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Temporal variation in the traits of individuals and the extrinsic environment that influence nest success… Crombie, Merle Dora 2016

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TEMPORAL VARIATION IN THE TRAITS OF INDIVIDUALS AND THE EXTRINSIC ENVIRONMENT THAT INFLUENCE NEST SUCCESS IN AN ISLAND SONGBIRD by Merle Dora Crombie B.Sc., The University of British Columbia, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  September 2016 © Merle Dora Crombie, 2016  ii Abstract Nest success is a key factor affecting the dynamics and life–history evolution of avian populations. While multiple factors affect nest success, their relative influence remains unclear, partly due to the short time periods over which many studies take place relative to the scales of temporal variation in the environment, and the traits of birds that make up populations. I examined the effects of two intrinsic (female age, inbreeding coefficient), two abiotic (rainfall, temperature), and three biotic (breeding densities, cowbird parasitism rates, and brood parasitism) factors potentially affecting nest success (≥ 1 fledged young) in an insular song sparrow (Melospiza melodia) population over 39 years. I also compared the influence of these factors in three 13–year intervals representing the early, mid and late periods of the study to estimate temporal influence. Over 39 years, song sparrow nesting success first increased (1 – 3 years), and then decreased with female age (3+ years), and declined in relation to the degree of inbreeding in females, increased rainfall during the nest period, and by increased breeding densities. Nests that were parasitized by brown–headed cowbirds, or that experienced increased risk of cowbird predation, tended to fail more often. Parallel analyses of nest success in the early, mid and late periods of the study showed that only female age and breeding densities explained success in all periods, whereas the effects of inbreeding, cowbirds, and rainfall were episodic through time. This discrepancy was due to temporal variation in abiotic or biotic conditions that affected which factors were most influential of success. Many studies of nest success in passerine birds are limited in duration and the number of variables that can be considered due to limits on the amount or quality of data, preventing the comparison of many biotic and abiotic factors reported to affect success in the  iii literature. I show that over 39 years, the intrinsic effects of inbreeding, abiotic effects of climate and biotic effects of brood parasites on nest success were each influential but varied through time, indicating that any ranking of their relative influence on demography will also vary temporally.     iv Preface My thesis used 39 years of data collected by dozens of field teams on Mandarte Island, BC, from 1975 – 2014. I collected data in 2010, 2014, and 2015. Drs. Peter Arcese, Richard Schuster, Valerie Lemay, and Ryan Germain advised my statistical analysis. My supervisor Dr. Peter Arcese provided great ideas, advice, and helpful comments on my thesis. My committee members, Drs. Kathy Martin and Patrick Kelley, and my friend, Dr. Elizabeth Gow, also provided useful feedback and comments on my thesis.  Chapter 2 will be submitted as a paper co–authored by Drs. Peter Arcese and Richard Schuster. Dr. Peter Arcese provided substantial guidance in the development of this chapter as well as statistical advice and edits. Dr. Richard Schuster provided useful programming code (R) to increase the efficiency of my analyses, and to error–check the dataset. I conducted all analyses on my own with the guidance of co–authors, and wrote the original drafts of all sections, which were subsequently improved upon by comments and edits by Dr. Peter Arcese.  The UBC Animal Care Committee (A07–0309) approved the animal–related protocols detailed in this thesis. A Canadian bird–banding permit was obtained from Environment Canada (Master Bander Permit No. 10596).    v Table of Contents Abstract .............................................................................................................................. ii Preface ............................................................................................................................... iv Table of Contents .............................................................................................................. v List of Tables ................................................................................................................... vii List of Figures ................................................................................................................. viii Acknowledgements .......................................................................................................... ix Dedication ......................................................................................................................... xi Chapter 1: General Introduction .................................................................................... 1 1.1 Nest success as a demographic parameter ..................................................................... 1 1.2 Factors that influence nest success ................................................................................. 2 1.3 Study system: Mandarte Island song sparrows ............................................................ 5 1.4 Thesis overview ................................................................................................................ 5 Chapter 2: Temporal Variation in the Traits of Individuals and the Extrinsic Environment that Influence Nest Success in an Island Songbird ................................ 7 2.1 Introduction...................................................................................................................... 7 2.2 Hypotheses and predictions ............................................................................................ 8 2.3 Methods .......................................................................................................................... 11 2.3.1 Study system ................................................................................................................ 11 2.3.2 Factor definitions ......................................................................................................... 12 2.3.3 Temporal analysis ........................................................................................................ 15 2.3.4 Statistical analysis ........................................................................................................ 16 2.4 Results ................................................................................................................................ 17  vi 2.5 Discussion ........................................................................................................................... 19 Chapter 3: General Conclusion ..................................................................................... 27 3.1 Implications ....................................................................................................................... 27 3.2 Main findings ..................................................................................................................... 28 3.3 Strengths and limitations .................................................................................................. 29 3.4 Future directions ............................................................................................................... 30 References ........................................................................................................................ 39 Appendices ....................................................................................................................... 61 Appendix A: Proportion of Known Female Ages ................................................................. 61 Appendix C: Proportion of Known Inbreeding Coefficients .............................................. 63 Appendix D: Independent and Averaged Model Outputs using Non–Imputed Data ....... 64 Appendix E: Plot of Model Coefficient’s for the Entire, Early, Mid and Late Study Periods Using Non–Imputed Data ......................................................................................... 65 Appendix F: Cowbird Parasitism Throughout the Breeding Season ................................. 66 Appendix G: Parameter Variation in the Early, Mid and Late Study Periods ................. 67 Appendix H: Distribution of The Number of Young Fledged From 2900 Song Sparrow Nests Over 39 Years ................................................................................................................ 68 Appendix I: Averaged Model Coefficients for the Early, Mid, and Late Study Periods .. 69     vii List of Tables Table 1 Factors that influence avian nest success and associated predictions.………….41  Table 2 Variation in predictors of nest success over 39 years…..………………………42  Table 3 Individual vs. averaged models to predict nest success…………………….…..43   viii List of Figures Figure 1 Annual variation of the intrinsic, abiotic, and biotic factors tested in relation to variation in nest success on Mandarte Island from 1975 – 2014.………………………..44  Figure 2 Probability of nest success in relation to female song sparrow age…………...46 Figure 3 Coefficients for averaged models explaining nest success over the entire, early, mid, and late study periods………………………..……………………………………..47   ix Acknowledgements I owe the deepest gratitude to my supervisor, Dr. Peter Arcese, for believing I could pursue research when I didn’t believe this myself. I’m so fortunate he took a chance on me. I also thank Peter for his willingness to shed perspective and advice, as well as for his generosity with sushi.  