects models in R using the ‘lme4' library with host species as a random effect and a Gaussian error distribution (7, 68). First we tested for significant differences in prevalence and intensity between P. downsi in the Galapagos and native Philornis in other localities (no studies of P. downsi effects in its native range have been published to our knowledge). Then, we modeled the effect of parasitism based on the following fixed effects: Philornis prevalence and parasite identity (P. downsi vs. other Philornis spp.), again including host species as a random effect. We ran models first with the interaction term, and then in the case of a significant interaction, we modeled the effect of prevalence and host mass on cost of parasitism separately for P. downsi vs. other Philornis spp. We did not make other comparisons between species of Philornis because about a quarter of the studies identified parasites only to genus. 30 65 Supplemental Table 4: Relationship between host mass, Philornis species (binary: P. downsi or other Philornis) and cost of parasitism (reduction in fledging success of parasitized nests compared to non-parasitized nests).* 4a: All studies Model = Cost of parasitism ~ mass* Philornis species, random effect = host species N= 23 observations, 21 host species. Fixed Effects Estimate Standard error t Pr(>|t|) (Intercept) 21.967 4.908 4.476 0.000 P. downsi 0.260 0.133 1.953 0.066 Host mass 4.493 10.517 0.427 0.674 -1.482 0.507 -2.924 0.009 P. downsi *Host mass 31 66 4b: Studies of P. downsi only (Galapagos) Model = Cost of parasitism ~ mass, random effect = host species N= 9 observations, 8 host species. Fixed Effects Estimate Standard error t Pr(>|t|) (Intercept) 26.460 9.820 2.694 0.036 Host mass -1.222 0.516 -2.367 0.056 4c: Studies of native Philornis only (outside the Galapagos) Model = Cost of parasitism ~ mass, random effect = host species N= 14 observations, 13 host species. Fixed Effects Estimate Standard error t Pr(>|t|) (Intercept) 21.967 4.776 4.599 <0.001 Host mass 0.260 0.129 2.007 0.066 * Data collected as in Table 3. We modeled the effect of parasitism based on the fixed effects of host mass (extracted from Handbook of Birds of the World) (14) and parasite identity (P. downsi vs. other Philornis spp.), including host species as a random effect. 32 67 Supplemental Table 5. Data used in analyses reported in Supplemental Tables 3 and 4. Parasite Host Prev.i Costii Nests Loc.iii Ref. 15 26.1 3.2 23 Native (1) 48.4 22.0 53.0 18 Native (6) 3.0 11 Native (9) Host mass P. torquans Phacellodomus sibilatrix Philornis sp. Gubernatrix cristata P. porteri Myiarchus 33.4 crinitus P. trinitensis Mimus gilvus 45.5 76.5 20.0 35 Native (10) P. trinitensis Tiaris bicolor 9.3 52.6 31.8 39 Native (10) Philornis sp. Suiriri affinis 19.75 88.9 0.0 25 Native (13) Philornis sp. Suiriri islerorum 22 90.0 22.0 20 Native (13) P. pici and P. Anumbius 36 18.0 4.5 50 Native (14) seguyi annumbi P. pici and P. Pseudoseisura 71 16.4 5.5 67 Native (14) seguyi lophotes P. seguyi Troglodytes 11.55 25.0 42.0 157 Native (17) 64 44.4 47.0 29 Native (18) 36.75 28.2 26.0 131 Native (19) aedon P. seguyi Mimus saturninus P. seguyi Paroaria coronata 33 68 Philornis sp. Troglodytes 11.55 8.4 10 13.5 3.0 214 Native (20) 100.0 37.0 88 Galapagos (3) 100.0 56.0 76 Galapagos (3) 100.0 52.0 22 Galapagos (7) 34.5 91.0 20.0 11 Galapagos (8) aedon P. downsi Certhida olivacea P. downsi Camarhynchus parvulus P. downsi Geospiza spp.iv P. downsi Platyspiza crassirostris P. downsi Mimus parvulus 53.7 90.9 1.1 66 Galapagos (11) P. downsi Geospiza fortis 25 86.2 38.7 61 Galapagos (11) P. downsi Geospiza fortis 25 100.0 76.0 43 Galapagos (12) P. downsi Camarhynchus 16 100.0 41.0 13 Galapagos (16) 14.5 100.0 73.0 14 Galapagos (15) 102.6 96.0 272 Puerto (2) pauper P. downsi Geospiza fuliginosa Margarops P. deceptivusv fuscatus Philornis sp. 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Unlike resistance, tolerance does not reduce parasite burden, but instead compensates for damage done to the host by the parasite. Compared to resistance, relatively little is known about mechanisms or conditions underlying tolerance. However, just as resistance is diminished under stressful conditions, tolerance may also depend on environmental conditions. Here we demonstrate variation in the tolerance of Galápagos mockingbirds (Mimus parvulus) to an introduced nest parasite, Philornis downsi. We experimentally manipulated P. downsi abundance in mockingbird nests over four field seasons to test the effects of parasitism on mockingbird reproductive success. The effect of parasite abundance on host fitness varied greatly among years. In years where rainfall and green vegetation were abundant, mockingbirds provisioned nestlings more, and nestlings were more tolerant of P. downsi. However, in years of low rainfall and less vegetation, mockingbird provisioning rates were lower and nestlings were less tolerant. These results demonstrate that tolerance can vary within a host population in response to environmental conditions. To our knowledge, this is the first 79 demonstration of environmentally-determined tolerance to a parasite or pathogen in a wild animal host. Introduction The two main categories of host defense against parasitism are resistance and tolerance (Boots 2008, Medzhitov et al. 2012, Kutzer and Armitage 2016). Resistance protects the host by reducing parasite load (Råberg et al. 2009). In contrast, tolerance protects the host by minimizing the negative effects of parasites on host fitness. Unlike resistance, tolerance does not select for counter-adaptations in the parasite because it does not reduce parasite fitness. Resistance and tolerance thus have different implications for host-parasite coevolution (Miller et al. 2005). Tolerance has been well studied in plantherbivore interactions (Fornoni 2011), but has received relatively little attention as a strategy for defense by animal hosts (Mazé-Guilmo et al. 2014, Kutzer and Armitage 2016, Zeller and Koella 2017). As a result, little is known about the mechanisms and conditions governing tolerance in animals, especially in wild populations. Resistance mechanisms, such as immune responses, have well-documented associations with host nutritional status and condition (Schmid-Hempel 2003, Cotter et al. 2011). Resistance is assumed to vary in part because it is costly, and thus dependent on resource availability (Schmid-Hempel 2003, Owen et al. 2010, Cressler et al. 2014). For example, in a study of several species of wild bovids, resistance to nematodes was lower in a drought year compared to a year of average rainfall (Ezenwa 2004). The negative effect of drought on parasite resistance was stronger for species with low-quality 80 diets, suggesting that lack of adequate nutrition in poor environmental conditions can mediate the emergence of disease. Tolerance may also be limited by poor environmental conditions. However, most theoretical and empirical studies of tolerance in animals assume that tolerance is a fixed characteristic of a host (Jokela et al. 2000, Roy and Kirchner 2000, Restif and Koella 2004, Little et al. 2010). As a result, most have focused on identifying differences in tolerance among populations or species (Råberg et al. 2007, Lefèvre et al. 2011, MazéGuilmo et al. 2014, Parker et al. 2014, Knutie et al. 2016, Manzoli et al. 2018). Only a few recent experiments have investigated how tolerance may vary with "extrinsic" environmental factors, such as diet (Kutzer and Armitage 2016). The effects of diet differ among systems: some studies report that tolerance increases on a high resource diet (Clough et al. 2016, Knutie et al. 2017, Zeller and Koella 2017). In contrast, others report that tolerance decreases on a high resource diet (Cornet et al. 2014, Howick and Lazzaro 2014). This published work suggests a complex relationship between host tolerance and environmental resources. However, none, to our knowledge, have documented variation in tolerance in a natural animal population. Moreover, few studies of tolerance, in wild or captive populations, measure host fitness directly (Adelman and Hawley 2017). The ability to mount a successful defense may be particularly important to species restricted to islands, which are particularly vulnerable to introduced parasites and pathogens (Mooney and Cleland 2001, Smith and Banks 2014). A significant threat to landbirds in the Galápagos Islands is Philornis downsi, an introduced parasitic nest fly (Kleindorfer and Sulloway 2016, McNew and Clayton 2018). Adult P. downsi are not parasitic; however, the females lay their eggs in birds' nests. Upon hatching, the larvae 81 feed on brooding female birds and their nestlings. Following three larval instars, the fly larvae pupate in the nest material. Philornis downsi causes high mortality in most native passerines in the Galápagos that have been studied, including the critically endangered mangrove finch (Camarhynchus heliobates) (Fessl et al. 2010). It is considered a threat to rare and common Galápagos finch species alike (Koop et al. 2016, McNew and Clayton 2018). Neither Darwin's finches, nor Galápagos mockingbirds, appear to have effective immunological of behavioral defenses against P. downsi (Koop et al. 2013b, Knutie et al. 2016). Galápagos mockingbirds are noteworthy in that they show tolerance to P. downsi. Knutie et al. (2016) found no effect of parasitism on fledging success over two years of experimental study. The key to mockingbird tolerance appears to be increased provisioning of parasitized nestlings, which may allow them to recover energy lost to P. downsi. Studies of nest parasites in other avian systems have similarly found that parents increase provisioning to help nestlings tolerate parasitism in what has been called the "parental compensation hypothesis" (Christe et al. 1996, Tripet and Richner 1997). In situations where the key to tolerance is compensatory provisioning of parasitized nestlings, successful defense may depend on food availability. Rainfall is notoriously variable in the Galápagos Islands, leading to "boom and bust" food cycles. Meteorological data collected since 1965 by the Charles Darwin Research Station on Santa Cruz include annual rainfall totals from 64mm of rain in an extremely dry year to 2,769mm during a strong El Niño year (Charles Darwin Foundation 2018). Rains trigger the growth of vegetation in the arid zone, which in turn leads to higher arthropod abundance. Breeding of mockingbirds and other Galápagos passerines occurs during the 82 rainy season (Curry and Grant 1990, Grant and Grant 2014), because larval arthropods form the bulk of nestlings' diet. Breeding success of passerines is reduced in dry years when arthropod abundance is low (Curry and Grant 1990, Grant and Grant 2014). In dry years, when food is limited, mockingbirds may not have the resources to compensate for the added burden of parasitism by P. downsi. The goal of the current study was to test if mockingbird tolerance to P. downsi varies with environmental conditions. We compared tolerance of mockingbirds in two years of higher rainfall (2012 and 2013) to two years of lower rainfall (2015 and 2016). We experimentally manipulated parasite abundance in mockingbird nests to test the direct effects of parasitism on nestling growth and fledging success. We explored whether tolerance was related to measures of environmental quality including rainfall, normalized difference vegetation index (NDVI) values and nestling blood isotope ratios. We predicted that provisioning rates of nestlings would be lower in dry years, resulting in lower tolerance to P. downsi. Methods Study site The study was conducted from January - April 2015 and 2016 on Santa Cruz Island in the Galápagos archipelago. Previously published data collected from January - April 2012 and 2013 (Knutie et al. 2016) are also used in the analyses. The study site, El Garrapatero, is a 3 x 4 km area in the arid coastal zone, approximately 10km east of the town of Puerto Ayora. Galápagos mockingbirds are common year-round residents at our study site. Following the onset of the rainy season, mockingbirds build open cup-shaped 83 nests in Acacia rorudiana trees or giant prickly pear cacti (Opuntia echios). They lay between 1 and 5 eggs, which are incubated by the female for about 15 days (Knutie et al. 2016). Nestlings are fed by both parents until they fledge at about 14 days of age (Knutie et al. 2016). Mockingbirds may renest following the failure of a clutch, or if the rainy season is unusually long. However, mockingbirds construct a new nest for each reproductive attempt (Knutie et al. 2016). Environmental variation We used two metrics to evaluate environmental conditions: rainfall data and normalized difference vegetation index (NDVI) data. First, we used daily precipitation data collected by the Charles Darwin Research Station in Puerto Ayora, about 10 km from our field site (Charles Darwin Foundation 2018). We used these data to calculate the average rainfall during the breeding period (date of the first egg laid through date the last nestling fledged each season across all nests). We also used these data to calculate the cumulative seasonal rainfall at hatching per nest (the previous December through the date of hatching for each nest). Second, we analyzed the relationship between rainfall at Puerto Ayora and vegetation at our field site using NDVI, an index of photosynthesizing vegetation based on the absorption and reflectance of light in satellite images. NDVI data have been used widely to characterize vegetation dynamics in arid and semiarid environments (Anyamba and Tucker 2005). We used the MODIS Global Subsetting and Visualization Tool to access filtered, scaled NDVI data from a representative 2.25 x 2.25 km square at the center of our field site collected every 16 days during 2000-2016 (Didan 2015, ORNL-DAAC 2017). NDVI values during the typical rainy season (December - 84 April) ranged from 0.339 - 0.735. The total rainfall at Puerto Ayora and the mean NDVI at our field site during the rainy season were highly correlated (P < 0.001; adjusted Rsquared = 0.59). For our study, we evaluated: 1) mean NDVI values during the breeding period each year, and 2) NDVI values on the date closest to hatching for each nest in order to obtain a snapshot of relative vegetation conditions for each nest at our field site. Experimental manipulation and parasite quantification To manipulate parasite abundance, nests were either fumigated with a 1% aqueous permethrin solution (PermectrinTM II), or sham-fumigated with water as a control. Permethrin has been used in several previous studies to eliminate P. downsi larvae (Fessl et al. 2006, Koop et al. 2013b, O'Connor et al. 2014, Knutie et al. 2016) and has minor, if any, effects on nestlings (López-Arrabé et al. 2014). Nests were sprayed soon after the first nestling hatched and then 5-6 days later. Nestlings, unhatched eggs, and the top layer of the nest were removed during the spraying process, then replaced after the nest dried (<10 min). Parents quickly returned to the nest following treatment; we observed no cases of nest abandonment due to treatment. We quantified P. downsi by collecting each nest after nestlings had died or fledged. Nests were carefully dissected within 8 h of collection and the parasites in each nest were counted (Koop et al. 2013a, 2013b, Knutie et al. 2016). Because first instar larvae are too small to count reliably, only second and third instar larvae were counted. Thus, total abundance of P. downsi for each nest was the total number of second and third instar larva and pupae in the nest. Second instar larvae usually died soon after collection because they lacked the resources to complete development; however, most third instar 85 larvae pupated within 24 h of collection. Third instar larvae and pupae were reared to the adult stage to confirm that they were P. downsi (no other fly taxa were found in nests). Nestling growth, condition, and fledging success In 2012, each nestling was measured within 24 h of hatching and then again at 910 days of age (Knutie et al. 2016). In 2013, 2015 and 2016 nestlings were measured three times: at hatching, at 5-6 days of age and at 10-11 days of age. At each sampling point body mass and tarsus length were recorded; at the second and third sampling points we also recorded the length of the first primary feather (Koop et al. 2011). At the second and third sampling points we took a small blood sample (<30 ul) via brachial venipuncture. Hemoglobin concentration was immediately quantified in the field using a HemoCue® HB 201+ portable analyzer and approximately 10ul of blood (hemoglobin was not measured in 2012). The remainder of each blood sample was kept on wet ice in the field. Within 6 h of collection, samples were spun at 8000 rpm for 10 min to separate plasma and erythrocytes, which were frozen separately in a -20 °C freezer. Samples were transported to the University of Utah in a liquid nitrogen dry shipper for analysis of nestling blood isotope values. Nestlings were banded with an individually numbered monel band and a unique combination of color bands at 9-11 days of age. Fledging success was determined by identifying banded individuals after they had left the nest (at about 15 days of age). 86 Estimation of tolerance Host tolerance is often measured as a reaction norm, i.e., the slope of the relationship between host fitness and parasite abundance (Simms 2000). A slope of zero indicates completely tolerant hosts (i.e., no relationship between parasite abundance and host fitness). Nonzero slopes indicate less tolerance, with more negative slopes showing progressively less tolerance. We thus quantified mockingbird tolerance each year as the slope of the relationship between P. downsi abundance and the fledging success at each nest. Nestling and adult behavior Behavior was recorded at nests in 2013, 2015 and 2016 from a haphazard subsample of study nests. Small bullet cameras (31mm x 36mm; Sony SC-IRB) were suspended over nests and connected to portable DVRs (Lawmate PV700 Hi-res DVR) hidden at the base of each tree supporting a nest. Nestling and adult behaviors were scored by one author (SMM) to avoid interobserver variation. Videos were analyzed using the software Boris (version 3.60; 62). The following behaviors were scored for adults: "brooding," "standing," "provisioning," "allopreening," and "nest sanitation" (Knutie et al. 2016). Brooding was defined as the female sitting in direct contact with nestlings (male mockingbirds do not brood). Standing was time that parents spent either standing on the rim or in the nest. Provisioning (feeding) nestlings was defined as insertion of the bill with food into the mouths of nestlings. Allopreening was defined as the adult picking at or preening nestlings. Nest sanitation was defined as the adult probing the nest or manipulating nest 87 material. Because nest sanitation occurred rarely (< 1% of video time, on average) we excluded it from the analyses. Brooding, standing, feeding, and allopreening were considered mutually exclusive behaviors. Although we previously reported provisioning from 2013 as the percentage of time that parents are in attendance at the nest (Knutie et al. 2016), attendance time varied substantially between 2013 and subsequent years. Thus, in the current paper, all behaviors were scored as the percentage of total video time. The following behaviors were scored for nestlings: "begging," "agitation," and "calm" as in Koop et al. (2013b) and Knutie et al. (2016). Begging was defined as one or more nestlings tilting their head back, with neck extended and open mouth showing (Christe et al. 1996). Agitation was defined as shaking, repositioning, or jumping in the nest. Calm was defined as nestlings sitting undisturbed in the nest without agitation or begging. Behaviors were scored as a percentage of total video time. Blood isotope values We analyzed carbon (δ13C) isotope ratios in 10-day-old nestlings to evaluate variation in their diet between treatments and among years. Carbon stable isotope values can be used to distinguish the contributions of different plants in the food chain because carbon fixed by C3 plants is more depleted in C13 isotopes compared to CAM and C4 plants (Kelly 2000, Wolf and Martinez del Rio 2000, Herrera et al. 2006). C3 vegetation is common at the El Garrapatero field site during rainy conditions. However, during the dry season, and in years with little rain, Opuntia cacti, which use CAM photosynthesis, are the predominant plants. Therefore, in years of more rainfall, we expect C3 plants to be the main producers, and we thus expect to see more depleted δ13C values in 88 mockingbirds. In contrast, in drier years we expect a greater contribution from CAM cacti and consequently more enriched δ13C values in nestlings. Stable isotope analysis was performed at the Stable Isotope Ratio Facility for Environmental Research (SIRFER) at the University of Utah. Five microliters of erythrocytes (ca. 0.5 mg) were pipetted into tin capsules and dried for 48h at 65° C. Samples were analyzed alongside a set of laboratory reference materials using an elemental analyzer attached to an isotope ratio mass spectrometer (EA-IRMS, Thermo Fisher Scientific, Bremen, Germany) operated in continuous flow mode. Laboratory reference materials consisted of two glutamic acids and ground bovine muscle. Results for carbon isotope ratios are presented on the Vienna Pee Dee Belemnite (VPDB) scale. Stable isotope ratios are reported using the standard δ-notation relative to an international standard in units per mil (‰) using the following: δX = (Rsample/Rstandard - 1) * 1000, where X is the isotope of interest, Rsample and Rstandard are the molar ratios of the heavy to the light isotopes (e.g.13C/12C) of the sample and international standard, respectively. Statistical analysis Analyses were conducted in RStudio (2016, version 1.0.136; R version 3.3.3). We ran linear models (LMs), generalized linear models (GLMs), and linear mixed models (LMMs) using the packages MASS, lmer4, nlme, lmerTest, car and smart. Degrees of freedom and P-values for LMMs were calculated using a Satterthwaite approximation with the lmerTest package. Parasite abundance, nestling measurements and behavioral data from 2012 and 2013 were published earlier (Knutie et al. 2016). For the current study we directly compared those raw data to new data from 2015 and 2016. 89 We tested for differences in parasite abundance among years and between treatments using a GLM assuming a negative binomial distribution. Year and treatment were considered factors. We used LMMs to test the hypothesis that the effect of parasitism on nestling growth and condition depends on environmental conditions. Nestling survival was analyzed with GLMs with a binomial error distribution (logistic regression). We analyzed the number of nestlings that fledged from each nest based on the fixed effects of treatment x year and treatment x rain. We estimated tolerance using a binomial GLM comparing the relationship between the number of nestlings that fledged from each nest and P. downsi abundance. "Tolerance" each year was defined as the estimated effect of abundance from the GLM. Nestling and adult behaviors were logit transformed and the analyzed with LMMs. We tested for differences among years (2013, 2015 and 2016), between treatments, and with rainfall. We included age as a fixed effect initially and nest as a random effect. When age was nonsignificant (most behaviors), it was removed from the model. We tested for interactions between treatment and year; when the interaction was nonsignificant, it was removed. Nestling measurements (mass, tarsus length, first primary feather length, and hemoglobin) were analyzed with the three-way-interaction of nestling age, treatment, and rain, with individual nested within mockingbird nest, and year as random effects. Rain was quantified as the cumulative amount of seasonal rainfall by the date of hatching for each nest. The three-way interaction term was not significant for hemoglobin, and so was removed. The intercept was set to Age 1 for mass and tarsus (the day of hatching), and Age 5 for hemoglobin, first primary feather length and glucose (the first day of 90 measurement). Nestling isotope ratios were compared using LMMs with rain, NDVI, and year as fixed effects and nest as a random effect. Results Environmental variation Rainfall varied substantially among study years (Table 3.1). The mean rainfall at hatching was significantly higher in 2012 and 2013 compared to 2015 and 2016 (LM P < 0.001, Table S3.1) and slightly higher in 2016 compared to 2015 (P = 0.049). NDVI values during the breeding season ranged from 0.59 (2015) to 0.62 (2012; Table 3.1). Mean NDVI was not significantly different among years (LM P > 0.05 for all year comparisons, Table S3.2), although only a maximum of six NDVI readings were available each season because the data are taken by satellite only every 16 days. Philornis downsi abundance We studied 30 - 35 nests per year, for a total of 131 nests (Table S3.3). Clutch size did not differ significantly among years or between treatments (LM, P > 0.18, Table S3.4). Fumigated nests had significantly fewer parasites than nests sham-fumigated with water in all four years (GLM, P < 0.001). Philornis downsi abundance in sham-fumigated nests did not vary significantly among years (GLM, P > 0.362 for year by year comparisons; Tables S3.3, S3.5-S3.6). 91 Nestling growth and condition All measurements (mass, tarsus and first primary feather length) increased with age (LMM P < 0.001, Table S3.7-S11). Hemoglobin also increased with age, consistent with increased erythropoiesis as nestlings develop (LMM P < 0.001; Table S3.11) (Fair et al. 2007). For mass and tarsus, the main effects of treatment and rain were not significant (P > 0.05), meaning that, at hatching, nestlings were the same size regardless of treatment or rain. However, the interactions of treatment x age, rain x age, and treatment x rain x age were all significant (P ≤ 0.05), meaning that, over time, size depended on both treatment and rain. For first primary feather, the interactions between treatment x age and treatment x rain x age were also significant (P < 0.001 and P = 0.009, respectively). Thus, growth of nestlings was reduced in the sham-fumigated treatment, particularly when rainfall was limited (Figure 3.1, Tables S3.7-S3.10). Hemoglobin was consistently lower in sham-fumigated nestlings (P < 0.001). The effect of treatment on hemoglobin did not vary with rain (main effect rain: P = 0.15; interactions P > 0.05; Table S3.11). Nestling isotope values Nestling δ13 C values did not differ between treatments (LMM P = 0.75). However, the values did differ significantly among years. δ13 C values were significantly enriched (higher) in 2015, relative to the other three years (LMM P < 0.001 for all comparisons; Figure 3.2, Table S3.12), and significantly depleted (lower) in 2012, compared to the other three years (LMM P < 0.04 for all comparisons). δ13 C values were significantly negatively correlated with NDVI and rainfall (LMM rain P = 0.03; NDVI P < 0.001; Table S3.13, Figure 3.S1). δ13 C values were also significantly negatively 92 correlated with nestling mass: larger nestlings had more depleted carbon values (LMM P = 0.01). Nestling fledging success The fledging success of fumigated nestlings did not differ significantly among years (Logistic regression, 2013: P = 0.52; 2015: P = 0.58; 2016: P = 0.19; Tables S3.1 and S3.14). There was no difference in fledging success between treatments in 2012 or 2013 (treatment: P = 0.978, treatment x year 2013: P = 0.446). However, fledging success of sham-fumigated nestlings was significantly lower in 2015 and 2016 (treatment x year 2015: P < 0.0001; 2016: P = 0.008). Across all years, rainfall had a positive effect on fledging success (logistic regression, rain = 0.044, Table S3.15), and parasitism had a negative effect on fledging success (treatment P < 0.001). However, there was a significant interaction between rainfall and treatment: the likelihood of fledging for sham-fumigated nestlings improved with more rain (treatment x rain P< 0.001). Tolerance Tolerance varied among years. P. downsi abundance was not significantly associated with fledging success in 2012 or 2013 (Logistic regression, 2012 P = 0.137; 2013 P= 0.28, Table S3.16, Figure 3.3). In contrast, P. downsi abundance was significantly negatively correlated with fledging success in 2015 (logistic regression, P = 0.003), and marginally associated with fledging success in 2016 (logistic regression P = 0.07). Tolerance was significantly lower in 2015. 93 Adult behavior We quantified behavior from 51 video observation periods during 2013, 2015 and 2016, each recorded at a nest between 0600h and 1000h, totaling 118h of video. On average, adults spent more time attending nests in 2013 than in 2015 or 2016 (LMM both years P < 0.001; Tables S3.17-S3.18). There was a significant interaction between year and treatment: in 2016 parents of sham-fumigated nests spent more than twice as much time at the nest as parents of fumigated nests (year 2016 x treatment P < 0.001, Table S3.20). Provisioning rates did not differ between 2013 and 2016, but were significantly lower in 2015 (LMM 2016 P = 0.37, 2015 P = 0.002, Table S3.19). Provisioning rates did not differ significantly between treatments when years were analyzed separately or together (P > 0.05), nor was provisioning affected by rainfall on the date of hatching for a given nest (LMM, P = 0.286). However, tolerance increased linearly with mean provisioning rate (LM R2 = 0.99, P = 0.009; Figure 3.4). Parents at sham-fumigated nests tended to allopreen their nestlings more than parents at fumigated nests, but the trend was not significant, overall (LMM, P = 0.09, Table S3.20). The difference was significant in one year: parents of sham-fumigated nests in 2016 spent significantly more time allopreening than any other group (year 2016 x treatment P = 0.034). However, time spent allopreening was not correlated with parasite intensity in sham-fumigated nests (LMM, P = 0.709, Table S3.21). The proportion of time females spent brooding decreased with nestling age (LMM, P = 0.005, Table S3.22). Females spent significantly less time brooding chicks in 2015 and 2016 than in 2013 (2015 P < 0.001; 2016 P = 0.001). Across all years, brooding 94 time did not differ between treatments (P = 0.316); however, when years were analyzed separately, 2013 females brooded sham-fumigated chicks less than fumigated chicks (LMM, P = 0.024). Parents spent significantly less time standing in or at the nest in 2015 compared to 2013 or 2016 (LMM P = 0.002, Table S3.23); however, time standing did not differ between treatments (P = 0.200). Nestling behavior In all years, nestling begging increased with nestling age (LMM, age P = 0.033, Table S3.24) and there was a trend towards sham-fumigated nestlings begging more than fumigated nestlings (treatment P = 0.075; Tables S3.4, S3.24). Nestlings begged significantly more in 2015 and 2016 than in 2013 (2015 P = 0.007; 2016 P < 0.001). Nestlings were also significantly more agitated in 2015 and 2016 compared to 2013 (LMM, 2015 P = 0.026; 2016 P = 0.012, Table S3.25). However, agitation did not differ significantly between treatments (P = 0.13), nor was it associated with nestling age (P = 0.27). Combining agitation and begging, nestlings were significantly more active (i.e. less "calm") in 2015 and 2016 than in 2013 (LMM, both years P < 0.001, Table S3.26) and more active in sham-fumigated nests than in fumigated nests (P = 0.009). Discussion Introduced parasites and pathogens pose a threat to host populations (van Riper et al. 1986, Smith and Banks 2014). Hosts can avoid potential costs of parasitism if they are able to mount a successful defense. Knutie et al. (2016) reported that Galápagos mockingbirds tolerate the effects of P. downsi, based on two seasons of experimental 95 field studies in 2012 and 2013. These results were notable because mockingbirds appear to be one of the few native Galápagos passerines that do not suffer a reduction in reproductive success due to P. downsi. The data presented herein show that tolerance is not a constitutive trait of Galápagos mockingbirds. In two successive years of study, we documented low tolerance in one year (2015) and intermediate tolerance in another year (2016) (Figure 3.3). The ability of mockingbirds to adequately provision their nestlings may be key for tolerance. Across years, estimates of tolerance in our study increased linearly with provisioning rates (Figure 3.4). In years of lower provisioning, nestlings appeared to lack adequate resources to compensate for the effects of P. downsi. Tolerance was higher in years with more rainfall and higher NDVI (vegetation) values. The growth and fledging success of parasitized nestlings were directly associated with rainfall levels; in dry conditions sham-fumigated nestlings were significantly smaller and less likely to fledge. Carbon stable isotopes from nestlings also reflected environmental variation among years (Figure 3.2). Blood δ13 C values were strongly correlated with NDVI and rainfall. More depleted δ13 C values from tolerant years indicate a greater abundance of C3 plants at the base of the food web, consistent with strong vegetative growth in rainy years (See Methods; 42). In contrast, the significantly enriched values from 2015, the year with lowest tolerance, suggested that C3 plants were less abundant (Wolf and Martinez del Rio 2000). The lower abundance of C3 plants in 2015 likely corresponded with lower arthropod abundances and consequently, reduced provisioning rates that year. Other behavioral responses of nestling and parent mockingbirds may contribute to tolerance or a lack thereof. Nestlings begged and were agitated significantly more in 96 sham-fumigated nests and in dry conditions (Table S3.4). Estimates of the energetic costs of nestling activity vary, but begging and agitation may represent a significant proportion of the nestlings' daily energy budget (Bachman and Chappell 1998, Kilner 2001, Moreno-Rueda et al. 2012). Mockingbird nestlings may increase begging and agitation in response to P. downsi; however, they may incur a substantial energetic cost as a result. Extra energy expenditure may have reduced the ability of the nestlings to tolerate P. downsi in dry conditions. Likewise, some behavioral responses by parents may have exacerbated the effects of parasitism. Females spent nearly twice as much time brooding nestlings in the tolerant year (2013) as in subsequent years (Table S3.4). In some years females may have reduced brooding to avoid being fed on by larvae (Koop et al. 2013b), or to increase provisioning capacity of nestlings (Moreno 1987). Brooding allows nestlings to devote most of their energy to rapid growth during the first days after hatching (Diehl and Myrcha 1973, Klaassen et al. 1994). Thus, in our study, reduced brooding by females in less tolerant years may have resulted in a higher thermoregulatory burden for nestlings, reducing energy they could devote to growth. Variation in mockingbird tolerance could theoretically affect other hosts of P. downsi in the community. Tolerant hosts may serve as reservoirs that maintain the parasite population (Haydon et al. 2002, Samuel et al. 2011, Knutie et al. 2016). The reduction of tolerance in mockingbirds, in contrast, may have a negative effect on P. downsi's population size. As a result, the breeding success of vulnerable hosts, such as most species of Darwin's finches, could conceivably increase. However, in our study, the abundance of P. downsi in mockingbird nests did not differ among years. Most sham- 97 fumigated nests that failed in 2015 and 2016 survived at least to the 5-6 day mark, which may be enough time for P. downsi to complete the larval stages (McNew and Clayton 2018). Thus, mockingbirds still have the potential to serve as reservoir hosts even in lesstolerant years. To our knowledge, this is the first demonstration of resource-dependent tolerance to a parasite or pathogen in a natural animal host population. Our results indicate that tolerance depends on adequate provisioning, which is dependent on food availability. The idea that poor host nutritional status and environmental conditions can promote disease emergence is not new; however, it is typically thought of in the context of immune function (i.e., resistance) (Tompkins et al. 2015). Although many studies assume that tolerance is costly, few have investigated the conditions required to mount a successful defense, and how tolerance as a defense varies with environmental conditions. The interaction between tolerance and the environment has important implications for understanding disease ecology in a changing world. Hosts rarely resist all parasites; instead, healthy hosts may compensate for the effects of some parasites and pathogens by adjusting their food intake or other behaviors. Climate change may increase the frequency of extreme climatic events (Vasseur et al. 2014), which could exacerbate poor conditions in harsh environments, such as the Galápagos Islands. Stressful conditions may diminish tolerance to parasites and pathogens, potentially mediating an increase in outbreaks of disease. 98 Acknowledgments We thank the Galápagos National Park and Charles Darwin Foundation for logistical support and permits to do fieldwork in the Galápagos. We are grateful for field assistance from Emily DiBlasi, Jordan Herman, Daniela Vargas, Oliver Tiselma, Andrew Bartlow and Elena Arriero. We thank Jeb Owen, Fred Adler, Franz Goller, Scott Villa, Monte Neate-Clegg, and Phillip Dennison for helpful discussion. We are grateful to the SIRFER facility and Christy Mancuso, Suvankar Chakraborty, and Jim Ehleringer for supporting the isotopic analysis and for helpful discussion. This work was supported by NSF grant DEB- 0816877 to DHC, an NSF Graduate Research Fellowship and a University of Utah Global Change and Sustainability Center (GCSC) Research Grant to SMM, and a University of Utah GCSC Grant, a University of Utah Graduate Research Fellowship, and Frank Chapman Research Grant to SAK. All applicable institutional guidelines for the care and use of animals were followed. References Adelman, J. S., and D. M. Hawley. 2017. Tolerance of infection: a role for animal behavior, potential immune mechanisms, and consequences for parasite transmission. Hormones and Behavior 88:79-86. Anyamba, A., and C. J. Tucker. 2005. 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Climatic conditions during the four mockingbird breeding seasons Year 2012 2013 2015 2016 24 Jan - 29 Jan - 2 Feb - 3 Jan - 9 Apr 7 Apr 3 Apr 2 Mar Mean ± SE cumulative seasonal 182.30 ± 142.18 ± 69.54 ± 1.41 79.93 ± 1.62 rainfall prior to hatching (mm) 5.74 4.08 Mean ± SE daily rainfall during 4.68 ± 1.29 2.30 ± 0.74 1.83 ± 1.01 0.86 ± 0.41 0.621 ± 0.06 0.648 ± 0.04 0.593 ± 0.05 0.610 ± 0.02 Breeding period of mockingbirds breeding period (mm) Mean ± SE NDVI during breeding period 45 105 Fumigated 30 20 25 Mass (g) 35 40 Sham-fumigated 80 100 120 140 160 180 Rain (mm) Figure 3.1. Relationship between rainfall and mass of nestlings at 9-11 days of age. Lines indicate regression lines for fumigated (black, N = 139) and sham-fumigated (grey, N = 120) nestlings. Fumigated nestling mass is not significantly associated with rainfall; in contrast, sham-fumigated nestling mass is positively associated with rainfall (Table S3.11). 106 -20 c -22 a -24 -23 b -26 -25 δ13 Carbon (‰) -21 b 2012 2013 2015 2016 Year Figure 3.2. Boxplot showing the δ13 carbon values from nestling blood over the four field seasons (N = 92). Differences between treatments were not significant. Letters indicate significant differences among years (Table S3.15). 