DEMOGRAPHIC SHIFTS and the ROLE of CLIMATE WARMING in a SWITCH from MIGRANT to RESIDENT LIFE HISTORY By

DEMOGRAPHIC SHIFTS AND THE ROLE OF CLIMATE WARMING IN A SWITCH FROM MIGRANT TO RESIDENT LIFE HISTORY by

Hannah Visty

B.Sc., The University of British Columbia, 2015

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry)

THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)

November 2018

The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled:

Demographic shifts and the role of climate warming in a switch from migrant to resident life history

submitted by Hannah Visty in partial fulfillment of the requirements for the degree of Master of Science in Forestry

Examining Committee:

Dr. Peter Arcese, Forestry Supervisor

Dr. Kathy Martin, Forestry Supervisory Committee Member

Dr. Andrew Trites, Zoology Additional Examiner

Additional Supervisory Committee Members:

Dr. Scott Wilson, Environment Canada Supervisory Committee Member

Abstract

Identifying causes and consequences of variation in species life history should improve predictions about how climate and land use change will affect the demography and distribution of species in future. Sooty fox sparrows (Passerella iliaca unalaschcensis) were documented as obligate migrants, abundant in winter but with only three breeding records in coastal habitats of British Columbia and Washington prior to 1950. Because this subspecies has since established year-round resident populations in this region, I studied resident populations of sooty fox sparrows to test theory on how climate change and life history might affect the demography and distribution of a new partial migrant.

I estimated demographic vital rates in one recently established resident population on Mandarte Is., BC, using color-banded birds. Annual fecundity (F) was higher than reported in migrant populations studied previously in Alaska and Newfoundland, supporting the hypothesis that residents invest more in reproduction on average than migrants within species. I also estimated high juvenile and adult overwinter survival (Sj = 0.32 ± 0.06, and Sa = 0.69 ± 0.05) and population growth (λexp = 1.61 ± 0.57), implying rapid population growth.

I next tested the hypothesis that climate warming facilitated the establishment of resident populations by reducing the net benefit of migrating out of the wintering area to breed. Because resident sooty fox sparrows breed earlier and longer than migrants, I asked if climate warming during the pre-breeding period coincided with patterns of establishment by: 1) characterizing the pre-breeding climate niche of resident populations using species distribution models, occurrence data, and monthly climate records, and 2) testing if the emergence of the pre-breeding climate niche currently occupied by resident populations corresponds to the first reports of sooty fox sparrows breeding after 1920. Niche models suggest that the mild, near- shore climate niche now occupied by recently established resident populations expanded dramatically from 1920 to 2015 in a pattern matching early records of breeding by sooty fox sparrows within their historic wintering area from 1950.

Whereas prior studies focus on climate warming affecting overwinter survival, my results suggest warming may also affect migration through improved fecundity and breeding niches.

iii

Lay Summary

Climate warming is contributing to global declines and disruptions in animal migration, as species that once migrated long distances now remain resident year-round, migrate shorter distances, and occupy novel habitats. My work documents the loss of migration in some populations of a former migrant, the sooty fox sparrow (Passerella iliaca unalaschcensis), and uses theory to identify potential factors affecting migration tendency and its influence on population persistence and life history. I found that newly resident sooty fox sparrows bred earlier and had more nesting attempts annually than migrant fox sparrows, while maintaining high survival. These results suggest that sooty fox sparrows gain a reproductive benefit by remaining resident in their former wintering area, possibly facilitated by climate warming during the ‘pre-breeding’ season. My work illustrates the potential for climate change to influence trade-offs affecting the strategies of migrants and residents and the communities they occupy.

