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The effects of habitat preference, environmental heterogeneity, and inter-individual variation on fitness Germain, Ryan Ross 2015

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THE EFFECTS OF HABITAT PREFERENCE, ENVIRONMENTAL HETEROGENEITY, AND INTER-INDIVIDUAL VARIATION ON FITNESS   by Ryan Ross Germain  B.Sc. (Hons), Queen’s University, 2007 M.Sc., Queen’s University, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2015  © Ryan Ross Germain, 2015  ii  Abstract Theory predicts that animals breeding in heterogeneous landscapes should select habitat likely to maximize individual fitness, but identifying the fine-scale environmental characteristics which influence habitat preference and affect fitness is often problematic. While many studies quantify relationships between habitat preference and the reproductive success of their occupants, few are able to separate the independent effects of inter-individual variation among animals in a population from the effects of habitat on indices of fitness. In this thesis, I used up to 39 years of nesting, survival, and pedigree data from a resident, island population of song sparrows (Melospiza melodia) to identify fine-scale environmental characteristics which influenced habitat preference, determine whether preferred habitats positively affected fitness, and distinguish the relative effects of preferred habitats on indices of fitness from those due to inter-individual variation among song sparrows within the population. Song sparrows in this population exhibited marked preference for habitats that conferred positive effects on individual fitness via annual reproductive success and survival. Females nesting in preferred habitats also began breeding earlier, exhibited more energetically efficient incubation behaviour, and produced more offspring that recruited the population than those nesting in less-preferred sites. Preferred habitats in this system had more shrub cover, more edge, and deeper soil. The potential benefits of occupying preferred habitats included greater early season food availability and shelter from predators and inclement weather during both the breeding and non-breeding seasons. Despite the positive effects of preferred habitats on fitness, the relative contributions of habitat to indices of fitness were substantially less than those related to inter-individual variation in phenotype, genotype and developmental stage (measured as relative lifetime reproductive iii  success, additive genetic and permanent individual variance, and age). Together, results from this thesis suggest that inter-individual variation in ‘quality’ can be more influential of fitness than habitat quality in free-living populations, and highlight the importance of estimating the relative contributions of inter-individual variation when attempting to identify the environmental correlates of fitness in natural systems.  iv  Preface I have forgone the tradition of using the first-person throughout this thesis, as the work presented here would not have been possible without the dedicated assistance of several collaborators and colleagues who are gratefully acknowledged. A version of Chapter 2 has been published as Germain, R. R., and P. Arcese. 2014. Distinguishing individual quality from habitat preference and quality in a territorial passerine. Ecology 95: 436–445. I designed the study, conducted statistical analyses, and wrote the paper with input from Peter Arcese, who also helped design the study and provided extensive editorial advice.  A version of Chapter 3 has been published as Germain R. R., R. Schuster, K. E. Delmore, and P. Arcese. 2015. Habitat preference facilitates successful early breeding in an open-cup nesting songbird. Functional Ecology. Early view, DOI: 10.1111/1365-2435.12461. I designed the study, collected data, conducted statistical analyses, and wrote the paper with input from all co-authors. Richard Schuster helped model LiDAR and micro-climate data and import nest locations into an updated co-ordinate system. Kira Delmore helped with parameterizing and running models, and Peter Arcese helped design the study and edit the paper. Work for Chapter 4 was done in collaboration with Jane Reid, Matthew Wolak, Sylvain Losdat, and Peter Arcese. I designed the study, collected data, conducted statistical analyses, and wrote the paper with input from all co-authors. Jane Reid helped design the study and provided extensive editorial advice, and Matthew Wolak was instrumental in conducting statistical analyses. Sylvain Losdat provided helpful statistical advice and comments on the paper, and Peter Arcese helped design the study and edit the paper. v  Work for Chapter 4 was done in collaboration with Richard Schuster, Corey Tarwater, Wesley Hochachka, and Peter Arcese. I designed the study, collected data, conducted statistical analyses, and wrote the paper with input from all co-authors. Richard Schuster helped develop the analyses, model survival data, and generate predictions. Corey Tarwater helped design the study, collect data, and conduct analyses. Wesley Hochachka and Peter Arcese both provided helpful advice and comments on the paper. All protocols involving the use of animals in this thesis were approved by the UBC Animal Care Committee (A07- 0309). Permits for this work were obtained from Environment Canada (master banding permit no. 10596). This work was supported by grants from the Faculty of Graduate Studies and Department of Forest and Conservation Sciences at UBC, the Natural Sciences and Engineering Research Council of Canada (including a Michael Smith Foreign Study Supplement), The Nature Trust of British Columbia, Werner and Hildegard Hesse, American Ornithologists’ Union, Society of Canadian Ornithologists, and Wilson Ornithological Society. vi  Table of contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of contents .......................................................................................................................... vi List of tables.................................................................................................................................. xi List of figures ............................................................................................................................... xii Acknowledgments ...................................................................................................................... xiii Dedication .....................................................................................................................................xv Chapter 1: General introduction ..................................................................................................1 1.1 Overview ......................................................................................................................... 1 1.2 Background ..................................................................................................................... 2 1.3 Habitat preference and its effects on individual fitness .................................................. 3 1.4 Separating habitat quality and inter-individual variation................................................ 5 1.5 Study system: the song sparrows of Mandarte Island ..................................................... 6 1.6 Thesis chapters ................................................................................................................ 8 Chapter 2: Distinguishing individual quality from habitat preference and quality in a territorial passerine .....................................................................................................................10 2.1 Introduction ................................................................................................................... 10 2.2 Methods......................................................................................................................... 13 2.2.1 Study system ............................................................................................................. 13 2.2.2 Breeding site use ....................................................................................................... 14 2.2.3 Estimates of habitat preference ................................................................................. 14 2.2.4 Estimates of annual reproductive output per breeding site ....................................... 14 vii  2.2.5 Estimates of female quality per breeding site: age and rLRS ................................... 15 2.2.6 Vegetation characteristics of breeding sites .............................................................. 16 2.2.7 Statistical analysis ..................................................................................................... 17 2.2.7.1 Long-term patterns in habitat preference and quality ....................................... 17 2.2.7.2 Contributions of habitat versus individual quality to reproductive output ....... 18 2.3 Results ........................................................................................................................... 19 2.3.1 Long-term habitat preference and habitat quality ..................................................... 20 2.3.2 Vegetation traits in preferred habitat ........................................................................ 20 2.3.3 Contributions of habitat and individual quality to reproductive output .................... 21 2.4 Discussion ..................................................................................................................... 21 Chapter 3: Habitat preference facilitates successful early breeding in an open-cup nesting songbird ........................................................................................................................................34 3.1 Introduction ................................................................................................................... 34 3.2 Methods......................................................................................................................... 37 3.2.1 Study system ............................................................................................................. 37 3.2.2 Early season cell and nest-site preference ................................................................ 38 3.2.3 Components of early season and annual reproduction ............................................. 39 3.2.4 Incubation behaviour ................................................................................................ 41 3.2.5 Modelling cell relative micro-climate ....................................................................... 42 3.2.6 Phenology and food availability ............................................................................... 43 3.2.7 Statistical analyses .................................................................................................... 44 3.3 Results ........................................................................................................................... 46 3.4 Discussion ..................................................................................................................... 48 viii  Chapter 4: Quantitative genetics of breeding date – direct and indirect genetic and fine-scale environmental effects in song sparrows ............................................................................61 4.1 Introduction ................................................................................................................... 61 4.2 Methods......................................................................................................................... 65 4.2.1 Study population ....................................................................................................... 65 4.2.2 Pedigree and paternity data ....................................................................................... 67 4.2.3 Quantitative genetic analysis .................................................................................... 68 4.2.4 Spatial models ........................................................................................................... 70 4.2.5 Model implementation and parameter estimation ..................................................... 74 4.3 Results ........................................................................................................................... 77 4.3.1 Breeding date, pairing, and pedigree data ................................................................. 77 4.3.2 Non-spatial animal model ......................................................................................... 77 4.3.3 Grid model ................................................................................................................ 78 4.3.4 Overlap model ........................................................................................................... 79 4.3.5 Spatial autocorrelation model ................................................................................... 79 4.4 Discussion ..................................................................................................................... 80 4.4.1 Additive genetic variances ........................................................................................ 80 4.4.2 Variance due to breeding location ............................................................................ 82 4.4.3 Inbreeding depression ............................................................................................... 84 4.4.4 Conclusions ............................................................................................................... 85 Chapter 5: Habitat preference is linked to adult survival, reproductive rate, and the relative importance of each to annual population growth in a year-round resident bird ....91 5.1 Introduction ................................................................................................................... 91 ix  5.2 Methods......................................................................................................................... 93 5.2.1 Study area.................................................................................................................. 93 5.2.2 Predictors of adult over-winter survival ................................................................... 94 5.2.3 Habitat preference and annual reproductive rate ...................................................... 96 5.2.4 Statistical analysis ..................................................................................................... 97 5.3 Results ......................................................................................................................... 100 5.3.1 Predictors of adult over-winter survival ................................................................. 100 5.3.2 Site-specific estimates of survival, reproductive rate, and PRSS ........................... 101 5.3.3 Relationships between habitat preference and multiple fitness components .......... 101 5.3.4 Effects of survival and reproductive rate on population growth ............................. 102 5.4 Discussion ................................................................................................................... 102 5.4.1 Predictors of adult over-winter survival ................................................................. 103 5.4.2 Relationships between habitat preference and multiple fitness components .......... 104 5.4.3 Effects of survival and reproductive rate on population growth ............................. 106 Chapter 6: General discussion ..................................................................................................114 6.1 Identifying preferred habitat ....................................................................................... 114 6.2 Habitat preference as a metric of habitat quality ........................................................ 115 6.3 Influences of habitat versus inter-individual variation on fitness ............................... 116 6.4 Broader implications and future directions ................................................................. 118 References ...................................................................................................................................121 Appendices ..................................................................................................................................142 Appendix A Supplementary material for Chapter 2 ............................................................... 142 A.1 Full output from all models in top models subset ................................................... 142 x  Appendix B Supplementary material for Chapter 3 ............................................................... 167 B.1 Long-term patterns of early season grid cell preference ......................................... 167 B.2 Measuring incubation behaviour ............................................................................. 169 B.3 Assignment of time periods to account for sun location throughout the day ......... 170 B.4 Modelling relative micro-climate of grid cells ....................................................... 172 B.5 Shrub selection criteria for assessments of food availability .................................. 177 Appendix C Supplementary material for Chapter 4 ............................................................... 178 C.1 All alternate versions of the ‘non-spatial’ model .................................................... 178 C.2 All alternate versions of the ‘grid’ model ............................................................... 185 C.3 All alternate versions of the ‘overlap’ model ......................................................... 189 C.4 Spatial autocorrelation model ................................................................................. 194 Appendix D Supplementary material for Chapter 5 ............................................................... 196 D.1 Details of measuring habitat variables .................................................................... 196 D.2 Pairwise correlations among predictors of over-winter survival ............................ 197 D.3 Cell-specific estimates of predicted over-winter survival ...................................... 198 D.4 Details of elasticity analysis.................................................................................... 198 D.5 Full output from all models in top models subset ................................................... 200  xi  List of tables Table 2.1 Variance components, standard error (SE), and percentage of total variance accounted for by breeding site identity for three random effects models ...................................................... 27 Table 2.2 Parameter estimates (± SE) for variables identified as predictors of site-specific reproductive output in top-ranked models .................................................................................... 28 Table 3.1 Parameter estimates (± SE) from averaged generalized mixed models for each of six components of early season (relative breeding date, clutch size, average nesting condition, relative independent offspring per eggs produced [ROEP]) and annual (total annual independent offspring, total annual recruits) reproductive success in song sparrows ...................................... 55 Table 3.2 Parameter estimates (± SE) from averaged linear (Gaussian) mixed models for each of two measures of incubation behaviour (constancy and average off-bout duration) ..................... 57 Table 3.3 Island-wide differences in predicted relative micro-climate of 632 hexagonal cells (65 m2) representing potential nesting sites ........................................................................................ 58 Table 4.1 Variance component estimates (95% confidence intervals [CI]), fixed effect regression parameter estimates (± SE), and heritability estimates (± SE) from four separate univariate animal models for song sparrow breeding date ............................................................................ 87 Table 5.1 Summary statistics for predictors of adult over-winter survival ................................ 107 Table 5.2 Parameter estimates (± SE) from averaged linear mixed model of individual over-winter survival for adult song sparrows ...................................................................................... 108  xii  List of figures Figure 2.1 Observed versus expected (given Poisson distribution) pattern of occupation by nesting song sparrows in 146 breeding sites over 35 years .......................................................... 29 Figure 2.2 Relationship between the total number of years a breeding site was occupied and ‘habitat preference’ ....................................................................................................................... 30 Figure 2.3 Relationships between habitat preference and site-specific reproductive output, the mean age of females occupying the site, and the relative lifetime reproductive success (rLRS) of the individual females that initiated those nests ........................................................................... 31 Figure 2.4 Relative support for 11 vegetation traits used to predict habitat preference by nesting female song-sparrows ................................................................................................................... 33 Figure 3.1 Frequency of overlap between early season nesting attempts (100 m2 buffer on nest point location) and 65 m2 hexagonal cells .................................................................................... 59 Figure 4.1 Visual representation of spatial data used in the ‘grid’ and ‘overlap’ models ........... 89 Figure 4.2 Distribution of breeding dates from first annual song sparrow nesting attempts ....... 90 Figure 5.1 Estimates of a) predicted over-winter survival, b) predicted reproductive rate, and c) the combined effects of each fitness component (prime reproduction and survival sites [PRSS]) across 20 × 20 m grid cells (‘sites’) on Mandarte Island, BC, Canada....................................... 110 Figure 5.2 Relationships between site-specific estimates of habitat preference and predicted adult over-winter survival, predicted reproductive rate, and PRSS (prime reproduction and survival sites) .............................................................................................................................. 111 Figure 5.3 Elasticity analysis of the relative effects of adult over-winter survival (black bars) and annual reproductive rate (grey bars) on population growth across four categories of habitat preference .................................................................................................................................... 113 xiii  Acknowledgments First and foremost, I would like to thank my supervisor Peter Arcese for his encouragement and support over the course of my PhD. Peter has been an outstanding mentor who has helped me develop as a researcher and taught me the value of finding your own personal sweet spot along Fretwell’s (1972) axes of theoretical and empirical competence. He pushed me when I needed to pushed, but always gave me the freedom to pursue the questions I was interested in, and for that I will be forever grateful. My committee members Kathy Martin, John Richardson, and Wesley Hochachka likewise provided support and helpful comments throughout the course of this research. Many members of the Arcese and Martin labs have provided appreciated input during lab meetings and in personal discussions, particularly when my ideas were still in their half-baked stage. These people include (but are not limited to) Richard Schuster, Corey Tarwater, Merle Crombie, Kate Johnsson, Joe Bennett, and Jessica Krippel. Richard has been an especially invaluable labmate, and much of the work presented here would not be possible without his help and advice. Several of our ideas were hatched in pubs and scribbled on napkins, and this experience has granted me a lifelong collaborator, co-malcontent, and friend. I also had the great fortune of being adopted into a separate lab during my time in Aberdeen, Scotland, where Jane Reid very graciously agreed to host me in her group. Jane has been an excellent mentor with outstanding patience and I greatly look forward to continue learning from her. Matthew Wolak was likewise extremely helpful in teaching me the nuts and bolts of quantitative genetic analyses, and answering the same questions several times. Sylvain Losdat provided me with proper Scottish hospitality during my time in Aberdeen, including letting me crash on his (easily) inflatable mattress when locked out of my flat.  xiv  A substantial portion of my PhD (somewhere above 300 days total) was spent conducting field work on Mandarte Island. I am grateful to Pirmin Nietlisbach, Kathrin Näpflin, Martha Essak, Christophe Bousquet, Thomas Lameris, Sylvain Losdat, Andrew Cook, and Emily Sunter, for all their assistance and camaraderie while living on a barren rock in the middle of the Salish Sea. Pirmin in particular provided hours of thoughtful discussion, heated games of Risk, and was the perfect teammate to run the study system with. He is an ideal combination of great researcher, great naturalist, and great friend. Outside of PhD life I have had an excellent support network of friends and family who helped me reach the end of this long journey. Tom DeFalco made it his mission to not let me get too jammed up about science or life (and would commiserate when I did anyway), and continued the tradition of Ludachristmas long after the joke lost its timeliness. Cory Toth provided an endless supply of movies/shows, which were greatly appreciated (especially during fieldwork), and Diana and Graeme Rennison plied me with good food. My parents, to whom this thesis is dedicated, taught me the importance of compassion and perseverance, and were a constant source of encouragement. They are often sad to see me live so far away, but are happy that I am pursuing my goals wherever the lead me. Lastly, I thank Kira, my partner in life and in crime. You’ve put up with me for 8+ years and yet after falling off a mountain, staring blankly at you when you discuss computational problems, and damaging every nice thing you own, you’re still by my side. You are a pillar of strength, your patience and love know no bounds, and I cannot thank you enough.  xv  Dedication   For Mom and Dad. I got my ticket  1  Chapter 1: General introduction “…going to another country doesn’t make any difference. I’ve tried all that. You can’t get away from yourself by moving from one place to another. There’s nothing to that.”             – Ernest Hemingway, The Sun Also Rises (1926) 1.1 Overview Habitat selection is the process of choosing an appropriate location in which to reside or breed, and occurs at both the population level (defining a species’ niche), and at the level of individual organisms (resource availability versus use). Understanding why individuals preferentially select certain habitats over others, and how the environmental effects of preferred habitats may influence individual fitness can help inform our understanding of important ecological processes including dispersal and migration (Studds et al. 2008), species interactions (Danielson 1991), responses to climate change (Franklin et al. 2000), and conservation area prioritization (Sergio and Newton 2003). However, despite the long history of research on habitat selection, we still know relatively little about the specific fine-scale environmental characteristics of preferred habitats that can positively affect the fitness of individual animals (Johnson 2007), or even how to separate these environmental effects from the independent effects of inter-individual variation among animals in the population (Sergio et al. 2009). My overall goals in this thesis are to identify some of the fine-scale characteristics of environments that influence habitat preference in a free-living bird population, determine whether preferred habitats positively influence fitness, and distinguish the relative effects of habitat-based variation and inter-individual variation on indices of fitness.  2  1.2 Background Habitat selection can be viewed as one of the central concepts of modern animal ecology, given the widely recognized importance of the relationship between wildlife and the landscapes that they occupy (reviewed in Hall et al. 1997). Indeed, as pointed out by Southwood (1977), the origin of the word ecology (from the Greek ‘oikos’, ‘the household’) indicates that understanding the relationships between organisms and the environments around them is central to ecological research. However, habitat can also be viewed as “one of the most widely used and ambiguous terms in ecology” (Morris 2003) and, depending on context, can encompass work at very broad and fine spatial scales, from studies of whole ecosystems down to individual foraging patches (Addicott et al. 1987, Morris 2003). This ambiguity as to what actually constitutes habitat is one potential source of confusion surrounding the precise role of ‘habitat quality’ (i.e., the effects of habitat on the fitness of its occupants) in animal ecology despite its long history of study (reviewed in Johnson 2007). Thus, researchers examining the influences of habitat on individual animal fitness need to clarify not only the spatial scale under investigation (e.g., breeding site, territory, home range), but also how environmental heterogeneity at this scale may affect specific components of fitness for a habitat’s occupants. In this thesis, I define ‘habitat’ as a spatially bounded subset of biotic and abiotic conditions which differ in their effects on an occupant’s fitness (i.e., survival and reproduction) from those of neighbouring subsets (modified from Hall et al. 1997, Morris 2003). The generality of this definition stems from the general nature with which habitat is thought to influence individual fitness, and through the myriad of studies relating habitat to the life-history of its occupants. Much of this work as it pertains to animals, stems from Charles Elton’s (1927) notion that “…animals are not completely hemmed in by their environment, but by only a few 3  limiting factors which may, however, be difficult to discover”. The difficulty in determining the precise, fine-scale factors that influence individuals to occupy certain habitats over others (‘habitat preference’), and how this preference affects the fitness of those individuals, make up two key areas of investigation in modern animal ecology.  1.3 Habitat preference and its effects on individual fitness Quantifying habitat preference within a population is fundamental to our understanding of how habitat quality can affect individual fitness. Across a wide variety of taxa, individuals prefer habitats containing a suite of fine-scale environmental effects (e.g., availability of key resources, shelter from predators/inclement weather, etc.) that can lead to greater reproductive success and/or survival (e.g., Martin 1998, McLoughlin et al. 2007, Gripenberg et al. 2010). As a result, natural selection should reinforce processes that increase the probability of occupying high-quality habitat, and we would expect the most frequently or densely occupied habitats to be those with the strongest positive effects on fitness (Fretwell and Lucas 1970). However, following Van Horne’s (1983) clear demonstration that density can be a misleading indicator of habitat quality, most researchers now recognize that occupancy of a given habitat is not always a reliable indicator of its positive effects on individual fitness (Bock and Jones 2004). In extreme cases, particularly in human-dominated landscapes, individual animals have been shown to preferentially occupy habitats which reduce mean fitness (‘ecological traps’, Dwernychuk and Boag 1972), due to the presence of cues normally indicative of high expected fitness in other, less-disturbed landscapes (e.g., Arlt and Pärt 2007, Rodewald et al. 2011). Thus, studies which estimate habitat quality based on preference alone, and fail to consider the mean reproductive 4  success and/or survival of individuals occupying preferred versus less-preferred habitats run the risk of misclassifying the expected effects of habitat on individual fitness. In the past 30 years, an increasing number of studies have used the mean reproductive output of individuals occupying particular habitats to estimate habitat quality (reviewed in Johnson 2007). While this method may be considered a more direct measure of a habitat’s potential quality than occupancy, it remains mired by the issue of whether differences in reproductive output are due to variation in the environmental aspects of the habitats themselves, or in the quality of animals occupying those habitats. Fretwell and Lucas (1970) theorized that where competitive abilities differ, dominant or high-quality individuals may be more adept at securing high-quality habitat and thus relegate lower-quality individuals to habitats with lower expected fitness benefits, forming an ‘ideal despotic distribution’ (Fretwell and Lucas 1970, Fretwell 1972). Although few animal populations adhere to the strict rules of ideal despotic distributions (Kennedy and Gray 1993, Tregenza 1995), many examples of settlement patterns in the wild demonstrate the potential influences of individual variation in patterns of habitat occupancy which may lead to co-variation between habitat and individual quality (e.g., reptiles, Calsbeek and Sinervo 2002; fish, Haugen et al. 2006; birds, Sebastián-González et al. 2010, Quaintenne et al. 2011). Thus, analyses of the effects of habitat quality on fitness are complicated by the fact that indices of habitat quality are likely to be at least partly influenced by co-variation between the repeatable genetic or phenotypic quality of individuals and the habitats they occupy (Arcese 2003, Steele and Hogg 2003).  5  1.4 Separating habitat quality and inter-individual variation The need to separate the potentially confounding effects of habitat and individual quality on fitness is complicated by the ambiguous and inconsistent use of the term ‘individual quality’ (Wilson and Nussey 2010). In general, individual quality represents the intrinsic, persistent heterogeneity among individuals in a population, which may be due to a number of mechanisms including additive genetic variance in life-history or morphological traits, and spatiotemporal variance in the environment (Vindenes et al. 2008). Although the exact sources of variation in individual quality are seldom identified and likely multi-faceted (Wilson and Nussey 2010), analyses accounting for intrinsic differences among individuals can be used to estimate the relative influences of individual quality on indices of fitness (e.g., Cam et al. 2002, van de Pol and Verhulst 2006, Zabala and Zuberogoitia 2014). However, many studies that contrast individual and habitat quality are cross-sectional and rely on coarse categorizations of age (a trait related to both development and experience) to estimate individual quality (van de Pol and Verhulst 2006, Wilson and Nussey 2010). Indices of fitness such as annual reproductive success typically increase in prime-age animals (Forslund and Pärt 1995) but can decline in old age (e.g., Reid et al. 2003, Keller et al. 2008, McCleery et al. 2008, Bouwhuis et al. 2009). Thus, while often linked to fitness, age does not represent intrinsic variation in the quality of individuals in a population that persists over time. Disentangling the effects of individual and habitat quality on fitness therefore requires that researchers adopt more definitive metrics of ‘inter-individual variation’ and assess their independent contributions to fitness-related life-history traits. Long-term studies of known, free-living animals offer a particularly promising means of separating the environmental effects of habitat quality on fitness from the effects of inter-individual variation (Clutton-Brock and Sheldon 2010). Given repeat observations from the same 6  individuals in a number of habitats, and long-term observations of habitats containing a variety of individuals, modern analytical techniques can be applied to such systems to statistically separate environmental versus individual-based effects on indices of fitness. In particular, where individuals in long-term study populations are assigned to a pedigree, quantitative genetic analysis using the ‘animal model’ can be used to quantify the similarity in fitness-related life-history traits among relatives (additive genetic variance), and partition total phenotypic variance in such traits to their additive genetic, individual, and environmental components (Kruuk et al. 2014). Natural studies using long-term study populations can also provide unique insights into environmental influences on fitness which may be elusive in laboratory studies. While laboratory or experimental investigations are extremely useful in isolating the relative effects of key environmental sources of variation on fitness-related life-history traits (e.g., the effects of temperature and day length on reproductive phenology in birds: Perfito et al. 2005, Schaper et al. 2012), they may not represent the cues used by individuals to select preferred habitat under natural conditions. Further, many life-history traits critical to the biology of populations under study may not be expressed under laboratory conditions, and much of the natural variation in annual reproductive success and survival (and hence fitness) may only exist in free-living populations (Kruuk et al. 2014).  1.5 Study system: the song sparrows of Mandarte Island Fundamental to my ability to address the novel and basic ecological questions covered by my thesis research is the study system in which the work was conducted: the song sparrows (Melospiza melodia) of Mandarte Island, British Columbia, Canada. This small, relatively isolated population of resident sparrows has been monitored continuously since 1975, where the 7  location, attending parents, and fate of nearly every nesting attempt (c. 3300 at time of writing) has been documented. Further, all offspring hatched on the island as well as all immigrants (mean = 1.1/year) are uniquely colour banded for individual identification and assigned to a full pedigree (Smith 2006). This detailed knowledge of the relatedness, annual survival, and lifetime reproductive success of every individual in the population over 40 years offers a unique opportunity to investigate the relative contributions of inter-individual variation to commonly-studied indices of individual fitness. Mandarte (48°38’00 N, 123°17’13 W) is a small (c. 6 ha) island situated in the Southern Gulf Islands region of coastal British Columbia. This region is typically dominated by Douglas-fir forest and Garry oak meadow patches. While Mandarte exhibited a vegetation structure typical of Garry oak meadow over the last century (Lameris et al. unpublished data), the island now hosts a relatively simple vegetation structure of sea island scrub and open grass meadow (Smith 2006). In 1986 and 2006, vegetation surveys were conducted across the extent of the island, documenting the shrub height, soil depth, and percent cover of each dominant shrub species (see Chapter 2 for shrub species) on a 20 × 20 m (400 m2) grid. The detail of these vegetation surveys over two time periods enables an in-depth investigation into the vegetation-based targets of habitat preference in this system. Combined with even finer-scale (65 m2) surveys of topography, food availability, and habitat-specific micro-climate (Chapter 3), this system offers an unprecedented ability to determine the specific, fine-scale environmental targets of habitat preference over four decades of study, and to estimate the precise effects of occupying preferred and less-preferred habitats on individual fitness. Song sparrows themselves are a typical songbird species commonly found in a variety of landscapes across North America (Arcese et al. 2002). Thus, inferences drawn on the contributions of habitat quality versus inter-8  individual variation on measures of fitness from this well-studied and relatively simple system should be widely applicable to short-lived resident wildlife populations living in seasonal, heterogeneous environments.  1.6 Thesis chapters My thesis uses detailed observations of individual song sparrows and the habitats they occupy to determine the relative influences of each on variation in key indices of fitness. My overall goals are to identify the fine-scale environmental characteristics which influence habitat preference, and to distinguish the effects of occupying preferred habitats on indices of fitness from those related to inter-individual variation among sparrows in the population.  