107 100 2013 Fledging success (%) 40 60 80 2012 2016 0 20 2015 0 50 100 150 Mean abundance 200 Figure 3.3. Relationship between abundance of P. downsi and mockingbird fledging success. Each point represents an individual nest (N = 131). Points have been jittered for clarity. Logistic regression lines show the relationship between abundance and fledging success where more negative slopes indicate lower tolerance. Fledging success was not significantly associated with abundance in 2012 or 2013 (slopes not significantly different from 0). Fledging success was marginally associated with abundance in 2016, but was significantly negatively correlated with abundance in 2015 (Table S3.3). 2015 2016 -0.02 -0.01 2013 -0.05 -0.04 -0.03 Tolerance 0.00 0.01 108 1.0 1.5 2.0 2.5 Time provisioning (%) Figure 3.4. Relationship between mean (± SE) amount of time that parents spent provisioning nestlings and tolerance (± SE) for that year. Provisioning data were not collected in 2012. Annual provisioning rates were calculated from 15 observations in 2013 and 18 observations each 2015 and 2016. Annual tolerance was calculated from 34 nests in 2013, 35 nests in 2015 and 30 nests in 2016. 109 Supplemental tables and figures Table S3.1. Linear model comparing rainfall at the time of nest hatching.* Rain at hatching (Intercept) B CI P 69.54 62.51 - 76.58 <.001 relevel(year, ref = "2015")** 2012 112.76 102.58 - 122.94 <.001 2013 72.64 62.62 - 82.66 <.001 2016 10.39 0.04 - 20.75 .049 Observations R2 / adj. R2 131 .832 / .828 * Rainfall at time of hatching was calculated as the cumulative rainfall from December 1st of the previous year until the hatch date of each nest. ** The model has been leveled to 2015, the year with the lowest rainfall, i.e., P values reflect significant differences between any particular year and 2015. 110 Table S3.2. Linear model comparing breeding season NDVI among years. Mean NDVI during breeding B CI p 0.62 0.52 - 0.72 <.001 2013 0.03 -0.11 - 0.16 .671 2015 -0.03 -0.18 - 0.12 .694 2016 -0.01 -0.15 - 0.13 .877 (Intercept) year Observations R2 / adj. R2 20 .045 / -.134 111 Table S3.3. Comparison of P. downsi abundance and fledging success in fumigated (F) vs. sham-fumigated (SF) nests. Year 2012* 2013 2015 2016 F SF F SF F SF F SF Total nests (nestlings) 16 (51) 16 (54) 16 (44) 18 (57) 18 (51) 17 (52) 14 (44) 16 (45) Mean ± SE abundance 0.625 71.63 0.353 53.44 1.17 ± 57.29 0.643 51.00 of P. downsi ± 0.43 ± ± ± 0.39 ± 8.7 ± ±12.01 17.27 0.256 10.82 76.47 77.77 70.45 66.67 56.87 19.23 65.90 15.55 87.5 87.5 75.00 77.77 88.89 35.29 78.57 31.25 Percent nestlings that 0.643 fledged. Percent of nests that fledged at least one nestling *Mean ± SE rainfall in mm at hatching for each year: 2012: 182.30 ± 5.74; 2013: 142.18 ± 4.08; 2015: 69.54 ± 1.41; 2016: 79.93 ± 1.62 (Table 1). 112 Table S3.4. Linear model testing for differences in clutch size among years. Clutch Size B CI p 3.28 2.97 - 3.59 <.001 2013 -0.31 -0.74 - 0.12 .154 2015 -0.34 -0.76 - 0.09 .118 2016 -0.31 -0.76 - 0.13 .162 (Intercept) year Observations R2 / adj. R2 131 .025 / .002 113 Table S3.5. Negative binomial generalized linear model comparing P. downsi abundance between treatments and among years. Abundance IRR CI p 0.77 0.42 - 1.42 .381 2013 0.72 0.36 - 1.43 .362 2015 1.13 0.56 - 2.26 .735 2016 0.80 0.39 - 1.66 .548 85.03 50.25 - 145.48 <.001 (Intercept) year treatment (W)* Observations 130 *Treatments were: fumigated with permethrin ("P"), or sham-fumigated with water ("W"). 114 Table S3.6. Negative binomial generalized linear model comparing abundances of P. downsi among years in sham-fumigated nests only. Abundance, only sham-fumigated IRR CI p 71.62 44.25 - 126.76 <.001 2013 0.75 0.36 - 1.53 .424 2015 0.80 0.38 - 1.67 .548 2016 0.71 0.33 - 1.53 .376 (Intercept) year Observations 66 Table S3.7. Comparison of nestling size and hemoglobin concentration in fumigated (F) vs. sham-fumigated (SF) nests. Year 2012* F Mean (± SE) mass of 9-11 day old nestlings (g) 2013 SF F 2015 SF F 2016 SF F SF 33.94 ± 32.94 ± 36.15 ± 34.9 ± 34.18 ± 30.33 ± 35.87 ± 31.2 ± 0.58 0.77 0.57 0.85 0.98 1.25 0.83 1.48 Mean (± SE) tarsus length of 9-11 day old nestlings 31.18 ± 31.51 ± 32.3 ± 0.3 31.99 ± 32.01 ± 31.18 ± 32.71 ± 29.88 ± (mm) 0.29 0.3 0.38 0.47 0.66 0.3 0.86 Mean (± SE) first primary length of 9-11 day old 15.43 ± 15.12 ± 17.31 ± 16.60 ± 17.13 ± 14.26 ± 17.74 ± 14.42 ± nestlings (mm) 0.46 0.39 0.46 0.57 0.87 0.75 0.45 0.79 Mean (± SE) hemoglobin concentration 9-11 day old --- --- 10.58 ± 7.84 ± 9.54 6.83 ± 10.45 ± 4.36 ± 0.24 0.41 ±0.24 0.54 0.23 0.48 nestlings (g/dL) *Mean ± SE rainfall at hatching for each year: 2012: 182.30 ± 5.74; 2013: 142.18 ± 4.08; 2015: 69.54 ± 1.41; 2016: 79.93 ± 1.62 (Table 3.1) 115 115 116 Table S3.8. Linear mixed effects model of treatment, rainfall and age on nestling mass. Nestling mass B CI p (Intercept) 3.75 2.32 - 5.17 <.001 treatmentW 0.22 -1.75 - 2.19 .825 rain -0.00 -0.01 - 0.01 .552 I(age - 1) 3.07 2.88 - 3.27 <.001 treatmentW:rain -0.00 -0.02 - 0.01 .937 treatmentW:I(age - 1) -0.58 -0.88 - -0.29 <.001 rain:I(age - 1) 0.00 0.00 - 0.00 <.001 treatmentW:rain:I(age - 1) 0.00 -0.00 - 0.00 .052 Fixed Effects Random Effects Residual variance σ2 8.313 Variance (τ00), band:yearnest 1.206 τ00,, yearnest 1.488 τ00 year 0.000 Nband:yearnest 392 Nyearnest 131 Nyear 4 Intraclass Correlation Coefficient (ICC) band:yearnest 0.110 ICCyearnest 0.135 ICCyear 0.000 Observations R 2 / Ω 02 832 .957 / .957 117 Table S3.9. Linear mixed effects model of treatment, rainfall and age on tarsus length. Nestling tarsus length B CI p (Intercept) 7.00 5.82 - 8.18 <.001 treatmentW 0.39 -0.76 - 1.54 .504 rain -0.00 -0.01 - 0.01 .765 I(age - 1) 2.42 2.31 - 2.52 <.001 treatmentW:rain -0.00 -0.01 - 0.01 .629 treatmentW:I(age - 1) -0.29 -0.45 - -0.13 <.001 rain:I(age - 1) 0.00 0.00 - 0.00 <.001 treatmentW:rain:I(age - 1) 0.00 0.00 - 0.00 .023 Fixed Effects Random Effects σ2 2.528 τ00, band:yearnest 0.791 τ00, yearnest 0.478 τ00, year 0.178 Nband:yearnest 393 Nyearnest 131 Nyear 4 ICCband:yearnest 0.199 ICCyearnest 0.120 ICCyear 0.045 Observations R 2 / Ω 02 838 .980 / .980 118 Table S3.10. Linear mixed effects model of treatment, rainfall and age on primary length. Nestling first primary length B CI p (Intercept) 0.36 -1.32 - 2.04 .678 treatmentW 0.96 -1.20 - 3.13 .385 rain 0.01 -0.00 - 0.03 .132 I(age - 5) 2.85 2.55 - 3.16 <.001 treatmentW:rain -0.01 -0.03 - 0.01 .299 treatmentW:I(age - 5) -0.94 -1.35 - -0.52 <.001 rain:I(age - 5) -0.00 -0.00 - 0.00 .747 treatmentW:rain:I(age - 5) 0.01 0.00 - 0.01 .006 Fixed Effects Random Effects σ2 2.963 τ00, band:yearnest 1.294 τ00, yearnest 1.836 τ00, year 0.039 Nband:yearnest 315 Nyearnest 117 Nyear 4 ICCband:yearnest 0.211 ICCyearnest 0.299 ICCyear 0.006 Observations R 2 / Ω 02 457 .964 / .963 119 Table S3.11. Linear mixed effects model of treatment, rainfall and age on hemoglobin. Nestling hemoglobin concentration B CI p (Intercept) 4.18 3.19 - 5.17 .006 treatment (W) -2.87 -3.45 - -2.28 <.001 rain 0.01 0.00 - 0.02 .157 age 0.43 0.38 - 0.49 <.001 Fixed Effects Random Effects σ2 1.654 τ00, band:yearnest 0.000 τ00, yearnest 1.321 τ00, year 0.013 Nband:yearnest 216 Nyearnest 85 Nyear 3 ICCband:yearnest 0.000 ICCyearnest 0.442 ICCyear 0.004 Observations R 2 / Ω 02 326 .794 / .791 120 Table S3.12. Linear mixed effects model of year on nestling carbon isotope values. Nestling carbon isotopes B CI p (Intercept) -23.92 -24.37 - -23.47 <.001 relevel(year, ref = "2013") (2012) -0.74 -1.36 - -0.13 .020 relevel(year, ref = "2013") (2015) 1.77 1.23 - 2.30 <.001 relevel(year, ref = "2013") (2016) -0.11 -0.70 - 0.49 .728 Fixed Effects Random Effects σ2 0.743 τ00, nest 0.089 Nnest ICCnest Observations R 2 / Ω 02 49 0.107 92 .661 / .659 121 Table S3.13. Linear mixed effects model of rain and NDVI on nestling carbon isotope values. Nestling carbon isotopes B CI p (Intercept) -17.87 -19.34 - -16.41 <.001 rain -0.01 -0.01 - -0.00 .032 ndvi -7.97 -10.95 - -4.99 <.001 Fixed Effects Random Effects σ2 0.831 τ00, nest 0.117 Nnest ICCnest Observations R 2 / Ω 02 49 0.123 92 .623 / .619 122 Table S3.14. Binomial generalized linear model of differences among years and between treatments in fledging success. Fledging success (cbind #fledged/#died for each nest) Odds Ratio CI p 2.38 1.28 - 4.72 .009 2012 1.49 0.59 - 3.83 .404 2015 0.55 0.23 - 1.28 .173 2016 0.81 0.33 - 1.99 .647 treatmentW 0.72 0.31 - 1.65 .448 relevel(year, ref = "2013")2012:treatmentW 1.36 0.39 - 4.78 .626 relevel(year, ref = "2013")2015:treatmentW 0.25 0.07 - 0.84 .025 relevel(year, ref = "2013")2016:treatmentW 0.13 0.03 - 0.48 .003 (Intercept) relevel(year, ref = "2013") Observations 131 123 Table S3.15. Binomial generalized linear model of the effects of rain and treatment on fledging success. Fledging success (cbind #fledged/#died for each nest) Odds Ratio CI p (Intercept) 0.99 0.45 - 2.16 .970 cumulative.rain 1.01 1.00 - 1.01 .045 treatmentW 0.03 0.01 - 0.10 <.001 cumulative.rain:treatmentW 1.02 1.01 - 1.03 <.001 Observations 131 124 Table S3.16. Yearly tolerance based on logistic regression of the effect of abundance on fledging success per nest each year. Year (number of nests) Tolerance* 95% CI P-value 2012 (N = 32) -0.005 -0.012 - 0.002 0.137 2013 (N = 34) -0.005 -0.013 - 0.004 0.284 2015 (N=35) -0.02 -0.042 - -0.01 0.003 2016 (N=30) -0.01 -0.026 - 0.00 0.07 *Tolerance is the slope of the relationship between P. downsi abundance and fledging success. A slope of 0 indicates complete tolerance; significant negative slopes indicate lower tolerance. Table S3.17. Behavior of adult and nestling mockingbirds in fumigated (F) and sham-fumigated (SF) nests. Year 2013 2015 2016 (N = 15) (N = 18) (N = 18) F SF F SF F SF Attending 52.44 ± 5.43 46.64 ± 2.38 20.81 ± 5.6 28.89 ± 5.24 18.09 ± 3.19 53.8 ± 2.97 Provisioning 1.96 ± 0.25 2.33 ± 0.41 1.2 ± 0.35 1.14 ± 0.37 2.03 ± 0.17 1.56 ± 0.25 Allopreening 5.1 ± 1.91 9.51 ± 1.67 4.68 ± 1.35 12.44 ± 3.83 3.14 ± 1.12 29.41 ± 6.77 Brooding 39.73 ± 6.07 17.43 ± 4.01 10.42 ± 5.37 6.39 ± 2.26 6.62 ± 2.88 9.64 ± 6.55 Standing 4.7 ± 0.71 11.61 ± 3.97 3.45 ± 0.6 3.7 ± 0.99 5.95 ± 0.85 6.1 ± 0.88 Begging 2.86 ± 0.42 5.35 ± 0.77 8.16 ± 1.75 11.63 ± 2.34 11.37 ± 1.41 11.9 ± 2.69 Agitated 0.16 ± 0.08 0.94 ± 0.41 1.53 ± 0.38 1.31 ± 0.53 1.11 ± 0.26 2.28 ± 0.97 Calm 96.64 ± 0.5 89.15 ± 3.13 81.94 ± 3.44 74.85 ± 5.07 87.09 ± 1.44 81.3 ± 4.94 Adults* Nestlings *The total amount of time that parents spent present at the nest ("Attending"), as well as the primary behaviors of parents while at the 125 nest are listed. Values are the mean ± SE percent of total video time. 125 126 Table S3.18: Linear mixed effect model of year and treatment on the time that parents spent present at the nest. logit(present) B CI p (Intercept) 0.09 -0.47 - 0.65 .756 year2015 -1.78 -2.51 - -1.04 <.001 year2016 -1.75 -2.49 - -1.02 <.001 treatmentW -0.23 -1.00 - 0.54 .566 year2015:treatmentW 0.91 -0.14 - 1.95 .097 year2016:treatmentW 2.04 1.00 - 3.09 <.001 Fixed Effects Random Effects σ2 0.578 τ00, yearnest 0.000 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.000 51 .552 / .552 127 Table S3.19: Linear mixed effect model of year and treatment on the time that parents spent provisioning nestlings. logit(feeding.nestlings) B CI p (Intercept) -3.05 -3.18 - -2.92 <.001 year (2015) -0.25 -0.41 - -0.10 .002 year (2016) -0.07 -0.22 - 0.08 .376 treatment (W) -0.02 -0.15 - 0.10 .706 Fixed Effects Random Effects σ2 0.045 τ00, yearnest 0.003 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.058 51 .300 / .293 128 Table S3.20: Linear mixed effect model of year and treatment on the time that parents spent allopreening at the nest. logit(allopreening) B CI p (Intercept) -3.35 -4.19 - -2.52 <.001 year2015 -0.18 -1.28 - 0.92 .748 year2016 -0.58 -1.70 - 0.55 .321 treatmentW 0.99 -0.15 - 2.13 .097 year2015:treatmentW 0.17 -1.41 - 1.74 .838 year2016:treatmentW 1.80 0.20 - 3.41 .035 Fixed Effects Random Effects σ2 1.016 τ00, yearnest 0.247 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.196 51 .629 / .615 129 Table S3.21: Negative binomial generalized linear model of parasite abundance and time that parents spent allopreening. P. downsi abundance IRR CI p (Intercept) 33.24 21.59 - 51.17 <.001 allopreening 1.00 0.99 - 1.01 .709 Fixed Effects Random Effects Nyearnest 19 Observations 24 130 Table S3.22: Linear mixed effect model of year and treatment on the time that parents spent brooding nestlings. Brooding B CI p (Intercept) 0.54 -0.57 - 1.66 .344 year (2015) -1.50 -2.25 - -0.75 <.001 year (2016) -1.42 -2.21 - -0.63 .001 treatment (W) -0.32 -0.93 - 0.30 .316 age -0.31 -0.52 - -0.11 .005 Fixed Effects Random Effects σ2 0.905 τ00, yearnest 0.222 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.197 51 .637 / .623 131 Table S3.23: Linear mixed effect model of year and treatment on the time that parents spent standing at the nest. logit(standing) B CI p (Intercept) -2.83 -3.23 - -2.42 <.001 year (2015) -0.81 -1.29 - -0.33 .003 year (2016) -0.14 -0.64 - 0.35 .573 treatment (W) 0.27 -0.13 - 0.67 .200 Fixed Effects Random Effects σ2 0.343 τ00, yearnest 0.126 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.268 51 .628 / .574 132 Table S3.24: Linear mixed effect model of year and treatment on the time that nestlings spent begging. logit(begging) B CI p (Intercept) -2.89 -3.20 - -2.57 <.001 year (2015) 0.58 0.21 - 0.96 .004 year (2016) 0.86 0.46 - 1.25 <.001 treatment (W) 0.32 0.01 - 0.64 .052 Fixed Effects Random Effects σ2 0.109 τ00, yearnest 0.165 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.601 51 .891 / .862 133 Table S3.25: Linear mixed effect model of year and treatment on the time that nestlings were agitated. logit(agitation) B CI p (Intercept) -3.72 -4.04 - -3.39 <.001 year (2015) 0.25 0.02 - 0.49 .038 year (2016) 0.29 0.04 - 0.54 .027 treatment (W) 0.14 -0.06 - 0.33 .169 age 0.03 -0.03 - 0.09 .275 Fixed Effects Random Effects σ2 0.043 τ00, yearnest 0.059 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.580 51 .873 / .811 134 Table S3.26: Linear mixed effect model of year and treatment on the time that nestlings were calm (neither agitated nor begging). logit(calm) B CI p (Intercept) 3.25 2.76 - 3.73 <.001 year (2015) -1.47 -2.05 - -0.89 <.001 year (2016) -1.12 -1.72 - -0.51 <.001 treatment (W) -0.69 -1.17 - -0.20 .009 Fixed Effects Random Effects σ2 0.276 τ00, yearnest 0.373 Nyearnest ICCyearnest Observations R 2 / Ω 02 40 0.574 51 .889 / .868 0.75 135 NDVI Rain (mm) 0.45 80 0.50 100 0.55 120 0.60 NDVI 140 0.65 160 0.70 180 Rainfall −26 −25 −24 −23 −22 −21 −20 δ13 C (‰) Supplemental Figure S3.1. Nestling carbon isotope values are negatively correlated with both NDVI (LM slope = -7.97, P < 0.001) and rainfall (slope = 0.006, P = 0.03). CHAPTER 4 INDIRECT COSTS OF PHILORNIS DOWNSI TO FUTURE REPRODUCTION IN GALÁPAGOS MOCKINGBIRDS Abstract Animals have limited resources to allocate among various life-history traits. Increased investment in one activity, such as reproduction, can often result in a trade-off in another, such as survival. In this study we investigated whether the introduction of a novel parasite can affect resource allocation in an avian host. We studied the effects of the invasive nest parasite, Philornis downsi, on condition, survival, and investment in future reproduction in Galápagos mockingbirds (Mimus parvulus). We experimentally manipulated P. downsi abundance by fumigating nests with a mild insecticide. P. downsi reduced the fledging success of nestlings but also had a surprising effect on future reproductive investment of parents: parents whose first brood was parasitized by P. downsi reduced the second brood size when they renested. These results demonstrate that parasitism by P. downsi can have carry-over effects for future reproductive investment of mockingbirds. 137 Introduction Birds have the ability to control their investment in any particular reproductive attempt. Individuals adjust such aspects of reproduction as the timing of breeding (Brinkhof et al. 2002), the number of eggs in a clutch (Monaghan and Nager 1997), and how long they care for young after fledging (Verhulst et al. 1997). Experimental studies demonstrate that parents have the ability to produce more eggs and fledge more young than they generally do under natural conditions (Monaghan and Nager 1997, Monaghan et al. 1998, Ardia 2005). Given that natural selection should favor those who produce the greatest number of offspring, what limits individuals from investing more in a reproductive attempt? The intuitive answer to this question is that reproduction is costly, and investment in reproduction trades-off with investment with other life history traits (van Noordwijk and de Jong 1986, Gustafsson et al. 1994, Harshman and Zera 2007). Because birds typically have more than one opportunity to reproduce, individuals maximize their fitness by maximizing the number of offspring over all reproductive attempts, rather than maximizing the number of fledglings from any particular clutch (Lack 1947, 1954, van Noordwijk and de Jong 1986). Consistent with predictions, empirical studies find that investment in current reproduction trades off with investment in self-maintenance (Hanssen et al. 2005), immune function, (Gustafsson et al. 1994), and future reproduction (Brinkhof et al. 2002). The optimum balance of investment in reproduction versus survival may vary according to the likelihood of future reproductive opportunities. For example, investment in immunocompetence versus reproduction differs between two populations of migratory 138 tree swallows (Tachycineta bicolor) (Ardia 2005). In Alaska, where annual return rates from wintering grounds are low and individuals generally have few opportunities to reproduce, swallows increase investment in the current reproductive attempt at the expense of immunocompetence. In Tennessee, where return rates are higher and individuals therefore have more opportunities to reproduce, they maintain immune function at the expense of the current reproductive effort. Thus, long-lived birds that generally have multiple opportunities to reproduce are expected to maintain body condition and survival at the expense of any particular reproductive attempt. The intensity of trade-offs between reproduction and other life history traits is mediated by environmental conditions including resource availability and the presence of parasites and pathogens (Lope et al. 1993, Gustafsson et al. 1994, Erikstad et al. 1998, Descamps et al. 2009). In good environmental conditions birds have more resources to allocate to different tasks. Conversely, stressful conditions may increase the energy needed to survive, and exacerbate life-history trade-offs. For example, experimentally increasing clutch size in common eiders (Somateria mollissima) during a cholera outbreak resulted in reduced parental survival the following year (Descamps et al. 2009). However, in years in which avian cholera was absent, eiders could incubate a larger clutch with no cost to next-year survival. As a result, birds adjust reproductive investment in response to environmental conditions. For instance, birds may reduce clutch sizes or number of nesting attempts when food is limited (Wiggins et al. 1994, Husby et al. 2009, Grant and Grant 2014). The introduction of a novel stressor, such as a parasite or pathogen, may force birds to adjust resource allocation. In the Galápagos Islands, the invasive nest parasite 139 Philornis downsi drastically reduces the reproductive success of many species of native passerines and is considered a significant threat to the survival of some species (Causton et al. 2006, McNew and Clayton 2018). Adult P. downsi are not parasitic; however, they lay their eggs in the nests of birds and the larval flies feed on nestling birds and their mothers (McNew and Clayton 2018). The effects of P. downsi on nestling growth and fledging success have been well-documented in a number of species (Kleindorfer and Dudaniec 2016, McNew and Clayton 2018), but it is unknown if parasitism forces parents to make trade-offs in other areas. We studied parental resource allocation in Galápagos mockingbirds (Mimus parvulus) in response to environmental conditions and P. downsi during a long breeding season in which mockingbirds bred more than once. We tested two predictions regarding the relationship between mockingbird breeding success and the environment: 1) in the absence of parasitism, mockingbird investment in second clutches will increase as conditions improve over the season; and, 2) when parasitized by P. downsi, parents will diminish investment in future reproduction because parasitism increases the demands of immediate reproduction and/or survival. Methods Study site The study was conducted from January-May 2015 on Santa Cruz Island in the Galápagos, Ecuador. Breeding success data from the first breeding attempt of mockingbirds also appear in Chapter 3. The field site, El Garrapatero, is a 3 x 4 km area located in arid scrub habitat dominated by tree cacti (Opuntia echios), acacia (Acacia 140 rorundiana) and palo santo trees (Bursera graveolens). Galápagos mockingbirds are common, year-round residents at the site. Breeding of mockingbirds is triggered by seasonal rains that typically fall during January-April. Mockingbirds construct open cupshaped nests in Opuntia cacti or acacia trees. They lay between 1 and 5 eggs, which are incubated by the female for roughly 15 days. After hatching, nestlings are fed by both parents until they fledge at approximately 14 days of age (Knutie et al. 2016). Parents feed chicks a varied diet consisting primarily of larval arthropods (Grant and Grant 1979). Mockingbirds may renest if their initial clutch fails or if the breeding season is particularly long (Curry and Grant 1990); however they construct a new nest with each nesting attempt. The effects of P. downsi on Galápagos mockingbird reproductive success vary among years and depend on environmental conditions (Chapter 3). In years where food resources are abundant, mockingbirds tolerate P. downsi, meaning reproductive success is not diminished by parasitism. In contrast, in years in which rainfall and food are limited, mockingbird tolerance is reduced and few parasitized nestlings survive. Environmental conditions We used rainfall and normalized difference vegetation index (NDVI) data to characterize conditions at the field site over the season. NDVI is an index of photosynthesizing vegetation based on satellite imagery; higher values correspond to more vegetation (Anyamba and Tucker 2005). Data were collected as in Chapter 3. Briefly, we used daily precipitation data from the Charles Darwin Research Station in Puerto Ayora (Charles Darwin Foundation 2018) along with NDVI data from a 141 representative 2.25 x 2.25 square at the center of our field site, collected every 16 days and obtained from the MODIS Global Subsetting and Visualization Tool (Didan 2015, ORNL-DAAC 2017). Experimental manipulation Mockingbirds began nesting following the onset of the rainy season. When nestlings hatched, they were removed briefly while the nest was sprayed with either 1% aqueous permethrin (PermectrinTM II) or water, as a control. Permethrin has been used in several previous studies to eliminate P. downsi larvae from birds' nests (Fessl et al. 2006, Koop et al. 2013b, O'Connor et al. 2014, Knutie et al. 2016) and has minor, if any, effects on nestlings (Causton and Lincango 2014, López-Arrabé et al. 2014). Nests were sprayed soon after the first nestling hatched and then a second time 5-6 days later. Nestlings and any unhatched eggs were removed during the spraying process. Parents quickly returned to the nest following treatment and there were no cases of nest abandonment due to treatment. The first nest of the season was randomly assigned to a treatment via coin flip. As the clutches in subsequent nests hatched, they were assigned to alternating treatments. The same treatment was applied to both the first and second nests of individual pairs that renested. Nestlings were weighed at hatching and then banded with an individually numbered monel band and a unique combination of color bands at 911 days of age. Fledging success was determined by resighting banded individuals once they left the nest (at about 15 days of age). The abundance of P. downsi was determined by collecting the nest after nestlings had died or fledged. Nests were carefully dissected within 8h of collection to quantify P. 142 downsi larvae and pupae (Koop et al. 2013a, 2013b, Knutie et al. 2016). Although first instar larvae are typically too small to be counted reliably, we could recover second and third instar larvae, as well as pupae from the nest. The total abundance was the sum of counts of larvae and pupae. Third instar larvae and pupae were reared to the adult stage to confirm that they were P. downsi. Parental identification and condition Parents were opportunistically captured with mist-nets during their first reproductive attempt (i.e., during incubation and caring for nestlings). Parents were sexed using size and reproductive condition and were banded with an individually numbered monel band and a unique combination of color bands. We took measurements of adult mass and tarsus length to quantify body condition. Body condition of parents was estimated using a scaled mass index following methods described by Peig and Green (2009). We also collected a small blood sample via brachial venipuncture in order to quantify parental immune response to P. downsi. Next-year survival for 2015 parents was estimated by resighting color-banded parents in the study area during January - April the following year (2016). Parental immune response We used enzyme-linked immunosorbent assays (ELISA) to quantify P. downsibinding antibodies in parental mockingbirds following methods in Knutie et al. (2016). Ninety-six well plates were coated in 100 µl/well of P. downsi protein extract (capture antigen) diluted 1:100 in carbonate coating buffer, followed by incubation for one hour 143 on an orbital table. Plates were then washed and coated with 200 µl of blocking buffer (bovine serum albumin, BSA), and then incubated for 30 min. After blocking, plates were washed and coated with 100 µl of mockingbird plasma diluted 1:100 in sample buffer and incubated for 1h, followed by washing and coating with 100 ul of Goat- αBird- IgG (diluted 1:10,000) (Antibodies Online, Atlanta GA, USA). Plates were incubated with the detection antibody for 1h and then washed. Finally, wells were filled with 100 µl of peroxidase substrate tetramethylbenzidine, TMB; Bethyl Laboratories, Montgomery, TX, USA) and incubated for exactly 15 min. The reaction was stopped using 100 µl/well of stop solution (Bethyl Laboratories). Optical density (OD) was measured with a spectrophotometer (BioTek, Winooski, VT, USA; PowerWave HT, 450-nanometer filter). Plates were washed five times between each step with a Tris-buffered saline wash solution. Samples were run in triplicate on each plate. Each plate also included a positive control sample of pooled adult plasma, which was used to control for interplate variation. Each plate also included three other methodological controls: 1) wells in which P. downsi antigen and the detection antibody were added but no plasma was added, to test for nonspecific binding (NSB) of the detection antibody to the antigen; 2) wells in which the antigen was excluded but the rest of the procedure was followed to ensure that samples were binding exclusively to the antigen; and 3) blank wells, in which coating, sample, and blocking buffers were added, without antigen or plasma, followed by the detection antibody, to ensure that the buffers were uncontaminated. OD values were calculated as the mean OD from each sample minus the mean NSB value for that plate, scaled to the positive control OD to control for interplate variation. Higher OD values correspond to higher antibody binding levels. 144 Analyses We used linear models (LMs) to test for an increase in NDVI values over the season. We used generalized linear models (GLMMs) to test for differences between treatments and breeding attempts on parasite abundance, clutch size (number of eggs laid), brood size (number of nestlings hatched) and reproductive success (percent fledging success and number of fledglings). We used a negative binomial distribution to model parasite abundance because the variance was much greater than the mean. Percent fledging success was modeled with a binomial error structure, and number of fledglings was modeled with a negative binomial error structure. Models included the fixed effects of treatment, nesting attempt, and treatment x nesting attempt, and mockingbird pair as a random effect. Models were also run separately for each treatment to confirm results because small sample sizes may fail to detect interactions (Leon and Heo 2009). Variation in parental condition and P. downsi antibody binding response (measured as optical density, OD) were evaluated using linear models (LM) with sex, treatment and nestling age as fixed effects. Models were first run with the interaction of age of nestlings and treatment. When the interaction was nonsignificant it was removed. All analyses were conducted in RStudio (2016, version 1.0.136; R version 3.3.3) using the packages car, viridis, lmer4, MASS and smatr. Degrees of freedom and Pvalues for linear mixed-models (LMMs) were calculated using a Satterthwaite approximation with the lmerTest package. 145 Results The rainy season began at the start of February and lasted through May (Figure 4.1). NDVI values correspondingly increased over the season (LM P = 0.002; Figure 4.1). Mockingbirds started breeding following the first significant rainfall and most parents nested twice (Table 4.1). First reproductive attempt There was no significant difference in the clutch size or brood size of fumigated vs. sham-fumigated pairs in their first nesting attempt (clutch mean ± SE fumigated: 3.38 ± 0.12; sham-fumigated: 3.41 ± 0.17, GLMM P = 0.91; brood mean ± SE fumigated: 2.83 ± 0.23; sham-fumigated: 3.05 ± 0.18, GLMM P = 0.44) (Figure 4.2A-B). Fumigation of the nest after hatching significantly decreased the number of P. downsi (mean ± SE fumigated: 1.17 ± 0.39; sham-fumigated: 57.29 ± 8.70; GLMM P < 0.001). Parents of fumigated nests fledged significantly more nestlings than parents of sham-fumigated nests (mean ± SE fumigated: 1.61 ± 0.22; sham-fumigated: 0.59 ± .23; GLMM P < 0.001; Figure 4.2C). Correspondingly, percent fledging success was significantly higher in fumigated nests compared to sham-fumigated nests (mean ± SE fumigated: 59.25 ± 7.3; sham-fumigated: 19.11 ± 7.4; GLMM P < 0.001; Figure 4.2D). Probability of renesting and inter-nest interval The majority of parents renested following their first clutch (Table 4.1). On average, the mean time between the failure or fledging success of the first clutch and the laying date of the second clutch (inter-nest interval) was 27 days. The inter-nest interval 146 did not significantly differ between fumigated and sham-fumigated parents (LM P = 0.65). Second reproductive attempt The abundance of P. downsi did not differ between reproductive attempts (GLMM P = 0.44). When pairs renested, both fumigated and sham-fumigated parents laid significantly more eggs than in their first attempt, with no significant difference between treatments (mean ± SE fumigated: 4.40 ± 0.22; sham-fumigated: 4.0 ± 0.22, GLMM: attempt P < 0.001, treatment x attempt P = 0.26; Figure 4.2A). In contrast, brood size differed significantly between treatment groups when they renested: fumigated parents hatched significantly more chicks in their second attempt than sham-fumigated parents (mean ± SE fumigated: 4.10 ± 0.28; sham-fumigated: 2.86 ± 0.34, GLMM attempt P < 0.001, treatment x attempt P = 0.006; Figure 4.2B). The nestling mass at hatching did not differ between treatments in the first reproductive attempt, but was significantly higher for fumigated nestlings in the second attempt (second attempt mean ± SE fumigated: 5.15 ± 0.23, mean ± SE; sham-fumigated 4.64 ± 0.19, LMM treatment x attempt P = 0.02). Both the percent fledging success and the number of successful fledglings for fumigated pairs increased in the second breeding attempt (% fledging success GLMM P = 0.003; number of fledglings GLMM P < 0.001; Figure 4.2C-D). There was no significant difference in either percent fledging success or number of fledglings between breeding attempts for sham-fumigated pairs (% fledging success GLMM P = 0.14; number of fledglings GLMM P = 0.25; Figure 4.2C-D). 147 When treatments were analyzed together, percent fledging success and number of fledglings improved in the second nesting attempt (% fledging success: attempt P = 0.001; number of fledglings GLMM P < 0.001). However, the interaction term of treatment x attempt was not significant (% fledging success P = 0.11; number of fledglings P = 0.41) which suggests that reproductive success improved for both fumigated and sham-fumigated parents when they renested. However, the relatively small sample size may not have provided enough power to detect an interaction. Relationship between environmental conditions and reproductive success The number of fledglings that fumigated nests produced was highly positively correlated with NDVI (GLMM P < 0.001); however, NDVI was not correlated with fledglings in sham-fumigated nests (GLMM P = 0.44; Figure 4.3). Similarly, percent fledging success was correlated with NDVI for fumigated nests (GLMM P < 0.001) but not for sham-fumigated nests (GLMM P = 0.19). Parental condition, immune response, and survival The scaled mass of parents decreased over the nestling period (LM age of nestlings P = 0.002; Figure 4.4); however, the decrease did not differ between treatments (LM treatment x age of nestlings P = 0.97). Scaled mass did not differ significantly between males and females (P = 0.67) or between treatments, though there was a trend for water-treated parents to be heavier (P = 0.07). There was no difference between treatments in P. downsi antibody binding activity (optical density, OD) (LM P = 0.88; Figure 4.5), and OD was unrelated to age of nestlings (P > 0.05). OD in females was on 148 average higher than OD of males; however, the difference was not significant (P = 0.099). The abundance of P. downsi was uncorrelated with OD for both males and females (LM P > 0.05). Males were more likely to be resighted the following year than females (GLM sex P = 0.005); however, the probability of resighting did not differ between treatments (GLM treatment P = 0.63). Discussion Life history trade-offs are particularly acute in stressful conditions when resources are limited. Thus, for birds, the ability to judge environmental conditions and food availability accurately during the breeding season is likely under selection, because producing more nestlings than parents can adequately provision may have negative consequences for future survival and reproductive success (Lack 1947). Galápagos mockingbirds adjust reproductive investment in response to current environmental conditions. In drought years, mockingbirds may not attempt to reproduce at all, and in years of high rainfall mockingbirds may raise up to six broods (Curry and Grant 1990). In our study, rainfall increased over the course of the season and, in the absence of P. downsi, mockingbirds concomitantly increased their investment in reproduction and reproductive success. This pattern is consistent with our predictions that reproductive investment and success is correlated with current environmental conditions. P. downsi is a new environmental stressor that may influence mockingbird investment in life-history traits. Parasitism of the first clutch negatively affected brood size of the second; compared to fumigated parents, sham-fumigated parents hatched fewer chicks when they renested (Figure 2B). In addition, shortly after hatching (i.e., 149 before nests were experimentally manipulated) second clutch nestlings of shamfumigated parents were smaller in mass than the second clutch nestlings of fumigated parents. Thus, P. downsi reduced both the number and the quality of chicks of renesting parents. The reduction in brood size may have been an unavoidable consequence of the cost of P. downsi to parents themselves, or a facultative adaptive response to the presence of P. downsi in the environment. Unlike previous studies (Koop et al. 2013b), we did not find that parents mounted an immune response to P. downsi during their first reproductive attempt (Figure 4.4). Environmental conditions were fairly poor during the beginning of the season, which could have diminished the ability of parents to mount an immune response (Schmid-Hempel 2011). We did not find that parasitism of the first clutch by P. downsi negatively affected parental condition or survival (Figure 4.3); however, parents may have allocated more energy to self-maintenance, and/or to their first broods at the expense of second-brood egg quality. Some eggs in sham-fumigated nests may not have hatched either because they were not fertilized or because they failed to develop, though embryo failure is considered more common than infertility (Hemmings et al. 2012, Hemmings and Birkhead 2016). Surprisingly, the proximal causes of embryo failure in wild birds are not well known (Stewart and Westneat 2013). Embryo failure could in theory result from poor nutritional status or stress of the mother, which are factors linked to pregnancy loss in mammals (Wasser and Barash 1983, Wilmut et al. 1986, Beehner et al. 2006). Thus, if P. downsi had some cost to female mockingbirds during their first breeding attempt, egg quality in their second nest may have suffered. Although we did not see an effect of P. downsi on 150 parental body condition, parasitism may have adversely affected parents in ways we did not measure, such as by increasing oxidative stress (Fowler and Williams 2017). Eggs may also fail to hatch due to inconsistent or reduced brooding by mothers (males do not incubate) (Arnold et al. 1987). Although we did not record behavior of mockingbirds during incubation, nests are often infested by P. downsi at this stage, during which time the larvae feed exclusively on incubating mothers (McNew and Clayton 2018). After exposure to P. downsi in their first breeding attempt, females may have significantly reduced incubation in an effort to avoid the flies, which could have negatively affected chick development. Hatching failure could also conceivably be a strategy of parents to intentionally reduce brood size after experiencing parasitism by P. downsi. For example, collared flycatchers (Ficedula albicollis) reduced clutch size in a year following experimentally increased nest predation in what the authors concluded was an "individual decision" as opposed to "physiological constraint" (Doligez and Clobert 2003). The risk of nest predation, as well as the presence of nest parasites, have long been invoked as pressures that restrict clutch size among birds (Martin et al. 2000, 2001). In theory, reducing brood size could be an adaptive response to P. downsi, since parents could likely increase pernestling provisioning and tolerance to P. downsi increases with provisioning rates (Knutie et al. 2016, Chapter 3). However, reproductive success of sham-fumigated parents did not significantly improve in their second attempt. Future experiments manipulating clutch size in fumigated and sham-fumigated nests may better test whether brood size reduction in response to P. downsi is adaptive. 151 Whether or not reducing brood size is an adaptive response of parasitized parents, the potential of P. downsi to impact parental resource allocation is noteworthy. P. downsi has significant negative effects on nestling survival in many species of Galápagos birds (McNew and Clayton 2018). However, no previous study has investigated if parasitism has consequences for other life history traits in parent birds. Our results suggest that parasitism not only reduces current reproductive success of mockingbirds, but also has carry-over effects to future reproductive investment. Most attention on invasive parasites and pathogens globally focuses on their costs to host health and immediate fitness. Future work should also consider other how hosts shift resource allocation in response to novel stressors and the life-history trade-offs that may result. Acknowledgments This manuscript will be submitted for publication with Graham B. Goodman, Janai Yépez and Dale H. Clayton. We thank the Galápagos National Park and Charles Darwin Foundation for logistical support and fieldwork permits for the Galápagos. We are thankful to Monte Neate-Clegg, Scott Villa, Sarah Knutie, Jordan Herman and Sarah E. Bush for field assistance and/or helpful discussion. This work was supported by NSF grant DEB- 0816877 to DHC and an NSF Graduate Research Fellowship to SMM. 