Preface

Chapter two of this thesis utilized 45 years of song sparrow data on Mandarte Island, British Columbia collected by dozens of field teams between 1960 and 2016, as well as 6 years of fox sparrow data collected between 2010 and 2016. I participated in data collection during the field season of 2017 to contribute to the dataset moving forward. A version of Chapter 2 of this thesis has been published: Visty, H., Wilson, S., Germain, R., Krippel, J., and Arcese, P. 2018. Demography of sooty fox sparrows following a shift from migratory to resident life history. Can. J. Zool. 96: 436-440. I conducted all analyses and wrote the manuscript. Dr. R. Germain and J. Krippel led the data collection between 2010-2016. Dr. S. Wilson advised on the data analysis and helped extensively with editing. Dr. P. Arcese advised on data analysis and design of the paper. All authors provided editing and writing input, especially Dr. P. Arcese and Dr. S. Wilson.

The third chapter of this thesis utilized several years of point count data collected throughout southwest British Columbia and northwest Washington between 2007 and 2017 from multiple field teams. I collected all data in 2017. Dr. R. Schuster and Dr. T. Wang provided code and analysis help. Dr. P. Arcese provided input on experimental design and analysis. I created the final maps and models and wrote the manuscript. Extensive editing help was provided by Dr. P. Arcese. A version of this chapter will be submitted for publication with Drs. R. Schuster, T. Wang, and P. Arcese as co-authors.

All protocols involving the use of animals in this thesis were approved by the UBC Animal Care Committee (A14- 0366). Permits for this work were obtained from Environment Canada (master banding permit no. 10596, sub-banding permit 10596 T).

Table of contents

Abstract ...... iii Lay Summary ...... iv Preface ...... v Table of contents ...... vi List of Tables ...... viii List of Figures ...... ix Acknowledgements ...... x Dedication ...... xi 1. Introduction ...... 1 1.1 Climate warming impacts on demography and migration ...... 1 1.2 Study region ...... 3 1.3 Sooty fox sparrows as a novel partial migrant complex ...... 4 1.4 Thesis overview ...... 5 2. Demography of sooty fox sparrows (Passerella iliaca unalaschcensis) following a shift from migratory to resident life history ...... 6 2.1 Introduction ...... 6 2.2 Methods ...... 7 2.2.1 Study system ...... 7 2.2.2 Estimation of survival and fecundity ...... 8 2.2.3 Stochastic estimation of population growth...... 10 2.3 Results ...... 10 2.3.1 Clutch size and nest success ...... 10 2.3.2 Number of annual nests ...... 11 2.3.3 Survival and population growth rate ...... 12 2.4 Discussion ...... 14 2.5 Conclusion ...... 15 3. Climate warming and the establishment of resident populations in a former obligate migrant ...... 16 3.1 Introduction ...... 16 3.2 Methods ...... 18 3.2.1 Establishing locations with year-round presence ...... 19 3.2.2 Characterizing the pre-breeding climate niche of resident sooty fox sparrows ...... 20 3.2.3 Testing historical presence of the pre-breeding climate niche ...... 22

3.3 Results ...... 22 3.3.1 Pre-breeding climate niche of resident sooty fox sparrows ...... 22 3.3.2 Testing historical presence of the pre-breeding climate niche ...... 23 3.4 Discussion ...... 25 3.4.1 The current sooty fox sparrow pre-breeding climate niche ...... 25 3.4.2 Expansion of the pre-breeding climate niche and establishment of resident populations ...... 26 3.5 Conclusion ...... 27 4. Conclusion ...... 29 4.1 Summary of key findings ...... 29 4.2 Implications ...... 29 4.3 Caveats and suggestions for future study ...... 31 4.4 Conclusion ...... 32 References ...... 33 Appendices ...... 44 Appendix A: Hierarchical occupancy model ...... 44 Appendix B: Additions to tables and maps ...... 47

vii

List of Tables

Table 2.1 Model selection results examining variation in sooty fox sparrow annual survival ...... 13 Table 3.1 Top five most important climate variables ranked in the random forest model...... 23 Table A.1 Averaged occupancy model result from unmarked ...... 45 Table A.2 Covariates included in the final Bayesian model...... 46 Table B.1 Complete list ranking variables by importance in random forest model...... 47

viii

List of Figures

Figure 2.1 Comparison of sooty fox sparrow and song sparrow lay dates...... 12 Figure 2.2 The distribution of stochastically estimated population growth rates (λ) ...... 14 Figure 3.1 Map of study area comprising southwest coastal British Columbia, Canada and northwest Washington, USA...... 20 Figure 3.2 Current and historical probability of sooty fox sparrow occupancy predicted by random forest models ...... 24 Figure B.1 Average temperature in February in 1900-1910 and 2005-2015...... 48