In Chapter 2, I establish whether individuals in this population exhibit preference for certain habitats over others and identify the vegetation-based habitat characteristics associated with preference. Next, I evaluate whether preferred habitats conferred greater annual reproductive success to their occupants on average, thereby establishing whether habitat preference and habitat quality are positively related in this system. I then determine the relative contributions of preferred nesting habitat to female annual reproductive success, independent of the age and intrinsic quality (estimated as relative lifetime reproductive success, rLRS) of females occupying varying habitats. In Chapter 3, I extend this work by examining fine-scale cues that song sparrows use to assess breeding habitat quality, particularly during the early breeding season when resources are more limited. I then examine the components of both early season and annual reproductive success most influenced by occupying higher-quality habitat, again estimating the relative 9  contribution of habitat versus age and individual female quality (rLRS) on each component of reproduction. In Chapter 4, I use an ‘animal model’ to partition variance in breeding date, a key fitness-related life-history trait, and estimate its direct (female) and indirect (male) additive genetic and fine-scale environmental components. I further examine how quantifying fine-scale environmental variance in breeding date using different spatial methods affects estimates of additive genetic variance (and hence heritability) in this trait, and thereby provide insight into how estimating variance due to breeding habitat may influence evolutionary predictions of key fitness-related traits. In Chapter 5, I estimate the effects of habitat characteristics on adult over-winter survival, relative to those of winter weather, population density, sex, and age. I then determine whether preferred habitats lead to greater likelihood of adult over-winter survival, and whether the relative importance of survival and reproduction to annual population growth varies with habitat preference. Finally, I investigate whether habitat preference in this system is more closely linked to variation in survival, reproduction, or a combination of both fitness components. In Chapter 6, I highlight how work from this thesis contributes to our overall understanding of the influences of habitat to the fitness of its occupants. I summarize the different approaches used to estimate both habitat quality and inter-individual variation, and emphasize the importance of understanding the independent effects of each on indices of fitness. I further generalize the findings of this thesis to other, more complex systems of free-living animal populations in heterogeneous environments, and provide recommendations for both establishing the relationship between habitat preference and quality, and describing the relative contributions of habitat versus inter-individual variation to individual animal fitness.  10  Chapter 2: Distinguishing individual quality from habitat preference and quality in a territorial passerine1  2.1 Introduction Theory suggests that habitat quality plays a key role in regulating animal populations (Andrewartha and Birch 1954, Fretwell and Lucas 1970) and that individuals preferentially occupy habitats likely to confer high relative fitness (e.g., Dhondt et al. 1992, Clark and Shutler 1999, Gunnarsson et al. 2005, Mills 2005). However, despite decades of study we still lack consensus on how to measure habitat quality or its influence on individual animals (Johnson 2007). Problems arise because habitat quality is often estimated by indices of vegetation structure or primary productivity that have uncertain links to individual fitness (Johnson 2007), and because comparisons of observed fitness across habitat gradients often fail to identify and separate the independent effects of individual quality from the quality of the habitats they occupy (Sergio et al. 2009). We used a 35 year study of an insular song sparrow population to estimate correlations between habitat preference and habitat quality, and to ascribe the relative contributions of habitat quality, age, and individual female intrinsic quality (defined here as relative lifetime reproductive success; rLRS) to habitat-specific reproductive output. It is widely assumed that habitat quality is reflected in occupancy rate and/or reproductive performance in focal species. Occupancy can be a compelling proxy for habitat quality when compiled over long periods (Sergio and Newton 2003) but is also criticized because its underlying assumption, that individuals preferentially occupy habitats that confer high relative                                                  1 A version of this chapter has been published: Germain, R. R., and P. Arcese. 2014. Distinguishing individual quality from habitat preference and quality in a territorial passerine. Ecology  95: 436–445. 11  fitness, is often false (Van Horne 1983, Bock and Jones 2004, Arlt and Pärt 2007, Rodewald et al. 2011). Another approach is to use habitat-specific estimates of reproductive output to estimate habitat quality directly (Johnson 2007). For example, in pied flycatchers (Ficedula hypoleuca) breeding-site selection was manipulated by relocating nest-boxes of newly formed pairs and measuring fitness proxies to asses habitat quality at control and relocation sites (e.g., Siikamäki 1995, Huhta et al. 1999, Thomson et al. 2012). However, because we can expect positive correlations between individual and habitat quality when developmentally or intrinsically superior individuals settle preferentially in the most productive sites (Fretwell 1972, Lomnicki 1988), it has proved challenging to separate the independent effects of individual quality versus habitat quality on fitness in natural settings (Sergio et al. 2009). Further complicating this issue, the term ‘individual quality’ can be ambiguous or inconsistently applied (Wilson and Nussey 2010). For example, studies that contrast individual and habitat quality often use cross-sectional data and equate age with quality (e.g., Balbontín and Ferrer 2008). However, because reproductive success tends to increase and decrease with age across a wide range of taxa (reviewed in Nussey et al. 2008), this definition implies that individual quality varies temporally. In contrast, quantitative genetic theory implies that individual quality is better estimated as the permanent environmental and genetic effects of phenotype on survival and reproductive success, which is consistent with its characterization as a repeatable, intrinsic trait of individual animals that is positively related to relative fitness (Arcese 2003, Steele and Hogg 2003, Kruuk 2004, Wilson and Nussey 2010, Bergeron et al. 2011). However defined, estimating the independent contributions of individual and habitat quality to site-specific annual reproductive output requires that reliable metrics of habitat and individual 12  quality are available, and that the processes acting to distribute animals across focal habitats and the relationships between individual and habitat quality are known. We used 2543 breeding records by 528 female song sparrows over 35 years to quantify long-term preference for individual breeding sites on Mandarte Island, BC, Canada, and to test if the long-term mean and annual reproductive output assessed in preferred sites was predicted better by vegetation traits measured in those sites or the mean rLRS or age of the females that nested in them annually or on average. Specifically, we asked: 1) were breeding sites occupied non-randomly by females over the study period, 2) did preferred breeding sites produce more offspring on average, 3) were preferred breeding sites occupied primarily by prime-age females or those displaying high rLRS on average, 4) could female preference for particular breeding sites be predicted by the vegetation traits measured in those sites, and 5) what were the relative contributions of female age, rLRS, and site-specific vegetation traits to annual variation in site-specific reproductive output measured over 35 years? The population of song sparrows resident on Mandarte Island is ideally suited to these questions because >98% of the birds breeding there after 1975 were hatched locally, individually-marked as nestlings and re-sighted thereafter to quantify lifetime fitness (e.g., Smith et al. 2006c, Sardell et al. 2012). Moreover, because female population size has varied widely (4-71 females) over time, the area of habitat used by nesting females is limited in extent and described in detail, territory ownership is dynamic in both sexes and individual differences among birds affect territory tenure, there exists the potential for many different females to have nested in many different breeding sites, and for most breeding sites to have been occupied by many different females over 35 years of study (Arcese 1987, 1989a, 1989b, Smith et al. 2006a).  13  2.2 Methods  2.2.1 Study system Song sparrows are an archetypal, open-cup nesting passerine species where females alone incubate eggs and both parents provide care for young (Arcese et al. 2002). The resident, individually marked population of song sparrows on Mandarte Island has been monitored in detail since 1975 (Smith 2006) by recording the life histories of all locally hatched birds (99% of all breeders on average) that recruit to the population for survival and reproductive output over their lifetimes. Song sparrows are socially monogamous but genetically polygamous (Sardell et al. 2010). Both sexes engage in sometimes protracted contests for breeding sites and individual phenotype can predict the outcome of these contests (Arcese 1987, 1989a, 1989b). Once settled, song sparrows typically breed within a neighborhood of adjacent sites on Mandarte Island (Smith et al. 2006a). However, because territory size varies inversely with the number of adults on the island (Smith et al. 2006a), the range of sites available to any individual female depends mainly on population size. Each year all nests on the island were located using a co-ordinate system based on an air photo, composed of 157 grid cells (20 x 20 m each) representing potential nesting habitat (i.e., including grass and/or shrub cover), which we define as ‘breeding sites’. Habitat preference and quality were estimated at the scale of grid cells instead of ‘territories’ because grid cell location was static over the study whereas territory size varied dramatically and inversely to population size (Smith et al. 2006a). We focused on 146 breeding sites with at least one known nesting attempt from 1975-2010, but excluded estimates of reproductive output for nests potentially influenced by feeding experiments in 1979 (n = 65), 1985 (n = 85), and 1988 (n = 113) due to the demonstrated effects of supplemental food on reproduction (Arcese and Smith 1988). 14   2.2.2 Breeding site use To estimate breeding site use and preference by female sparrows, we mapped the locations of 2543 nests from 1975–2010 with one or more eggs to a resolution of ~2.5 m in ArcGIS 9.2 (ESRI 2006). We then created 100 m2 circular buffers around each nest to define the ‘nest area’, under the assumption that buffers reflect habitat used intensively by the parents and offspring associated with each nesting attempt. We then used HawthsTools (Beyer 2004) to allocate a proportion of each 100 m2 nest area to each breeding site that it intersected (i.e., m2 overlap). Allocated in this way, each nest area overlapped 1 to 4 breeding sites, depending on its location near the center of a site or intersection of adjacent sites.  2.2.3 Estimates of habitat preference We quantified habitat preference as the number of years a breeding site included at least some fraction of 1 nest area, and also by calculating the fraction of all nest areas in a year that overlapped a given breeding site. We then calculated the mean fractional use by females of all breeding sites in all years to create a long-term metric of female preference for particular sites; defined here as ‘habitat preference’.  2.2.4 Estimates of annual reproductive output per breeding site We used the number of offspring that survived to independence from parental care (~24 days post-hatch) as the metric of reproductive output for each mapped nest. To estimate annual reproductive output at individual breeding sites, we distributed independent young from each nest to one or more sites in proportion to overlapping nest area (see above). Because annual 15  reproductive success on Mandarte varies with breeding density (Arcese et al. 1992), climate (Wilson and Arcese 2003), weather (Marr et al. 2006) and cowbird parasitism (Arcese et al. 1996), we also standardized reproductive output at each occupied breeding site in each year using Z-scores:  𝑅𝑠𝑖𝑡𝑒 − µ𝑡𝜎𝑡 where Rsite represents the total yearly reproductive output of a site (yearly sum of reproductive output of each nest in a breeding site, proportional to nest area overlap) in year t, and µt and σt represent the mean and standard deviation of reproductive output for all occupied sites in year t. Thus, our final data set included one estimate of the reproductive output of each occupied breeding site in each year.  2.2.5 Estimates of female quality per breeding site: age and rLRS We assessed individual female quality in two ways: as a developmental trait, female age (years since hatch), and as an intrinsic trait of females, their lifetime number of independent young, relative to other females hatched in the same year (relative lifetime reproductive success; rLRS). The mean age of females observed in each breeding site annually was calculated as female age, weighted by the fraction of each female’s nest area overlapping the site. We then standardized annual mean values (as above) to reduce the influence of age structure of the population on annual estimates of reproductive output (Smith et al. 2006d). Females of unknown age in 1975 were considered to belong to a single hatch-year cohort (n = 34). As with mean age and annual reproductive output of breeding sites (above), we then calculated the annual weighted mean 16  rLRS of females with a nest area overlapping the breeding site and standardized those values by the annual mean and SD of all occupied sites.  2.2.6 Vegetation characteristics of breeding sites To examine the vegetation characteristics of breeding sites we selected a number of predictors believed to be related to song sparrow habitat preference in our study system. More than 99% of nests occur in or adjacent to shrubs on Mandarte Island (Smith 2006). Thus, in 1986 and 2006 we surveyed each breeding site to record the total area (m2) of shrub cover, as well as the cover (m2) of the 9 most common shrubs occurring on the island (snowberry [Symphoricarpos albus], Nootka rose [Rosa nutkana], serviceberry [Amelanchier alnifolia], Pacific willow [Salix lucida], trailing blackberry [Rubus ursinus], Himalayan blackberry [Rubus armeniacus], currant [Ribes divaricatum], oceanspray [Holodiscus discolor] and red elderberry [Sambucus racemosa]), each of which may be subject to preference/avoidance by song sparrows as a potential nesting substrate. In addition, we surveyed the linear distance (m) of shrub/grass interface (‘edge’) in each breeding site as song sparrows typically prefer to nest in edge habitat (Arcese et al. 2002). We also measured the mean soil depth (cm above rock) and shrub height (m) at 5 evenly spaced points in each breeding site, as these predictors may reflect micro-habitat variation related to the size or age of shrubs not accounted for by the total shrub area or shrub species composition of a breeding site. We then averaged estimates from each survey (1986 and 2006) to describe ‘vegetation traits’ at each site.  17  2.2.7 Statistical analysis 2.2.7.1 Long-term patterns in habitat preference and quality Analyses were performed in R 2.15.1 (R Development Core Team 2012). We used Pearson’s chi-square to test if sparrows nested randomly with respect to breeding site location across years by comparing the distribution of the number of years a site was occupied versus expected values generated from the Poisson distribution, and pooling the number of years a site was occupied into 14 categories (≤ 12, 13, 14,…, 23, 24, ≥ 25) with expected frequencies greater than 5 to meet the assumptions of the χ2 statistic. We used the ncf package (Bjørnstad 2009) to test for spatial autocorrelation in habitat preference using Moran’s I, which calculates the summed covariation from each sampling location at a given distance, divided by the number of location pairs, to estimate spatial autocorrelation (Fortin and Dale 2005); however, none was detected beyond 20 m (i.e., width of 1 breeding site; Moran’s I < 0.2 for increments over 20 m). Estimates of habitat preference were then normalized by square root transformation and used as the dependent variable in linear analyses to test if breeding sites occupied in a greater number of years were also occupied preferentially. To test if preferred breeding sites produced more offspring on average, we used a random-effects model with breeding site identity as a random factor to calculate a best linear unbiased predictor (BLUP) of the standardized reproductive output of each site over all years, and then used linear regression to test if site-specific reproductive output increased with habitat preference. We then repeated this procedure for female age and female rLRS to test if breeding site preference was predicted by the age or intrinsic quality of its female occupants. BLUPs are to be used with caution in populations where many individuals have few observations (or a single observation) for traits under investigation (e.g., Hadfield et al. 2010). In our application of 18  BLUPs to estimate breeding site quality, we observed all breeding sites in all years to precisely estimate the proportion of all nests in each breeding site in each year. To identify the vegetation traits that characterized preferred breeding sites, we adopted an information-theoretic (IT) approach and used linear multiple regression with habitat preference as the response variable and 11 site-specific vegetation traits as predictors (all related at r ≤ 0.7; shrub area, edge, soil depth, rose, serviceberry, oceanspray, willow, trailing blackberry, Himalayan blackberry, elder, and currant). Each species of shrub in this predictor set formed the substrate of at least one song sparrow nest over the 35 year study. In total, we ran 2048 models (all possible models from n = 11 predictor variables excluding interactions), and selected those with partial support (i.e., ΔAIC ≤ 7 from the best model, Burnham and Anderson 2002). We chose a cut-off of ΔAIC ≤ 7 rather than ΔAIC ≤ 2 to incorporate all models which fall within the range of plausibility described by Burnham and Anderson (2002) and Burnham et al. (2011). We re-ran models from this subset to re-calculate relative AIC weights and assess the relative support for each predictor by summing its AIC weight for each model in which it was included. Relative support for predictors ranged from 0 (absent from all models) to 1 (present in all models); we included all those with ≥ 0.5 relative support as potential predictors of habitat preference.  2.2.7.2 Contributions of habitat versus individual quality to reproductive output Our data consisted of 2741 observations of the annual reproductive output of breeding sites overlapped by one or more nest areas in that year. To estimate the relative contributions of female and habitat quality to observed variation in annual reproductive output at a breeding site, we again applied an IT approach, linear mixed-effects models and model averaging. Specifically, 19  we used standardized annual reproductive output at a breeding site as the response variable and breeding site identity as a random effect. As predictors we included total nest overlap (TNO; the annual fraction of all nest areas that overlapped a site) to estimate what fraction of annual variation in site-specific reproductive output was accounted for solely by the sum of nest area overlap in a site and year. We did so because we expect that if female settlement was random across sites with respect to rLRS or age, the total overlap in a site would account for most of the variation observed in relative reproductive output between years, and thus reflect only the shared preference of females for a site rather than the mean phenotype of females nesting there. We also included mean age and rLRS, the interaction of these variables with total nest overlap, and 4 vegetation traits with relative support ≥ 0.5 (see Chapter 2.3.2) because these were our main predictors of interest. Vegetation traits and total nest overlap were standardized to mean = 0, SD = 1 to reduce any influence of measurement scale (White and Burnham 1999). Because equal numbers of observations are required to compare AIC in competing models (Burnham and Anderson 2002), we pruned our data set to a sample of 2555 observations across 144 breeding sites by excluding females of unknown age. We ran a tailored model set of 208 models (all possible combinations of 7 predictor variables plus age × TNO and rLRS × TNO interaction terms) and estimated parameters by averaging models with ΔAIC ≤ 7 from the top model, following Burnham and Anderson (2002).  2.3 Results Occupied breeding sites had a mean (± SD) of 2.5 (± 1.6) overlapping nest areas annually over 35 years, with a mean overlap of 95.6 (± 93.4) m2 per year. Individual female sparrows bred a mean of 2.2 (± 1.4) years, and occupied a mean of 12.7 (± 9.8) breeding sites over their lifetimes. 20  Females in a given cohort produced a mean total of 6.46 (± 3.17) independent offspring over their lifetimes.  2.3.1 Long-term habitat preference and habitat quality Female song sparrows nested non-randomly with respect to breeding site location over 35 years (Figure 2.1), and the number of years a site was occupied was strongly positively related to habitat preference, indicating that sites occupied in more years were also more densely overlapped by female nest areas on average (Figure 2.2). Breeding site identity accounted for 19.8% of the observed variance in reproductive output over 35 years (Table 2.1), indicating modest repeatability in the performance of sites across years. Preferred breeding sites also displayed high average reproductive output (Figure 2.3a). In contrast, breeding site identity accounted for only 2.3% of variation in the mean age of females whose nest areas overlapped the site, and only 5.4% of variation in mean female rLRS (Table 2.1). Neither female age nor rLRS were related to long-term average habitat preference (Figure 2.3b, c).  2.3.2 Vegetation traits in preferred habitat A global model of vegetation traits in preferred breeding sites was a good fit to the observed values of habitat preference (R2 = 0.78). Of 2048 total models (see Chapter 2.2.7.1), 261 were within ΔAIC ≤ 7 of the top model and summed to a cumulative AIC weight of 0.90. This subset included 4 traits with strong support as predictors of habitat preference (Figure 2.4): edge, shrub area, and soil depth (positive predictors), and serviceberry cover (negative predictor; see Appendix A.1 for parameter estimates and standard errors for all models in this subset). Only edge and total shrub area were included in all 262 subset models. 21   2.3.3 Contributions of habitat and individual quality to reproductive output A global model of female- and habitat-specific contributions to reproductive output at a breeding site was moderately well fit to the observed values of site-specific reproductive output (R2 = 0.55). Of 208 original models, 2 were within ΔAIC ≤ 7 from the best model, and the cumulative AIC weight of all 3 models in this subset was 0.94. Variables included in one or more of these models were total nest overlap (TNO), age, rLRS, edge and the interaction of age × TNO and rLRS × TNO (Table 2.2). AIC weights for this model subset and model averaging indicated that TNO, rLRS, and the interaction rLRS × TNO emerged as strongly supported predictors of the annual variation in reproductive output of breeding sites (Table 2.2). Edge, age, and age × TNO were relatively weak predictors of site-specific reproductive output (Table 2.2). In particular, rLRS was 2.8× more influential in predicting site- reproductive output than female age, and 85× more influential than edge, the only vegetation trait which emerged as a predictor from our IT approach.  2.4 Discussion Female song sparrows preferentially nested in breeding sites that produced high relative numbers of young on average. However, because the number of breeding females varied from 4–71 over 35 years (Smith et al. 2006b), most breeding sites were occupied by a wide range of females with respect to age and rLRS. Annual variation in the reproductive output of breeding sites was strongly predicted by the mean rLRS of females that occupied the site, but was not closely related to vegetation traits or mean female age in sites. Nevertheless, vegetation was a good predictor of long-term preference for high quality breeding sites by female song sparrows. Thus, 22  while habitat preference and quality were positively related on average, annual reproductive output at particular breeding sites was mainly a consequence of the rLRS, or quality, of the females that occupied them. Below we address our earlier questions and discuss the implications of our finding that individual quality and habitat preference were not positively related. Female song sparrows strongly preferred particular breeding sites over 35 years, leading to non-random patterns of site occupancy (Figure 2.1). Sergio and Newton (2003) concluded that birds occupied breeding habitat non-randomly in all 22 studies and 17 species included in a review of site occupancy as a measure of quality, indicating that habitat preference may act as a reliable proxy for habitat quality. In contrast, several studies report non-ideal patterns of habitat occupancy wherein individuals preferentially occupy sites with low reproductive output (i.e., ‘ecological traps’; Van Horne 1983, Bock and Jones 2004, Arlt and Pärt 2007, Rodewald et al. 2011). On Mandarte Island, habitat preference by female song sparrows was positively related to long-term, site-specific reproductive output (Figure 2.3a), indicating that habitat preference in this system is adaptive. Adaptive habitat preference is posited in many systems (e.g., Southwood 1977, Lurz et al. 1997, Clark and Shutler 1999), but relatively few studies estimate the components of variation in site-specific reproductive output that are a consequence of variation in the quality of the animals that occupy them annually or over longer periods. Our results suggest that where individual quality is not easily estimated, site-specific estimates of reproductive output based on short time-frames (relative to the longevity or site-fidelity of its occupants) cannot be assumed to represent a repeatable trait of sites that will be conferred to different occupants. We found that preference for individual breeding sites by female song sparrows was not closely related to the mean age or rLRS of the females that occupied them (Figure 2.3b, c). This 23  indicates that the ability of females to settle in preferred breeding sites was largely independent of their age or intrinsic quality, measured as rLRS. These results are in contrast to the idea of ‘Resource Holding Potential’ (Parker 1974), which assumes a positive correlation between habitat preference and individual quality as an outcome of contest competition and individual variation in competitive ability (see also Fretwell 1972, Lomnicki 1988). Although many studies report that adults or socially dominant individuals regularly restrict access by young or subordinate animals to resource-rich habitats (e.g., Arcese and Smith 1985, Petit and Petit 1996, Marra 2000, Buston 2003, Clutton-Brock et al. 2006, Wittemyer et al. 2007), few studies use longitudinal data to quantify variation in habitat use over an individual’s lifetime, or identify repeatable indices of habitat quality that are closely linked to individual fitness (but see Pärt 2001, Reid et al. 2006). On Mandarte Island, breeding site acquisition and loss depends in part on transient aspects of individual phenotype which are related to development (e.g., maturation, senescence, learning, experience, etc.), leading to frequent turnover in site ownership (Arcese 1987, 1989a, 1989b). Female numbers also varied greatly over 35 years, allowing all females to breed in high quality sites at low density, but preventing many high quality females from access to those same sites at high density, because the year-round nature of territoriality in this population limits opportunities for the re-assortment of females annually (Keller and Arcese 1998). As a consequence, strong correlations between habitat preference and female rLRS did not develop. Four of 13 vegetation traits we measured were strongly supported as predictors of habitat preference (Figure 2.4). Vegetation traits are often used to characterize habitat and predict demographic performance across habitat gradients, particularly where habitat preference is related to the reproductive output of its occupants (reviewed in Johnson 2007, Homyack 2010). 24  Vegetation traits can reflect breeding resources (e.g., nest sites, food; Burke and Nol 1998, Rodenhouse et al. 2003), as well as shelter from predators (Chalfoun and Martin 2009) or inclement weather (D’Alba et al. 2009). However, relatively few studies that use vegetation traits to assess habitat quality demonstrate clear links between preference for particular habitats and the fitness of occupants (but see Rodenhouse et al. 2003). We show that although female song sparrows strongly preferred breeding sites that could be identified by vegetation cover and composition, vegetation traits were up to 85× less influential of site-specific reproductive output than were the attributes (age and rLRS) of the females that occupied them on average (Table 2.2). This result indicates that the vegetation traits that predict habitat occupancy, and are sometimes assumed to indicate habitat quality, may be of little value in predicting the fitness of different female occupants (see also Morrison 2001, Johnson 2007). Female song sparrows preferred to nest in breeding sites that contributed more offspring to the population on average (Figure 2.3a). As expected, the best predictor of annual reproductive output at a site was the annual fraction of nests overlapping the site (total nest overlap), which should be the only predictor of output under the null hypothesis of random settlement (Fretwell 1972, Lomnicki 1988; see Chapter 2.2.7.2). However, we also found that the mean rLRS of female occupants and interaction of the mean total nest overlap of sites and female rLRS were positive predictors of annual variation in reproductive output among sites (Table 2.2). This indicates that annual variation in reproductive output among sites was influenced by the mean rLRS of females nesting in them, and also that the influence of female rLRS on reproductive output at a site increased with its mean total nest overlap. Thus, although females with high rLRS did not have priority of access to the most preferred sites (Figure 2.3c), our 25  results suggest that mean reproductive output in preferred breeding sites, which is often assumed to indicate habitat quality, was biased by the intrinsic quality of its occupants. Understanding how the environmental features of habitats shape individual phenotype and estimating how genotype and phenotype affect population growth rate are shared goals in ‘eco-evolutionary’ research (Hairston et al. 2005, Pelletier et al. 2007, 2009, Wilson et al. 2010). Gradients in habitat quality are also extensively discussed with respect to their influence on population growth and persistence (e.g., Donovan et al. 1995, Newton 1998, Rodewald et al. 2011) and much evidence suggests that fitness often co-varies with genotype across habitat gradients (e.g., Nussey et al. 2005, Charmantier et al. 2008, Quinn et al. 2009). However, when estimates of habitat quality are biased by the quality of the individuals that occupy them, care must be taken to reliably estimate the influence of habitat on population growth or individual fitness. Although vegetation traits predicted breeding site preference by female sparrows, they had little-to-no influence on site-specific reproductive output over 35 years. Nevertheless, site-identity did account for 19.8% of the annual variation in reproductive output that we observed (Table 2.1), which suggests that repeatable differences between sites exist and influence the performance of the females in them. We suggest some candidate factors not measured here include those linked to repeatable micro-climate, food availability or breeding date, all known to influence reproductive output in female song sparrows. Females that breed earlier on Mandarte typically produce more offspring annually (Arcese and Smith 1988, Hochachka 1990, Wilson and Arcese 2003) and also raise offspring more likely to achieve social dominance and to recruit to the population and breed (Arcese and Smith 1985, Hochachka 1990). Breeding sites with favorable micro-climates may enhance female fitness by facilitating early breeding or 26  minimizing physiological stress early in the season when incubation costs can be high (Bryan and Bryant 1999) and poor weather can induce nest failure (Marr et al. 2006). Our results highlight the need to estimate habitat quality independent of individual quality when the goal is to make predictions about site-specific contributions to population growth or individual fitness. Although female song sparrows exhibited strong preference for certain breeding sites over our 35 year study, we found that female intrinsic quality measured as rLRS, rather than habitat quality per se, accounted for most of variation in site-specific annual reproductive output we observed. Our findings imply that separating these contributions will be easier in populations where habitat preference, quality and individual phenotype are not closely correlated. In general, those correlations will be lower when short lifespan, low site fidelity, high variation in population density or related factors prevent the monopolization of breeding sites by a subset of individuals in populations.  27  Table 2.1 Variance components, standard error (SE), and percentage of total variance accounted for by breeding site identity for three random effects models partitioning variance in site-specific reproductive output, female age, and female rLRS.  Response  Variance component SE % of total variance Reproductive output1 Site ID 0.20 0.45 19.8  Residual 0.80 0.90 80.2 Age2 Site ID 0.02 0.15 2.3  Residual 0.97 0.99 97.7 Female rLRS1 Site ID 0.05 0.01 5.4  Residual 0.95 0.03 94.6 1n = 2740 observations across 146 breeding sites; 2n = 2555 observations across 144 breeding sites 28  Table 2.2 Parameter estimates (± SE) for variables identified as predictors of site-specific reproductive output in top-ranked models (ΔAIC ≤ 7). Parameter estimates for averaged model are calculated via AIC weights (see Burnham and Anderson 2002). TNO and rLRS refer to total nest overlap and relative lifetime reproductive success, respectively.  Model rank Intercept TNO Female age Female rLRS Edge Female age × TNO Female rLRS × TNO ΔAIC AICwt 1 -0.001 (0.02) 0.70 (0.01) -0.06 (0.01) 0.17 (0.014) - - 0.11 (0.01) 0 0.82 2 -0.001 (0.02) 0.70 (0.01) -0.06 (0.01) 0.17 (0.014) - -0.03 (0.01) 0.12 (0.01) 3.82 0.12 3 -0.01 (0.02) 0.69 (0.01) -0.06 (0.01) 0.17 (0.014) 0.03 (0.02) - 0.11 (0.01) 5.46 0.05           Averaged Model -0.002 (0.02) 0.70 (0.01) -0.06 (0.01) 0.17 (0.014) 0.002 (0.002) -0.004 (0.002) 0.11 (0.01)     29  Figure 2.1 Observed versus expected (given Poisson distribution) pattern of occupation by nesting song sparrows in 146 breeding sites over 35 years. Female song sparrows nested non-randomly with respect to breeding site location over the study period (χ213 = 350.92, p < 0.0001).    30  Figure 2.2 Relationship between the total number of years a breeding site was occupied and ‘habitat preference’, the annual fraction of nesting effort allocated to a breeding site, averaged across all years of study (R2 = 0.82, F1,144 = 665.38, p < 0.0001) indicates that females concentrated annual nesting effort in sites most often occupied over 35 years.     31  Figure 2.3 Relationships between habitat preference and site-specific reproductive output, the mean age of females occupying the site, and the relative lifetime reproductive success (rLRS) of the individual females that initiated those nests. Habitat preference was positively related to (a) the reproductive output of a breeding site (R2 = 0.63, F 1,144 = 242.53, p< 0.0001), but unrelated to (b) the mean age of females that nested in those sites (R2 = 0.0006, F 1,142 = 0.09, p = 0.76), or (c) their mean rLRS (R2 = 0.02, F 1,144 = 2.20, p = 0.14).  32    33  Figure 2.4 Relative support for 11 vegetation traits used to predict habitat preference by nesting female song-sparrows. Dashed line indicate cut-off at 0.5 relative support using all models within ΔAIC ≤ 7 subset (n = 268).    34  Chapter 3: Habitat preference facilitates successful early breeding in an open-cup nesting songbird2  3.1 Introduction Habitat selection by animals involves a complex series of decisions aimed at maximizing individual fitness within limits imposed by the environment (Morris 2003, Johnson 2007, Homyack 2010). When habitats vary in quality, exhibiting preference for sites likely to maximize foraging and breeding opportunities and/or survival rates can be adaptive (Clark & Shutler 1999; Calsbeek & Sinervo 2007). For instance, cavity-nesting endotherms often prefer to breed or roost in sites with stable, predictable micro-climates which can reduce individual heat loss and energy expenditure (e.g., bats: Sedgeley 2001; woodpeckers: Wiebe 2001). However, it may be more difficult for non-cavity nesting species to evade environmental limits on fitness given frequent and prolonged exposure to ambient conditions during breeding (Martin and Ghalambor 1999). Although many studies investigate the effects of habitat quality on reproductive success, fewer identify the potential cues responsible for selecting higher quality habitat or the components of reproduction most affected (reviewed in Johnson 2007). Further, the effects of habitat quality on fitness can be mediated by individual behaviour (Willis and Brigham 2007, Ardia et al. 2009) or phenotypic quality (Kim and Monaghan 2005, D’Alba et al. 2009, Germain and Arcese 2014), potentially confounding estimates of expected fitness in focal habitats. In this                                                  2 A version of this chapter has been published: Germain, R. R., R. Schuster, K. E.  Delmore, and P. Arcese . 2015. Habitat preference facilitates successful early breeding in an open-cup nesting songbird. Functional Ecology. Early view, DOI: 10.1111/1365-2435.12461.  