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Activity of mockingbird pairs following first reproductive attempts (First attempt: fumigated N = 18, sham-fumigated N = 17). Fumigated Sham-fumigated Total Left territory 4 5 9 Remained on territory but did not renest 2 2 4 Remained on territory and renested 12 10 22 2nd clutch included in study 10 7 17 2nd clutch excluded* 2 3 5 *Some 2nd clutches were excluded from study either because they were not found until after nestlings were several days old or because treatment was inadvertently changed between nests. 157 0.40 0.35 10 0 February March April May Date Figure 4.1. Cumulative rainfall at Puerto Ayora, Santa Cruz in 5-day windows from February - May 2015 (bars) and mean normalized difference vegetation index (NDVI) values at our study site (plotted line) over the same period. NDVI 0.55 0.45 0.50 30 20 Rain (mm) 40 0.60 50 0.65 60 0.70 Figure 1 158 Figure 2 B 5.0 4.0 * 40 60 80 100 * 0 20 4 3 0 1 * Mean (± SE) % fledging success 5 D * Shamfumigated 1st 2nd Nesting Attempt C 2 * 3.0 Mean (± SE) brood size 1st 2nd Nesting Attempt Mean (± SE) fledglings Fumigated 2.0 5.0 4.0 3.0 2.0 Mean (± SE) clutch size A 1st 2nd Nesting Attempt 1st 2nd Nesting Attempt Figure 4.2. Mean (± SE) clutch size (A), brood size (B), number of successful fledglings (C) and percent fledging success (D) for fumigated and sham-fumigated nests in their first and second nesting attempts. Asterisks indicate significant differences between treatments. See text for additional statistical comparisons. 159 0.45 0.40 0.35 0 Mar 15 Apr 01 Apr 15 May 01 Date Figure 4.3. NDVI values over the course of the season (line) and number of fledglings produced from fumigated nests (grey points) and sham-fumigated nests (black points). Points have been jittered for clarity. Reproductive success was positively correlated with NDVI for fumigated parents, but not for sham-fumigated parents (see text for details). NDVI 0.55 0.50 3 2 1 Fledglings 4 0.60 0.65 5 0.70 Figure 3 160 52 Figure 4 48 46 44 42 38 40 Scaled mass of parents (g) 50 Fumigated Sham-fumigated 5 10 15 Eggs 20 25 Nestlings Nesting stage (days) Figure 4.4. Scaled mass of parents captured over the first reproductive attempt (i.e., during incubation of eggs and caring for nestlings). Parental condition decreases over the nestling period; however, the decrease does not differ between treatments (LM treatment x age P = 0.97). 161 Fumigated 0.1 0.2 0.3 Shamfumigated 0.0 Optical Density (OD) 0.3 Figure 5 Females Males Figure 4.5. Immune response (Measured as optical density, OD) to P. downsi antigens in female (N = 11) and male (N = 18) mockingbirds. There was no significant difference between either sexes (LM P = 0.09) or treatments (P = 0.88). CHAPTER 5 DO GALÁPAGOS MOCKINGBIRDS BIAS SEX RATIOS OF THEIR BROODS? Abstract One way that birds can adjust investment in reproduction is through altering sex ratios of their broods. Producing either more sons or daughters can lower the costs of reproduction in cases where one sex is more costly to produce. Biasing sex ratios can also be adaptive when the value of sons versus daughters differs. In this chapter I investigate whether Galápagos mockingbirds (Mimus parvulus) bias sex ratios either in response to environmental conditions, or to the presence of an added environmental stressor, the invasive parasite, Philornis downsi. We found no evidence of sex ratio manipulation either in response to the environment or to P. downsi. Introduction Birds adjust investment in reproduction depending on environmental conditions. In favorable conditions they may attempt more clutches, lay more eggs, or increase provisioning rates to nestlings (Weimerskirch et al. 2001, Grant and Grant 2014). Conversely, in stressful conditions, parents may attempt to reduce investment in reproduction. One way in which parents may adjust their investment in reproduction is by 163 preferentially producing either sons or daughters. Adjusting sex ratios may be adaptive in cases where the costs and benefits of raising sons and daughters differ (Komdeur and Pen 2002). Thus, individuals may bias sex ratios in favor of the less costly sex, especially in poor environmental conditions (Ligon and Ligon 1990, Pryke and Rollins 2012). For example, in nutritionally stressed conditions, male nestling parrot finches (Erythrura trichora) grow faster and are more likely to survive than female nestlings (Pryke and Rollins 2012). Correspondingly, females overproduce sons in nutritionally stressed environments. Parents may also bias sex ratios because the value of sons versus daughters depends on conditions. Fitness of sons is expected to be more correlated with their quality than that of daughters (Trivers and Willard 1973). High quality sons may receive a large proportion of the mating opportunities, and thus sire a much higher number of offspring than low quality males (Ellegren et al. 1996). In contrast, since females are typically the choosier sex, the fitness differential between low and high quality daughters is expected to be less. As a result, in good conditions, parents may opt to invest more in sons, because the inclusive fitness benefit of a high quality son is greater than a high quality daughter (Hasselquist and Kempenaers 2002, Robertson et al. 2006, Pryke and Rollins 2012). For instance, conservation biologists discovered that heavy supplemental feeding of the critically endangered kakapo (Strigops habroptilus) improved female condition, but had the unexpected result of producing heavily male-biased offspring (Robertson et al. 2006). When conservationists switched to a moderate supplemental feeding scheme, kakapo produced equal numbers of sons and daughters. 164 Biasing offspring sex ratios can also be beneficial in cooperatively breeding birds where one sex is more likely to assist parents with the care of subsequent broods (often referred to as "helpers"). When helpers significantly improve the fitness of parents, parents may bias sex ratios in favor of the helping sex (Komdeur and Pen 2002). For example, in Seychelles Warblers (Acrocephalus sechellensis), the value of helpers, which are mostly female, depends on whether the parents inhabit poor or good quality territories. Correspondingly, parents in high-quality territories, which benefit most from helpers, produce significantly more daughters than sons (Komdeur et al. 1997). Here, I investigate whether Galápagos Mockingbirds (Mimus parvulus) manipulate sex ratios either in response to environmental conditions or in response to parasitism by Philornis downsi. Mockingbirds adjust reproductive investment in response to environmental conditions: in dry years in which food is limited, they may lay smaller clutches or fail to reproduce altogether, and in wet years they attempt more clutches (Curry and Grant 1990). Mockingbirds on Santa Cruz Island are also facultative cooperative breeders: in some years, especially when conditions are drier, offspring from the previous year will not establish their own territories and will instead remain in their parents' territory and assist with the raising of subsequent broods (Curry and Grant 1990, pers obs.). Fledgling dispersal is sex biased with females being more likely to disperse and males more likely to assist parents. Presumably, females are more quickly recruited into the breeding population because between-year survival is lower for females than males (Curry and Grant 1990, Chapter 4). Environmental conditions may influence the value of sons versus daughters in mockingbirds. In dry years, in which offspring quality may be limited and sons are less 165 likely to found their own territories, mockingbirds may bias sex ratios in favor of daughters. In contrast, wet years in which high quality male fledglings are likely to be recruited into the breeding population would favor the production of sons. Parasitism by P. downsi also has the potential to affect brood sex ratios if the costs of raising sons differ from those of raising daughters. P. downsi decreases fledging success of mockingbirds in dry conditions, likely because parents cannot provision nestlings enough to compensate for the effects of parasitism (Chapter 3). Parasitism also causes a reduction in brood size when parents renest, potentially because parents reduce reproductive investment in response to parasitism (Chapter 4). Male mockingbirds are slightly larger than females, so raising males may require a larger provisioning investment from parents. If P. downsi causes parents to reduce future reproductive investment by reducing brood size, parasitism may also cause parents to shift sex-ratios in favor of the less-costly sex. Methods I surveyed sex ratios of nestlings in mockingbird nests over 4 years of field experiments. The field methods are described in detail in Chapters 3 and 4. Briefly: the study took place at the El Garrapatero field site on Santa Cruz Island, Galápagos. Mockingbird reproductive success was studied during January through April of 2012, 2013, 2015 and 2016. The years varied substantially in rainfall, with 2012 and 2013 being wetter than 2015 or 2016 (Table 5.1). However, in 2015, the unusual timing of the rainfall caused the mockingbirds to nest twice. While initially the 2015 season was quite dry, rains started falling later than usual and the mockingbirds renested after their first nesting attempt (Chapter 4). 166 Mockingbirds primarily nest in Acacia rorudiana or Opuntia echios trees. Males and females both construct the nest, after which females lay between 1 and 5 eggs (Knutie et al. 2016). Eggs are incubated by the female for about 15 days and then both parents, and occasionally juvenile helpers, provision nestlings for about 15 days before fledging. Nestlings are fed a varied diet consisting primarily of larval arthropods (Knutie et al. 2016). Shortly after hatching, nestlings and the top layer of the nest were briefly removed while the nest was fumigated with either 1% aqueous permethrin (PermectrinTM II) to exclude P. downsi larvae, or water, as a control. First nests for each pair were randomly assigned to treatment; if mockingbirds renested (2015 only), the second nest was treated the same as the first. The nestlings and nest lining were replaced once the nest had dried (< 10 min). Nests were fumigated again at 5-6 days. Nestling mockingbirds were banded with an individually numbered monel band and a unique combination of color bands at 911 days of age. Fledging success was determined by resighting of color-banded individuals after fledging. At 9-10 days of age in 2012, and 5-6 days of age in subsequent years, a small blood sample was collected via brachial venipuncture from each nestling. Samples were kept on wet ice in the field and then later centrifuged to separate erythrocytes and plasma, which were frozen separately. Samples were kept in a −20°C degree freezer while in the Galápagos. Following the field season, the samples were transferred in a liquid nitrogen dry shipper to the University of Utah where they were stored in a −80°C freezer. Genomic DNA was extracted from frozen erythrocytes using DNEasy kits (QIAGEN). Nestlings were sexed using PCR amplification of the CHD-W and CHD-Z 167 genes, which differ in size on the W and Z chromosomes (Hoeck et al. 2009). Genes were amplified using primers specifically designed for Mimus mockingbirds: (5'GAGRAAYTGTGCRAAA- CAGG-3', 5'-PET-GAGAYKGAGTCACTATCAGATCCAG-3'; Hoeck et al. 2009). PCR reactions were run in a total volume of 25 µl containing 2.5µl of 10X standard reaction buffer, 0.5 µl dNTP mix, 0.2 µl Taq polymerase (all reagents, New England BioLabs), 1 µl of each primer and 2µl of genomic DNA. PCR conditions were as follows: an initial denaturation of 95°C for 15 min, 32 cycles of 94°C for 30 s, 60°C for 90 s, and 72°C for 1 min, followed by a final elongation step of 60°C for 25 min. Fragments were visualized on an agarose gel, where one fragment corresponded to a male nestling, and two to a female (the heterogametic sex). Each plate was run with both a negative and positive (known adult female) control. Sex ratios were analyzed using generalized linear models (GLM) with binomial errors (logistic regression) with treatment, year, and nesting attempt as fixed effects. I compared the number of males and females in each nest among years (first nests only) and the number of males and females in each nest between first and second nests of mockingbirds in 2015. I tested for differences in mass between male and female nestlings using a linear model (LM). I tested for differences in fledging success between male and female nestlings using a generalized linear mixed effects model (LMM) with treatment and sex as fixed effects and year as a random effect. The overall differences between treatments and among years in fledging success are presented in Chapters 3 and 4. 168 Results I sexed 327 nestlings out of a total of 459 (71%) in the field study (Table 5.1). Of the remainder, 111 nestlings (24%) either died before samples could be taken or samples were not taken, and 21 (5%) samples did not amplify successfully. The proportion of males in each clutch varied slightly from year to year, but generally was close to 0.5 (Figure 5.1). The sex ratio did not differ significantly among years (GLM P > 0.18; Figure 5.1). In 2015, when most parents nested twice, the sex ratio of parents' second clutch was not significantly different from their first clutch (GLM nesting attempt P = 0.65). The sex ratio of the second clutch did not depend on whether the first clutch was fumigated or sham-fumigated (treatment P = 0.85, Figure 5.2). Male nestlings were significantly larger by 9-11 days of age than females (LM P = 0.003). There was no difference in the likelihood of fledging between males and females (GLMM P = 0.26). Discussion Galápagos mockingbirds do not appear to adjust sex ratios of their broods either in response to the environment or to P. downsi. Environmental conditions in the years of study varied significantly, with higher rainfall in the first 2 years compared to the second 2 years (Table 5.1). However, in all years the ratio of males to females was close to 50:50 (Figure 5.1). Likewise, there is no evidence that parasitism of an initial nest by P. downsi caused parents to bias the sex ratio of their second clutch. However, the sample size of nests when comparing first and second nesting attempts was fairly limited (N = 35 and N = 16, respectively). As a result, my power to detect the effects of P. downsi on sex ratios 169 may have been limited. Future work following breeding attempts of the same pairs over time may help confirm my results. My results estimate the "secondary" sex ratio, as opposed to the "primary" sex ratio, because I did not determine the sex of eggs that did not hatch or chicks that died before samples were taken (Komdeur and Pen 2002). However, there was no evidence of a trend towards sex ratio bias that more complete sampling would have detected. Mockingbirds may not have adjusted sex ratios because neither sex has a strong benefit over the other or because parents cannot anticipate conditions that favor sons vs. daughters. In a study of woodhoopoes (Phoeniculus purpureus), daughters appear to be favored in dry conditions both because they are less costly to rear than sons and because they are more likely to be nest helpers than males (Ligon and Ligon 1990). In the case of mockingbirds, sons have a potential cost of being slightly larger. However, while male woodhoopoes are approximately 20% larger than females (Ligon and Ligon 1990), male mockingbirds are only about 1% larger than females. In addition, males have a potential benefit of being the helping sex. However, it may be difficult for mockingbirds to predict the conditions that favor helpers. Helpers at mockingbird nests are typically fledglings from the previous years' clutch that did not disperse in time for the subsequent breeding season. Environmental conditions in the Galápagos are highly variable and adjusting sex ratios to provide more or fewer helpers would, in theory, require parents to anticipate breeding season conditions a year in advance. If mockingbirds cannot anticipate whether the next year will be wet or dry there is no advantage to biasing broods for or against the helping sex. In mockingbird populations on Santa Cruz, it is also unclear how helpful "helpers" are. In behavioral observations of mockingbird behavior in 2015 and 2016, the 170 contributions of helpers to nestling provisioning ranged from individuals that appeared rarely at the nest and did not provision nestlings, to those that provisioned as frequently as parents (SMM personal observations). Despite theoretical predictions that favor sex-ratio manipulation, many studies in birds find no evidence for sex ratio bias (Koenig and Dickinson 1996, Radford and Blakey 2000, Saalfeld et al. 2013). Publication bias of positive cases of brood sex manipulation may have made the phenomenon seem more common than it actually is (Hasselquist and Kempenaers 2002). In addition, few mechanisms of prehatching sex ratio manipulation have been identified in birds (Hasselquist and Kempenaers 2002, Komdeur and Pen 2002). Because female birds are the heterogametic sex, one plausible mechanism of generating sex-biased broods is the resorption of eggs of a particular sex (Emlen 1997). However, this mechanism has clear costs in terms of both egg production as well as potential risks of delaying the completion of the clutch and increasing hatching asynchrony (Emlen 1997). Theory predicts that even minor costs of brood-ratio manipulation may cause it to be selected against (Hasselquist and Kempenaers 2002). As a result, it may simply not be adaptive for mockingbirds to adjust sex ratios. Males may be more costly, which would select for the production of females during difficult environmental conditions, and/or in the presence of P. downsi; however, the cost of sex ratio manipulation may be greater than the advantage of producing more female offspring. References Curry, R. L., and P. R. Grant. 1990. Galapagos mockingbirds: territorial cooperative breeding in a climatically variable environment. Pages 291-331 in P. B. Stacey and 171 W. D. Koenig, editors. Cooperative Breeding in Birds: Long-term Studies of Ecology and Behavior. Cambridge University Press, Cambridge. Ellegren, H., L. Gustafsson, and B. C. Sheldon. 1996. Sex ratio adjustment in relation to paternal attractiveness in a wild bird population. Proceedings of the National Academy of Sciences of the United States of America 93:11723-11728. Emlen, S. T. 1997. When mothers prefer daughters over sons. Trends in Ecology and Evolution 12:291-292. Grant, P. R., and B. R. Grant. 2014. 40 Years of Evolution. Princeton University Press, Princeton, NJ. Hasselquist, D., and B. Kempenaers. 2002. Parental care and adaptive brood sex ratio manipulation in birds. Philosophical Transactions of the Royal Society B: Biological Sciences 357:363-372. Hoeck, P. E. A., T. B. Bucher, P. Wandeler, and L. F. Keller. 2009. Microsatellite primers for the four Galápagos mockingbird species (Mimus parvulus, Mimus macdonaldi, Mimus melanotis and Mimus trifasciatus). Molecular Ecology Resources 9:1538-1541. Knutie, S. A., J. P. Owen, S. M. McNew, A. W. Bartlow, E. Arriero, J. M. Herman, E. Diblasi, M. Thompson, J. A. H. Koop, and D. H. Clayton. 2016. Galápagos mockingbirds tolerate introduced parasites that affect Darwin's finches. Ecology 97:940-950. Koenig, W. D., and J. L. Dickinson. 1996. Nestling sex-ratio variation in western bluebirds. The Auk 113:902-910. Komdeur, J., S. Daan, J. Tinbergen, and C. Mateman. 1997. Extreme adaptive modification in sex ratio of the Seychelles warbler's eggs. Nature 385:522-525. Komdeur, J., and I. Pen. 2002. Adaptive sex allocation in birds: the complexities of linking theory and practice. Philosophical Transactions of the Royal Society B: Biological Sciences 357:373-380. Ligon, J. D., and S. H. Ligon. 1990. Female-biased sex ratio at hatching in the green woodhoopoe. The Auk 107:765-771. Pryke, S. R., and L. A. Rollins. 2012. Mothers adjust offspring sex to match the quality of the rearing environment. Proceedings of the Royal Society B: Biological Sciences 279:4051-4057. Radford, A. N., and J. K. Blakey. 2000. Is variation in brood sex ratios adaptive in the great tit (Parus major)? Behavioral Ecology 11:294-298. Robertson, B. C., G. P. Elliott, D. K. Eason, M. N. Clout, and N. J. Gemmell. 2006. Sex 172 allocation theory aids species conservation. Biology Letters 2:229-231. Saalfeld, S. T., W. C. Conway, D. A. Haukos, and W. P. Johnson. 2013. Seasonal variation in offspring sex ratio in the Snowy Plover. Western North American Naturalist 73:60-71. Trivers, R. L., and D. E. Willard. 1973. Natural selection of parental ability to vary the sex ratio of offspring. Science 179:90-92. Weimerskirch, H., L. Zimmermann, and P. A. Prince. 2001. Influence of environmental variability on breeding effort in long-lived seabirds, the yellow-nosed albatross. Behavioral Ecology 12:22-30. 173 Table 5.1. Summary of environmental conditions and sample sizes. Year 2012 2013 2015 2016 182.30 ± 142.18 ± 69.54 ± 79.93 ± 5.74 4.08 1.41 1.62 Total nests (nestlings) 32 (105) 34 (101) 52 (164)* 30 (89) Total nests (nestlings) sexed 25 (60) 32 (78) 51 (124)* 26 (65) Mean (± SE) seasonal rainfall at hatching (mm) *Totals for 2015 include both first and second nests of mockingbirds. 174 Figure 5.1. Effect of environmental conditions on offspring sex ratio of mockingbirds. Mean sex ratios in mockingbird broods (first nesting attempts only) did not vary significantly among years (GLM P > 0.05 for all year x year comparisons). 175 Figure 5.2. Effect of prior parasite exposure on the offspring sex ratio of mockingbirds. Sex ratios of mockingbird's second broods did not depend on prior exposure to P. downsi (GLM treatment x attempt P = 0.7). CHAPTER 6 DOES PHILORNIS DOWNSI AFFECT DNA METHYLATION IN GALÁPAGOS MOCKINGBIRD NESTLINGS? Abstract Phenotypic variation in a population is essential for adaptation. In addition to underlying genetic variation, epigenetic processes - such as DNA methylation - are also a potentially important source of novel phenotypes. Changes in methylation can be induced by the environment and occur more frequently than DNA mutations. Thus, when faced with a new selective pressure, such as an introduced parasite, epigenetic modifications may be key for rapid adaptation and survival. We investigated the epigenetic effects of a recently introduced parasitic nest fly, Philornis downsi, on nestling Galapagos mockingbirds (Mimus parvulus). Mockingbirds vary in tolerance to P. downsi: in years when food is abundant most parasitized nestlings survive, but in years where food is limited, most parasitized nestlings die. Parasitism causes changes in gene expression of nestlings, which may reflect both the costs of parasitism, and mechanisms of tolerance. To understand the regulatory framework underlying tolerance to P. downsi, we studied methylation patterns of parasitized and nonparasitized nestlings in tolerant and nontolerant years. We used a new reduced representation bisulfite conversion method - epiGBS - to test for differentially methylated sites. We did not find any differences either 177 between treatments or between tolerant and nontolerant years. However, poor sequence quality likely limited our power to identify methylation differences. Introduction The introduction of a novel parasite or pathogen into a host population forces hosts to either defend themselves against attack, or suffer a decrease in fitness. Investigating the effects of infection on a molecular level in the host can reveal much about both the costs of parasitism as well as mechanisms of host defense. Traditionally, genetic variation is thought to underlie differences in susceptibility to disease (Archie et al. 2009, Fumagalli et al. 2011). Thus, a population that survives the introduction of a virulent parasite or pathogen must either have standing genetic variants that confer protection, or a novel mutation must occur and spread through the population. However, recent work has identified other molecular mechanisms that can generate a rapid response to an emerging threat. For example, changes in gene expression related to resistance are one way in which animals can quickly react to a new parasite (Navajas et al. 2008, Bonneaud et al. 2011). While changes in gene expression have been linked to parasite defense, we know less about the processes that regulate gene expression in the first place. One candidate mechanism is DNA methylation, the binding of methyl groups to nucleotides (Angers et al. 2010). Epigenetic changes of this kind can affect gene expression and the resulting phenotype without changing the DNA sequence itself (Jaenisch and Bird 2003, Robertson 2005, Jones 2012, Duncan et al. 2014). Studies in human and other animal systems suggest that methylation profiles are altered by exposure to toxicants or stressors early in 178 development (Onishchenko et al. 2008, Baccarelli and Bollati 2009, Rubenstein et al. 2016). Moreover, some methylation changes are passed on to the germ line, meaning that they are potentially heritable and could affect subsequent generations (Crews et al. 2007; Latzel et al. 2012; Verhoeven et al. 2016). A handful of studies have evaluated methylation changes in wild populations of birds and other animals, particularly in relation to sources of environmental stress. For example, in a study of superb starlings (Lamprotornis superbus), Rubenstein et al. (2016) found associations between rainfall and DNA methylation in the glucocorticoid receptor. In this study, DNA methylation levels were also linked to the reproductive success of male starlings, suggesting that methylation changes can mediate an adaptive response to environmental conditions early in life. In a study of red grouse (Lagopus lagopus scotica) populations, Wenzel and Piertney (2014) found associations between parasite load and epigenetic differentiation in a subset of genes. These studies provide evidence that epigenetic changes may be important for birds rapidly adapting to environmental stressors, including parasites and pathogens. The degree to which epigenetic mechanisms may be involved in the response to a novel parasite or pathogen is unknown. In this study, we investigate the epigenetic effects of an introduced parasitic nest fly, Philornis downsi, on Galápagos mockingbirds (Mimus parvulus). Philornis downsi, a generalist parasite of nestling birds, is one of the most significant threats to Galápagos bird populations (Causton et al. 2006, O'Connor et al. 2010, McNew and Clayton 2018). Adult P. downsi are free living; however, the larval stages live in the nests of birds and feed on brooding mothers and their nestlings. Philornis downsi causes high mortality in Darwin's finch species; however, the effects of 179 parasitism on Galápagos mockingbirds vary (Chapter 3). In some years mockingbirds tolerate P. downsi with no cost to nestling fledging success. In others, mockingbird tolerance is reduced and parasitized nestlings typically die. Tolerance appears mediated by the environment: in rainy years when resources are abundant mockingbirds are more tolerant, while in drought years tolerance is reduced. Philornis downsi has host-specific effects on gene expression of nestling birds (Knutie 2014). In a comparison of medium ground finches (Geospiza fortis) and tolerant mockingbirds, P. downsi altered expression in both hosts, but affected different genes in each species. Parasitism resulted in upregulation of genes associated with DNA repair in finches, suggesting that P. downsi causes significant cell damage in this nontolerant host. In contrast, genes related to metabolism, signaling, and transcription were differentially expressed in mockingbirds, suggesting that parasitism has broad effects on mockingbirds at a cellular level, even when they are tolerant and ultimately survive. Gene expression changes may reflect potentially both the costs of P. downsi, as well as mechanisms of defense of mockingbirds against parasitism. However, gene expression is transient, and it is unknown what molecular changes govern these gene expression changes. In order to further investigate these processes, we tested for effects of P. downsi on DNA methylation profiles of nestling mockingbirds. We experimentally manipulated P. downsi abundance in mockingbird nests over four field seasons that varied in resource availability and tolerance to parasitism (Chapter 3). We examined DNA methylation differences in nestling erythrocytes to test for differences between parasitized and nonparasitized nestlings and between tolerant and nontolerant nestlings. 180 Methods Field experiments The experimental field study took place during January - April of 2012, 2013, 2015 and 2016. The methods are described in detail in Chapter 3. Briefly, we studied the effects of P. downsi on mockingbird fledging success at El Garrapatero, a 3 x 4 km site in arid scrub habitat along the coast of Santa Cruz Island. Mockingbird nests were identified during the beginning of the breeding season. Mockingbirds lay between 1 and 5 eggs, which are incubated by the female for about 15 days (Knutie et al. 2016). Both parents feed nestlings until they fledge at about 14 days of age (Knutie et al. 2016). Once the chicks hatched, they were briefly removed while nests were fumigated with either 1% aqueous permethrin (PermectrinTM II), or sham-fumigated with water as a control. While fumigating the nest we removed the top layer of nesting material and sprayed the interior of the nest where P. downsi typically reside. After the nest dried (< 10 min) the top layer of the nest and the nestlings were replaced. Permethrin is commonly used to eliminate nest parasites and has minor, if any, effects on nestlings (Koop et al. 2013, Causton and Lincango 2014, Hund et al. 2015). Nests were sprayed again at 5-6 days after hatching. We quantified P. downsi by collecting each nest after the nestlings either fledged or died. Nests were dissected within 8h and P. downsi larvae and pupae were carefully counted. The fumigation method does not completely eliminate exposure to parasites: fumigated nestlings may have been parasitized during the first few hours after hatching, before nests were treated. However, once nests are treated, permethrin is extremely effective at eliminating P. downsi (Koop et al. 2013, Knutie et al. 2016). 181 At 9-11 days of age we took a small blood sample from each nestling via brachial venipuncture. A small portion of blood was used to measure hemoglobin concentration (hemoglobin was not measured in 2012). The remainder of the blood was kept on wet ice in the field. Within 6 h of collection the blood samples were centrifuged at 8000 rpm for 10 min to separate plasma and erythrocytes, which were frozen separately in a -20 °C freezer. Samples were transported to the University of Utah in a liquid nitrogen dry shipper, where they were permanently stored in a -80 °C freezer. Controls for permethrin exposure To control for potentially confounding effects of permethrin exposure on nestling methylation patterns we ran a control experiment exposing captive zebra finch nestlings to permethrin at similar stages of development. Zebra finches were bred in captivity in an aviary at the University of Utah. They were supplied with nesting material (shredded paper) and wall-mounted nest cups. When chicks hatched, they were briefly removed while the nest was sprayed with either permethrin or water, following field methods. Nests were treated again 7 days after hatching (the nestling period for zebra finches is longer than that of mockingbirds, at 21 vs. 14 days, respectively). At 7 and 15 days after hatching, we took blood samples via brachial venipuncture from the nestlings. Blood samples were centrifuged following field methods. Erythrocytes were frozen in TRIzolTM Reagent (Life Technologies). 182 DNA extraction Genomic DNA was extracted from frozen erythrocytes using DNEasy kits (QIAGEN) following manufacturer's protocols. DNA was isolated from TRIzolpreserved samples using the following chloroform protocol: samples were first incubated in 1ml TRIzol for 5 min at room temperature. Next, 200 µl chloroform was added and samples were incubated for 15 min. Samples were then centrifuged at 20,000 RPM for 15 min at 8°C. The supernatant was discarded, and then the DNA was precipitated in 100% ethanol. After 2-3 min incubation, the sample was centrifuged for 5 min at 12,000 RPM. The supernatant was again discarded and the DNA pellet was washed twice in 0.1 M sodium citrate. DNA was resuspended in 75% ethanol, followed by 5 min of centrifugation at 12,000 RPM. The supernatant was removed and the pellet was dried for 5 min on the bench. The DNA pellet was dissolved in 100µl sterile water and incubated at 55°C for 10 min. Samples were then centrifuged for 10 min at 12,000 RPM and the supernatant was removed and placed in a new tube. DNA concentration from extracted DNA samples was quantified using a Qubit 4 Fluorometer (Invitrogen). Library preparation We used epiGBS, a reduced representation bisulfite sequencing method to quantify DNA methylation following a protocol modified from van Gurp et al. (2016). Briefly, 400 ng of DNA was digested for 17h at 37°C in a 40 µl reaction containing the restriction enzyme PstI, NEBuffer 3.1 (New England Biolabs; NEB), and BSA (NEB). Next, unique barcode pairs were ligated onto each sample to enable sample identification after pooling. Barcodes (between 4 and 6 bp long) were paired in combinations so that 183 each pair of barcodes differed from all others by a minimum of three mutational steps. Adaptor ligation took place in a 60 µl reaction containing 40 µl of digested DNA, along with T4 DNA ligase buffer and T4 DNA ligase (NEB) and the adaptors. Reactions were run for 3h at 22°C and then overnight at 4°C. Samples were then pooled 1:1 in groups of 8. Next, we did a PCR clean-up step using a Qiaquick PCR Purification Kit (Qiagen) following the manufacturer's protocol. Then, the libraries were size selected using SPRI beads (Magbio) to purify fragments >200 bp long, which were eluted in a total volume of 24 µl. Libraries were then nickrepaired to correct gaps between the barcode adaptors and DNA fragments. Nick repair took place in a 1h reaction at 15 °C containing 18 µl of the purified DNA, 2.5 µl dNTPs (Zymo Research), 2.5 µl 10x NEB buffer (NEB), 0.75 µl DNA polymerase I (NEB). Bisulfite conversion We then bisulfite-converted the libraries using the EZ DNA MethylationLightning kit (Zymo research) following the manufacturer's protocol. Immediately following bisulfite conversion we amplified the libraries with KAPA PCR. Reactions included 5 µl of KAPA HiFi HotStart Uracil + ReadyMix (Kapa Biosystems) and 6 pmol of primers (van Gurp et al. 2016). PCR cycles involved an initial denaturation step for 3 min at 95 °C followed by 18 cycles of 98 °C for 10 s, 65 °C for 15 s and 72 °C for 15 s, with a final extension step at 72 °C for 5 min. Libraries were bioanalyzed using 1 µl of DNA on a high-sensitivity DNA chip on a 2100 Bioanalyzer (Agilent). Libraries were pooled in equimolar quantities into a single 184 pool containing approximately 400 ng of DNA. The library was then sequenced in a single lane on an Illumina Hiseq PE150 sequencer at Novogene (Hong Kong). Bioinformatics and analysis Analysis was done using custom Python scripts modified from those developed for epiGBS and available on GitHub (https://github.com/thomasvangurp/epiGBS) (van Gurp et al. 2016). The bioinformatics pipeline was run at the Center for High Performance Computing (CHPC) at the University of Utah. First, the sequences were demultiplexed using a barcode library containing the appropriate sample name and barcode combination. The multiplexing step creates a fastq file containing the sample reads, stripped of the barcode sequence, each labeled by sample ID and strand ("Watson" or "Crick"). Next, a de-novo reference was constructed from the consensus of all fragments using PEAR v9.0.5 (Zhang et al. 2014). Reads were merged using the following settings: minimum P value: 0.001, minimum overlap: 10, no trimming, minimum assembly length: 0. Reads were separated into Watson and Crick reads using Unix grep according to the fastq label. Methylation polymorphisms were removed in silico for the development of the reference sequence. Identical reads were grouped together and a reference sequences for Watson and Crick reads was assembled using SAMtools mpileup and BCFtools for variant calling using vcfutils.pl vc2fq (Li et al. 2009, van Gurp et al. 2016). Reads were mapped to the reference sequence using a modified version of bwameth using the default settings (Pedersen et al. 2014). Variant calling was done using Freebayes (Garrison and Marth 2012) to produce variant call files (VCFs) for both 185 Watson and Crick strands. Methylation calling was done using the "walktogether" method of the PyVCF package. This method simultaneously iterates over the Watson and Crick VCF files to identify SNPs and methylation polymorphisms. C/T polymorphisms in the Watson strand combined with C only on the Crick strand indicate a methylated cytosine on the Watson strand. A G/A polymorphism on the Crick strand combined with a G on the Watson strand identifies a methylated site on the Crick strand. Per-site, perindividual methylation levels were exported as .bed files for downstream analysis and visualization. Statistical analysis We tested for differential methylation in mockingbirds and using logistic regressions in in RStudio (2016, version 1.0.136; R version 3.3.3) where the proportion of methylated to nonmethylated reads at each site was predicted as a function of treatment (permethrin or water) and nesting conditions (rainy years vs. dry years). For analyses we only included sites that had reads from six or more samples. P-values were adjusted to a false-discovery rate of ≤ 0.05 (Benjamini and Hochberg 1995). Differentially methylated sites between permethrin and water treatments in zebra finches were similarly identified using logistic regressions. Manhattan plots were generated using the package qqman version 0.1.4 (Turner 2014). Results We obtained 460 million reads that averaged 150 bp in length. FastQC indicated a high proportion of duplicated reads (~75%) and an over-representation of Illumina PCR 186 adaptors. When reads were de-duplicated, trimmed and filtered, we identified 263,148 unique methylated sites in Galápagos mockingbirds. Of those, most were identified from only a few samples (Figure 6.1). We did not identify any significant methylation differences either between treatments or between wet and dry conditions (Figure 6.2). We also did not identify any differentially methylated sites between treatments for zebra finches. Discussion Our results suggest that parasitism by Philornis downsi does not cause differential methylation in Galápagos mockingbirds. We also did not identify any differences between rainy and dry years in methylation patterns. However, our sequence coverage was fairly low, with most methylated sites identified from just a few samples in our dataset. Further work is needed to optimize sequencing coverage and read quality. The reduced-representation method that we used, epiGBS, also has the potential to miss regions of differential methylation because only a subset of the genome is sequenced. Thus, if parasitism has strong effects on a few particular loci, epiGBS may have not detected them. However, epiGBS is also a cost-effective way to survey across an entire genome in cases where candidate loci are not chosen a priori, and also provides single base-pair resolution, unlike some other methods (e.g., MeDIP). Although previous work suggested P. downsi has effects on nestling gene expression, parasitism may not affect nestling erythrocyte methylation profiles. We were only able to investigate methylation differences in erythrocytes, because nestlings could not be destructively sampled. Because DNA methylation is cell-line specific, parasitism 187 may have had different effects on different tissue types (Duncan et al. 2014). Indeed, another study found relationships between parasite load and methylation states in liver tissue of red grouse, a tissue that the authors chose because of its immunological importance (Wenzel and Piertney 2014). However, other studies have found that methylation differences associated with the environment can be detected in avian erythrocytes (Rubenstein et al. 2016, McNew et al. 2017). While previous studies have identified methylation differences associated with environmental correlates in wild populations, few have experimentally manipulated those environmental variables. Thus, previous correlational studies may have erroneously attributed methylation differences to unrelated factors. Although we did not identify any effects of P. downsi on mockingbird DNA methylation, this experiment is one of the first to experimentally investigate the effects of a parasite or pathogen on methylation patterns in a wild animal population. Acknowledgments This manuscript will be submitted for publication with Sarah Knutie, Teresa Boquete, Niels Wagemaker, Christina Richards and Dale Clayton. We thank the Galápagos National Park and Charles Darwin Foundation for logistical support and permits to do fieldwork in the Galápagos. We are grateful for field assistance from Emily DiBlasi, Jordan Herman, Daniela Vargas, Oliver Tiselma, Andrew Bartlow and Elena Arriero. We thank Franz Goller and Natasha Verzhbitskiy for the use of their zebra finch colony and assistance with experimental manipulations. Thanks to Jeannie Mounger, Sandra Voors for assistance with library preparation and analysis. We are especially grateful to Andre Kurlovs, James Baldwin Brown, Brett Milash, Anita Orendt, Wim 188 Cardoen and the University of Utah Center for High Performance Computing for assistance with bioinformatics. This work was supported by NSF grant DEB- 0816877 to DHC, an NSF Graduate Research Fellowship and NSF DDIG to SMM and a University of Utah Global Change and Sustainability Center (GCSC) Research Grant, University of Utah Graduate Research Fellowship, and Frank Chapman Research Grant to SAK. All applicable institutional guidelines for the care and use of animals were followed. References Angers, B., E. Castonguay, and R. Massicotte. 