Acknowledgements

I hold enduring gratitude towards the faculty, staff, and fellow students at UBC who have supported me in my degree and my work. I am also thankful to those that supported this work financially, including the Faculty of Graduate Studies, the Department of Forest and Conservation Sciences, Werner and Hildegard Hesse, and the American Ornithologists’ Union.

I owe particular thanks to my supervisor, Peter Arcese for sharing his extensive knowledge in this field and always encouraging me to see the bigger picture.

A huge thanks to Richard Schuster for his stellar coding skills and invaluable analysis help. Additional thanks to Scott Wilson and Tongli Wang for lessons in statistical analyses; I learned so much more than I imagined I could.

I thank my field team, lab mates, and co-workers, Jessica Krippel, Dominic Janus, Pablo Vaz, Pirmin Nietlisbach, Nina Morrell, Cora Skein, Micah Scholer, Devin DeZwaan, and Tomás Altamirano for support along the way, for engaging conversations, and for bringing fun to our work lives.

A special thanks to my friends for enduring my constant chatting about birds, and to my boyfriend Conrad for his endless support and for acting as my audience and editor at home.

Dedication

For my Mom (Meh), who ceaselessly encouraged me to follow my curiosity wherever it led, and who gave me the courage and confidence to follow my dreams—my biggest champion. Your love endures.

1. Introduction

1.1 Climate warming impacts on demography and migration Identifying factors affecting species distribution and demography is essential to designing conservation plans that are resilient to ongoing climate change. In particular, climate warming has been identified as a factor with the potential to affect species life history, including the expression of novel phenotypic traits linked to phenology, morphology, or survival and reproductive rate, often in regions outside the species’ historic range (Robillard et al. 2015, Feeley et al. 2017, MacLean and Beissinger 2017). However, despite theoretical frameworks establishing the connection between climate warming and loss of migration (Shaw 2016), there are as yet just a handful of examples demonstrating the establishment of resident populations in species formerly known as obligate migrants (e.g., Zuckerberg et al. 2010, Møller et al. 2014, Plummer et al. 2015, Shephard et al. 2015).

Many migratory species are in decline due to novel barriers to migration, habitat loss and fragmentation, as well as to the direct and indirect effects of climate change (Sanderson et al. 2006, Wilcove and Wikelski 2008, Cox 2010). My overarching goal in this thesis was to document the establishment of a resident population of sooty fox sparrows, a subspecies formerly known to be an obligate migrant (Swarth 1920), and to use my observations to inform theory on potential trade-offs in migratory versus residential life histories subject to climate change (Shaw 2016, Reid et al. 2018).

Current theory about the causes of migration predict that in many cases animals improve survival and reproduction by moving away from areas where seasonal abiotic conditions makes survival unlikely (e.g., Berthold 1999, Boyle and Conway 2007, Hsiung et al. 2018). However, cases wherein a single species shifts between migratory and residential tactics offer unique opportunities to test whether expectations built on comparisons across species also predict the demographic traits of newly resident populations (Chapman et al. 2011, Reid et al. 2018). In partial migrant bird species, in which some individuals or populations migrate and others do not, factors affecting life history traits such as lay date, brood number, clutch size,

1 annual reproductive success, and adult or juvenile survival, are thought to be linked to the trade-offs of migration versus year-round residency (Chapman et al. 2011, Reid et al. 2018). Several recent case studies document shifts in these trade-offs to favour residency, driven by climate amelioration and human-induced improvements of resource availability such as backyard bird feeders and agriculture lands providing food through winter (Møller et al. 2014, Plummer et al. 2015, Shephard et al. 2015). Climate warming is a broad driver of migratory change, including the loss of migratory behaviour (e.g., in blackbirds Møller et al. 2014), earlier migrations and subsequent breeding seasons (Townsend et al. 2013, Saino et al. 2003, Marra et al. 2005, Kelly et al. 2016), and reduced migration distance (Zuckerberg et al. 2010). Although recent theoretical work on the evolution and maintenance of partial migration now offers a framework in which to study the phenomenon (Boyle and Conway 2007, Chapman et al. 2011, Reid et al. 2018), empirical examples remain scarce (Berthold 1999, Boyle 2008, Jahn et al. 2010) in part due to challenges of tracking and comparing individuals following different strategies (Wikelski et al. 2007, Reid et al. 2018).