Past and present members of the Arcese lab provided invaluable resources such as R–code, statistical advice, sharing papers, editing my work, and good old quality time. I owe Ryan Germain a million thanks for holding my hand through my first manuscript. I thank Richard Schuster for his unwavering willingness to help in all R– and stats–related problems. I thank Kate Johnson for her encouragement and insight, Nina Morrell for digitizing 3000+ nest cards (!!), and Jessica Krippel for her laughter. Other faculty affiliates helped me too. Notably, Danielle Courcelles, and Dr. Trzcinski. Thank you! I wouldn’t have survived the past 2+ years without those that brought me painful bouts of laughter, countless pep talks, and a million reasons to escape the ordinary: Kelsey Ngai, Bonnie McGrew, Lydia Hol, Kimia Abhar, Veronica Augustin, and Caitlin Sinclair. I also owe Warren Scheske a shout–out for lending me his iMac, and my parents for their faithful support in every path I choose to follow; I’m incredibly lucky. I also want to acknowledge my funders. My work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Werner and Hildegard Hesse, and by grants from UBC’s Faculty of Graduate Studies and Department of Forest and Conservation Sciences. Dr. Peter Arcese provided funding from his NSERC, and helped in my successful application for an NSERC CGS–M (2014).  x Last, I want to thank the Mandarte song sparrows for igniting my interest in animal behaviour, ecology, and birds in general. Their vivacious nature and individual personalities made this work so much fun.   xi Dedication To my former self, who didn’t believe this was possible. 1 Chapter 1: General Introduction 1.1 Nest success as a demographic parameter Identifying the factors that influence the survival of nestling birds is fundamental to understanding the dynamics and evolution of bird populations (Shaffer 2004, Martin 2015). Nest success can affect individual fitness, and is often used to predict population growth and inform management (Clark and Martin 2007, Jones and Geupel 2007). Numerous studies have investigated the drivers of nest success, including (1) the intrinsic qualities of the parental bird(s), (2) stage of the nest cycle, and (3) abiotic and biotic factors that directly or indirectly affect the nest contents or parental birds. However, very few studies are sufficiently detailed, or span a sufficiently wide range of environmental conditions to simultaneously consider more than a few of the many well–supported relationships between nest success and the classes of factors noted above.  Variation in nest success can dramatically affect reproductive success in birds (Rolland et al. 2011) and may therefore select for traits that maximize success subject to potential trade–offs between reproductive effort and survival (Ricklefs 1969, Götmark et al. 1995, Sæther and Bakke 2000, Öst and Steele 2010, Seltmann et al. 2014). However, these traits, or the factors that describe them, are diverse across systems, and often indirectly influence the proximate causes of nest failure. For example, older female song sparrows that had prior experience with brood parasites were more likely to suffer cowbird nest depredation due to their ‘enemy recognition’ behaviour, that served to ‘tip–off’ cowbirds to an active nest (Smith et al. 1984). In contrast, nest success increased  2 with breeding experience in eiders (Somateria mollissima) and Daito white–eyes (Zosterops japonicas daitoensis) that selected safer nest sites less prone to predation (Öst and Steele 2010, Horie and Takagi 2012). Additionally, inbred female song sparrows that experienced high rainfall during nesting abandoned their nests more often than outbred females (Marr et al. 2006), and red–winged blackbirds that selected nest sites in a thorny invasive shrub (Himalayan blackberry; Rubus armeniacus) experienced greater nest success (Cook and Toft 2005). These are just a sample of factors shown to influence the proximate causes of nest failure. Quantifying variation in the influence of multiple drivers of nest success simultaneously should help reveal how temporal variation in the environment and structure of populations could affect the strength of selection on nest success (Ricklefs 1969). This would be best achieved by using large data sets distributed over a wide range of environmental conditions and parental phenotypes (Clutton–Brock and Sheldon 2010). 1.2 Factors that influence nest success Nest predation is widely considered to be the main cause of nest failure in birds (Ricklefs 1969, Martin 1995b), but its influence varies among populations (Wilson and Arcese 2006, Nagy and Holmes 2012), years (Arcese et al. 1992, Forstmeier and Weiss 2004, Schmidt et al. 2005, Mahon and Martin 2006) and within seasons (Arcese et al. 1996). The diversity and abundance of nest predators can each influence nest success, including species introduced or facilitated by humans (Holdaway 1999, Martin and Joron 2003) or via land–use change (Rodewald and Yahner 2001, Evans 2004). Within seasons, nest predation rates may vary as a consequence of the foraging decisions of predators, seasonal changes in nest or predator abundance (Johnson et al. 1989), or nest stage (e.g.,  3 Burhans et al. 2002; Collister & Wilson 2007). Accordingly, much evidence suggests that natural selection has favoured adaptations that reduce the likelihood of predation, including by selecting nesting habitat that reduces detection or accessibility to predators (Ghalambor and Martin 1999, Öst and Steele 2010, Horie and Takagi 2012), by attempting to thwart predators (Montgomerie and Weatherhead 1988; Bize et al. 2012; Krams et al. 2014), or by adjusting breeding date to maximize success (Hatchwell 1990).  Like predation, inclement weather can also cause total nest failure (Martin 1992, Newton 1998), may act throughout the nesting cycle, and operate via direct or indirect routes (Ricklefs 1969). Inclement weather may cause parents to abandon nests due to weather–induced stress (Schroeder 1972, Marr et al. 2006) or an inability to deliver sufficient food to offspring (Wingfield et al. 1983). Weather–related abandonment has also been linked to nest stage (Wingfield 1988) and inbreeding level in nesting females (Marr et al. 2006). Inclement weather can also affect nests directly when rain or wind dislodge or flood nests (McClure 1942, Best and Stauffer 1980, Wingfield 1985) or extreme temperatures cause egg or nestling death (Webb 1987, Cunningham et al. 2013). Adaptations to ameliorate the effects of inclement weather on nest failure include the construction of sturdy, insulated or well–placed nests able to withstand dislodgement (Crook 1963, Rohwer et al. 2015), and regulate nest temperature (Finch 1983, Kern et al. 1993, Hilton et al. 2004, Eggers et al. 2006). Additionally, birds may also adjust their incubation rhythms to cope with inclement weather and temperatures (Macdonald et al. 2014).  Breeding density can also influence nest success in birds by several mechanisms. In territorial birds, high breeding density can reduce paternal care to nestlings as a result  4 of elevated testosterone in breeding males that frequently engage in conspecific interactions such as territory disputes and mate guarding (Ball and Wingfield 1987, Wingfield et al. 1990). Tompa (1971) attributed a ~10% reduction in hatching rate in song sparrows to increases in the frequency of territorial encounters at high breeding densities. In pied flycatchers (Ficedula hypoleuca) and house sparrows (Passer domesticus), experimentally elevating testosterone levels in adults reduced both offspring provisioning rates and breeding success (Silverin 1980, Hegner and Wingfield 1987). High breeding densities can also reduce nest success by limiting food availability (Arcese and Smith 1988).  It is also possible that breeding density and the traits of individual birds co–vary temporally or spatially. For instance, territoriality at high densities can sort individuals among habitats by phenotype (Duckworth 2006, Duckworth et al. 2015) with consequences for reproductive success (Darolová et al. 2014). Males may also trade–off the frequency and intensity of territorial interactions with neighbours with their ability to provide parental care (Hyman and Hughes 2006), thereby causing females to compensate by increasing offspring provisioning rate (Silverin 1980, Mutzel et al. 2013). Breeding densities can also indirectly influence nest success by being functionally related to increased rates of nest predation. On Mandarte Island, the occurrence of brown–headed cowbirds correlates with high song sparrow populations, and their presence results in decreased fledgling numbers, and increased rates of nest failure (Smith and Arcese 1994, Arcese et al. 1996, Smith et al. 2006). Other studies have also shown increased nest failure via predation at higher host densities (McGeen 1972, Roos 2002, Sofaer et al. 2014).  5 Given the potential for multiple factors to act on individual fitness via variation in nest success, our ability to predict which factors are most influential of success requires a better empirical understanding of how biotic and abiotic drivers of success vary through time. Long–term studies of individual birds that include multiple cohorts observed over varied environmental conditions therefore offers an unusual opportunity to identify the key drivers of nest success and test whether they vary temporally or with changing population structure.  