2010. Environmentally induced phenotypes and DNA methylation: how to deal with unpredictable conditions until the next generation and after. Molecular Ecology 19:1283-1295. Archie, E. A., G. Luikart, and V. O. Ezenwa. 2009. Infecting epidemiology with genetics: a new frontier in disease ecology. Trends in Ecology and Evolution 24:21-30. 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Bioinformatics 30:614-620. 192 Figure 6.1. A histogram of loci by the number of samples (out of 83) that were sequenced at that locus. Most loci were sequenced in fewer than 10 samples. 193 Figure 6.2. Manhattan plot of differentiation between treatments at methylated sites in mockingbird nestlings. Each point represents a polymorphic locus, where the P value was calculated based on a logistic regression of methylation by treatment. The line represents the genome-wide significance. A locus with a -log10(P) value above that line would be considered significantly differentially methylated between treatments. CHAPTER 7 EPIGENETIC VARIATION BETWEEN URBAN AND RURAL POPULATIONS OF DARWIN'S FINCHES Printed with permission from: McNew, S. M., D. Beck, I. Sadler-Riggleman, S. A. Knutie, J. A. H. Koop, D. H. Clayton and M.K. Skinner. 2017. Epigenetic variation between urban and rural populations of Darwin's finches. BMC Evolutionary Biology 17:183. 195 McNew et al. BMC Evolutionary Biology (2017) 17:183 DOI 10.1186/s12862-017-1025-9 RESEARCH ARTICLE Open Access Epigenetic variation between urban and rural populations of Darwin's finches Sabrina M. McNew1, Daniel Beck2, Ingrid Sadler-Riggleman2, Sarah A. Knutie1, Jennifer A. H. Koop1, Dale H. Clayton1 and Michael K. Skinner2* Abstract Background: The molecular basis of evolutionary change is assumed to be genetic variation. However, growing evidence suggests that epigenetic mechanisms, such as DNA methylation, may also be involved in rapid adaptation to new environments. An important first step in evaluating this hypothesis is to test for the presence of epigenetic variation between natural populations living under different environmental conditions. Results: In the current study we explored variation between populations of Darwin's finches, which comprise one of the best-studied examples of adaptive radiation. We tested for morphological, genetic, and epigenetic differences between adjacent "urban" and "rural" populations of each of two species of ground finches, Geospiza fortis and G. fuliginosa, on Santa Cruz Island in the Galápagos. Using data collected from more than 1000 birds, we found significant morphological differences between populations of G. fortis, but not G. fuliginosa. We did not find large size copy number variation (CNV) genetic differences between populations of either species. However, other genetic variants were not investigated. In contrast, we did find dramatic epigenetic differences between the urban and rural populations of both species, based on DNA methylation analysis. We explored genomic features and gene associations of the differentially DNA methylated regions (DMR), as well as their possible functional significance. Conclusions: In summary, our study documents local population epigenetic variation within each of two species of Darwin's finches. Keywords: Epigenetics, Geospiza, Copy number variation, Galápagos Islands, DNA methylation Background Studies of the molecular basis of evolutionary change have focused almost exclusively on genetic mechanisms. However, recent work suggests that heritable modifications to gene expression and function, independent of changes to DNA sequence, may also be involved in the evolution of phenotypes [1-3]. One of the most common of these epigenetic mechanisms is DNA methylation, i.e. the chemical attachment of methyl groups (CH3) to nucleotides (usually a cytosine followed by a guanine- "CpG") [4]. Methylation can be induced by the environment and affect gene expression and phenotypic traits without changing the DNA sequence itself [5-8]. Importantly, some patterns of methylation are * Correspondence: [e-mail] 2 Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA 99164-4236, USA Full list of author information is available at the end of the article heritable, meaning they have the potential to evolve [9-14]. Indeed, because DNA methylation modifications (epimutations) are more common than genetic mutations [15], they may play a role in the rapid adaptation of individuals to new or variable environments [16]. Environmentally-induced epimutations may be a component of the adaptive radiation of closely related species to new environments [17]. For example, Skinner et al. [18] showed that epigenetic variation is significantly correlated with phylogenetic distance among five closely related species of Darwin's finches in the Galápagos Islands. Although the adaptive significance of this epigenetic variation is unknown, some of the variants are associated with genes related to beak morphology, cell signaling, and melanogensis. The results of this study suggest that epigenetic changes accumulate over macroevolutionary time and further suggest that epigenetic changes may contribute to the evolution of adaptive phenotypes. © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 196 McNew et al. BMC Evolutionary Biology (2017) 17:183 Epigenetic variation also occurs among populations within single species [15, 19-24]. Some population epigenetic studies report correlations between methylation patterns and environmental factors, suggesting that differences in methylation are involved in local adaptation to different environments [21, 22, 24]. For example, in a study of populations of two salt marsh specialist plants living along a salinity gradient, Foust et al. [24] found that ground salinity is more closely correlated with epigenetic variation than genetic variation. The purpose of our study was to investigate epigenetic variation between populations of each of two species of Darwin's finches: the medium ground finch (Geospiza fortis) and the small ground finch (G. fuliginosa) (Fig. 1). Darwin's finches are a closely related group of about 16 species endemic to the Galápagos Islands [25-28]. Longterm studies show rapid phenotypic changes in populations of finches in response to environmental pressures, including competition [26]. The molecular basis of these phenotypic changes is poorly known. Although recent genomic studies have identified alleles in several putative genes associated with beak size and shape [28-30], most genetic markers show little differentiation among populations or species [28, 30-34]. Page 2 of 14 Epigenetic variation may contribute to the phenotypic diversity of Darwin's finch populations that cannot be detected through genomic studies. As an initial test of this hypothesis, we compared components of the morphology, genetics, and epigentics in populations of finches living at El Garrapatero, a relatively undisturbed locality, to populations living near Puerto Ayora (Academy Bay), the largest town in the Galápagos Islands (Fig. 1). Hereafter, we refer to these as the "rural" and "urban" sites, respectively. The two sites, which are only 10 km apart, are both arid, lowland scrub habitat along the south and south-eastern coast of the island. Vegetative cover, based on remote sensing spectroradiometric indicies, is slightly higher at the urban site; however, cover is highly correlated between the two sites year-round (Additional file 1: Figure S1). Despite the overall ecological similarity of the sites, anthropogenic disturbance at the urban site has increased dramatically over the past fifty years [35]. Observational studies suggest urbanization has effects on finch behavior and diet: birds in the urban population incorporate novel, human foods into their diets, whereas finches in the rural popuation do not [36]. To further explore potential impacts of urbanization of Puerto Ayora on ground finches, we tested for morphological, genetic, and Fig. 1 Study sites and species. a The Galápagos Archipelago. b Santa Cruz Island; Roads are indicated by narrow grey lines and study sites by red Xs. c Geospiza fortis; photo by J.A.H.K. d Geospiza fuliginosa; photo by S.A.K. Maps in (a) and (b) are modified from © 2016 Google 197 McNew et al. BMC Evolutionary Biology (2017) 17:183 epigenetic differences between urban and rural populations in each of two species of finches. Methods Study sites and species We studied each of two populations of G. fortis and G. fuliginosa living in urban and rural environments (urban: Academy Bay; 0° 44′ 21.3″ S, 90° 18 ‘06.3″ W; rural: El Garrapatero; 0° 41′ 15.7″ S, 90° 13′ 18.3″ W). The two localities, which are separated by about 10 km, are both in the arid coastal zone of Santa Cruz Island (Fig. 1). Geospiza fortis and G. fuliginosa are among the most abundant species of finches at these study sites. There appears to be little movement of finches between populations. Over the course of a decade-long banding study (2002-2012), during which more than 3700 finches were captured- and more than 300 individuals recaptured- only one bird (a female G. fortis) was shown to have moved between the two sites (J. Raeymaekers pers. comm.). Field work and sample collection Finches were captured at the two study sites January- April 2008-2016. The birds were mist-netted and banded with individually numbered Monel bands in order to track individuals. They were aged and sexed using size and plumage characteristics [37]. Morphological measurements were taken from each individual including beak depth, beak width, beak length, tarsus length, wing chord, and body mass, following Grant and Grant (2014) [26], with the exception that wing chord was measured unflattened. Principle components were calculated from untransformed data for the three body measurements (mass, wing chord, and tarsus) and for the three beak measurements (length, width, and depth) to provide aggregate measures of body size and beak size and shape [38]. We evaluated morphological differences between urban and rural sites using linear mixed effects models (LMM), with site as a fixed effect, and year as a random effect to control for variation among years and investigators. Separate models were run for each morphological measurement, as well as body size (PC1 body) and beak size and shape (PC1 beak and PC2 beak). P-values were adjusted with a Bonferroni correction for multiple tests. Morphological analyses were run in the program RStudio using R version 3.2.1 with the packages pwr, plotrix, lme4, and lmerTest [39-42]. Blood and sperm samples for epigenetic and genetic analyses were collected from a subset of birds captured January-April 2009-2013 at the two study sites. Blood samples (<90 μl) were taken from finches via brachial venipuncture. The samples were stored on wet ice in the field and, within six hours of collection, erythrocytes were purified by centrifugation. Sperm samples (~5 μl) Page 3 of 14 were taken from a subset of males. The sperm samples were obtained by gentle squeezing of the cloacal protuberance of reproductively active males. Blood erythrocytes and sperm samples were stored in a − 20 °C freezer in the Galápagos. Following each field season, they were transferred to a − 80 °C freezer in the USA for long-term storage. All field procedures were approved by the University of Utah Institutional Animal Care and Use Committee (protocols #07-08004, #10-07003 and #13-06010) and by the Galápagos National Park. Genomic DNA preparation Genomic DNA from finch red blood cells (erythrocytes) was prepared using the Qiagen DNAeasy Blood and Tissue Kit (Qiagen, Valencia CA). The manufacturer's instructions for nucleated blood samples were followed, but in the final DNA elution step H2O was used instead of the buffer provided in the kit. Genomic DNA from finch sperm was prepared as follows: collected sperm suspension was adjusted to 100 μl with 1 x Phosphate Buffered Saline (PBS) then 820 μl DNA extraction buffer (50 mM Tris pH 8, 10 mM EDTA pH 8, 0.5% SDS) and 80 μl 0.1 M dithiothreitol (DTT) were added and the sample was incubated at 65 °C for 15 min. Next, 80 μl Proteinase K (20 mg/ml) were added and the sample was incubated on a rotator at 55 °C for 2 h. After incubation, 300 μl of protein precipitation solution (Promega, A795A) were added, then the sample was mixed and incubated on ice for 15 min, then spun at 4 °C at 13,000 rpm for 20 min. The supernatant was transferred to a fresh tube, then precipitated over night with the same volume of 100% isopropanol and 2 μl glycoblue at −20 °C. The sample was then centrifuged and the pellet washed with 75% ethanol, then air-dried and re-suspended in 100 μl H2O. DNA concentration was measured using a Nanodrop Spectrophotometer (Thermo Fisher). CNV-Seq protocol To test for genetic differences between the urban and rural populations we sequenced DNA extracted from red blood cells (erythrocytes) and compared genetic copy number variation (CNV) [18]. CNV, defined as the changes in the number of repeat element copies of more than >1 kb of DNA, is increasingly recognized as one of the most common and functionally important markers of genetic variation [43]. The basic copy number variation (CNV) was determined through genomic sequencing of the same samples used for epigenetic analysis. Read numbers at specific loci were compared genome wide to identify CNV [18]. Erythrocyte DNA pools were generated by combining equal amounts of extracted DNA from five individuals. Each pool contained a total of 2 μg of genomic DNA. Three pools of five individuals each were created per species, per site. 198 McNew et al. BMC Evolutionary Biology (2017) 17:183 Pooling samples for genomic analysis provides an accurate and cost-effective way of comparing populations [44]. Pooling decreases power, compared to sequencing individual samples. Although minor differences in copy number between populations may be missed [45], large differences between groups should be detected. The pools were diluted to 130 μl with 1 x TE buffer and sonicated in a Covaris M220 with the manufacturer's preset program to create fragments with a peak at 300 bp. Aliquots of the pools were run on a 1.5% agarose gel to confirm fragmentation. The NEBNext DNA Library Kit for Illumina was used to create libraries for each pool, with each pool receiving a separate index primer. The libraries were sent to the University of Nevada, Reno Genomics Core for NGS on the Illumina HiSeq 2500 using a paired end PE50 application. All 6 pooled sequencing libraries for each species were run in one sequencing lane to generate approximately 30 million reads per pool. The read depth across the genome was then assessed to identify CNV and statistically assessed with a Bayesian analysis. The genome-wide paired end read depth was approximately 2× with the CNV read depth being a total of 300 to 6000 reads per CNV detected. Methylated DNA Immunoprecipitation (MeDIP) Following Skinner et al. [18], we used erythrocytes as a purified somatic cell type to compare differentially methylated regions (DMRs) between populations of each of the two species. For a subset of birds, we also compared DMR of germ line cells (sperm). DMRs between urban and rural populations were identified by the methylated DNA immunoprecipitation (MeDIP) of genomic DNA. MeDIP is an enrichment-based technique that uses an antibody to preferentially precipitate methylated regions of the genome that are then sequenced [46]. DMRs are identified by comparing coverage between groups of interest. MeDIP is a cost-effective way to evaluate genomic CpG methylation, and provides highly concordant results to other sequencing-based DNA methylation methods, such as bisulfite sequencing [47]. Because MeDIP surveys methylation genome-wide, it can be used to identify genomic characteristics associated with methylation. For instance, studies have found relationships between CpG density, methylation, and effects on gene transcription [6]. For analysis of erythrocytes, genomic DNA was extracted from the same individuals as used in the CNV pools. Each erythrocyte pool included five individuals and contained a total of 6 μg of genomic DNA. Sperm pools included two individuals and contained a total of 1.8 μg of genomic DNA. Three pools were generated per species, per site (for a total of n = 6 individuals per species, per site for sperm and n = 15 individuals per species per site for erythrocytes to consider biological variation of the pools and analysis). All pools were diluted to 150 μl Page 4 of 14 with 1× Tris-EDTA (TE, 10 mM Tris, 1 mM EDTA) and sonicated with a probe sonicator using 5 × 20 pulses at 20% amplitude. Fragment size (200-800 bp) was verified on 1.5% agarose gel. Sonicated DNA was diluted to 400 μl with 1xTE and heated to 95 °C for 10 min, then shocked in ice water for 10 min. Next, 100 μl of 5 x immunoprecipitation (IP) buffer (50 mM Sodium Phosphate pH 7, 700 mM NaCl, 0.25% Triton X-100) and 5 μg of 5-mC monoclonal antibody (Diagenode, C15200006-500) were added and the sample was incubated on a rotator at 4 °C over night. The next day Protein A/G Agarose Beads from Santa Cruz Biotechnology, Santa Cruz CA, were prewashed with 1xPBS/0.1% BSA and re-suspended in 1 x IP buffer. Eighty μl of the bead slurry were added to each sample and incubated at 4 °C for 2 h on a rotator. The bead-DNA-antibody complex was washed 3 times with 1 x IP buffer by centrifuging at 6000 rpm for 2 min and resuspending in 1 x IP buffer. After the last wash the beadcomplex was re-suspended in 250 μl of digestion buffer (50 mM Tris pH 8, 10 mM EDTA pH 8, 0.5% SDS) with 3.5 μl Proteinase K (20 mg/ml) per sample and incubated on a rotator at 55 °C for 2 h. After incubation, DNA was extracted with the same volume of Phenol-ChloroformIsoamyalcohol, then with the same volume of chloroform. To the supernatant from chloroform extraction, 2 μl glycoblue, 20 μl 5 M sodium chloride and 500 μl 100% cold ethanol were added. DNA was precipitated at −20 °C over night, then spun for 20 min at 13,000 rpm at 4 °C, washed with 75% ethanol, and air-dried. The dry pellet was resuspended in 20 μl H2O and concentration measured in Qubit using a Qubit ssDNA Assay Kit (Life Technologies, Carlsbad, CA). MeDIP-Seq protocol The next step for DMR identification involved sequencing the MeDIP DNA to identify differential methylation at specific genomic loci by assessing read numbers for the different samples. The MeDIP pools were used to create sequencing libraries for next generation sequencing (NGS) at the University of Nevada, Reno Genomics Core Laboratory using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina®, starting at step 1.4 of the manufacturer's protocol to generate double stranded DNA. After this step the manufacturer's protocol was followed. Each pool received a separate index primer. NGS was performed at the same laboratory using the Illumina HiSeq 2500 with a paired end PE50 application, with a read size of approximately 50 bp and approximately 100 million reads per pool. Two separate sequencing libraries, one rural and one urban, were run in each lane. The read depth for identified differential DNA methylated regions (DMRs) ranged from approximately 100 to >1000 total reads per DMR. 199 McNew et al. BMC Evolutionary Biology (2017) 17:183 Page 5 of 14 Bioinformatics Basic read quality was verified using summaries produced by the FastQC program [48]. The reads for each sample for both CNV and DMR analyses were mapped to the zebra finch (Taenopygia guttata) genome using Bowtie2 [49] with default parameter options. The mapped read files were converted to sorted BAM files using SAMtools [50]. The cn.MOPS R package [51] was used to identify potential CNV. The cn.mops default information gain thresholds were used for this analysis. The cn.MOPS analysis detects CNVs by modeling read depth across all samples. The model predicts copy number for a given window based on observed read counts. The model uses a Bayesian framework to determine whether copy number for a give window differs significantly from 2. The length of the CNV is determined by comparing copy number of adjacent windows on the genome and joining those with the same copy number into one segment. A CNV call occurs when copy number for a given genomic segment varies from that of other samples. CNV detection with cn.MOPS is robust to low-coverage sequencing data (0.18-0.46 for 75 bp reads) and performs well when comparing 6 or more samples [51]. The window size used by the cn.MOPS analysis was chosen dynamically for each chromosome based on the read coverage. The chromosomes' window size ranged from approximately 25 kb to 60 kb. Only CNV that occurred in either all urban or all rural pools were compared. Although some individual pools had higher numbers of CNV, only CNV that occur red among all the pools were included in the analysis. The CNV are identified using the difference between the posterior and prior distributions from the Bayesian analysis to estimate information gain. To identify DMR, the reference genome was broken into 100 bp windows. The MEDIPS R package [52] was used to calculate differential coverage between the urban and rural localities. The edgeR p-value [53] was used to determine the relative difference between the two localities for each genomic window. Windows with an edgeR p-value less than 10−3 were considered DMR. The DMR edges were extended until no genomic window with an edgeR p-value less than 0.1 remained within 1000 bp of the DMR. The DMR that included at least two windows with an edgeR p-value <10−3 ("multiple-window DMR") were then selected for further analysis. Because no fully assembled or annotated genome exists for any Darwin's finch species, we aligned DMR with the zebra finch genome. CpG density and gene associations were then calculated for the DMR, based on alignment with the reference genome. Though we previously found high (>98%) homology between Darwin's finch and zebra finch genomes using tiling arrays [18], some differences were expected. Thus, associations of DMR with genes are likely to be under-estimates. To validate the epigenetics and gene associations, a similar analysis was also done with the draft G. fortis genome [54]. All the DMR sequence and genomic data obtained in the current study have been deposited in the NCBI public GEO database (GEO # GSE87825). DMR clusters were identified with an in-house R script (www.skinner.wsu.edu under genomic data) using a 2 Mb sliding window with 50 kb intervals. DMR were annotated using the biomaRt R package [55] to access the Ensembl database [56]. The genes that overlapped with DMR were then input into the KEGG pathway search [57, 58] to identify associated pathways. A 10 kb flanking sequence was added to each DMR to consider potential localization in promoter regions of the gene as previously described [18, 59]. The DMR associated genes were manually sorted into gene classification groups by consulting information provided by the DAVID, Panther, and Uniprot databases incorporated into an internal curated database (www.skinner.wsu.edu under Table 1 Mean (± 1SE) values for morphological characteristics of G. fortis and G. fuliginosa at rural vs. urban sites. G. fortis G. fuliginosa Morphological Rural Urban Rural Character N = 560 N = 245 N = 171 N = 121 Beak depth 11.48 ± 0.06 11.98 ± 0.09** 7.40 ± 0.04 7.42 ± 0.06 Beak width 9.89 ± 0.04 10.24 ± 0.07** 6.8 ± 0.03 6.82 ± 0.04 Beak length 11.71 ± 0.04 12.02 ± 0.07*** 8.56 ± 0.04 8.46 ± 0.09 Tarsus length 21.00 ± 0.06 21.15 ± 0.09 18.83 ± 0.11 18.67 ± 0.09 Wing chord 69.3 ± 0.19 70.4 ± 0.29** 61.26 ± 0.31 61.1 ± 0.30 Body mass 21.23 ± 0.13 22.2 ± 0.23* 13.87 ± 0.15 13.76 ± 0.14 PC1 Body −0.13 ± 0.06 0.29 ± 0.09*** 0.07 ± 0.09 −0.10 ± 0.10 PC1 Beak −0.17 ± 0.07 0.40 ± 0.11*** 0.01 ± 0.09 −0.01 ± 0.15 PC2 Beak −0.01 ± 0.02 0.02 ± 0.03 0.07 ± 0.04 −0.09 ± 0.10 Statistically significant differences between populations at P < 0.01, 0.001, and <0.0001 are indicated by *, ** and ***, respectively Urban 200 McNew et al. BMC Evolutionary Biology (2017) 17:183 Table 2 Differentially methylated regions (DMR) between urban and rural populations based on different cell types Species/Cell Type Number of Windows* 1 2 3 4 5 Sum of Multiple (≥2) Window DMR** G. fortis Erythrocytes 2742 125 4 0 0 129 G. fortis Sperm 1160 97 9 3 1 110 G. fuliginosa Erythrocytes 4339 314 9 1 0 324 G. fuliginosa Sperm 133 6 0 0 139 1765 Only DMR that were significant at P < 0.001 are included *DMR detected in one window alone were considered "single-window" variants (Fig. 2) **DMR detected in two or more adjacent windows were considered "multiplewindow" variants and used in subsequent analyses (Figs. 2, 3, 4, 5 and 6) genomic data). To assess that the DMR were not false positives due to random biological variation within populations, a pairwise comparison analysis (individual pool comparison) on the genomic sequence data within the individual urban or rural sites and cell populations was performed [60]. Results Morphology We used 1097 birds captured between 2008 and 2016 for morphological analyses. We controlled for slight variation among years in traits by including year as a random effect in all analyses. At both sites, G. fortis was significantly Page 6 of 14 larger than G. fuliginosa in all morphological traits (linear mixed-effects models P < 0.0001). Within species, urban G. fortis was significantly larger than rural G. fortis for all direct morphological measurements, except tarsus length (Table 1). Composite measures of G. fortis body and beak size (PC1 body and PC1 beak) also differed between the two sites; however, there was no difference in beak shape (PC2 beak). In contrast to G. fortis, G. fuliginosa did not differ significantly between the urban and rural populations in any of the morphological measurements or composite measures (Table 1). Because we captured more G. fortis than G. fuliginosa we did a power analysis for G. fuliginosa, using the effect size of morphological differences found in the G. fortis populations (smallest effect size = 0.256 (wing chord); largest effect size = 0.358 (beak depth)). Power for comparisons of G. fuliginosa appeared adequate for detecting similar effect sizes (0.69-0.91). Copy number variation (CNV) Mean read depth genome-wide for pools used in CNV analysis varied between 1.08× and 1.30× (overall mean = 1.22×). The total read depth of individual variants ranged from 300 to 6000. We identified unique CNV in three of six G. fortis pools and five of six G. fuliginosa pools. The total number of variants per pool ranged from 1 to 20. However, no variants were exclusive to all urban or all rural pools for either G. fortis or G. fuliginosa (Additional file 2: Fig. S2). Fig. 2 DMR overlap between species and cell types. Each value is the number of differentially methylated regions between the urban and rural populations. Overlapping colors in the figure show the number of DMR that are shared between the two species or the two cell types. DMRs detected within a single 100 bp windows, b 2-5 adjacent 100 bp windows, c 2-7 Mb regions 201 McNew et al. BMC Evolutionary Biology (2017) 17:183 Therefore, while there was variation within populations in copy number at various loci in both G. fortis and G. fuliginosa (e.g., FB2 & 12), there were no fixed differences between the urban and rural populations for either species. It is unclear why certain pools had more variants than others; however variation was consistent among chromosomes. To control for underestimation of CNV differences due to reads that did not align to the zebra finch genome, we performed a similar analysis aligning reads to the un-assembled Geospiza fortis genome [54]. The average proportion of reads aligned to the G. fortis genome was higher (two-fold). However, we still did not find any differences in CNV between the urban and rural populations for either species of Darwin's finch. A limitation of this CNV analysis is that only large variants (>24 Kbp) can be detected reliably; smaller variants (<10 Kbp or less) may have escaped detection. Differential DNA methylation regions (DMRs) DMRs were found between populations for both cell types and both species (Table 2). We report the number of DMRs at p-value cut-offs ranging from <0.01 to <1e05 in Additional file 3: Table S1; Additional file 4: Table S2 and Additional file 5: Table S3. The analyses Page 7 of 14 reported below are restricted to DMRs significant at a level of P < 0.001. We evaluated differences on three "regional" scales (Fig. 2): single 100 bp window DMRs, multiple window DMRs, and "DMR clusters", i.e. statistically over-represented DMR clusters of 3-10 DMRs spanning 2-7 Mb [18] (Additional file 6: Table S4A-D). We focus on multiple-window DMRs (Additional file 4: Table S2 and Additional file 5: Table S3), i.e. DMRs detected independently in adjacent windows, because they further reduce the likelihood of false positives and provide a set of highly reproducible DMRs [18]. Multiple-window DMRs were used in the analysis of the genomic features of DMRs reported below. There was little overlap between species or cell types in the regions that were differentially methylated between urban and rural populations (Fig. 2). A small proportion of single window DMRs (Fig. 2A) was shared between species and/or cell types. However, there were virtually no shared multiple-window DMRs (Fig. 2B) or clusters of DMRs (Fig. 2C) between species and/or cell types. For both species and cell types, multiple-window DMRs usually were detected in only two multiple 100 bp windows; however, a limited number (<10% of total DMRs) were found in 3-5 multiple windows (Table 2). Based on extension of edges of multiple-window DMRs (extension Fig. 3 DMR length (kb) in a G. fortis sperm. b G. fuliginosa sperm. c G. fortis erythrocytes. d G. fuliginosa erythrocytes. Only multiple-window DMR significant at a p-value threshold of <10−3 are included 202 McNew et al. BMC Evolutionary Biology (2017) 17:183 Fig. 4 Chromosomal locations of DMR identified in Geospiza fortis sperm a and erythrocytes (b) and G. fuliginosa sperm (c) and erythrocytes (d). Locations are based on alignment to the zebra finch (Taeniopygia guttata) genome. Red arrowheads indicate DMR and black boxes indicate DMR clusters. Only multiple-window DMR significant at a p-value threshold of <10−3 are shown G. fortis (Sperm) Chromosomes A Chromosome Size G. fortis (Erythrocytes) Chromosomes B Chromosome Size G. fuliginosa (Sperm) Chromosomes C Chromosome Size G. fuliginosa (Erythrocytes) Chromosomes D Page 8 of 14 Chromosome Size of adjacent 100 bp windows with p < 0.1; see Methods) we estimated that most DMRs were 500-1000 bp in length (Fig. 3). Many of the DMRs in this study were clustered together, consistent with previous studies showing that DMRs are not evenly distributed across the genome [59]. Based on alignment to the zebra finch genome, we plotted the chromosomal locations of multiple-window DMRs and DMR clusters (Fig. 4). DMRs were present on all chromosomes in both sperm and erythrocytes of both species; however, the chromosomal locations of DMRs differed between the cell types and species. We evaluated the location of DMRs with respect to nucleotide composition. CpG density was highest in DMRs of G. fortis sperm cells (Fig. 5A). DMRs in G. fortis erythrocytes and both cell types of G. fuliginosa were most often found in lower density CpG regions of the genome (<1 CpG site/100 bp; Fig. 5B-D). We estimated that the DMRs typically had approximately 10 CpG sites clustered within 1 kb regions. We identified potential genes associated with DMRs through alignment with the zebra finch reference genome. DMRs within 10 kb of a gene (such that the promoter is included) have the potential to influence the gene's expression and/or pathways associated with that gene [59]. Different categories of genes were methylated in the two cell types and species (Fig. 6, specific genes listed in Additional file 7: Table S5). The most common gene categories associated with DMRs were metabolism, cell signaling and transcription (Fig. 6). Gene categories associated with DMRs differed significantly between the two species (Chi-square test, p = 0.039) and marginally between the two cell types (Chi-square test; p = 0.078). Pathway analysis (KEGG) showed DMRs associated with several genes (GALNT14, SGMS1, ENO2, PLCH2) in metabolic pathways of G. fortis sperm. DMRs were associated with different genes (GCLC, PRIM2, ALD1A3, AK4, ACACA) in metabolic pathways of G. fuliginosa sperm. Geospiza fortis erythrocyte DMRs were associated with genes (CACNA1H, FGF8, MRAS, RAP1A) in the MAPK signaling pathway. Geospiza fuliginosa erythrocyte DMRs were not associated with any particular pathway. When the DMR data sets for both species and cell types were compared, KEGG pathways with the most DMR-associated genes were metabolic pathways, and MAPK and TGFß/BMP signaling pathways. Metabolic pathways included glycolysis, in which genes involved with pyruvate and acetate production were associated with 203 McNew et al. BMC Evolutionary Biology (2017) 17:183 Page 9 of 14 Fig. 5 The CpG density of DMR in Geospiza fortis sperm (a), G. fuliginosa sperm (b), G. fortis erythrocytes (c) and G. fuliginosa erythrocytes (d). Only multiple-window DMR significant at a p-value threshold of <10−3 are included DMRs in both finch species (Additional file 8: Figure S3 and Additional file 9: Figure S4). Other metabolic pathways associated with DMRs included genes involved in purine metabolism and glycosylation (Additional file 7: Tables S5). Signaling pathways were also associated with DMRs in both species and cell types. Three genes in the TGFß/BMP pathway were associated with DMRs between G. fuliginosa populations (erythrocytes and sperm combined): BMP5, BMP7 and FST (Fig. 7). MAPK, a common pathway for many regulatory processes, such as cell growth, contained a high number of DMR-associated genes in both finch species (Additional file 8: Figure S3 and Additional file 9: Figure S4). Genomic correlates of our DMR and CNV data were analyzed using the well-annotated zebra finch genome. In addition, our sequencing data were also compared to the G. fortis shotgun sequence database [54]. In contrast to the zebra finch genome, the G. fortis genome is neither assembled, nor annotated, meaning that limited data analysis is possible. The pooled individual sample read number was approximately 100 million reads for both genome analyses. The overall read alignment rate was 47- 48% for the zebra finch analysis and 70-75% for the G. fortis genome analysis. Although previous analysis using tiling arrays suggested a 98% similarity in tiling array hybridization of the genome [18], the next generation sequencing analysis shows that more differences exist, likely in non-coding regions. The zebra finch genome analysis revealed twice the number of DMRs compared to the G. fortis genome analysis. This was largely due to the incomplete nature of the G. fortis genome. Nevertheless, analysis with both the zebra finch and G. fortis genomes identified epigenetic alterations between the rural and urban sites. To test whether methylation variation between sites was greater than within sites we conducted a pairwise comparison analysis (comparison of individual pools) within each species and rural or urban populations for specific cell types. We identified a number of DMRs between individual pools, which suggests that there is epigenetic variation within the study populations. However, few DMRs were found in multiple pools from the same population. Moreover, almost none of these DMRs were also found between urban and rural populations (Additional file 10: Figure S5). Thus, the DMRs identified between urban and rural populations are not an artifact of sampling withinpopulation variation. Discussion Darwin's finches are well known for their phenotypic variability and evolution in response to changing environmental conditions [26]. In addition to genetic variation, 204 McNew et al. BMC Evolutionary Biology (2017) 17:183 Fig. 6 Gene categories associated with DMR detected in (a) G. fortis and (b) G. fuliginosa. Only multiple-window DMR significant at a p-value threshold of <10−3 are included epigenetic variation - such as differential DNA methylation - may exist between natural populations living under different environmental conditions. The goal of this paper was to test for morphological, genetic, and epigenetic differences between urban and rural populations within each of two species of Darwin's finches. We found that G. fortis individuals at the urban site (Academy Bay) were larger than those at the rural site (El Garrapatero). In contrast, G. fuliginosa individuals did not differ morphologically between the sites. We did not find genetic differentiation between populations of either species based on CNV comparisons. However, we did find epigenetic (DMR) differences between urban and rural populations of both species of finches. We found urban G. fortis were larger in nearly all morphological measurements compared to rural