Migrants and residents of the same species will also theoretically show differences in demography related to trade-offs in survival and reproductive success, which can provide clues to the environmental drivers behind such differences. Partial migrant complexes make it easy to compare differences in the demographic costs and benefits to migrants versus residents, especially when migrants and residents overlap in part of the annual cycle, thereby experiencing similar conditions for part of the year and narrowing down key differences across the annual cycle. For example, studies of partial migrants which reveal residents have higher fecundity over migrants have led researches to test whether drivers of reproductive success, like dominance traits including behaviour and body size, allow residents to remain on territories all year (Adriaensen and Dhondt 1990, Belthoff and Gauthreaux 1991, Gillis et al. 2008, Grayson et al. 2011). In cases of species switching between migratory and residential tactics, such demographic differences can theoretically inform hypotheses on the drivers of change.

Despite many studies documenting the effects of climate change on migration and demography, relatively few connect climate warming to the demographic rates that influence migratory behavior, despite migratory species’ potential vulnerability to such changes (Newson

2 et al. 2016, Taylor and Stutchbury 2016, La Sorte et al. 2017, McDermott and DeGroote 2017). Furthermore, such shifts in migratory behavior and demography can have community-wide implications by affecting interspecific competitive interactions (e.g., in trees Uriarte et al. 2016; in birds, Johnson et al. 2018), patterns of herbivory and predation (e.g., Cohen et al. 2015, Ramos-Robles et al. 2016), or parasitism and disease (Pan et al. 2015, Leung and Koprivnikar 2016).

1.2 Study region In the Pacific Northwest of British Columbia (BC) and Washington (WA), the Coastal Douglas Fir (CDF) ecozone is particularly in flux, due to both extensive land use change and climate warming (Meidinger and Pojar 1991, Austin et al. 2008, Schuster et al. 2014). Over 60% of habitat in this imperiled region has now been converted for exclusive human use, leaving less than 0.3% of historic old growth forest (Austin et al. 2008). The area historically contained Douglas Fir and Garry Oak forests with shallow meadows, the remnants of which still support 117 Species at Risk—the greatest number of any BC ecozone (Meidinger and Pojar 1991, Austin et al. 2008). The bird community is undergoing especially noticeable shifts, threatened by loss of habitat (Martin et al. 2011) and undergoing community composition changes driven by climate, urbanization, and land conversion. Recent reduced breeding success in songbirds of the region is linked to several key habitat changes, including loss of shrubby nesting habitat which has been browsed out by abundant deer (Martin et al. 2011), as well as nest failures from nest-parasitic cowbirds which are thriving on rural farmland patches fragmenting forest breeding sites (Jewell et al. 2007, Jewell and Arcese 2008). Further instability in the bird community is arising from poleward shifts of species at their northern range limits in this region (Cannings 2018). For example, the Anna’s hummingbird is now resident throughout southwest BC, having expanded its winter range over 700km in the past 20 years by utilizing warmer urban areas and artificial feeders (Greig et al. 2017). Additional work on bird communities at northern range limits indicate poleward shifts are likely to be species specific, related to both climate amelioration and suitability of habitat (La Sorte and Thompson 2007, Princé and Zuckerberg 2014). Careful study of continuing land use and climate change will allow more informed protection for this threatened biodiversity hotspot (Martin et al. 2011, Schuster et al. 2014).