1.3 Study system: Mandarte Island song sparrows I studied patterns of nest success on Mandarte Island, BC, in relation to seven variables describing the intrinsic traits of nesting birds, and the abiotic and biotic conditions experienced during the nest period. The Mandarte Island system was ideal for my work because it hosts a resident population of song sparrows that has been intensively studied since 1975, providing detailed data on reproductive rate, population age and genetic structure, breeding density, and cowbird parasitism rates with approximately 3000 nesting records observed over 39 years. My work was designed to advance understanding about the key components of nest success by using a dataset with the power to simultaneously test a suite of variables for their relative influence on nest success and subsequently estimate temporal variance in these predictors. This study offers a unique opportunity to investigate long–term patterns of nest success and has the potential to clarify the mechanisms driving the dynamics of bird populations in general. 1.4 Thesis overview I used 39 years of nest monitoring data from an island song sparrow (Melospiza melodia) population to test multiple factors to explain variation in nest success, related to the (1)  6 intrinsic traits of nesting females, and their (2) abiotic and (3) biotic breeding environment. I also tested whether factors influential of nest success over the entire study period were also identified over shorter time periods to understand temporal variation in factors affecting success through time. Overall, my goal in this thesis was to identify and rank the drivers that explain variation in nest success. The intrinsic factors I tested in relation to nest success were the age and degree of inbreeding (f) in female song sparrows, given that experience and/or senescence can affect female reproductive performance (Sæther 1990, Forslund and Pärt 1995) and that inbreeding typically reduces individual fitness (Keller 1998). I expected nest success to first increase in 1+ year old females, and then decrease after a prime breeding age of 3 – 4 years, as this pattern has been found with other measures of reproductive success in this and other bird populations (Robertson and Rendell 2001, Smith et al. 2006a). I also expected nest success to decline with increased inbreeding (f) in females. Abiotic factors predicted to reduce nest success included increased rainfall and colder temperatures during the nesting period, given that inclement weather can reduce nest success by challenging the energy budgets of nesting females and their nestlings (Wingfield et al. 1983, Marr et al. 2006). Biotic factors predicted to reduce nest success were increases in breeding densities and parasitism rates by brown–headed cowbirds (Molothrus ater) within the vicinity of song sparrow nests, as well as whether a nest was parasitized, to address hypotheses linked to parental care (Wingfield et al. 1990), resource competition (Arcese and Smith 1988), nest depredation (Arcese et al. 1992) and increased brood costs (Hauber 2003). In Chapter 2, I develop the ideas above further, describe the analytical approach developed, and discuss my key results in the context of current literature.   7 Chapter 2: Temporal Variation in the Traits of Individuals and the Extrinsic Environment that Influence Nest Success in an Island Songbird 2.1 Introduction Nest success is a key factor affecting the dynamics, life history and evolution of avian populations (Martin 2015), but the most influential factors can vary due to variation among individuals and the environment (Arcese 2003) and temporal variation in the influence of those factors individually or in combination (Lebreton et al. 1992, Dinsmore et al. 2002, Clutton–Brock and Sheldon 2010). This variation creates uncertainty in those variables most influential of success and arises in part due to the short time periods over which most studies take place relative to the scales of temporal change in the abiotic and biotic environment (Franklin 1989, Norris et al. 2007, Blight et al. 2015). However, it also arises due to systematic variation in the traits of individual birds that make up populations (Clutton–Brock and Sheldon 2010, Martínez–Padilla et al. 2014). Comprehensive, long–term studies have the potential to help us understand the interplay of factors potentially affecting nest success and, subsequently, to determine which factors are most likely to predict management outcomes and evolutionary response (Arcese 2003, Benton et al. 2006, Lovett et al. 2007, Clutton–Brock and Sheldon 2010). In this work, I first estimate the influence of two intrinsic (female age and inbreeding coefficient), two abiotic (rainfall, temperature), and three biotic (breeding densities, parasitism rates, brood parasitism) factors potentially affecting nest success (≥ 1 fledgling) in an insular song sparrow (Melospiza melodia) population subject to marked  8 environmental variation over 39 years. To do so, I use an information theoretic approach and model all combinations of intrinsic, abiotic, and/or biotic factors to identify the best–supported models. I then compare effect sizes of factors included in those models to estimate their relative influence on nest success and test hypotheses about how each factor above is thought to affect success (Table 1). Secondly, I identify top models to explain success in 3 consecutive 13–year periods of the study represented by different environmental conditions to ask whether the effects identified in each period predict success over the 39 years of this study. 2.2 Hypotheses and predictions The long history of study on nest success in birds provides a rich suite of hypothesized effects that can be classified into the intrinsic, abiotic, and biotic factors examined here (Table 1). I now review this work briefly as background to the models explored.  Intrinsic Factors – The intrinsic traits of individuals arise via experience, development, or inheritance and are often used to define individual differences in ‘quality’, including those linked to fitness traits such as nest success (Wilson and Nussey 2010). A female’s age (years) and her inbreeding coefficient (f) are two intrinsic traits known to influence the reproductive performance of birds, including nest success. Reproductive performance varies in many bird species with age, with increases commonly ascribed to the acquisition of skill with age (Forslund and Pärt 1995, Decker et al. 2012, Duckworth et al. 2012, Horie and Takagi 2012), and declines in advanced age to senescence (e.g. Keller et al. 2008). On Mandarte, 2 – 3 year old females tend to have higher reproductive performance than 1 and 4+ year old females, as measured by increased clutch sizes, earlier laying dates, and higher fledgling numbers and recruitment  9 (Smith et al. 2006a). Inbred birds may suffer reduced reproductive performance and individual fitness (Chen 1993, Jiménez et al. 1994, Keller et al. 1994, Keller 1998), especially when faced with environmental stress (Kristensen et al. 2005, Marr et al. 2006). On Mandarte Island, inbred birds suffer reduced hatching success (Keller 1998) and are more likely to abandon nests during extended periods of rainy weather (Marr et al. 2006). Given the patterns above, I predicted that nests tended by 2 – 3 year old (hereafter ‘prime breeding age’) and less inbred birds would succeed more often. Abiotic Factors – Abiotic factors affecting populations often include weather, which can be highly unpredictable and exert strong effects (McDonald et al. 2004, Reichert et al. 2012). In birds, temperature and precipitation influence reproductive performance by affecting energetic costs (Wingfield et al. 1990, Reid et al. 2000, Macdonald et al. 2013). Heavy or extended rainfall during nesting can result in nest failure or reduced fledgling numbers due to reduced foraging opportunities by adults to feed themselves and nestlings, and/or disruptions to incubation (Wingfield 1983, Radford et al. 2001, Syroechkovsky et al. 2002, Macdonald et al. 2013, Öberg et al. 2015). Rain, cold temperatures, and high winds can also cause nest failure directly by stressing embryos, nestlings or adults or by dislodging nests (McClure 1942, Best and Stauffer 1980, Wingfield 1985, McDonald et al. 2004). Accordingly, I predicted that nest success would decline in nests that experienced high cumulative rainfall and low minimum temperatures during the nesting period. Biotic Factors – Biotic effects on reproductive performance in birds may act via the direct or indirect effects of predation, brood parasitism, and population density (Lack 1966, Ricklefs 1969, Martin 1992). Nest predation is a key cause of nest failure in birds  10 (Ricklefs 1969, Martin 1992, 1995b), and can affect nest success via multiple mechanisms. For example, parent birds may reduce feeding rates to nestlings when the perceived risk of predation is high (Martin 2011, Zanette et al. 2011), or nest success may decline as predator abundances increase (Schmidt and Ostfeld 2003, Borgmann et al. 2013). Brood parasites such as brown–headed cowbirds (Molothrus ater) can double as nest predators by removing host eggs or young to expedite re–nesting (Arcese et al. 1996, Elliott 1999, Hauber 2000), and reduce fledgling numbers by puncturing or removing host eggs around the time of parasitism (Sealy 1992, Smith and Arcese 1994). Brood parasites also indirectly influence nest success by increasing the energetic costs of brood rearing (Woodworth 1997, Hauber 2003, Hoover 2003, Ludlow et al. 2014). Accordingly, parasitized nests on Mandarte Island fledge 0.5 fewer young than non–parasitized nests (Smith and Arcese 1994, Arcese et al. 1996), and nest failure increases linearly with the number of cowbird eggs laid in the population (Arcese et al. 1996). Because parasitized nests are more costly to rear, and because parasitism intensity is correlated with nest failure, I predicted that parasitized nests, and those subject to higher cowbird predation risk (locally high rates of cowbird parasitism) would fail more often. Testing each of these predictions allows me to separately estimate increased brood costs from cowbird parasitism, and increased predation risk from the intensity of local cowbird parasitism. The effects of breeding density on the reproductive performance in birds is diverse, but includes effects on parental care, food availability, and the likelihood of predation and brood parasitism (Ball and Wingfield 1987, Smith and Arcese 1994, McKellar et al. 2014). Increased testosterone in male and female birds has been linked to reduced parental care (Ball and Wingfield 1987, Wingfield et al. 1990), and may arise from  11 increased conspecific aggression at high breeding densities (Veiga et al. 2002, Smith et al. 2005, Cantarero et al. 2015). The per capita availability of food also declines as density increases, potentially affecting a range of behaviours linked to reproductive performance (Lack 1966, Arcese and Smith 1988, Duncan Rastogi et al. 2006). Predators may also form search images or otherwise adjust their behaviour to search for nests more effectively or often at high prey or host densities (Arcese et al. 1992, Arcese and Marr 2006, McKellar et al. 2014). Accordingly, I predicted that nests initiated at higher breeding densities would fail more often. 