G. fortis (Table 1), which may be due to increased food availability at the urban site. Previous work suggests that urbanization around Academy Bay has relaxed selection on finch beak size [35, 36]. Urbanization is associated Page 10 of 14 with a shift in the distribution of beak size in G. fortis: beak size is strongly bimodal at the rural site, whereas bimodality has decreased at the urban site concurrently with human population growth [35]. Both studies propose that increased food availability at the urban site has altered the selective landscape for G. fortis [35, 36]. Beak size is highly heritable in Geospiza finches; e.g. mid-parent vs. midoffspring values estimate heritability of beak depth in G. fortis to be 0.74 [61]. In contrast, G. fuliginosa showed no morphological differentiation between sites (Table 1). Geospiza fortis is phenotypically more variable than G. fuliginosa on Santa Cruz Island [61]. As a result, G. fortis may have undergone more rapid local adaptation than G. fuliginosa. Although G. fuliginosa and G. fortis have overlapping dietary niches, they do show some degree of specialization [27]. It is possible that urbanization has had a greater selective effect on G. fortis than G. fuliginosa. Alternatively, morphological differences in G. fortis may be driven by hybridization between G. fortis and the slightly larger G. magnirostris. Hybridization between G. fortis and G. magnirostris has been documented on Santa Cruz [62]. While we have no information on relative rates of hybridization at our study sites, G. magnirostris is more abundant at the urban site than the rural site (4.56% of urban birds captured, compared to 1.86% of rural birds captured; unpublished data 2008-2016). Despite differences in morphology between populations of G. fortis, we found no genetic differences between the urban and rural populations, based on the CNV comparisons made. Because CNV sequence coverage was limited, we may have overlooked small CNV, but larger CNV should have been detected between the two populations. CNV is a sensitive index of genetic differentiation between populations; indeed, some studies have found that CNV accounts for more genetic variation than SNPs [63-65]. Recent work has also linked CNV to rapid evolution in pepper moths [66] and primates [67]. Our study is first to explore genetic variation between populations of Darwin's finches using large-scale genomic features (CNV). Like our study, previous studies using smaller-scale genomic markers (microsatellites, nuclear introns, and mitochondrial DNA) detected little or no genetic structure within populations of either G. fortis or G. fuliginosa [31, 34, 68]. Two recent studies of genomic variation among Darwin's finches using SNPs did identify variable sites associated with variation in beak morphology [29, 30]. However, most of the genes associated with beak morphology in the two studies were different. These inconsistent results suggest that other forms of variation, such as large scale CNVs, could underlie phenotypic differences. However, our 205 McNew et al. BMC Evolutionary Biology (2017) 17:183 Page 11 of 14 Fig. 7 TGFB/BMP pathway. Genes associated with DMR are listed and outlined in red in the pathway results show that negligible large size CNV changes exist between the rural and urban populations of G. fortis or G. fuliginosa. In contrast to our genetic results, we found a large number of epigenetic differences between urban and rural populations in both species of finches and both cell types (Fig. 2). Although DMRs were found in both species, few of the same genomic regions were differentially methylated in G. fortis and G. fuliginosa. These data suggest that methylation patterns are species-specific, even when comparing closely related species. This may mean that G. fortis and G. fuliginosa are responding to environmental changes at the urban site in different ways. The lack of overlap in DMRs between the two species may reflect differences in their diets [27]. As discussed above, dietary differences may also have contributed to the morphological differences between urban and rural populations of G. fortis. Although DMRs were also found in both cell types, few of the same genomic regions were differentially methylated in sperm and erythrocytes. Because methylation is involved with cell differentiation [6, 69], some lack of similarity in erythrocyte and sperm DMR is expected. The differences between the genomic regions that were differentially methylated in sperm and erythrocytes may provide clues as to the functional significance of the epimutations. DMRs in somatic cells, such as erythrocytes, potentially reflect effects of the environment on physiology of the birds. DMRs in germ cells, such as sperm, are more likely to be transgenerationally inherited and contribute to evolution. Recent studies show that heritability of methylation variants can be high, but that this varies among loci [12]. However, without following multiple generations of individuals with known ancestry, we cannot determine which of the DMRs in our study are heritable. It is possible that many of the DMRs we detected were plastic responses to the environment. Analysis of Darwin's finches with known pedigrees - from long-term studies of banded populations - may be a way in which to distinguish heritable from non-heritable epimutations in the future. While locations of DMRs varied between species and cell types (Fig. 4), they had genomic features in common. DMRs were typically 500-1000 bp in length (Fig. 3) and 206 McNew et al. BMC Evolutionary Biology (2017) 17:183 many were clustered in 2-7 Mb regions. Most DMRs were in areas of low CpG density known as "CpG deserts" (Fig. 5). Many studies of DNA methylation have focused on the gene-silencing effects of methylation in high-density "CpG islands" near transcriptional start sites [6]. However, DMRs in other genomic regions, such as CpG deserts, can have other important effects on gene regulation and expression [6, 70]. Methylation of cytosines increases the rate of cytosine to thymine transitions [71]. Thus, over time, methylation can cause CpG-poor regions in the genome to accumulate. The persistence of conserved clusters of methylated CpG sites within CpG deserts suggests that these regions are likely conserved and under purifying selection [70]. Thus, these types of DMRs may have a functional role in regulating gene expression and could be subject to selection. Many of the DMRs we detected were associated with metabolic and signaling genes (Fig. 6). Previous work has suggested that novel food sources at the urban site are changing the diet of finches [27]. While we did not quantify phenotypic traits related to metabolism, it is possible that DMRs associated with metabolic genes are associated with other physiological differences between the urban and rural populations. We also found DMRs associated with genes in the bone morphogenic protein (BMP) / transferring growth factor beta (TGFß) pathway (Fig. 7). Expression of Bmp4 is related to beak shape in Geospiza finches [72]; however, it is unknown what factors regulate gene expression at this locus. We previously found that this pathway was differentially methylated among species of Darwin's finches [18]. These data suggest that DNA methylation may play a role in regulating expression of genes in this pathway and therefore may influence finch morphology. Our study compared just two populations - one rural and one urban - and therefore we cannot be certain that urbanization is the key environmental change influencing finch morphology and/or epigenetics in our study. Moreover, it is possible that differences between the two populations are the result of epigenetic drift, rather than differential selection. Some dispersal of G. fortis between the urban and rural populations has been documented through mark-recapture studies; but it is not very common (J. Raeymaekers pers. comm.). Low levels of gene flow between populations would preclude divergence of the rural and urban populations due to drift. However, much more work is needed to understand the basis of epigenetic variation and its relationship to phenotypic variation in populations of Darwin's finches. Conclusions We found epigenetic differences between adjacent populations of each of two species of Darwin's finches. We do not know which of the DMRs are responses to Page 12 of 14 environmental differences between the urban and rural sites, versus the result of random epigenetic drift. However, as the environmental differences between our sites are recent (<60 years) any methylation changes associated with urbanization have spread quickly. As in other recent studies [19, 20, 22], the functional relationship between environmental and epigenetic variation is not well understood. Nevertheless, these results are consistent with a potential role of epigenetic variation in rapid adaptation to changing environments. Future studies are needed to determine what effects DMR have on phenotypes, and to what extent these methylation patterns may play a role in evolution. Additional files Additional file 1: Figure S1. Comparison of vegetative cover at the rural site (El Garrapatero) versus urban site (Puerto Ayora, Academy Bay) over the course of the study. Cover was dervied from Normalized Difference Vegetative Index (NDVI) values generated from satellite imagery (ORNL DAAC. 2008. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed May 08, 2017 http://dx.doi.org/10.3334/ORNLDAAC/1241). Values range from 0-1 with 1 reprensenting the highest vegetation cover. (PDF 850 kb) Additional file 2: Figure S2. Copy number variation (CNV) between the rural and urban populations. (A) CNV analysis summary for the G. fortis erythrocytes showing read depth and alignment, and CNV numbers per pool with chromosomes containing CNV indicated, and no overlap between rural and urban pools indicated. (B) CNV analysis summary for the G. fuliginosa erythrocytes with Read Mapping Summary, overall CNV per pool and chromosome, and no overlapping CNV identified. (PDF 20 kb) Additional file 3: Table S1. The number of DMR detected at single window and multiple window scales at increasing levels of significance. (PDF 61 kb) Additional file 4: Table S2. Description of multiple-window DMR detected in G. fortis sperm (A) and erythrocytes (B). Description includes DMR name, chromosome number, DMR start site, length in base pair (bp), number of multiple sites, minimum p-value, CpG number per sequence length, CpG density (CpG number / 100 bp) and DMR gene association. "NA" indicates DMR associated with a gene that did not align to the zebra finch reference genome. (PDF 126 kb) Additional file 5: Table S3. Description of multiple-window DMR detected in G. fuliginosa sperm (A) and erythrocytes (B). Description includes DMR name, chromosome number, DMR start site, length in base pair (bp), number of multiple sites, minimum p-value, CpG number per sequence length, CpG density (CpG number / 100 bp) and DMR gene association. "NA" indicates DMR associated with a gene that did not align to the zebra finch reference genome. (PDF 154 kb) Additional file 6: Table S4. Description of DMR clusters detected in G. fortis sperm (A) and erythrocytes (B) and G. fuliginosa sperm (C) and erythrocytes (D). Description includes DMR in cluster, chromosome number, cluster start site, cluster stop site, length in bp, and minimum p-value. (PDF 103 kb) Additional file 7: Table S5. Gene associations with DMR detected in G. fortis sperm (A) and erythrocytes (B) and G. fuliginosa sperm (C) and erythrocytes (D). Description includes DMR name, gene symbol, entrez gene identification, chromosome number, start position site, ensemble gene identification number, gene description and gene classification category. (PDF 225 kb) Additional file 8: Figure S3. MAPK signaling pathway. Genes associated with DMR are listed and outlined in red in the pathway. (PDF 109 kb) 207 McNew et al. BMC Evolutionary Biology (2017) 17:183 Additional file 9: Figure S4. Glycolysis metabolism pathway. Genes associated with DMR are listed and outlined in red in the pathway. (PDF 66 kb) Additional file 10: Figure S5. DMRs identified in pairwise comparison of pools within populations: (A) G. Fuliginosa RBC urban analysis, (B) G. fuliginosa-RBC rural analysis, (C) G. fortis RBC urban analysis, and (D) G. fortis rural analysis. Numbers indicate DMRs between urban (U) or rural (R) individual pools (1-3). "Full analysis" are DMRs identified between urban and rural pools. DMRs identified in the full analysis were found independently of within-site variation. (PDF 98 kb) Abbreviations BMP: bone morphogenic protein; CNV: copy number variation; DDT: dithiothreitol; DMR: differentially DNA methylated region; IP: immunoprecipitation; LMM: linear mixed effects models; NGS: next generation sequencing; PBS: Phosphate Buffered Saline; TGFß: transferring growth factor beta Acknowledgments We acknowledge the advice and critical review of Dr. Eric Nilsson (WSU). The following people contributed to sample collection: Céline Le Bohec, Sarah Bush, Roger Clayton, Miriam Clayton, Oliver Tiselma, Elena Arriero, Andrew Bartlow, Daniela Vargas, Emily DiBlasi, Jordan Herman, Pricilia Espina, Kiyoko Gotanda, Sofia Carvajal, Joost Raeymakers and Janaí Yepez. We thank Ms. Jayleana Barton for molecular technical assistance and Ms. Heather Johnson for assistance in preparation of the manuscript. The research was supported by a Templeton grant to MKS, National Science Foundation grants DEB-0816877 and DEB-1342600 to DHC, and an NSF Graduate Research Fellowship to SMM. Dr. Sarah A. Knutie's present address: Department of Ecology and Evolutionary Biology, Storrs, CT 06269-3043, USA. Dr. Jennifer A. H. Koop's present address: Biology Department, University of Massachusetts-Dartmouth, Dartmouth, MA 02747-2300, USA. Funding The research was supported by a Templeton grant to MKS, a National Science Foundation grant to DHC, and an NSF Graduate Research Fellowship to SMM. Availability of data and materials All the DMR sequence and genomic data obtained in the current study have been deposited in the NCBI public GEO database (GEO # GSE87825). Author contributions DHC and MKS designed the study; SMM, SAK and JAHK collected the samples, DB and ISR analyzed the genomic data, SMM, DHC and MKS analyzed the data and wrote the manuscript with help from the other authors. All authors read and approved the final manuscript. Ethics approval and consent to participate All field procedures were approved by the University of Utah Institutional Animal Care and Use Committee (protocols #07-08004, #10-07003 and #13-06010) and by the Galápagos National Park. Competing interests The authors declare that they have no competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1 Department of Biology, University of Utah, Salt Lake City, UT 84112-0840, USA. 2Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA 99164-4236, USA. Page 13 of 14 Received: 26 January 2017 Accepted: 26 July 2017 References 1. Bossdorf O, Richards CL, Pigliucci M. Epigenetics for ecologists. Ecol Lett. 2008;11(2):106-15. 2. Day T, Bonduriansky R. 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Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Date Dec-13 Dec-14 Dec-15 Dec-16 210 Supplemental Figure S2 CNV Analysis of Individual Sample Pools (A) CNV analysis summary for the G.fortis RBC (erythrocytes) Read Mapping Summary: Read Number Overall Alignment Rate FB1 FB2 FB3 FB4 FB5 FB6 31943702 48.12% 35390572 48.85% 35292876 48.30% 32866339 48.10% 32098287 48.44% 33303537 47.99% The number of reads present for each sample and the overall alignment rate calculated by bowtie2. Overall CNV numbers per pool and chromosome sFB1.bam sFB2.bam sFB6.bam 18 7 27 3 9 Un 11 Z 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 The number of CNV found and separated by sample and chromosome Overlapping CNVs between rural and urban populations There were no CNV called in all rural pools or all urban pools. (B) CNV analysis summary for the G. fuliginosa RBC Read Mapping Summary: Read Number Overall Alignment Rate FB1 FB2 FB3 FB4 FB5 FB6 29702409 44.77% 35909278 47.91% 32460939 48.62% 31290666 48.11% 31033219 46.76% 39220844 45.51% The number of reads present for each sample and the overall alignment rate calculated by bowtie2. Overall CNV numbers per pool and chromosome sFB10.bam sFB11.bam sFB12.bam sFB8.bam sFB9.bam 4 20 1 10 11 13 14 18 1A 2 3 5 6 7 8 9 Un Z 1 0 32 0 0 0 1 0 0 1 0 0 51 0 0 0 0 4 0 0 0 0 8 0 0 0 0 1 0 0 0 0 3 0 0 0 0 1 0 0 0 0 34 0 0 0 0 41 1 0 0 0 34 0 0 0 0 29 0 0 0 0 13 0 0 0 0 18 0 0 0 0 16 0 0 0 0 6 0 0 0 0 21 0 0 0 0 20 0 0 The number of CNV found and separated by sample and chromosome Overlapping CNVs between rural and urban populations There were no CNV called in all rural or all urban pools exclusively. 211 Table S1. The number of DMR detected at single window and multiple window scales at increasing levels of significance. A. G. fortis Sperm Number of DMRs vs. p-value cutoff p-value Single Multiple Window Windows 0.01 24,058 44,423 0.001 1270 110 1e-04 79 2 1e-05 5 0 B. G. fuliginosa Sperm Number of DMRs vs. p-value cutoff p-value Single Multiple Window Windows 0.01 42,495 8,800 0.001 1,904 139 1e-04 22 1 1e-05 1 0 C. G. fortis Erythrocytes Number of DMRs vs. p-value cutoff p-value Single Multiple Window Windows 0.01 27,127 3,087 0.001 2,871 129 1e-04 252 4 1e-05 20 0 D. G. fuliginosa Erythrocytes Number of DMRs vs. p-value cutoff p-value Single Multiple Window Windows 0.01 35,541 5,432 0.001 4,663 324 1e-04 493 11 1e-05 66 3 212 Supplemental Table S2A DMR Name DMR1:2405001 DMR1:17460701 DMR1:89900301 DMR1A:31501 DMR1A:9608801 DMR1A:33384201 DMR2:1016201 DMR2:1382701 DMR2:11077401 DMR2:49493301 DMR2:52702101 DMR2:57021501 DMR2:57355101 DMR2:105076801 DMR2:152511201 DMR2:155551401 DMR3:19616201 DMR3:22144501 DMR3:82421001 DMR3:110673201 DMR3:111805401 DMR3:112515501 DMR4:5948301 DMR4:6462901 DMR4:10513201 DMR4:58976401 DMR4:67704101 DMR4A:4743001 DMR4A:15730101 DMR4A:17926101 DMR4A:19342701 DMR5:6667801 DMR5:10950801 DMR5:14025601 DMR5:43833301 DMR5:58425401 DMR6:3861701 DMR6:21155001 DMR7:1620001 DMR7:2068201 DMR8:24996701 DMR9:3092101 DMR9:16765501 DMR9:20816401 DMR9:23196601 DMR10:2121101 DMR10:14143801 DMR10:14466301 DMR10:15617101 DMR10:17182001 DMR11:2277401 DMR11:14550401 DMR11:16380101 DMR12:1610001 DMR12:4785401 Chr 1 1 1 1A 1A 1A 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4A 4A 4A 4A 5 5 5 5 5 6 6 7 7 8 9 9 9 9 10 10 10 10 10 11 11 11 12 12 G. fortis Multiple-Window DMR Sperm List Start 2405001 17460701 89900301 31501 9608801 33384201 1016201 1382701 11077401 49493301 52702101 57021501 57355101 105076801 152511201 155551401 19616201 22144501 82421001 110673201 111805401 112515501 5948301 6462901 10513201 58976401 67704101 4743001 15730101 17926101 19342701 6667801 10950801 14025601 43833301 58425401 3861701 21155001 1620001 2068201 24996701 3092101 16765501 20816401 23196601 2121101 14143801 14466301 15617101 17182001 2277401 14550401 16380101 1610001 4785401 Length (bp) 1400 2200 300 200 400 400 3000 4200 500 800 2600 300 1100 2000 3100 1000 200 500 1200 1600 200 900 300 2700 1000 500 800 400 800 1200 1400 9500 2000 2000 1100 1000 900 1400 1100 4000 500 2700 500 1200 500 10600 2500 200 500 2800 600 400 800 800 2400 # Sites 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 3 2 2 3 2 2 2 2 2 2 2 4 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 min P Value 1.88E-05 0.00083337 0.00024454 0.00056486 0.00039257 0.00042233 0.00017649 6.06E-05 7.82E-05 0.00017788 0.00047911 0.00055811 0.00043181 3.16E-05 0.00032751 0.00018367 0.00018836 9.00E-05 8.39E-05 0.00013009 7.92E-05 0.00018417 0.00012392 0.00050397 0.00034909 3.21E-05 4.79E-05 0.00050104 0.0003688 0.00036737 0.00048117 0.00032085 7.05E-05 9.59E-05 0.0004157 0.00024649 0.00024665 0.00036418 0.00045746 0.00011985 0.00031729 0.00018478 0.00028966 0.00045795 0.00089336 0.00027567 0.00038843 2.98E-05 0.0008557 2.67E-05 8.54E-05 0.00015349 0.00052102 5.31E-05 0.00014665 CpG # 1 4 10 0 10 1 56 32 16 8 115 3 24 19 115 24 2 1 80 31 2 47 5 111 63 26 56 1 20 12 44 378 17 19 30 15 55 37 25 55 15 19 14 1 24 542 78 10 17 61 25 4 7 3 119 CpG Density (#/100bp) 0.07 0.1 3.3 0 2.5 0.2 1.8 0.7 3.2 1 4.4 1 2.1 0.9 3.7 2.4 1 0.2 6.6 1.9 1 5.2 1.6 4.1 6.3 5.2 7 0.2 2.5 1 3.1 3.9 0.8 0.9 2.7 1.5 6.1 2.6 2.2 1.3 3 0.7 2.8 0.08 4.8 5.1 3.1 5 3.4 2.1 4.1 1 0.8 0.3 4.9 Gene Association ENO2;LRRC23 HGF MSRB3 CCDC12 NA RPL15 NA RHPN1 GALNT14 NRXN1 HTR1B PAQR8 MSRA FZD3 FAT4 LRBA JAKMIP1 CTNNA2;LRRTM1 RPS6KA6 CNGA2 TMEM132A;CD6 SOX6 SYT8 SGMS1 NA HDAC4 NFIA NA PLEC NA SV2B IGF1R NA MST1R 213 DMR12:5901001 DMR13:11920001 DMR14:1181901 DMR14:3090701 DMR14:8978801 DMR14:15047701 DMR15:2627801 DMR15:2915401 DMR15:5142701 DMR17:410701 DMR17:3431501 DMR18:991001 DMR18:1081001 DMR18:1084101 DMR19:514001 DMR19:5685501 DMR20:353601 DMR20:553601 DMR20:1139801 DMR20:12107601 DMR20:13641501 DMR21:3356301 DMR23:4735301 DMR25:797501 DMR26:2781201 DMR27:383501 DMR27:4437801 DMR28:3706001 DMR28:4853401 DMRZ:528401 DMRZ:34124901 DMRZ:38519001 DMRUn:1762101 DMRUn:13189701 DMRUn:37518901 DMRUn:38440401 DMRUn:42636001 DMRUn:52965301 DMRUn:64017401 DMRUn:65244001 DMRUn:70786601 DMRUn:70799501 DMRUn:78679901 DMRUn:93117201 DMRUn:93590701 DMRUn:116431501 DMRUn:116729701 DMRUn:124049301 DMRUn:132241501 DMRUn:134552701 DMRUn:142781801 DMRUn:144276901 DMRUn:155480601 DMRUn:156914801 DMRUn:173298101 12 13 14 14 14 14 15 15 15 17 17 18 18 18 19 19 20 20 20 20 20 21 23 25 26 27 27 28 28 Z Z Z Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un 5901001 11920001 1181901 3090701 8978801 15047701 2627801 2915401 5142701 410701 3431501 991001 1081001 1084101 514001 5685501 353601 553601 1139801 12107601 13641501 3356301 4735301 797501 2781201 383501 4437801 3706001 4853401 528401 34124901 38519001 1762101 13189701 37518901 38440401 42636001 52965301 64017401 65244001 70786601 70799501 78679901 93117201 93590701 116431501 116729701 124049301 132241501 134552701 142781801 144276901 155480601 156914801 173298101 1100 300 300 1300 1100 1300 200 200 800 3000 700 2200 1100 6900 1000 300 1000 1700 300 1100 300 4000 1500 1500 800 2100 7000 300 2900 600 300 200 300 400 200 1800 200 1000 1000 200 11700 3000 300 700 1200 2000 1400 300 2200 1500 200 700 400 2200 300 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 3 2 2 2 2 2 2 2 2 3 2 2 5 3 2 2 2 2 2 2 2 2 2 4 2 3 2 0.00029715 0.00067146 8.90E-05 0.00065447 0.00038178 0.00039071 1.97E-05 0.00019932 0.00032215 0.00021266 4.00E-05 2.96E-06 0.00044212 0.00040113 0.00048372 0.00010526 0.00011803 2.41E-05 3.27E-05 0.00037116 0.00010363 0.00035826 0.00070343 0.00080997 0.00034282 7.14E-06 0.00019975 0.00051934 0.00043561 0.00020996 1.80E-05 0.000281 0.00091328 4.06E-05 0.00010471 0.0006965 0.00035246 7.17E-05 0.00024876 5.10E-05 0.00015993 0.00012449 0.00051026 6.42E-05 0.00018951 0.00050843 0.00079537 3.29E-05 0.00067437 0.00012924 0.00017126 2.93E-05 0.00036774 0.00021333 0.00040971 23 22 20 28 37 0 7 9 2 80 34 144 40 216 36 11 16 19 10 2 15 87 47 59 14 108 211 0 170 28 0 7 1 2 15 112 4 3 9 12 25 23 3 1 4 79 14 1 86 7 13 5 1 128 20 2 7.3 6.6 2.1 3.3 0 3.5 4.5 0.2 2.6 4.8 6.5 3.6 3.1 3.6 3.6 1.6 1.1 3.3 0.1 5 2.1 3.1 3.9 1.7 5.1 3 0 5.8 4.6 0 3.5 0.3 0.5 7.5 6.2 2 0.3 0.9 6 0.2 0.7 1 0.1 0.3 3.9 1 0.3 3.9 0.4 6.5 0.7 0.2 5.8 6.6 NA GABRB2 RAB40C RHBDF1 NA IFT81 NA BTBD17 SLC38A10 SLC38A10 RNF43 RFFL RALGAPB NA NA PLCH2 EPHA10 NA DDX42 NA KLHL26 RPP25 ARHGAP39 SYCP1 SYCP1 DNMT1 DOCK7 USP21 214 Supplemental Table S2B DMR Name DMR1:2682401 DMR1:5421101 DMR1:15978201 DMR1:17714201 DMR1:26248601 DMR1:30831101 DMR1:34061101 DMR1:48999201 DMR1:52621901 DMR1:59413201 DMR1:68474101 DMR1:83012401 DMR1:93094801 DMR1:93731001 DMR1:98009701 DMR1:103888201 DMR1:116098201 DMR1:117036401 DMR1A:14851501 DMR1A:33667601 DMR1A:37330801 DMR1A:37641201 DMR1A:46028501 DMR1A:47095601 DMR1A:51403801 DMR1A:68987201 DMR1A:70257101 DMR2:1798801 DMR2:6567801 DMR2:25864201 DMR2:44270401 DMR2:48865301 DMR2:74709601 DMR2:81217201 DMR2:106386901 DMR2:106836801 DMR2:109922901 DMR2:127963201 DMR2:135732501 DMR2:139628601 DMR2:148037901 DMR2:153195201 DMR3:16726301 DMR3:17261701 DMR3:24857301 DMR3:26735901 DMR3:31219401 DMR3:35391201 DMR3:78517101 G. fortis Multiple-Window DMR Erythrocyte List Chr 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1A 1A 1A 1A 1A 1A 1A 1A 1A 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 Start 2682401 5421101 15978201 17714201 26248601 30831101 34061101 48999201 52621901 59413201 68474101 83012401 93094801 93731001 98009701 103888201 116098201 117036401 14851501 33667601 37330801 37641201 46028501 47095601 51403801 68987201 70257101 1798801 6567801 25864201 44270401 48865301 74709601 81217201 106386901 106836801 109922901 127963201 135732501 139628601 148037901 153195201 16726301 17261701 24857301 26735901 31219401 35391201 78517101 Length (bp) 900 200 1100 600 300 200 1200 1300 400 200 300 300 300 200 1700 700 300 200 1200 200 200 800 500 2200 400 300 700 1100 200 300 200 200 200 200 1700 1200 1300 200 800 800 800 1300 2600 200 200 200 200 500 300 # Sites 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 min P Value 0.00027256 1.02E-06 3.20E-05 4.03E-05 0.00014309 0.00018354 4.96E-05 1.53E-05 0.00030553 0.00022223 0.00045777 0.00041654 0.00019656 0.00013012 0.00042053 0.00037939 8.32E-05 0.00020596 0.00028406 0.00014771 0.00050789 7.67E-06 4.03E-05 0.0002731 4.03E-05 3.61E-05 4.78E-05 0.00015174 7.19E-05 0.00022459 0.00053621 0.00040403 0.00012255 0.00020906 0.00072119 0.00067838 0.00024475 0.00027855 6.12E-05 0.00094242 2.14E-05 0.00039388 0.00049046 1.87E-06 0.00027559 7.67E-06 0.00040117 4.11E-05 1.11E-06 CpG # 0 2 7 3 6 1 2 6 5 1 0 1 1 0 9 5 1 1 5 0 0 9 6 13 1 1 6 26 0 4 3 0 4 0 15 11 8 1 6 4 3 9 14 1 1 1 4 1 3 CpG Density (#/100bp) 0 1 0.6 0.5 2 0.5 0.1 0.4 1.2 0.5 0 0.3 0.3 0 0.5 0.7 0.3 0.5 0.47 0 0 1.1 1.2 0.5 0.2 0.3 0.8 2.3 0 1.3 1.5 0 2 0 0.8 0.9 0.6 0.5 0.7 0.5 0.3 0.6 0.5 0.5 0.5 0.5 2 0.2 1 Gene Association NA CXorf36 GEMIN8 NA NA PIBF1 NA NA CPNE8 IKBIP NA MICAL3 FARS2 NA CRIM1 215 DMR3:78536501 DMR3:91910801 DMR3:104372501 DMR4:34475501 DMR4:50910901 DMR4:61178101 DMR4A:6107001 DMR5:2675901 DMR5:2884401 DMR5:11814401 DMR5:14504501 DMR5:17085201 DMR5:25257301 DMR5:27884501 DMR5:38477401 DMR5:47907301 DMR6:4069801 DMR6:10248601 DMR6:15841201 DMR6:22127301 DMR6:24244501 DMR6:24443901 DMR7:4454501 DMR7:7103301 DMR7:7578001 DMR7:22822301 DMR7:37235801 DMR7:37271201 DMR7:38670601 DMR8:1651101 DMR8:9281501 DMR9:19271201 DMR10:11658001 DMR10:11904101 DMR10:14265201 DMR10:18696001 DMR11:7908101 DMR12:7343801 DMR12:9477701 DMR12:10036401 DMR12:11939801 DMR12:20282601 DMR13:246201 DMR13:11691501 DMR14:781701 DMR14:8553401 DMR14:10140501 DMR15:5515601 DMR15:9613601 DMR15:12868101 DMR15:13976501 DMR15:14137901 3 78536501 3 91910801 3 104372501 4 34475501 4 50910901 4 61178101 4A 6107001 5 2675901 5 2884401 5 11814401 5 14504501 5 17085201 5 25257301 5 27884501 5 38477401 5 47907301 6 4069801 6 10248601 6 15841201 6 22127301 6 24244501 6 24443901 7 4454501 7 7103301 7 7578001 7 22822301 7 37235801 7 37271201 7 38670601 8 1651101 8 9281501 9 19271201 10 11658001 10 11904101 10 14265201 10 18696001 11 7908101 12 7343801 12 9477701 12 10036401 12 11939801 12 20282601 13 246201 13 11691501 14 781701 14 8553401 14 10140501 15 5515601 15 9613601 15 12868101 15 13976501 15 14137901 1300 400 200 900 900 300 200 1400 200 200 1200 1100 700 200 200 400 200 200 200 1500 200 200 200 200 200 2700 1800 200 2100 200 300 1500 300 1000 800 300 300 900 400 600 200 300 300 900 200 700 200 200 300 200 400 900 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0.00027855 3.96E-05 0.00011622 0.00061095 0.00012234 3.99E-05 0.0004287 7.94E-05 0.00025439 7.66E-05 1.05E-05 0.00018656 8.59E-06 5.89E-05 5.06E-05 1.10E-05 1.96E-05 0.00024475 0.00016098 4.03E-05 0.00052526 0.00022223 0.00056941 0.00048932 0.00024475 2.60E-05 0.00023034 0.00014816 0.00023782 6.12E-05 0.00034517 0.00022223 4.96E-05 0.00054934 0.00033345 0.00039166 0.00012243 0.00048932 9.44E-05 4.39E-05 0.00070523 0.00060805 0.00020988 0.00026847 0.00034848 0.00056809 0.00042141 0.00014552 0.00024475 0.0002835 4.04E-06 1.20E-05 4 2 1 4 52 0 3 12 0 2 7 6 35 0 4 1 2 0 1 4 2 1 0 3 0 6 16 2 25 1 3 9 3 9 2 1 1 4 1 8 7 2 0 5 1 6 1 1 1 1 3 5 0.3 0.5 0.5 0.4 5.7 0 1.5 0.8 0 1 0.5 0.5 5 0 2 0.2 1 0 0.5 0.2 1 0.5 0 1.5 0 0.2 0.8 1 1.1 0.5 1 0.6 1 0.9 0.2 0.3 0.3 0.4 0.2 1.3 3.5 0.6 0 0.5 0.5 0.8 0.5 0.5 0.3 0.5 0.7 0.5 ELP4 NA NA GBF1 FBXW4;FGF8 SORCS3 MRAS PTBP2 ABTB1 ITPR1 STK32A NA CACNA1H;B9D1 MN1 NA AIFM3 216 DMR17:7618801 DMR18:7751401 DMR18:10818401 DMR19:6307401 DMR19:9135801 DMR20:4414401 DMR20:5555701 DMR20:6871001 DMR20:10911801 DMR20:15138501 DMR21:4415901 DMR26:3225201 DMR26:4154601 DMRZ:25875601 DMRZ:32978101 DMRZ:45594501 DMRZ:45691301 DMRZ:70647701 DMRUn:31643901 DMRUn:38098901 DMRUn:39304701 DMRUn:48843301 DMRUn:73071501 DMRUn:81523201 DMRUn:115914401 DMRUn:130440301 DMRUn:166943301 DMRUn:167343801 17 18 18 19 19 20 20 20 20 20 21 26 26 Z Z Z Z Z Un Un Un Un Un Un Un Un Un Un 7618801 7751401 10818401 6307401 9135801 4414401 5555701 6871001 10911801 15138501 4415901 3225201 4154601 25875601 32978101 45594501 45691301 70647701 31643901 38098901 39304701 48843301 73071501 81523201 115914401 130440301 166943301 167343801 300 200 500 200 300 300 300 1500 1600 1400 200 300 400 200 900 200 300 1200 900 200 200 300 400 1000 200 200 800 300 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0.00021721 0.0001428 5.76E-05 0.00015843 5.29E-05 3.52E-05 4.22E-05 0.00065159 0.00067837 4.46E-05 0.00041562 1.55E-05 0.00064105 0.00097829 0.00038839 6.12E-05 0.00030731 2.69E-05 0.00012679 0.00014552 6.12E-05 6.12E-05 1.06E-05 0.00063847 0.00097829 0.00073111 1.35E-05 3.12E-05 3 5 2 3 3 4 5 7 7 21 0 3 5 0 5 0 4 0 1 1 1 1 4 14 0 2 4 3 1 2.5 0.4 1.5 1 1.3 1.6 0.4 0.4 1.5 0 1 1.2 0 0.5 0 1.3 0 0.1 0.5 0.5 0.3 1 1.4 0 1 0.5 1 ST6GALNAC2 NA KCNB1 ACAP3 RAP1A ISL1 EDIL3 NA NA 217 Supplemental Table S3A DMR Name DMR1:10654201 DMR1:14148101 DMR1:25398601 DMR1:51009601 DMR1:57226101 DMR1:60559501 DMR1:66164301 DMR1:71703301 DMR1:80766701 DMR1:89065301 DMR1:89740001 DMR1:94245001 DMR1:96225201 DMR1:110763501 DMR1A:5308001 DMR1A:6286301 DMR1A:11577701 DMR1A:13180201 DMR1A:22795501 DMR1A:24114101 DMR1A:25971701 DMR1A:27064701 DMR1A:29521501 DMR1A:33582601 DMR1A:37122201 DMR1A:48389101 DMR1A:59545101 DMR1A:61248301 DMR2:7094701 DMR2:8947601 DMR2:19980701 DMR2:21027601 DMR2:21459501 DMR2:22412201 DMR2:44117701 DMR2:46614201 DMR2:47305801 DMR2:57223201 DMR2:60537101 DMR2:67804701 DMR2:74604401 DMR2:76623901 DMR2:81234601 DMR2:91707301 DMR2:94031401 DMR2:96346101 DMR2:108428901 DMR2:118325701 DMR2:123640001 DMR2:134150201 Chr 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1A 1A 1A 1A 1A 1A 1A 1A 1A 1A 1A 1A 1A 1A 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 G. fuliginosa Multiple-Window DMR Sperm List Start 10654201 14148101 25398601 51009601 57226101 60559501 66164301 71703301 80766701 89065301 89740001 94245001 96225201 110763501 5308001 6286301 11577701 13180201 22795501 24114101 25971701 27064701 29521501 33582601 37122201 48389101 59545101 61248301 7094701 8947601 19980701 21027601 21459501 22412201 44117701 46614201 47305801 57223201 60537101 67804701 74604401 76623901 81234601 91707301 94031401 96346101 108428901 118325701 123640001 134150201 Length (bp) 1200 1300 1900 1800 2300 200 1600 400 300 1100 400 3100 1400 700 2500 800 1100 600 1300 1600 900 500 600 1000 1200 3200 1100 200 600 1100 2800 1800 1300 1300 600 300 500 700 1600 500 600 400 600 1200 1600 900 300 300 200 1500 # Sites 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 min P Value 0.00028226 0.00065919 0.00056475 0.00074605 0.00059756 0.0008456 3.79E-05 9.86E-05 0.00042544 0.00010216 0.00026565 3.25E-05 0.00088771 0.00033142 0.0004006 0.00012642 0.00010458 0.0005454 7.56E-05 0.00061683 0.00052497 0.0003543 0.00068445 0.00050694 4.58E-05 0.0001117 0.00084561 0.00041825 0.00067655 0.00056393 0.00056404 0.00072248 4.60E-05 0.0001609 0.00017683 0.00078566 0.00022533 0.00081186 0.00035886 0.00053012 0.00044348 0.00027441 0.00047515 0.0006222 0.00027513 0.00070456 0.00048682 0.00025038 0.00029415 0.00041082 CpG # 3 1 73 4 10 0 5 1 1 4 1 13 3 1 3 2 2 3 4 101 1 1 0 2 5 8 3 0 0 4 109 6 4 2 5 0 1 2 5 2 5 1 3 5 4 5 0 1 2 0 CpG Density (#/100bp) 0.2 0.07 3.8 0.2 0.4 0 0.3 0.2 0.3 0.3 0.2 0.4 0.2 0.1 0.1 0.2 0.1 0.5 0.3 6.3 0.1 0.2 0 0.2 0.4 0.2 0.2 0 0 0.3 3.8 0.3 0.3 0.1 0.8 0 0.2 0.2 0.3 0.4 0.8 0.2 0.5 0.4 0.2 0.5 0 0.3 1 0 Gene Association IL1RAPL1 FAT3 PTPN12 WNT2 IMMP2L TMEM117 NA TTC26 KDM7A C2CD5 GALNT11 NA NA NA OSBPL10 NA TOX NIPAL2 218 DMR2:144740601 DMR3:12996601 DMR3:14233101 DMR3:17642301 DMR3:20417201 DMR3:27575701 DMR3:37439001 DMR3:41905401 DMR3:43634901 DMR3:47822101 DMR3:52144601 DMR3:53617101 DMR3:57319601 DMR3:58282801 DMR3:62782501 DMR3:67318501 DMR3:72011901 DMR3:78687001 DMR3:85093801 DMR3:89692801 DMR3:92570201 DMR3:95496201 DMR3:102676701 DMR4:30010001 DMR4:40007701 DMR4:46638801 DMR4:54765801 DMR4:57167101 DMR4:65124401 DMR4A:4645401 DMR5:11178401 DMR5:17116201 DMR5:19330701 DMR5:20105501 DMR5:22209801 DMR5:25054401 DMR5:25225401 DMR5:29800501 DMR5:36949201 DMR5:41558801 DMR5:53829801 DMR6:5313901 DMR6:28395601 DMR6:29758301 DMR6:32221801 DMR7:2821801 DMR7:7413001 DMR7:8134501 DMR7:9858501 DMR7:27843701 DMR7:32399701 DMR8:4830301 DMR8:6695101 2 144740601 3 12996601 3 14233101 3 17642301 3 20417201 3 27575701 3 37439001 3 41905401 3 43634901 3 47822101 3 52144601 3 53617101 3 57319601 3 58282801 3 62782501 3 67318501 3 72011901 3 78687001 3 85093801 3 89692801 3 92570201 3 95496201 3 102676701 4 30010001 4 40007701 4 46638801 4 54765801 4 57167101 4 65124401 4A 4645401 5 11178401 5 17116201 5 19330701 5 20105501 5 22209801 5 25054401 5 25225401 5 29800501 5 36949201 5 41558801 5 53829801 6 5313901 6 28395601 6 29758301 6 32221801 7 2821801 7 7413001 7 8134501 7 9858501 7 27843701 7 32399701 8 4830301 8 6695101 400 300 600 1800 1600 300 1500 1300 400 1900 1100 900 2700 2600 1900 1800 400 4500 300 1000 1500 1700 400 500 1100 2000 3000 1400 400 500 2000 500 600 300 1100 400 300 500 2400 300 2100 900 1100 300 1000 1300 2500 1200 1400 2400 300 600 1500 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 0.00072872 0.00014089 0.00016186 0.00026862 0.00029158 0.00051085 0.00078426 0.00041533 0.00067468 0.00077027 0.00065509 0.00041859 0.00018767 0.00050741 0.00033142 0.00062474 0.00041523 0.00024516 0.00047423 0.00029415 6.93E-05 0.00050741 0.00022277 0.00030323 0.00066563 0.00038729 0.00034629 0.00043234 0.00033129 0.00020126 5.92E-05 0.00022882 0.0003843 5.25E-05 0.00031993 0.00055585 0.00016997 0.00038454 0.00052011 0.00047342 0.00035467 0.00016145 0.0005434 0.00050694 0.00020736 0.00048644 0.0005313 0.00051493 0.00061522 0.0004597 0.00029298 0.00043102 9.23E-05 1 1 5 6 5 0 1 82 1 7 5 2 13 10 4 8 1 18 1 0 7 4 2 3 3 7 7 9 3 2 60 0 0 1 6 1 0 1 7 0 9 2 5 0 2 9 11 3 8 12 1 2 8 0.2 0.3 0.8 0.3 0.3 0 0.06 6.3 0.2 0.3 0.4 0.2 0.4 0.3 0.2 0.4 0.2 0.4 0.3 0 0.4 0.2 0.5 0.6 0.2 0.3 0.2 0.6 0.7 0.4 3 0 0 0.3 0.5 0.2 0 0.2 0.2 0 0.4 0.2 0.4 0 0.2 0.6 0.4 0.2 0.5 0.5 0.3 0.3 0.5 FBXO32 UBR2 LCLAT1 PARK2 DLL1 SIPA1L2 SNX9 AKIRIN2 FAM135A BMP5 CSMD1 PXDN RAPGEF2 HDX NA EXT2 AMBRA1 UBR1 RYR3 C10orf107 NA IQCA1 ZNF804A NA 219 DMR8:11398901 DMR8:14216901 DMR8:14857401 DMR8:25817401 DMR9:7480901 DMR9:11711001 DMR9:15033901 DMR10:7071601 DMR10:8862401 DMR10:11607101 DMR10:16087901 DMR11:14774301 DMR11:16902201 DMR11:17270401 DMR11:17582801 DMR11:18477501 DMR13:7280601 DMR13:10352201 DMR13:13887701 DMR14:12007301 DMR14:15125101 DMR15:8420501 DMR19:8815901 DMR20:9459301 DMR20:12224401 DMR26:1999201 DMRZ:36983401 DMRZ:42084601 DMRZ:45791301 DMRZ:58234701 DMRZ:66392601 DMRUn:15781801 DMRUn:31802501 DMRUn:55354101 DMRUn:99227501 DMRUn:152715201 8 11398901 8 14216901 8 14857401 8 25817401 9 7480901 9 11711001 9 15033901 10 7071601 10 8862401 10 11607101 10 16087901 11 14774301 11 16902201 11 17270401 11 17582801 11 18477501 13 7280601 13 10352201 13 13887701 14 12007301 14 15125101 15 8420501 19 8815901 20 9459301 20 12224401 26 1999201 Z 36983401 Z 42084601 Z 45791301 Z 58234701 Z 66392601 Un 15781801 Un 31802501 Un 55354101 Un 99227501 Un 152715201 500 1200 200 3700 800 600 400 300 2100 600 1400 1300 1800 2500 2200 2000 1500 500 3700 900 1300 900 1100 300 3000 1400 1000 600 2200 5200 600 700 600 200 400 200 2 2 2 2 2 3 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0.00074667 0.00010694 0.00041888 0.00045392 0.00038451 0.0003283 0.00017153 0.00055586 1.62E-05 0.00066354 0.00018825 0.00051978 0.00025038 0.00014437 0.00020408 0.00032749 0.00032367 0.00061683 0.00052392 0.0006074 0.00016353 0.0003794 0.0002476 0.00037744 0.0002881 0.00022374 0.00068726 0.00049473 0.00055305 0.00016185 0.00049011 0.00016516 0.00026326 0.00018537 0.00020246 0.00067783 0 0 0 8 3 2 2 1 14 0 9 9 3 10 5 5 3 2 11 2 11 1 3 1 67 3 7 4 8 25 0 3 5 1 3 1 0 0 0 0.2 0.3 0.3 0.5 0.3 0.6 0 0.6 0.6 0.1 0.4 0.2 0.2 0.2 0.4 0.2 0.2 0.8 0.1 0.2 0.3 2.2 0.2 0.7 0.6 0.3 0.4 0 0.4 0.8 0.5 0.7 0.5 NA MB21D2 TMOD2 CDH8 NA TOP3B GDPD1 KLHL12 DENND4C NA 220 Supplemental Table S3B DMR Name DMR1:4372401 DMR1:4717301 DMR1:8796601 DMR1:15514901 DMR1:30194301 DMR1:34868501 DMR1:49804201 DMR1:55662401 DMR1:55714201 DMR1:56745701 DMR1:60281601 DMR1:69585701 DMR1:79976701 DMR1:80849001 DMR1:80975101 DMR1:88272501 DMR1:88280001 DMR1:91117201 DMR1:91608001 DMR1:94220601 DMR1:94822701 DMR1:95189901 DMR1:109208501 DMR1:116004201 DMR1A:536501 DMR1A:3291201 DMR1A:19893901 DMR1A:25828501 DMR1A:37988101 DMR1A:38048601 DMR1A:48081501 DMR1A:50021401 DMR1A:59233601 DMR1A:60465701 DMR1A:67777301 DMR1A:70315301 DMR1A:72702301 DMR1A:73391301 DMR2:5091301 DMR2:6286001 DMR2:9116901 DMR2:11574001 DMR2:13073201 DMR2:13156501 DMR2:14049001 DMR2:14069501 DMR2:16620301 DMR2:18882501 G. fuliginosa Multiple-Window DMR Erythrocyte List Chr Start 1 4372401 1 4717301 1 8796601 1 15514901 1 30194301 1 34868501 1 49804201 1 55662401 1 55714201 1 56745701 1 60281601 1 69585701 1 79976701 1 80849001 1 80975101 1 88272501 1 88280001 1 91117201 1 91608001 1 94220601 1 94822701 1 95189901 1 109208501 1 116004201 1A 536501 1A 3291201 1A 19893901 1A 25828501 1A 37988101 1A 38048601 1A 48081501 1A 50021401 1A 59233601 1A 60465701 1A 67777301 1A 70315301 1A 72702301 1A 73391301 2 5091301 2 6286001 2 9116901 2 11574001 2 13073201 2 13156501 2 14049001 2 14069501 2 16620301 2 18882501 Length (bp) # Sites min P Value 200 2 7.68E-06 2700 2 4.51E-05 200 2 0.0001461 700 2 7.48E-05 500 2 0.00020125 1400 2 0.00097878 400 2 1.76E-05 600 2 3.07E-05 1400 2 8.18E-05 600 2 1.26E-05 200 2 0.00024493 300 2 7.68E-06 300 2 0.00045066 200 2 0.00046822 300 2 7.87E-05 1400 2 0.00030252 400 2 0.00012253 300 2 0.00038876 1600 2 0.00013341 900 3 0.00012598 200 2 3.07E-05 3700 2 0.00048961 2200 2 4.95E-05 200 2 0.00024493 2300 2 0.00026491 800 2 8.20E-05 200 2 6.13E-05 300 2 0.00053859 200 2 0.00036327 2000 2 3.55E-05 800 2 0.00044329 700 2 0.00075736 1600 2 7.32E-05 300 2 3.07E-05 300 2 0.00012253 1100 2 7.89E-05 300 2 0.00012253 1700 2 0.00068727 3300 2 6.37E-05 300 2 0.00029475 1200 2 0.00055161 1200 2 0.00012903 1100 2 8.15E-05 400 2 0.00023991 1100 2 0.00075338 200 2 0.00010969 400 2 0.00038799 1200 2 0.00048961 CpG # 7 17 0 0 2 7 1 3 7 3 0 2 0 0 1 15 0 2 14 8 3 24 9 0 83 3 1 2 1 8 2 6 2 1 0 11 1 9 16 4 4 2 1 1 1 0 2 15 CpG Density (#/100bp) 3.5 0.6 0 0 0.4 0.5 0.2 0.5 0.5 0.5 0 0.6 0 0 0.3 1 0 0.6 0.8 0.8 1.5 0.6 0.4 0 3.6 0.3 0.5 0.6 0.5 0.4 0.2 0.8 0.1 0.3 0 1 0.3 0.5 0.4 1.3 0.3 0.1 0.09 0.2 0 0 0.5 1.2 Gene Association NA RNF149 NA LMO7 NA FAT3 LSAMP NET1 SFMBT2 PPP1R3A NAV3 CACNA1I SOX5 PLEKHA5 221 DMR2:21606201 DMR2:30149401 DMR2:34396601 DMR2:39353301 DMR2:49454001 DMR2:66551001 DMR2:69476301 DMR2:71040901 DMR2:71432601 DMR2:75127401 DMR2:82366501 DMR2:84797701 DMR2:87015801 DMR2:88439501 DMR2:88582201 DMR2:93255301 DMR2:94902101 DMR2:97747101 DMR2:100091801 DMR2:102792501 DMR2:107228101 DMR2:109112201 DMR2:110670701 DMR2:112067801 DMR2:115575001 DMR2:118036701 DMR2:119271201 DMR2:120341401 DMR2:121488201 DMR2:132400801 DMR2:133334401 DMR2:133357301 DMR2:138528101 DMR2:140587701 DMR2:141908201 DMR2:144011701 DMR2:144085301 DMR2:144294901 