35  study, we use spatial mapping of all early season nesting attempts across a 38 year study of song sparrows to evaluate the specific benefits provided by preferred nest-sites to early season reproduction, given that the challenges posed by the abiotic environment during this period can be high. Specifically, we estimated the relative contributions of nest-site preference and phenotypic quality to annual fecundity in females by measuring success at consecutive components of reproduction from timing of breeding to the number of offspring recruiting to the breeding population the following year. We also evaluate two cues linked to localized micro-climate and food availability that represent candidate factors potentially affecting habitat preference early in the breeding season. For most species in the northern temperate zone, early spring is the predominant time for territory establishment and the decision of where and when to breed. The early breeding season presents a unique set of challenges to reproduction, as cooler temperatures, shorter day-length, and harsh weather can all negatively affect reproductive success (Dunn 2004). One strategy for mitigating the challenges of early spring reproduction is to optimize the location of a breeding attempt. For species breeding in relatively exposed conditions, such as open-cup nesting birds, this usually involves selecting nest-sites with sufficient shelter, surrounding food availability, and/or thermal properties to facilitate successful breeding while minimizing costs to the incubating parent (e.g., Kim & Monaghan 2005; D’Alba et al. 2009). This need to minimize reproductive costs arises because time spent on the nest represents a potential trade-off between investment in offspring development versus parental self-maintenance and survival (Conway and Martin 2000, Hainsworth and Voss 2002, Williams 2012). In experimental studies, warmer nesting micro-climates have been linked to increased reproductive investment and success for various components of breeding, including breeding 36  date, clutch size, nestling body condition, incubation behaviour, and the number of offspring produced (e.g., Nager 1990; Meijer et al. 1999; Wiebe 2001; Pérez et al. 2008; D’Alba et al. 2009). Other studies link some or all of the measures above to food availability during the early breeding season (e.g., Arcese & Smith 1988; Hoi-Leitner et al. 2001; Nager 2006; Williams 2012). These results are intuitive because individuals in cooler micro-climates or with less access to food must allocate more time and energy to self-maintenance and offspring development, potentially leading to a negative energy balance and reduced fitness (Conway and Martin 2000, Reid et al. 2000). However, most experimental studies of habitat quality that involve local heating or food supplementation focus on cavity-nesters or occur after habitat selection has taken place, and thus may not reflect the contributions each play as cues for habitat selection in species nesting in more exposed environments. For open-cup nesting birds, the features and benefits of high-quality early season nest- sites may also differ from those better suited to environmental conditions later in the season (e.g., change in vegetation cover, predation risk, heat load). Thus, ignoring temporal differences in nest-site selection may also obscure relationships between habitat preference and individual fitness (reviewed in Chalfoun & Schmidt 2012). Despite the challenges of early season breeding, initiating successful early nests can dramatically improve individual performance for several life-history traits, including increasing the annual number of reproductive attempts and offspring produced, and the viability or recruitment of offspring to breeding age (reviewed in Williams 2012). This implies that individuals seeking to maximize fitness should select nest-sites that facilitate successful early breeding. We tested the hypothesis that song sparrows prefer nest-sites that facilitate successful early breeding and estimated the influence of preferred nest-sites on the reproductive 37  performance and incubation behaviour of occupants. Specifically, we predicted that individuals in preferred sites would: 1) breed earlier, 2) lay and hatch more eggs, 3) exhibit more energetically efficient incubation behaviour, and 4) produce more independent offspring and offspring that recruited to the population. We also estimated the relative contributions of site preference and female phenotypic quality (age and relative lifetime reproductive success; rLRS) at each investigated component of reproductive success, as ‘prime-aged’ (2–3 years old) females and those with higher rLRS tend to have greater annual reproductive success regardless of the breeding sites they occupy (Smith et al. 2006d, Germain and Arcese 2014). Further, for a subset of breeding seasons we investigated two potential cues (relative micro-climate and food availability) by which female sparrows may evaluate the quality of potential early season nest-sites. By determining the specific benefits of preferred early season nest-sites to individual fitness, and the potential cues used to identify high quality sites, we aimed to clarify the specific contributions of nest-site quality to individual fitness in systems where both individuals and habitats vary in quality.  3.2 Methods 3.2.1 Study system The resident population of song sparrows on Mandarte Island, BC, Canada has been monitored continuously since 1975; each individual is uniquely marked and all nesting locations are known (Smith 2006). Song sparrows are an archetypal, open-cup nesting songbird with female-only intermittent incubation, though males aid in feeding and care of offspring once hatched (Arcese et al. 2002). Breeding pairs reside on the territories year-round, with the majority of territory establishment/turnover occurring several weeks before breeding begins (Arcese 1989a). Females 38  typically begin egg-laying in late March or early April, producing 2-3 broods per season through early-mid July (Smith et al. 2006d). For this study, we focused on the early breeding season, which we define as any nesting attempt with a breeding date (date of first egg) on or before April 21. We chose this date as an arbitrary cut-off to coincide with timing of our assessments of early season relative micro-climate and food availability (below), and to capture variation in site use and reproduction at the earliest stage of breeding each year, before peak breeding begins in late April/early May. Further, by limiting our observations to the early breeding period we minimized the influence of nest parasitism/predation by brown-headed cowbirds (Molothrus ater). Cowbirds are the main source of nest predation in this system (Arcese et al. 1996), and typically begin nesting on average 40 days later than sparrows (Smith et al. 2006b). We excluded nesting attempts from females that had access to supplemental food during experimental studies in 1979, 1985, and 1988 (Arcese and Smith 1988), and no nesting data were available for 1980. Incorporating these restrictions resulted in a dataset of 844 nesting attempts over 38 years of study from 1975–2013, with a mean of 22.3 ± 12.7 (SD) nesting attempts per year.  3.2.2 Early season cell and nest-site preference We quantified preference for potential nesting sites following Germain & Arcese (2014) using all early season nesting attempts. Briefly, we estimated the features of available breeding habitat as 65 m2 hexagonal cells. This cell size was chosen to account for potential error in nest location (± 2.5 m) while incorporating fine-scale spatial resolution and topographical features included in our analyses of relative micro-climate (see Chapter 3.2.5). We quantified the fractional overlap (m2) of all early season nesting attempts (100 m2 buffer on nesting point location, see Germain & Arcese 2014) in each cell in each year, and then averaged yearly fractional overlap values for 39  each cell over the 38 year study period as a metric of long-term ‘cell preference’. In total, 632 cells were overlapped by at least one nesting attempt in one or more years (Figure 3.1), and non-random patterns of occupancy within this subset indicate that song sparrows exhibit strong preference for certain cells during the early breeding season (Appendix B.1). We tested for the presence of spatial autocorrelation in cell preference using Moran’s I (ncf package: Bjørnstad 2009), and found none beyond 10 m (i.e., the diameter of one 65 m2 cell; Moran’s I < 0.2 for increments over 10 m). To estimate the value of nest-site location for each individual nesting attempt (hereafter ‘nest-site preference’), we used Geospatial Modelling Environment (Beyer 2012) to calculate an area-weighted-mean based on the area of overlap between a given 100 m2 nesting buffer and the long-term preference values of each surrounding cell.  3.2.3 Components of early season and annual reproduction We chose six components of reproductive investment/success to determine if female sparrows nesting in preferred sites had greater early season and overall annual reproductive success. By investigating each component of reproduction separately, we were further able to determine if increased success in one reproductive measure (e.g., early breeding date) may be related to or in opposition with later components of success. Four components (relative breeding date, clutch size, nestling condition, and relative independent offspring per egg produced [ROEP)]) were specific to the early season nesting attempts under investigation. The last two measures (total independent offspring and total recruits) were included to determine if nesting in preferred early season sites may carry-over to greater annual reproductive success or a greater number of offspring recruiting to the breeding population the following year. 40  We ranked breeding date of nests each year to remove the effects of annual variation on timing of breeding (hereafter ‘relative breeding date’). Because the overwhelming majority of nests on Mandarte contain either 3 or 4 eggs, we analysed clutch size as a binomial variable following Hochachka (1990) by pooling nests with 1 (n = 7), 2 (n = 43) and 3 eggs (n = 364) into the category ‘three eggs or less’ (n = 414) and pooling two clutches of 5 eggs into the category ‘four eggs or more’ (total n = 430). We calculated nestling condition independent of nestling age following Hochachka & Smith (1991), by extracting residuals from a cubic regression (R2 = 0.72, F3, 4252 = 3600, p < 0.0001) between wing chord length (mm) and mass (g) and averaging the condition of all nestlings in a given nesting attempt. Next, we calculated ROEP as the proportion of offspring that survived to independence (~24 days after hatching) from the number of eggs laid, weighted by clutch size relative to the maximum recorded clutch size on Mandarte (5 eggs) using the formula:  Relative independent offspring per egg produced (ROEP) = 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔√𝑀𝑎𝑥 𝑐𝑙𝑢𝑡𝑐ℎ × 𝐶𝑙𝑢𝑡𝑐ℎ 𝑠𝑖𝑧𝑒 Weighting reproductive success in this way incorporates the costs associated with both egg production and provisioning offspring, each of which may be limiting on reproductive output (e.g., Monaghan et al. 1998). Our measure of ROEP allocates higher values to females for successfully rearing larger broods, but penalizes females for smaller returns on larger energetic investments in egg production and brood rearing. We described the annual reproductive success of individual females as the total number of independent offspring produced over the entire season (‘total independent’) and as the number that recruited to the breeding population the following season (‘total recruits’). The number of recruited offspring is a classical fitness index because it represents an individual’s genetic 41  contribution to the focal population, but it is rarely quantified in the wild due to the difficulty of following the fate of offspring after they become independent of parental care. Recruitment in our system may be under-estimated if some offspring survive and breed after dispersing from the population, but these instances are relatively rare (Arcese 1989c, Smith et al. 2006c, Wilson and Arcese 2008, Reid et al. 2014b) and re-sighting probability within the population is approximately 1.0 (Wilson et al. 2007).  3.2.4 Incubation behaviour To determine whether females in preferred nest-sites experience a lower cost to early season reproduction during the incubation period, we monitored the incubation behaviour of a subset of females in four years of the study (1997–1998, 2012–2013). We used StowAway XTI (1997–98) and HOBO U23 (2012–13) temperature loggers (Onset Computer Corporation, Bourne, MA, USA) to record the internal nest temperature at 30 s intervals continuously over 24–125 hours (mean 65.5 ± 34.5 [SD] hours), and utilized temperature profiles (warmer during on-nest bouts, cooler during off-bouts) to infer transitions in female timing on and off the nest (see Appendix B.2 for details on incubation measures). We did not record actual egg/embryo temperatures to minimize likelihood of abandonment.  We chose two metrics of female incubation behaviour indicative of the trade-offs related to offspring development versus parental self-maintenance: incubation constancy and average off-bout duration. Constancy represents the percentage of time spent incubating eggs (total time on nest/total period under observation), where higher incubation constancy is correlated with increased embryo development and nestling mass upon hatching (Kim and Monaghan 2006). Off-bout duration is positively correlated with ambient temperature in cooler environments (i.e., 42  closer to the incubating parent’s lower critical temperature), as eggs cool quickly and re-warm slowly at lower temperatures, forcing females to take shorter off-bouts and invest more time and energy maintaining optimal embryo developmental temperature (Conway and Martin 2000). Thus, females attempting to optimize offspring growth and minimize energetic investment in early spring conditions should take fewer, longer off bouts (Drent 1975, Conway and Martin 2000).  3.2.5 Modelling cell relative micro-climate To quantify cell-specific differences in the relative micro-climate experienced by nesting females, we deployed ten Kestrel 4000 miniature weather meters (Nielsen-Kellerman Co., Boothwyn, PA, USA) across the island for three full breeding seasons (2011–2013). Weather meters were mounted on 18 cm high tripods, representative of the height of an average song sparrow nest, and recorded wind-chill temperature (°C: incorporating air temperature [°C] and average wind speed [m/s]) at a given location every 30 min continuously for 30–668 hours (mean 140.3 ± 113.7 [SD] hours) before being transferred to a new location. Wind-chill temperature may represent a more appropriate measure of the energetic costs of site selection in open-cup nesting species than temperature alone, since forced convection from even relatively low wind speeds can dramatically increase heat-loss from a nest (Heenan and Seymour 2012). Over the early stage (March 9–April 21) of three breeding seasons, we recorded the relative wind-chill temperature of 315 cells over 18,103 total meter-hours of effort, and obtained multiple recordings (i.e., same location over separate months/years) for 30% of cells surveyed. For each deployment we recorded the location of the weather meter (± 2.5 m) and the relative cover of vegetation immediately surrounding it based on visibility of the meter from 5 m distance on a 43  scale of 0–5 (0 = no vegetation, meter fully visible, 5 = full vegetation, meter not visible). Further, we separated measurements of cell-specific wind-chill temperature into four separate time periods, corresponding to morning (07:30–11:00), mid-day (11:30–15:00), evening (15:30–20:00), and overnight (20:30–07:00; see Appendix B.3 for details of time separation) to account for sun location throughout the day. We modelled the micro-climate of each of the 315 cells with wind-chill temperature data, incorporating detailed measures of the island surface (slope and aspect), site-specific vegetation characteristics (total area of shrub cover [m2], linear distance of shrub/grass interface [‘edge’, m], and soil depth [mm]), and regional weather (mean daily temperature [°C] and total daily precipitation [mm]) to estimate the contributions of each to cell-specific wind-chill temperature (see Appendix B.4 for model details). We then used these models to generate predictions (separated by time period) of the relative differences in wind-chill temperature (hereafter ‘relative micro-climate’) for each of the 632 cells that females nested in over the 38 year study.  3.2.6 Phenology and food availability Adult and nestling song sparrows rely heavily on leaf-rollers (Lepidoptera: Tortricidae) as early season food on Mandarte Island, where food available in the early breeding period can limit breeding date, clutch size, and offspring production (Arcese 1987, Arcese and Smith 1988). We estimated early season food availability in a subset of cells by recording bud phenology (a predictor of insect phenology, reviewed in van Asch and Visser 2007), and insect abundance in March–April, 2012–2013 (for detailed methods see Appendix B.5). To assess early season phenology we randomly selected four terminal branches (~1.5 m above ground, with ≥ six buds) from focal shrubs and recorded the fraction of buds burst (green growth visible between bud 44  scales) and leafed out (leaves fully extended, folded or unfolded). To estimate insect abundance we returned to each focal shrub after 2–3 weeks to record the fraction of leaves damaged by Lepidopterans (edges chewed or leaves rolled into shelters) on a separate set of four randomly selected terminal branches, using the same criteria as above. We then estimated differences in plant phenology and insect abundance across individual cells as deviation from the island-wide mean for each measurement.  3.2.7 Statistical analyses All statistical analyses were performed in R 3.0.1 (R Development Core Team 2014). We used an information theoretic (IT) approach (Burnham and Anderson 2002), mixed effects models, and model averaging to determine whether nesting in preferred sites leads to increased success in four components of early season reproduction (relative breeding date, clutch size, nestling condition, ROEP), two components of annual reproductive success (total independent, total recruits), and two measures of early season incubation behaviour (constancy and average off-bout duration). We accounted for individual female phenotype in each analysis using two estimates of female quality; age and relative lifetime reproductive success (rLRS; Germain and Arcese 2014). We grouped age into three categories (1, 2–3, 4+ years) due to demonstrated increases in fecundity and success at ages 2–3 (hereafter ‘prime age’; Smith et al. 2006d), and low samples sizes of individuals older than 4 years of age. rLRS was calculated as the total number of independent offspring a female produced over her lifetime relative (z-standardized) to all other females in her cohort. This metric is linked statistically to context-dependent conditions experienced during early life (Tarwater, Germain, & Arcese, unpublished data), represents an individual female’s ability to produce offspring regardless of the stochastic environmental 45  conditions she experiences over her lifetime, and is a strong positive predictor of site-specific reproductive output, independent of site quality (Germain and Arcese 2014). Further, neither measure of female quality is correlated with access to preferred, higher quality nesting sites, as territory acquisition in this system is opportunistic and females remain relatively site faithful over their lifetimes (Arcese 1987, 1989a, 1989b, Germain and Arcese 2014). To determine the contributions of early season nest-site preference to reproductive performance, we constructed a global generalized linear mixed model for each of our six components of reproduction, including year as a random effect and female age, female rLRS, nest-site preference, and interactions between site preference and both age and rLRS as fixed effects. We included both male age and the interaction of male age and nest-site preference as fixed effects in our analyses of two particular components of reproduction likely to be directly affected by male parental investment at the nest (nestling condition and relative independent offspring per egg produced). We also added brood size to our model of mean nestling condition. All continuous fixed effects were standardized to mean = 0, SD = 1 to reduce any influence of measurement scale (White and Burnham 1999). For each measure of reproduction we ran a tailored model set that included all possible combinations of predictors and pre-selected interactions (above, see Table 3.1 for total number of models and distribution family for each response variable), selected those within ∆AICc ≤ 2 from the top model, and estimated parameters for each predictor included in these ∆AICc ≤ 2 subsets by model averaging following Burnham and Anderson (2002). To determine whether preferred nest-sites allowed females to exhibit more energetically efficient incubation behaviour, we evaluated early season nest-site preference as a predictor of both incubation constancy and average off-bout duration, again using mixed-effects models and 46  an IT approach. For each of our two behavioural measures, we constructed a global linear mixed model with year as a random effect, and nest-site preference, female age, female rLRS, mean daily temperature (°C) and total daily precipitation (mm) as fixed effects. Daily temperature and precipitation were included given their demonstrated influences on the incubation behaviour of females in many systems (reviewed in Deeming 2006), and were obtained from the Victoria International Airport weather station (http://climate.weather.gc.ca), located approximately 10 km northwest of Mandarte Island. As with components of reproduction (above), we ran a series of models with all possible combinations of our fixed effects and selected those within ∆AICc ≤ 2 from the top model for model averaging. To evaluate two potential mechanisms by which preferred nest-sites might facilitate early season reproduction, we conducted a correlative investigation between long-term cell preference and both cell-specific relative micro-climate (based on measurements from three breeding seasons) and food availability (based on measurements from two breeding seasons). We calculated the Spearman’s rank correlation coefficient (ρ) between the predicted relative micro-climate of cells and cell preference for four separate time periods (overnight, morning, mid-day, evening) to determine if female sparrows preferred cells with warmer relative micro-climates at a specific period during the day. Next, we determined if long-term cell preference was correlated with each measure of phenology/food availability (bud burst, leaf out, Lepidopteran larvae damage) again using Spearman’s rank.  3.3 Results Nesting in preferred sites affected individual female performance for several components of early season and annual reproduction. Early season nests in more preferred sites had earlier 47  relative breeding dates and fewer independent offspring per egg produced, but there was no significant relationship between nest-site preference and either clutch size or average nestling condition (Table 3.1). In terms of annual reproductive success, females that nested in more preferred early-season sites did not produce more total independent offspring on average, but did produce more offspring that recruited to the breeding population the following year (Table 3.1).  Nest-site preference was present in the averaged best model for all six components of early seasonal and annual reproduction, but in each instance had a lower relative contribution than both measures of female quality (age and rLRS). The interaction between nest-site preference and rLRS was a predictor of relative breeding date and clutch size, indicating that higher quality females nesting in preferred sites laid earlier, larger clutches than lower quality females (Table 3.1). Further, the interaction between site preference and female age predicted relative breeding date and total recruits. In this case, prime age and older females in preferred sites began breeding earlier than first-time breeders, and prime age females in preferred sites had fewer total recruits (Table 3.1). Females nesting in preferred early season sites exhibited more attentive, energetically efficient incubation behaviour via higher incubation constancy and longer average off-bouts, as predicted (Table 3.2). In contrast, female rLRS and mean daily temperature were negative predictors of incubation constancy, but positive predictors of average off-bout length (Table 3.2).  Long-term cell preference was significantly negatively correlated with relative micro-climate across all four time periods (Table 3.3), indicating that female song sparrows preferred relatively cooler cells overall. Variance in cell micro-climate across the island was relatively small during the evening and overnight periods and highest at mid-day (Table 3.3), and cooler cells were those with increased vegetation structure (Appendix B.4). Long-term cell preference 48  was positively related to the fraction of leaves damaged by Lepidopteran larvae (ρ = 0.27, n = 80, p = 0.02) in two breeding seasons, supporting our prediction that food availability may influence cell preference. However, cell preference was unrelated to plant bud burst (ρ = -0.07, n = 109, p = 0.48) or leaf out (ρ = -0.10, n = 109, p = 0.30), perhaps because sample variance in both measures was lower (0.67 and 0.59, respectively) than for leaf damage (0.92).  3.4 Discussion We observed that female song sparrows nesting in preferred early season sites exhibited earlier relative breeding dates, maintained more energetically efficient incubation behaviour, and produced more offspring that recruited to the population. Contrary to our predictions, long-term preference for potential breeding habitat (cells) was negatively correlated with cell-specific relative micro-climate in all periods of the day, indicating that females may not choose nest-sites based on warmer micro-climate per se. However, long-term cell preference was positively related to estimated food availability, measured as leaf damage by Lepitopteran larvae in two years of the study. These results suggest that preferred early season nest-sites could offer females a fitness advantage by facilitating early breeding and thus the production of offspring likely to recruit to the population at a lower reproductive cost to themselves. For many migratory and resident species that breed in the northern temperate zone, timing of breeding is strongly influenced by early season environmental conditions, and birds in particular tend to begin laying eggs earlier under warmer spring temperatures (reviewed in Dunn 2004). At the population level, an earlier breeding date is a strong predictor of individual reproductive success in many species (reviewed in Williams 2012). Because early-hatched offspring are often more likely to survive, achieve social dominance and recruit to their local 49  population (e.g., Arcese and Smith 1985, Hochachka 1990, Williams 2012), any advantage obtained by female sparrows that breed successfully early in the season could also influence individual life history. Our finding that preferred breeding sites facilitated the production of offspring that recruited to the breeding population has important implications for the ecology and conservation of populations in seasonal environments. Given that females in preferred sites produce more offspring that recruit to the population, but not more offspring per unit investment (ROEP) or more offspring overall (total independent; Table 3.1), nesting in preferred, early season sites may represent a trade-off in offspring quality (probability of recruitment) versus quantity. Specifically, by facilitating early breeding, preferred nest-sites may allow their occupants to contribute a disproportionate number of breeders to future populations by producing early-hatched offspring with a higher likelihood of local recruitment (Arcese and Smith 1985, Hochachka 1990). Thus, although prior results indicated that early-breeding females produced more offspring annually partly by increasing their total number of breeding attempts (Wilson and Arcese 2003), our current results suggest that the relationship between early-breeding and recruitment success varies more among females than it does among nest-sites (Table 3.1). This suggests that the specific advantages of preferred, early season nest-sites may be difficult to detect in analyses of habitat preference alone, or in analyses of either the nest specific (e.g., ROEP) or annual number of independent offspring produced in the absence of data on offspring recruitment probability. Our results offer unique insights on the potential influence of nesting habitat on female fitness by partitioning its effects into successive components of early season and annual reproduction. Moreover, by estimating individual female quality (age and rLRS) we were able to 50  quantify the contributions of, and interactions between, habitat and individual quality to each component of reproduction. We confirmed that female rLRS contributed relatively more to success in a given nest than site preference (cf Germain and Arcese 2014), but also found that female age was more influential than site preference to variation in relative breeding date and clutch size, because prime-aged females (2–3 years-old) tended to lay earlier and larger clutches on average (Table 3.1). However, prime-age females and/or high rLRS females did not have priority of access to high-quality sites (Germain and Arcese 2014) because annual population density has varied widely on Mandarte Island (4–71 females, Smith et al. 2006c), thus allowing most females to settle in high-quality nest-sites in low density years, but excluding many high-quality females from settling in preferred sites at high density (cf Arcese 1989b). Indeed, the interaction between rLRS and nest-site preference contributed relatively more to variation to breeding date and clutch size than did site preference alone (Table 3.1). This suggests that females nesting in preferred sites lay earlier on average, but that high-quality females nesting in preferred sites laid earlier and produced larger clutches than females with lower rLRS. While our finding that prime-age females in preferred breeding sites produce fewer recruits is counter-intuitive, age may be a less informative measure of ‘individual quality’ than often assumed, and may only reflect developmental quality or experience (Wilson and Nussey 2010). Further, similar results on the covariance between individual genotype/phenotype and the fitness achieved in higher versus lower-quality habitats (reviewed in Wilson and Nussey 2010) highlight the need for in-depth investigations of individual variation across habitat gradients to fully understand the contributions of habitat quality to both individual fitness and population dynamics. 51  Female song sparrows nesting in preferred early season sites were also found to have higher incubation constancy and longer off-bouts (Table 3.2). These results suggest that females in preferred nest-sites exhibited more attentive incubation behaviour on average and attempted to optimize available energy by minimizing the cost of rewarming eggs (Drent 1975, Conway and Martin 2000). Indeed, the relationships we observed indicate that strong causal links exist between female rLRS, incubation and nest-site preference, despite the substantial variation in incubation behaviour (Table 3.2). Previous studies suggest that females in cooler conditions spend less time incubating (lower constancy) but more time foraging due to the energetic constraints associated with maintaining egg temperatures for optimal embryo development (Conway and Martin 2000, Ardia et al. 2009). Although preferred nest-sites in our study did not offer warmer relative micro-climates to incubating females, it is possible that they experienced increased food intake (below). Incubating parents in species with single-parent, intermittent incubation expend energy on the nest at ~5× their basal metabolic rate, which requires a 100% increase in energy intake as compared to the period of offspring care (Tinbergen and Williams 2002). This represents a sizable energy flux, particularly for females not fed by a mate during incubation, and given that food availability during incubation tends to be low relative to later in the nesting cycle (e.g., Visser et al. 2006). Thus, nest-sites that facilitate efficient incubation behaviour may improve female energy balance and alleviate some costs associated with early season reproduction. We found that females preferred to nest in grid cells which, for the three years measured, presented cooler relative micro-climates across four time periods (overnight, morning, mid-day, evening; Table 3.3). Our results indicate that differences in micro-climate among cells were similar throughout the day (i.e., regardless of orientation of the sun), but that female sparrows 52  did not appear to seek out warmer cells in which to nest. Elsewhere, warmer temperatures have been shown to alleviate the energetic costs associated with incubation and brood rearing (Bryan and Bryant 1999, Pérez et al. 2008, Ardia et al. 2009), and many species show preferences for nest-sites orientated towards or away from direct sunlight, presumably as a response to thermal conditions in the nest (e.g., Walsberg 1981, With and Webb 1993, Wiebe 2001, Robertson 2009). Other studies suggest that the use of thermally sub-optimal nest-sites may reduce predation risk (reviewed in Lima 2009), or emphasize food availability and concealment from inclement weather as having more influence in nest-site selection than micro-climate (reviewed in Refsnider and Janzen 2010). We found that the warmest relative micro-climates on Mandarte Island occurred in areas with little shrub cover and shallow soils (see Appendix B.4). In contrast, cooler conditions prevailed in cells with high shrub cover, deeper soil and more edge habitat at the shrub-grass interface, all of which are positive predictors of nest-site preference in this system (Germain and Arcese 2014). Thus, although nests located in areas of little shrub cover (i.e., tall-grass meadow) may receive more direct sunlight than those in sites with more shrub cover, our results indicate that vegetation structure (e.g., nesting substrate, foraging opportunities, and concealment from the elements/predators) was more influential than relative micro-climate in nest-site selection. Nest predation in particular, while relatively low in this system in the absence of cowbirds (Arcese et al. 1996), could play a role in preference for cooler, more densely vegetated nesting sites as found in other studies (e.g., Chalfoun and Martin 2009). Future work should strive to understand the relative influences of shelter from predators versus shelter from abiotic sources of both early and late season nest failure (e.g., inclement weather, limited access to freshwater) on nest-site selection in this system. 53  Grid cell preference was positively related to leaf damage by Lepidopterans, as expected if females preferred sites with high relative food abundance in the early breeding season. However, plant phenology and leaf damage were only measured in two of the 38 years, perhaps explaining why we found no marked differences in plant phenology across our small island, despite demonstrations of dramatic variation across larger areas or more heterogeneous habitats (e.g., Wilkin et al. 2006, Møller 2008, Burger et al. 2012). Food availability prior to and at the onset of breeding is often identified as a constraint on female reproduction (Nager 2006). Success in the early nests of great tits (Parus major) depends on the synchronization of breeding and peak of Lepidopteran biomass and can influence population dynamics (Visser et al. 2006). Female song sparrows with ad libitum supplemental food in the early breeding season advance laying, produce larger and more successful broods, and raise 300% more independent offspring than control birds, demonstrating its potential to regulate the timing of breeding and reproductive success in this species (Arcese and Smith 1988). If preferred early nest-sites on Mandarte Island also offer an energetic advantage to females via increased food availability, future studies might also expect females to express higher metabolic activity and increased feeding rates to offspring in those sites. The early breeding season presents a unique set of challenges to temperate bird species, but overcoming these challenges may confer a substantial fitness advantage. Studies of how habitat preference and quality mitigate these challenges and influence individual fitness should help us predict how changes in habitat quality may affect population dynamics and life history evolution. Our findings confirm that even in relatively homogenous habitats, female preference for high-quality nest-sites can yield fitness benefits by facilitating early breeding and the production of offspring that recruit to breed, but that these benefits may not be apparent in 54  analyses of the number of fledged or independent offspring produced annually. We further show that an estimate of female quality (rLRS) and habitat quality interacted statistically, indicating that female song sparrows varied in their ability to capitalize on the potential benefits of nesting in preferred habitat. As a consequence, our findings advance understanding about the effects of nesting habitat on early-season reproduction in birds and highlight the need to consider the independent contributions of female quality and habitat quality on fitness indices.  55  Table 3.1 Parameter estimates (± SE) from averaged generalized mixed models for each of six components of early season (relative breeding date, clutch size, average nesting condition, relative independent offspring per eggs produced [ROEP]) and annual (total annual independent offspring, total annual recruits) reproductive success in song sparrows. Significant predictors (where SEs do not overlap zero) are depicted in bold. For each measure of reproduction the distribution family is reported, t = total models ran (all possible combinations of predictor variables), s = number of models in ΔAICc ≤ 2 subset (i.e., models incorporated into averaged model), and n = total number of observations. Pref and rLRS refer to nest-site preference and relative lifetime reproductive success (female lifetime reproductive success z-standardized by cohort), respectively. Age is given as ‘Prime’ (2–3 years) and ‘4+’ (older than 4 years) for females (♀) and males (♂). Blank cells occur where a predictor was not present in ΔAICc ≤ 2 subset, dashes occur when a predictor was not included in the model (see Chapter 3.2.7 for details).56   † - Goodness of fit assessed by conditional coefficient of determination of global model (Nakagawa and Schielzeth 2013) * - Goodness of fit assessed by linear regression of predicted values from global model on observed values  ♀ Age  ♀ Age × Pref  ♂ Age  Reproductive component R2 Intercept Prime 4 + ♀ rLRS Pref Prime 4 + ♀ rLRS × Pref Prime 4 + Brood size Breeding date (Poisson, t = 13,  s= 1, n = 793) 0.74† 2.60 (0.08) -0.25 (0.03) -0.22 (0.05) -0.04 (0.01) -0.03 (0.02) -0.07 (0.03) -0.05 (0.04) -0.03 (0.01) - - - Clutch size (Binomial, t = 13, s = 3, n = 793) 0.12† -0.35 (0.17) 0.50 (0.17) 0.62 (0.26) 0.10 (0.11) 0.02 (0.06)   0.05 (0.04) - - - Nestling condition (Gaussian, t = 70,  s = 3, n = 463) 0.13† 0.43 (0.24)   0.13 (0.06) -0.02 (0.02)    -0.05 (0.05) -0.01 (0.04) -0.19 (0.07) ROEP (Beta, t = 35,  s = 1, n = 771) 0.08* -0.99 (0.13) 0.06 (0.10) -0.39 (0.15) 0.33 (0.04) -0.13 (0.05)      - Total independent (Poisson, t = 13,  s = 3, n = 793) 0.39† 1.13 (0.07) 0.02 (0.05) -0.25 (0.07) 0.23 (0.02) -0.01 (0.02)   -0.005 (0.006) - - - Total recruits (Zero-Inflated Poisson, t = 13,  s = 2, n = 793) 0.30* 0.27 (0.09) -0.05 (0.12) -0.42 (0.17) 0.24 (0.04) 0.12 (0.06) -0.07 (0.05) -0.03 (0.06)  - - - 57  Table 3.2 Parameter estimates (± SE) from averaged linear (Gaussian) mixed models for each of two measures of incubation behaviour (constancy and average off-bout duration). Significant predictors (where SEs do not overlap zero) are depicted in bold. For each behaviour t = total models ran (all possible combinations of predictor variables), s = number of models in ΔAICc ≤ 2 subset (i.e., models incorporated into averaged model), n = total number of observations, and R2 refers to the goodness-of-fit of the global model (Nakagawa and Schielzeth 2013). ‘Pref’ and ‘rLRS’ refer to nest-site preference and relative lifetime reproductive success, respectively.   R2 Intercept ♀ rLRS Pref Mean daily temp (°C) Incubation constancy (t = 32, s = 4, n = 68) 0.17 0.70 (0.43) -0.51 (0.11) 0.13 (0.09) -0.02 (0.02) Avg. off-bout duration (s) (t = 32, s = 2, n = 68)  0.41 169.83 (74.26) 99.99 (13.19) 18.37 (11.79) 22.41 (6.33)   58  Table 3.3 Island-wide differences in predicted relative micro-climate of 632 hexagonal cells (65 m2) representing potential nesting sites on Mandarte Island, BC, Canada across four time periods. ‘Variance’ represents total island-wide variance in predicted relative micro-climate, ‘Predictors’ refers to habitat-based predictors of cell-specific relative micro-climate (see Appendix B.4 for parameter estimates), and ‘Relationship with cell preference’ represents the Spearman’s rank correlation coefficient between relative micro-climate and cell preference for each time period.  