2.3 Methods 2.3.1 Study system A resident song sparrow population on Mandarte Island, British Columbia, Canada (6 ha, 48° 38' N, 123° 17' W), has been monitored continuously from 1975 – 2014. Mandarte Island experiences a mild maritime climate averaging 12.5°C from March to August, and 7°C from September to February, and receives more than double the rainfall in fall–winter than spring–summer (means = 100 and 40 mm respectively; Victoria International Airport weather station; http://climate.weather.gc.ca). Song sparrows in this region are an approximately 24g, socially monogamous, open–cup nesting passerine wherein females incubate 1–5 eggs in 1 – 4 nests per year, with both social parents providing care for young (Arcese et al. 2002, Smith et al. 2006a). Territories were mapped annually in late April and visited every 2 – 5 days to record ownership and breeding performance. Most nests (95% of 3274) were discovered during incubation, and their locations mapped using aerial photographs (± 2m), and then  12 converted to UTM coordinates. Nestlings were colour–banded and monitored to independence from parental care (aged 24 – 30 days), immigrants were colour–banded soon after their arrival on the island, and all birds were sampled for DNA from 1987 onwards (detailed methods in Smith 2006). As a result, all song sparrows on the island were individually marked, and population size, age structure, and reproductive rate were precisely known. Most birds are included in a social pedigree initiated in 1975, but a genetic pedigree exists for all birds hatched after 1991. Precise coefficients of inbreeding (f) were available for >75% of all birds after 1993, based on the identity of all four social and/or genetic grandparents (Sardell et al. 2010, Reid et al. 2014). Prior to 1994, f was estimated for 0 – 88% of birds annually, derived mainly via the social pedigree, which reduces the precision of those estimates via extra–pair–paternity (~27% of young; Reid et al. 2014). 2.3.2 Factor definitions  I used seven factors to represent intrinsic, abiotic and biotic factors potentially affecting nest success (Tables 1 & 2). Intrinsic Factors included the age and inbreeding coefficient of nesting females, each of which can be interpreted as an intrinsic index of quality (Keller et al. 2008, Wilson and Nussey 2010). Adult age was unknown in 1975, but by 1979 was known for 97% of birds and for 2710 of 2900 nest records overall (Appendix A). Individual females bred for 1 – 9 years (median = 2; mean = 2.09; Table 2; Appendix B) and both age and age2 were included as fixed effects to model the quadratic relationship between female age and reproductive performance (see Hypotheses above; Nol and Smith 1987; Smith et al. 2006a).  13 Inbreeding coefficients, f, were estimated from the genetic pedigree for females that initiated 1081 nests (37% of 2900 nests), mainly later in the study period (see Appendix C). For all other females I imputed the mean value of f over the study (mean = 0.06, SE = 0.001; n = 1081; Table 2). Imputation allowed me to compare all factors potentially influencing success over all years of the study without excluding the earliest years, when populations were larger and fluctuations more extreme. I provide the consequences of imputation by comparing models with and without those data but report imputed model estimates here because those models provided similar parameter estimates to models based only on precisely–known individuals (see Appendices D and E). The Abiotic Factors I included were cumulative rainfall (mm) and minimum temperature (°C) during the nesting period, defined as a 31–day window from initiation of nest building to fledge date (e.g., 3 days to build a nest, 3 to lay, 13 to incubate, and 12 to feed young to fledging; Arcese et al. 2002). In the data used for this study, the earliest nest was initiated on February 23rd, and the latest nest was completed on August 18th. Weather data were recorded 11km west of Mandarte Island by Environment Canada (Victoria International Airport, http://climate.weather.gc.ca). Biotic Factors included breeding density and cowbird parasitism rates in the vicinity of individual nests, and brood parasitism of individual nests as determined by the presence of one or more cowbird eggs. Local breeding density was estimated at each nest as the number of nests initiated the same year by other breeding female(s) within a 50m radius of the focal nest. I estimated local parasitism similarly, as the fraction of those nests parasitized within a 50m radius. A 50m radius (7854m2) represents 2–8 territories depending on density (Smith et al. 2006b). The presence of a cowbird egg (brood  14 parasitism) was treated as a binary variable to separate the direct effect of parasitism from the potential effects of parasite activity (Smith and Arcese 1994). The number of cowbird eggs laid annually predicts nest failure rate on Mandarte Island but varies markedly (Arcese et al. 1996, Arcese and Marr 2006), but other nest predators on the island include Northwestern crows (Corvus caurinus), deer mice (Mus musculus), glaucous–winged gulls (Larus glaucescens), river otters (Lontra canadensis), red–winged blackbirds (Agelaius phoeniceus) and European starlings (Sturnus vulgaris). However, in the absence of cowbirds, annual nest depredation averages < 20% and is relatively constant (Arcese et al. 1996, Arcese and Marr 2006). I used the date of the first egg in each nest (hereafter ‘lay date’) as a covariate in models to account for variation in nest success throughout the breeding season (e.g. from cowbird parasitism rates; Appendix F). Lay date refers to the Julian day on which the first egg of each clutch was laid and was known via direct observation or backdating from nestling age. Lay date was unknown for 164 nests (6% of 2897 nests) because the nest was found after the young fledged, after it had failed, or was not found but known to exist based on female behaviour. I imputed missing lay dates with the mean value of known lay dates for nests initiated during the same part of the breeding season in the same year and explored the consequences of doing so as noted above for f. I used female identity (female ID) and year of study as random effects in models. Female ID has the potential to account for the repeatable differences of females in their ability to tend nests and rear young, and it helped to account statistically for uneven sampling across females. Using year as a random effect controlled for the influence of annual variation in models not accounted for by the factors defined above.  15 2.3.3 Temporal analysis I tested for temporal variation in the effects of factors potentially affecting nest success (see above; Table 1) by re–running models (see Statistical analysis below) using three consecutive 13–year subsets from the entire dataset (39 years). These subsets represented the early (1975 – 1988), mid (1989 – 2001) and late (2002 – 2014) study periods, and were chosen to equally divide the dataset. However, each subset reflected different environmental conditions on Mandarte associated with rates of nest failure that were largely characterized by differing breeding densities and associated cowbird occurrence (Arcese et al. 1996, Arcese and Marr 2006; Figure 1; Appendix G). The frequency of visitation and number of eggs laid by cowbirds on Mandarte is a function of population size (Arcese and Marr 2006), which has declined 0.6% per annum over the entire study (Johnson 2015). Thus, the early, mid and late study periods represented episodes of declining cowbird parasitism and population size. Cowbirds visited Mandarte 11, 7, and 1 of each 13–year period and parasitized 21%, 8% and 2% of total nests for the early, mid, and late study periods, respectively (Appendix G). Local nest densities also declined among periods, where there were an average of 3.52 (CV = 0.51), 0.81 (CV = 0.45), and 0.33 (CV = 0.40) nests within a 50m buffer of nests for each the early, mid and late study periods, respectively (Appendix G). Additionally, two population crashes occurred over the study whereby the population declined by ≥80% in both the early and mid study periods (1979–80 and 1988–89, respectively; Keller et al. 2006). Inbreeding levels rapidly increased following the 1988–89 crash (Keller et al. 2006).  16 2.3.4 Statistical analysis Analyses were performed in R 3.0.2 (R Development Core Team 2013). I tested whether intrinsic, abiotic and/or biotic factors influenced nest success using nest records from 39 years and 2900 nests, after removing 338 nests subject to experiments over the study period. I used generalized linear mixed models (R package lme4 v1.1–7; Bates et al. 2015) with a binomial error distribution to estimate the probability of nest success (success = 1; failure = 0).  