DMR2:149096501 DMR2:151126601 DMR2:151971901 DMR2:153992101 DMR2:155785401 DMR3:3457301 DMR3:5223801 DMR3:5355701 DMR3:7621301 DMR3:9086001 DMR3:13404601 DMR3:21546801 DMR3:26913201 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 21606201 30149401 34396601 39353301 49454001 66551001 69476301 71040901 71432601 75127401 82366501 84797701 87015801 88439501 88582201 93255301 94902101 97747101 100091801 102792501 107228101 109112201 110670701 112067801 115575001 118036701 119271201 120341401 121488201 132400801 133334401 133357301 138528101 140587701 141908201 144011701 144085301 144294901 149096501 151126601 151971901 153992101 155785401 3457301 5223801 5355701 7621301 9086001 13404601 21546801 26913201 300 200 200 700 300 400 400 300 500 400 800 900 200 600 200 800 1200 200 200 200 1500 300 500 1100 300 300 1800 200 1900 200 1300 1700 200 200 400 200 300 200 300 1100 1200 700 2000 500 400 200 1700 400 200 5100 1600 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4.12E-05 0.00051448 1.68E-07 0.00040467 3.72E-05 0.00022321 0.00032254 2.32E-05 0.00083302 0.00039202 7.33E-05 0.00017431 0.00021576 0.00048961 9.21E-05 0.00029452 0.0006423 8.18E-05 0.00020101 0.00024493 0.00012253 8.82E-05 0.00026973 4.25E-05 0.00023639 7.67E-05 0.00026098 0.00052692 8.26E-05 0.00036063 0.00018133 0.00048961 0.00075338 0.00027586 0.00014206 0.00031234 0.00046914 3.84E-06 0.00015574 1.60E-05 0.0002323 0.00012885 0.00058204 3.09E-05 0.00066758 0.00027129 0.00019112 0.00027586 0.00040864 0.00016536 4.21E-05 0 1 0 5 0 2 1 2 1 0 4 8 0 2 0 3 12 0 0 0 6 3 2 4 0 2 8 4 9 0 74 7 1 0 0 4 1 0 0 15 5 7 28 1 1 0 38 0 0 21 13 0 0.5 0 0.7 0 0.5 0.2 0.6 0.2 0 0.5 0.8 0 0.3 0 0.3 1 0 0 0 0.4 1 0.4 0.3 0 0.6 0.4 2 0.4 0 5.6 0.4 0.5 0 0 2 0.3 0 0 1.3 0.4 1 1.4 0.2 0.2 0 2.2 0 0 0.4 0.8 NA EEPD1 MYH6 FHOD3 C18orf21 GNAL OSBPL1A CDH2 NA CDH17 NA EIF3H FAM135B KCNK9 ZC3H3 TASP1 FOXN3 222 DMR3:32739301 DMR3:47496101 DMR3:54732001 DMR3:58937401 DMR3:64095701 DMR3:68932001 DMR3:70631201 DMR3:72222201 DMR3:74530101 DMR3:77533001 DMR3:89074901 DMR3:89714201 DMR3:90528101 DMR3:95828401 DMR3:102264401 DMR3:103500401 DMR3:105780201 DMR3:110489001 DMR4:5567801 DMR4:6440901 DMR4:11522601 DMR4:21854601 DMR4:23287201 DMR4:30011901 DMR4:35035201 DMR4:38514801 DMR4:47123001 DMR4:54085101 DMR4:59851201 DMR4A:3765401 DMR4A:6082701 DMR4A:9160301 DMR4A:9445601 DMR4A:12525201 DMR4A:12646701 DMR4A:13403801 DMR4A:13875801 DMR4A:17382501 DMR4A:17926601 DMR4A:18342901 DMR5:143201 DMR5:5322501 DMR5:7862001 DMR5:15807101 DMR5:23278601 DMR5:23337901 DMR5:26249001 DMR5:28428001 DMR5:28566801 DMR5:28908101 DMR5:31294901 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4A 4A 4A 4A 4A 4A 4A 4A 4A 4A 4A 5 5 5 5 5 5 5 5 5 5 5 32739301 47496101 54732001 58937401 64095701 68932001 70631201 72222201 74530101 77533001 89074901 89714201 90528101 95828401 102264401 103500401 105780201 110489001 5567801 6440901 11522601 21854601 23287201 30011901 35035201 38514801 47123001 54085101 59851201 3765401 6082701 9160301 9445601 12525201 12646701 13403801 13875801 17382501 17926601 18342901 143201 5322501 7862001 15807101 23278601 23337901 26249001 28428001 28566801 28908101 31294901 400 500 200 500 600 1600 200 300 500 3000 1000 800 800 400 700 1400 200 1300 200 2000 300 400 1200 1000 300 1700 300 400 500 1100 1000 1000 500 1100 200 400 500 400 900 200 2400 300 1000 1100 2800 400 200 2400 200 600 2400 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3.14E-07 0.00027586 0.00097878 3.37E-05 2.29E-05 0.00016801 0.00014652 0.00086697 3.50E-06 0.00028017 3.07E-05 2.64E-05 0.00048961 0.00029828 0.00027586 6.87E-07 0.00024493 4.53E-05 0.0003297 5.60E-05 0.00028556 0.00024493 0.00044914 7.46E-05 0.00018333 5.70E-05 0.00015385 0.00058308 0.00097878 0.00053963 0.00071146 2.37E-05 0.00012253 0.00012253 0.00087502 0.00048961 0.00026475 1.81E-05 1.81E-05 0.00060352 0.00024493 0.00011009 9.51E-05 0.00059893 0.00091069 0.00077175 4.81E-05 4.03E-05 0.00024493 0.00044855 0.00023909 2 3 0 2 1 3 0 2 3 22 2 4 3 5 0 7 0 5 0 16 1 3 10 4 0 10 0 0 2 5 17 1 2 1 1 1 3 3 6 0 14 4 23 2 36 1 1 5 0 3 10 0.5 0.6 0 0.4 0.1 0.1 0 0.6 0.6 0.7 0.2 0.5 0.3 1.2 0 0.5 0 0.3 0 0.8 0.3 0.7 0.8 0.4 0 0.5 0 0 0.4 0.4 1.7 0.1 0.4 0.09 0.5 0.2 0.6 0.7 0.6 0 0.5 1.3 2.3 0.1 1.2 0.2 0.5 0.2 0 0.5 0.4 UST NOX3 PRDM1 PRIM2 BMP5 GCLC NA SPATA5 FAT4 ARHGAP10 LEF1 RAPGEF2 ENPP6 NA MCTS1;AKAP14 NELL1 NA RPLP2 INO80 GPHN 223 DMR5:34525601 DMR5:36924001 DMR5:40106301 DMR5:44895801 DMR5:48919901 DMR5:53365301 DMR5:53490201 DMR5:53560501 DMR5:54589001 DMR5:54610101 DMR5:57026201 DMR5:58011201 DMR5:58386501 DMR6:16318101 DMR6:25933601 DMR6:36172101 DMR7:8264601 DMR7:10812401 DMR7:11920001 DMR7:17993901 DMR7:26104901 DMR7:36578601 DMR7:39775501 DMR8:1072401 DMR8:4542101 DMR8:10439801 DMR8:11860301 DMR8:12439401 DMR8:13603601 DMR8:15006401 DMR8:19866501 DMR8:22901801 DMR8:23883401 DMR8:24123401 DMR8:24651101 DMR8:24810801 DMR8:24850501 DMR8:26306401 DMR9:659301 DMR9:5238901 DMR9:7457601 DMR9:8221501 DMR9:14240601 DMR9:14369701 DMR9:19526001 DMR9:20093101 DMR9:20439401 DMR9:22520301 DMR9:24201901 DMR9:25227801 DMR9:25731701 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 34525601 36924001 40106301 44895801 48919901 53365301 53490201 53560501 54589001 54610101 57026201 58011201 58386501 16318101 25933601 36172101 8264601 10812401 11920001 17993901 26104901 36578601 39775501 1072401 4542101 10439801 11860301 12439401 13603601 15006401 19866501 22901801 23883401 24123401 24651101 24810801 24850501 26306401 659301 5238901 7457601 8221501 14240601 14369701 19526001 20093101 20439401 22520301 24201901 25227801 25731701 1000 300 1500 800 800 300 1600 200 400 1000 400 300 1000 2600 1600 1200 200 3400 200 1400 300 1000 200 1700 300 1300 200 1100 200 300 1100 300 2400 600 200 800 300 1100 300 1000 300 300 300 400 600 1800 1000 200 900 2600 300 2 2 2 2 2 2 2 2 2 2 2 2 3 2 4 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0.00048961 0.00098308 0.00013108 0.00097878 0.00029446 0.00014568 0.00046167 0.00071995 2.63E-05 0.00034145 9.21E-05 5.69E-05 1.54E-05 1.13E-06 6.13E-05 6.29E-05 4.35E-05 5.96E-05 4.37E-05 7.96E-05 0.00051055 0.00029064 4.89E-07 7.68E-06 5.51E-06 0.00012253 0.00018568 6.13E-05 8.65E-05 0.00052699 0.00017471 6.51E-05 0.00013798 0.00018994 0.00052459 0.00048961 9.01E-05 0.00010926 0.00047856 0.00052077 0.00014515 0.00040869 9.65E-05 0.00021154 0.00012253 1.03E-05 0.00048961 5.23E-05 0.00059108 0.000835 0.00097878 4 3 15 3 6 0 5 4 5 17 1 5 16 15 8 6 1 21 1 8 7 17 2 7 1 15 1 4 1 0 13 2 22 1 0 0 1 5 1 1 1 1 4 1 1 8 3 1 1 15 1 0.4 1 1 0.3 0.7 0 0.3 2 1.2 1.7 0.2 1.6 1.6 0.5 0.5 0.5 0.5 0.6 0.5 0.5 2.3 1.7 1 0.4 0.3 1.1 0.5 0.3 0.5 0 1.1 0.6 0.9 0.1 0 0 0.3 0.4 0.3 0.1 0.3 0.3 1.3 0.2 0.1 0.4 0.3 0.5 0.1 0.5 0.3 NA TTC7B NA CEP170B BRF1 BRF1 TMEM260 NA SLC4A3 IFIH1 ORMDL1 CRB1 RPL5;SNORD21 AK4 NA SPSB4 MFN1 MFSD1 224 DMR9:26210101 DMR9:27015201 DMR10:7027301 DMR10:7163301 DMR10:10285701 DMR10:13256501 DMR10:13316301 DMR10:15606601 DMR10:17519401 DMR10:17985101 DMR10:18730201 DMR11:3177001 DMR11:3675801 DMR11:8358501 DMR11:9094101 DMR11:9926601 DMR11:10915301 DMR11:11887801 DMR11:15791101 DMR11:15948901 DMR11:17071401 DMR11:19202501 DMR12:424701 DMR12:1074001 DMR12:2681801 DMR12:15878101 DMR12:16143901 DMR12:18973401 DMR12:19028901 DMR12:19307701 DMR12:20529201 DMR12:21383601 DMR13:4235701 DMR13:4487901 DMR13:5482801 DMR13:6425801 DMR13:8068401 DMR13:9793401 DMR13:11390101 DMR13:16263501 DMR13:16826401 DMR14:922901 DMR14:1865701 DMR14:7746201 DMR14:12143301 DMR15:3320201 DMR15:9116301 DMR15:10710801 DMR15:11293101 DMR17:6864401 DMR17:11018301 9 9 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 14 14 14 14 15 15 15 15 17 17 26210101 27015201 7027301 7163301 10285701 13256501 13316301 15606601 17519401 17985101 18730201 3177001 3675801 8358501 9094101 9926601 10915301 11887801 15791101 15948901 17071401 19202501 424701 1074001 2681801 15878101 16143901 18973401 19028901 19307701 20529201 21383601 4235701 4487901 5482801 6425801 8068401 9793401 11390101 16263501 16826401 922901 1865701 7746201 12143301 3320201 9116301 10710801 11293101 6864401 11018301 300 200 1900 300 200 500 1600 200 3400 300 300 300 1100 1200 500 300 1600 700 1200 200 1700 1600 1300 300 200 300 2300 1200 400 300 200 1700 200 200 1100 600 1700 600 200 700 2700 1800 500 200 1400 200 300 1400 2300 1100 200 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3.16E-05 0.00038638 1.36E-05 5.03E-05 5.76E-05 0.00010529 0.00014568 5.72E-05 4.07E-05 0.00023797 0.00086225 5.05E-06 0.00042951 7.67E-05 0.00070224 3.07E-05 0.00057165 0.00035386 0.00048961 0.00040956 0.00024067 7.67E-05 0.00060191 0.00011721 0.00024493 1.30E-05 1.65E-05 6.03E-06 0.00010033 0.00014568 3.84E-06 0.00023678 4.92E-05 0.00022422 5.36E-05 0.0003907 0.00014568 0.00059893 0.00070224 0.00044215 1.85E-05 0.00010341 0.00097974 0.00024493 0.00043873 0.00048961 9.07E-05 0.00044301 0.00010906 0.00023253 0.00066212 2 1 7 1 2 2 11 3 10 5 1 1 1 1 1 1 9 3 3 2 12 5 7 0 0 3 17 12 1 6 4 6 1 3 5 1 12 3 1 15 378 11 3 1 13 3 1 4 20 13 34 0.6 0.5 0.3 0.3 1 0.4 0.6 1.5 0.2 1.6 0.3 0.3 0.09 0.08 0.2 0.3 0.5 0.4 0.2 1 0.7 0.3 0.5 0 0 1 0.7 1 0.2 2 2 0.3 0.5 1.5 0.4 0.1 0.7 0.5 0.5 2.1 14 0.6 0.6 0.5 0.9 1.5 0.3 0.2 0.8 1.1 17 NA PFN2 CGNL1 DET1 ALDH1A3 DENND4A BANP NA NA FAM19A1 EBF1 RAB11FIP3 MYH11 EIF4ENIF1 PRRC2B PBX3 225 DMR18:126301 DMR18:1033201 DMR18:3560201 DMR18:8856901 DMR18:9121601 DMR19:5744201 DMR19:7251301 DMR19:8830401 DMR19:10177801 DMR20:12291301 DMR20:12433001 DMR20:13029701 DMR21:149301 DMR21:1575701 DMR21:3489101 DMR21:3618001 DMR21:3922101 DMR21:4701101 DMR21:4941801 DMR21:5494101 DMR22:3011601 DMR23:2525401 DMR24:1935801 DMR26:3370501 DMR26:4774101 DMR27:3371401 DMR27:3902601 DMR28:538701 DMRZ:7053001 DMRZ:8349501 DMRZ:11290201 DMRZ:22572801 DMRZ:33485401 DMRZ:36664001 DMRZ:38648801 DMRZ:38745301 DMRZ:38864501 DMRZ:39510601 DMRZ:39662301 DMRZ:40335601 DMRZ:40862001 DMRZ:46606301 DMRZ:49883001 DMRZ:53351401 DMRZ:54426901 DMRZ:66310201 DMRUn:1724101 DMRUn:7287501 DMRUn:9275501 DMRUn:13837801 DMRUn:19838801 18 18 18 18 18 19 19 19 19 20 20 20 21 21 21 21 21 21 21 21 22 23 24 26 26 27 27 28 Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Un Un Un Un Un 126301 1033201 3560201 8856901 9121601 5744201 7251301 8830401 10177801 12291301 12433001 13029701 149301 1575701 3489101 3618001 3922101 4701101 4941801 5494101 3011601 2525401 1935801 3370501 4774101 3371401 3902601 538701 7053001 8349501 11290201 22572801 33485401 36664001 38648801 38745301 38864501 39510601 39662301 40335601 40862001 46606301 49883001 53351401 54426901 66310201 1724101 7287501 9275501 13837801 19838801 1400 700 1300 800 200 600 1000 1700 200 200 1600 1000 300 200 200 2100 3700 400 200 1400 200 400 200 2300 600 300 1100 600 200 300 400 400 200 900 300 1700 400 200 3300 200 400 700 300 1700 1100 500 200 1300 200 900 1700 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 8.78E-05 0.00050364 6.13E-05 0.00025713 6.13E-05 1.26E-05 6.13E-05 0.00013425 7.70E-05 0.00063673 2.27E-05 0.00097878 5.95E-05 1.18E-05 6.13E-05 0.00024527 0.00027586 1.53E-05 0.00011491 0.00031974 0.0003452 0.00060005 0.00059565 0.0002477 0.00015107 0.00052077 0.00012399 0.00031694 0.00023076 0.00023147 0.00032687 0.00052077 0.00014568 0.00048961 0.00014568 0.00010258 0.00046444 2.24E-05 0.00034602 0.00065736 0.00052901 0.00011601 2.14E-06 1.88E-05 0.00048728 6.13E-05 5.20E-06 0.00010725 1.92E-08 0.00010152 1.58E-06 6 4 6 12 2 2 5 13 2 1 20 9 3 1 0 26 32 3 0 7 3 4 5 22 4 1 7 6 2 0 1 1 1 2 1 12 2 1 32 1 2 22 3 6 3 2 6 12 0 5 8 0.4 0.5 0.4 1.5 1 0.3 0.5 0.7 1 0.5 1.2 0.9 1 0.5 0 1.2 0.8 0.7 0 0.5 1.5 1 2.5 0.9 0.6 0.3 0.6 1 1 0 0.2 0.2 0.5 0.2 0.3 0.7 0.5 0.5 0.9 0.5 0.5 3.1 1 0.3 0.2 0.4 3 0.9 0 0.5 0.4 NPLOC4 MRPS7 ACACA NA VAPB BMP7 SKI NA UBE4B RERE NA FAM76A NA RAP1A NA TLK2 NA NA SETBP1 NA FST IPO11 FCHO2 226 DMRUn:26079901 DMRUn:28474101 DMRUn:44797501 DMRUn:63885501 DMRUn:81577201 DMRUn:101350901 DMRUn:102335201 DMRUn:111843701 DMRUn:125483701 DMRUn:128057501 DMRUn:130868301 DMRUn:133170701 DMRUn:138655101 DMRUn:139521501 DMRUn:141968301 DMRUn:147075801 DMRUn:148458601 DMRUn:151192001 DMRUn:158423601 DMRUn:163874501 DMRUn:165781501 Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un Un 26079901 28474101 44797501 63885501 81577201 101350901 102335201 111843701 125483701 128057501 130868301 133170701 138655101 139521501 141968301 147075801 148458601 151192001 158423601 163874501 165781501 1000 200 300 200 500 500 200 200 200 500 200 300 200 1800 200 2400 400 200 300 200 1300 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0.00014707 0.00052077 0.00024493 0.00028473 0.00029103 8.15E-07 6.13E-05 0.00024493 0.00034005 6.11E-05 0.00012253 3.07E-05 0.00027129 1.03E-05 0.00026973 0.00024345 7.68E-06 0.00013794 0.00034419 0.00040692 3.66E-05 7 2 3 1 1 6 0 0 0 15 3 2 2 20 1 20 1 0 2 1 10 0.7 1 1 0.5 0.2 1.2 0 0 0 3 1.5 0.6 1 1.1 0.5 0.8 0.2 0 0.6 0.5 0.7 227 Supplemental Table S4 Cluster Analysis Lists and Number Supplemental Table S4A G. fortis DMR Sperm List DMR in Cluster Chr Start Stop Length (bp) Min pvalue DMR10:14143801;DMR10:14466301;DMR10:15617101;DMR10:17182001 DMR11:14550401;DMR11:16380101 DMR12:4785401;DMR12:5901001 DMR14:1181901;DMR14:3090701 DMR15:2627801;DMR15:2915401 DMR18:991001;DMR18:1081001;DMR18:1084101 DMR2:1016201;DMR2:1382701 DMR2:57021501;DMR2:57355101 DMR20:353601;DMR20:553601;DMR20:1139801 DMR20:12107601;DMR20:13641501 DMR28:3706001;DMR28:4853401 DMR3:110673201;DMR3:111805401;DMR3:112515501 DMR4:5948301;DMR4:6462901 DMR4A:17926101;DMR4A:19342701 DMR7:1620001;DMR7:2068201 DMRUn:37518901;DMRUn:38440401 DMRUn:64017401;DMRUn:65244001 DMRUn:70786601;DMRUn:70799501 DMRUn:93117201;DMRUn:93590701 DMRUn:116431501;DMRUn:116729701 DMRUn:142781801;DMRUn:144276901 DMRUn:155480601;DMRUn:156914801 10 11 12 14 15 18 2 2 20 20 28 3 4 4A 7 Un Un Un Un Un Un Un 12500000 14400000 3950000 1100000 950000 50000 50000 55400000 50000 11650000 2900000 109850000 4500000 17350000 1.00E+05 36450000 63250000 68850000 91600000 114750000 142300000 154950000 17550000 16500000 6700000 3100000 4550000 3.00E+06 3.00E+06 58950000 2550000 14050000 5650000 113750000 7850000 19850000 3550000 39500000 6.60E+07 72750000 95100000 118400000 144750000 157400000 5050000 2100000 2750000 2.00E+06 3600000 2950000 2950000 3550000 2500000 2400000 2750000 3900000 3350000 2500000 3450000 3050000 2750000 3900000 3500000 3650000 2450000 2450000 2.26E-06 0.030 0.030 0.030 0.030 2.26E-06 0.030 0.030 0.030 0.030 0.030 2.26E-06 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 DMR in Cluster Chr Start Stop Length (bp) Min pvalue DMR1:15978201;DMR1:17714201 DMR1:93094801;DMR1:93731001 DMR1:116098201;DMR1:117036401 DMR10:11658001;DMR10:11904101 DMR12:9477701;DMR12:10036401;DMR12:11939801 DMR14:8553401;DMR14:10140501 DMR15:12868101;DMR15:13976501;DMR15:14137901 DMR1A:37330801;DMR1A:37641201 DMR1A:46028501;DMR1A:47095601 DMR1A:68987201;DMR1A:70257101 DMR2:106386901;DMR2:106836801 DMR20:4414401;DMR20:5555701;DMR20:6871001 DMR26:3225201;DMR26:4154601 DMR3:16726301;DMR3:17261701 DMR3:24857301;DMR3:26735901 DMR3:78517101;DMR3:78536501 DMR5:2675901;DMR5:2884401 DMR6:24244501;DMR6:24443901 DMR7:7103301;DMR7:7578001 DMR7:37235801;DMR7:37271201;DMR7:38670601 DMRUn:38098901;DMRUn:39304701 DMRUn:166943301;DMRUn:167343801 DMRZ:45594501;DMRZ:45691301 1 1 1 10 12 14 15 1A 1A 1A 2 20 26 3 3 3 5 6 7 7 Un Un Z 15750000 91750000 115050000 9950000 8050000 8150000 1.20E+07 35650000 45100000 68300000 104850000 3600000 2200000 15300000 24750000 76550000 9.00E+05 22450000 5600000 35300000 37350000 165350000 43700000 17950000 95050000 1.18E+08 13600000 11950000 10500000 15900000 39300000 4.80E+07 70900000 108300000 7500000 5150000 18700000 26850000 80450000 4600000 26150000 9100000 39200000 40050000 168850000 47500000 2200000 3300000 2950000 3650000 3900000 2350000 3900000 3650000 2900000 2600000 3450000 3900000 2950000 3400000 2100000 3900000 3700000 3700000 3500000 3900000 2700000 3500000 3800000 0.020 0.020 0.020 0.020 0.020 0.020 4.77E-07 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.0202 0.020 0.020 0.020 0.020 4.77E-07 0.020 0.020 0.020 Supplemental Table S4B G. fortis DMR Cluster Erythrocyte List 228 Supplemental Table S4C G. fuliginosa Cluster Sperm List DMR in Cluster DMR1:89065301;DMR1:89740001 DMR10:7071601;DMR10:8862401 DMR11:16902201;DMR11:17270401;DMR11:17582801;DMR11:18477501 DMR1A:5308001;DMR1A:6286301 DMR1A:11577701;DMR1A:13180201 DMR1A:22795501;DMR1A:24114101;DMR1A:25971701;DMR1A:27064701 DMR1A:59545101;DMR1A:61248301 DMR2:7094701;DMR2:8947601 DMR2:19980701;DMR2:21027601;DMR2:21459501;DMR2:22412201 DMR2:46614201;DMR2:47305801 DMR3:12996601;DMR3:14233101 DMR3:41905401;DMR3:43634901 DMR3:52144601;DMR3:53617101 DMR3:57319601;DMR3:58282801 DMR5:19330701;DMR5:20105501 DMR5:25054401;DMR5:25225401 DMR6:28395601;DMR6:29758301 DMR7:7413001;DMR7:8134501;DMR7:9858501 DMR8:4830301;DMR8:6695101 DMR8:14216901;DMR8:14857401 Chr 1 10 11 1A 1A 1A 1A 2 2 2 3 3 3 3 5 5 6 7 8 8 Start 87750000 6900000 15300000 4300000 11200000 22150000 59250000 6950000 19050000 45350000 12250000 41650000 51650000 56300000 18150000 23250000 27800000 6150000 4700000 12900000 Stop 9.10E+07 9.00E+06 19500000 7300000 13550000 27950000 61450000 9050000 23450000 48550000 14950000 43900000 54100000 59250000 21300000 2.70E+07 30300000 10050000 6800000 16150000 Length 3250000 2100000 4200000 3000000 2350000 5800000 2200000 2100000 4400000 3200000 2700000 2250000 2450000 2950000 3150000 3750000 2500000 3900000 2100000 3250000 Min pvalue 0.007 0.007 7.22E-18 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 Chr 1 1 1 10 11 Start 54750000 7.90E+07 93200000 16750000 7950000 Stop 57650000 81950000 96150000 19450000 11900000 Length (bp) 2900000 2950000 2950000 2700000 3950000 Min pvalue 0.017 0.017 0.017 0.017 0.017 11 12 15100000 17350000 17700000 20950000 2600000 3600000 13 2 3500000 11200000 6200000 15150000 2700000 3950000 0.017 0.0001446 29 0.017 0.017 2 2 2 2 20 21 69450000 86600000 131400000 142300000 11050000 1950000 71450000 8.90E+07 134400000 145950000 14200000 6650000 2.00E+06 2400000 3.00E+06 3650000 3150000 4700000 0.017 0.017 0.017 0.017 0.017 1.67E-07 3 3 4A 4A 5 5 3400000 88550000 11450000 16350000 26950000 51600000 5450000 9.10E+07 14600000 19300000 30400000 58950000 2050000 2450000 3150000 2950000 3450000 7350000 0.017 0.017 0.017 0.017 0.017 1.67E-07 8 8 11650000 22150000 13850000 26750000 2200000 4600000 0.017 2.63E-11 9 9 18450000 23750000 21500000 27650000 3050000 3900000 0.017 0.00014 Z 36900000 41600000 4700000 2.63E-11 Supplemental Table S4D G. fuliginosa Cluster Erythrocyte List DMR in Cluster DMR1:55662401;DMR1:55714201;DMR1:56745701 DMR1:79976701;DMR1:80849001;DMR1:80975101 DMR1:94220601;DMR1:94822701;DMR1:95189901 DMR10:17519401;DMR10:17985101;DMR10:18730201 DMR11:8358501;DMR11:9094101;DMR11:9926601;DMR11:10915301;DM R11:11887801 DMR11:15791101;DMR11:15948901;DMR11:17071401 DMR12:18973401;DMR12:19028901;DMR12:19307701;DMR12:20529201 DMR13:4235701;DMR13:4487901;DMR13:5482801 DMR2:11574001;DMR2:13073201;DMR2:13156501;DMR2:14049001;DMR 2:14069501 DMR2:69476301;DMR2:71040901;DMR2:71432601 DMR2:87015801;DMR2:88439501;DMR2:88582201 DMR2:132400801;DMR2:133334401;DMR2:133357301 DMR2:144011701;DMR2:144085301;DMR2:144294901 DMR20:12291301;DMR20:12433001;DMR20:13029701 DMR21:3489101;DMR21:3618001;DMR21:3922101;DMR21:4701101;DMR 21:4941801;DMR21:5494101 DMR3:3457301;DMR3:5223801;DMR3:5355701 DMR3:89074901;DMR3:89714201;DMR3:90528101 DMR4A:12525201;DMR4A:12646701;DMR4A:13403801;DMR4A:13875801 DMR4A:17382501;DMR4A:17926601;DMR4A:18342901 DMR5:28428001;DMR5:28566801;DMR5:28908101 DMR5:53365301;DMR5:53490201;DMR5:53560501;DMR5:54589001;DMR 5:54610101;DMR5:57026201;DMR5:58011201;DMR5:58386501 DMR8:11860301;DMR8:12439401;DMR8:13603601 DMR8:22901801;DMR8:23883401;DMR8:24123401;DMR8:24651101;DMR 8:24810801;DMR8:24850501;DMR8:26306401 DMR9:19526001;DMR9:20093101;DMR9:20439401 DMR9:24201901;DMR9:25227801;DMR9:25731701;DMR9:26210101;DMR 9:27015201 DMRZ:38648801;DMRZ:38745301;DMRZ:38864501;DMRZ:39510601;DMR Z:39662301;DMRZ:40335601;DMRZ:40862001 229 G. fortis Multiple-Window Sperm DMR Gene Associations Supplemental Table S5A DMR Name DMR1:89900301 DMR1:89900301 DMR1A:9608801 Gene Symbol ENO2 LRRC23 HGF entrezgene 100231650 NA NA DMR1A:33384201 DMR2:1016201 DMR2:1016201 DMR2:11077401 DMR2:57355101 DMR2:152511201 DMR2:155551401 DMR3:19616201 MSRB3 CCDC12 NA RPL15 NA RHPN1 GALNT14 100222850 NA 100228451 100232012 100190572 NA 100230992 100229575 1A 2 2 2 2 2 2 3 DMR3:22144501 DMR3:82421001 NRXN1 HTR1B 100228660 751996 3 3 22119248 ENSTGUG00000005731 82421033 ENSTGUG00000012613 DMR3:110673201 PAQR8 100220736 3 110674600 ENSTGUG00000013277 DMR3:111805401 DMR3:112515501 DMR4:6462901 DMR4:10513201 MSRA FZD3 FAT4 LRBA NA NA 100222872 100226445 3 3 4 4 111667595 112498936 6366597 10348610 DMR4:58976401 JAKMIP1 101233368 4 58954507 ENSTGUG00000010025 DMR4:67704101 CTNNA2 100221417 4 67538300 ENSTGUG00000010908 DMR4:67704101 LRRTM1 100218569 4 67703594 ENSTGUG00000010929 DMR4A:4743001 RPS6KA6 100217905 4A 4707076 ENSTGUG00000002510 DMR4A:19342701 DMR5:6667801 DMR5:6667801 DMR5:10950801 DMR5:14025601 DMR6:3861701 DMR6:21155001 DMR7:1620001 DMR8:24996701 DMR9:23196601 DMR10:2121101 DMR10:14143801 DMR10:14466301 DMR10:17182001 DMR10:17182001 DMR11:16380101 DMR12:4785401 DMR12:5901001 DMR13:11920001 CNGA2 TMEM132A CD6 SOX6 SYT8 SGMS1 NA HDAC4 NFIA NA PLEC NA SV2B NA NA NA 100225773 100232540 100229900 NA 100222021 100221182 NA NA 100217734 100222606 NA 100227374 NA NA NA 100227722 4A 5 5 5 5 6 6 7 8 9 10 10 10 10 10 11 12 12 13 19342297 6667834 6674726 10806780 14010649 3861953 21137374 1612998 24945852 23120500 2115301 14143898 14442653 17184323 17184337 16377691 4785348 5885739 11919977 ENSTGUG00000006426 ENSTGUG00000006351 ENSTGUG00000006362 ENSTGUG00000008522 ENSTGUG00000009405 ENSTGUG00000005065 ENSTGUG00000009180 ENSTGUG00000003197 ENSTGUG00000009745 ENSTGUG00000011013 ENSTGUG00000004052 ENSTGUG00000008405 ENSTGUG00000008436 ENSTGUG00000008635 ENSTGUG00000008636 ENSTGUG00000009150 ENSTGUG00000005035 ENSTGUG00000005918 ENSTGUG00000001428 DMR14:1181901 DMR14:3090701 DMR15:2627801 DMR15:2915401 DMR17:3431501 DMR18:991001 DMR18:991001 DMR18:1081001 DMR18:1084101 DMR19:514001 DMR19:5685501 RAB40C RHBDF1 NA IFT81 NA NA 100230264 NA 100224782 NA 100190374 100225927 NA NA NA 101233851 14 14 15 15 17 18 18 18 18 19 19 1176454 3088544 2627887 2910879 3419406 897346 991544 1064419 1064419 494931 5681157 ENSTGUG00000003408 ENSTGUG00000004429 ENSTGUG00000005468 ENSTGUG00000005815 ENSTGUG00000003291 ENSTGUG00000002914 ENSTGUG00000002937 ENSTGUG00000002960 ENSTGUG00000002960 ENSTGUG00000003301 ENSTGUG00000004921 IGF1R NA MST1R NA GABRB2 BTBD17 SLC38A10 SLC38A10 RNF43 RFFL Chr start_position Ensembl # 1 89898643 ENSTGUG00000013292 1 89846656 ENSTGUG00000018128 1A 9584500 ENSTGUG00000002584 33347429 992603 1006110 11020995 57352106 152312910 155541405 19573963 ENSTGUG00000006603 ENSTGUG00000000185 ENSTGUG00000000186 ENSTGUG00000000783 ENSTGUG00000003325 ENSTGUG00000012639 ENSTGUG00000012739 ENSTGUG00000004629 ENSTGUG00000013321 ENSTGUG00000013332 ENSTGUG00000002042 ENSTGUG00000002514 Classification Gene Description Category enolase 2 (gamma - neuronal) Metabolism leucine rich repeat containing 23 Unknown hepatocyte growth factor (hepapoietin A; scatter factor) Growth Factor methionine sulfoxide reductase B3 Metabolism Uncharacterized protein Unknown coiled-coil domain containing 12 Transcription Uncharacterized protein Unknown ribosomal protein L15 Translation Uncharacterized protein Unknown rhophilin - Rho GTPase binding protein 1 Signaling polypeptide Nacetylgalactosaminyltransferase 14 Golgi neurexin 1 Receptor 5-hydroxytryptamine (serotonin) receptor 1B - G protein-coupled Receptor progestin and adipoQ receptor family member VIII Receptor methionine sulfoxide reductase A Metabolism frizzled class receptor 3 Receptor FAT atypical cadherin 4 Receptor LPS-responsive vesicle trafficking - beach and anchor containing Transport janus kinase and microtubule interacting protein 1 Signaling catenin (cadherin-associated protein) alpha 2 Signaling leucine rich repeat transmembrane neuronal 1 Receptor ribosomal protein S6 kinase - 90kDa polypeptide 6 Signaling cyclic nucleotide gated channel alpha 2] Transport transmembrane protein 132A Unknown CD6 molecule Receptor SRY (sex determining region Y)-box 6 Transcription synaptotagmin VIII Transport sphingomyelin synthase 1 Metabolism Uncharacterized protein Unknown histone deacetylase 4 Epigenetic nuclear factor I/A Transcription Uncharacterized protein Unknown plectin Cytoskeleton Uncharacterized protein Unknown synaptic vesicle glycoprotein 2B Protein Interaction Uncharacterized protein Unknown insulin-like growth factor 1 receptor Receptor Uncharacterized protein Unknown macrophage stimulating 1 receptor Receptor Uncharacterized protein Unknown gamma-aminobutyric acid (GABA) A receptor - beta 2 Receptor RAB40C - member RAS oncogene family Signaling rhomboid 5 homolog 1 (Drosophila) Protease Uncharacterized protein Unknown intraflagellar transport 81 Unknown Uncharacterized protein Unknown Uncharacterized protein Unknown BTB (POZ) domain containing 17 Unknown solute carrier family 38 - member 10 Transport solute carrier family 38 - member 10 Transport ring finger protein 43 Unknown ring finger and FYVE-like domain containing E3 ubiquitin protein ligase Proteolysis 230 DMR20:553601 RALGAPB DMR20:1139801 NA DMR20:13641501 NA DMR21:3356301 PLCH2 DMR23:4735301 EPHA10 DMR25:797501 NA DMR25:797501 NA DMR27:383501 DDX42 DMR27:4437801 NA DMR27:4437801 NA DMR28:4853401 KLHL26 DMRUn:38440401 ARHGAP39 DMRUn:70786601 SYCP1 DMRUn:70786601 DMRUn:70799501 SYCP1 DMRUn:70799501 DMRUn:116431501 DNMT1 DMRUn:116729701 DOCK7 DMRUn:132241501 USP21 DMRZ:38519001 DMRZ:38519001 RPP25 100224256 20 100230956 NA NA NA 100219169 100222004 100217897 NA NA 100230034 100223465 NA NA NA NA NA NA 100231115 NA NA 20 20 21 23 25 25 27 27 27 28 Un Un Un Un Un Un Un Un Z Z 523559 ENSTGUG00000003151 1136740 13633879 3357561 4734790 797650 797650 383542 4277732 4317779 4852037 38437090 70782305 70765920 70782305 70765920 116426172 116727282 132242124 38512069 38519149 ENSTGUG00000003444 ENSTGUG00000008516 ENSTGUG00000002915 ENSTGUG00000001509 ENSTGUG00000004304 ENSTGUG00000004304 ENSTGUG00000001931 ENSTGUG00000003306 ENSTGUG00000003320 ENSTGUG00000001064 ENSTGUG00000015026 ENSTGUG00000015209 ENSTGUG00000015195 ENSTGUG00000015209 ENSTGUG00000015195 ENSTGUG00000013708 ENSTGUG00000013835 ENSTGUG00000016056 ENSTGUG00000001724 ENSTGUG00000001727 Ral GTPase activating protein - beta subunit (non-catalytic) Uncharacterized protein Uncharacterized protein phospholipase C - eta 2 EPH receptor A10 Uncharacterized protein Uncharacterized protein DEAD (Asp-Glu-Ala-Asp) box helicase 42 Uncharacterized protein Uncharacterized protein kelch-like family member 26 Rho GTPase activating protein 39 synaptonemal complex protein 1 Uncharacterized protein synaptonemal complex protein 1 Uncharacterized protein DNA (cytosine-5-)-methyltransferase 1 Uncharacterized protein ubiquitin specific peptidase 21 Uncharacterized protein ribonuclease P/MRP 25kDa subunit Signaling Unknown Unknown Signaling Receptor Unknown Unknown Translation Unknown Unknown Unknown Signaling Cell Cycle Unknown Cell Cycle Unknown Epigenetic Signaling Proteolysis Unknown Unknown 231 Supplemental Table S5B G. fortis Multiple-Window Erythrocyte DMR Gene Associations DMR Name DMR1:2682401 DMR1:5421101 DMR1:17714201 DMR1:52621901 DMR1:59413201 DMR1:68474101 Gene Symbol NA CXorf36 GEMIN8 NA NA PIBF1 entrezgene NA 100227072 NA 100225399 100219571 100218618 DMR1:83012401 DMR1:93094801 DMR1A:14851501 DMR1A:46028501 DMR1A:47095601 DMR1A:70257101 NA NA CPNE8 IKBIP NA MICAL3 100228758 100219809 NA NA 100232057 NA DMR2:44270401 FARS2 NA DMR2:148037901 DMR3:35391201 NA CRIM1 NA NA DMR5:2884401 DMR5:17085201 DMR6:4069801 DMR6:4069801 DMR6:15841201 ELP4 NA NA NA GBF1 NA NA NA NA 100227027 DMR6:22127301 DMR6:22127301 DMR6:24244501 FBXW4 FGF8 SORCS3 100229466 NA 100229522 DMR7:7103301 DMR8:9281501 DMR12:10036401 MRAS PTBP2 ABTB1 100225544 100219685 100223102 DMR12:20282601 DMR13:246201 DMR13:11691501 DMR14:10140501 ITPR1 STK32A NA CACNA1H NA NA 100227780 NA DMR14:10140501 DMR15:5515601 B9D1 MN1 NA 100223802 DMR15:12868101 DMR15:14137901 NA AIFM3 NA 100227335 DMR18:7751401 ST6GALNAC2 100224588 DMR19:9135801 DMR20:15138501 NA KCNB1 DMR21:4415901 ACAP3 NA DMR26:3225201 RAP1A 100151697 DMRUn:115914401 DMRUn:167343801 DMRZ:45691301 DMRZ:70647701 NA NA ISL1 EDIL3 NA NA 100231918 100232785 NA NA Classification Chr start_position Ensemb # Gene Description Category 1 2421764 ENSTGUG00000005151 Uncharacterized protein Unknown 1 5410116 ENSTGUG00000006067 chromosome X open reading frame 36 Unknown 1 17700218 ENSTGUG00000008162 gem (nuclear organelle) associated protein 8 Translation 1 52426258 ENSTGUG00000011857 Uncharacterized protein Unknown 1 59285996 ENSTGUG00000012398 Uncharacterized protein Unknown 1 68431741 ENSTGUG00000012511 progesterone immunomodulatory binding factor 1 Immune 1 82998831 ENSTGUG00000012920 Malic enzyme Metabolism 1 93077213 ENSTGUG00000013396 Uncharacterized protein Unknown 1A 14799211 ENSTGUG00000003596 copine VIII Protein Interaction 1A 46028147 ENSTGUG00000008755 IKBKB interacting protein Protein Interaction 1A 47068604 ENSTGUG00000009105 Uncharacterized protein Unknown 1A 70210553 ENSTGUG00000012499 microtubule associated monooxygenase calponin and LIM domain containing 3 Unknown 2 44124991 ENSTGUG00000002269 phenylalanyl-tRNA synthetase 2 mitochondrial Translation 2 148037992 ENSTGUG00000012535 Uncharacterized protein Unknown 3 35362730 ENSTGUG00000008626 cysteine rich transmembrane BMP regulator 1 (chordin-like) Development 5 2764406 ENSTGUG00000004816 elongator acetyltransferase complex subunit 4 Transcription 5 17073049 ENSTGUG00000010089 Amino acid transporter Transport 6 3961424 ENSTGUG00000005123 Uncharacterized protein Unknown 6 3970891 ENSTGUG00000005129 Uncharacterized protein Unknown 6 15830427 ENSTGUG00000007414 golgi brefeldin A resistant guanine nucleotide exchange factor 1 Signaling 6 21890389 ENSTGUG00000009903 F-box and WD repeat domain containing 4 Unknown 6 22049471 ENSTGUG00000009956 fibroblast growth factor 8 (androgen-induced) Growth Factor 6 24203189 ENSTGUG00000010448 sortilin-related VPS10 domain containing receptor 3 Receptor 7 7083869 ENSTGUG00000004727 muscle RAS oncogene homolog Signaling 8 9249676 ENSTGUG00000005558 polypyrimidine tract binding protein 2 Translation 12 10018786 ENSTGUG00000008002 ankyrin repeat and BTB (POZ) domain containing 1 Unknown 12 20139304 ENSTGUG00000010241 inositol 1 -4 -5-trisphosphate receptor - type 1 Receptor 13 246166 ENSTGUG00000000062 serine/threonine kinase 32A Signaling 13 11677740 ENSTGUG00000001419 Phospholipid-transporting ATPase Transport 14 10031279 ENSTGUG00000006881 calcium channel - voltage-dependent - T type alpha 1H subunit Transport 14 10093952 ENSTGUG00000007019 B9 protein domain 1 [ Extracellular Matrix 15 5486760 ENSTGUG00000007793 meningioma (disrupted in balanced translocation) 1 Unknown 15 12853825 ENSTGUG00000010495 Uncharacterized protein Unknown 15 14135686 ENSTGUG00000010632 apoptosis-inducing factor - mitochondrionassociated - 3 Apoptosis 18 7751042 ENSTGUG00000007819 ST6 (alpha-N-acetyl-neuraminyl-2 -3-betagalactosyl-1 -3)-N-acetylgalactosaminide alpha2 -6-sialyltransferase 2 Gogli 19 9132699 ENSTGUG00000007767 Uncharacterized protein Unknown 20 15135879 ENSTGUG00000008781 potassium channel - voltage gated Shab related subfamily B - member 1 Transport 21 4404694 ENSTGUG00000003217 ArfGAP with coiled-coil - ankyrin repeat and PH domains 3 Signaling 26 3158273 ENSTGUG00000001456 Taeniopygia guttata RAP1A - member of RAS oncogene family (RAP1A) - mRNA. Signaling Un 115904314 ENSTGUG00000016835 Uncharacterized protein Unknown Un 167343490 ENSTGUG00000016875 Uncharacterized protein Unknown Z 45690056 ENSTGUG00000002375 ISL LIM homeobox 1 Transcription Z 70483471 ENSTGUG00000006826 EGF-like repeats and discoidin I-like domains 3 Development 232 Supplemental Table S5C DMR Name Gene Symbol entrezgene DMR1:10654201 IL1RAPL1 100219378 DMR1:80766701 FAT3 DMR1A:11577701 PTPN12 100223418 NA DMR1A:24114101 WNT2 100221216 DMR1A:27064701 IMMP2L 100226070 DMR1A:29521501 DMR1A:37122201 DMR1A:48389101 DMR1A:59545101 DMR1A:61248301 TMEM117 NA TTC26 KDM7A C2CD5 100228057 NA 100225372 100223451 NA DMR2:7094701 GALNT11 100231024 DMR2:19980701 DMR2:21459501 DMR2:57223201 DMR2:60537101 DMR2:94031401 DMR2:118325701 NA NA NA OSBPL10 NA TOX NA NA NA 100222583 NA 100232611 DMR2:134150201 NIPAL2 DMR2:144740601 FBXO32 DMR3:14233101 UBR2 NA 100224944 100225781 DMR3:20417201 DMR3:37439001 LCLAT1 PARK2 NA 100221229 DMR3:41905401 DMR3:43634901 DLL1 SIPA1L2 100230776 100228885 DMR3:53617101 DMR3:78687001 DMR3:85093801 SNX9 AKIRIN2 FAM135A 100225096 NA 100227848 DMR3:89692801 DMR3:92570201 DMR3:95496201 DMR4:30010001 BMP5 CSMD1 PXDN RAPGEF2 100232671 100230644 100230769 100224541 DMR4A:4645401 DMR5:17116201 DMR5:20105501 DMR5:22209801 DMR5:25054401 HDX NA EXT2 AMBRA1 UBR1 100223075 NA 100231605 NA NA DMR5:29800501 DMR6:5313901 RYR3 C10orf107 100219104 NA DMR6:28395601 DMR7:2821801 NA IQCA1 DMR7:27843701 DMR7:32399701 DMR8:25817401 DMR9:15033901 DMR10:8862401 DMR11:17270401 DMR14:15125101 ZNF804A NA NA MB21D2 TMOD2 CDH8 NA DMR15:8420501 TOP3B NA NA 100221331 NA 100218377 100221081 100225462 NA 100220325 NA G. fuliginosa Multiple-Window Sperm DMR Gene Associations Chr start_position Ensembl # Gene Description 1 10652160 ENSTGUG00000007085 interleukin 1 receptor accessory protein-like 1 1 80634098 ENSTGUG00000012873 FAT atypical cadherin 3 1A 11558479 ENSTGUG00000002717 protein tyrosine phosphatase - nonreceptor type 12 1A 24114687 ENSTGUG00000005016 wingless-type MMTV integration site family member 2 1A 26702220 ENSTGUG00000005588 IMP2 inner mitochondrial membrane peptidase-like (S. cerevisiae) 1A 29424286 ENSTGUG00000005962 transmembrane protein 117 1A 37109977 ENSTGUG00000007370 Uncharacterized protein 1A 48361671 ENSTGUG00000009550 tetratricopeptide repeat domain 26 1A 59545795 ENSTGUG00000011686 lysine (K)-specific demethylase 7A 1A 61201716 ENSTGUG00000011766 C2 calcium-dependent domain containing 5 2 7087083 ENSTGUG00000000608 polypeptide Nacetylgalactosaminyltransferase 11 2 19983441 ENSTGUG00000001217 Uncharacterized protein 2 21459108 ENSTGUG00000001259 Uncharacterized protein 2 57030969 ENSTGUG00000003303 Uncharacterized protein 2 60501996 ENSTGUG00000003656 oxysterol binding protein-like 10 2 93945597 ENSTGUG00000008651 Uncharacterized protein 2 118235600 ENSTGUG00000011174 thymocyte selection-associated high mobility group box 2 134144014 ENSTGUG00000012018 NIPA-like domain containing 2 2 144729030 ENSTGUG00000012442 F-box protein 32 3 14191156 ENSTGUG00000003261 ubiquitin protein ligase E3 component n-recognin 2 3 20382321 ENSTGUG00000005391 lysocardiolipin acyltransferase 1 3 37384893 ENSTGUG00000009367 parkin RBR E3 ubiquitin protein ligase 3 41897719 ENSTGUG00000009780 delta-like 1 (Drosophila) 3 43583239 ENSTGUG00000010103 signal-induced proliferationassociated 1 like 2 3 53592751 ENSTGUG00000011096 sorting nexin 9 3 78683482 ENSTGUG00000012437 akirin 2 3 85067721 ENSTGUG00000012721 family with sequence similarity 135 member A 3 89677349 ENSTGUG00000012830 bone morphogenetic protein 5 3 92239007 ENSTGUG00000012896 CUB and Sushi multiple domains 1 3 95422869 ENSTGUG00000012962 peroxidasin 4 30007643 ENSTGUG00000005715 Rap guanine nucleotide exchange factor (GEF) 2 4A 4645348 ENSTGUG00000002499 highly divergent homeobox 5 17093478 ENSTGUG00000010095 Amino acid transporter 5 20077856 ENSTGUG00000010249 exostosin glycosyltransferase 2 5 22154488 ENSTGUG00000010628 autophagy/beclin-1 regulator 1 5 25031883 ENSTGUG00000011165 ubiquitin protein ligase E3 component n-recognin 1 5 29645499 ENSTGUG00000011653 ryanodine receptor 3 6 5284088 ENSTGUG00000005288 chromosome 10 open reading frame 107 6 28226525 ENSTGUG00000010983 Uncharacterized protein 7 2756467 ENSTGUG00000003628 IQ motif containing with AAA domain 1 7 27813179 ENSTGUG00000011155 zinc finger protein 804A 7 32359427 ENSTGUG00000011682 Uncharacterized protein 8 25813953 ENSTGUG00000009906 Uncharacterized protein 9 15029216 ENSTGUG00000009252 Mab-21 domain containing 2 10 8857237 ENSTGUG00000006888 tropomodulin 2 (neuronal) 11 17141697 ENSTGUG00000009281 cadherin 8 - type 2 14 15124742 ENSTGUG00000008831 Ubiquitin carboxyl-terminal hydrolase 15 8411299 ENSTGUG00000008644 topoisomerase (DNA) III beta Classification Category Receptor Extracellular Matrix (ECM) Signaling Growth Factor Proteolysis Unknown Unknown Unknown Epigenetic Unknown Gogli Unknown Unknown Unknown Binding Protein Unknown Immune Development Transcription Proteolysis Metabolism Proteolysis Receptor Signaling Transport Unknown Unknown Growth Factor Signaling Metabolism Signaling Unknown Transport Gogli Apoptosis Proteolysis Receptor Unknown Unknown Unknown Unknown Unknown Unknown Unknown Cytoskeleton Extracellular Matrix (ECM) Proteolysis Transcription 233 DMR19:8815901 GDPD1 DMR26:1999201 KLHL12 DMRUn:152715201 NA DMRZ:58234701 DENND4C 100224315 19 NA NA 100230179 26 Un Z 8812582 ENSTGUG00000007493 glycerophosphodiester phosphodiesterase domain containing 1 1977574 ENSTGUG00000001234 kelch-like family member 12 152582138 ENSTGUG00000018382 Uncharacterized protein 58237261 ENSTGUG00000004350 DENN/MADD domain containing 4C Metabolism Cytoskeleton Unknown Unknown 234 G. fuliginosa Multiple-Window Erythrocyte DMR Gene Associations Supplemental Table S5D DMR Name DMR1:15514901 DMR1:30194301 DMR1:49804201 DMR1:69585701 DMR1:79976701 DMR1:80849001 DMR1:94822701 Gene Symbol entrezgene NA NA RNF149 100221793 NA NA LMO7 NA NA 100229192 FAT3 100223418 LSAMP NA DMR1A:536501 NET1 DMR1A:3291201 SFMBT2 DMR1A:25828501 PPP1R3A 100228711 100218182 NA DMR1A:38048601 NAV3 DMR1A:50021401 CACNA1I 100229468 NA DMR1A:60465701 SOX5 DMR1A:67777301 PLEKHA5 100220574 NA DMR2:21606201 DMR2:66551001 NA EEPD1 NA NA DMR2:84797701 MYH6 NA DMR2:88439501 FHOD3 NA DMR2:88582201 C18orf21 DMR2:102792501 GNAL 100218608 100219216 DMR2:109112201 OSBPL1A DMR2:110670701 CDH2 100223128 100229697 DMR2:121488201 NA DMR2:132400801 CDH17 100231693 NA DMR2:138528101 NA DMR2:141908201 EIF3H 100223993 100190645 DMR2:151126601 FAM135B 100227463 DMR2:151971901 KCNK9 100230281 DMR2:155785401 DMR3:5223801 DMR3:21546801 DMR3:47496101 DMR3:54732001 DMR3:70631201 ZC3H3 TASP1 FOXN3 UST NOX3 PRDM1 NA NA NA 100218364 100228883 100219562 DMR3:89074901 DMR3:89714201 DMR3:90528101 PRIM2 BMP5 GCLC 100228796 100232671 100218278 DMR3:110489001 DMR4:5567801 DMR4:6440901 DMR4:11522601 DMR4:23287201 DMR4:30011901 NA SPATA5 FAT4 ARHGAP10 LEF1 RAPGEF2 NA 100221993 100222872 100227425 100228376 100224541 DMR4:38514801 ENPP6 100225148 Classification Chr start_position ensembl_gene_id Gene Description Category 1 15501404 ENSTGUG00000007781 Uncharacterized protein Unknown 1 30188297 ENSTGUG00000009945 ring finger protein 149 Proteolysis 1 49704389 ENSTGUG00000011664 Uncharacterized protein Unknown 1 69547644 ENSTGUG00000012539 LIM domain 7 Cytoskeleton 1 79958222 ENSTGUG00000012829 Uncharacterized protein Unknown 1 80634098 ENSTGUG00000012873 FAT atypical cadherin 3 ECM 1 94669017 ENSTGUG00000013402 limbic system-associated membrane protein ECM 1A 528641 ENSTGUG00000001945 neuroepithelial cell transforming 1 Signaling 1A 3209575 ENSTGUG00000002052 Scm-like with four mbt domains 2 Epigenetic 1A 25815970 ENSTGUG00000005399 protein phosphatase 1 - regulatory subunit 3A Signaling 1A 38040492 ENSTGUG00000007519 neuron navigator 3 Development 1A 50003360 ENSTGUG00000010198 calcium channel - voltage-dependent - T type - alpha 1I subunit Transport 1A 60317901 ENSTGUG00000011749 SRY (sex determining region Y)-box 5 Transcription 1A 67772119 ENSTGUG00000012354 pleckstrin homology domain containing family A member 5 Signaling 2 21604293 ENSTGUG00000001271 Uncharacterized protein Unknown 2 66510181 ENSTGUG00000005472 endonuclease/exonuclease/phosphatas e family domain containing 1 Signaling 2 84795322 ENSTGUG00000007833 myosin - heavy chain 6 - cardiac muscle alpha Cytoskeleton 2 88300265 ENSTGUG00000007999 formin homology 2 domain containing 3 Unknown 2 88579126 ENSTGUG00000008036 chromosome 18 open reading frame 21 Unknown 2 102746499 