Time period Variance Predictors Relationship with  cell preference Overnight (20:30 – 07:00) 0.0002 Time, Date, Edge, Slope, Soil depth Ρ = -0.38, p < 0.0001 Morning (07:30 – 11:00) 0.50 Time, Date, Edge, Area shrub, Precipitation Ρ = -0.52, p < 0.0001 Mid-Day (11:30 – 15:00) 0.92 Time, Date, Edge, Area shrub, Precipitation, Soil Depth, Slope Ρ = -0.50, p < 0.0001 Evening (15:30 – 20:00) 0.02 Time, Date, Edge, Area shrub, Precipitation, Soil depth, Aspect Ρ = -0.50, p < 0.0001   59  Figure 3.1 Frequency of overlap between early season nesting attempts (100 m2 buffer on nest point location) and 65 m2 hexagonal cells on Mandarte Island, BC, Canada across 38 years of monitoring. Grey outline depicts the extent of the island (rocky intertidal area), beige represents tall-grass meadow, and black line shows extent of total shrub cover across the island.60     61  Chapter 4: Quantitative genetics of breeding date – direct and indirect genetic and fine-scale environmental effects in song sparrows  4.1 Introduction Quantifying genetic versus environmental contributions to population-wide variation in key life-history traits can provide insight into population dynamics and the potential for evolutionary responses to selection, and is consequently a major goal in ecology (Réale et al. 2003b, Charmantier and Garant 2005, Kruuk et al. 2014). However, partitioning variance in life-history traits for wild populations into their genetic and environmental components is still extremely challenging, despite the increasing availability of appropriate datasets and analytical methods (Kruuk et al. 2014). This is partly because observed phenotypic variation might reflect the direct genetic and environmental effects of focal individuals expressing the trait as well as indirect effects of individuals interacting with them (Moore et al. 1997, Wolf 2003, Hall et al. 2013, Reid et al. 2014a, Edward et al. 2014). Further, related individuals might be clustered into similar local environments, meaning that genetic effects cannot be reliably distinguished from correlated environmental effects (Kruuk and Hadfield 2007, Stopher et al. 2012, Shaw and Shaw 2014). Consequently, developing evolutionary predictions requires studies that can partition variance in life-history traits into their direct and indirect genetic and environmental components, as well as accurate data describing individual phenotype in populations where relatives are distributed across local environmental variation. The timing of breeding within a season (e.g., egg laying date or parturition date, hereafter ‘breeding date’) is a life-history trait that can substantially affect individual fitness in 62  bird, mammal, and reptile populations (e.g., Sinervo and Doughty 1996, Réale et al. 2003a, Sheldon et al. 2003). Breeding earlier within seasons often leads to higher annual reproductive or adult survival rates (e.g., Sheldon et al. 2003, Wilson and Arcese 2003, Blums et al. 2005, Charmantier et al. 2008), or high offspring survival and recruitment (Festa-Bianchet 1988, Hochachka 1990, Verboven and Visser 1998, Naef-Daenzer et al. 2001). Despite resulting strong selection on earlier breeding date, generational changes in this trait due to selection (i.e., micro-evolution) are not always observed (Gienapp and Brommer 2014, Charmantier and Gienapp 2014). This discrepancy between initial prediction and observation might arise because the total additive genetic variance in breeding date, and hence its evolutionary potential, has not always been adequately estimated (Liedvogel et al. 2012), meaning that evolutionary predictions might be biased. Inaccuracies might arise because of indirect effects of males on a female’s expression of breeding date (Brommer and Rattiste 2008, Teplitsky et al. 2010, Brommer et al. 2015), and/or because local environmental effects affecting relatives might confound estimates of additive genetic variance (e.g., van der Jeugd and McCleery 2002a, Kruuk and Hadfield 2007, Stopher et al. 2012). It is increasingly clear that indirect genetic effects of mates can substantially influence variance in mating and reproductive traits (Moore et al. 1997, Wolf 2003, Hall et al. 2013, Reid et al. 2014a, Edward et al. 2014), including male effects on female breeding date (Brommer and Rattiste 2008, Teplitsky et al. 2010). Selection on such indirect effects may cause indirect selection on breeding date, meaning that any micro-evolution will depend on both the direct genetic effects of the female and the indirect effects of her social mate, and on the cross-sex genetic correlation between the two. Resulting micro-evolution might then differ, or even be opposite in direction, from that predicted given observed genetic variation in and selection on 63  females only (Wolf et al. 1998, Bijma et al. 2007a, 2007b). Thus, studies that estimate direct and indirect genetic effects on breeding date and the cross-sex genetic correlation are required to provide insights into adaptation regarding this fitness-based trait shared by a breeding pair. The influences of broad-scale environmental effects on breeding date (e.g. climate, temperature, day length, and phenology of food resources) have received considerable attention (Réale et al. 2003b, Wilson and Arcese 2003, Both et al. 2004, Visser et al. 2006, Dunn et al. 2011). In contrast, fewer studies have quantified influences of fine-scale aspects of the local breeding environment. Some evidence suggests that breeding in higher quality locations can lead to earlier breeding date and higher fitness (Lambrechts et al. 2004, Wilkin et al. 2007, Germain et al. 2015). Studies that decompose variance in breeding date typically control for the effects of breeding location (e.g., Liedvogel et al. 2012), or assume that that any variance due to location is accounted for by permanent individual variation among territorial males (e.g., Auld et al. 2013). However, explicit estimates of phenotypic variance in breeding date due to fine-scale environmental versus genetic effects are rare. Further, failure to account for potential confounding of genetic and fine-scale environmental effects, for example stemming from correlated breeding locations of relatives, might upwardly bias estimates of additive genetic variance in breeding date (van der Jeugd and McCleery 2002). Additionally, both the method and spatial scale by which location-based variance is taken into account can affect estimates of both environmental and additive genetic variance, and consequent estimates of heritabilities (Stopher et al. 2012). Rigorous empirical studies that can accurately estimate fine-scale environmental components of variance are therefore required to accurately predict micro-evolution of key life-history traits such as breeding date. 64  Quantitative genetic analyses, and in particular the ‘animal model’, have been increasingly used to decompose phenotypic variance in key life-history traits expressed in wild populations into additive genetic, individual (repeatable non-genetic), and environmental components (Kruuk 2004, Wilson et al. 2010, Kruuk et al. 2014). However, although the animal model is often assumed to reduce bias in estimates of additive genetic variance due to shared environments among relatives, it has recently been emphasised that such models might still yield greatly inflated estimates if correlations between observations due to shared fine-scale habitat use are not taken into account (Stopher et al. 2012). Conversely, when genetic and fine-scale environmental effects co-vary (e.g. due to social structure or shared habitat use by parents and offspring), overcorrecting for such fine-scale environmental effects might cause additive genetic variance in traits of interest to be underestimated (Shaw and Shaw 2014). Therefore, accurately partitioning of variance in traits such as breeding date is only possible in systems where these two sources of variance are not confounded. While small, relatively closed populations are ideal study systems from which to derive the complete pedigrees needed to accurately partition variance in life-history traits to their additive genetic and fine-scale environmental components, they are also populations likely to express high rates of inbreeding. Inbreeding depression in small, closed populations can affect fitness, including delaying breeding date (Keller 1998, Keller and Waller 2002). Recent evidence also suggests that inbred mates can also affect the fitness of their outbred partners (e.g., Mattey and Smiseth 2015). However, relatively few studies of wild populations are able to accurately quantify the inbreeding coefficient (f) of both individuals in a breeding pair, or estimate the independent effects of each on traits such as breeding date. This represents a substantial gap in our understanding of the contributions of inbreeding to individual fitness, and our ability to 65  accurately partition variance in many key life-history traits to their additive genetic and environmental components. One of the few natural systems in which estimates of f are available for essentially all females and males is the song sparrow population resident on Mandarte Island, British Columbia, Canada. We fitted animal models to 39 years of pedigree and breeding data from this population to partition variance in breeding date while simultaneously controlling for female and male f. Specifically, we quantified: the contributions of direct (female) and indirect (male) additive genetic variances to the total phenotypic variance in breeding date, the cross-sex genetic correlation in breeding date, the effects of female and male f on breeding date, and the contribution of breeding location to the total phenotypic variance in breeding date. In so doing, we tested whether accounting for the effects of breeding location altered estimates of either direct or indirect additive genetic variance.  4.2 Methods 4.2.1 Study population Mandarte Island holds a resident song sparrow population whose members have been monitored intensively since 1975 (Smith 2006). Song sparrows typically form socially monogamous breeding pairs, where males and females cooperate to defend breeding territories and rear offspring (Arcese et al. 2002). Pairs occupy territories year-round, with peak territorial intrusion/defence occurring in early spring, just prior to breeding (Arcese 1989a). Immigrants to Mandarte (mean = 1.1/year) are mist-netted and uniquely colour-banded soon after arrival, ensuring that all adults are individually recognisable (Smith 2006). Females typically lay the first egg of their first annual nesting attempt during March or April, and produce 2–3 broods per 66  season (Smith et al. 2006d). Although song sparrows are primarily socially monogamous, extra-pair paternity is commonplace (~28% of offspring sired by extra-pair males, Sardell et al. 2010). However, extra-pair males do not provide obvious resources or care to either the extra-pair females with which they mate or extra-pair offspring that they sire. Rather, all offspring (within-pair and extra-pair) are exclusively reared on their natal territories by their mother and her socially-paired male. Each year, song sparrow nests (‘breeding locations’) on Mandarte are located by systematic close observation of all social pairs. Breeding locations are recorded to ~2.5 m accuracy on maps drawn from aerial photos and then converted into Universal Transverse Mercator (UTM) co-ordinates. Nests are visited every 3–5 days, and offspring are uniquely colour-banded 5–6 days post-hatch. The lay date of the first egg was observed directly for nests found with incomplete clutches, or back-calculated from observed hatch date or chick age for nests found subsequently (Wilson and Arcese 2003, Smith 2006, Wilson et al. 2007). To maximise accuracy, all lay date records were checked by back-calculating from nestling age and banding date and checking all discrepancies against original field notes of parental nest building/incubation behaviour. Overall, the location, lay date (± 2 days), outcome, and attending male and female of ≥ 99% of all 3350 nests initiated on Mandarte since 1975 are known (data are less complete for 1980, due to reduced fieldwork). We defined the response variable ‘breeding date’ as the day of year (days since January 1) on which the first egg was laid in each female's first annual clutch during 1975–2014. We excluded data from twenty-two active breeding attempts (as inferred from observed parental behaviour) for nests which failed before they were found. A further 98 observations were excluded because adults were involved in supplemental feeding experiments in 1979, 1985, or 67  1988 (Arcese and Smith 1988). We excluded these observations rather than model effects of each experiment because the numbers of manipulated individuals were small.  4.2.2 Pedigree and paternity data We compiled a full pedigree for all chicks banded on Mandarte during 1975–2014 using the observed identities of the male and female attending offspring at each nest (Keller 1998, Reid et al. 2015a). Beginning in 1993, all offspring were blood sampled at banding and genotyped at up to 170 highly polymorphic microsatellite loci to assign genetic parentage (Nietlisbach et al. 2015). All genetic mothers matched those assigned from behavioural observations, and Bayesian full probability models assigned sires to > 99% of sampled offspring with ≥ 99% individual-level statistical confidence (Sardell et al. 2010, Reid et al. 2014b, 2015a). Paternity of song sparrows hatched before 1993 that survived to breed subsequently was also genetically verified so far as available samples allowed (Reid et al. 2014b). We used all available genetic paternity assignments to correct the pedigree for extra-pair paternity so far as feasible (see Sardell et al. 2010, Reid et al. 2014b, 2015a for full details). We applied standard algorithms (e.g., Keller 1998, Lynch and Walsh 1998) to the full pedigree to calculate individual f relative to the 1975 pedigree baseline (Reid et al. 2014b). Because we censored our dataset by age (below), 109 observations of breeding date from individuals breeding in 1975 and hatched in 1980 (with unknown age and/or unknown parents) were excluded. We assumed that new immigrants to Mandarte were unrelated to each other and to all existing residents at the time of arrival (Keller et al. 2006, Marr et al. 2006), and therefore defined the offspring of immigrant-resident pairings as outbred (f = 0). We also assumed that immigrants themselves were outbred (f = 0), having originated from larger and less isolated 68  islands with much higher rates of immigration and turnover (Wilson and Arcese 2008). Results remained qualitatively similar when analyses were repeated after excluding phenotypic data from immigrants (Appendix C.1). Unobserved extra-pair paternity prior to 1993 presumably introduces error into the 1975–1992 portion of the pedigree, potentially affecting estimates of both VA and inbreeding depression in breeding date (e.g., Reid et al. 2014b). However, approximately 90% of all pedigree links are likely to be correct (100% of 1975-2014 maternal links with no missing data, ~100% of 1993–2014 paternal links, and ~72% of 1975–1992 paternal links assuming a similar extra-pair paternity rate to that observed subsequently). Such pedigree error is likely to cause relatively little bias in estimates of VA (Charmantier and Réale 2005, Firth et al. 2015). Furthermore, analyses that were restricted to the period for which the pedigree was fully corrected for extra-pair paternity (1993–2014) returned qualitatively similar estimates, but with higher uncertainty due to reduced statistical power (Appendix C.1).  4.2.3 Quantitative genetic analysis We fitted a series of univariate ‘animal models’ to partition phenotypic variance in breeding date into key genetic and fine-scale environmental components. Animal models are mixed effects models that estimate additive genetic variances from the covariance in phenotypes attributable to genes shared through common ancestry (Kruuk 2004, Kruuk and Hadfield 2007). They can minimise bias in estimates of VA stemming from common environmental effects, and allow flexible estimation of direct and indirect additive genetic and non-genetic components of phenotypic variance (Kruuk and Hadfield 2007). 69  Our initial (hereafter ‘non-spatial’) univariate animal model can be written in matrix notation as: 𝐲 = 𝐗𝛃 +  𝐙𝟏𝐚♀ +  𝐙𝟐𝐚♂ +  𝐙𝟑𝐏𝐈♀ +  𝐙𝟒𝐏𝐈♂ + 𝐙𝟓𝐘 + 𝐞   (1) Where y is a vector of phenotypic observations of breeding date, X and Z are design matrices relating observations of y to the relevant fixed or random effect, β is a vector of fixed effects, and a, PI, Y, and e are vectors of random additive genetic, permanent individual, year, and residual effects, respectively. This model estimates female (direct) and male (indirect, sensu Bijma et al. 2007a, 2007b, Brommer and Rattiste 2008) additive genetic effects (a♀ and a♂), female and male permanent individual effects (PI♀ and PI♂, which are assumed to comprise permanent environmental and non-additive genetic variances, Quinn et al. 2009), year (Y) and residual effects (R) on breeding date. We modelled direct and indirect genetic and permanent individual effects in a univariate model, rather than as separate sex-specific traits in a bivariate model, since the interaction between social partners is non-reciprocal (i.e., male phenotype may influence female breeding date, but the interaction ends after egg laying and cannot influence the male trait retrospectively (Moore et al. 1997, Brommer and Rattiste 2008). Male and female additive genetic effects are assumed to be jointly distributed following a multivariate normal distribution (MVN): a = [a♀′, a♂′] ~ MVN (0, G ⨂ A), where prime (′) represents a vector transpose and ⨂ the Kronecker product. Here, A represents the additive genetic relationship matrix between all individuals calculated from the pedigree (Kruuk 2004), and G represents the variance-correlation matrix: 𝐆 = [𝜎2𝐴♀ 𝜌𝐴♀♂ 𝜎2𝐴♂]     (2) 70  where σ2A♀ and σ2A♂ represent the variances in female and male additive genetic effects. We formulated our univariate model to directly estimate the cross-sex genetic correlation (ρA♀♂) rather than the additive genetic covariance for breeding date between the sexes (σA♀♂), since the former facilitated model implementation and variance component estimation, and represents a value bounded by -1 and 1 which is directly comparable among studies. We thus retain the genetic correlation in our calculations of sex-specific and total heritability (below) for simplicity of presentation, but note that the terms are easily interchanged as: 𝜎𝐴♀♂  =  𝜌𝐴♀♂  × √𝜎2𝐴♀ ×  𝜎2𝐴♂     (3) Note that we use the notation σ2 and ρ to designate expectations of variance and correlation, whereas model-derived estimates are denoted by ‘V’ and ‘Corr’.  4.2.4 Spatial models We then extended the non-spatial animal model to estimate phenotypic variance associated with breeding location of the first annual nesting attempt, and thereby partition any variance in breeding date arising through shared fine-scale environmental effects that might inflate estimates of VA♀ or VA♂. In seasonally breeding bird species, timing of breeding is thought to be largely determined by local environmental cues at the scale perceived by individuals during their daily movements (Caro et al. 2009, Thomas et al. 2010). Individual nest-sites may therefore represent a more biologically meaningful scale for heterogeneity in such environmentally sensitive traits than broad habitat classifications (Wilkin et al. 2007). Indeed, female song sparrows breed earlier when nesting in certain sites (Germain et al. 2015). We therefore focussed on estimating variance in breeding date associated with breeding location, which is assumed to capture multi-dimensional fine-scale effects of the local breeding environment, rather than modelling effects of 71  vegetation, topography, or any other specific attributes of nest-sites on breeding date. While some environmental characteristics of Mandarte have changed somewhat during 1975–2014 (e.g., vegetation composition/extent; PA, unpublished data), these changes are relatively minor. We constructed three different spatial models, hereafter ‘grid’, ‘overlap’, and ‘spatial autocorrelation (SAC)’, to estimate variance in breeding date associated with breeding location. Each spatial model formed an independent extension of the non-spatial model (equation 1) by adding a vector of random location effects (Loc): 𝐲 = 𝐗𝛃 +  𝐙𝟏𝐚♀ +  𝐙𝟐𝐚♂ +  𝐙𝟑𝐏𝐈♀ +  𝐙𝟒𝐏𝐈♂ + 𝐙𝟓𝐘 + 𝐙𝟔𝐋𝐨𝐜 + 𝐞  (4a) Since each of the three spatial models uses a separate method to describe location-based effects on breeding date, the additional component ZLoc differs among them. However, we use a common notation to represent variance due to the effects of breeding location for simplicity of presentation. For each spatial method, we estimated the scale (i.e., area [m2]) over which location-based variance in breeding date was largest, and compared estimates of VA♀ and VA♂ from each spatial method’s best-scale model with those estimated from the non-spatial model. The ‘grid’ model quantified the contribution of breeding location within a grid system to phenotypic variance in breeding date. Previous analyses of habitat quality on Mandarte used grid cells to identify fine-scale environmental characteristics that influence song sparrow habitat preference and reproductive success (Germain and Arcese 2014, Germain et al. 2015). We applied a similar approach to assign breeding locations to spatial groups, and hence to discrete areas of breeding habitat. Using ArcGIS 10.1 (ESRI 2012) we overlaid a fixed grid of tessellated hexagons (diameter = 16m, area = 166.3m2) covering all breeding locations on Mandarte and assigned a unique identifier to each grid cell (Figure 4.1). Random location effects of the identity of the cell that contained each observed breeding location were then fitted in our grid model, 72  where the spatial effect of cell identity is univariate normally distributed as Loc ~ N (0, VLoc × I), and where I represents the correlation among location effects that are independently and identically distributed. Our choice of grid cell size was based on previous analyses investigating local environmental effects on breeding date, which utilized a grid system with hexagonal cell diameter = 10m (area = 65 m2) based on the accuracy of local topographic features (Germain et al. 2015). To verify the spatial scale most appropriate for our analysis of breeding date using the grid model, we constructed a hierarchy of eight models, with different cell diameters spanning 4m to 18m (in 2m increments), giving a range of cell areas from 10.4–210.4 m2. These values approximate the scale up to which song sparrows exhibit nest-site preference (200 m2, Germain and Arcese 2014), and are each smaller than the mean area of defended territory on Mandarte (mean territory area = 1340 m2, mean area of nesting shrub cover in territory = 380 m2; RRG, unpublished data), given our focus on effects due to breeding (and not territory) location. We then compared all grid models to identify the cell size that provided the best fit to our data based on several criteria (Appendix C.2); the model with cell diameter = 16 m was best supported and is therefore reported. All other grid models are presented in Appendix C.2, and show that cell diameter had little influence on the estimated variance in breeding date due to breeding location, or hence on estimates of VA♀ and VA♂. The spatial ‘overlap’ model quantifies the degree to which areas surrounding each pair’s breeding location overlapped across years. We used ArcGIS to construct circular spatial buffers around each nest location (Germain and Arcese 2014, Germain et al. 2015). We then calculated a matrix (S) describing the area of buffer overlap between all pairwise combinations of observations, scaled so that a given breeding location had a 'spatial relatedness' of 1 with itself 73  and 0 with all breeding locations with non-overlapping buffers (Stopher et al. 2012). Thus, S describes the covariances among breeding locations based on their area of buffer overlap, analogous to how A describes covariances among the additive genetic effects of individuals based on their shared genes. The random location effect fitted in our overlap model was assumed to be univariate normally distributed as Loc ~ N (0, VLoc × S). This method thus estimates variance in breeding date attributable to shared space, given that covariance in breeding dates between neighbouring observations are expected to be greater than those spaced further apart in a heterogeneous environment (e.g., Stopher et al. 2012, Wilson 2014). We present results from analyses in which we used a 100 m2 buffer (radius = 5.6 m) because previous analyses show that this scale predicts both habitat preference and reproductive success, and encapsulates the breeding location and its immediate surroundings while accounting for potential error in a nest’s geographic location (Germain and Arcese 2014, Germain et al. 2015). However, model estimates remained similar when buffer area was sequentially increased from 50 m2 to 1000 m2 (Appendix C.3). The ‘spatial autocorrelation’ (SAC) animal model included a spatial autocovariate and estimated the distance between breeding locations at which differences in breeding date are expected to be zero, thereby directly quantifying the spatial scale of phenotypic covariance (Fortin and Dale 2005). We rounded the X and Y co-ordinates of all breeding locations to the nearest 1m, and then jittered all co-ordinates by a value of d/5, where d is the smallest distance between locations. This procedure ensured that no two observations had identical coordinates, which may impede estimation of spatial autocorrelation (Fortin and Dale 2005). We then fitted a two-dimensional spherical spatial correlation structure to the residual variance of our initial non-spatial model, where the estimated location-based variance component quantifies the spatial 74  range over which observations of breeding date are non-independent (i.e., are spatially autocorrelated; Appendix C.4). We used a spherical model rather than a 1st order separable autoregressive (AR1) process (e.g., Stopher et al. 2012), due to the uneven distribution of breeding locations on Mandarte (i.e., most nests located in and along shrub, Germain and Arcese 2014). This creates numerous empty row and column data and hinders model fitting. We also implemented an independent evaluation of the degree of spatial autocorrelation in phenotypic breeding date by calculating Moran’s I (Fortin and Dale 2005, Appendix C.4).  4.2.5 Model implementation and parameter estimation All analyses were conducted using R 3.02 (R Development Core Team 2013). Animal models were fitted using ASReml-R (Butler et al. 2007), facilitated by the MasterBayes and nadiv packages (Hadfield et al. 2006, Wolak 2012). All four animal models (non-spatial, grid, overlap, and SAC models) included separate fixed effect regressions of breeding date on female and male f, thereby directly estimating sex-specific inbreeding depression in breeding date and ensuring that estimates of VA♀ and VA♂ were not upwardly biased due to unmodelled inbreeding depression (Reid and Keller 2010). All four animal models also included sex-specific fixed effects of three age groups (1, 2–4, 5+) following previous evidence that middle-age individuals breed earlier than yearlings or older individuals in the study population (Smith et al. 2006d) and other systems (Forslund and Pärt 1995). All immigrants were assumed to be aged one year at arrival because detailed analyses from Mandarte and the surrounding islands meta-population indicate that song sparrows disperse solely as juveniles (Arcese 1989c, Wilson and Arcese 2008). 75  We used profile likelihoods to estimate 95% confidence intervals for each variance component (Meyer 2008), and likelihood-ratio tests to determine whether any of the three spatial animal models (i.e. grid, overlap and SAC) fit the data better than the initial non-spatial model. Since we model breeding date as a joint (‘emergent’) trait stemming from direct effects of the breeding female and indirect effects of her socially paired male, the total phenotypic variance (VP) for breeding date, conditioned on the fitted fixed effects, is estimated as: 𝑉𝑃 = 𝑉𝐴♀ + 𝑉𝐴♂ + 2([𝐶𝑜𝑟𝑟𝐴♀♂ × √𝑉𝐴♀ × 𝑉𝐴♂ ] × 𝑟𝑚𝑒𝑎𝑛) + 𝑉𝑃𝐼♀  + 𝑉𝑃𝐼♂ + 𝑉𝑌 + 𝑉𝐿𝑜𝑐 + 𝑉𝑅  (4b) where the spatial variance component VLoc is zero for the non-spatial model. Here, rmean is the mean relatedness between all observed breeding pairs, and was calculated from the pedigree as twice the pairwise coefficient of kinship (Bijma et al. 2007a, 2007b, Bouwman et al. 2010). The sex-specific narrow-sense heritabilities (h2♀ and h2♂) of breeding date can then be calculated separately for each sex as: ℎ♀2 =𝑉𝐴♀𝑉𝑃       (5) The total additive genetic variance in breeding date arising from the combined female and male additive genetic effects is estimated as: 𝑉𝐴𝑇𝑜𝑡 =  𝑉𝐴♀ + 𝑉𝐴♂ + 2(𝐶𝑜𝑟𝑟𝐴♀♂ × √𝑉𝐴♀ × 𝑉𝐴♂)   (6) (Bijma et al. 2007a, 2007b, Bouwman et al. 2010). The ratio of total additive genetic variance to total phenotypic variance (T2), which represents the total amount of additive genetic variance in breeding date which selection may act upon, is calculated as: 𝑇2 =𝑉𝐴𝑇𝑜𝑡𝑉𝑃       (7) We calculated approximate standard errors for female and male heritabilities and T2 using the delta method (Lynch and Walsh 1998). 76  To confirm that estimates of VA♀ and VA♂ were not biased by parental environmental effects, we fitted a subsequent non-spatial animal model that included additional random maternal and social paternal effects (Appendix C.1). Because song sparrows frequently re-pair between breeding seasons (Reid et al. 2015b), our estimates of additive genetic and permanent individual effects should be identifiable between individual females and males, and not confounded as they can be in systems where individual females and males only form one mutual social pairing over their lifetime (e.g., Brommer and Rattiste 2008, Teplitsky et al. 2010). However, to confirm that estimates of VA and VPI were not confounded by pair identity, we fitted an additional non-spatial animal model to a restricted dataset that excluded breeding dates of female-male pairs that bred exclusively with each other (Appendix C.1). Further, because sex-specific effects on breeding date may be confounded over repeated observations from the same social pair, we removed all genetic effects (σ2A♀, σ2A♂, and ρA♀♂) and calculated the sex-specific repeatability of our non-spatial model (Appendix C.1). We further removed all genetic effects and σ2PI♀ to calculate male-specific repeatability of our non-spatial model in the absence of direct female influences on breeding date (Appendix C.1). Finally, to ensure that our interpretations were not driven by any restrictions of the ASReml restricted maximum likelihood algorithm, we fitted our non-spatial model using Bayesian inference (Appendix C.1). Previous analyses in this system showed that relatives are not clumped in space, meaning that genetic and spatial effects on any phenotypic trait are unlikely to be confounded (Arcese 1989c). The mean kinship between female song sparrows and males on neighbouring territories does not differ from the mean kinship with more distant males (Reid et al. 2015a). Indeed, there is essentially no correlation between our relatedness and breeding location matrices, and hence no correlation between relatedness and inter-nest distances for females (Pearson correlation 77  coefficient [r] = - 0.035, 95% CI = -0.034, -0.037) or males (r = - 0.030, 95% CI = -0.029, -0.032). Thus, our models should separate the additive genetic and location-based effects on breeding date since these two sources of variance are not confounded.  4.3 Results 4.3.1 Breeding date, pairing, and pedigree data Our final dataset comprised 1040 observations of breeding date from 38 years (1976–1979, 1981–2014) from a mean of 28.5 (± 15.3 SD, range 2–60) pairs per year. There was substantial phenotypic variance in breeding date across the study period (Figure 4.2), with an overall day of year mean of 107.1 (i.e., April 17th, ± 12.9 SD days), but a range that spanned days 57 (Feb 26th) to 171 (June 20th). The 1040 breeding attempts were made by 518 individual female and 483 individual male song sparrows, comprising 782 unique social pairings, and including 122 (16%) social pairings where the female and male paired exclusively with each other. The 518 females contributed means of 2.1 (± 1.3 SD) observations per individual (range 1–7; 247 [48%] individuals contributed 1 observation), while the 483 males contributed means of 2.2 (± 1.4 SD) breeding observations per individual (range 1–9, 205 [42%] individuals contributed 1 observation). The pedigree (pruned to focal females and males and all assigned ancestors) comprised 1088 individuals. Mean relatedness (rmean) across the 782 social pairings that contributed phenotypic data was 0.117 (± 0.125 SD). Mean f was 0.041 (± 0.051 SD) across the 518 females and 0.037 (± 0.05 SD) across the 483 males.  4.3.2 Non-spatial animal model  The non-spatial model estimated moderate female (direct; VA♀ = 12.3) and smaller male 78  (indirect; VA♂ = 3.6) additive genetic variance for breeding date, with 95% confidence intervals that did not overlap zero (Table 4.1). The estimated cross-sex genetic correlation (CorrA♀♂) was effectively one (Table 4.1). There was also moderate permanent individual variance for females (VPI♀ = 12.3), but the estimated permanent individual variance for males was zero (Table 4.1). The year and residual variances were substantial, comprising the largest proportions of total phenotypic variance (Table 4.1). The sex-specific heritabilities were estimated to be 0.07 (± 0.03 SE) and 0.02 (± 0.01 SE) for females and males respectively (Table 4.1), and T2 was 0.18 (± 0.06 SE). The regression of breeding date on female f was significantly positive, showing that more inbred females had later annual breeding dates (Table 4.1). However, breeding date did not vary significantly with male f (Table 4.1). Middle-aged (2–4 years) and older females bred earlier than one-year old females, with middle-aged females breeding earliest (Table 4.1). This pattern was similar for males, but effects of male age were smaller than those of female age (Table 4.1).  4.3.3 Grid model The 1040 observations of breeding date were allocated to 212 discrete grid cells (diameter 16 m), with a mean of 5.1 (± 3.9 SD, range 1–21) observations per cell. There was small, but significant, variance in breeding date attributed to cell identity (Table 4.1). Indeed, the grid model had a greater (closer to zero) log-likelihood than the non-spatial model and therefore fitted the data significantly better (likelihood ratio test, p = 0.03; Table 4.1), providing evidence of persistent local environment effects on breeding date. Estimates of VA♀, VA♂, VY and CorrA♀♂ were quantitatively similar to those estimated by the non-spatial model, while the estimates of VPI♀ and VR were slightly smaller (Table 4.1). Consequently, the estimated sex-specific heritabilities and T2 were unchanged (Table 4.1). The estimated effects of female f were slightly smaller than 79  those estimated in the non-spatial model, and age effects for both sexes were quantitatively similar between the grid and non-spatial models (Table 4.1).  4.3.4 Overlap model The mean pairwise overlap between the 100 m2 buffers surrounding all 1040 nest locations was 569.8 m2 (± 279.7 SD, range 0–1297.9). When the resulting S matrix was fitted in the ‘overlap’ animal model, the estimated VLoc was significantly greater than zero, but substantially lower than that estimated by the grid model (Table 4.1). The overlap model did not fit the data better than the non-spatial model (Table 4.1). All other variance component estimates, the cross-sex genetic correlation, the sex-specific heritabilities, T2, and the sex-specific effects of f and age remained very similar to those estimated by the non-spatial model (Table 4.1).  4.3.5 Spatial autocorrelation model The spatial autocorrelation (SAC) model produced the lowest estimate of variance in breeding date due to habitat similarity, which was only marginally greater than zero (Table 4.1). This indicates that there was essentially no spatial autocorrelation in breeding date across the island, which we confirmed via a Moran's I test on the raw phenotypic data (Appendix C.4). Furthermore, the SAC model did not fit the data better than the initial non-spatial model (Table 4.1). All other variance component estimates, heritabilities, T2, and sex-specific f and age effects remained very similar to those from the non-spatial model (Table 4.1).  80  4.4 Discussion We partitioned total phenotypic variance in song sparrow breeding date into its direct and indirect additive genetic and fine-scale environmental components in a population where these potential sources of variance in breeding date are not confounded. These analyses estimated significant female (direct) and male (indirect) additive genetic variances (VA), a strong, positive cross-sex genetic correlation, and significant female (but not male) inbreeding depression in breeding date. Estimates of variance associated with breeding location exceeded zero but were relatively small, and modelling location variance did not alter our estimates of VA for breeding date in either sex.  4.4.1 Additive genetic variances We estimated substantial female and moderate male additive genetic variance, highlighting that phenotypic variance in breeding date stems partly from genetic effects of both sexes in a breeding pair. Our estimate of female heritability (0.07 ± 0.03SE) falls at the lower end of the range (0.02–0.43) reported by other studies estimating heritability of breeding date as either a female-only or joint trait (reviewed in Liedvogel et al. 2012). Our estimates of male additive genetic variance and heritability, while lower than VA♀ and h2♀, indicate that the indirect genetic effects of a female's social mate can influence her breeding date. While indirect genetic effects among relatives (e.g., maternal effects) are widely recognized across taxa, those among non-relatives are more rare (reviewed in McAdam et al. 2014). Of these, indirect genetic effects on mating traits are particularly documented in seasonally-breeding bird species, where males can affect female fitness through several pathways (reviewed in Brommer et al. 2015). For instance, a superior social mate may improve a female’s pre-reproductive condition and advance breeding 81  date by providing or defending exclusive access to high quality resources (Brommer and Rattiste 2008). In the Mandarte song sparrow population, both males and females defend territories (Arcese and Smith 1988, Arcese 1989b), and access to breeding locations with increased shelter and food resources may advance breeding date (Germain et al. 2015). Thus, while male song sparrows have the capacity to affect breeding date through defending female access to high-quality breeding locations, the indirect genetic effects of this access (and hence h2♂) may be less influential than in species where females are more reliant on males for pre-breeding resources (Brommer and Rattiste 2008, Teplitsky et al. 2010). Our finding of a strong, positive cross-sex genetic correlation suggests that the direction and magnitude of sex-specific responses to selection on breeding date are likely parallel between female and male song sparrows. These results imply that if an earlier breeding date is selected for in both sexes, this trait may evolve at a faster rate than if breeding date was not genetically correlated across sex, or if the correlation was antagonistic (Wolf et al. 1998, Brommer and Rattiste 2008). For instance, Brommer and Rattiste (2008) found evidence for a strong, negative cross-sex genetic correlation, and suggested that this sexually antagonistic genetic relationship may maintain population-wide variation in breeding date. Further, analyses of mating traits from several insect species suggests that accounting for indirect genetic effects allows for the identification of sexual conflict, and may substantially reduce estimated responses to selection than those which consider direct genetic effects alone (Wolf 2003, Edward et al. 2014). Although the strength and direction of selection on breeding date in male song sparrows has not yet been quantified, natural selection strongly favours earlier breeding in female song sparrows, as it is positively related to annual reproductive success (Wilson and Arcese 2003, Essak 2013), and because offspring produced early in the season survive locally and recruit to breed at high 82  relative rates (Arcese and Smith 1985, Hochachka 1990). Our results emphasize the need to quantify selection on both members of a social breeding pair to understand the evolution of phenological traits such as breeding date, as well as mating traits in general (e.g., Hall et al. 2013, Edward et al. 2014).  