I first developed models for each of the intrinsic, abiotic, and biotic factors in Table 1 to assess their individual effects on nest success (seven models). I then modeled all possible combinations of these factors (excluding interactions) to identify those that best explained nest success from a set of candidate models defined by AIC criteria (ΔAIC < 2 from the top model; Burnham and Anderson 2002). Candidate models were averaged and variables were considered influential if model coefficients (β) ± 95% confidence intervals (CI) did not overlap zero (hereafter the ‘averaged model’). Finally, to assess whether those variables found to comprise the averaged nest success model varied in their relative effects through time, I repeated the steps above for each of the early, mid and late study periods (see Temporal Analysis above). I then compared effect sizes among these periods to evaluate the consistency in which each factor influenced nest success over the entire study. All models described above included female ID and year as random effects, and lay date as a covariate. Most factors were uncorrelated (r < 0.5), with the exception of minimum temperature with lay date, and female age with age2 (r > 0.8). Continuous variables were standardized to mean = 0 and SD = 0.5 following Gelman (2008), allowing for the direct comparison of all model coefficients, including untransformed  17 binary predictors (e.g. brood parasitism; see Schielzeth 2010 and Grueber, et al. 2011 for applications of this scaling approach). Lastly, all models described above were also run with non–imputed values for f and lay date for comparison (see appendices D and E). 2.4 Results I analyzed 2900 nest attempts over 39 years; 64% of these fledged 1–5 young (4631 in total; Appendix H). Variation in all intrinsic, abiotic and biotic variables observed across years was substantial (Figure 1). Female age and f varied from 1 to 9 years (CV = 0.62) and 0 to 0.28 (CV = 0.91), respectively (Table 2). Cumulative rainfall and minimum temperature during nesting varied from 0 to 128 mm (CV = 0.59) and -3.2 to 10.2 °C (CV = 0.91), respectively (Table 2). Local density, local parasitism, and the annual proportion of nests parasitized varied from 0 to 46 nests (CV = 0.60), 0 to 12 nests (CV = 1.37), and 0 to 44% (CV = 2.78) across years, respectively (Table 2).  Five of the seven hypotheses tested in Table 1 were supported when intrinsic, abiotic and biotic factors were tested for their individual effects on nest success. Nest success increased with female age (95% CI: 0.11, 1.41), but declined with female age2 (CI: 1.48, -0.17), indicating a quadratic relationship between nest success and female age (Table 3). When these coefficients were back–transformed and plotted, a quadratic relationship was evident, whereby nest success increased slightly for females aged 1 to 3 years (0.64% to 0.69%; Figure 2), and gradually declined thereafter (0.68% to 0.38%, ages 4 – 8, respectively; Figure 2). Nests associated with inbred females (CI: -0.43, -0.03), and increased rainfall (CI: -0.60, -0.13), local breeding densities (CI: -0.83, -0.27), and local parasitism rates (CI: -0.90, -0.25) were all less likely to succeed, as predicted (Table 3). Minimum temperatures during nesting and brood parasitism did not individually  18 explain nest success over the study period (CI: -0.20, 0.69 and CI: -0.36, 0.21, respectively; Table 3). When all possible combinations of factors were modeled to predict nest success, all factors were included in the top model (AIC = 0). Notable differences in effects from individual models were that brood parasitism became a negative predictor of success (CI: -0.6, 0.01), whereas local parasitism became less influential of success (CI: -0.74, 0.03; Table 3). Overall, based on the averaged model coefficients, nests were less likely to succeed when initiated by inbred females (CI: -0.42, -0.03), subjected to high rainfall during the nesting period (CI: -0.57, -0.12), and in years with high breeding densities (CI: -0.84, -0.18), and if they were parasitized by brown–headed cowbirds (CI: -0.6, 0.01; Table 3). As above, nest success increased with female age (CI: 0.17, 1.46), but declined with female age2 (CI: 1.5, -0.2; Table 3), revealing a quadratic relationship when back–transformed coefficients were plotted against nest success (Figure 2). When the study was examined for temporal differences in the factors affecting nest success, no factor was identified as a consistently significant driver of nest success in each of the early (1975 – 1988), mid (1989 – 2001) and late (2002 – 2014) study periods (Figure 3; Appendix I). However, female age and local nest density had similar non–significant effects in all periods (Figure 3; Appendix I). From 1975 – 88, nest success tended to decrease at high local densities (CI: -0.80, 0.00; Figure 3; Appendix I). From 1989 – 2001, success declined as f  (CI: -0.99, -0.36), rainfall (CI: -0.83, -0.15), and brood parasitism (CI: -1.39, -0.15) increased. Local density (CI: -2.68, -0.34) was the only statistically significant predictor of success in 2002 – 14 (Figure 3; Appendix I).  19 2.5 Discussion  Intrinsic, abiotic, and biotic factors all influenced nest success in song sparrows, but the strength of those effects varied greatly over 39 years. This implies that our ability to identify the most influential factors affecting nest success may be complicated by temporal variation in the traits of individuals and the environment. I now discuss some of the potential mechanisms underlying temporal variation in the drivers of success. As predicted, nest success tended to decline as the influence of cowbirds increased, measured here as the risk of predation (i.e., local parasitism rates), and brood parasitism (Tables 1 & 3; Figure 3). Not surprisingly, these effects were not consistent over the 3 study periods assessed, as cowbirds were not always present on Mandarte (see Methods) and became infrequent visitors as the study progressed (Figure 1). The temporal variation in cowbird effects is perhaps an intuitive one, although important, because nest predation and parasitism are commonly ascribed as the dominant factors causing nest failure (Tewksbury et al. 2002), but I show here that these effects can be temporal in nature (Table 3; Figure 3). It is therefore possible that the weight we place on predation/parasitism effects on nest success in bird populations may be exaggerated because of the relatively short time periods considered in most studies (e.g. 2 – 5 years for most graduate studies). As datasets become longer with the push to increase study longevity (e.g. the National Science Foundation’s Long–Term Ecological Research program; NSF 2016), future work will allow for the investigation of variation in historical frequencies of predators/parasites and their effects on host bird populations in order to appropriately weight their effects through time.   20 Interestingly, the risk of cowbird predation, and the effects of parasitism did not exert the same effects on nest success through time, even though these effects would have been acting concurrently when cowbirds were present (see Table 3; Figure 3). For example, nest success tended to decline in relation to increased local parasitism in the early period, whereas brood parasitism appeared to have no effect on nest success in the same period (Table 3; Figure 3). The opposite finding was true for the mid–period, where nest success declined in relation to brood parasitism, but not with increased local parasitism (Table 3; Figure 3). These discrepancies may be due to the fact that parasitized nests in years when cowbirds are common are more likely to be successful, given that non–parasitized nests are likely to be depredated by cowbirds to facilitate re–nesting by the host species (Arcese et al. 1996, Hauber 2000). As the early period included the highest frequency of cowbird occurrence (11 of 13 years; see Methods and Appendix G), parasitized nests in this time period may have actually experienced greater success than nests in the mid period, where cowbirds were only present for 7 of 13 years, and thus the sample of nests included without cowbirds was much higher, revealing the effect of parasitism on nest success. As predicted, inbred females were more likely to experience nest failure than outbred females (Tables 1 & 3; Figure 3). This effect was modest over 39 years, but as with cowbirds, the magnitude of effects varied in time (Table 3; Figure 3). Variation in the effect of inbreeding on success could be due to sampling error, mean and distribution of inbred individuals in the population, or the fact that inbreeding depression is often expressed more so in the face of environmental stress (Coltman et al. 1999, Keller et al. 2002). Inbreeding had its strongest negative effect on nest success during the middle  21 period of this study (1989–2001), which coincided with a rapid increase in inbreeding following a population crash (1988–9; Figure 3; Table 3; Keller et al. 2006). In contrast, inbreeding appeared to have no effect on nest success in the early and late study periods (Figure 3). Thus, inbreeding depression may only be apparent in this population when it is highly inbred. Further work assessing interactions between inbreeding and other ecological factors would aid our understanding of how inbreeding depression affects reproductive success in birds, and may have particular relevance to small, isolated populations with limited gene flow (see Briskie and Mackintosh 2004). It has already been shown on Mandarte that inbred females are more likely than outbred females to abandon their nests during inclement weather (Marr et al. 2006), but further exploration could assess interactions with other abiotic and biotic variables hypothesized to increase stress while breeding, which appears to be the primary condition that discloses the effects of inbreeding depression (Coltman et al. 1999, Meagher et al. 2000, Kristensen et al. 2005). Nests that experienced more rainfall during the nesting period were also less successful than those experiencing drier conditions (Figure 3; Table 3), as predicted (Table 1). However it is unclear how rainfall affects success because it was not consistently linked to success in all study periods (Figure 3; Table 3; Appendix I). Relatively few studies have investigated the effect of rainfall on nest success (but see Collister and Wilson 2007, Öberg et al. 2015), perhaps because it is more often used as a index of food abundance or breeding conditions generally (Morrison and Bolger 2002, Rodríguez and Bustamante 2003, Chase et al. 2005, Skagen and Adams 2012, Sherry et al. 2015). However, it is clear that rainfall can be a stressor during breeding as it is often  22 reported as a proximate cause of failure (Ricklefs 1969, Frederick and Collopy 1989, Aguilar et al. 2000, McDonald et al. 2004, Ouyang et al. 2012), as well as one that exacerbates other stressors present during breeding (Sillett et al. 2004). For example, Ouyang et al. (2012) report that great tits (Parsus major) with high, stress–induced plasma corticosterone levels abandoned their nests more often during inclement weather. Sillett et al. (2004) also showed that reproductive success in black–throated blue warblers declined at high breeding density, but did so more in El Niño years characterized by high rainfall. As previously noted, rainfall on Mandarte Island increased nest failure more so in inbred females (Marr et al. 2006). I suggest that rainfall increased nest failure mainly in concert with other stress–inducing environmental factors, but occasionally acts as a proximate cause of failure when extreme conditions provoke abandonment.  