ENSTGUG00000009707 guanine nucleotide binding protein (G protein) - alpha activating activity polypeptide - olfactory type Signaling 2 109039838 ENSTGUG00000010525 oxysterol binding protein-like 1A Binding Protein 2 110571137 ENSTGUG00000010620 cadherin 2 - type 1 - N-cadherin (neuronal) ECM 2 121486449 ENSTGUG00000011308 Uncharacterized protein Unknown 2 132378941 ENSTGUG00000011871 cadherin 17 - LI cadherin (liver-intestine) ECM 2 138507126 ENSTGUG00000012216 Uncharacterized protein Unknown 2 141896849 ENSTGUG00000012302 eukaryotic translation initiation factor 3 subunit H Translation 2 151114975 ENSTGUG00000012621 family with sequence similarity 135 member B Unknown 2 151900356 ENSTGUG00000012636 potassium channel - two pore domain subfamily K - member 9 Transport 2 155762072 ENSTGUG00000012743 zinc finger CCCH-type containing 3 Translation 3 5146840 ENSTGUG00000002456 taspase - threonine aspartase - 1 Proteolysis 3 21548956 ENSTGUG00000005623 forkhead box N3 Unknown 3 47457997 ENSTGUG00000010673 uronyl-2-sulfotransferase Metabolism 3 54716130 ENSTGUG00000011124 NADPH oxidase 3 Metabolism 3 70624594 ENSTGUG00000012204 PR domain containing 1 - with ZNF domain Transcription 3 88989762 ENSTGUG00000012790 primase - DNA - polypeptide 2 (58kDa) Metabolism 3 89677349 ENSTGUG00000012830 bone morphogenetic protein 5 Growth Factor 3 90521029 ENSTGUG00000012854 glutamate-cysteine ligase - catalytic subunit Metabolism 3 110359582 ENSTGUG00000013268 Uncharacterized protein Unknown 4 5549460 ENSTGUG00000002029 spermatogenesis associated 5 Development 4 6366597 ENSTGUG00000002042 FAT atypical cadherin 4 Signaling 4 11426980 ENSTGUG00000002594 Rho GTPase activating protein 10 Signaling 4 23247602 ENSTGUG00000004013 lymphoid enhancer-binding factor 1 Transcription 4 30007643 ENSTGUG00000005715 Rap guanine nucleotide exchange factor (GEF) 2 Signaling 4 38502012 ENSTGUG00000006653 ectonucleotide pyrophosphatase/phosphodiesterase 6 Signaling 235 DMR4:47123001 DMR4A:9445601 DMR4A:9445601 DMR5:143201 NA MCTS1 AKAP14 NELL1 100225702 100190027 100218817 100228095 4 4A 4A 5 47122672 9359760 9443728 75638 ENSTGUG00000008603 ENSTGUG00000003338 ENSTGUG00000003440 ENSTGUG00000004458 DMR5:7862001 DMR5:15807101 DMR5:23337901 DMR5:28566801 DMR5:40106301 DMR5:44895801 DMR5:48919901 DMR5:53490201 DMR5:54589001 NA RPLP2 INO80 GPHN NA TTC7B NA CEP170B BRF1 100224878 100221892 100227760 NA NA NA NA NA NA 5 5 5 5 5 5 5 5 5 7862469 15762188 23289026 28455478 39895138 44815864 48861713 53432980 54573873 ENSTGUG00000007330 ENSTGUG00000009801 ENSTGUG00000010786 ENSTGUG00000011577 ENSTGUG00000012355 ENSTGUG00000012501 ENSTGUG00000012709 ENSTGUG00000012907 ENSTGUG00000012926 DMR5:54610101 BRF1 NA 5 54573873 ENSTGUG00000012926 DMR5:58386501 DMR6:25933601 DMR7:10812401 TMEM260 NA SLC4A3 100230578 NA 100223699 5 6 7 58386478 ENSTGUG00000013057 25926844 ENSTGUG00000010665 10813277 ENSTGUG00000006252 DMR7:11920001 IFIH1 NA 7 11915850 ENSTGUG00000006914 DMR7:26104901 ORMDL1 NA 7 26101329 ENSTGUG00000010880 DMR8:4542101 CRB1 100223846 8 4494806 ENSTGUG00000004300 DMR8:10439801 DMR8:10439801 DMR8:26306401 DMR9:5238901 DMR9:7457601 RPL5 SNORD21 AK4 NA SPSB4 100190411 NA NA 100221456 100225409 8 8 8 9 9 10438158 10440268 26304563 5228092 7410458 DMR9:20093101 DMR9:25731701 MFN1 MFSD1 100220202 100222737 9 9 20085820 ENSTGUG00000010693 25730655 ENSTGUG00000011199 DMR9:26210101 DMR9:27015201 DMR10:7027301 DMR10:13256501 DMR10:17985101 NA PFN2 CGNL1 DET1 ALDH1A3 NA NA NA 100217762 100231202 9 9 10 10 10 26202250 27012835 6994325 13250087 17976236 ENSTGUG00000011240 ENSTGUG00000011405 ENSTGUG00000006242 ENSTGUG00000008339 ENSTGUG00000008854 DMR10:18730201 DMR11:10915301 DMR11:19202501 DMR12:424701 DMR12:16143901 DENND4A BANP NA NA FAM19A1 100229269 NA NA NA 100228414 10 11 11 12 12 18728541 10820357 19195065 347145 16075500 ENSTGUG00000009220 ENSTGUG00000007690 ENSTGUG00000009520 ENSTGUG00000003635 ENSTGUG00000009669 DMR13:4235701 DMR14:1865701 EBF1 RAB11FIP3 100228980 NA 13 14 4019793 ENSTGUG00000000593 1810964 ENSTGUG00000003751 DMR14:7746201 MYH11 100230210 14 7725174 ENSTGUG00000005378 NA 15 11294615 ENSTGUG00000009790 DMR17:6864401 PRRC2B DMR17:11018301 PBX3 DMR18:1033201 DMR18:1033201 NPLOC4 NA NA 100190374 NA 17 17 18 18 6861636 10940614 897346 1020952 DMR18:8856901 MRPS7 DMR19:10177801 ACACA DMR20:12291301 NA NA NA 100220955 18 19 20 8759169 ENSTGUG00000008655 10154218 ENSTGUG00000008074 12289073 ENSTGUG00000008146 DMR15:11293101 EIF4ENIF1 ENSTGUG00000005988 ENSTGUG00000018060 ENSTGUG00000010010 ENSTGUG00000006742 ENSTGUG00000007647 ENSTGUG00000004903 ENSTGUG00000007364 ENSTGUG00000002914 ENSTGUG00000002958 Uncharacterized protein malignant T cell amplified sequence 1 A kinase (PRKA) anchor protein 14 NEL-like 1 (chicken) [Source:HGNC Symbol;Acc:HGNC:7750] Uncharacterized protein ribosomal protein - large - P2 INO80 complex subunit gephyrin Uncharacterized protein tetratricopeptide repeat domain 7B Uncharacterized protein centrosomal protein 170B BRF1 - RNA polymerase III transcription initiation factor 90 kDa subunit BRF1 - RNA polymerase III transcription initiation factor 90 kDa subunit transmembrane protein 260 Uncharacterized protein solute carrier family 4 (anion exchanger) - member 3 interferon induced with helicase C domain 1 ORMDL sphingolipid biosynthesis regulator 1 crumbs family member 1 photoreceptor morphogenesis associated ribosomal protein L5 Small nucleolar RNA SNORD21 adenylate kinase 4 Uncharacterized protein splA/ryanodine receptor domain and SOCS box containing 4 mitofusin 1 major facilitator superfamily domain containing 1 Uncharacterized protein profilin 2 cingulin-like 1 de-etiolated homolog 1 (Arabidopsis) aldehyde dehydrogenase 1 family member A3 DENN/MADD domain containing 4A BTG3 associated nuclear protein Uncharacterized protein Uncharacterized protein family with sequence similarity 19 (chemokine (C-C motif)-like) - member A1 early B-cell factor 1 RAB11 family interacting protein 3 (class II) myosin - heavy chain 11 - smooth muscle eukaryotic translation initiation factor 4E nuclear import factor 1 proline-rich coiled-coil 2B pre-B-cell leukemia homeobox 3 Uncharacterized protein nuclear protein localization 4 homolog (S. cerevisiae) mitochondrial ribosomal protein S7 acetyl-CoA carboxylase alpha Uncharacterized protein Unknown Receptor Signaling Development Unknown Translation Transcription Receptor Unknown Metabolism Unknown Unknown Transcription Transcription Unknown Unknown Transport Transcription Metabolism Development Translation Unknown Signaling Unknown Unknown Signaling Transport Unknown Cytoskeleton Cytoskeleton Proteolysis Metabolism Unknown Transcription Unknown Unknown Cytokine Transcription Signaling Cytoskeleton Translation Unknown Transcription Unknown Proteolysis Translation Metabolism Unknown 236 DMR20:12433001 VAPB NA 20 DMR20:13029701 DMR21:3618001 DMR21:3922101 DMR21:4941801 DMR21:5494101 BMP7 SKI NA UBE4B RERE NA NA NA NA NA 20 21 21 21 21 DMR22:3011601 DMR23:2525401 NA FAM76A NA 100232402 22 23 DMR24:1935801 DMR26:3370501 NA RAP1A 100227293 100151697 24 26 DMR26:4774101 DMR27:3371401 DMR28:538701 DMRZ:8349501 DMRZ:33485401 DMRZ:39662301 DMRZ:46606301 DMRZ:49883001 DMRZ:66310201 NA TLK2 NA NA SETBP1 NA FST IPO11 FCHO2 NA NA NA NA 100228402 NA 100226154 100222001 100218413 26 27 28 Z Z Z Z Z Z 12407416 ENSTGUG00000008176 VAMP (vesicle-associated membrane protein)-associated protein B and C 12994772 ENSTGUG00000008348 bone morphogenetic protein 7 3567060 ENSTGUG00000002944 SKI proto-oncogene 3829014 ENSTGUG00000002984 Uncharacterized protein 4917870 ENSTGUG00000003533 ubiquitination factor E4B 5384095 ENSTGUG00000003924 arginine-glutamic acid dipeptide (RE) repeats 3005531 ENSTGUG00000005141 Uncharacterized protein 2514050 ENSTGUG00000000951 family with sequence similarity 76 member A 1915428 ENSTGUG00000000375 Uncharacterized protein 3158273 ENSTGUG00000001456 Taeniopygia guttata RAP1A - member of RAS oncogene family (RAP1A) - mRNA 4749296 ENSTGUG00000001836 Uncharacterized protein 3357401 ENSTGUG00000003073 tousled-like kinase 2 507677 ENSTGUG00000000077 Uncharacterized protein 8261519 ENSTGUG00000000475 Uncharacterized protein 33303203 ENSTGUG00000001615 SET binding protein 1 39660746 ENSTGUG00000001811 Uncharacterized protein 46604913 ENSTGUG00000002425 follistatin 49853180 ENSTGUG00000002759 importin 11 66303495 ENSTGUG00000005780 FCH domain only 2 Transport Growth Factor Development Unknown Proteolysis Unknown Unknown Unknown Unknown Signaling Unknown Signaling Unknown Unknown Transcription Unknown Growth Factor Transport Cytoskeleton G. fortis - FGF, CACN, RAS, RAP G. fuliginosa - CACN, RAP, RSK, LNasGEF 237 Supplemental Figure S3 238 Supplemental Figure S4 G. fortis - (4.2.1.11) ENO2 G. fuliginosa - (1.2.1.5) AID1A3 239 Supplemental Figure S5 (A) Fuliginosa RBC Urban (U) Pairwise DMR Comparison U1 vs U3 U2 vs U3 U1 vs U2 Full analysis (B) Fuliginosa RBC Rural (R) Pairwise DMR Comparison R1 vs R3 Full analysis R2 vs R3 R1 vs R2 240 Supplemental Figure S5 (continued) (C) Fortis RBC Urban (U) Pairwise DMR Comparison U1 vs U3 U2 vs U3 Full analysis U1 vs U2 (D) Fortis RBC Rural (R) Pairwise DMR Comparison R1 vs R3 Full analysis R2 vs R3 R1 vs R2 APPENDIX A DOES HAEMOPROTEUS COLUMBAE AFFECT HOMING BEHAVIOR IN FERAL PIGEONS (COLUMBA LIVIA)? Introduction Parasites survive and reproduce by taking resources from a host, thus causing harm to the host (Schmid-Hempel 2011). Because of the costs of parasitism, parasites are assumed to be an important selective force influencing traits of their hosts such as appearance, geographic distribution, behavior and physiology (Hamilton and Zuk 1982, Clayton et al. 2010, Ricklefs 2010). Somewhat surprisingly, however, the costs of many parasites to their hosts' survival and reproduction are unknown. Attention tends to focus on a subset of parasites, often new to a host population, that clearly cause disease and have obvious implications for host health or conservation (van Riper et al. 1986, Fischer et al. 1997, Berger et al. 1998, Daszak et al. 2000). In contrast, although many native parasites are assumed to be damaging to the host, little is known about their costs. Malaria parasites of birds (Haemosproidia) are one such group of parasites that are ubiquitous, diverse, and have uncertain consequences for the host. "Avian malaria" includes organisms in the genera Plasmodium, Leucocytozoon, and Haemoproteus, that are globally distributed and found in the majority of bird orders (Valkiūnas 2004, Outlaw and Ricklefs 2011). Introduced species of Plasmodium in Hawaii and other archipelagos 242 are associated with the outbreak of disease and decline of native host populations (Samuel et al. 2011, Howe et al. 2012). Most studies that investigate costs of avian malaria infection tend to study Plasmodium parasites, in part because of their relevance for conservation, and also because they are easier to manipulate in laboratory conditions than Haemoproteus or Leucocytozoon species. As a result, it is unclear how costly most malaria parasites are to native hosts. Some studies report a negative effect of infection on host condition (Valkiūnas et al. 2006), survival (Marzal et al. 2008, Lachish et al. 2011) and/or reproductive success (Knowles et al. 2010, Asghar et al. 2015). However, several others have found no relationship between parasitism and host condition, survival, or reproductive success (Davidar and Morton 1993, Bensch et al. 2007, Szöllősi et al. 2009, Knutie et al. 2013), or report ambiguous results (Dawson and Bortolotti 2000, Ortego et al. 2008). These findings raise the question of whether malaria parasites are truly "parasites." Many studies have attempted to test the pathogenicity of avian malaria parasites in captive birds (Garvin et al. 2003, Cornet et al. 2014). While in captivity, birds are not subject to the stressors of wild life, such as foraging for food, flying, or avoiding predation. As a result, they may easily tolerate or resist parasitism. On the other hand, studies of the effects of parasitism in wild birds are often correlational, where the causes and effects of infection cannot be easily distinguished. In this study, I used an experimental approach under seminatural conditions to test the effects of Haemoproteus columbae infection on rock pigeons (Columba livia). I took advantage of a natural, physiologically demanding behavior of pigeons - homing - in order to determine if H. columbae infection has a sublethal, but significant, cost to the host. 243 Study system Rock pigeons are a globally distributed, human-commensal species. Originally domesticated from populations in Iran, the rock pigeons that inhabit the cities of North America are largely feral descendants of escaped homing pigeons (Stringham et al. 2012). Homing pigeons are exceptional fliers that are capable of traveling over 1800 km during racing competitions (Walcott 1996). Feral pigeons maintain much of this navigational and flying ability. Adult feral pigeons show high site-fidelity to their breeding colony (Hetmański 2007) and generally forage within a few kilometers of the colony (Rose et al. 2006). However, birds released up to 160 km away will return to their home colony (D.H. Clayton unpublished data). Homing is an energetically and physically demanding activity (Butler et al. 1977, Costantini et al. 2008, Usherwood et al. 2011) that may be diminished by parasitism. Pigeons are native hosts of Haemoproteus columbae, a generalist blood parasite that infects more than 40 species of pigeons and doves (Valkiūnas 2004). H. columbae is transmitted to pigeons by the pigeon fly Pseudolynchia canariensis (Diptera: Hippoboscidae). H. columbae reproduces sexually in P. canariensis, and asexually in avian hosts. Infections of H. columbae are associated with lower survival in juvenile pigeons (Sol et al. 2003); however, the direct effects of infection are unknown. There are anecdotal reports of H. columbae causing pathology in some Columbiformes (Earle et al. 1993); however, observational and experimental studies in feral pigeons often find no relationship between infection and host condition or survival (Rupiper 1998, Paperna and Smallridge 2002, Knutie et al. 2013). 244 Methods Breeding colony and homing training Feral pigeons were caught in Salt Lake City, Utah, using walk-in traps. Birds were housed in free-flight lofts at the University of Utah where they were fed ad libitum and allowed to nest in provided nest boxes. Pigeon squabs (F1s) were raised by their parents and banded with a uniquely numbered metal band and a combination of color bands shortly before fledging. F1s were introduced to homing at about two months of age. We began by teaching pigeons to enter the loft from holding cages via one-way bobs attached to the windows. Then, pigeons were allowed to freely explore the exterior of the loft and gradually were released at increasing distances from the loft, culminating in releases from 15 km away. We tagged each pigeon with a unique 23mm radio-frequency identification (RFID) passive integrative transponder (PIT) tag (Biomark) that was encased in shrink-wrap tubing and then attached with super glue to the upper shaft of the rectrices. A tag reader (Biomark) was placed beneath the landing platform at the entrance to the loft. Thus, the precise entry time for each pigeon could be recorded for each flight. When F1s pigeons reached adulthood (~ 6 months of age) and had successfully completed two releases from 15 km away, they were eligible for experimental infection. Before experimentally infecting pigeons, we confirmed they were infection-free through blood smear microscopy. Smears were stained with Geimsa stain and inspected under oil immersion at 1000x magnification for 100 fields. 245 Experimental infections Experimental infections were generated following methods developed by Knutie et al. (2013). We identified heavily infected wild-caught pigeons through blood smear microscopy and fed captive P. canarensis (the fly vector) on these donor birds for 10-12 days to develop infectious stages. Control P. canarensis were fed on uninfected pigeons. Following the incubation period, batches of five infected or control flies were macerated in 1000 µl of phosphate buffered saline for 3 min. Each experimental bird was injected intramuscularly with 500 µl of supernatant from either macerated infected or control flies. Control and experimental birds were all injected on the same day. On the day of injection (day 0), each pigeon was weighed and randomly assigned to a treatment. Following infection, birds were examined at 21 days postinjection and at 3-day intervals thereafter. At each examination day the birds were weighed, and a blood sample was taken to measure hematocrit (the proportion of erythrocytes to total blood volume) and to quantify parasitemia using blood smear microscopy. Parasitemia was defined as the number of infected blood cells in 100 fields at 1000X magnification under oil immersion. By day 27 all experimental birds had mature gametocytes in the peripheral red blood cells. Homing test On day 28 postinjection, experimental and control pigeons were taken to Antelope Island State Park, located in the Great Salt Lake, approximately 40 kilometers from the home loft. Pigeons were released individually, in random order, and the time from release until they returned home was recorded. 246 Analyses We used linear models (LMs), and generalized linear models (GLMs) to analyze the effect of treatment on parasitemia, infection status, and return status. Parasitemia was transformed using log (X +1) where X is the number of infected cells per 100 microscopy fields. Infection status and return status (returned or not) were modeled using binomial errors. We used linear mixed effects models (LMMs) with treatment as a fixed effect and bird ID as a random effect to test for differences in mass and hematocrit between experimentally infected and control groups. We used a Wilcoxon rank sum test to test for differences between treatments in return time. Statistical analyses were conducted using R Studio (2016, version 1.0.136; R version 3.3.3) using lme4 and lmerTest packages. Results Twelve birds were assigned to experimental and twelve to control treatments. All experimental birds had mature gametocytes in the blood by day 27. Most of the control birds showed no signs of infection; however, four out of 12 were infected. Experimental birds overall had higher parasitemia (infected blood cells / 100 fields) than control birds (LM P < 0.001). Counting only infected birds from both treatments, parasitemia was also higher in experimental birds (LM P = 0.003; Figure A.1). There was no difference between treatments in either mass (LMM P = 0.08) or hematocrit (LMM P = 0.35). Six control birds and four experimental birds returned to the loft (Figure A.2). The mean time to return for control birds was 3.75 days and 3.23 days for experimental birds. There was no significant difference between treatments in either the number of birds that returned to the loft (GLM P = 0.41) or their travel time to return home 247 (Wilcoxon rank sum test P = 0.76). There was no effect of parasitemia on probability of return (GLM P = 0.69) or time to return (LM P = 0.74). Discussion We observed no cost of H. columbae on homing ability in feral pigeons. These results are consistent with previous studies that suggest that H. columbae infection has little or no effect on pigeon condition or fitness (Paperna and Smallridge 2002, Knutie et al. 2013). We tested relatively few birds; thus, we may have failed to detect minor costs of infection on homing performance. Feral pigeons, although fairly site-faithful, were inconsistent homers. Of more than 100 birds hatched in the breeding colony, fewer than 25% learned to home consistently. The birds used in this experiment were largely trained in groups of five or more birds, but were tested alone. Intraindividual variation in navigational ability or motivation therefore may have influenced which individuals returned home, regardless of physiological condition. The use of homing breeds, which are artificially selected for homing behavior, may improve the assay. Haemoproteus and Leucocytozoon parasites have been rarely used in experimental infection studies because, unlike Plasmodium, infective stages cannot be generated from the blood. As a result, the arthropod vector is typically required to create experimental infections (Garvin et al. 2003). The requirement of the arthropod vector increases the complexity of experimental infections. Some of our control pigeons may have been inadvertently infected by infected flies, which may have been infected by feeding on pigeons with low-level chronic infections. Nevertheless, experimental studies are important to distinguish between correlative and causative effects of parasitism. 248 Future studies, potentially over longer time periods and with more animals, may provide better insight into the effects of infection on host physiology and condition. Many malaria parasites appear to have negligible effects on their hosts, especially during the acute stage of infection (Garvin et al. 2003, Knutie et al. 2013). The studies that do find negative effects of infection often focus on long-term consequences of chronic infection for survival or reproductive success (Sol et al. 2003, Knowles et al. 2010, Asghar et al. 2015). These results suggest that hosts may be able to tolerate many of the acute effects of infection without obvious signs of disease. However, over a lifetime of exposure, malaria parasites may have a significant effect on fitness, potentially mediated through subtle effects such as telomere degradation (Asghar et al. 2015). Whether or not Haemosporidians are in fact parasites likely depends on the scale at which they are evaluated. Acknowledgments Eric Middleton, Angela Hansen, Dale Clayton, and Sarah Bush were integral to the experimental design and data collection. We are grateful to Jon Gale and the staff of the Utah Animal Facilities for their assistance in maintaining the breeding colony. Thanks also to Scott Villa, Sarah Knutie, Emily DiBlasi, Monte Neate-Clegg, and Jessie Waite for helpful discussion. We thank Marsha Gilford and Smiths Marketplace for their assistance in trapping pigeons. All procedures were approved by the University of Utah IACUC (protocol #11-07018). Funding was provided by an NSF GRFP to SMM and an NSF grant DEB-1342600 to DHC and SEB. 249 References Asghar, M., D. Hasselquist, B. Hansson, P. Zehtindjiev, H. Westerdahl, and S. Bensch. 2015. Hidden costs of infection: chronic malaria accelerates telomere degradation and senescence in wild birds. Science 347:436-438. Bensch, S., J. Waldenström, N. Jonzén, H. Westerdahl, B. Hansson, D. Sejberg, and D. Hasselquist. 2007. Temporal dynamics and diversity of avian malaria parasites in a single host species. The Journal of Animal Ecology 76:112-22. Berger, L., R. Speare, P. 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Control Experimental 0.5 0.0 253 Control Experimental 10 5 0 Time until return (days) 15 Control Experimental 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Log10 (Parasitemia+1) Figure A.2. The effect of parasitemia at release (infected blood cells per 100 fields) on time to return. Each point represents an individual bird. Points at parasitemia = 0 have been jittered slightly for clarity. APPENDIX B GALÁPAGOS MOCKINGBIRDS TOLERATE INTRODUCED PARASITES THAT AFFECT DARWIN'S FINCHES Reprinted with permission from: Knutie, S. A., J. P. Owen, S. M. McNew, A. W. Bartlow, E. Arriero, J. M. Herman, E. Diblasi, M. Thompson, J. A. H. Koop, and D. H. Clayton. 2016. Galápagos mockingbirds tolerate introduced parasites that affect Darwin's finches. Ecology 97:940-950. 255 Ecology, 97(4), 2016, pp. 940-950 © 2016 by the Ecological Society of America Galápagos mockingbirds tolerate introduced parasites that affect Darwin's finches SARAH A. KNUTIE,1,4,6 JEB P. OWEN,2 SABRINA M. MCNEW,1 ANDREW W. BARTLOW,1 ELENA ARRIERO,3 JORDAN M. HERMAN,1 EMILY DIBLASI,1 MICHAEL THOMPSON,1 JENNIFER A. H. KOOP,1,5 AND DALE H. CLAYTON1 1 Department of Biology, University of Utah, Salt Lake City, Utah 84112, USA Department of Entomology, Washington State University, Pullman, Washington 99164, USA Department of Zoology and Physical Anthropology, Complutense University of Madrid, E-28040, Madrid, Spain 2 3 Abstract. Introduced parasites threaten native host species that lack effective defenses. Such parasites increase the risk of extinction, particularly in small host populations like those on islands. If some host species are tolerant to introduced parasites, this could amplify the risk of the parasite to vulnerable host species. Recently, the introduced parasitic nest fly Philornis downsi has been implicated in the decline of Darwin's finch populations in the Galápagos Islands. In some years, 100% of finch nests fail due to P. downsi; however, other common host species nesting near Darwin's finches, such as the endemic Galápagos mockingbird (Mimus parvulus), appear to be less affected by P. downsi. We compared effects of P. downsi on mockingbirds and medium ground finches (Geospiza fortis) on Santa Cruz Island in the Galápagos. We experimentally manipulated the abundance of P. downsi in nests of mockingbirds and finches to measure the direct effect of the parasite on the reproductive success of each species of host. We also compared immunological and behavioral responses by each species of host to the fly. Although nests of the two host species had similar parasite densities, flies decreased the fitness of finches but not mockingbirds. Neither host species had a significant antibody-mediated immune response to P. downsi. Moreover, finches showed no significant increase in begging, parental provisioning, or plasma glucose levels in response to the flies. In contrast, parasitized mockingbird nestlings begged more than nonparasitized mockingbird nestlings. Greater begging was correlated with increased parental provisioning behavior, which appeared to compensate for parasite damage. The results of our study suggest that finches are negatively affected by P. downsi because they do not have such behavioral mechanisms for energy compensation. In contrast, mockingbirds are capable of compensation, making them tolerant hosts, and a possible indirect threat to Darwin's finches. Key words: Galápagos Islands; Geospiza fortis; host defense; Mimus parvulus; nest parasite; Philornis downsi; tolerance. INTRODUCTION Introduced parasites can threaten native host populations that lack effective defenses (Daszak et al. 2000, Keesing et al. 2010). Not all hosts are vulnerable to introduced parasites, however. The fitness of some host species is clearly reduced, while the fitness of other hosts is relatively unaffected. "Unaffected" hosts may alleviate parasite damage with defense mechanisms that can include both resistance and tolerance. These two forms of defense are important to distinguish, because resistant hosts lower parasite populations, whereas tolerant hosts do not negatively affect parasite populations. Therefore, tolerant hosts provide a more stable resource for the Manuscript received 19 January 2015; revised 28 September 2015; accepted 5 November 2015. Corresponding Editor: K. P. Huyvaert. 4 Present address: Department of Integrative Biology, University of South Florida, Tampa, Florida 33620, USA. 5 Present address: Biology Department, University of Massachusetts-Dartmouth, Dartmouth, Massachusetts 02747, USA. 6 E-mail: [e-mail] introduced parasite (Schmid-Hempel 2011). By supporting the parasite population, tolerant hosts sustain, or increase, the "force of infection" for vulnerable host populations, defined as the fraction of the susceptible host population that the infected hosts can infect per unit of time (Anderson and May 1991, Hudson et al. 2002). For example, the introduction of parapoxvirus to Great Britain by tolerant, nonnative grey squirrel hosts has been implicated in the decline of native red squirrels (Tompkins et al. 2003). Tolerant hosts, such as the grey squirrel, maintain high levels of the parasite in the environment, while more vulnerable host species decline (Nokes 1992). For this reason, tolerant hosts can represent an indirect threat to populations of more vulnerable host species (Daszak et al. 2001, McCallum 2012). Small island populations are particularly vulnerable to the effects of introduced parasites (Wikelski et al. 2004, Atkinson and Lapointe 2009). A classic example involves the historical introduction of avian malarial parasites and their mosquito vectors to the Hawaiian Islands. This 940 256 April 2016 HOST TOLERANCE OF INTRODUCED PARASITES introduction is thought to be partly responsible for the extinction of 17 endemic honeycreeper species (Atkinson and Lapointe 2009). Some species of honeycreepers have been relatively unaffected by introduced malarial parasites, however. Experiments with captive birds suggest that the amakihi honeycreeper (Hemignathus virens virens) is tolerant of the malarial parasite, and that this species of honeycreeper may therefore be a reservoir host that helps maintain the parasite in the local environment (Atkinson et al. 2000). In other words, the amakihi may be a host that essentially amplifies the negative effect of the malarial parasite on more vulnerable and declining honeycreeper species (Atkinson and Lapointe 2009). The concept of host tolerance has seldom been tested directly under natural conditions (Read et al. 2008, Råberg et al. 2009, Svensson and Råberg 2010, Medzhitov et al. 2012). Testing for tolerant hosts requires comparing the fitness of different host genotypes or species under similar environmental conditions. Such studies are challenging because the most rigorous method for assessing the relative effects of parasites is to experimentally manipulate parasite abundance, which can be very difficult under natural conditions (McCallum and Dobson 1995). Introduced parasites have colonized the Galápagos Islands of Ecuador in recent decades, threatening endemic birds as well as other groups of animals and plants (Wikelski et al. 2004). A notorious example of an introduced parasite is the nest fly Philornis downsi, which has been implicated in the decline of critically endangered species of Darwin's finches, such as the mangrove finch (Camarhynchus heliobates) (O'Connor et al. 2009, Fessl et al. 2010). Adult flies, which are not parasitic, lay their eggs in the nests of finches and other land birds in the Galápagos. Once the eggs hatch, fly larvae feed on the blood of nestlings and adult females as they brood the nestlings. Several studies have shown that P. downsi reduces the reproductive success of the medium ground finch (Geospiza fortis) and other species of Darwin's finches (reviewed in Koop et al. 2011). In some years, 100% of finch nests fail to produce fledglings due to P. downsi (Koop et al. 2011, 2013a, O'Connor et al. 2013). Moreover, Kleindorfer et al. (2014) recently suggested that Philornis-related mortality in finches has increased over the past decade, with nestling age at mortality decreasing due to P. downsi infestations earlier in the nestling developmental period. Other host species nesting near Darwin's finches, such as the endemic Galápagos mockingbird (Mimus parvulus), may be less affected by P. downsi infestation. Anecdotal observations suggest that mockingbird nestlings often do not die when parasitized by P. downsi (personal observation). If so, then mockingbirds could be tolerant hosts that effectively amplify the force of infection for vulnerable hosts, such as Darwin's finches. The goal of the current study was to test this hypothesis by comparing the effects of P. downsi on the fitness of mockingbirds and medium ground finches at the same time and location. 941 We measured the effects of parasites on nestling mockingbirds and finches over two field seasons, then compared the reaction norms of host nestling survival and parasite density between the two host species. During the first season, we compared the effect of P. downsi on the size and fledging success of nestling mockingbirds and finches. We predicted a significant negative effect of P. downsi on the size and fledging success of finches, but not mockingbirds. We also tested for evidence of nestling immune responses that combat P. downsi in mockingbirds and finches. During the second field season, we repeated these comparisons, and also explored possible mechanisms of tolerance, such as the rapid replacement of blood lost to the parasite. To test this possibility, we compared the effect of P. downsi on the hemoglobin of finch and mockingbird nestlings. Another possible mechanism of tolerance is increased parental care of parasitized nestlings (Tripet and Richner 1997, Hurtrez-Bousses et al. 1998, Tripet et al. 2002). Parents of such nestlings might increase their feeding rates to compensate for energy lost to parasites. One cue known to lead to increased feeding rates is increased begging by parasitized nestlings (Bengtsson and Rydén 1983, Christe et al. 1996). In some cases, however, parasitized nestlings appear to be too weak to solicit more food by begging. We compared parental and nestling behavior, as well as energy levels (via glucose), of finches and mockingbirds with and without parasites in the nest. We predicted that mockingbird nestlings in parasitized nests would beg more than nestlings in unparastized nests, and that parents in parasitized nests would provision nestlings more than parents in nonparasitized nests. We further predicted that increased begging would lead to increased glucose levels in parasitized nestlings. In contrast, finch parental provisioning does not differ in response to parasitism (Koop et al. 2013a). Thus, we predicted that parasitized and nonparasitized finch nestling begging and glucose levels would decrease or not differ because parasitized nestlings are too weak to increase begging. METHODS Study system The study was conducted January-April in both 2012 and 2013 on the island of Santa Cruz in the Galápagos Archipelago. Our 3 × 4 km field site, known as El Garrapatero, is located in the arid coastal zone; it is located approximately 10 km east of the main town of Puerto Ayora. Galápagos mockingbirds and medium ground finches are abundant at the site. Mockingbirds build open, cup-shaped nests, which are made of Acacia thorns (bottom layer), moss (middle layer), and coarse grasses (top layer/nest liner) and primarily found in giant prickly pear cacti (Opuntia echios gigantea) or Acacia trees. Mockingbird clutch size ranges from 1 to 5 eggs, and females incubate the eggs for about 15 d. Nestlings 257 942 SARAH A. KNUTIE ET AL. spend an average of 15 d in the nest, and both the adult females and males feed them. Mockingbirds feed their nestlings by placing food items in the nestling's open mouth rather than by regurgitating food, as in the case of finches (see below). Mockingbirds usually lay a single clutch of eggs per breeding season; however, they have been reported to re-nest in a new location when the first clutch fails, sometimes due to delayed rains. Mockingbirds normally do not reuse the same nest. Finches build dome-shaped nests, which are made of coarse grasses (exterior layer) and fine grasses (nest liner that contacts the nestlings in the cup of the nest) and primarily found in giant prickly pear cacti or Acacia trees (Grant 1999). Their clutch sizes range from 2 to 5 eggs, with females incubating the eggs for 10-14 d. Nestlings spend an average of 12 d in the nest, and adult females and males both feed the nestlings by regurgitating food into the nestling's throat. In years of favorable weather and food resources, medium ground finches may lay additional clutches of eggs over the course of a single breeding season; however, like mockingbirds, they do not reuse the same nest (Grant 1999). Experimental manipulation of parasites To quantify the effect of P. downsi on host fitness, experimental nests were fumigated with a 1% aqueous permethrin solution (Permectrin™ II). Control nests were sham-fumigated with water. Permethrin, which has been used in previous studies (Fessl et al. 2006, Koop et al. 2013a,b, O'Connor et al. 2013), is harmless to birds, including newly hatched nestlings. Nests were sprayed soon after the first nestling hatched, then again 4-6 d later. Nest contents (nestlings, unhatched eggs, and the nest liner) were removed during the spraying process. Nest contents were then returned to the nest once it had dried (<10 min). Parents quickly returned to the nest following treatment, with no cases of nest abandonment due to treatment observed for either bird species. Nestling size and fledging success In 2012, each nestling was weighed twice: within 24 h of hatching, then again at 9-10 d of age. In 2013, each nestling was weighed three times: within 24 h of hatching, then at one-third and again at two-thirds of the nestling developmental period. Thus, the second weighing occurred when finch nestlings were 4-5 d old, and mockingbird nestlings were 5-6 d old. The third weighing occurred when finch nestlings were 8-9 d old, and mockingbird nestlings were 10-11 d old. Each nestling was banded with a unique darvic color band combination. Successful fledging was confirmed by identifying birds once they left the nest, as in previous studies (Koop et al. 2011, 2013a,b). After the birds in a nest had all fledged or died, the nest was collected and Ecology, Vol. 97, No. 4 placed in a sealed plastic bag. The number of P. downsi in the nest was then quantified, as described below. Fledging success for finches from 2013 was first reported in Knutie et al. (2014). Quantifying P. downsi Each nest was carefully dissected within 8 h of collection and P. downsi larvae, pupae, and eclosed pupal cases were counted (Koop et al. 2011, 2013a,b). First instar larvae can live subcutaneously in nestlings, making them impossible to quantify reliably. Therefore, parasite abundance was the sum of counts of second and third instar larvae, pupae, and eclosed pupal cases (for both infested and uninfested nests). Parasite abundance was used to calculate parasite density, which is the number of parasites per unit of host (Bush et al. 1997). For mockingbirds and finches, density was calculated by dividing the number of parasites per nest by the total mass of nestlings for a given nest at 2/3 of the mean nestling developmental period. Larvae and pupae were reared to the adult stage to confirm that they were P. downsi (Dodge and Aitken 1968). Most larvae were third instars when the nests were collected; these larvae usually pupated within 24 h. Younger larvae, which require a blood meal, died soon after they were collected from the nest and were therefore not reared to adulthood. The length and width of pupae were measured with digital calipers in mm. These measurements were then used to estimate pupal volume as a measure of individual parasite size, which is related to lifetime fitness in other Muscid flies (Schmidt and Blume 1973, Moon 1980). Nestling hemoglobin In 2012, blood was sampled from 9 to 10 d old nestlings. In 2013, blood was sampled from nestlings when they were at one-third and two-thirds of the nestling period. A small blood sample (<30 µL) was collected in a microcapillary tube via brachial venipuncture. Using a portion of this blood, hemoglobin concentration was quantified immediately in the field (2013 only). Hemoglobin concentration can provide an accurate estimate of ectoparasite-induced anemia (O'Brien et al. 2001, Dudaniec et al. 2006, Carleton 2008). Hemoglobin was measured with a HemoCue® HB 201+ portable analyzer, using ten microliters of whole blood per disposable microcuvette. Hemoglobin was measured in g/dL. The remainder of each blood sample was stored on wet ice in the field. Within 6 h of collection, samples were spun at 8000 rpm for 10 min in a centrifuge. Plasma and red blood cells were stored separately in 0.5 mL vials in a −20°C freezer at the Charles Darwin Research Station. Samples were later frozen at −80°C after being transported in liquid nitrogen to the University of Utah. The samples were ultimately used for the immunological and glucose assays described below. 