1.3 Sooty fox sparrows as a novel partial migrant complex The sooty fox sparrow (Passerella unalaschcensis J.F. Gmelin, 1789; also known as Passerella iliaca, unalaschcensis B. Merrem, 1786) is a subspecies within the polytypic fox sparrow complex, known to science as an obligate migrant prior to this study (Swarth 1920, Munro and Cowan 1947). The fox sparrow species complex occurs throughout most of the US and Canada in some part of its annual cycle, with sooty fox sparrows occurring on the west coast of North America from southern California in winter to Alaska in summer (Weckstein et al. 2002). Quantitative evidence based on range, morphology, and genetics suggests that sooty fox sparrows are one of four distinct fox sparrow species (Zink 1994, Zink and Weckstein 2003, Zink 2008). However, despite detailed research on the taxonomy of fox sparrows, there remains substantial uncertainty about migration patterns, life history, and sub-specific ranges (Swarth 1920, Zink 1994, Zink and Weckstein 2003, Zink 2008, Fraser et al. 2018). Historically, sooty fox sparrows in the Pacific Northwest of BC and WA overwintered at coastal low elevation sites and were assumed to undergo short-distance altitudinal migrations to breed at high elevation sites (Swarth 1920). Bell (1997) suggested the existence of five additional races of sooty fox sparrow which wintered further south and were thought to ‘leapfrog’ over southern BC to breed further north through coastal Alaska (Swarth 1920, Bell 1997). However, contemporary evidence indicates extensive overlap in the northern Alaska breeding range and a lack of diagnostic morphological or genetic differences among sooty fox sparrow populations, making the leapfrog migration structure unlikely (Zink 1994, Pyle and Howell 1997, Fraser et al. 2018).

Historical records demonstrate that sooty fox sparrows have long been abundant in southern BC overwinter (Swarth 1920, Munro and Cowan 1947, Campbell et al. 2001), but lacked breeding populations, with only three regional nest records prior to 1975 when the subspecies was confirmed as a year-round resident on Mandarte Island, BC (Munro and Cowan 1947, Johnson et al. 2018). Subsequent secondary and tertiary sources list resident populations on several Gulf and San Juan Islands of BC and WA, as well as at select locations along the Olympic Peninsula coast and western Vancouver Island (Campbell et al. 2001, Weckstein et al. 2002, Wahl et al. 2005, eBird Northwest team 2016). Local climatic data also suggest that winter freezes capable of limiting the survival of song sparrows on Mandarte Is. have

4 dramatically reduced in frequency and severity since 1900, facilitating the colonization of the island by resident populations of song sparrows in the last half century (Arcese and Norris, unpub data). The sooty fox sparrow population resident on Mandarte Is. since 1975 has steadily grown, now outnumbering long-established song sparrows by about 2:1 and potentially driving their decline to extinction via resource competition (Johnson et al. 2018).

1.4 Thesis overview Theory suggests that ameliorating environmental conditions in one part of a migrant’s range may alter the costs and benefits of migration to favour residency if residents survive or reproduce better than migrants. For example, winter climate amelioration may improve overwinter survival for would-be residents compared to migrants. Similarly, spring climate amelioration may allow residents to breed earlier, longer, or more successfully than migrants while avoiding the cost of migration. Such benefits of residency are potentially measureable in demographic parameters like size and number of broods, nest success, and juvenile and adult survival. To identify factors that may have shifted the trade-offs in migration and residency for sooty fox sparrows, I first compared the demography of migratory and resident populations to identify potential differences. Second, I tested the hypothesis that climate warming facilitated the establishment of resident populations by enhancing conditions for breeding in the region over the last century, thereby reducing the net benefit of migrating out of the wintering area to breed.