4.4.2 Variance due to breeding location Estimates of location-based variance in phenotypic traits can vary based on both the method and spatial scale used (Stopher et al. 2012), and each method may describe different aspects of the fine-scale environment affecting the trait of interest. There may, therefore, be no single best way to model location-based variance in life-history traits, and so we used three complimentary methods and a variety of spatial scales to estimate VLoc in breeding date. Our estimate of VLoc was greater than zero in each of our spatial animal models, suggesting that the location of a breeding attempt (and hence its underlying fine-scale environmental attributes) can influence breeding date in this system. However, because estimates of VLoc varied substantially between spatial models (0.03–3.61, Table 4.1) our results illustrate that investigations into location-based effects on life-history traits should adopt multiple methods and/or spatial scales to quantify multiple underlying aspects of fine-scale environmental variance, each of which may form a significant component of total phenotypic variance (see also Stopher et al. 2012). While the grid model fitted the data best, including location effects did not alter our estimates of VA♀ or VA♂, their associated heritabilities, or the total heritability (T2) of breeding date from those estimated in the non-spatial model (Table 4.1). This result contrasts with van der Jeugd and McCleery (2002) and Stopher et al. (2012), who investigated the contributions of spatial covariance to fitness traits in systems where habitat use and genes themselves often co-83  vary, which may lead to under-estimates of VA relative to location-based variance (Shaw and Shaw 2014). Although Mandarte Island is small, natal dispersal distances across the island is dictated by hatch location and not parentage, since individuals hatched nearer the ends of the island can disperse greater distances than those hatched near the centre (Arcese 1989c). Thus, relatives in this system are not clumped in space, allowing us to separate the additive genetic and location-based components of variance in breeding date. Our results imply that in systems where habitat use is not strongly correlated with relatedness (e.g., where relatives breed in different locations), or where location-based effects are analysed at a fine scale such that genetic and environmental effects are separated statistically, estimates of VA and VLoc may not be confounded, as previously reported. There are many demonstrations that breeding date is influenced by a number of broad-scale environmental effects (e.g., Réale et al. 2003b, Both et al. 2004, Visser et al. 2006, Dunn et al. 2011). For instance, coastal songbirds can exhibit substantial annual variation (i.e., YY, Table 4.1) in breeding date correlated with the El Niño Southern Oscillation, which in turn affects the reproductive output of individual females (Wilson and Arcese 2003). In many cases, individuals within a population synchronize their timing of breeding to coincide with the most favourable conditions for reproduction, through such mechanisms as phenotypic plasticity (Réale et al. 2003b, Charmantier et al. 2008), genetic variation in sensitivity to phenological cues (Visser et al. 2011), or prior experience (Grieco et al. 2002). However, relatively little research has sought to quantify how fine-scale environmental effects may influence estimates of additive genetic variance in traits closely associated with broad-scale environmental heterogeneity. We found that in addition to the effects of broad-scale environmental variation (captured by year, VY), fine-scale environmental variation did affect breeding date; however, its influences were less 84  substantial than those related to individual song sparrows (VA♀, VA♂, and VPI♀). Previous research in this system suggests that females exhibit plasticity in breeding date dependent upon their prior experience with broad-scale climate conditions (Wilson et al. 2007). Further, indices of female quality and their interactions with breeding habitat quality exhibit greater influences on breeding date than breeding habitat quality alone, on average (Germain et al. 2015). Thus, while the fine-scale environmental effects associated with breeding location may affect song sparrow breeding date, its effects on total phenotypic variance in breeding date, and ultimately the effects of underlying habitat quality on this life-history trait, are relatively small compared to additive genetic variance.  4.4.3 Inbreeding depression Although the effects of female inbreeding depression on life-history traits have been demonstrated in a number of systems (Keller and Waller 2002), the indirect effects of inbred males on joint traits such as breeding date are less known. We estimated significant female inbreeding depression on breeding date, such that more inbred females laid later in the season. In contrast, breeding date did not vary with male f. These results suggest that inbreeding depression affected individual female expression of breeding date much more than that of females mated to inbred males, as also noted by Keller et al. (2006) using earlier data from the same population. However, the effects of inbreeding depression on male fitness traits such as survival and reproductive success have previously been shown to be greater than those of females in song sparrows (Keller et al. 2008), and inbred social mates have been found to substantially influence the fitness of their outbred partners in other systems (Mattey and Smiseth 2015). Given the limited number of studies which examine the male basis to variation in breeding date, there are, 85  to the best of our knowledge, no estimates of how breeding date varies with male f outside of this study system (Keller et al. 2006, present study). Our findings of significant VA♂ but no effect of male f suggests that while male inbreeding depression in breeding date may be less pronounced than that of females, studies investigating joint traits such as breeding date should, where feasible, consider the potential indirect effects of male f on phenotypic observations of key life-history traits.  4.4.4 Conclusions Our results from this small population have important implications for studies estimating the additive genetic and environmental components of variance in life-history traits, which may not be identified across larger geographic areas. For instance, in an analysis of shorebirds that spanned 35 sites along the Great Lakes of eastern North America, Saunders and Cuthbert (2014) found that breeding location explained the greatest proportion of phenotypic variance in breeding date, and noted no significant female additive genetic variance in this trait. However, because dispersal distances between locations ranged up to ~450km (mean ~80km), differences in breeding date between them were more likely related to geographic variation in early-season temperature and phenology than the inherent quality of the breeding locations themselves (Saunders and Cuthbert 2014). Thus, although our study took place in a small, relatively homogenous geographic area, our detailed knowledge of both breeding location and relatedness among all individuals in the population facilitated our ability to partition variance in breeding date to its fine-scale environmental and direct and indirect additive genetic components with high resolution. The conclusions we draw from these analyses are readily applicable to many other study systems across a variety of taxa, and imply that adequately partitioning variance in 86  key life-history traits should include estimates of direct and indirect genetic and fine-scale environmental effects.   87  Table 4.1 Variance component estimates (95% confidence intervals [CI]), fixed effect regression parameter estimates (± SE), and heritability estimates (± SE) from four separate univariate animal models for song sparrow breeding date. VA, and VPI refer to additive genetic and permanent individual variances for females (♀) and males (♂), while VY, VLoc, and VR refer to year, breeding location, and residual variance, respectively. CorrA♀♂ represents the cross-sex genetic correlation, and f quantifies the effect of an individual’s inbreeding coefficient on breeding date. Sex-specific heritability and the ratio of total additive genetic variance to phenotypic variance in breeding date are denoted h2 and T2. Lambda (Λ) and p values are from likelihood ratio tests comparing focal spatial model (grid, overlap, spatial autocorrelation [SAC]) to the initial non-spatial model. df for each model = 1033.88    Non-spatial Grid Overlap SAC Variance components Est CI Est CI Est CI Est CI  VA♀ 12.30 (6.10–20.72) 12.20 (6.03–20.57) 12.44 (6.19–20.91) 12.51 (6.31–20.85)  VA♂ 3.63 (1.41–6.88) 3.15 (1.08–6.25) 3.42 (1.23–6.65) 3.43 (1.30–6.58)  VPI♀ 12.27 (5.68–19.29) 10.79 (4.15–17.84) 11.74 (5.03–18.87) 10.57 (4.08–17.49)  VPI♂ 0.00 (<0.00–0.40) 0.00 (<0.00–0.40) 0.00 (<0.00–0.40) 0.00 (<0.00–0.40)  VY 76.40 (54.88–109.61) 76.37 (54.86–109.54) 76.17 (54.71–109.28) 77.08 (55.41–110.50)  VLoc - - 3.61 (1.20–6.68) 1.55 (0.10–5.66) 0.03 (0.01–0.05)  VR 60.87 (55.75–66.50) 58.81 (53.65–64.49) 59.80 (54.17–65.92) 62.41 (57.19–68.16)  CorrA♀♂ 0.99 (0.70–0.99) 0.99 (0.70–0.99) 0.99 (0.70–0.99) 0.99 (0.70–0.99) Fixed effects Est SE Est SE Est SE Est SE  Intercept 114.36 1.89 114.46 1.88 114.37 1.89 114.32 1.89 ♀ Age  2-4 years -6.61 0.61 -6.54 0.61 -6.59 0.61 -6.63 0.61 5+ years -6.14 1.33 -6.11 1.33 -6.12 1.33 -6.28 1.33  ♀ f 29.57 8.35 28.50 8.28 29.31 8.34 29.22 8.27 ♂ Age 2-4 years -2.93 0.69 -3.07 0.69 -2.94 0.69 -3.01 0.69 5+ years -1.47 1.11 -1.52 1.11 -1.44 1.11 -1.30 1.11  ♂ f -2.42 7.52 -2.96 7.49 -2.52 7.52 -2.53 7.43 Heritability Est SE Est SE Est SE Est SE  h2♀ 0.07 0.03 0.07 0.03 0.07 0.03 0.07 0.03  h2♂ 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01  T2 0.18 0.06 0.17 0.06 0.17 0.06 0.17 0.06  Loglik -2857.24  -2854.78 Λ = 4.93 -2857.06 Λ = 0.37 -2855.95 Λ = 2.58      p = 0.03  p = 0.55  p = 0.11   89  Figure 4.1 Visual representation of spatial data used in the ‘grid’ and ‘overlap’ models from the four most recent years of nesting data (2010–2014, for simplicity of presentation) on Mandarte Island. Grey outline represents the rocky intertidal area, beige region the tall grass meadow, and green represents the current extent of shrub cover. Red points indicate breeding locations (i.e., nests; ± 2.5 m), and blue circles represent 100 m2 buffers around each location, used in the ‘spatial overlap’ model. Black grid represents the grid system utilized in the ‘grid’ model (cells 16 m diameter, area = 166.3 m2).    90  Figure 4.2 Distribution of breeding dates from first annual song sparrow nesting attempts (n = 1040 nests) across 39 years of study on Mandarte Island. Black line represents the mean value of 107.1, or April 17. Individual females and males are represented by means of 2.1 (± 1.3 SD) and 2.2 (± 1.4 SD) times respectively, given longevity.     91  Chapter 5: Habitat preference is linked to adult survival, reproductive rate, and the relative importance of each to annual population growth in a year-round resident bird  5.1 Introduction Natural selection is expected to favour individuals able to recognize and acquire high-quality habitat that positively influences fitness, inspiring many studies on the effects of habitat on reproductive rate (reviewed in Johnson 2007, Gaillard et al. 2010). However, in medium to long-lived species variation in adult survival tends to be more influential of individual fitness and population performance than reproductive rate (Sæther and Bakke 2000, Heppell et al. 2000, Crone 2001) and a more limited literature indicates that adult survival during the non-breeding (i.e., ‘over-winter’) period can be affected by habitat quality or type (e.g., Hochachka et al. 1989, Sherry and Holmes 1996, Coulson et al. 1999, Morris and Davidson 2000). Because mortality rates in many species peak in the non-breeding period (Arcese et al. 1992, Camphuysen et al. 1996, Hostetler et al. 2015) it is possible that habitat features affecting over-winter survival differ from those affecting annual reproductive rate. Thus, hypotheses about how habitat affects individual fitness or population performance should consider how local environmental factors linked to habitat may affect over-winter survival as well as reproductive rate (Webster and Marra 2005, Hostetler et al. 2015). Links between habitat and over-winter survival may be particularly critical in species that reside year-round in northern latitudes where cold temperatures, heavy rains or severe storms can cause catastrophic mortality (e.g., Tompa 1971, Rogers et al. 1991, Camphuysen et al. 1996), 92  and where variation in vegetation structure or complexity, or access to food may affect over-winter survival (Newton 1998). Resident species may also face trade-offs if habitats differ in their likelihood of maximizing annual reproductive rate, over-winter survival, or both components of annual fitness (van Noordwijk and de Jong 1986, Franklin et al. 2000). The lack of empirical examples of such trade-offs may stem from the difficulty of discriminating these effects of habitat due to stochastic variation in environmental factors affecting survival or annual reproductive rate, or to systematic variation in population size or the traits of individual animals. Here we quantify the influences of habitat selection on adult over-winter survival, and test whether individuals prefer habitats that maximize fitness through annual reproductive rate during the breeding season, adult survival during the over-winter season, or through both fitness components over the entire annual cycle. We use 37 years of detailed observations on a resident, individually-marked, island song sparrow population to test whether birds occupying preferred habitats had both higher adult over-winter survival and annual reproductive rates, or whether preference was more strongly linked to one of these fitness components at the expense of the other. Earlier results suggest that up to 90% of adult mortality occurs outside the breeding season in this population (Arcese et al. 1992), that spatial variation in adult over-winter survival exists (Hochachka et al. 1989), and that habitat preference by song sparrows has predictable, positive effects on individual annual reproductive rate (Germain and Arcese 2014). We extend these results here by estimating the effects of habitat features on adult over-winter survival relative to those of winter weather, population size, sex, and individual age. We further test whether annual reproductive rate, over-winter survival, or a combination of these fitness components is the most likely target of habitat selection in this population by evaluating their relationships with a long-term pattern of habitat preference. Finally, we test whether the relative importance of annual 93  reproductive rate or over-winter survival to annual population growth (λ) varies with habitat preference. By evaluating how habitat selection can positively affect both survival and reproduction in a year-round resident species, we provide insight into the effects of habitat preference on individual fitness and population performance throughout the entire annual cycle.  5.2 Methods 5.2.1 Study area Our study was carried out on Mandarte Island, British Columbia, Canada, where a resident population of song sparrows has been individually marked and monitored since 1975 (see Smith 2006 for details of study system and methods). Briefly, each breeding season territory boundaries of paired and unpaired territorial males are mapped via behavioural observations onto an orthorectified air photo of the island. A complete census of all individuals present on the island is conducted annually from about April 20 to May 10 to determine apparent survival over the preceding winter (hereafter ‘over-winter survival’), since natal dispersal occurs before an individual’s first breeding season (Arcese 1989c, Wilson and Arcese 2008) and re-sighting probability within the population is c. 1 given our survey methods (Wilson et al. 2007). Males and females spend most of their time on or close to their territories year-round (Arcese 1989c, Hochachka et al. 1989). Peaks in territory settlement and turnover occur in March, just prior to breeding (Arcese 1989a), and females as well as males compete for sites (Arcese and Smith 1988, Arcese 1989b). For our analysis of adult over-winter survival, we included data from all males and females that maintained a territory and were alive at the end of breeding in year t that survived to year t + 1 (‘survivors’). If an individual bred in t but did not survive to t + 1 (‘non-survivors’), it 94  was included in our dataset if the lay date of the first egg in its last annual breeding attempt was later than the minimum last lay date of all survivors that year (i.e., the individual survived at least to the end of the breeding season). If the lay date of a non-survivor was earlier than the minimum last lay date of all survivors, it was removed since it was unknown whether the individual survived to the end of the breeding season. These restrictions removed 116 records from our original total of 2751 records of individual overwinter survival. We further included all territorial individuals who were consistently observed in year t but did not breed (i.e., unmated males) in our analyses since previous work in this system suggests that most adult mortality occurs during the winter (Arcese et al. 1992), and since individuals not producing/caring for offspring are less likely to engage in physiologically demanding or ‘risky’ behaviour during the breeding season. Our final dataset consisted of 250 records of individual overwinter survival from non-breeding territorial males, thus their inclusion is not likely to alter our results/interpretations. Although females remain on a set territory and breed in every year (Hochachka et al. 1989, Smith et al. 2006d), some males may not gain a territory until two years of age or older (Arcese 1989c). These ‘floater’ males were not included in our analyses for years in which they did not hold a territory the previous breeding season, but were included in years where they maintained a territory.  5.2.2 Predictors of adult over-winter survival Based on field-drawn territory maps from 1976-2012, we digitally mapped the extent of each territory defended in the prior breeding season using ArcGIS 10.1 (ESRI 2012), except for data from 1981 due to reduced field effort in 1980 precluding accurate estimates of territories or annual reproductive rate. We calculated the total area (m2) of each mapped territory, as well as 95  the area of shrub cover (‘area shrub’, m2) and length of shrub/grass interface (‘edge’, m) within each territory polygon (see Appendix D.1 for details). Prior results indicate that shrub area and edge length are strong predictors of breeding habitat preference in this system (Germain and Arcese 2014) since over 99% of nests occur in or adjacent to shrubs (Smith 2006). We also calculated the total length (m) of intertidal coastline in each territory (‘length intertidal’, see Appendix D.1 for details). The intertidal zone provides sparrows with consistent access to marine invertebrates (Rogers et al. 1991, Arcese et al. 2002), potentially giving owners of territories closer to the edge of the island or with more coastline priority access to this food resource. Regionally, the Pacific North-west of North America is characterized by a Mediterranean climate, with cool summers and relatively mild, wet winters (Smith 2006). Data to describe winter weather were obtained from the Victoria International Airport weather station (http://climate.weather.gc.ca), c. 10 km north-west of Mandarte Island. In each year we created four summaries of weather over January–February, which are typically the coldest months of the year and those with the highest documented juvenile and adult mortality (Arcese 1989c, Rogers et al. 1991, Arcese et al. 1992). Summary variables were: average temperature (mean of daily mean temperature, °C), total rainfall (mm), total precipitation (rainfall + liquid equivalent of snowfall, mm), and average temperature over the three coldest days of January–February (lowest cumulative mean daily temperature, °C, hereafter ‘3 coldest days’). The latter variable was indicative of episodic periods of extreme inclement weather, and was created based on findings that three or more consecutive days of sub-zero temperatures on Mandarte can drastically reduce survival (Tompa 1971, P. Arcese, unpublished results), suggesting that song sparrows reach a physiological threshold when exposed to inclement weather for periods longer than 3 days 96  (Arcese et al. 1992, Marr et al. 2006), as found in similar species (e.g., white-crowned sparrow: Ketterson and King 1977). We also included in our analyses three potential predictors of adult over-winter survival unrelated to winter weather but reported in this and other systems. These included individual sex and age, since males and middle-aged birds tend to survive better over-winter than females and younger or older individuals (Hochachka et al. 1989, Smith et al. 2006d, Keller et al. 2008). We thus pooled individuals older than 5 years (5+) and included age as a quadratic term (age + age2) in our analyses. Because over-winter survival may also be influenced by population density (Arcese et al. 1992, Coulson et al. 2001, Smith et al. 2006c), we included population size as a covariate, calculated as the sum of adults alive at the end of breeding and independent offspring produced from the prior breeding season. We chose to include independent offspring in this metric, rather than simply the number of breeding pairs (Arcese et al. 1992), since this value represented the total number of individuals competing for winter resources annually.  5.2.3 Habitat preference and annual reproductive rate Site-specific habitat preference and the predicted annual reproductive rate of birds occupying particular sites were estimated following Germain and Arcese (2014). Briefly, we assigned the locations of all nests (plus 100 m2 circular buffer, radius = 5.64 m) found between 1975 and 2011 (N = 2590) to a series of 20 × 20 m grid cells (hereafter ‘sites’) and calculated the fractional area of overlap between all nest buffers and a given site in each year. We then estimated ‘habitat preference’ for each site as the mean fractional overlap of all nests in all years, such that preferred sites are occupied more frequently and/or used more heavily in low density years when individual females have greater choice of nest location (Germain and Arcese 2014, 97  Germain et al. 2015). Next, we estimated predicted reproductive output at each site using a random-effects model with cell identity as a random factor and the standardized (by year) number of offspring that reached independence (~24 d after hatch) from all nests in a given site each year as the response variable. The resulting output represents the predicted ‘reproductive rate’ at each site in all years of study, which accounted for ~20% of all observed variance in reproductive output, independent of the age and quality of females occupying each site (Germain and Arcese 2014). Prior results show that site-specific habitat preference and reproductive rate are highly positively related, indicating that song sparrows preferred to nest in sites that consistently produced more offspring annually on average, regardless of the age or identity of the individual birds breeding in the site (Germain and Arcese 2014).  5.2.4 Statistical analysis All analyses were performed in R 3.1.1 (R Core Development Team 2014). We used generalized linear mixed models, an information theoretic approach, and model averaging (Burnham and Anderson 2002) via the lme4 (Bates et al. 2014) and MuMIn (Barton 2015) packages to determine the best suite of habitat, weather, and individual or population-based predictors of over-winter survival. We first tested for pairwise correlations between all of our predictor variables, and excluded one member of each highly correlated (r ≥ 0.7) pair (see Appendix D.2 for details). We then constructed a global model with survival (‘yes/no’) as a binomial response variable, year as a random effect (to account for stochastic annual variation in over-winter survival), and our remaining non-collinear variables (area shrub, length intertidal, average temperature, total precipitation, 3 coldest days, sex, age + age2, and population size) as fixed effects. All continuous fixed effects were standardized to mean = 0, SD = 1 to reduce any 98  influence of measurement scale on results (White and Burnham 1999). We also included all two-way interactions between our habitat variables (area shrub and length intertidal) and each winter weather variable (average temperature, total precipitation, and 3 coldest days) as fixed effects to determine if habitat features are more likely to influence survival during cooler or more extreme winter conditions. We ran a tailored set of 1495 models (all possible combinations of eight fixed effects plus six interaction terms) and selected those with ΔAICc ≤ 7 from the top model. We chose a cut-off of ΔAICc ≤ 7 to ensure that estimates of the relative influences of each predictor on adult over-winter survival were as conservative and inclusive as possible (Burnham and Anderson 2002). We then recalculated relative AIC weights from this ‘top models subset’, assessed the relative support of each predictor by summing the AIC weight of each model in which it was included, and averaged parameter estimates and standard errors for each predictor weighted by the cumulative AIC weight of each model in which it was included (Burnham and Anderson 2002). Statistical significance for each fixed effect was assessed via z-values (absolute value of parameter estimate divided by SE) where z-values greater than 1.96 were considered significant at α = 0.05. We used output from our final averaged model based on fine-scale territory attributes (above) to estimate predicted ‘over-winter survival’ for each mapped grid cell, thus allowing us to directly compare the effects of habitat on annual reproductive rate and over-winter survival at the same spatial scale (see Appendix D.3 for details). Song sparrows on Mandarte Island are highly philopatric to territories year-round (Arcese 1989c), but range more widely and are therefore likely to utilize a larger number of sites within their territories in winter as compared to the breeding season. Because territory size and shape vary by year and population size (Smith et al. 2006a), we restricted our analysis to the effects of individual grid cells on survival, since 99  these represent a static spatial unit that can be directly compared across the 37 year study period. However, we assumed that territories which include more ‘high quality’ sites in terms of predicted over-winter survival were more beneficial to their occupant. We used the ncf package (Bjørnstad 2009) to test for the presence of spatial autocorrelation in the residuals from predicted values using Moran’s I, and found none beyond 20 m (i.e., the width of each grid cell; Moran’s I < 0.2 for increments over 20 m). Next, we created a metric to combine our predictions of site-specific over-winter survival with site-specific reproductive rate, based on complementarity between these two metrics for each site. Prime reproduction and survival sites (PRSS) were calculated as: 𝑃𝑅𝑆𝑆 =  2 × 𝑅𝑅 × 𝑂𝑊𝑆𝑅𝑅 + 𝑂𝑊𝑆 where RR represents reproductive rate, and OWS over-winter survival. This metric is analogous to a β diversity score, where the metric is scaled by a factor of 2 to create a score between 0 and 1, and where higher scores are allocated to sites with higher values for both over-winter survival and reproductive rate. While we estimated over-winter survival for all 160 grid cells across the island, we present PRSS values only in sites for which we have estimates of predicted reproductive rate (i.e., those which contained at least one nest over the study period, n = 146 sites, Germain and Arcese 2014). We used linear regression to compare site-specific estimates of over-winter survival and reproductive rate and test if sites wherein song sparrows were more likely to survive were those where they exhibited higher reproductive rates on average. Next, we used linear regressions to compare the relationships between habitat preference and over-winter survival, reproductive rate, and PRSS of each site, and test whether song sparrows in this population preferentially select 100  year-round habitats which positively affect their over-winter survival, reproductive rate, or the best combination of both fitness components. Finally, we conducted an elasticity analysis using the popbio package (Stubben and Milligan 2007) to evaluate the relative effects of proportional changes in annual reproductive rate and adult over-winter survival on λ in preferred versus less-preferred habitats. To do so, we binned habitat preference into 4 categories (low, med-low, med-high, and high) and estimated average annual reproductive rate and average adult over-winter survival for each category based on site-specific predictions of each (above, see Appendix D.4 for details). Juvenile survival was calculated as mean annual survival of all independent offspring produced in each breeding season (1975-2011) to the following spring (0.37), and held constant in all models. Elasticity analyses were based on a 1% change in vital rates, and we assumed an equal sex ratio in all models (Postma et al. 2011).  5.3 Results 5.3.1 Predictors of adult over-winter survival We used 2586 observations of 496 female and 550 male song sparrows (Nobs = 1103 female and 1483 male, respectively) to estimate site-specific variation in over-winter survival over 37 years. Summary statistics for each predictor of over-winter survival are presented in Table 5.1. Our model averaging approach indicated that adult over-winter survival was significantly lower in territories with increased length of intertidal coastline, and greater for both males and middle-aged individuals than females and younger or older individuals (Table 5.2). Our global model of the habitat, weather, and individual or population-based influences on adult over-winter survival was a relatively good fit to the data (R2 = 0.19) and of the 1495 models, 193 were within 101  the top models subset (within ΔAICc ≤ 7 from top model) with a cumulative AICc weight of 0.88 (parameter estimates for entire top models subset presented in Appendix D.5). ‘Sex’ and ‘age + age2’ were included in all top models, and each fixed effect was included at least once in a top model (Table 5.2). Over-winter survival increased with total shrub area (p = 0.06), but declined with the length of intertidal habitat (p = 0.02). Population size, total precipitation, and interactions between total shrub and 3 coldest days, and length of intertidal and average temperature were weakly associated with over-winter survival based on relative support, but were not statistically significant (Table 5.2).  5.3.2 Site-specific estimates of survival, reproductive rate, and PRSS Site-specific predictions of adult over-winter survival suggested that survival was higher in sites along the center of the island, coinciding with areas of dense shrub cover, and that survival was lower adjacent to intertidal areas (Figure 5.1a). Site-specific estimates of predicted reproductive rate and PRSS exhibited more spatial heterogeneity than over-winter survival, but were likewise highest in sites with higher shrub cover (Figure 5.1b,c).  5.3.3 Relationships between habitat preference and multiple fitness components Site-specific over-winter survival and reproductive rate were positively related (R2 = 0.34, F1,144 = 74.99, p < 0.001), indicating that sites predicting high over-winter survival were also those with higher annual reproductive rate on average. Over-winter survival was also positively related to site-specific estimates of habitat preference (Figure 5.2a) although a somewhat bimodal distribution in survival estimates reduced the strength of this relationship and that between habitat preference and PRSS (Figure 5.2c). Overall, the strongest relationship between site-102  specific estimates of habitat preference and our measured fitness components was that between habitat preference and reproductive rate (Figure 5.2b).  5.3.4 Effects of survival and reproductive rate on population growth  Elasticity analyses revealed that adult over-winter survival was more influential of λ than annual reproductive rate in all four categories of habitat preference (Figure 5.3). However, the magnitude of this difference declined markedly in more preferred sites because annual reproductive rate was higher in preferred sites, but adult over-winter survival was similar in more and less-preferred sites (Figure 5.3).  5.4 Discussion Many studies focus on links between habitat selection and reproductive rate or survival but few test whether habitat selection in resident animals tends to maximize annual fitness or one fitness component at the expense of another. We asked how habitat influenced adult over-winter survival and reproductive rate over 37 years and whether song sparrows preferred to occupy sites more predictive of high adult over-winter survival, annual reproductive rate, or both fitness components. We detected significant heterogeneity in adult over-winter survival across sites and found that survival was predicted better by fine-scale habitat attributes than by population size or winter weather (Table 5.2). We further found that both adult survival and reproductive rate influenced habitat selection and that sites that increased reproductive rate also tended to increase the probability of survival, highlighting the importance of obtaining high quality sites for maximizing annual fitness. Although adult survival had greater effects on λ than annual reproductive rate, the relative influences of reproductive rate on λ become greater in preferred 103  sites, due to higher site-specific variation in reproductive rate than over-winter survival. Given the overwhelming importance of longevity to lifetime reproductive success in this and other species (Clutton-Brock 1988, Newton 1998), our results indicate that natural selection for traits linked to the ability to recognize and occupy high-quality habitat that maximizes fitness over the entire annual cycle will be under strong directional selection.  5.4.1 Predictors of adult over-winter survival Adult over-winter survival was linked positively to total shrub cover in an individual’s territory but negatively to the length of adjacent intertidal habitat (Table 5.2). Shrub cover is a strong positive predictor of habitat preference on Mandarte Island (Germain and Arcese 2014). Densely-vegetated sites exhibit slightly cooler mean daytime temperatures in the early spring than more open sites, but also earlier breeding dates, more efficient incubation rhythms, and higher food availability (Germain et al. 2015), suggesting that densely-vegetated sites may offer their occupants an energetic advantage. High shrub cover may also provide superior protection from predators and inclement weather (Kim and Monaghan 2005, D’Alba et al. 2009, Lima 2009), each of which can dramatically affect survival (Newton 1998). In contrast, the negative relationship between intertidal coastline and adult over-winter survival was counter-intuitive, given that intertidal habitats are used extensively by foraging sparrows in spring and summer (Rogers et al. 1991, Arcese et al. 2002). Our current results therefore suggest that exposure to predators or high winds may counteract the potential foraging benefits of these areas (cf Rogers et al. 1991). Interestingly, neither total population size nor weather extremes predicted adult over-winter survival in our current results (Table 5.2). The former finding is consistent with prior 104  results indicating that population size primarily acts on juvenile survival in this system (Arcese et al. 1992, Smith et al. 2006c). In contrast, severe winter weather has been previously but inconsistently linked to events of severe adult mortality in this population (Tompa 1971, Rogers et al. 1991, Arcese et al. 1992, Keller et al. 1994) and reproductive failure has been variously linked to extended rainfall events, inbreeding, and the interaction of these factors (Marr et al. 2006), suggesting that a wide range of weather events may influence individual fitness but nevertheless remain hard to predict at the level of individuals or sites.  5.4.2 Relationships between habitat preference and multiple fitness components Site-specific predictions of adult survival and reproductive rate were positively related, indicating that habitats conferring high adult over-winter survival were also those linked to high annual reproductive rate, independent of the individual birds that occupied those sites or the year of study (cf Germain and Arcese 2014). Further, individuals in this population strongly preferred habitats which positively affected both measured fitness components (Figure 5.2a, b). Therefore, a trade-off between adult over-winter survival and annual reproductive rate was not observed, in contrast to Franklin et al. (2000), who reported opposite effects of habitat metrics on survival and reproduction for a long-lived owl species. Sites with lower reproductive rates in our study typically corresponded to areas conferring average over-winter survival, whereas those with higher reproductive rates typically conferred higher over-winter survival (Figure 5.1), likely because increased shrub cover offered benefits to both survival and reproduction throughout the year. Our metric combining these effects (PRSS) was also strongly positively related to habitat preference, but preference was more closely linked to reproductive rate than survival or PRSS (Figure 5.2). This indicates that although song sparrows in this population may, on average, 105  prefer habitat that maximizes annual fitness, site selection may be based primarily on habitat characteristics most closely linked to reproduction. For short-lived species such as song sparrows (median lifespan of each sex ~2 years, Smith et al. 2006c), we might expect individuals to employ ‘fast-living’ strategies that favour current reproduction over survival or future reproduction (Sæther and Bakke 2000), in contrast to longer-lived species that employ ‘bet-hedging’ strategies favouring survival over reproduction (e.g., Franklin et al. 2000, Testa 2004, Hamel et al. 2009). However, the relative strength of the relationship we report between habitat preference and annual reproductive rate (Figure 5.2b) may stem in part from the fact that nest locations are much more precisely estimated than over-wintering sites, which include roosting and feeding areas that were not mapped in detail. Due to the difficulty of observing individuals outside of breeding, when activities are not focused at a nest site, there may be a higher sampling error in estimates of the relationship between local habitat and overwinter survival than for reproductive rate. For instance, an individual’s current reproductive rate or those of its neighbours has been regularly used to estimate habitat suitability and is often linked to the occupancy of ‘high-quality’ breeding habitat (e.g., Danchin et al. 1998, Bled et al. 2011, Kivelä et al. 2014). However, this relationship is less well-established for over-winter survival, particularly in short-lived species, perhaps because habitat-based cues related to survival may be harder to detect than those related to reproduction (Gaillard et al. 2010). Thus, individuals more often rely on larger-scale cues such as population density to evaluate habitat suitability (Fletcher 2006), which can lead to mismatches between habitat preference and quality (Van Horne 1983, Bock and Jones 2004, Arlt and Pärt 2007, Rodewald et al. 2011).   