258 April 2016 HOST TOLERANCE OF INTRODUCED PARASITES Nestling immunology Enzyme-linked immunosorbent assays (ELISA) were used to detect the presence of P. downsi-binding antibodies in the plasma of finches and mockingbirds, with a modification of the protocol in Koop et al. (2013a). Ninety-six well plates were coated with 100 µL/well of P. downsi protein extract (capture antigen) diluted in carbonate coating buffer (0.05 mol/L, pH 9.6). Plates were incubated overnight at 4°C, then washed and coated with 200 µL/ well of bovine serum albumin (BSA) blocking buffer and incubated for 30 min at room temperature on an orbital table. Between each of the following steps, plates were washed five times with a Tris-buffered saline wash solution, loaded as described, and incubated for 1 h on an orbital table at room temperature. Triplicate wells were loaded with 100 µL/well of individual host plasma (diluted 1:100 in sample buffer). Plates were then loaded with 100 µL/ well of Goat-αBird-IgG (diluted 1:50,000) (Antibodies Online, Atlanta, GA, USA; ABIN351982). Finally, plates were loaded with 100 µL/well of peroxidase substrate (tetramethylbenzidine, TMB; Bethyl Laboratories, Montgomery, TX, USA) and incubated for exactly 30 min. The reaction was halted using 100 µL/well of stop solution (Bethyl Laboratories). Optical density (OD) was measured with a spectrophotometer (BioTek, Winooski, VT, USA; PowerWave HT, 450-nanometer filter). On each plate, a positive control of pooled plasma from naturally P. downsi parasitized adult female finches from the 2013 field season was used in triplicate to correct for inter-plate variation (Koop et al. 2013a). In addition, each plate contained a non-specific binding (NSB) sample in which capture antigen and detection antibody were added, but plasma was excluded. Finally, each plate included a blank sample in which only the detection antibody was added, but plasma and capture antigen were excluded. NSB absorbance values were subtracted from the mean OD value of each sample to account for background binding of the detection antibody to the capture antigen. Nestling glucose Plasma glucose was measured using blood samples taken from mockingbird and finch nestlings at about the same time their behavior was quantified; see below. An Endpoint Autokit (Wako, Diagnostics, Mountain View, CA, USA) was used to measure plasma glucose for mockingbirds and finches with a modified protocol based on Guglielmo et al. (2013). The kit provided 500 mg/dL and 200 mg/dL standards. Following the manufacturer's protocol, the buffer solution and color reagent were mixed together, then refrigerated at 4°C until they were used in the assay. Three microliters of sample or standard were run primarily in duplicate, assuming sufficient sample was available, on Nunc® MicroPlate™ 96-well polystyrene plates (Sigma-Aldrich, St. Louis, MO, USA). The buffer solution was pre-warmed to 37°C, then 300 µL were 943 added to each well. The plate was incubated at 37°C on a microplate incubator shaker (Stat Fax® 2200) for 10 min, then shaken for 10 s on low speed. Optical density (OD) was measured using a spectrophotometer (BioTek; PowerWave HT, 505-nanometer filter). Samples were corrected for intraplate variation based on the 500 mg/ dL standard. From the standards, a standard curve was created ranging from 50 to 500 mg/dL. Glucose concentration (mg/dL) for each sample was calculated using the OD value of the sample (x), and the slope and intercept of the line from the standard curve (y = 0.003x + 0.0352). Nestling and adult behavior Mockingbird behavior was recorded during the 2013 field season. Because we had a limited number of nest cameras and recording devices, and because we collected behavioral data from fumigated and sham-fumigated finch nests from the same field site in 2010, we chose to concentrate on recording mockingbird data in 2013; see Koop et al. (2013a) for details on finch behavior. Behavior was monitored with battery-powered Sony® video camera systems. Small nest cameras (31 mm in diameter, 36 mm in length) were suspended above nests; seven-meter long cables connected the cameras to small recording devices (PV700 Hi-res DVR; StuntCams, Grand Rapids, MI, USA) hidden near the base of the tree supporting the nest. Behavior was recorded between 0600 and 1000 using haphazard subsamples of fumigated and sham-fumigated nests. Mockingbird behavior was quantified from videos by one of the authors (M.T.) who was blind to nest identity or treatment. A similar "blind" approach was used for finch behavior (Koop et al. 2013a). Videos were analyzed with the software VLC media player (VideoLAN, Paris, France), except in the case of begging, which was analyzed using CowLog v.2.1 (Hänninen and Pastell 2009). A single day of video from each of two nests was paired between treatments to control for hatch date, brood size, and nestling age. There was no significant difference in brood size or nestling age between treatments. Nestling begging was defined as one or more nestlings tilting their head back, with the neck extended and the open mouth showing (Christe et al. 1996). Begging time was calculated as the proportion of total video time. The proportion of video time with nestling agitation behavior, defined as shaking, repositioning, or jumping in the nest, was also quantified. Adult behaviors included the proportion of time each adult spent at the nest. We were unable to distinguish female and male mockingbirds because they are not sexually dimorphic. The following adult behaviors were quantified: brooding nestlings, standing erect in the nest, standing motionless on the rim of the nest, nest sanitation, self-preening, allopreening nestlings, and provisioning (feeding) nestlings. Brooding was defined as the adult sitting on the nest in direct contact with nestlings. Nest sanitation was defined as the adult contacting or 259 944 Ecology, Vol. 97, No. 4 SARAH A. KNUTIE ET AL. manipulating nest material with its bill. Provisioning of nestlings was defined as the insertion of the bill into the mouths of nestlings by adults; note, however, that we were unable to determine how much food was actually delivered. Because adults often preen themselves while brooding nestlings, self-preening was analyzed separately from the other behaviors. All other behaviors were analyzed as the proportion of total time that adults were observed. Mockingbird behaviors were quantified from a total of 41 h of video, with an average of 2.5 h of video for each of the 16 mockingbird nests (eight fumigated, eight sham-fumigated). Mockingbird nestlings in the videos ranged in age from 3 to 6 d, and brood size ranged from 1 to 5 nestlings. Finch behaviors were quantified from a total of 54 h of video, with an average of 3 h of video for each of the 18 finch nests (nine fumigated, nine shamfumigated; Koop et al. 2013a). Finch nestlings in the videos ranged in age from 2 to 6 d, and brood size ranged from 1 to 5 nestlings. The data for adult finch behavior were reported separately by sex in Koop et al. (2013a). For our analyses these data were pooled. Statistical analyses Parasite abundance, density, and volume were analyzed using generalized linear models (GLM) with a negative binomial family and logit link function for abundance and a Gaussian family and identity link function for density and volume; year (2012 or 2013) and host species (mockingbird or finch) were fixed effects for all three variables and treatment (fumigated or sham-fumigated) was a fixed effect for parasite abundance. Data for individual nestlings were analyzed with generalized linear mixed models (GLMM) using nest as a random effect and year, host species, age, and treatment as fixed effects. Fledging success was modeled with a binomial family and logit link function; year, host species, and treatment were fixed effects. Mass, immune response, hemoglobin, and glucose were modeled with a Gaussian family and identity link function; year, host species, age, and treatment were fixed effects for mass and immune response, host species, age, and treatment were fixed effects for hemoglobin, and host species and treatment were fixed effects for glucose. For each of the GLM and GLMM analyses, we developed a set of a priori models that included single, additive, and interactive effects of variables that we hypothesized had biologically meaningful effects of the response variables of interest. For example, year, host species, and treatment were predicted to affect parasite abundance; therefore, we analyzed the effect of year, species, and treatment alone, and all two and three-way interactions (Appendix S1). We ranked models using Akaike's Information Criterion with adjustment for small sample size (AICc). We report AICc differences (∆AICc) and Akaike weights (ω) to determine the strength of evidence for each model, relative to the set of candidate models (Burnham and Anderson 2002). To account for model selection uncertainty, we averaged across all models to calculate model-averaged parameter ≃ estimates (β) with shrinkage, as well as z-values and P-values, for each variable and their interaction(s). Host nestling and adult behavior were compared between treatments using a chi-square test to match previously reported analyses of finch behavior from Koop et al. (2013a); specific behaviors were compared between treatments using Fisher's exact tests. GLMM and GLM analyses were performed in the program RStudio, version 3.1.1 (R Core Team 2014) using the lme4, MuMln, nlme, and MASS packages. Prism® v.5.0b (GraphPad Software, Inc., La Jolla, CA, USA) was used for all other analyses and to create figures. RESULTS Quantifying P. downsi Top ranked models included the effect of treatment and host species on parasite abundance and both variables were in every model with an Akaike weight of >0.10, indicating their importance (Appendix S1: Table S2). The experimental treatment of nests with permethrin was effective at reducing parasite abundance, compared to sham-fumigated control nests for both host≃species in both years of the study (GLM, Treatment, β ± SE = -5.06 ± 0.57, P < 0.0001; Appendix S1: Table S3). Parasite abundance was significantly higher in mockingbird nests than finch nests in both years (Species, ≃ β ± SE = 1.11 ± 0.39, P < 0.001; Appendix S1: Table S3). However, variation in parasite density (parasites per gram of nestling) and parasite size (pupal volume) was not explained by any of the predictors that we measured ≃ in our study (Density: Species, β ± SE = −0.06 ± 0.21, P = 0.80;≃ Table 1; Appendix S2: Tables S2 and S3 Size: Species, β ± SE = 0.01 ± 0.04, P = 0.67; Table 1; Appendix S3: Tables S2 and S3). Nestling mass For nestling mass, all top models included the effect of age (ω > 0.10; Appendix S4: Table S3). Nestling mass increased significantly with increasing age in both species ≃ (GLMM, Age, β ± SE = 1.43 ± 0.05, P < 0.0001; Appendix S4: Table S4). Top models (ω > 0.10) also included the interaction between age and species and age and treatment. As expected, mockingbirds weighed significantly more than finches (Age × Species, ≃ β ± SE = 1.96 ± 0.05, P < 0.0001; Appendix S4: Table S4). P. downsi had a significant effect on the mass of both mockingbird and finch nestlings, but only in older nest≃ lings (Age × Treatment, β ± SE = 0.13 ± 0.05, P = 0.01; Appendix S4: Table S4). The body mass of older nestlings in fumigated nests was significantly greater than the body mass of older nestlings in sham fumigated nests (Appendix S4: Table S1). 260 April 2016 HOST TOLERANCE OF INTRODUCED PARASITES 945 TABLE 1. Comparison of Philornis downsi number and size, and host fledging success in mockingbirds and finches in fumigated (F) and sham-fumigated (SF) nests. Parasite density is the number of parasites per gram of host. Galápagos mockingbird 2012 Mean ± SE parasite density (Number of nests) Mean ± SE pupal volume, mm3 (Number of nests) Fledglings, % (Number of nestlings) Nests with at least one fledgling, % (Number of nests) Medium ground finch 2013 2012 2013 F SF F SF F SF F SF - 1.00 ± 0.29 (14) - 1.06 ± 0.47 (13) - 1.49 ± 0.52 (8) - 1.06 ± 0.42 (12) - 115.20 ± 6.57 (13) - 120.10 ± 8.52 (14) - 108.30 ± 6.93 (9) - 117.50 ± 5.70 (9) 76.5 (51) 87.5 77.8 (54) 87.5 70.0 (47) 76.5 66.7 (54) 76.5 86.0 (43) 91.7 34.2 (38) 50.0 83.3 (60) 95.0 53.7 (54) 64.7 (16) (16) (17) (17) (12) (12) (20) (17) Fledging success For fledging success, all top models (ω > 0.10) included the effect of an interaction between species and treatment (Appendix S5: Table S1). Treatment had a significant effect on the fledging success of finches, but not mockingbirds (GLMM, Species × Treatment, ≃ β ± SE = −4.33 ± 1.14, P < 0.001; Tables 1; Fig. 1; Appendix S5: Table S2). That is, P. downsi reduced fledging success of finches but had no effect on mockingbirds. Parasite density was a significant predictor of fledging success in finches, but not mockingbirds (GLM, Density × Species, χ2 = 16.24, df = 1, P < 0.0001; Fig. 1). Nestling immunology Philornis downsi was not a significant predictor of antibody levels because treatment was not included in any of the top models (ω > 0.10; Appendix S6: Table S3). Antibody levels within each species were low (Appendix S6: Table S1). However, the top models included an effect of year, species, and their interaction, on antibody levels (Appendix S6: Table S3). For finches, antibody levels were significantly higher in 2012 than 2013 (GLMM, ≃ Species × Year, β ± SE = 0.16 ± 0.04, P < 0.001; Appendix S6: Table S4). Finches also had significantly higher antibody levels than mockingbirds (Species, ≃ β ± SE = −320.70 ± 88.40, P < 0.001; Appendix S6: Table S4). Nestling hemoglobin Top models included the effect of age and an age × treatment interaction on nestling hemoglobin levels (ω > 0.10; Appendix S7: Table S1). Mockingbird and finch nestling hemoglobin increased significantly ≃ with age (GLMM, Age, β ± SE = 0.33 ± 0.04, P < 0.0001; Appendix S7: Table S3). There was a significant effect FIG. 1. Reaction norms for fledging success in finches and mockingbirds across different Philornis downsi densities. Each point represents percentage of fledging success, or the percentage of hatchlings that successfully left the nest, plotted against mean parasite density within a treatment and year. Mockingbirds are more tolerant to P. downsi; parasite density is not a significant predictor of fledging success. In contrast, parasite density is a significant predictor of fledging success in finches. of P. downsi on the hemoglobin of older nestlings in both species of hosts (Age × Treatment, β ± SE = 0.30 ± 0.06, P < 0.0001; Appendix S7: Table S3). Older mockingbird nestlings in fumigated nests had 40% more hemoglobin than similar aged nestlings in sham-fumigated nests (Fig. 2; Appendix S7: Table S1). Older finch nestlings in fumigated nests had 14% more hemoglobin than similar aged nestlings in shamfumigated nests (Fig. 2; Appendix S7: Table S1). ≃ 261 946 Ecology, Vol. 97, No. 4 SARAH A. KNUTIE ET AL. FIG. 2. Mean (±SE) hemoglobin in nestlings from fumigated and sham-fumigated nests. Nestlings in fumigated nests had significantly higher hemoglobin levels than nestlings in shamfumigated nests for both species of birds. Nestling glucose The top model included the effect of a species by treatment interaction on nestling glucose levels and carried nearly all of the Akaike weight (ω = 0.99; Appendix S8: Table S2). Mockingbird nestlings in fumigated nests had significantly lower plasma glucose levels than mockingbird nestlings in ≃ sham-fumigated nests (GLMM, Species × Treatment, β ± SE = −31.10 ± 15.79, P = 0.05; Fig. 3; Appendix S8: Table S3). In contrast, parasite abundance was not a significant predictor of glucose concentration in finch nestlings (Fig. 3). Nestling and adult behavior Agitation behavior did not differ significantly between mockingbird nestlings from fumigated and shamfumigated nests (Table 2). However, mockingbird nestlings from sham-fumigated nests spent significantly more time begging than nestlings from fumigated nests (Table 2; Fig. 4A). The amount of time adult mockingbirds spent at fumigated and sham-fumigated nests did not differ significantly (Table 2). There was no significant effect of treatment on self-preening by adult mockingbirds (W = −3.00, P = 0.81); note, however, that the birds spent <0.01% of their time engaged in self-preening at the nest. We did not collect data on self-preening or other behaviors in birds away from the nest. Adult mockingbirds differed significantly in the time they devoted to other (mutually exclusive) behaviors at fumigated vs. sham-fumigated nests (Chi-square test: χ2 = 18.90, df = 5, P < 0.001). The largest difference was in the time adult mockingbirds spent brooding nestlings, with adults at fumigated nests spending significantly more time brooding than adults at sham-fumigated nests (Table 2). When mockingbirds from shamfumigated nests were not brooding, they were either standing erect in the nest, or standing erect on the rim of the nest (Table 2); however, these behaviors did not differ significantly between treatments. Adults on the rim of nests occasionally probed nest material (nest sanitation behavior), allopreened nestlings, or provisioned nestlings. Adult mockingbirds spent very little time in nest sanitation behavior, and there was no significant effect of treatment on this behavior (Table 2). When adult mockingbirds from sham-fumigated nests were not brooding, but were still present at the nest, they spent most of their time allopreening nestlings while standing on the rim of the nest; however, there was no significant difference in allopreening between treatments (Table 2). Adults from fumigated nests spent significantly less time in provisioning behavior, compared to adults from sham-fumigated nests (Table 2; Fig. 4A). The amount of time parents spent in provisioning behavior was positively correlated with the amount of time nestlings spent begging (Spearman rank correlation: rS = 0.52, P = 0.04). In contrast to mockingbirds, nestlings in fumigated finch nests did not beg more than nestlings in shamfumigated finch nests (Table 2; Fig. 4B). The time adult finches spent at fumigated and sham-fumigated nests did not differ significantly (Table 2). The time parents spent in provisioning behavior was correlated with nestling begging time (Spearman rank correlation: rS = 0.81, P < 0.0001). However, adult finches did not differ significantly in the amount of time they spent in provisioning behavior at fumigated and sham-fumigated nests (Table 2; Fig. 4B). See Koop et al. (2013a) for further details of finch behavior. DISCUSSION FIG. 3. Mean (±SE) plasma glucose levels in mockingbird and finch nestlings from fumigated and sham-fumigated nests. Mockingbird nestlings in sham-fumigated nests had higher glucose levels than nestlings in fumigated nests. In contrast, glucose levels did not differ significantly between treatments for finches. The effect of P. downsi varied considerably between finches and mockingbirds within the same years and location. P. downsi reduced the fledging success of Darwin's finch nestlings; however, despite a similar density of flies in mockingbird nests, P. downsi had no significant effect on mockingbird fledging success. Thus, 262 April 2016 HOST TOLERANCE OF INTRODUCED PARASITES 947 TABLE 2. Behavior of nestling and adult mockingbirds and finches in fumigated and sham-fumigated nests. For mockingbirds, each treatment contained eight nests; for finches, each treatment contained nine nests. For nestlings, values are the mean ± SE percent time out of total video time. For adults, most values are the mean ± SE percent time out of total attendance time at nest. Attendance time is the total time at the nest out of total video time. Wilcoxon signed rank tests were used to compare treatments within each behavior. See Koop et al. (2013a) further details of finch behavior. Fumigated (%) Galápagos mockingbird Nestlings Begging Agitation Adults Attendance at nest Brooding Standing erect in nest Standing on rim of nest Nest sanitation Allopreening Provisioning behavior Medium ground finch Nestlings Begging Adults Attendance at nest Provisioning behavior Sham-fumigated (%) W statistic P-value 3.12 ± 0.74 10.38 ± 2.16 5.78 ± 0.98 12.86 ± 3.07 −32.00 −6.00 0.02 0.74 54.59 ± 5.00 70.35 ± 5.05 2.27 ± 0.57 7.77 ± 1.85 0.92 ± 0.43 15.18 ± 3.85 3.50 ± 0.56 50.45 ± 6.78 41.12 ± 8.11 9.03 ± 7.46 11.87 ± 2.65 1.24 ± 0.32 30.77 ± 7.36 5.98 ± 1.04 10.00 32.00 8.00 −18.00 −16.00 −24.00 −32.00 0.55 0.02 0.64 0.25 0.31 0.11 0.02 6.85 ± 0.90 5.53 ± 0.86 25.00 0.16 47.29 ± 6.10 11.05 ± 2.19 57.72 ± 9.14 10.45 ± 4.90 −23.00 27.00 0.20 0.13 FIG. 4. Nestling and parental behavior (mean ±SE) in fumigated and sham-fumigated nests for (A) mockingbirds and (B) finches. Time allocated to nestling begging and parental provisioning behavior was significantly higher in sham-fumigated mockingbird nests than in fumigated nests. In contrast, the amount of time spent on these behaviors did not differ significantly between treatments in finch nests. we suggest that this provides evidence that mockingbirds are tolerant hosts, whereas finches are highly vulnerable to the parasite. We then explored potential tolerance mechanisms used by mockingbirds to deal with P. downsi. We found that mockingbird nestlings from shamfumigated nests begged significantly more than nestlings from fumigated nests. Greater begging was correlated with increased parental provisioning, which may have been responsible for the higher glucose concentration in nestlings in sham-fumigated nests. In contrast to mockingbirds, finch nestling begging and parental provisioning did not change in response to P. downsi, nor was there a difference in the plasma glucose levels of nestlings in fumigated and sham-fumigated nests. We suggest that these behavioral differences indicate adaptive tolerance of P. downsi by mockingbirds. The difference in the effect of P. downsi on tolerant vs. non-tolerant hosts motivates the question: How can hosts vary in their susceptibility to the same parasite at the same time in the same place? Neither mockingbird nor finch nestlings produced a significant antibody-mediated immune response to P. downsi in our study. In fact, antibody levels in nestling finches (and mockingbirds) were nearly undetectable, compared to those measured in adult finches in an earlier study (Koop et al. 2013a). Captive house sparrows (Passer domesticus) are capable of producing independent antibody-mediated immune responses at 3 d of age when challenged with non-specific antigens (King et al. 2010). However, we found no evidence of such responses by finch or mockingbird nestlings parasitized by P. downsi. 263 948 SARAH A. KNUTIE ET AL. It is possible that our assay was not sensitive enough to detect low concentrations of antibodies. Antibody levels did increase with nestling age, but this increase did not differ significantly between experimental treatments. One other possibility is that these hosts are exposed to relatively few native parasites in the Galápagos, meaning that non-specific antibody-mediated immune responses are not primed as much as they would be on the mainland. On the other hand, the antibodies we detected may not be specific to P. downsi. Instead, the antibodies may have also been influenced by other biting insects, such as mosquitoes, which have antigens in their saliva that induce similar responses to those induced by P. downsi (e.g., IgG) (Peng et al. 1996). Nevertheless, our results suggest that nestling immune responses do not ameliorate the effects of P. downsi on mockingbirds or finches. Mockingbird parents from sham-fumigated nests brooded their nestlings less than parents from fumigated nests. Mockingbird parents were still present at the nest, but they may have been avoiding the parasites by standing on the rim of the nest. Koop et al. (2013a) found that adult finches in sham-fumigated nests also brood their nestlings less, and stand erect more, compared to adult finches in fumigated nests. Mockingbird parents also allopreen their nestlings; however, it was not clear from our video analyses whether allopreening removes or damages P. downsi (cf. Clayton et al. 2010). It is possible that allopreening does provide at least some defense against P. downsi. Further tests are needed to determine the extent to which mockingbirds can reduce P. downsi on their nestlings by allopreening them. Mockingbirds may tolerate the effects of P. downsi by increasing parental provisioning of nestlings to compensate for energy lost to parasites. In other systems, parasitic flies are known to increase host metabolic rate, which depletes host energy resources (Careau et al. 2010). Several studies of other systems have also shown that parents in parasitized nests feed their nestlings more than parents in nonparasitized nests, leading to increased fledging success (Tripet and Richner 1997, HurtrezBousses et al. 1998, Tripet et al. 2002). Our study suggests that increased begging by mockingbird nestlings in shamfumigated nests led to increased provisioning by parents, which likely contributed to the improved survival of nestlings in these nests. A more definitive test of this hypothesis would involve comparison of the quality and quantity of food being delivered to nestlings between experimental treatments and species. It would also be interesting to test the begging-provisioning hypothesis by using recordings to simulate increased begging in nests to see if parents respond with the delivery of more food to the nestlings (Bengtsson and Rydén 1983, Ottosson et al. 1997). Why do finch nestlings not beg more in sham-fumigated nests, the way that mockingbird nestlings do? The answer may lie in the smaller body size of finch nestlings, which are only half the size of mockingbird nestlings. Smaller birds require more energy per gram of body mass because Ecology, Vol. 97, No. 4 they have a higher surface-to-volume ratio than larger birds (Schmidt-Nielsen 1984). Thus, small-bodied species tend to beg more than large-bodied species (Price and Ydenberg 1995, Christe et al. 1996, Leech and Leonard 1996, Kitaysky et al. 2001, Saino et al. 2001, Simon et al. 2005). As a result, they may also be fed more by their parents (Christe et al. 1996). Interestingly, nestlings in fumigated finch nests spent more than twice as much time begging as nestlings in fumigated mockingbird nests. Because begging by small birds is more energetically costly (per gram) than begging by large birds (Jurisevic et al. 1999), finch nestlings may experience an energetic ceiling beyond which they are simply incapable of additional begging. On the other hand, some small-bodied species of birds are known to increase begging in response to native parasitic flies (Christe et al. 1996). However, O'Connor et al. (2013) similarly found that P. downsi does not have a significant effect on another small-bodied species of Darwin's finch. This topic clearly requires further exploration. Similar to finches, older parasitized mockingbird nestlings had lower hemoglobin and mass compared to nonparasitized nestlings. Dudaniec et al. (2006) similarly found that hemoglobin decreases as P. downsi abundance increases in Darwin's finches. Because hemoglobin and mass are indicators of body condition, we cannot discount the possibility that the post-fledging survival of parasitized mockingbirds was less, compared to nonparasitized mockingbirds. For example, Streby et al. (2009) found that, despite similar fledging success, parasitized ovenbirds (Seiurus aurocapilla) had lower post-fledging survival than nonparasitized fledglings. Alternatively, fledglings from parasitized nests may recover body mass and hemoglobin once they have left the nest, especially given that mockingbird parents continue to feed fledglings for at least a month after they leave the nest (S.A.K. personal observation). A future study could track postfledgling survival of parasitized and nonparasitized mockingbird and finch nestlings to determine if P. downsi has a delayed effect on survival. Our study is one of the first to show differential effects of an introduced parasite on different host species under natural conditions, including evidence for possible tolerance mechanisms. Only recently has the idea of animal host tolerance to parasitism become widely recognized as an important defense strategy (Read et al. 2008, Råberg et al. 2009, Baucom and de Roode 2011, Medzhitov et al. 2012, Sorci 2013). Tolerant hosts may be important ecological mediators of the effects of parasites on vulnerable hosts. Tolerant hosts may serve as parasite reservoirs, amplifying the effects of parasites on nontolerant hosts. Identifying reservoir hosts can have important conservation implications if the vulnerable host population is declining, or if the reservoir host population is increasing. However, rigorous studies of reservoir hosts are difficult because they ideally would require experimental manipulations at the population level. For example, suspected reservoir hosts could be 264 April 2016 HOST TOLERANCE OF INTRODUCED PARASITES removed from the community, and the consequences of removal assessed for more vulnerable host species (Haydon et al. 2002, Laurenson et al. 2003). This approach is typically not feasible, particularly if the reservoir host is a protected species. In the mockingbirdfinch-fly system, a future study could control or eliminate parasites from mockingbird nests at some sites, then compare the population dynamics of finches (and flies) across all sites. ACKNOWLEDGMENTS We thank Daniela Vargas, Michael Skinner, Oliver Tisalema, and Joost Raeymaekers for assistance in the field, and Christopher Guglielmo for advice on the glucose assays. We thank the Galápagos National Park and Charles Darwin Foundation for logistical and permit support in the Galápagos. We thank Fred Adler, Franz Goller, Wayne Potts, Denise Dearing, Luis Ortiz Catedral, Francesca Cunningham, Paquita Hoeck, and Sarah Bush for the loan of equipment and/or helpful discussion. We thank the Rohr lab for comments on the manuscript, and especially Jason Rohr, David Civitello, and Betsy Roznik for statistical advice. We also thank Kathryn Huyvaert and two anonymous reviewers for helpful comments on the manuscript. The work was supported by NSF grant DEB-0816877 to DHC, and a University of Utah Global Change and Sustainability research grant and a Frank Chapman student research grant to SAK. SAK was also supported by a University of Utah Graduate Research Fellowship. AWB and SMM were supported by NSF Graduate Research Fellowships. EA was supported by grant BFU2011-25957 from MINECO, Spain. LITERATURE CITED Anderson, R. M., and R. M. May. 1991. 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Galápagos birds and diseases: invasive pathogens as threats for island species. Ecology and Society 9: online. http:// www.ecologyandsociety.org/vol9/iss1. SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article at http://onlinelibrary.wiley.com/ doi/10.1890/15-0119.1/suppinfo APPENDIX C DARWIN'S FINCHES COMBAT INTRODUCED NEST PARASITES WITH FUMIGATED COTTON Reprinted with permission from: Knutie, S. A., S. M. McNew, A. W. Bartlow, D. A. Vargas and D. H. Clayton. Darwin's finches combat introduced nest parasites with fumigated cotton. 2014 Current Biology 24(9): R1-R2. 267 Magazine R1 Correspondence Darwin's finches combat introduced nest parasites with fumigated cotton Sarah A. Knutie1,*, Sabrina M. McNew1, Andrew W. Bartlow1, Daniela A. Vargas2 and Dale H. Clayton1 Introduced parasites are a threat to biodiversity when naïve hosts lack effective defenses against such parasites [1]. Several parasites have recently colonized the Galápagos Islands, threatening native bird populations [2]. For example, the introduced parasitic nest fly Philornis downsi (Diptera: Muscidae) has been implicated in the decline of endangered species of Darwin's finches, such as the mangrove finch (Camarhynchus heliobates) [3]. Here, we show that Darwin's finches can be encouraged to ‘self-fumigate' nests with cotton fibers that have been treated with permethrin. Nests with permethrin-treated cotton had significantly fewer P. downsi than control nests, and nests containing at least one gram of cotton were virtually parasite-free. Nests directly fumigated with permethrin had fewer parasites and fledged more offspring than nests treated with water. Adult P. downsi flies, which are not parasitic, lay their eggs in the nests of Darwin's finches and other land birds in the Galápagos. Once the eggs hatch, the fly larvae feed on the blood of nestlings and adult females when they sit on the nest. Several previous studies have shown that P. downsi reduces the reproductive success of Darwin's finches [4]. In some years, 100% of nests at a given location can fail due to P. downsi [4-6]. It is therefore critical that control measures be developed to help reduce the effect of P. downsi on endangered Darwin's finches and other birds [3,7]. Our study was conducted January- April, 2013 at the El Garrapatero field site on Santa Cruz island [4,5]. The study was prompted by observations of several species of Darwin's finches incorporating cotton fibers from laundry lines into their nests (Figure 1A). To determine whether finches can be encouraged to self-fumigate their nests, we placed 30 cotton dispensers (Figure 1B) at 40-meter intervals along two transects through our study site (Supplemental information). Preliminary trials showed that finches transport cotton up to 20 meters (Supplemental information). We used two types of (interspersed) dispensers: experimental dispensers, which contained cotton treated with a 1% permethrin solution, and control dispensers, which contained cotton treated with water. Processed and unprocessed cotton were used to distinguish between the treatments. The two types of cotton were similar in appearance, but could be distinguished upon close inspection. A coin toss determined which treatment was assigned to which cotton type: processed cotton was used for the experimental treatment and unprocessed cotton for the control treatment. A preliminary experiment showed that finches do not discriminate on the basis of cotton type or fumigant (Figure 1C; Supplemental information). Over the course of the study, we searched once a week for active nests within 20 meters of each dispenser. When a nest was found, it was checked with a camera on a long pole to confirm breeding activity. After the birds finished breeding, the nests were collected and dissected to quantify the number of P. downsi in each nest. Cotton and natural nest materials were separated and weighed. We located 26 active Darwin's finch nests, 22 (85%) of which contained cotton (Figure 1D). None of the nests contained more than one type of cotton. Thirteen nests had experimental (permethrin) cotton and nine nests had control (water) cotton. Nests were constructed by four species of Darwin's finches: Geospiza fortis, G. fuliginosa, Camarhynchus parvulus, and Platyspiza crassirostris. Nests with experimental cotton had a mean (± SE) of 14.69 ± 9.54 parasites; control nests had a mean of 29.89 ± 7.69 parasites (Mann-Whitney test: U = 31.00, P = 0.03). The effect of the experimental cotton was dosedependent. Of the eight nests that contained at least one gram of experimental cotton, seven had no parasites and the eighth had only four parasites (Figure 1E). There was no relationship between cotton and parasite load among control nests (Figure 1E). Monitoring reproductive success requires climbing to nests and banding nestlings, which could interfere with self-fumigation behavior. We therefore quantified the effect of fumigation on host reproductive success using another 37 Darwin's finch nests adjacent to the self-fumigation transects. We sprayed experimental nests with a 1% permethrin solution and control nests with water. Nestlings were banded with color bands, enabling us to confirm fledging success by identifying individual birds after they left the nest [4,5]. Once all of the nestlings in a nest had fledged or died, the nest was collected and dissected to quantify the number of parasites. The twenty experimental nests sprayed with permethrin had no parasites, while the 17 control nests sprayed with water had a mean of 17.00 ± 3.89 parasites (Mann-Whitney test, U = 20.00, P < 0.0001). Nineteen of the twenty experimental nests (95%) fledged at least one offspring, while only 11 of the 17 control nests (65%) fledged any offspring (Fisher's Exact, P = 0.03). Overall, 50 of 60 nestlings (83%) fledged from experimental nests, compared to just 29 of 54 nestlings (54%) from control nests (Figure 1F). Our study shows that Darwin's finches can control P. downsi with permethrin-treated cotton, and that fumigation increases fledging success. There are currently no other effective methods for controlling P. downsi. Self-fumigation may thus be a viable approach for combatting P. downsi in the nests of Darwin's finches. The mangrove finch is the most critically endangered species of Darwin's finch, with a population of less than 100 individuals restricted to a home range of less than 1km2 on Isabela Island [3]. Sixty cotton dispensers could treat this entire population. Self-fumigation may be a particularly efficient approach because mangrove finches often build their nests high in mangrove trees, where they are relatively inaccessible [3]. Our study is the first to demonstrate the effectiveness of self-fumigation against parasites. This approach has been tried previously where mice were encouraged to incorporate fumigated 268 Current Biology Vol 24 No 9 R2 Supplemental Information Supplemental Information including experimental procedures and one figure can be found with this article online at http://dx.doi. org/10.1016/j.cub.2014.03.058. Acknowledgments We thank the Galápagos National Park, Charles Darwin Research Station, Fred Adler, Elena Arriero, Emily DiBlasi, Jordan Herman, Michael Thompson and Sarah Windes for assistance. We thank Charles Brown, Sarah Bush, Franz Goller, Jennifer Koop, Cagan Sekercioglu, and two anonymous reviewers for comments on the manuscript. The work was supported by NSF grant DEB-0816877 to D.H.C. and a University of Utah GCSC grant and RocketHub Crowdfunding to S.A.K. We are grateful to U.S. Cotton™ for donating supplies. References Figure 1. Incorporation of permethrin-treated cotton into nests by Darwin's finches. (A) Female medium ground finch (Geospiza fortis) removing fibers from a cotton laundry line at the Charles Darwin Research Station, Galápagos. (B) Cotton dispenser at the field site; cotton has been removed from the lower half by finches. (C) Small ground finch (G. fuliginosa) removing cotton from a dispenser in a preliminary experiment. (D) Finch nest containing about one gram of cotton. (E) Parasite abundance was negatively correlated with the mass of experimental cotton (Spearman rank correlation: rs = -0.62, P = 0.03), but not with the mass of control cotton (rs = 0.22, P = 0.58). (F) Experimental nests treated with permethrin fledged more offspring than control nests treated with water (Fisher's Exact test: P = 0.001). Orange bars are the total number of nestlings monitored; green bars are the total number of nestlings that fledged. cotton into their nests to kill ticks that vector Lyme Disease. However, the effectiveness of the method is not clear [8]. Self-fumigation might also be useful for controlling the fleas that vector plague, which can contribute to the local extinction of black-tailed prairie dogs (Cynomys ludovicianus) [9]. Because prairie dogs incorporate plant fibers into their burrows, it might be possible to encourage them to use fumigated materials. Self-fumigation also has potential for the control of parasites in other threatened and endangered bird species. 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