Because both of these effects may vary temporally, they may explain the temporal effect of rainfall on nest success in this and other systems. In contrast to rainfall, minimum temperature during nesting was unrelated to nest success over the entire study and in three consecutive periods (Figure 3; Table 3). Although warmer temperatures may alleviate energetic costs in some species (Bryan and Bryant 1999, Pérez et al. 2008, Ardia et al. 2009), it is possible that conditions on Mandarte Island are sufficiently mild during breeding as to rarely challenge nesting females. Interestingly, Germain et al. (2015) showed that female song sparrows preferred cooler nest sites on average, perhaps because they prioritized features that reduced nest predation (reviewed in Lima 2009) or increased their proximity to food (reviewed in Refsnider and Janzen 2010).  23 As predicted, nest success declined as population density increased in song sparrows over 39 years (Table 1; Figure 3; Table 3). This relationship was relatively consistent in each of three, 13–year consecutive periods of the study (Figure 3; Table 3; Appendix I). Prior work on Mandarte Island suggests this relationship might have resulted via increased brown–headed cowbird visitation at high host densities (Smith and Arcese 1994). To test whether the decline in success with increasing song sparrow density was linked to the occurrence of cowbirds, I re–ran the full model (using the same coefficients as the averaged model; see Methods) on years with (n = 16) and without (n = 23) cowbirds. Because nest success declined significantly with increasing song sparrow density in years with cowbirds present (β = -0.46; CI: -0.77, -0.14), but not in years with cowbirds absent (β =  -0.40; CI: -0.91, 0.11), my results suggest that cowbirds influence the effect of song sparrow density on nest success. However, even in periods with few cowbirds (i.e., the late study period), a positive relationship between song sparrow density and nest failure remained evident (Figure 3; Table 3). Most studies attribute increased nest failure at high densities to functionally related increases in predator abundance or activity (e.g. McGeen 1972, Arcese et al. 1996). That was likely not the case for Mandarte (in the absence of cowbirds) as nest failure was low overall (~20% per annum; see Methods), with reduced variation as the study progressed (Figure 1). As I did not test for interactions in this study, I cannot conclude what other mechanisms might have contributed to this result. However, it is apparent that high densities can affect reproductive performance in more ways than just by increasing predation. For example, black–throated blue warblers (Dendroica caerulescens) with experimentally reduced neighbour densities experienced increased reproductive success, even though nest  24 predation rates did not differ from birds breeding under normal densities (Sillett et al. 2004). Birds breeding at high densities may also have higher circulating testosterone (Cantarero et al. 2015), which can reduce the likelihood of successful breeding (Gerlach and Ketterson 2013). As well, prior work on Mandarte Island suggested that food limitation at high densities reduced nest defense, thus increasing the likelihood of parasitism when cowbirds were present (Arcese and Smith 1988). Overall, nest success declines in relation to increased breeding densities, even when functional increases in predator abundances aren’t the primary mechanism at work. As predicted, the probability of nest success increased and then decreased with increasing age in female song sparrows (Table 1). Specifically, the likelihood of nest success increased from 64 – 69% in 1 – 3 year old female song sparrows, and declined thereafter to a likelihood of 38% in 8 year olds (Figure 2). This pattern was similar in all three study periods (Figure 3), although prediction intervals around estimates were larger with only 13 years included (Figure 3; Appendix I). My results are consistent with prior work in this system (Smith et al. 2006a), and with many others who have reported age–dependent reproductive success in birds (Sæther 1990, Forslund and Pärt 1995, Martin 1995a, Keller et al. 2008). While the exact mechanisms behind this relationship are not clear, most generally attribute improved success as a result of age–related abilities to acquire food and avoid predation, and reduced success to senescence (e.g. Desrochers and Magrath 1993, Robertson and Rendell 2001, Smith et al. 2006a). However, while female age appears to be a persistent contributor of nest success (Figure 3) in this and other populations (e.g. Robertson and Rendell 2001), it’s unlikely that age–dependent nest success plays a large role in the demography of free–living populations where  25 mortality rates are often high (Nussey et al. 2008) and few individuals reach senescent age. For example, Brommer et al. (2007) showed that only 7% of 4992 marked collared flycatchers (Ficedula albicollis) over 25 years reached senescent age. Thus, while I’ve shown an age–dependent relationship with nest success in song sparrows, consistent with the prevalence of age–dependent reproductive success in birds reported elsewhere, the overall contribution of this relationship to demography may be small. Overall, I showed that a variety of factors influenced song sparrow nest success over the long–term, and that some were more reliable predictors of success than others when assessed across shorter time periods. This information has the potential to inform management decisions when determining what factors may have the strongest effects on important demographic parameters in the short vs. long–term, and lends some insight as to how the effects of some factors can be more or less pronounced under differing environmental scenarios. In this study, for example, age–dependent nest success, as well as the negative effects of higher breeding densities exhibited consistent relationships with nest success among all study periods considered, implying that these factors may always underlie how populations respond to varying environmental conditions. In contrast, the effects of inbreeding, cowbirds, and rainfall were episodic through time, implying that while these factors may strongly influence nest success, they may only do so during punctuated events in time. Further work on the potential interactions between factors may clarify further underlying mechanisms affecting nest success, and provide a better understanding of how to manage populations under differing environmental scenarios. While long–term studies can provide the data to explore complex interactions of the type suggested above, experimental studies are likely to be needed to test for the action of  26 specific factors as synergistic agents of nest failure (e.g. Arcese et al. 1992, Sillet et al. 2004).     27 Chapter 3: General Conclusion 3.1 Implications The planet is experiencing unprecedented change. The climate is warming, seasonal weather extremes are more frequent, exotic species invasions are commonplace, and natural areas are continuously being converted for human use (Vitousek et al. 1997, Root et al. 2003, Foley 2005). The consequences of such change have already been detrimental for a variety of species (Benning et al. 2002, Lemoine et al. 2007), and are predicted to have an even greater effect into the future (Sala et al. 2009, Bellard et al. 2012). In the case of birds, nearly 1/3 of North American species alone need urgent conservation action (NABCI 2016), with the range of threats identified as relating to invasive species introductions, agriculture practices and expansion, and climate change (BirdLife International 2013, NABCI 2016). For this reason alone, it is more important than ever that we place value on understanding how populations have responded to a variety of environmental conditions in the past, in order to better predict how they may respond in future. Developing a detailed understanding of demography, especially for already imperiled species, is and will be crucial for successful managerial intervention. Studies that are able to investigate several drivers of important demographic rates, and relate these to historical (environmental) conditions, are better equipped to plan for future conditions, and may serve to inform other, less well–studied systems.  