106  5.4.3 Effects of survival and reproductive rate on population growth As is commonly found in other studies (e.g.,Sæther and Bakke 2000, Heppell et al. 2000) adult survival had larger effects on population growth than reproductive rate (Figure 5.3). However, the importance of annual reproductive rate to λ increased with increasing habitat preference. For individuals in low and med-low preference sites, the effects of adult over-winter survival to λ were over 4× more influential than those of reproductive rate (Figure 5.3). Survival and reproductive rate were nearly equally important for individuals in med-high and high preference sites (Figure 5.3), owing to higher estimates of each fitness component in preferred sites. Such dramatic variation in multiple components of fitness between preferred and less-preferred habitats will likely lead to differential selective pressure among habitats, and strong directional selection for occupying habitats that maximize fitness over the entire annual cycle (Coulson and Tuljapurkar 2008). Because adult survival is often more influential than reproductive rate on population growth (reviewed in Crone 2001), some studies argue that this fitness component should be the target of conservation efforts for species at risk (e.g., Heppell et al. 2000, Reid et al. 2004, Schmidt et al. 2005). However, our results suggest that the greater influence of survival on λ is limited to individuals in less-preferred sites that produce fewer offspring and thus contribute relatively less to population growth than those occupying preferred sites. Therefore, since both fitness components may contribute equally to population growth in ‘high-quality habitat’, conservation efforts should seek to identify and prioritize habitat that maximizes fitness throughout the annual cycle.  107  Table 5.1 Summary statistics for predictors of adult over-winter survival. Habitat features (‘Area shrub’ and ‘Length intertidal’) refer to the total area of shrub cover and total length of intertidal coastline in an individual’s territory. Winter weather predictors (‘Avg. temp’, ‘Total precip.’, and ‘3 coldest days’) refer to January-February average temperature, total precipitation, and lowest cumulative temperature for a three day period, respectively. Note that for both female and male age we pooled individuals older than 5 years (5+) for our analyses (see Chapter 5.2.2).   Mean SD Range Area shrub (m2) 438.47 324.04 0–3194.33 Length intertidal (m) 25.14 39.16 0–267.06 Avg. temp (°C) 4.61 1.13 1.86–6.80 Total precip. (mm) 230.65 82.99 88.6–501 3 coldest days (°C) -1.01 2.63 -8.93–3.67 Population size 175 66 28–294 Female age 2.05 1.21 1–5 Male age 2.33 1.35 1–5 108  Table 5.2 Parameter estimates (± SE) from averaged linear mixed model of individual over-winter survival for adult song sparrows. Significant predictors (at α = 0.05) are depicted in bold. Relative support represents the summed AIC weight of all N models in which each predictor was included, where relative support = 1 represents predictors included in all 193 models in the ‘top models subset’. ‘Area shrub’ and ‘Length intertidal’ refer to the total area (m2) of shrub cover and total length (m) of intertidal coastline in an individual’s territory. ‘Avg. temp’, ‘Total precip.’, and ‘3 coldest days’ refer to January-February average temperature (°C), total precipitation (mm), and lowest cumulative temperature (°C) for a three day period, respectively.   109    Estimate SE z-value p Relative support N models Intercept 0.49 0.24     Area shrub 0.12 0.06 1.91 0.06 0.85 154 Length intertidal -0.12 0.05 2.43 0.02 0.96 176 Age 0.17 0.17 1.00 0.32 1 193 Age2 -0.06 0.03 2.12 0.03 1 193 Sex (male) 0.30 0.09 3.28 0.001 1 193 Population size -0.16 0.14 1.13 0.26 0.41 85 Avg. temp 0.11 0.19 0.56 0.58 0.50 122 Total precip. 0.16 0.15 1.09 0.28 0.53 121 3 coldest days -0.08 0.16 0.50 0.61 0.49 126 Area shrub: Avg. temp -0.01 0.08 0.15 0.88 0.12 34 Area shrub: Total precip. -0.04 0.06 0.67 0.50 0.12 33 Area shrub: 3 coldest days -0.08 0.06 1.36 0.17 0.20 52 Length intertidal: Avg. temp -0.08 0.06 1.40 0.16 0.26 59 Length intertidal: Total precip. -0.02 0.05 0.31 0.76 0.11 33 Length intertidal:   3 coldest days -0.01 0.06 0.13 0.90 0.11 39   110  Figure 5.1 Estimates of a) predicted over-winter survival, b) predicted reproductive rate, and c) the combined effects of each fitness component (prime reproduction and survival sites [PRSS]) across 20 × 20 m grid cells (‘sites’) on Mandarte Island, BC, Canada. All values are scaled between 0 and 1 for equal visualization, and cooler colours (green) represent higher predicted values for each fitness component. Outer edge of island represents intertidal zone, inner black line represents the current extent of shrub cover. Sections of the island outside the grid system (southern edge) represent areas with steep cliff faces, no intertidal zone, and effectively no shrub layer.     111  Figure 5.2 Relationships between site-specific estimates of habitat preference and predicted adult over-winter survival, predicted reproductive rate, and PRSS (prime reproduction and survival sites), where all independent variables are scaled for direct comparison. Habitat preference was a) positively related with predicted over-winter survival (R2 = 0.29, F1,144 = 58.63, p < 0.001), and strongly positively related with both b) predicted reproductive rate (R2 = 0.63, F1,144 = 243.01, p < 0.001) and c) PRSS (R2 = 0.53, F1,144 = 165.2, p < 0.001).  112     113  Figure 5.3 Elasticity analysis of the relative effects of adult over-winter survival (black bars) and annual reproductive rate (grey bars) on population growth across four categories of habitat preference.   114  Chapter 6: General discussion In this thesis I show that song sparrows exhibited marked preference for habitats that conferred positive effects on individual fitness via reproductive success and survival. However, despite the positive effects of preferred, high-quality habitats on fitness documented here, the relative contributions of habitat to indices of fitness were substantially less than those due to inter-individual variation among song sparrows in the study population. My results imply that investigations into the effects of habitat quality on indices of fitness that fail to account for inter-individual variation may result in biased interpretations of the influences of habitat on individual fitness and/or population growth. There is a long history of research on how variation in environmental attributes can lead to natural selection for preferring habitats that positively affect individual reproductive success and survival (reviewed in Johnson 2007). However, the importance of habitat quality to individual fitness is not always clear given that 1) free-living animals have been shown to sometimes prefer seemingly sub-optimal habitats (i.e., ‘ecological traps’), and 2) few studies are able to distinguish the relative effects of habitat quality on indices of fitness from those due to inter-individual variation among animals within a population. Below, I summarize the main results of my thesis and the implications of these findings for future research that will be necessary to precisely estimate the independent contribution of habitat quality to individual fitness.  6.1 Identifying preferred habitat In three chapters of this thesis (Chapters 2, 3, and 5) I quantified habitat preference based on breeding location, and identified key, fine-scale characteristics of the environment associated with population-wide preference for certain habitats. Female song sparrows preferred to breed in 115  sites (grid cells) with more shrub cover, more edge, and deeper soil (Chapter 2). Thus, the cover and complexity of shrubby vegetation may be the primary means by which song sparrows select breeding habitat on Mandarte Island. Among the potential benefits that preferred habitats may confer to their occupants, I found evidence for greater food availability and decreased incubation cost during the early breeding season (Chapter 3), and likely greater shelter from predators and inclement weather in the breeding (Chapter 3) and non-breeding seasons (Chapter 5). The research presented in these chapters addresses a fundamental goal of ecology: to understand the processes that give rise to patterns of distribution, especially when habitats of varying quality are distributed heterogeneously in space (Beyer et al. 2010). By investigating whether animals in a population use certain habitats disproportionately, and identifying the fine-scale environmental targets of such habitat ‘preference’, we can begin to identify key habitat features that are attractive to individual animals or avoided by them, and thus gain insight into how animals perceive their environments (Beyer et al. 2010). These insights are particularly important in conservation biology, as changes in the availability, size, or distribution of what individuals perceive as high-quality habitat can profoundly affect population dynamics (e.g., Fahrig 2001).  6.2 Habitat preference as a metric of habitat quality I demonstrated that sparrows preferred to occupy higher-quality habitats, as long-term preference for certain sites was related positively to the mean annual reproductive success (Chapter 2) and survival (Chapter 5) of their occupants. Individual female song sparrows that nested in preferred early-season habitat also began egg-laying earlier and produced more offspring that were successful at recruiting into the breeding population (Chapter 3). Further, occupancy of preferred 116  habitats was independent of the age or intrinsic quality (relative lifetime reproductive success) of individual female song sparrows (Chapter 2), indicating that this population does not form the settlement patterns of an ideal despotic distribution (Fretwell and Lucas 1970, Fretwell 1972). Taken together, these results indicate that habitat preference is a reliable proxy for habitat quality in this system, and that occupying preferred habitat can enhance fitness in song sparrows regardless of age or individual quality. Quantifying relationships between habitat use and individual fitness is fundamental to ecology and necessary for the testing of key ecological concepts such as niche theory and source-sink dynamics (McLoughlin et al. 2007). By investigating patterns of individual performance in more- and less-preferred habitats, researchers can also test whether habitat selection is adaptive (e.g., Martin 1998, Clark and Shutler 1999) and predict how habitat occupancy and preference can affect population dynamics (e.g., Franklin et al. 2000, Chapter 5). This is true because environmental processes affecting reproduction and survival are more likely to drive variation in individual fitness and population dynamics than those affecting habitat occupancy alone (Clark and Shutler 1999, McLoughlin et al. 2006, 2007, Morris 2006). Marked yearly variation in population size can also clarify the contributions of habitat versus individual quality to indices of fitness, because at low population densities, low-quality individuals are more likely to occupy high-quality habitat, thus reducing the potential correlation between these two sources of variation.  6.3 Influences of habitat versus inter-individual variation on fitness In Chapter 4 I used a specialized version of a mixed-effect model, the ‘animal model’, to estimate the similarity in a fitness-related life-history trait (breeding date) among relatives 117  (additive genetic variance). I showed that song sparrows exhibited significant female (direct) and male (indirect) additive genetic variance in breeding date. Despite the demonstrated link between habitat and breeding date in this system (Chapters 3, 4), both sources of additive genetic variance represented substantially greater components than breeding habitat of total phenotypic variance in breeding date. These results indicate that investigations into the contributions of habitat to fitness-related life-history traits should, where feasible, account for similarity in observations among related individuals to estimate the genetic basis to individual quality that may influence fitness. Despite the importance of habitat quality to individual fitness, metrics of inter-individual variation in song sparrows (rLRS, age, additive genetic, and permanent individual variance) contributed substantially more than habitat to each of the indices of fitness I used here. These results indicate that inter-individual variation can have a greater influence on fitness than variation in habitat quality in free-living populations, even when they do not form despotic distributions, and they highlight the importance of estimating the relative contributions of inter-individual variation when attempting to identify the environmental correlates of fitness in natural systems. The observation that not all individuals in a population are ecologically equivalent is also a concept gaining support from studies of the effects of ‘individual quality’ on indices of fitness (e.g., Cam et al. 2002, Steele and Hogg 2003, Espie et al. 2004, Zabala and Zuberogoitia 2014), and of the demographic and environmental factors that regulate population growth (e.g., Cardador et al. 2012, Hostetler et al. 2015). However, factors related to individual quality that also contribute to variation in fitness may not always be apparent and/or easily quantified without repeat observations from many individuals in a population. Thus, analyses designed 118  explicitly to account for inter-individual variation, such as linear mixed-effects models with individual random effects, have the potential to offer unique insights into the normally unmeasured traits representing individual quality. The conclusions presented in Chapter 4 were made possible by analytical methods that took advantage of the most precise and complete dataset yet available on the relatedness of individuals in a free-living vertebrate population. Where relatedness data are unavailable, variance decomposition of fitness-related life-history traits via mixed-effects models remains one of the best methods for estimating the contributions of inter-individual variation to population-wide phenotypic variance in fitness (Cam et al. 2002, Steele and Hogg 2003, van de Pol and Wright 2009), because most unaccounted for additive genetic variance will be allocated to variance among individuals (e.g., VPI: Chapter 4, Appendix C.1). This approach thus facilitates estimation of the contributions of individual intrinsic quality to variance in fitness-related life-history traits by accounting for unmeasured aspects of inter-individual variation that are difficult to quantify in systems with less detailed data (Wilson and Nussey 2010). In such cases, researchers should aim to maximize both the number of measurements of a trait of interest per individual and the number of individuals for which repeat measurements are available to achieve more accurate estimates of the contributions of inter-individual variation to indices of fitness (Steele and Hogg 2003) relative to those due to habitat quality.  6.4 Broader implications and future directions The main conclusion of this thesis is that, even where free-living animals exhibit preference for habitats that affect fitness positively, the contributions of habitat to variance in fitness may be substantially less than contributions due to intrinsic differences in the quality of the animals 119  under study. These findings have important consequences for many areas of research, including conservation planning for species at risk. For instance, when establishing protected areas for species at risk, funding limitations require the prioritization of habitats likely to have the strongest positive effect on population growth (Margules and Pressey 2000, Thomas et al. 2001, Hodgson et al. 2009). However, if habitat quality is assessed by occupancy alone, or cross-sectional analyses of the reproductive output of individuals of unknown age/quality in occupied habitats, there is a severe risk of over/under-estimating the quality of a focal habitat and its potential benefits to conservation (Arcese 2003). This implies that future research effort should be allocated to refining estimates of inter-individual variation in species at risk to understand the relative contributions of habitat and individual quality to population persistence and growth. Insights from my study of this small island population of song sparrows also have implications for understanding the evolutionary ecology of free-living populations generally. For instance, because of the small area over which this population is distributed and the detail with which habitat characteristics were recorded over time, I was able to focus on features of the environment that vary over fine spatial scales and which were likely to be missed in broad-scale analyses over larger geographic areas. These analyses thus estimate the contributions of the immediate breeding environment to key components of individual fitness, and additionally provide a methodological framework using different newer statistical methods that can be easily applied elsewhere, even if the environmental characteristics affecting habitat preference remain elusive. Other fitness-related benefits of occupying preferred habitats in addition to those considered here may also exist. However, to understand how habitat preference affects individual fitness or population growth researchers must differentiate the benefits that drive habitat 120  preference from those that are a by-product of those preferences. Future research is therefore warranted into the physiological mechanisms by which preferred habitat confers higher individual fitness, such as those linked to environmental constraints on metabolism, nutrition, endocrine control, or thermal relationships, as each may influence life-history variation and natural selection (Ricklefs and Wikelski 2002). For example, studies of migratory songbirds that use broad-scale classifications of habitat quality have found that individuals in preferred habitats may access greater food resources (Johnson and Sherry 2001) and as a consequence display lower baseline corticosterone concentrations, increased muscle mass, and greater physiological condition than those in less-preferred habitats (Marra and Holberton 1998). Individuals that over-winter in preferred habitat may in turn migrate and breed earlier (Tonra et al. 2011), which can translate to increased reproductive success (Reudink et al. 2009). Given that song sparrows in preferred habitats on Mandarte Island experienced higher rates of overwinter survival (Chapter 5) and began breeding earlier in the season (Chapters 3,4), occupying preferred habitats may lead to increased body condition in this population. If so, future studies investigating the contributions of habitat preference to individual fitness should find that individuals in preferred habitats are under less physiological stress and are more likely to maintain high body condition than those in less-preferred sites. 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PLoS One 9:e90254.    142  Appendices Appendix A  Supplementary material for Chapter 2 A.1 Full output from all models in top models subset   Table A.1.1 Parameter estimates (± standard error, SE) for all models in the ΔAIC ≤ 7 subset of 262 models (arranged by increasing ΔAIC) describing vegetation-based predictors of habitat preference. Column names of predictor variables refer to: Edge (linear distance [m] of shrub/grass interface), Area shrub (total area [m2] of shrub cover), Currant (m2), Red elderberry (m2), Oceanspray (m2), Trailing blackberry (m2), Himalayan blackberry (m2), Nootka rose (m2), Pacific willow (m2), Serviceberry (m2), and Soil depth (cm). ‘AICwt’ and ‘Cumltv wt’ refer to the AIC weight of the focal model cumulative AIC weight of the focal model and all preceding models, respectively.  143  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 1 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0001)      -0.0002 (0.0001) 0.0007 (0.0003) 140 0.00 0.03 0.03 2 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)         0.0006 (0.0003) 142 0.60 0.02 0.06 3 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)        -0.0002 (0.0001) 0.0006 (0.0003) 141 0.68 0.02 0.08 4 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)     0.0006 (0.0004)    0.0007 (0.0003) 141 0.83 0.02 0.10 5 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0001)       0.0007 (0.0003) 141 0.86 0.02 0.12 6 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0003 (0.0001)    -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 139 1.03 0.02 0.14 7 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)     0.0006 (0.0004)   -0.0001 (0.0001) 0.0007 (0.0003) 140 1.14 0.02 0.16 8 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)   0.0004 (0.0005)   -0.0002 (0.0001) 0.0008 (0.0003) 139 1.58 0.01 0.17 9 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0004 (0.001) -0.0002 (0.0001)      -0.0002 (0.0001) 0.0007 (0.0003) 139 1.71 0.01 0.18 10 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0003 (0.0002)     0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 139 1.94 0.01 0.20 11 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)       -0.0002 (0.0001) 0.0005 (0.0003) 140 2.01 0.01 0.21 144  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 12 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0001 (0.0001)   0.0005 (0.0005)    0.0008 (0.0003) 140 2.04 0.01 0.22 13 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00003 (0.0001)    -0.0002 (0.0001) 0.0007 (0.0003) 139 2.13 0.01 0.23 14 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001) -0.00004 (0.0002)     -0.0002 (0.0001) 0.0008 (0.0003) 139 2.19 0.01 0.24 15 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)    -0.0001 (0.0001)   0.0007 (0.0003) 140 2.24 0.01 0.25 16 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)        0.0006 (0.0003) 141 2.41 0.01 0.26 17 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0002)     0.0001 (0.0002)  0.0007 (0.0003) 140 2.48 0.01 0.27 18 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)       -0.0001 (0.0001) -0.0002 (0.0001) 0.0007 (0.0003) 140 2.48 0.01 0.28 19 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0001) -0.0001 (0.0002)      0.0008 (0.0003) 140 2.54 0.01 0.29 20 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)    0.0006 (0.0004)   -0.0002 (0.0001) 0.0006 (0.0003) 139 2.55 0.01 0.30 21 0.015 (0.004) 0.001 (0.0001) 0.0002 (0.00002)      -0.00003 (0.0001)   0.0006 (0.0003) 141 2.58 0.01 0.31 22 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)      -0.00003 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 140 2.65 0.01 0.31 145  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 23 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)       -0.00004 (0.0001)  0.0006 (0.0003) 141 2.68 0.01 0.32 24 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)    0.0006 (0.0004)    0.0007 (0.0003) 140 2.69 0.01 0.33 25 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)   0.0001 (0.0002)     -0.0002 (0.0001) 0.0006 (0.0003) 140 2.69 0.01 0.34 26 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)    -0.00004 (0.0001)    -0.0002 (0.0001) 0.0007 (0.0003) 140 2.70 0.01 0.35 27 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)        -0.0002 (0.0001)  142 2.71 0.01 0.36 28 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0001)  0.00005 (0.0001)     0.0007 (0.0003) 140 2.75 0.01 0.36 29 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)    -0.00001 (0.0001)     0.0006 (0.0003) 141 2.76 0.01 0.37 30 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)   -0.00001 (0.0002)      0.0006 (0.0003) 141 2.77 0.01 0.38 31 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)          143 2.78 0.01 0.39 32 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0003 (0.001) -0.0002 (0.0001)    -0.0001 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 138 2.88 0.01 0.40 33 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)     0.0006 (0.0004) -0.00002 (0.0001)   0.0007 (0.0003) 140 2.89 0.01 0.40 146  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 34 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)   0.0003 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 138 2.90 0.01 0.41 35 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0002 (0.001) -0.0002 (0.0001)       0.0007 (0.0003) 140 2.92 0.01 0.42 36 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    0.00003 (0.0001) 0.0006 (0.0005)    0.0007 (0.0003) 140 2.95 0.01 0.43 37 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)       -0.0002 (0.0001)  141 2.97 0.01 0.43 38 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)     0.0006 (0.0005)  0.00002 (0.0001)  0.0007 (0.0003) 140 3.01 0.01 0.44 39 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)   -0.00002 (0.0002)  0.0006 (0.0004)    0.0007 (0.0003) 140 3.02 0.01 0.45 40 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0003 (0.0001) -0.0001 (0.0002)   -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 138 3.08 0.01 0.45 41 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)     0.0006 (0.0004) -0.00003 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 139 3.19 0.01 0.46 42 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0003 (0.0002)    -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 138 3.21 0.01 0.47 43 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)   0.0001 (0.0002)  0.0006 (0.0004)   -0.0002 (0.0001) 0.0007 (0.0003) 139 3.24 0.01 0.47 44 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)  -0.0002 (0.0001)  -0.00002 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 138 3.26 0.01 0.48 147  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 45 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001) -0.0002 (0.0001)   0.0004 (0.0005)   -0.0002 (0.0001) 0.0007 (0.0003) 138 3.30 0.01 0.48 46 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)     0.0005 (0.0005)  -0.00003 (0.0001) -0.0002 (0.0001) 0.0007 (0.0003) 139 3.34 0.01 0.49 47 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)   0.0004 (0.0005)  0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 138 3.36 0.01 0.50 48 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    0 (0.0001) 0.0006 (0.0005)   -0.0001 (0.0001) 0.0007 (0.0003) 139 3.38 0.01 0.50 49 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)   0.0005 (0.0005)  0.0002 (0.0002)  0.0007 (0.0003) 139 3.39 0.01 0.51 50 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0003 (0.0001) -0.0002 (0.0002)   -0.0001 (0.0001)   0.0007 (0.0003) 139 3.54 0.01 0.51 51 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00005 (0.0001) 0.0004 (0.0005)   -0.0002 (0.0001) 0.0007 (0.0003) 138 3.61 0.01 0.52 52 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)    -0.0001 (0.0001)  -0.0002 (0.0001)  140 3.61 0.01 0.52 53 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0005 (0.001) -0.0002 (0.0001) -0.0001 (0.0002)     -0.0002 (0.0001) 0.0007 (0.0003) 138 3.66 0.01 0.53 54 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)  -0.0001 (0.0001)      -0.0002 (0.0001)  141 3.67 0.01 0.53 55 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00002) 0.0004 (0.001) -0.0002 (0.0002)     0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 138 3.67 0.01 0.54 148  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 56 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)   0.0004 (0.0005) -0.0001 (0.0001)   0.0007 (0.0003) 139 3.69 0.01 0.54 57 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00007 (0.0001) 0.0005 (0.0005)    0.0007 (0.0003) 139 3.70 0.00 0.55 58 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)         142 3.83 0.00 0.55 59 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001) -0.00003 (0.0002)  0.0004 (0.0005)   -0.0002 (0.0001) 0.0008 (0.0003) 138 3.83 0.00 0.56 60 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)      -0.0001 (0.0001)  -0.0002 (0.0001)  141 3.90 0.00 0.56 61 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0001) -0.0001 (0.0002)  0.0004 (0.0005)    0.0008 (0.0003) 139 3.91 0.00 0.57 62 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)      -0.0001 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 139 3.94 0.00 0.57 63 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001) -0.0002 (0.0001)  0.00002 (0.0001)    -0.0002 (0.0001) 0.0007 (0.0003) 138 3.94 0.00 0.58 64 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)     0.0004 (0.0004)     142 3.96 0.00 0.58 65 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)   -0.00005 (0.0001)    -0.0002 (0.0001) 0.0006 (0.0003) 139 3.97 0.00 0.59 66 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)      -0.0001 (0.0001)    142 4.02 0.00 0.59 149  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 67 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)     -0.00003 (0.0001)  -0.0002 (0.0001) 0.0005 (0.0003) 139 4.03 0.00 0.59 68 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)     0.0004 (0.0004)   -0.0002 (0.0001)  141 4.05 0.00 0.60 69 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0002 (0.001) -0.0001 (0.0001)   0.0005 (0.0005)    0.0007 (0.0003) 139 4.10 0.00 0.60 70 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)      -0.0001 (0.0001) -0.0001 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 139 4.12 0.00 0.61 71 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001) -0.0001 (0.0001)      -0.0002 (0.0001)  140 4.12 0.00 0.61 72 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0003 (0.0002)    -0.0001 (0.0001) 0.0001 (0.0002)  0.0007 (0.0003) 139 4.13 0.00 0.61 73 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0003 (0.0002)  0.00002 (0.0001)   0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 138 4.18 0.00 0.62 74 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001) -0.0002 (0.0001) -0.0002 (0.0002)      0.0007 (0.0003) 139 4.19 0.00 0.62 75 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0003 (0.0002) -0.00002 (0.0002)    0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 138 4.20 0.00 0.63 76 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    -0.00008 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 139 4.22 0.00 0.63 77 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)  0.00001 (0.0002)     -0.0002 (0.0001) 0.0005 (0.0003) 139 4.25 0.00 0.63 150  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 78 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)    0.0004 (0.0004)   -0.0002 (0.0001)  140 4.27 0.00 0.64 79 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)  -0.0001 (0.0001)        142 4.30 0.00 0.64 80 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0001) -0.0001 (0.0002) 0.00003 (0.0001)    -0.0002 (0.0001) 0.0007 (0.0003) 138 4.34 0.00 0.65 81 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0002 (0.001) -0.0002 (0.0001)    -0.0001 (0.0001)   0.0007 (0.0003) 139 4.38 0.00 0.65 82 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)     -0.0001 (0.0001)  -0.0002 (0.0001)  140 4.39 0.00 0.65 83 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0003 (0.0002) -0.0001 (0.0002)    0.0001 (0.0002)  0.0007 (0.0003) 139 4.42 0.00 0.66 84 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0002) -0.0001 (0.0002) 0.00006 (0.0001)     0.0007 (0.0003) 139 4.42 0.00 0.66 85 0.015 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)     -0.00003 (0.0001)   0.0005 (0.0003) 140 4.44 0.00 0.66 86 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00002 (0.0001)  -0.0001 (0.0001)   0.0007 (0.0003) 139 4.44 0.00 0.67 87 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0004 (0.001)  -0.0001 (0.0002)      0.0006 (0.0003) 140 4.47 0.00 0.67 88 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0006 (0.001) -0.0002 (0.0001)    -0.0001 (0.0001)  -0.0002 (0.0001)  139 4.48 0.00 0.67 151  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 89 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0005 (0.001) -0.0003 (0.0001) -0.0002 (0.0002)   -0.0001 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 137 4.54 0.00 0.68 90 0.015 (0.004) 0.001 (0.0001) 0.0002 (0.00003)      -0.00004 (0.0001) -0.0001 (0.0001)  0.0006 (0.0003) 140 4.56 0.00 0.68 91 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00002) 0.0002 (0.001) -0.0002 (0.0002)     0.0001 (0.0002)  0.0007 (0.0003) 139 4.56 0.00 0.68 92 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)      -0.00003 (0.0001)  0.0006 (0.0003) 140 4.57 0.00 0.69 93 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)  -0.0003 (0.0002)  0.00004 (0.0001)   0.0001 (0.0002)  0.0007 (0.0003) 139 4.59 0.00 0.69 94 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)   -0.00001 (0.0001)     0.0006 (0.0003) 140 4.60 0.00 0.69 95 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    -0.00004 (0.0001)  -0.00004 (0.0001)   0.0006 (0.0003) 140 4.63 0.00 0.70 96 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)    0.00004 (0.0001)      142 4.65 0.00 0.70 97 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)    0.0006 (0.0004) -0.00003 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 138 4.65 0.00 0.70 98 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)   0.00004 (0.0002)    -0.0001 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 139 4.68 0.00 0.70 99 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)    -0.00001 (0.0001)   -0.0001 (0.0001) -0.0002 (0.0001) 0.0007 (0.0003) 139 4.70 0.00 0.71 152  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 100 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   0.0001 (0.0002)   -0.00003 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 139 4.72 0.00 0.71 101 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)    -0.0001 (0.0001)    141 4.73 0.00 0.71 102 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0003 (0.001) -0.0002 (0.0001)   0.0003 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 137 4.74 0.00 0.72 103 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0004 (0.001)  -0.0001 (0.0002)  0.0006 (0.0004)    0.0007 (0.0003) 139 4.75 0.00 0.72 104 0.015 (0.004) 0.001 (0.0001) 0.0002 (0.00003)      -0.00003 (0.0001)   0.0006 (0.0003) 140 4.78 0.00 0.72 105 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   0.0001 (0.0002) -0.00003 (0.0001)    -0.0002 (0.0001) 0.0006 (0.0003) 139 4.78 0.00 0.73 106 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)    0.0006 (0.0004) -0.00002 (0.0001)   0.0006 (0.0003) 139 4.80 0.00 0.73 107 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)   -0.00001 (0.0001) 0.0005 (0.0005)   -0.0002 (0.0001) 0.0006 (0.0003) 138 4.81 0.00 0.73 108 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)    0.0005 (0.0005)  -0.00002 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 138 4.81 0.00 0.73 109 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)  0.000001 (0.0002)  0.0006 (0.0004)   -0.0002 (0.0001) 0.0006 (0.0003) 138 4.83 0.00 0.74 110 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)   -0.00005 (0.0002)    -0.0001 (0.0001)  0.0007 (0.0003) 140 4.83 0.00 0.74 153  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 111 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)    0.00002 (0.0001)    -0.0002 (0.0001)  141 4.84 0.00 0.74 112 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0003 (0.001)   0.00002 (0.0001) 0.0006 (0.0005)    0.0006 (0.0003) 139 4.86 0.00 0.75 113 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)       -0.00002 (0.0001) -0.0002 (0.0001)  141 4.87 0.00 0.75 114 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0003 (0.001)    0.0006 (0.0005)  0.00003 (0.0001)  0.0006 (0.0003) 139 4.88 0.00 0.75 115 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)       0.00002 (0.0001)   142 4.89 0.00 0.75 116 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002)    0 (0.0001)   -0.00004 (0.0001)  0.0006 (0.0003) 140 4.89 0.00 0.76 117 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00002) 0.0002 (0.001) -0.0002 (0.0001)  0.00005 (0.0001)     0.0007 (0.0003) 139 4.90 0.00 0.76 118 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)   0.00002 (0.0002)       142 4.92 0.00 0.76 119 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   -0.00002 (0.0002) -0.00001 (0.0001)     0.0006 (0.0003) 140 4.96 0.00 0.76 120 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)    0.0004 (0.0004)     141 4.97 0.00 0.77 121 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)  -0.0003 (0.0002)   0.0003 (0.0005) -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 4.99 0.00 0.77 154  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 122 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0002 (0.0002) -0.0001 (0.0002)  0.0003 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 137 5.05 0.00 0.77 123 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0003 (0.001) -0.0003 (0.0002)    -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001) 0.0006 (0.0003) 137 5.08 0.00 0.78 124 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0004 (0.001) -0.0002 (0.0001)  -0.00003 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 137 5.08 0.00 0.78 125 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001) -0.0002 (0.0002)   0.0004 (0.0005)  0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.09 0.00 0.78 126 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   -0.00002 (0.0002)  0.0006 (0.0004) -0.00002 (0.0001)   0.0007 (0.0003) 139 5.11 0.00 0.78 127 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)    0.00001 (0.0001) 0.0006 (0.0005) -0.00002 (0.0001)   0.0007 (0.0003) 139 5.12 0.00 0.79 128 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)     0.0006 (0.0005) -0.00002 (0.0001) 0.000001 (0.0001)  0.0007 (0.0003) 139 5.13 0.00 0.79 129 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00008 (0.0001)    -0.0002 (0.0001)  140 5.16 0.00 0.79 130 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)      -0.00001 (0.0001) -0.0002 (0.0001)  140 5.16 0.00 0.79 131 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)   0 (0.0001)    -0.0002 (0.0001)  140 5.17 0.00 0.79 132 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   -0.00001 (0.0002) 0.00003 (0.0001) 0.0006 (0.0005)    0.0007 (0.0003) 139 5.18 0.00 0.80 155  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 133 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    0.00003 (0.0001) 0.0006 (0.0005)  0.000004 (0.0001)  0.0007 (0.0003) 139 5.19 0.00 0.80 134 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)  -0.0002 (0.0001)  0 (0.0001) 0.0003 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 137 5.20 0.00 0.80 135 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0004 (0.001) -0.0003 (0.0001) -0.0003 (0.0002)   -0.0001 (0.0001)   0.0007 (0.0003) 138 5.20 0.00 0.80 136 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)     0.0004 (0.0004) -0.0001 (0.0001)    141 5.21 0.00 0.81 137 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)     0.0004 (0.0004) -0.0001 (0.0001)  -0.0002 (0.0001)  140 5.25 0.00 0.81 138 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)   -0.00001 (0.0002)  0.0006 (0.0005)  0.00002 (0.0001)  0.0007 (0.0003) 139 5.25 0.00 0.81 139 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)     -0.0001 (0.0001)    141 5.26 0.00 0.81 140 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0002 (0.0002) -0.0002 (0.0002)  0.0003 (0.0005) -0.0001 (0.0001)   0.0007 (0.0003) 138 5.27 0.00 0.82 141 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)     0.0005 (0.0005) -0.00004 (0.0001) -0.0001 (0.0001) -0.0002 (0.0001) 0.0007 (0.0003) 138 5.29 0.00 0.82 142 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0002)     0.0001 (0.0002) -0.0002 (0.0001)  140 5.30 0.00 0.82 143 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)    0.00008 (0.0001) 0.0006 (0.0005)     141 5.32 0.00 0.82 156  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 144 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   0.0001 (0.0002)  0.0006 (0.0004) -0.00003 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 138 5.34 0.00 0.82 145 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0003 (0.0002) -0.0001 (0.0002) -0.00002 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 137 5.34 0.00 0.83 146 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)   0.0005 (0.0005) -0.00004 (0.0001) 0.0001 (0.0002)  0.0007 (0.0003) 138 5.34 0.00 0.83 147 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)    -0.00004 (0.0001) 0.0005 (0.0005) -0.00004 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 138 5.35 0.00 0.83 148 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0003 (0.0002) -0.0001 (0.0002)   -0.0001 (0.0001) 0.00003 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.35 0.00 0.83 149 0.013 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0005 (0.001) -0.0002 (0.0001) -0.0001 (0.0002)  0.0004 (0.0005)   -0.0002 (0.0001) 0.0007 (0.0003) 137 5.36 0.00 0.84 150 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0001)  0.0001 (0.0001)      141 5.36 0.00 0.84 151 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)  0.00005 (0.0001) 0.0006 (0.0005)  0.0001 (0.0002)  0.0007 (0.0003) 138 5.38 0.00 0.84 152 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)   -0.00009 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 138 5.44 0.00 0.84 153 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)  -0.0003 (0.0002)  -0.00003 (0.0001)  -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.44 0.00 0.84 154 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0002 (0.001) -0.0002 (0.0002)   0.0005 (0.0005)  0.0002 (0.0002)  0.0007 (0.0003) 138 5.45 0.00 0.85 157  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 155 