In this thesis, I conducted a detailed study on the factors that influence a common demographic rate in birds: nest success (≥ 1 fledged young). Nest success is a simple, yet informative demographic parameter of most bird species (Conway and Martin 2000). In  28 fact, it is one of the most commonly used metrics to measure population productivity for being relatively easy to collect, and the product of both environmental and individual conditions (Ricklefs 1969). For these reasons, understanding the factors that influence nest success in light of unprecedented environmental change, and devastating declines being experienced by 30% of North American birds, may aid in managing species persistence into the future. Thus, the results of this thesis stem from a robust, detailed study on the factors that affect nest success in a resident population of song sparrows on Mandarte Island BC, Canada. Generally, my work documents a detailed method of understanding a widely used demographic parameter in birds with the potential to inform the factors that drive nest success in other, less well–studied systems. Robust studies such as detailed in this thesis are increasingly important as we enter a time of environmental novelty. 3.2 Main findings The main findings of my work showed that the factors driving nest success stem from both the intrinsic traits of birds, and their environment, and that the strength of these effects vary through time. Specifically, I show that female song sparrow nest success increases from age 1 – 3, and gradually declines thereafter, consistent with the patterns of improved and senescent reproductive performance reported in this and other bird populations (Martin 1995a, Keller et al. 2008, Tarwater and Arcese in prep). I also found that inbred females are more likely to suffer nest failure, but that this relationship is not consistent through time, perhaps reflecting inbreeding depression only in the context of interactive environmental stressors (Kristensen et al. 2005, Marr et al. 2006). Nest failure also increases with greater rainfall during the nesting period, consistent with the  29 hypothesis that rainfall is a stressor while breeding and thus influences nest failure probability (see Table 1). Like inbreeding, however, this effect is not consistent through time, likely reflecting differences in rainfall intensity (Skagen and Adams 2012), and/or how rainfall interacts with other stressors present (Sherry et al. 2015). Breeding density is a relatively consistent predictor of reduced nest success; the effects of which are partially related to a functional relationship with brown–headed cowbird presence. Nest success tends to decline in relation to increased predation risk (i.e., local parasitism), and for parasitized nests, but the effects of each vary temporally as a result of varying intensities of cowbird parasitism, as well as how these stressors may interact with other components of the breeding environment (Arcese and Smith 1988, Arcese et al. 1996, Latif et al. 2012). 3.3 Strengths and limitations The primary strength of this thesis research is the dataset used in the analysis of nest success. Few studies are able to simultaneously assess several factors hypothesized to influence important demographic rates, let alone over extended timeframes where such factors have varied in relation to the individuals that make up populations, and the environmental conditions they’ve experienced. Such datasets allow us to make reliable predictions about how populations will respond to future environmental conditions, thus providing reliable information with which to incorporate into management. However, there are limitations to this study concerned with the applicability of findings to other systems. Island systems are typically characterized as having fewer predators (MacArthur et al. 1972, Yeaton 1974, Sofaer et al. 2014), and the same appears true for Mandarte Island. The resident population of song sparrows on Mandarte Island experience low nest  30 failure overall (~20% per annum; Arcese and Marr 2006), compared to the high failure rates experienced by most mainland bird populations (~ 50 – 65% per annum; Ricklefs 1969, Martin 1993). Second, the proximate causes of nest failure in this system are unknown given lack of nest video surveillance and ~3 – 5 day intervals between nest checks to limit disruption. While this uncertainty is common in many systems for similar reasons, many are adopting video surveillance to identify the proximate causes of nest failure as this can help identify underlying mechanisms (Benson et al. 2010). Identifying specific nest predators in relation to nest failure rates throughout the breeding season, stages of the nest cycle, and among years with differing predominant environmental conditions (e.g. El Niño, high food pulses, etc.), can further empower managers to predict bird population dynamics (Benson et al. 2010, Sherry et al. 2015). Thus, the relatively low nest predation rate and uncertainty of proximate causes of nest predation may limit the applicability of these findings to other systems where nest failure rates are often higher, and causes of failure are becoming known with certainty.  3.4 Future directions A major outcome from this thesis was being able to show that the factors affecting nest success vary in their strength through time. Thus, long–term management plans for bird populations concerning productivity should consider changing circumstances and adjust their action and monitoring protocols accordingly. With this in mind, I propose two possible research directions drawing from the work presented in this thesis. First, I suggest that the long–term dataset analyzed in this thesis be used in ‘scenario development’, whereby the environmental changes experienced by this system, and the resultant effect on nest success, be generalized into ‘scenarios’ used to predict how this  31 and other populations may respond under similar conditions in future. Briefly, this would entail defining effect–size thresholds for the factors explored in this thesis (and potentially more), and grouping them into relevant ‘scenarios’ that define particularly low or high rates of nest success over the course of the study. Knowing the general combination and strength of factors that predict varying outcomes of nest success can help inform management programs for a variety of systems involving birds with similar life histories.  The second research direction I suggest from my work is to use the same long–term dataset to quantify the temporal influence on results. This would entail running the full nest success model (described in section 2.2.3), or variations thereof, over consecutive and randomized year–groupings typical of most short–term studies (e.g. 2 – 4 year periods, consistent with years for data collection during M.Sc. or Ph.D. degrees). Any differences in effect sizes for all factors considered among the consecutive versus randomized subsets of the dataset would represent temporal influence on results. This would comprise novel and useful research with the potential to change the way we evaluate and interpret the results of short–term studies.   32 Table 1: Factor classes, associated variables, usage rationale and the expected relationships of each variable in predicting nest success.  Factor Class Variable   Rationale References Prediction       Intrinsic Female age (1) Prior nesting experience improves reproductive performance until senescence  Nol & Smith 1987; Sæther 1990; Forslund & Pärt 1995; Smith et al. 2006; Horie & Takagi 2012 Nest success rates increase and then decrease with female age             Inbreeding coefficient (f) (2) Inbred birds suffer higher rates of hatching failure and may be more likely to abandon nests during periods of inclement weather Keller 1998; Marr et al. 2006 Nest success rates decrease with increased inbreeding coefficients       Abiotic Cumulative rainfall (mm) (3) Influences level of environmental stress through various mechanisms and correlated with nest abandonment Wingfield et al. 1983; Wingfield 1985; Radford et al. 2001; Oberg et al. 2014 Nest success rates decrease with high cumulative rainfall during the nest period            Minimum temperature (°C) (4) Influences energy required for thermoregulation of nest female and eggs or nestlings and correlated with nest abandonment Wingfield et al. 1983; Syroechkovsky et al. 2002; MacDonald et al. 2013 Nest success rates decrease with low minimum temperatures during the nest period           Biotic Local breeding density (5) Influences level of male parental care, food availability, and likelihood of predation/parasitism Wingfield et al. 1990; Arcese & Smith 1988; Arcese et al. 1992; Sofaer et al. 2014 Nest success rates decrease with increasing breeding population densities            Local brood parasitism rate (6) Local parasitism rates influence likelihood of being parasitized and/or nest failure Arcese et al. 1996; Hauber 2000; Smith et al. 2006 Nest success rates decrease with increasing rates of local brood parasitism        Brood parasitism (7) Influences host fledgling survival, and can influence energy expenditure of nest parents due to increased brood costs Woodworth 1997; Elliot 1999; Hauber 2003; Ludlow et al. 2014 Nests parasitized by brown-headed cowbirds are less successful       	 33 Table 2: The range, median, and 1st: 3rd quartiles of the intrinsic, abiotic, and biotic variables used to predict nest success over 39 years. Annual rates of brood parasitism reported here to show variation in likelihood of parasitism, but modeled as a binary variable associated with each nest (see Methods).    