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0003 (0.001) -0.0002 (0.0001)  0.00004 (0.0001) 0.0004 (0.0005)   -0.0002 (0.0001) 0.0007 (0.0003) 137 5.47 0.00 0.85 156 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)   0.0001 (0.0002)  0.0006 (0.0005)  -0.00001 (0.0001) -0.0002 (0.0001) 0.0007 (0.0003) 138 5.51 0.00 0.85 157 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)   0.0001 (0.0002) 0.000001 (0.0001) 0.0006 (0.0005)   -0.0002 (0.0001) 0.0007 (0.0003) 138 5.51 0.00 0.85 158 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001) -0.0001 (0.0001)        141 5.52 0.00 0.85 159 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0002) -0.0001 (0.0002)  0.0005 (0.0005)  0.0001 (0.0002)  0.0008 (0.0003) 138 5.53 0.00 0.86 160 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)  -0.0002 (0.0002)     0.0002 (0.0002)   141 5.55 0.00 0.86 161 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)  0.00003 (0.0001) 0.0005 (0.0005)  0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.55 0.00 0.86 162 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0001 (0.0001)   0.0003 (0.0005)   -0.0002 (0.0001)  140 5.57 0.00 0.86 163 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0002) -0.0001 (0.0002) 0.00007 (0.0001) 0.0005 (0.0005)    0.0007 (0.0003) 138 5.59 0.00 0.86 164 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    0.00001 (0.0001) 0.0005 (0.0005)  -0.00003 (0.0001) -0.0002 (0.0001) 0.0007 (0.0003) 138 5.61 0.00 0.87 165 0.013 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001) -0.0002 (0.0001) -0.0002 (0.0002)  0.0004 (0.0005)    0.0007 (0.0003) 138 5.61 0.00 0.87 158  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 166 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0002 (0.0002) 0.000004 (0.0002)  0.0004 (0.0005)  0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.66 0.00 0.87 167 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)    -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001)  139 5.67 0.00 0.87 168 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001)     -0.00005 (0.0001) -0.0001 (0.0001) -0.0002 (0.0001) 0.0005 (0.0003) 138 5.69 0.00 0.87 169 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)    0.0004 (0.0004) -0.0001 (0.0001)  -0.0002 (0.0001)  139 5.72 0.00 0.88 170 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)   0.0002 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001)  139 5.73 0.00 0.88 171 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0003 (0.0002) -0.0002 (0.0002)   -0.0001 (0.0001) 0.0001 (0.0002)  0.0007 (0.0003) 138 5.73 0.00 0.88 172 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)      -0.0001 (0.0001) -0.0001 (0.0001) -0.0002 (0.0001)  140 5.76 0.00 0.88 173 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)  -0.0002 (0.0001)  0.00005 (0.0001) 0.0005 (0.0005) -0.00003 (0.0001)   0.0007 (0.0003) 138 5.76 0.00 0.88 174 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0005 (0.001) -0.0003 (0.0002) -0.0001 (0.0002)    0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.78 0.00 0.88 175 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)  -0.0003 (0.0002) -0.0002 (0.0002) 0.00001 (0.0001)  -0.0001 (0.0001)   0.0007 (0.0003) 138 5.80 0.00 0.89 176 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)   0.0001 (0.0002)   -0.0001 (0.0001)  -0.0002 (0.0001)  140 5.81 0.00 0.89 159  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 177 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)     0.0005 (0.0005)  0.0001 (0.0001)   141 5.81 0.00 0.89 178 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001) -0.0002 (0.0002)     0.0001 (0.0002) -0.0002 (0.0001)  139 5.81 0.00 0.89 179 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0006 (0.001)  -0.0001 (0.0002)       141 5.81 0.00 0.89 180 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00004)  -0.0002 (0.0001)  0.00001 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001)  139 5.83 0.00 0.90 181 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0002 (0.001) -0.0002 (0.0001)   0.0004 (0.0005) -0.00005 (0.0001)   0.0007 (0.0003) 138 5.83 0.00 0.90 182 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00004)  -0.0002 (0.0001) -0.00002 (0.0002)   -0.0001 (0.0001)  -0.0002 (0.0001)  139 5.84 0.00 0.90 183 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)   0.00003 (0.0001)      141 5.84 0.00 0.90 184 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0002 (0.001) -0.0002 (0.0001)  0.00007 (0.0001) 0.0005 (0.0005)    0.0007 (0.0003) 138 5.87 0.00 0.90 185 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0002 (0.0002) -0.00004 (0.0002) 0.00005 (0.0001) 0.0004 (0.0005)   -0.0002 (0.0001) 0.0007 (0.0003) 137 5.88 0.00 0.90 186 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)   0.0001 (0.0002)  0.0004 (0.0004)   -0.0002 (0.0001)  140 5.89 0.00 0.91 187 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)  -0.0001 (0.0001)   0.0003 (0.0005)     141 5.90 0.00 0.91 160  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 188 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0005 (0.001) -0.0002 (0.0002) -0.0001 (0.0002) 0.00002 (0.0001)    -0.0002 (0.0001) 0.0007 (0.0003) 137 5.92 0.00 0.91 189 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)    0.00005 (0.0001) 0.0005 (0.0005)   -0.0001 (0.0001)  140 5.92 0.00 0.91 190 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)      0.00003 (0.0001)   141 5.93 0.00 0.91 191 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)    -0.00004 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001)  140 5.93 0.00 0.91 192 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001) -0.0003 (0.0002)  0.000005 (0.0001)   0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 5.98 0.00 0.92 193 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0007 (0.001) -0.0001 (0.0001)   0.0003 (0.0005)   -0.0002 (0.0001)  139 5.98 0.00 0.92 194 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0006 (0.001) -0.0002 (0.0001)  0.00005 (0.0001)    -0.0002 (0.0001)  139 6.04 0.00 0.92 195 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)    -0.00006 (0.0001)  -0.0001 (0.0001) -0.0001 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 138 6.08 0.00 0.92 196 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)   -0.00003 (0.0001)   -0.00005 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 138 6.12 0.00 0.92 197 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)   0.00001 (0.0002)  0.0004 (0.0004)     141 6.13 0.00 0.92 198 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)      -0.0001 (0.0001) -0.00002 (0.0001)   141 6.16 0.00 0.92 161  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 199 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0005 (0.001)  -0.00004 (0.0002)    -0.0001 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 138 6.18 0.00 0.93 200 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001) -0.0003 (0.0002) -0.0002 (0.0002)    0.0001 (0.0002)  0.0007 (0.0003) 138 6.18 0.00 0.93 201 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)   0.000001 (0.0002)   -0.0001 (0.0001)    141 6.20 0.00 0.93 202 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)    0 (0.0001)  -0.0001 (0.0001)    141 6.20 0.00 0.93 203 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001) -0.0002 (0.0002) -0.0002 (0.0002) 0.00005 (0.0001)     0.0007 (0.0003) 138 6.22 0.00 0.93 204 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)  -0.000004 (0.0002) -0.00005 (0.0001)    -0.0002 (0.0001) 0.0006 (0.0003) 138 6.24 0.00 0.93 205 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0007 (0.001)   -0.00006 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001)  139 6.24 0.00 0.93 206 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001) -0.0001 (0.0001)    -0.0001 (0.0001)    140 6.27 0.00 0.94 207 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0002 (0.001) -0.0003 (0.0002)    -0.00005 (0.0001) 0.0001 (0.0002)  0.0006 (0.0003) 138 6.29 0.00 0.94 208 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)  0.00001 (0.0002)   -0.00003 (0.0001)  -0.0002 (0.0001) 0.0005 (0.0003) 138 6.30 0.00 0.94 209 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00004) 0.0007 (0.001) -0.0002 (0.0001) -0.0001 (0.0002)   -0.0001 (0.0001)  -0.0002 (0.0001)  138 6.34 0.00 0.94 162  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 210 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0006 (0.001)     -0.0001 (0.0001) -0.0001 (0.0001) -0.0002 (0.0001)  139 6.37 0.00 0.94 211 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)   0.000001 (0.0002)   -0.0001 (0.0001) -0.0001 (0.0002) -0.0002 (0.0001) 0.0006 (0.0003) 138 6.39 0.00 0.94 212 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)  -0.0003 (0.0002)  0.00001 (0.0001)  -0.00005 (0.0001) 0.0001 (0.0002)  0.0007 (0.0003) 138 6.40 0.00 0.94 213 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0007 (0.001)   0.00003 (0.0001) 0.0005 (0.0005)   -0.0002 (0.0001)  139 6.40 0.00 0.95 214 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)   0.0001 (0.0002) -0.00008 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 138 6.40 0.00 0.95 215 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)    0.0004 (0.0004) -0.0001 (0.0001)    140 6.41 0.00 0.95 216 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0001 (0.0001) -0.00004 (0.0002)       141 6.42 0.00 0.95 217 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0003 (0.0002) -0.00003 (0.0002) 0.00002 (0.0001)   0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 137 6.46 0.00 0.95 218 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)    -0.0001 (0.0001) 0.0001 (0.0002)   140 6.46 0.00 0.95 219 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0003 (0.001)   -0.00004 (0.0001)  -0.00004 (0.0001)   0.0006 (0.0003) 139 6.50 0.00 0.95 220 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)  -0.0003 (0.0002) -0.0001 (0.0002) 0.00004 (0.0001)   0.0001 (0.0002)  0.0007 (0.0003) 138 6.50 0.00 0.95 163  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 221 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001)  -0.0001 (0.0002)   -0.00003 (0.0001)   0.0005 (0.0003) 139 6.51 0.00 0.96 222 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001)  -0.0001 (0.0002)    -0.0001 (0.0001)  0.0006 (0.0003) 139 6.51 0.00 0.96 223 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)  0.000002 (0.0002)  0.0004 (0.0004)   -0.0002 (0.0001)  139 6.51 0.00 0.96 224 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0003 (0.001)     -0.00004 (0.0001) -0.0001 (0.0001)  0.0006 (0.0003) 139 6.52 0.00 0.96 225 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0005 (0.001) -0.0003 (0.0002) -0.0002 (0.0002)  0.0003 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 136 6.57 0.00 0.96 226 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)   0.00007 (0.0001) 0.0006 (0.0005)     140 6.57 0.00 0.96 227 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0006 (0.001) -0.0002 (0.0002)    -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001)  138 6.57 0.00 0.96 228 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00003) 0.0006 (0.001) -0.0002 (0.0001)   0.0002 (0.0005) -0.0001 (0.0001)  -0.0002 (0.0001)  138 6.58 0.00 0.96 229 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0001 (0.0001)   0.0003 (0.0005) -0.0001 (0.0001)    140 6.58 0.00 0.96 230 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00004)  -0.0002 (0.0001) -0.0001 (0.0002)   -0.0001 (0.0001)    140 6.59 0.00 0.97 231 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0001 (0.0001)  0.00011 (0.0001) 0.0005 (0.0005)     140 6.60 0.00 0.97 164  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 232 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004)   -0.0001 (0.0002)   -0.00005 (0.0001) -0.0001 (0.0002)  0.0006 (0.0003) 139 6.63 0.00 0.97 233 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)  0.000001 (0.0002)   -0.0001 (0.0001)  -0.0002 (0.0001)  139 6.63 0.00 0.97 234 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)   0.0001 (0.0002) 0.00002 (0.0001)    -0.0002 (0.0001)  140 6.63 0.00 0.97 235 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0001 (0.001) -0.0002 (0.0001)  0.00001 (0.0001)  -0.0001 (0.0001)   0.0006 (0.0003) 138 6.64 0.00 0.97 236 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001)  -0.0001 (0.0002) -0.00002 (0.0001)     0.0006 (0.0003) 139 6.65 0.00 0.97 237 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)   0.0001 (0.0002)    0.00001 (0.0001) -0.0002 (0.0001)  140 6.69 0.00 0.97 238 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00005 (0.0001)  -0.0001 (0.0001)    140 6.69 0.00 0.98 239 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0006 (0.001) -0.0003 (0.0002) -0.0002 (0.0002) -0.00004 (0.0001)  -0.0001 (0.0001)  -0.0002 (0.0001) 0.0007 (0.0003) 136 6.73 0.00 0.98 240 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0006 (0.001)    0.0005 (0.0005)  0.0001 (0.0001)   140 6.73 0.00 0.98 241 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00002) 0.0002 (0.001) -0.0002 (0.0002)  0.00003 (0.0001)   0.0001 (0.0002)  0.0007 (0.0003) 138 6.74 0.00 0.98 242 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003)    -0.00003 (0.0001)  -0.00005 (0.0001) -0.0001 (0.0001)  0.0006 (0.0003) 139 6.74 0.00 0.98 165  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 243 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00004) 0.0006 (0.001) -0.0002 (0.0001)  -0.00001 (0.0001)  -0.00009 (0.0001)  -0.0002 (0.0001)  138 6.75 0.00 0.98 244 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004) 0.0005 (0.001)   -0.00005 (0.0001) 0.0005 (0.0005) -0.00005 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 137 6.75 0.00 0.98 245 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0005 (0.001)      0.0002 (0.0002)   140 6.78 0.00 0.98 246 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)   0.00003 (0.0002) 0.00004 (0.0001)      141 6.80 0.00 0.98 247 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00002) 0.0003 (0.001)   -0.000005 (0.0001)   0 (0.0001)  0.0006 (0.0003) 139 6.81 0.00 0.98 248 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002)    0.00005 (0.0001)   0 (0.0001)   141 6.82 0.00 0.99 249 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00004)   -0.00004 (0.0002) -0.00004 (0.0001)  0 (0.0001)   0.0006 (0.0003) 139 6.82 0.00 0.99 250 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0001)  0.00009 (0.0001) 0.0004 (0.0005)   -0.0002 (0.0001)  139 6.82 0.00 0.99 251 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0004 (0.001) -0.0002 (0.0002)   0.0004 (0.0005) -0.0001 (0.0001) 0.0001 (0.0002) -0.0002 (0.0001) 0.0006 (0.0003) 136 6.84 0.00 0.99 252 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003) 0.0004 (0.001)    0.0005 (0.0005) -0.00003 (0.0001) -0.00005 (0.0001) -0.0002 (0.0001) 0.0006 (0.0003) 137 6.85 0.00 0.99 253 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001)  -0.0001 (0.0002)  0.0006 (0.0004) -0.00002 (0.0001)   0.0006 (0.0003) 138 6.87 0.00 0.99 166  Model rank Intercept Edge Area shrub Currant Elder Ocean Trailing bb Himal. bb Rose Willow Service Soil depth df ΔAIC AICwt Cumltvwt 254 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0005 (0.001) -0.0003 (0.0002) -0.0002 (0.0002)   -0.0001 (0.0001) 0.00001 (0.0002) -0.0002 (0.0001) 0.0006 (0.0003) 136 6.88 0.00 0.99 255 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)  -0.0002 (0.0002)   0.0004 (0.0005)  0.0002 (0.0002)   140 6.88 0.00 0.99 256 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00003)   0.00004 (0.0002) -0.00002 (0.0001)   -0.0001 (0.0002) -0.0002 (0.0001) 0.0007 (0.0003) 138 6.92 0.00 0.99 257 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0004 (0.001) -0.0001 (0.0001)  0.00008 (0.0001)      140 6.95 0.00 1.00 258 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0005 (0.001)  -0.000004 (0.0002)  0.0006 (0.0004) 0 (0.0001)  -0.0002 (0.0001) 0.0006 (0.0003) 137 6.96 0.00 1.00 259 0.018 (0.003) 0.001 (0.0001) 0.0002 (0.00002) 0.0007 (0.001)  -0.0001 (0.0002)  0.0004 (0.0004)     140 6.96 0.00 1.00 260 0.019 (0.003) 0.001 (0.0001) 0.0002 (0.00003)    0.00003 (0.0001)   -0.00004 (0.0001) -0.0002 (0.0001)  140 6.96 0.00 1.00 261 0.014 (0.004) 0.001 (0.0002) 0.0002 (0.00004) 0.0004 (0.001) -0.0002 (0.0002) -0.0002 (0.0002)  0.0003 (0.0005) -0.0001 (0.0001)   0.0007 (0.0003) 137 6.97 0.00 1.00 262 0.014 (0.004) 0.001 (0.0001) 0.0002 (0.00003) 0.0004 (0.001)  -0.0001 (0.0002) 0.00002 (0.0001) 0.0006 (0.0005)    0.0006 (0.0003) 138 6.99 0.00 1.00  167  Appendix B  Supplementary material for Chapter 3 B.1 Long-term patterns of early season grid cell preference To evaluate whether female song sparrows selected grid cells non-randomly during the early breeding season, we used Pearson’s chi-square to compare the distribution of observed cell occupancy versus expected values of random cell occupancy from a Poisson distribution following Germain and Arcese (2014). For the 632 cells included in the analysis (see Chapter 3.2.2), the mean frequency of overlap was 6.80 ± 5.04 (SD) nesting attempts per grid cell. We pooled the frequency of overlap (cell overlapped by at least one 100 m2 buffer) between early season nesting attempts and a given grid cell into 13 categories (< 2, 3, 4,...,12, 13, > 14) with expected frequencies greater than 5 to meet the assumptions of the χ2 statistic. We determined that grid cells were selected non-randomly during the early season (χ212 = 1921.72, p < 0.0001), with certain cells occupied more frequently than expected and many cells occupied only 1–3 times over the long-term study (Figure B.1.1).  168  Figure B.1.1 Observed versus expected (given Poisson distribution) pattern of occupation by nesting song sparrows in 632 grid cells over 38 years    169  B.2 Measuring incubation behaviour Selection of monitored nests was based on the availability of un-deployed temperature loggers and the stage of incubation when the nest was discovered, with higher priority given to nests found earlier in the incubation period. In each instance, we inserted the temperature probe into the lining of the nesting cup when the female left the nest to forage, and allowed 30 minutes to pass before beginning to record temperature to account for any disturbance effects on female behaviour. Once loggers were removed from the nest, we used Rhythm 1.0 (Cooper and Mills 2005) to identify transitions between incubation on/off bouts based on changes in nest temperature (off bouts determined by decrease in temp by 1.0˚C in 5 min). We then visually inspected transition selections using Raven Pro 1.4 (Bioacoustics Research Program 2011) to ensure consistent selection criteria in delineating transitions between incubation on/off bouts. For each measure of incubation behaviour (constancy and average off-bout duration) we limited our observation period to between 2–11 days before hatching (average Mandarte song sparrow incubation period = 13 days). Because of this nine day span in measured incubation behaviour, we extracted studentized residuals from a linear regression between the behaviour (cubed-root transformation) and number of days before hatch to remove variation in incubation behaviour due to stage of embryo development (Deeming 2006): constancy- (R2 = 0.07, F1, 67 = 5.10, p = 0.03); average off duration- (R2 = 0.08, F1, 67 = 5.94, p = 0.02). We found no significant relationships between incubation behaviour and either Julian date or clutch size (80% of nests monitored contained 4 eggs), and thus excluded both from further analyses.  170  B.3 Assignment of time periods to account for sun location throughout the day To determine appropriate cut-off points in our separation of cell-specific micro-climate by time of day, we plotted all measures of air temperature (°C) during the early breeding season (March 9–April 21, 2011–2013) versus ‘decimal time’ (time of day expressed as a value between 0 [00:01] and 1 [23:59]). Next, we fit a cubic spline with Gaussian error structure through the data with a smoothing parameter (λ) that minimized the generalized cross-validation (GCV) score, indicating optimal separation of a smooth function from white noise (Figure B.3.1). We then visually inspected the output and classified four distinct time periods which represented the general state of temperature throughout the day (i.e., increasing, peak, decreasing, and stable) independent of wind-speed. These time periods (represented by decimal time with Pacific Daylight Time in square brackets) consisted of: Morning (0.31–0.46 [7:30–11:00]), Mid-Day (0.48–0.63 [11:30–15:00]), Evening (0.65–0.83 [15:30–20:00]), and Overnight (0.85–0.0 [20:30–24:00] + 0.0–0.29 [0:00–7:00]).  171  Figure B.3.1 Cubic regression spline based on ‘best smoother’ (λ = 0.000026, R2 = 0.49, n = 22857) of relative temperature versus time of day expressed as a value between 0–1 (00:00–23:59). Vertical black lines represent cut-off points delineating the four time periods of overnight (a), morning (b), mid-day (c), and evening (d).     172  B.4 Modelling relative micro-climate of grid cells Detailed measures of the slope and aspect of the entire surface of Mandarte Island were retrieved via remote sensing, using aerial flyovers of Mandarte and surrounding islands and Light Detection and Ranging (LiDAR) sensor technology following Jones et al. (2010, 2013). LiDAR sensors produce pulses of near-infrared light which can penetrate vegetation canopies and retrieve information on ground surface elevation and orientation (Wehr and Lohr 1999). For each grid cell, detailed measures of vegetation characteristics previously found to be significant positive predictors of site preference (total area of shrub cover [m2], linear distance of shrub/grass interface [‘edge’, m], and soil depth [mm], Germain and Arcese 2014) were calculated by averaging two island-wide vegetation surveys conducted in 1986 and 2006. Although shrub species composition of individual grid cells has changed over time, the percent change in total island-wide shrub cover is estimated to be only 7% between these two vegetation surveys (Crombie, Germain, Arcese, unpublished data). Daily summaries of local weather (average temperature [°C] and total precipitation [mm]) were obtained from the National Climate Archive of Environment Canada (http://climate.weather.gc.ca) for the Victoria International Airport station (48°38'50.010" N, 123°25'33.000" W). Daily average temperature and total precipitation were highly positively correlated (r > 0.7), thus only precipitation was included in our predictive models of cell-specific micro-climate. We incorporated year, location of the weather meter (Lat, Long) and relative vegetation cover (0–5, see Chapter 3.2.5) surrounding the weather meter as random effects in our analysis. We further accounted for temporal autocorrelation by using a first order autocovariate, which estimated the dependence between observations at time t and t-1. We generated separate models 173  for measured wind-chill temperature (°C) for each of our four time periods (see Appendix B.3), using all possible combinations of fixed effects (decimal time, Julian date, total daily precipitation, slope, aspect, total shrub cover, edge, and soil depth) following an information theoretic approach and averaging all models within ΔAICc of 7 from the top model (Table B.4.1). We chose ΔAICc ≤ 7 here (as opposed to ΔAICc ≤ 2 as used for models of reproduction and incubation behaviour) to ensure that estimates of the relative influences on wind-chill temperature were as conservative and inclusive as possible. We then used the averaged models to generate a surface of relative differences in cell-specific micro-climate (‘relative micro-climate’) across the island to be used in further analyses, and found no spatial autocorrelation in this metric beyond 20 m (i.e., roughly the diameter of two adjacent cells; Moran’s I < 0.2 for increments over 20 m) for any of the four time periods. The predicted micro-climate of each grid cell was highly correlated with the vegetation characteristics of the cell, indicating that areas of the island with greater vegetation structure (total shrub cover, deeper soil) were relatively cooler overall (Figures B.4.1, B.4.2). Linear distance of edge exhibited a quadratic relationship with micro-climate, as cells with minimal/no edge could represent either tall grass meadow or areas completely immersed in vegetative cover. 174  Table B.4.1 Final averaged models for cell-specific wind-chill temperature (°C) across four time periods on Mandarte Island. Significant predictors (where SEs do not overlap zero) are depicted in bold. Total models ran for each time period = 256, s = number of top models in ΔAICc ≤ 7 subset, n = number of observations in each time period. Blank cells occur where a predictor was not present in ΔAICc ≤ 7 subset.  Time period Intercept Aspect Decimal time Date Edge (m) Daily precip. (mm) Area shrub (m2) Slope Soil depth (mm) Overnight                                 s =4, n = 15976 -3.17(1.01)  0.44 (0.03) 0.08 (0.008) -0.001 (0.001)   0.001 (0.001) -0.0004 (0.001) Morning                                           s = 4, n = 5554 -12.00 (1.97)  39.37 (4.18) 0.06 (0.02) -0.0007 (0.001) -0.01 (0.01) -0.02 (0.005)   Mid-Day                                           s = 6, n = 5776 13.28(1.48)  3.27 (0.75) 0.001 (0.002) -0.0008 (0.002) -0.29 (0.02) -0.03 (0.008) -0.001 (0.002) -0.0001 (0.001) Evening                                                s = 6, n = 7459 30.96 (2.41) 0.0004 (0.0003) -36.18 (2.09) 0.06 (0.01) -0.0004 (0.001) -0.18 (0.02) -0.003 (0.003)  -0.001 (0.001) 175  Figure B.4.2 Predicted relative micro-climate (shown as °C) for 632 cells versus total area of shrub cover (shown as percentage of cell under shrub cover), linear distance of edge (m) and soil depth (mm) of the cell, averaged across two vegetation surveys conducted in 1986 and 2006.   176  Figure B.4.3 Predicted relative micro-climate (shown as °C) for 632 cells across Mandarte Island. Areas of increased shrub cover (central line of the island) are relatively cooler overall throughout the Morning, Mid-Day, and Evening periods. Grid cells are nearly uniformly cool during the Overnight period (note small scale bar).   177  B.5 Shrub selection criteria for assessments of food availability We sampled bud phenology and food availability on the two most abundant deciduous shrub species present on Mandarte Island (snowberry and Nootka rose). The selection of individual shrubs from which food/phenology measures were taken was independent of the system of grid cells, and based solely on the distribution of snowberry and rose in the local area following a 2006 vegetation survey of the island using 400 m2 plots (Germain and Arcese 2014). Individual shrubs were selected using a stratified random sampling design, where four pairs of locations where chosen for each species from a map of the local area depicting the extent of total shrub cover (but not shrub species/height, etc.), and the four sampling locations were determined via coin flip from the original eight selected. Once at one of the four sampling locations, two shrubs from each species were selected and the sample shrub was again determined via coin flip, and the locations of the sampled shrubs were mapped. RRG conducted over 95% of surveys, and supplemental sampling was conducted under the supervision of RRG. To estimate differences in plant phenology and insect abundance across grid cells, we quantified cell-level deviation from the island-wide mean for each measure (studentized residuals), following a series of mixed effects models with shrub species as a random effect and Julian date as a fixed effect, weighted by the number of shrubs sampled in each cell.  178  Appendix C  Supplementary material for Chapter 4 C.1 All alternate versions of the ‘non-spatial’ model We ran a total of seven alternate versions of our initial non-spatial model to validate assumptions regarding our dataset and model interpretations. For each restricted version of the model, we began with the 1040 nest records included in our non-spatial model, and excluded observations following the criteria below. We include estimates from the non-spatial model (Table 4.1) for reference. For each alternate version, we retained the same fixed effect structure as the non-spatial model, and (save the ‘Bayesian’ model) report standard errors for all random and fixed effects. We first excluded all nest records with either a female (n = 50) and/or male (n = 16) immigrant parent, for a restricted sample size of n = 974 (‘no immigrants’ model). These restrictions produced highly similar results to our initial non-spatial model (Table C.1.1). Although estimates VA♂ was slightly larger and VPI♀ and VR slightly smaller than those in the genetic model, excluding immigrants had no influence on the regression of breeding date on male f, and resulted in very little change (11.45 ± 4.74 [SE] versus 12.30 ± 5.31[SE]) in the estimate of female f. Next, we restricted our dataset to only those years of the study (1993–2014) for which the pedigree was fully corrected for extra-pair paternity (‘genetic pedigree’ model), as opposed to the grafted social/genetic pedigree spanning 1975–2014 used in our main analyses. This dataset consisted of n = 594 observations, with a mean breeding date of 108.4 (± 10.8 SD) and mean coefficient of inbreeding (f) of 0.061 (± 0.048 SD) for females and 0.057 (± 0.049 SD) for males. Despite the substantially reduced sample size, this model again produced qualitatively similar results to our initial non-spatial model (Table C.1.1). Only estimates of VPI♀ and VY 179  changing noticeably from the non-spatial model, likely due to the highly reduced density of this population in more recent years (Smith et al. 2006c). We then removed all observations of breeding date from the 122 social pairs that bred exclusively with each other (see Chapter 4.3.1), resulting in n = 854 observations of lay date (‘no exclusive pairs’ model). In this instance, our estimate of female additive genetic variance (VA♀) was slightly higher than the initial genetic non-spatial model whereas all other estimates from both random and fixed effects remained relatively unchanged (Table C.1.1). To further investigate the sex-specific effects on breeding date, we re-ran our non-spatial model removing all genetic effects (σ2A♀, σ2A♂, and ρA♀♂, ‘non-genetic’ model). Doing so allocated all variance (including VA) due to each parent to their respective VPI, allowing for a direct estimate of the total repeatability in breeding date for each sex. As expected if our results were not driven by repeat observations from the same social pair (confounding sex-specific estimates of VA and VPI), estimates of VPI♀ and VPI♂ in the non-genetic model were almost exactly the sum of VA and VPI for each sex in the initial non-spatial model (Table C.1.1). We then re-ran the ‘non-genetic’ model but further removed female permanent individual effects. This model (‘male effects’) thus allocates all individual-level male variance in breeding date to VPI♂, independent of female social mate. VPI♂ for the ‘male effects’ model was slightly greater than the non-genetic model (9.41 ± 3.40 [SE] versus 3.64 ± 3.11 [SE]), suggesting that some male-specific variance may be confounded by repeat observations from the same breeding pairs (and thus allocated to VPI♀). However, the majority of variance allocated to VPI♀ in the non-genetic model was allocated to the residual variance (VR) in the male effects model (Table C.1.1), indicating that most female-specific variance was accounted for in the male-specific model and that the potential confounding due to repeat observations was not substantial.180  Table C.1.1 Variance component (± SE) and fixed effects estimates (± SE) from the initial non-spatial animal model (from Table 1, for comparison) and five alternate versions of this model, using restricted datasets or random effects. VA, and VPI refer to additive genetic and permanent individual variance for females (♀) and males (♂), while VY and VR refer to year and residual variance, respectively. CorrA♀♂ represents the cross-sex genetic correlation, and f refers to an individual’s inbreeding coefficient. Star (*) denotes variance estimates constrained to a boundary parameter, thus SE was not estimated. Dash (–) denotes random effects not included in model.181    Non-spatial No Immigrants Genetic pedigree No exclusive pairs Non-genetic Male effects  Variance components Est SE Est SE Est SE Est SE Est SE Est SE  VA♀ 12.30 5.31 11.45 4.74 10.60 5.94 15.27 6.63 - - - -  VA♂ 3.63 2.20 6.62 2.81 2.18 2.36 3.48 2.35 - - - -  VPI♀ 12.27 4.90 9.42 4.29 5.79 5.24 11.18 5.64 24.14 4.36 - -  VPI♂ <0.00 * <0.00 * 1.84 3.40 <0.00 * 3.64 3.11 9.41 3.40  VY 76.40 19.00 82.44 20.46 48.04 16.03 78.36 19.62 74.11 18.35 72.45 18.04  VR 60.87 3.84 53.39 3.57 50.34 4.35 58.83 4.03 60.55 4.16 78.07 4.45  CorrA♀♂ 0.99 * 0.99 * 0.99 * 0.99 * - - - -                Fixed effects              Intercept 114.36 1.89 112.11 2.06 114.42 2.25 113.65 1.99 111.83 1.59 111.89 1.57 ♀ Age 2-4 years -6.61 0.61 -6.51 0.60 -6.11 0.71 -6.34 0.65 -6.53 0.62 -6.46 0.66 5+ years -6.14 1.33 -5.81 1.31 -6.96 1.34 -6.03 1.36 -5.77 1.36 -6.04 1.40  ♀ f 29.57 8.35 30.24 7.87 23.39 9.80 30.94 9.30 18.82 7.88 21.09 6.80 ♂ Age 2-4 years -2.93 0.69 -2.38 0.68 -3.15 0.86 -1.84 0.76 -2.88 0.70 -3.44 0.72 5+ years -1.47 1.11 -1.57 1.10 -2.46 1.26 -0.77 1.18 -1.33 1.13 -1.05 1.14  ♂ f -2.42 7.52 -0.50 7.52 -0.47 8.13 -7.74 8.23 -7.71 7.53 -5.30 7.54  df 1033  967  587  847  1033  1033   logLik -2857.24  -2626.37  -1560.71  -2343.57  -2867.72  -2889.46    182  Next, we sought to confirm that our estimates of VA♀ and VA♂ were not biased by parental environmental effects. We thus added random maternal effects to our non-spatial model (Chapter 4.2.3, equation 1), by including identity of the mother of both the female (Z6MAT♀) and social male (Z7MAT♂) for each observation of breeding date (‘maternal effects’ model). We opted to include maternal identity only, rather than maternal and/or social paternal identity for each member of a breeding pair, since male permanent individual variance in breeding date is essentially zero (non-spatial model, Table C.1.1). Further, due to extra-pair paternity, there would be uncertainty as to whether paternal effects associated with either member of a breeding pair would stem directly from the social male attending them as nestlings or indirectly from extra-pair sires. We excluded all observations where maternal identity of either the female or male was unknown (mostly corresponding to immigrants), resulting in a reduced sample size of n = 918 observations of breeding date. We detected some evidence of female maternal effects, but essentially no maternal effects on the social male (Table C.1.2). More importantly, female additive genetic variance remained very similar to that estimated in our initial non-spatial model, indicating that these two sources of variance are not confounded. Instead, the majority of variance due to female maternal effects (VMAT♀) appears to come from VPI♀, which was substantially less than that estimated in the non-spatial model, whereas all other variance estimates were similar between the two models (Table C.1.2). This is expected given that a large proportion (48%) of females in our dataset contributed only one observation of breeding date (see Chapter 4.3.1), and thus any maternal effects from the female would be highly correlated with VPI♀ and difficult to distinguish. Finally, we also ran our non-spatial model using Bayesian inference (‘Bayesian’ model) to ensure that variance component estimates were not influenced by any restrictions of the 183  restricted maximum likelihood algorithm (e.g., models getting stuck on a local maximum). Bayesian analyses were implemented in the R package MCMCglmm (Hadfield 2010). Fixed effect priors were normally distributed with mean zero and large variance, and variance component priors were inverse Wishart distributed. Analyses used 3,005,000 iterations, with a burn-in of 5000 and thinning interval of 3000, ensuring low-autocorrelation among thinned samples (< 0.05). For simplicity, our Bayesian model estimated the additive genetic covariance (σA♀♂) rather than the cross-sex genetic correlation (ρA♀♂), and we calculated the estimate of CorrA♀♂ for this model via equation 3 (Chapter 4.2.3). Variance component estimates from our Bayesian and non-spatial model were similar overall, although VA♀ and VY were slightly larger and VPI♀ smaller in the Bayesian mode. Fixed effects estimates between the two models were nearly identical, indicating that the use of Bayesian versus maximum-likelihood frameworks had no substantial influence on our results or interpretations.  Table C.1.2 Variance component and fixed effects estimates from the initial non-spatial animal model (from Table 4.1, for comparison) and two alternate versions of this model, using additional random maternal effects or estimated via Bayesian inference. Standard errors (SE) are presented for models fit via restricted maximum likelihood, and 95% credibility intervals (CI) are presented for the Bayesian model. VA, VPI, VMAT refer to additive genetic, permanent individual, and maternal-effects variance for females (♀) and males (♂), while VY and VR refer to year and residual variance, respectively. CorrA♀♂ represents the cross-sex genetic correlation, and f refers to an individual’s inbreeding coefficient. Star (*) denotes variance estimates constrained to a boundary parameter, thus SE was not estimated. Dash (–) denotes random effects not included in model.  184    Non-spatial Maternal effects Bayesian  Variance components Est SE Est SE Est CI  VA♀ 12.30 5.31 11.12 5.48 15.76 (3.55-28.17)  VA♂ 3.63 2.20 5.46 3.20 3.63 (0.13-7.68)  VPI♀ 12.27 4.90 6.06 5.04 8.72 (3.43-19.45)  VPI♂ <0.00 * <0.00 * 0.59 (<0.00-3.17)  VMAT♀ - - 4.75 3.68 - -  VMAT♂ - - 1.96 2.49 - -  VY 76.40 19.00 81.31 20.38 80.63 (45.94-119.3)  VR 60.87 3.84 52.14 3.71 61.89 (54.57-70.71)  CorrA♀♂ 0.99 * 0.99 * 0.98 (0.93-0.99)          Fixed effects Est SE Est SE Est CI  Intercept 114.36 1.89 112.69 2.07 114.53 (111.09-118.21) ♀ Age 2-4 years -6.61 0.61 -6.39 0.62 -6.61 ([-7.73]-[-5.27]) 5+ years -6.14 1.33 -5.94 1.37 -6.20 ([-8.98]-[3.51])  ♀ f 29.57 8.35 29.85 8.23 29.73 (13.18-47.91) ♂ Age 2-4 years -2.93 0.69 -2.70 0.70 -2.99 ([-4.33]-[-1.61]) 5+ years -1.47 1.11 -2.10 1.14 -1.52 (-3.81-0.74)  ♂ f -2.42 7.52 -4.12 7.80 -2.24 (-17.44-12.33)  df 1033  911   1033  logLik -2857.24  -2476.55       185  C.2 All alternate versions of the ‘grid’ model We ran a total of eight models estimating the influences of grid cell identity on breeding date by varying the diameter of hexagonal cells in increments of 2 m from a starting point of diameter = 10 m, a scale consistent with previous analyses investigating the effects of habitat preference and quality in this system (Germain et al. 2015). The full list of grid cell sizes were: diameter = 4 m (area = 10.4 m2), 6 m (23.4 m2), 8 m (41.6 m2), 10 m (64.9 m2), 12 m (93.5 m2), 14 m (127.3 m2), 16 m (166.3 m2), and 18 m (210.4 m2). We chose diameter = 4 m and 18 m as our lower and upper limits for cell sizes since they approached the estimated accuracy with which nest locations were mapped (~2.5 m), as well as the upper limit over which song sparrows have previously been shown to exhibit habitat preference in this system (200 m2, Germain and Arcese 2014). Full model results (variance component estimates ± SEs) for all grid cell sizes are presented in Table C.2.1, along with log likelihood and AIC values for each. The model with grid cell diameter = 16m had the greatest (closest to zero) log likelihood, the lowest AIC value (ΔAIC ≥ 2 for all alternate models, Burnham and Anderson 2002), and the greatest estimate of VLoc  of all models tested, and was thus selected for the ‘grid’ model used in our main analyses (see Chapter 4.2.4).   