Factor Unit MedianFemale Age years 1 - 9 2.00 1 : 3Inbreeding Coef. - 0 - 0.28 0.05 0.01 : 0.09Imputed Inbreeding Coef. - 0 - 0.28 0.06 0.04 : 0.06Cumulative Rainfall mm 0 - 128 31.4 20.2 : 49.6Min. Temperature °C -3.2 - 10.2 2.7 0.4 : 5.7Local Parasitism Rate parasitized nests/50m buffer 0 - 12 0 0 : 3Local Breeding Density nests/50m buffer 0 - 46 12 7 : 17Brood Parasitism Rate annual proportion 0 - 0.44 0 0 : 0.22Laydate Julian day 57 - 202 131 109 : 151Imputed Laydate Julian day 57 - 202 132 110 : 151Range 1st : 3rd Quartiles 34 Table 3: Standardized coefficients (β [95% CI]) for independent models vs. the averaged model to predict nest success over 39 years. For the independent models, each factor listed represents a single model to predict nest success (except female age and female age2 were modeled together to test a non–linear relationship of female age with nest success). Sample sizes (n) are indicated for each independent model. Both the independent and averaged model(s) included year and female ID as random effects, and lay date as a covariate. The averaged model coefficients were averaged from 4 candidate models within a ΔAIC < 2 from the top model, based on all possible model combinations. Hypotheses and rationale for each factor are summarized in Table 1.    Factor n β βFemale Age 0.76 [0.11, 1.41] 0.81 [0.17, 1.46]Female Age2 -0.82 [-1.48, -0.17] -0.85 [-1.5, -0.2]Inbreeding 2895 -0.23 [-0.43, -0.03] -0.22 [-0.42, -0.03]Rainfall 2895 -0.37 [-0.60, -0.13] -0.35 [-0.57, -0.12]Min. Temp. 2895 0.25 [-0.20, 0.69] 0.38 [-0.07, 0.83]Local Density 2818 -0.55 [-0.83, -0.27] -0.51 [-0.84, -0.18]Local Parasitism 2812 -0.57 [-0.90, -0.25] -0.36 [-0.74, 0.03]Brood Parasitism 2893 -0.08 [-0.36, 0.21] -0.29 [-0.6, 0.01]Lay Date - - - - -0.45 [-0.95, 0.05]95% CI95% CIBest Model, n = 26392706Independent Models 35  Z-Score Year  36 Figure 1: Temporal variation in nest success (grey line) and six potential drivers of success (labeled on each plot). The black dashed line in the Inbreeding plot represents inbreeding estimates largely based on the social pedigree, which reduces the precision of those estimates via extra–pair–paternity (~27% of young; Reid et al. 2014). The precision of f increased through time as genetic data was accumulated and lineages refined. Detailed monitoring did not occur in 1980.    37 Figure 2: The probability of nest success in relation to the age of female song sparrows (solid line) with 95% upper and lower confidence limits (dashed lines). Values plotted are logit back–transformed partial effects of female age and female age2 from the averaged model to predict nest success over 39 years (see Table 3), while keeping all other intrinsic, abiotic, and biotic factors at their mean. Plotting back–transformed effects from the independent model of female age and female age2 resulted in a similar curve, and is thus comparable to the figure shown here.    38  Figure 3: Standardized coefficients (with 95% CI) of the averaged models to predict nest success from the entire (1975 – 2014), early (1975 – 1988), mid (1989 – 2001) and late (2002 – 2014) study periods (Appendix I).    39  References Aguilar, T. M., Maldonado–Coelho, M., & Marini, M. Â. (2000). Nesting biology of the gray–hooded flycatcher (Mionectes rufiventris). Ornitologia Neotropical, 11, 223–230. Arcese, P., & Smith, J. N. M. (1988). Effects of population density and supplemental food on reproduction in song sparrows. Journal of Animal Ecology, 57(1), 119–136. 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Perceived predation risk reduces the number of offspring songbirds produce per year. Science, 334(6061), 1398–1401.   61 Appendices Appendix A: Proportion of Known Female Ages Open bars represent the total number of nests for which female age was known. Solid bars represent the total number of nest records in the dataset. Complete overlap indicates years wherein female ages were known for all nest records.     0 20 40 60 80 100 120 140 160 180 1975 1977 1979 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Number of Observations Year Total nest records Known female ages  62 Appendix B: Age Distribution of Breeding Female Song Sparrows Cumulative age distribution of breeding female song sparrows on Mandarte Island (1975 – 2014; n = 2710).   0 200 400 600 800 1000 1200 1400 1 2 3 4 5 6 7 8 9 Number of Female Song Sparrows Female Song Sparrow Age  63 Appendix C: Proportion of Known Inbreeding Coefficients Open bars represent the total number of observations for which the inbreeding coefficient of the female associated with each nest was known (based on knowing all 4 social and/or genetic grandparents). Solid bars represent the total number of nest records in the dataset. Complete overlap indicates years wherein all female inbreeding coefficients could be estimated.     0 20 40 60 80 100 120 140 160 180 1975 1977 1979 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Number of Observations Year Total nest records Known inbreeding coef.  64 Appendix D: Independent and Averaged Model Outputs using Non–Imputed Data Standardized coefficients (β [95% CI]) for independent models vs. the averaged model to predict nest success over 39 years using non–imputed data for f and lay date (see Methods). For the independent models, each factor listed represents a single model to predict nest success (except female age and female age2 were modeled together to test a non–linear relationship of female age with nest success). Sample sizes (n) are indicated for each independent model. Both the independent and averaged model(s) included year and female ID as random effects, and lay date as a covariate. The averaged model coefficients were averaged from 7 candidate models within a ΔAIC < 2 from the top model, based on all possible model combinations. Hypotheses and rationale for each factor are summarized in Table 1.   Factor n β βFemale Age 0.75 [0.07, 1.43] 0.63 [-0.32, 1.57]Female Age2 -0.83 [-1.49, -0.17] -0.62 [-1.62, 0.39]Inbreeding 1730 -0.32 [-0.58, -0.06] -0.36 [-0.62, -0.11]Rainfall 2745 -0.38 [-0.62, -0.14] -0.28 [-0.54, -0.02]Min. Temp. 2745 0.34 [-0.12, 0.80] 0.35 [-0.18, 0.89]Local Density 2688 -0.49 [-0.77, -0.21] -0.42 [-0.79, -0.04]Local Parasitism 2682 -0.49 [-0.83, -0.15] -0.62 [-1.06, -0.18]Brood Parasitism 2745 -0.31 [-0.59, -0.03] -0.56 [-0.94, -0.18]Lay Date - - - - -0.18 [-0.66, 0.29]Independent Models Best Model, n = 170295% CI 95% CI2578 65 Appendix E: Plot of Model Coefficient’s for the Entire, Early, Mid and Late Study Periods Using Non–Imputed Data Standardized coefficients (with 95% CI) of the averaged models to predict nest success from the entire (1975 – 2014), early (1975 – 1988), mid (1989 – 2001) and late (2002 – 2014) study periods using non–imputed data for lay date and f (see Methods).     66 Appendix F: Cowbird Parasitism Throughout the Breeding Season Fraction of nests parasitized by brown–headed cowbirds throughout the breeding season over 39 years. Points reflect the proportion of parasitized nests from all nests throughout the study that had similar lay dates (n = 2900).     67 Appendix G: Parameter Variation in the Early, Mid and Late Study Periods The range, median, and 1st : 3rd percentiles of the intrinsic, abiotic, and biotic variables used to predict nest success and fledgling numbers in the early– (1975 – 1988), mid– (1989 – 2001), and late– (2002 – 2014) study periods.   Factor Unit MedianEarly-Period (1975 - 1988)Female Age years 1 - 6 2.0 1.0 : 3.0Inbreeding Coef - 0 - 0.25 0.00 0.00 : 0.01Imputed Inbreeding Coef. - 0 - 0.25 0.06 0.00 : 0.06Cumulative Rainfall mm 0.2 - 99.3 30.0 19.4 : 47.0Min. Temperature °C -1.9 - 8.5 2.7 -0.2 : 4.8Local Parasitism Rate parasitized nests/50m buffer 0 - 12 3 1 : 5Local Breeding Density nests/50m buffer 0 - 46 16 11 : 23Brood Parasitism Rate annual proportion 0 - 0.44 0.22 0.17 : 0.25Laydate Julian day 57 - 194 131 110 : 151New laydate Julian day 57 - 194 132 110 : 151Mid-Period (1989 - 2001)Female Age years 1 - 7 2.0 1.0 : 3.0Inbreeding Coef - 0 - 0.28 0.05 0.03 : 0.09Imputed Inbreeding Coef. - 0 - 0.28 0.06 0.03 : 0.07Cumulative Rainfall mm 0 - 115.8 33.0 21.8 : 59.8Min. Temperature °C -3.2 - 9.6 2.9 2.0 : 6.1Local Parasitism Rate parasitized nests/50m buffer 0 - 7 0 0 : 1Local Breeding Density nests/50m buffer 0 - 26 12 8 : 16Brood Parasitism Rate annual proportion 0 - 0.36 0.01 0 : 0.13Laydate Julian day 66 - 200 132 109 : 151New laydate Julian day 66 - 200 131 109 : 151Late-Period (2002 - 2014)Female Age years 1 - 9 2.0 1.0 : 3.0Inbreeding Coef - 0 - 0.2 0.07 0.05 : 0.10Imputed Inbreeding Coef. - 0 - 0.2 0.06 0.05 : 0.09Cumulative Rainfall mm 1.2 - 128 31.2 19.2 : 46.3Min. Temperature °C -2.9 - 10.2 3.0 1.2 : 6.0Local Parasitism Rate parasitized nests/50m buffer 0 - 6 0 0 : 0Local Breeding Density nests/50m buffer 1 - 17 7 5 : 10Brood Parasitism Rate annual proportion 0 - 0.28 0 0 : 0Laydate Julian day 80 - 202 132 109 : 151New laydate Julian day 80 - 202 132 110 : 151Range 1st : 3rd Quartiles 68 Appendix H: Distribution of The Number of Young Fledged From 2900 Song Sparrow Nests Over 39 Years     0 200 400 600 800 1000 1200 0 1 2 3 4 5 Number of Nests Fledgling Number  69 Appendix I: Averaged Model Coefficients for the Early, Mid, and Late Study Periods Averaged, standardized parameter estimates (β), 95% confidence intervals, and relative importance of variables from top–ranked models within ΔAIC <2 of the averaged model explaining nest success in the early, mid and late study periods. Models included year and female ID as random effects, and lay date as a covariate.     70  Factor β SEEarly Period (1975 - 1988) Female Age 0.54 0.7726 models ΔAIC < 2 Female Age2 -0.65 0.60n = 999 nests Inbreeding 0.09 0.15Rainfall -0.29 0.22Min. Temperature 0.34 0.38Local Density -0.40 0.20Local Parasitism -0.40 0.23Brood Parasitism -0.04 0.18Lay Date -0.34 0.30Mid Period (1989 - 2001) Female Age 0.49 0.597 models ΔAIC < 2 Female Age2 -0.87 0.56n = 862 nests Inbreeding -0.68 0.16Rainfall -0.49 0.17Min. Temperature -0.12 0.19Local Density -0.28 0.27Local Parasitism 0.20 0.42Brood Parasitism -0.77 0.31Lay Date -0.08 0.17Late Period (2002 - 2014) Female Age 0.93 0.6424 models ΔAIC < 2 Female Age2 -0.93 0.54n = 778 nests Inbreeding -0.31 0.24Rainfall 0.17 0.24Min. Temperature 0.80 0.44Local Density -1.51 0.60Local Parasitism -1.04 0.57Brood Parasitism -0.42 0.53Lay Date -0.84 0.53

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