186  Table C.2.1 Variance component (± SE) and fixed effects estimates (± SE) from the eight models investigating random location effects of cell identity on breeding date. VA, and VPI refer to additive genetic and permanent individual variance for females (♀) and males (♂), while VLoc, VY and VR refer to location-based, year and residual variance, respectively. CorrA♀♂ represents the cross-sex genetic correlation, and f refers to an individual’s inbreeding coefficient. Star (*) denotes variance estimates constrained to a boundary parameter, thus SE was not estimated.187    Diam 4 m Diam 6 m Diam 8 m Diam 10 m Diam 12 m Diam 14 m  Variance components Est SE Est SE Est SE Est SE Est SE Est SE  VA♀ 12.30 5.33 12.33 5.36 12.34 5.36 12.46 5.37 12.35 5.36 12.30 5.33  VA♂ 3.63 2.20 3.34 2.16 3.35 2.16 3.45 2.17 3.29 2.15 3.63 2.21  VPI♀ 12.27 4.90 12.06 4.93 12.06 4.93 11.97 4.93 11.82 4.94 12.26 4.92  VPI♂ <0.00 * <0.00 * <0.00 * <0.00 * <0.00 * <0.00 *  VLoc <0.00 * 2.39 2.60 2.38 2.60 1.83 2.26 2.43 2.07 0.03 1.65  VY 76.40 19.00 76.01 18.90 76.01 18.90 75.86 18.87 75.91 18.87 76.40 19.00  VR 60.87 3.84 58.84 4.33 58.84 4.33 59.34 4.18 59.04 4.04 60.85 4.03  CorrA♀♂ 0.99 * 0.99 * 0.99 * 0.99 * 0.99 * 0.99 *                Fixed Effects              Intercept 114.36 1.89 114.34 1.88 114.34 1.88 114.41 1.89 114.46 1.88 114.36 1.89 ♀ Age 2-4 years -6.61 0.61 -6.58 0.61 -6.58 0.61 -6.60 0.61 -6.55 0.61 -6.61 0.61 5+ years -6.14 1.33 -6.13 1.33 -6.13 1.32 -6.09 1.33 -6.04 1.33 -6.14 1.32  ♀ f 29.57 8.35 29.38 8.34 29.39 8.34 29.24 8.36 28.50 8.34 29.56 8.34 ♂ Age 2-4 years -2.93 0.69 -2.92 0.69 -2.92 0.69 -2.96 0.69 -3.01 0.69 -2.93 0.69 5+ years -1.48 1.11 -1.42 1.11 -1.42 1.11 -6.09 1.33 -1.51 1.11 -1.47 1.11  ♂ f -2.42 7.52 -2.33 7.51 -2.33 7.51 -2.63 7.51 -2.81 7.50 -2.42 7.52  LogLik -2857.24  -2856.77  -2856.77  -2856.90  -2856.45  -2857.24   ΔAIC 4.93  3.99  3.99  4.24  3.34  4.93      188    Diam 16 m Diam 18 m  Variance components Est SE Est SE  VA♀ 12.20 5.27 12.39 5.36  VA♂ 3.15 2.09 3.45 2.18  VPI♀ 10.79 4.87 11.65 4.93  VPI♂ <0.00 * <0.00 *  VLoc 3.61 1.93 1.13 1.55  VY 76.37 18.98 76.44 19.00  VR 58.81 3.90 60.29 3.92  CorrA♀♂ 0.99 * 0.99 *        Fixed Effects      Intercept 114.46 1.88 114.39 1.89 ♀ Age 2-4 years -6.54 0.61 -6.58 0.61 5+ years -6.11 1.32 -6.13 1.33  ♀ f 28.50 8.28 29.02 8.33 ♂ Age 2-4 years -3.07 0.69 -2.96 0.69 5+ years -1.52 1.11 -1.46 1.11  ♂ f -2.96 7.49 -2.62 7.52  logLik -2854.78  -2856.95   ΔAIC 0  4.35  189  C.3 All alternate versions of the ‘overlap’ model We ran a total of thirteen models estimating variance in breeding date attributable to shared space (buffer overlap).The full list of breeding location buffer sizes were: area = 50 m2 (radius = 3.99 m), 100m2 (5.64 m), 150 m2 (6.91 m), 200 m2 (7.98 m), 250 m2 (8.92 m), 300 m2 (9.77 m), 400m2 (11.28 m), 500 m2 (12.62 m), 600 m2 (13.82 m), 700 m2 (14.93 m), 800 m2 (15.96 m), 900 m2 (16.93 m), 1000 m2 (17.84 m). Increments were altered from 50 m2 to 100 m2 to cover the widest spatial range possible in fewer total models. Using ArcGIS 10.1 (ESRI 2012), we first jittered each breeding location (nest) in our dataset by 2 m (i.e., a value lower than our estimated mapping accuracy of c. 2.5 m) before adding spatial buffers to ensure that breeding locations did not exactly overlap. For each buffer size listed above, we then used the ‘Tabulate Intersection’ function to calculate the area of buffer overlap between all pairwise combinations of breeding locations to calculate the S matrix of ‘spatial relatedness’ (see Chapter 4.2.4). Full model results from each buffer size are presented in Table C.3.1. Unlike our comparison between cell sizes in the grid model (above), no one overlap model received unambiguous support as the top (most parsimonious) model (e.g., all models ΔAIC ≤ 2 from top model, Burnham & Anderson 2002). We therefore used the 100 m2 buffer model for consistency with previous analyses of nest-site preference and habitat quality in this system (Germain and Arcese 2014, Germain et al. 2015).  190  Table C.3.1 Variance component (± SE) and fixed effects estimates (± SE) from the thirteen models investigating random location effects of buffer overlap between all pairwise combinations of breeding locations, ranging from buffer area = 50 m2 to 1000 m2. VA, and VPI refer to additive genetic and permanent individual variance for females (♀) and males (♂), while VLOC, VY and VR refer to location-based, year and residual variance, respectively. CorrA♀♂ represents the cross-sex genetic correlation, and f refers to an individual’s inbreeding coefficient. Star (*) denotes variance estimates constrained to a boundary parameter, thus SE was not estimated. 191    50 m2 100 m2 150 m2 200 m2 250 m2 300 m2  Variance components Est SE Est SE Est SE Est SE Est SE Est SE  VA♀ 12.35 5.34 12.44 5.36 12.49 5.37 12.52 5.38 12.53 5.38 12.55 5.39  VA♂ 3.49 2.18 3.42 2.17 3.39 2.17 3.39 2.17 3.40 2.17 3.40 2.17  VPI♀ 11.94 4.92 11.74 4.95 11.61 4.96 11.57 4.97 11.53 4.97 11.47 4.98  VPI♂ <0.00 * <0.00 * <0.00 * <0.00 * <0.00 * <0.00 *  VLoc 1.65 3.38 1.55 2.70 1.50 2.40 1.33 2.20 1.22 2.06 1.22 1.97  VY 76.27 18.96 76.17 18.94 76.10 18.92 76.09 18.92 76.10 18.92 76.09 18.92  VR 59.54 4.73 59.80 4.30 59.95 4.14 60.14 4.14 60.26 4.01 60.32 4.01  CorrA♀♂ 0.99 * 0.99 * 0.99 * 0.99 * 0.99 * 0.99 *                Fixed effects              Intercept 114.36 1.89 114.36 1.89 114.37 1.89 114.38 1.89 114.38 1.89 114.37 1.89 ♀ Age 2-4 years -6.60 0.61 -6.60 0.61 -6.59 0.61 -6.59 0.61 -6.59 0.61 -6.59 0.61 5+ years -6.12 1.33 -6.11 1.33 -6.11 1.33 -6.11 1.33 -6.12 1.33 -6.11 1.33  ♀ f 29.40 8.34 29.31 8.34 29.31 8.34 29.33 8.34 29.34 8.34 29.35 8.34 ♂ Age 2-4 years -2.94 0.69 -2.94 0.69 -2.94 0.69 -2.94 0.69 -2.94 0.69 -2.94 0.69 5+ years -1.46 1.11 -1.44 1.11 -1.44 1.12 -1.44 1.12 -1.44 1.12 -1.44 1.12  ♂ f -2.50 7.51 -2.52 7.52 -2.61 7.52 -2.68 7.52 -2.71 7.52 -2.75 7.52  LogLik -2857.11  -2857.06  -2857.03  -2857.04  -2857.05  -2857.03   ΔAIC 0.30  0.20  0.13  0.16  0.18  0.15          192    400 m2 500 m2 600 m2 700 m2 800 m2 900 m2  Variance components              VA♀ 12.56 5.39 12.61 5.40 12.67 5.41 12.68 5.41 12.68 5.41 12.69 5.41  VA♂ 3.41 2.17 3.38 2.17 3.36 2.16 3.37 2.16 3.38 2.16 3.38 2.16  VPI♀ 11.43 4.98 11.31 4.96 11.19 4.99 11.19 4.99 11.21 4.99 11.21 4.99  VPI♂ <0.00 * <0.00 * <0.00 * <0.00 * <0.00 * <0.00 *  VLoc 1.11 1.82 1.16 1.82 1.21 1.69 1.13 1.63 1.05 1.56 1.02 1.52  VY 76.10 18.92 76.07 18.92 76.04 18.91 76.03 18.91 76.03 18.90 76.03 18.90  VR 60.45 3.94 60.50 3.94 60.53 3.90 60.60 3.89 60.60 3.88 60.45 3.88  CorrA♀♂ 0.99 * 0.99 * 0.99 * 0.99 * 0.99 * 0.99 *                Fixed effects              Intercept 114.37 1.89 114.37 1.89 114.36 1.89 114.36 1.89 114.36 1.89 114.35 1.89 ♀ Age 2-4 years -6.58 0.61 -6.58 0.61 -6.58 0.61 -6.58 0.61 -6.57 0.61 -6.57 0.61 5+ years -6.11 1.33 -6.10 1.33 -6.09 1.33 -6.09 1.33 -6.08 1.33 -6.08 1.33  ♀ f 29.36 8.33 29.35 8.33 29.35 8.32 29.38 8.33 29.41 8.33 29.41 8.33 ♂ Age 2-4 years -2.94 0.69 -2.94 0.69 -2.94 0.69 -2.94 0.69 -2.93 0.69 -2.93 0.69 5+ years -1.44 1.11 -1.44 1.12 -1.43 1.12 -1.43 1.12 -1.43 1.11 -1.43 1.11  ♂ f -2.80 7.52 -2.86 7.51 -2.92 7.51 -2.92 7.51 -2.92 7.51 -2.93 7.51  logLik -2857.04  -2856.99  -2856.96  -2856.97  -2856.99  -2856.99   ΔAIC 0.16  0.08  0  0.03  0.06  0.05     193    1000 m2  Variance components    VA♀ 12.71 5.41  VA♂ 3.38 2.16  VPI♀ 11.18 4.99  VPI♂ <0.00 *  VLoc 1.02 1.49  VY 76.00 18.90  VR 60.70 3.87  CorrA♀♂ 0.99 *      Fixed effects    Intercept 114.35 1.89 ♀ Age 2-4 years -6.57 0.61 5+ years -6.08 1.33  ♀ f 29.41 8.33 ♂ Age 2-4 years -2.93 0.69 5+ years -1.43 1.12  ♂ f -2.94 7.51  logLik -2856.97   ΔAIC 0.03   194  C.4 Spatial autocorrelation model The spatial autocorrelation (SAC) model included an autocovariate to account for spatial similarity between X and Y co-ordinates in the variance-covariance matrix (R) of residual values. In most instances, residuals are assumed to be independent and R is defined as I ×VR, where I represents the identity matrix (Kruuk 2004). When accounting for spatial autocorrelation, residuals (R, equation 1, Chapter 4.2.3) can be split into spatially dependent and spatially independent components (Stopher et al. 2012), which for the SAC model represent VLoc and VR, respectively. We modelled spatial variance in ASReml-R using a two-dimensional isotropic spherical spatial autocovariate with homogenous variance, where ‘isotropic’ indicates that the correlation between two observations (Cij) is based on distance and independent of direction. This correlation model is described algebraically as: 𝐶𝑖𝑗 = [3 𝑑𝑖𝑗𝜙−12 ( 𝑑𝑖𝑗𝜙)3] , for 0 < 𝑑𝑖𝑗 < 𝜙 Where dij represents the Euclidian distance between two observations (i and j), and ϕ the spatial (or practical) range, the distance at which correlations decrease to ~ 0.05 or less (Fortin and Dale 2005, Schabenberger and Gotway 2005, Gilmore et al. 2009). In a spherical model, Cij = 0 when dij = ϕ. We further tested for the presence of spatial autocorrelation in observations of lay date via Moran’s I using the ncf package (Bjørnstad 2009), which estimates the summed covariation in lay date from each sampling location (nest) at a given distance, divided by the number of location pairs (Fortin and Dale 2005). We first scaled each observation of lay date by year to remove any variance associated with broad-scale year effects. We then spatially resampled covariance in lay date in increments of 25 m (a distance slightly larger than the width of two 195  overlapping 100 m2 spatial buffers [diameter = 11.28, overlap model]) for 1000 permutations. The resulting correlogram (Figure C.4.1) indicates no spatial autocorrelation in lay date beyond our starting distance of 25 m (-0.2 < Moran’s I < 0.2 for all spatial increments), or two overlapping nest buffers. Exploratory analyses using adjusted increments (10 m–40 m) indicated that increment size had little to no effect on this result.   Figure C.4.1 Moran’s I correlogram of spatial dependence at discrete distances increments of 25 m across Mandarte Island (total width c. 600 m, Figure 4.1). Dashed lines indicate cut-off values of 0.2, beyond which spatial autocorrelation is significant at that distance.    196  Appendix D  Supplementary material for Chapter 5 D.1 Details of measuring habitat variables Polygons for each territory on Mandarte Island from 1975–2011 were drawn in ArcGIS 10.1 (ESRI 2012) from annual end-of-breeding-season summary maps. For polygynous males, we assessed the habitat attributes of their entire defended territory, whereas females of polygynous males were assigned attributes of the region of her male’s territory in which she nested during the breeding season. We assessed total territory area (m2) as the area of available meadow/shrub habitat in each territory, up to the boundary with neighbouring territories or edge of the island (i.e., cliff face or edge of rocky intertidal area). Total area of shrub cover (‘area shrub’) was calculated as the area of overlap between each territory polygon and a polygon representing the extent of shrub cover across the island. Because the extent of shrub cover on the island has changed somewhat over the 37 year study (Smith 2006), we utilized two separate shrub layers in ArcGIS: one based off a vegetation survey of the island conducted in 1986, and one from a vegetation survey conducted in 2006. We measured the area of overlap of both shrub layers with each territory polygon, and weighted them by proximity to each vegetation survey. Thus, territories in 1975–1986 were fully weighted by the 1986 survey, territories in 2006–2012 were fully weighted by the 2006 survey, territories in 1996 were weighted by 50% from each survey, and those in between were weighted on a sliding scale of 5% change per year (e.g., 1987 had a 0.95 weighting for the 1986 survey and a 0.05 weighting for the 2006 survey, whereas 1988 was weighted 0.90 and 0.10, respectively). We measured the extent of ‘edge’ (linear length of shrub/grass meadow interface) in each territory in the same manner. Next, we used Geospatial Modelling Environment (GME; Beyer 2012) to calculate the total length (m) of intertidal coastline in each territory (‘intertidal length’). We used length of 197  intertidal coastline rather than total area of the intertidal zone since we had no information on tide level at the time of the air photo from which our maps were drawn, but assume that areas of the island with greater intertidal length also have greater intertidal area. The southern shore of the wedge-shaped island is dominated by steep cliffs and contains little-to-no intertidal zone. Thus, we restricted measures of intertidal length to the east and west points of the island and the entire northern shoreline between these points.  D.2 Pairwise correlations among predictors of over-winter survival Pairwise correlations between all predictors of over-winter survival revealed strong correlations (r ≥ 0.7) between three habitat variables and two winter weather variables. Of the three correlated habitat variables (area shrub and edge, r = 0.82; area shrub and territory area, r = 0.77; edge and territory area, r = 0.84), we retained area of shrub cover (‘area shrub’) in our analyses since increased vegetation cover within a territory is expected to provide greater concealment from predators and inclement weather (Lima 2009, Germain et al. 2015), and thus is likely to increase an individual’s probability of survival during the non-breeding season. Of the two winter weather variables found to be highly correlated (total rainfall and total precipitation, r = 0.98), we retained total precipitation in our models since this metric accounted for infrequent, episodic periods of snowfall, which has previously been shown to influence over-winter survival in this population (Tompa 1971, Rogers et al. 1991).  198  D.3 Cell-specific estimates of predicted over-winter survival We used parameter estimates from our final averaged model to predict overwinter survival of any given individual occupying a grid cell, based on the area of shrub overlap (see weighting shrub layers, above) and length of intertidal coastline in each grid cell. We first separated predicted values from our final averaged model by sex (male, female) and age (1, mean of 2–4, 5+) and then averaged these values to produce one final estimate of predicted overwinter survival for each grid cell. Our rationale behind this separation/averaging by sex and age was to reduce the effects of these individual-level predictors on overwinter survival, as their strong relative influences (Table 5.2) led to a somewhat bimodal distribution of grid cell-specific values of predicted overwinter survival.  D.4 Details of elasticity analysis We investigated how proportional changes in adult over-winter survival and annual reproductive success will affect population growth as habitat preference ranges from low to high preference. To do so, we binned site-specific habitat preference into four categories (‘low’, ‘med-low’, ‘med-high’, ‘high’) with 36 sites (grid cells) in the low and high categories, and 37 sites in med-low and med-high categories. Mean preference values (± SD) for each category (from low to high preference) were -0.42 (± 0.06), -0.19 (± 0.07), 0.08 (± 0.11), and 0.54 (± 0.25), respectively. We estimated mean adult survival of each category (0.57, 0.59, 0.62, 0.63, from low to high preference) based on site-specific predictions of adult over-winter survival (Chapter 5.3.1). 199  We then used site-specific predictions of annual reproductive rate (Chapter 5.3.1) to estimate the average site-specific probability of producing independent offspring per category (0.04, 0.04, 0.09, and 0.11, from low to high preference). Because these values represent the average site-specific probability of reproductive success and not the actual average annual reproductive rate per category, we then multiplied these values by the total number of offspring produced in each category annually to calculate average annual reproductive rate per category. To calculate the total number of offspring produced in each category, we divided the average number of independent offspring produced annually across the population (91.6) into our four categories of preference based on their different probabilities of producing young (above). For example, we estimated that song sparrows nesting in sites in the ‘low’ category produced a total of 13.09 offspring annually, and those nesting in sites in the ‘med-high’ category produced a total of 28.35 offspring annually, since sites in the med-high category had a 2.17 fold higher probability of producing offspring than those in the low category. The resulting estimates of total number of offspring produced in each category annually were 13.09, 13.87, 28.35, and 36.14, from low to high preference. Our final estimates of the average annual reproductive rate per category (probability of reproductive success per category × total number of offspring produced per category), which represented annual reproductive rate in our elasticity analyses were 0.52, 0.58, 2.44, and 3.96, from low to high preference.   200  D.5 Full output from all models in top models subset  Table D.5.1 Parameter estimates (± SE) for all 193 models in ‘top models subset’ (ΔAIC ≤ 7 from top model, arranged by increasing ΔAIC) describing habitat, weather, and individual/population-based predictors of adult overwinter survival. ‘Area shrub’ and ‘Length intertidal’ refer to the total area (m2) of shrub cover and total length (m) of intertidal coastline in an individual’s territory. ‘Pop. size’ refers to population size. ‘Avg. temp’, ‘Total precip.’, and ‘3 coldest’ refer to January–February average temperature (°C), total precipitation (mm), and lowest cumulative temperature (°C) for a three day period, respectively. ‘Cumltv AICwt’ refers to the cumulative AIC weight of the focal model and all preceding models.201   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 1 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)           0 0.04 2 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.17 (0.14)        0.58 0.07 3 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14)          0.79 0.1 4 0.46 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14)  0.16 (0.14)        1.62 0.11 5 0.51 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)    0 (0.14)   -0.08 (0.06)    1.73 0.13 6 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.15)      -0.08 (0.05)   1.77 0.15 7 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.07 (0.15)         1.82 0.16 8 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)    -0.05 (0.13)       1.87 0.18 9 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14)   -0.04 (0.06)     2.12 0.19 10 0.49 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.02 (0.14)   -0.08 (0.06)    2.2 0.21 11 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.06 (0.13)       2.36 0.22 12 0.46 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.17 (0.14) 0.1 (0.15)         2.37 0.23 13 0.45 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.22 (0.14)          2.4 0.24 14 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.17 (0.14)      -0.02 (0.05)  2.43 0.25 202   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 15 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.11 (0.15)      -0.07 (0.05)   2.43 0.27 16 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0 (0.16) 0.17 (0.15)        2.6 0.28 17 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.01 (0.16) 0.17 (0.15)     -0.08 (0.05)   2.6 0.29 18 0.47 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.15 (0.14)   -0.05 (0.13)       2.67 0.3 19 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)   -0.01 (0.14)   -0.07 (0.06)    2.89 0.31 20 0.51 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.17 (0.19)  -0.09 (0.17)   -0.08 (0.06)    2.92 0.32 21 0.52 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)           2.92 0.33 22 0.5 (0.24) 0.11 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.17 (0.19)  -0.08 (0.17)   -0.08 (0.06) -0.07 (0.05)   2.96 0.34 23 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.14 (0.17)       3.16 0.34 24 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)  0.16 (0.14)   -0.04 (0.06)     3.21 0.35 25 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.13 (0.17)    -0.07 (0.05)   3.22 0.36 26 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.15)   -0.04 (0.06)   -0.08 (0.05)   3.24 0.37 27 0.44 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.2 (0.14)  0.16 (0.14)        3.25 0.38 28 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.15)   -0.04 (0.06)      3.32 0.38 203   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 29 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.19 (0.14) 0.22 (0.19)  -0.16 (0.17)       3.42 0.39 30 0.47 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14)  0.16 (0.14) -0.06 (0.13)       3.42 0.4 31 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09)   0.18 (0.14)        3.43 0.4 32 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14)  0.16 (0.14)      -0.02 (0.05)  3.46 0.41 33 0.46 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.15 (0.14) 0.04 (0.16) 0.14 (0.15)        3.57 0.42 34 0.51 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)    0 (0.14)   -0.08 (0.06)   -0.02 (0.05) 3.58 0.43 35 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.11 (0.14)  0.16 (0.14) -0.02 (0.14)   -0.07 (0.06)    3.59 0.43 36 0.45 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.16 (0.14) 0.22 (0.19)  -0.12 (0.17)   -0.07 (0.06)    3.61 0.44 37 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.18 (0.14) 0.21 (0.19)  -0.15 (0.17)    -0.07 (0.05)   3.61 0.44 38 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.05 (0.16) 0.14 (0.15)     -0.07 (0.05)   3.66 0.45 39 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)    -0.05 (0.13)      -0.02 (0.05) 3.71 0.46 40 0.45 (0.24) 0.09 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.16 (0.14) 0.22 (0.19)  -0.11 (0.17)   -0.07 (0.06) -0.07 (0.05)   3.77 0.46 41 0.51 (0.24) 0.12 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)           3.77 0.47 42 0.44 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09) -0.23 (0.14) 0.1 (0.15)      -0.08 (0.05)   3.8 0.48 204   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 43 0.5 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.02 (0.14)  -0.04 (0.06) -0.08 (0.06)    3.87 0.48 44 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.06 (0.13)  -0.04 (0.06)     3.92 0.49 45 0.51 (0.24) 0.14 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14)   -0.04 (0.06)   -0.02 (0.05)  3.96 0.49 46 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.02 (0.14)   -0.08 (0.06)  -0.02 (0.05)  4 0.5 47 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.06 (0.17)   -0.08 (0.06)    4.01 0.5 48 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.11 (0.15)   -0.04 (0.06)      4.02 0.51 49 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.01 (0.16) 0.17 (0.15)   -0.05 (0.06)  -0.08 (0.05)   4.04 0.51 50 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.12 (0.15)   -0.04 (0.06)   -0.07 (0.05)   4.04 0.52 51 0.44 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.24 (0.14) 0.09 (0.15)         4.05 0.52 52 0.49 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.01 (0.14)   -0.08 (0.06)   -0.02 (0.05) 4.05 0.53 53 0.49 (0.23) 0.1 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.05 (0.17)   -0.08 (0.06) -0.07 (0.05)   4.08 0.53 54 0.45 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.22 (0.14)   -0.07 (0.13)       4.12 0.54 55 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0 (0.16) 0.18 (0.15)   -0.04 (0.06)     4.14 0.54 56 0.5 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.03 (0.16) 0.16 (0.15)  -0.04 (0.06)   -0.08 (0.05)   4.15 0.55 205   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 57 0.5 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09)  0.02 (0.16) 0.16 (0.15)  -0.04 (0.06)      4.18 0.55 58 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.3 (0.09)   0.18 (0.14) -0.06 (0.13)      -0.02 (0.05) 4.2 0.56 59 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.06 (0.13)     -0.02 (0.05)  4.21 0.56 60 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.15 (0.15) -0.11 (0.17)       4.22 0.57 61 0.51 (0.23) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.15 (0.15) -0.09 (0.17)    -0.07 (0.05)   4.3 0.57 62 0.52 (0.24)  -0.1 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  0.05 (0.15)      -0.08 (0.05)   4.38 0.58 63 0.5 (0.23) 0.12 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)   0.17 (0.14)        4.42 0.58 64 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0 (0.16) 0.17 (0.15)      -0.02 (0.05)  4.44 0.59 65 0.47 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.15 (0.14)   -0.04 (0.13)      -0.02 (0.05) 4.55 0.59 66 0.51 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.18 (0.19)  -0.1 (0.17) 0.05 (0.08)  -0.11 (0.07)    4.56 0.6 67 0.52 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)    -0.08 (0.14)       4.56 0.6 68 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.01 (0.16) 0.16 (0.15)     -0.08 (0.06) 0.01 (0.05)  4.59 0.6 69 0.44 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09) -0.24 (0.14) 0.23 (0.19)  -0.18 (0.17)    -0.08 (0.05)   4.64 0.61 70 0.51 (0.24) 0.11 (0.06) -0.11 (0.05) 0.15 (0.17) -0.06 (0.03) 0.31 (0.09)  0.17 (0.19)  -0.08 (0.17) 0.05 (0.08)  -0.11 (0.07) -0.07 (0.05)   4.66 0.61 206   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 71 0.5 (0.24) 0.11 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.17 (0.19)  -0.09 (0.17)   -0.08 (0.06) -0.1 (0.07)  0.03 (0.06) 4.7 0.61 72 0.44 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.25 (0.14) 0.23 (0.19)  -0.2 (0.17)       4.71 0.62 73 0.47 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)   0 (0.14)   -0.07 (0.06)   -0.02 (0.05) 4.77 0.62 74 0.51 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.17 (0.19)  -0.09 (0.17)   -0.08 (0.06)   -0.02 (0.05) 4.77 0.63 75 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.12 (0.17) -0.04 (0.06)      4.8 0.63 76 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.11 (0.17) -0.04 (0.06)   -0.08 (0.05)   4.82 0.63 77 0.45 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.2 (0.14)  0.16 (0.14) -0.08 (0.13)       4.86 0.64 78 0.52 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  0.04 (0.16)         4.87 0.64 79 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)   0.19 (0.14) -0.1 (0.13)       4.93 0.64 80 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.15 (0.21) 0.11 (0.16) -0.14 (0.17)       4.93 0.65 81 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.13 (0.17)    -0.09 (0.07)  0.03 (0.06) 4.97 0.65 82 0.51 (0.24)  -0.1 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  -0.02 (0.16) 0.18 (0.15)     -0.08 (0.05)   4.99 0.65 83 0.47 (0.24) 0.11 (0.06)  0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.12 (0.14)          4.99 0.66 84 0.44 (0.24)  -0.11 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.2 (0.14) 0.04 (0.17) 0.14 (0.15)     -0.08 (0.05)   5 0.66 207   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 85 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.13 (0.17)      -0.02 (0.05) 5 0.66 86 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)  0.17 (0.14) -0.06 (0.13)  -0.04 (0.06)     5.03 0.67 87 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)  0.17 (0.14)   -0.04 (0.06)   -0.02 (0.05)  5.04 0.67 88 0.45 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.15 (0.21) 0.11 (0.16) -0.09 (0.17)   -0.07 (0.06)    5.1 0.67 89 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.15 (0.21) 0.11 (0.16) -0.13 (0.17)    -0.07 (0.05)   5.13 0.68 90 0.44 (0.24)  -0.11 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.2 (0.14)  0.16 (0.14)      -0.02 (0.05)  5.14 0.68 91 0.47 (0.24) 0.12 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14) 0.04 (0.16) 0.15 (0.15)   -0.04 (0.06)  -0.08 (0.05)   5.17 0.68 92 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.04 (0.16) 0.15 (0.15)   -0.04 (0.06)     5.18 0.69 93 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.17 (0.14) 0.22 (0.19)  -0.12 (0.17) 0.05 (0.08)  -0.1 (0.07)    5.22 0.69 94 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.18 (0.14) 0.22 (0.19)  -0.15 (0.17) -0.03 (0.06)      5.22 0.69 95 0.44 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.21 (0.14) 0.03 (0.17) 0.14 (0.16)        5.23 0.69 96 0.45 (0.24) 0.09 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.13 (0.14) 0.15 (0.21) 0.11 (0.16) -0.08 (0.17)   -0.07 (0.06) -0.07 (0.05)   5.26 0.7 97 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.05 (0.17) 0.14 (0.15)  -0.03 (0.06)      5.26 0.7 98 0.47 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14)  0.16 (0.14) -0.06 (0.13)     -0.02 (0.05)  5.27 0.7 208   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 99 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.11 (0.14)  0.17 (0.14) -0.02 (0.14)  -0.04 (0.06) -0.07 (0.06)    5.28 0.71 100 0.47 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)  0.16 (0.14) -0.06 (0.13)      -0.02 (0.05) 5.29 0.71 101 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.19 (0.14) 0.22 (0.19)  -0.16 (0.17)      -0.02 (0.05) 5.3 0.71 102 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14) 0.06 (0.16) 0.13 (0.15)  -0.04 (0.06)   -0.07 (0.05)   5.32 0.71 103 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09)   0.18 (0.14)      -0.02 (0.05)  5.33 0.72 104 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.18 (0.14) 0.21 (0.19)  -0.16 (0.17)    -0.09 (0.07)  0.03 (0.06) 5.35 0.72 105 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.17 (0.14) 0.21 (0.19)  -0.14 (0.17) -0.03 (0.06)   -0.07 (0.05)   5.38 0.72 106 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.11 (0.14)  0.17 (0.14) -0.02 (0.14)   -0.07 (0.06)  -0.02 (0.05)  5.39 0.72 107 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09)  -0.03 (0.16) 0.19 (0.16)        5.41 0.73 108 0.46 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.15 (0.14) 0.04 (0.16) 0.14 (0.15)      -0.02 (0.05)  5.42 0.73 109 0.46 (0.24) 0.09 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.16 (0.14) 0.22 (0.19)  -0.11 (0.17) 0.05 (0.08)  -0.1 (0.07) -0.07 (0.05)   5.44 0.73 110 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.11 (0.14)  0.16 (0.14) -0.01 (0.14)   -0.07 (0.06)   -0.02 (0.05) 5.46 0.74 111 0.45 (0.24) 0.09 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.16 (0.14) 0.22 (0.19)  -0.12 (0.17)   -0.07 (0.06) -0.09 (0.07)  0.03 (0.06) 5.49 0.74 112 0.45 (0.24) 0.09 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.22 (0.19)  -0.12 (0.17)   -0.07 (0.06)   -0.02 (0.05) 5.49 0.74 209   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 113 0.51 (0.24) 0.13 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)  0.07 (0.15)         5.56 0.74 114 0.52 (0.24)  -0.1 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  0.16 (0.19)  -0.15 (0.17)    -0.08 (0.05)   5.59 0.75 115 0.5 (0.24) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.06 (0.17) 0.06 (0.08)  -0.11 (0.07)    5.6 0.75 116 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.05 (0.16) 0.14 (0.15)     -0.08 (0.06) 0.01 (0.05)  5.65 0.75 117 0.51 (0.24) 0.12 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)    -0.05 (0.13)       5.66 0.75 118 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.19 (0.14) -0.02 (0.14)  -0.04 (0.06) -0.08 (0.06)  -0.02 (0.05)  5.66 0.75 119 0.5 (0.23) 0.11 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.15 (0.15) -0.05 (0.17)  -0.04 (0.06) -0.08 (0.06) -0.08 (0.05)   5.69 0.76 120 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.01 (0.14)  -0.04 (0.06) -0.08 (0.06)   -0.02 (0.05) 5.7 0.76 121 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.16 (0.15) -0.06 (0.17)  -0.03 (0.06) -0.08 (0.06)    5.72 0.76 122 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.3 (0.09)   0.18 (0.14) -0.06 (0.13)  -0.04 (0.06)    -0.02 (0.05) 5.73 0.76 123 0.5 (0.23) 0.1 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.05 (0.17) 0.05 (0.08)  -0.11 (0.07) -0.07 (0.05)   5.74 0.77 124 0.52 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.19 (0.14) -0.06 (0.13)  -0.04 (0.06)   -0.02 (0.05)  5.76 0.77 125 0.51 (0.23) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.07 (0.2) 0.16 (0.15) -0.09 (0.17)  -0.04 (0.06)  -0.08 (0.05)   5.79 0.77 126 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.07 (0.2) 0.16 (0.15) -0.1 (0.17)  -0.04 (0.06)     5.81 0.77 210   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 127 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.06 (0.17)   -0.08 (0.06)  -0.02 (0.05)  5.82 0.78 128 0.49 (0.23) 0.11 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.14 (0.15) -0.06 (0.17)   -0.08 (0.06) -0.09 (0.07)  0.03 (0.06) 5.83 0.78 129 0.46 (0.24) 0.11 (0.06)  0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.11 (0.14)  0.15 (0.14)        5.83 0.78 130 0.45 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09) -0.21 (0.14)   -0.06 (0.13)      -0.03 (0.05) 5.84 0.78 131 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.01 (0.14)   -0.08 (0.06)  -0.02 (0.05) -0.02 (0.05) 5.86 0.78 132 0.5 (0.23) 0.13 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)   0.17 (0.14)   -0.05 (0.06)     5.86 0.79 133 0.5 (0.23) 0.11 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.06 (0.17)   -0.08 (0.06)   -0.02 (0.05) 5.86 0.79 134 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.02 (0.16) 0.17 (0.15)  -0.03 (0.06) -0.04 (0.07)  -0.08 (0.05)   5.88 0.79 135 0.5 (0.24) 0.12 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.09 (0.17) -0.03 (0.06)      5.9 0.79 136 0.52 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  0.16 (0.19)  -0.17 (0.17)       5.93 0.79 137 0.49 (0.24) 0.1 (0.06)  0.15 (0.17) -0.06 (0.03) 0.32 (0.09)    0 (0.14)   -0.07 (0.05)    5.95 0.8 138 0.5 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.15 (0.15) -0.08 (0.17) -0.04 (0.06)   -0.07 (0.05)   5.95 0.8 139 0.51 (0.24) 0.14 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0 (0.16) 0.18 (0.15)   -0.04 (0.06)   -0.02 (0.05)  5.97 0.8 140 0.51 (0.24) 0.13 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.01 (0.16) 0.17 (0.15)  -0.03 (0.06) -0.03 (0.07)     5.98 0.8 211   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 141 0.5 (0.24) 0.12 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.02 (0.16) 0.17 (0.15)  -0.04 (0.06)    -0.02 (0.05)  6.01 0.8 142 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.01 (0.16) 0.17 (0.15)   -0.05 (0.06)  -0.08 (0.06) 0.01 (0.05)  6.03 0.81 143 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.18 (0.14) -0.06 (0.13)     -0.02 (0.05) -0.02 (0.05) 6.06 0.81 144 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.15 (0.15) -0.1 (0.17)      -0.02 (0.05) 6.06 0.81 145 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.15 (0.15) -0.1 (0.17)    -0.09 (0.07)  0.03 (0.06) 6.07 0.81 146 0.51 (0.24) 0.13 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.16 (0.15) -0.1 (0.17)     -0.02 (0.05)  6.08 0.81 147 0.51 (0.24)   0.18 (0.17) -0.06 (0.03) 0.3 (0.09)           6.08 0.82 148 0.49 (0.23) 0.1 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.09 (0.2) 0.14 (0.15) -0.05 (0.17)   -0.08 (0.06) -0.08 (0.06) 0.01 (0.05)  6.08 0.82 149 0.5 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.03 (0.16) 0.16 (0.15)  -0.04 (0.06)   -0.08 (0.06) 0.01 (0.05)  6.15 0.82 150 0.52 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)    -0.07 (0.14)      -0.03 (0.05) 6.18 0.82 151 0.44 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09) -0.22 (0.14) 0.16 (0.21) 0.11 (0.16) -0.15 (0.17)    -0.08 (0.05)   6.19 0.82 152 0.5 (0.23) 0.12 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)   0.17 (0.14) -0.06 (0.13)       6.23 0.82 153 0.44 (0.24)   0.19 (0.17) -0.06 (0.03) 0.3 (0.09) -0.19 (0.14)          6.23 0.83 154 0.44 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.23 (0.14) 0.17 (0.21) 0.11 (0.16) -0.17 (0.17)       6.26 0.83 212   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 155 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.15 (0.15) -0.09 (0.17)    -0.08 (0.06) 0.01 (0.05)  6.29 0.83 156 0.51 (0.24) 0.11 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.17 (0.19)  -0.09 (0.17) 0.05 (0.08)  -0.11 (0.07) -0.09 (0.07)  0.03 (0.06) 6.4 0.83 157 0.51 (0.24) 0.11 (0.06) -0.12 (0.05) 0.15 (0.17) -0.06 (0.03) 0.31 (0.09)  0.18 (0.19)  -0.09 (0.17) 0.05 (0.08)  -0.11 (0.07)   -0.02 (0.05) 6.42 0.83 158 0.5 (0.23) 0.12 (0.06)  0.15 (0.17) -0.06 (0.03) 0.31 (0.09)  0.01 (0.16) 0.16 (0.15)        6.43 0.83 159 0.44 (0.24)  -0.12 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09) -0.25 (0.14) 0.23 (0.19)  -0.19 (0.17)      -0.03 (0.05) 6.44 0.84 160 0.48 (0.23) 0.1 (0.06)  0.16 (0.17) -0.06 (0.03) 0.31 (0.09)   0.17 (0.14) -0.02 (0.13)   -0.07 (0.05)    6.49 0.84 161 0.44 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.07 (0.03) 0.29 (0.09) -0.24 (0.14) 0.23 (0.19)  -0.19 (0.17)    -0.09 (0.07)  0.02 (0.06) 6.49 0.84 162 0.51 (0.24)  -0.1 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  0.07 (0.21) 0.16 (0.16) -0.12 (0.17)    -0.08 (0.05)   6.53 0.84 163 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)   0.19 (0.14) -0.09 (0.13)      -0.03 (0.05) 6.55 0.84 164 0.45 (0.24)  -0.11 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.19 (0.14)  0.16 (0.14) -0.08 (0.13)      -0.03 (0.05) 6.57 0.84 165 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.12 (0.17) -0.04 (0.06)   -0.1 (0.07)  0.03 (0.06) 6.57 0.85 166 0.46 (0.24) 0.11 (0.06)  0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.14 (0.14) 0.1 (0.15)         6.59 0.85 167 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.14 (0.21) 0.12 (0.16) -0.13 (0.17)  -0.04 (0.06)     6.61 0.85 168 0.49 (0.24)   0.19 (0.17) -0.06 (0.03) 0.3 (0.09)   0.17 (0.14)        6.64 0.85 213   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 169 0.51 (0.24) 0.12 (0.06) -0.11 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09)  0.16 (0.19)  -0.12 (0.17) -0.04 (0.06)     -0.02 (0.05) 6.65 0.85 170 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.15) 0.15 (0.21) 0.12 (0.16) -0.1 (0.17) 0.06 (0.08)  -0.11 (0.07)    6.67 0.85 171 0.51 (0.24) 0.12 (0.06) -0.12 (0.05) 0.15 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.21) 0.17 (0.16) -0.06 (0.17) 0.1 (0.1) -0.07 (0.07) -0.13 (0.08)    6.7 0.85 172 0.51 (0.24) 0.11 (0.06) -0.11 (0.05) 0.15 (0.17) -0.06 (0.03) 0.31 (0.09)  0.08 (0.2) 0.17 (0.15) -0.05 (0.17) 0.1 (0.1) -0.07 (0.07) -0.13 (0.08) -0.08 (0.05)   6.71 0.86 173 0.47 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.15 (0.14) 0.14 (0.21) 0.12 (0.16) -0.12 (0.17)  -0.04 (0.06)  -0.07 (0.05)   6.71 0.86 174 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.15) 0.15 (0.21) 0.11 (0.16) -0.13 (0.17) -0.03 (0.06)      6.74 0.86 175 0.45 (0.24)  -0.12 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.2 (0.14)  0.17 (0.14) -0.08 (0.13)     -0.02 (0.05)  6.75 0.86 176 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.15 (0.21) 0.12 (0.16) -0.14 (0.17)     -0.02 (0.05)  6.8 0.86 177 0.46 (0.24) 0.11 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.15 (0.21) 0.11 (0.16) -0.13 (0.17)      -0.02 (0.05) 6.81 0.86 178 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)   0.19 (0.14) -0.1 (0.13)     -0.02 (0.05)  6.83 0.86 179 0.51 (0.24)  -0.11 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  0.07 (0.21) 0.17 (0.16) -0.13 (0.17)       6.83 0.87 180 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.14 (0.21) 0.12 (0.16) -0.09 (0.17)  -0.03 (0.06) -0.07 (0.06)    6.85 0.87 181 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)  0.17 (0.14) -0.06 (0.13)  -0.04 (0.06)   -0.02 (0.05)  6.86 0.87 182 0.47 (0.24) 0.12 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.13 (0.14)  0.17 (0.14) -0.05 (0.13)  -0.04 (0.06)    -0.02 (0.05) 6.87 0.87 214   Intcept Area shrub Length Intidal Age Age2 Sex (male) Pop. size Avg. temp Total precip 3 coldest Shrub: Avg. temp Shrub: Total precip Shrub: 3 coldest Intidal: Avg. temp Intidal: Total precip Intidal: 3 coldest ΔAIC Cumltv  AICwt 183 0.46 (0.24) 0.11 (0.06) -0.11 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.16 (0.14) 0.15 (0.21) 0.11 (0.16) -0.13 (0.17)    -0.09 (0.07)  0.03 (0.06) 6.88 0.87 184 0.47 (0.24) 0.11 (0.06)  0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.12 (0.14)   -0.04 (0.13)       6.89 0.87 185 0.46 (0.24) 0.09 (0.06) -0.12 (0.05) 0.16 (0.17) -0.06 (0.03) 0.31 (0.09) -0.13 (0.14) 0.15 (0.21) 0.12 (0.16) -0.08 (0.17) 0.05 (0.08)  -0.11 (0.07) -0.07 (0.05)   6.9 0.87 186 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.15 (0.14) 0.15 (0.21) 0.11 (0.16) -0.11 (0.17) -0.03 (0.06)   -0.07 (0.05)   6.91 0.87 187 0.51 (0.24)  -0.1 (0.05) 0.19 (0.17) -0.06 (0.03) 0.29 (0.09)  -0.02 (0.16) 0.18 (0.15)     -0.09 (0.06) 0.02 (0.05)  6.91 0.88 188 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.14 (0.14) 0.15 (0.21) 0.12 (0.16) -0.09 (0.17)   -0.07 (0.06)  -0.02 (0.05)  6.92 0.88 189 0.46 (0.24) 0.1 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.13 (0.14) 0.14 (0.21) 0.12 (0.16) -0.08 (0.17)  -0.04 (0.06) -0.07 (0.06) -0.07 (0.05)   6.92 0.88 190 0.51 (0.24) 0.12 (0.06)  0.15 (0.17) -0.06 (0.03) 0.32 (0.09)  0.16 (0.19)  -0.13 (0.17)       6.93 0.88 191 0.44 (0.24)  -0.11 (0.05) 0.2 (0.17) -0.07 (0.03) 0.29 (0.09) -0.2 (0.14) 0.04 (0.17) 0.14 (0.16)     -0.09 (0.06) 0.01 (0.05)  6.94 0.88 192 0.45 (0.24) 0.09 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.3 (0.09) -0.14 (0.14) 0.15 (0.21) 0.11 (0.16) -0.09 (0.17)   -0.07 (0.06)   -0.02 (0.05) 6.98 0.88 193 0.45 (0.24) 0.09 (0.06) -0.12 (0.05) 0.17 (0.17) -0.06 (0.03) 0.31 (0.09) -0.13 (0.14) 0.15 (0.21) 0.11 (0.16) -0.09 (0.17)   -0.08 (0.06) -0.09 (0.07)  0.03 (0.06) 7 0.88   

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