2. Demography of sooty fox sparrows (Passerella iliaca unalaschcensis) following a shift from migratory to resident life history

2.1 Introduction Climate, land cover and species life histories often co-vary (e.g., Hudson and Keatley 2010, Gimona et al. 2015), which suggests that by identifying causal mechanisms we can improve predictions about how environmental change may affect species life histories in future (e.g., Winkler et al. 2002, Visser 2008, Pacifici et al. 2015, Beever et al. 2017). Fox sparrows (Passerella iliaca B. Merrem, 1786) offer an interesting case in point, given the species’ current status as a polytypic, single-brooded migrant (Bendire 1889, Swarth 1920, Threlfall and Blacquiere 1982, Garrett et al. 2000, Weckstein et al. 2002). In contrast, Zink (1994) provided genetic evidence in support of a phylogenetically distinct sooty fox sparrow (Passerella unalaschcensis J.F. Gmelin, 1789; sometimes recognized as a subspecies group within P. iliaca, e.g., Chesser et al. 2016) in coastal regions of the Pacific Northwest. Wahl et al. (2005) reported that sooty fox sparrows have become year-round residents of the coastal lowlands and Gulf and San Juan Islands of British Columbia (BC) and Washington State (WA), possibly producing multiple broods annually. I ask in this paper whether these differences in historical and modern accounts of sooty fox sparrows represent a life history shift from a migratory to resident life history, similar to shifts reported in a variety of species as an example of acclimatization to environmental change (e.g., Winkler et al. 2002, Visser 2008, Beever et al. 2017).

Historical records prior to ~1950 indicate that sooty fox sparrows were short distance migrants in coastal British Columbia (Swarth 1920), though contemporary evidence indicates extensive mixing occurs between all sooty fox sparrow along the west coast from California to Alaska (Zink 1994, Pyle and Howell 1997, Fraser et al. 2018). Three breeding records prior to 1975 document breeding attempts by sooty fox sparrows at lowland sites on Vancouver Island, but fall far short of the many dozens of winter records from the same period (Swarth 1920, Munro and Cowan 1947, Campbell et al. 2001). By 1983, Guiguet (1983) listed sooty fox sparrows as resident on Vancouver Island, and later sources recognize them as residents of the

Southern Gulf and San Juan Islands (Baron and Acorn 1997, Campbell et al. 2001, Wahl et al. 2005). Multiple lines of evidence also indicate that sooty fox sparrows are increasing in this region (National Audubon Society 2010, Hearne 2015), as might be expected following successful colonization and the adoption of a residential lifestyle. This expectation arises from well-known trade-offs in migratory and reproductive tactics, which influence species demography, ecology and evolution (Rolshausen et al. 2009, Tökölyi and Barta 2011). Shifts from migratory to residential lifestyles have been linked to increases in reproductive effort (Gillis et al. 2008, Bruderer and Salewski 2009). Comparative studies of birds also showed that short-distant migrant birds had a longer breeding season and initiated more broods with smaller clutches than long distance migrants of the same species (Sandercock and Jaramillo 2002).

In this chapter I estimate demographic vital rates and population growth in a resident, individually-marked population of sooty fox sparrows that colonized Mandarte Island, BC, in 1975. Based on the studies above, I expected to observe that the sooty fox sparrows I studied would have longer breeding seasons, initiate more nests annually, and lay smaller clutches than observed in migratory fox sparrows in Alaska (Rogers 1994) and Newfoundland (Threlfall and Blacquiere 1982). In the absence of commensurate reductions in survival (e.g., Sandercock and Jaramillo 2002), I also expected to observe evidence of positive population growth.

2.2 Methods 2.2.1 Study system I studied sooty fox sparrows on Mandarte Is. (c. 6 ha), located ~11km south of Victoria International Airport in southwestern BC, Canada, also the site of a long-term study of song sparrows (Melospiza melodia, 1960-63 and 1975-2017, Tompa 1963, Arcese et al. 1992, Smith et al. 2006). Sooty fox sparrows colonized Mandarte Is. in 1975 and became the most common passerine on the island ~2010 (Tompa 1963, Drent et al. 1964, Johnson et al. 2018). Researchers began collecting demographic data on sooty fox sparrows opportunistically in 2010 while also monitoring song sparrows. Observations typically spanned March – August annually, including 5-20 days per year of netting and census during overwinter trips in 2012 – 14, and up

7 to 60 days per winter from 1982 – 1987, indicating that most or all sooty fox sparrows observed on the island were resident year-round. From 2010 – 2016, 123 nestling, 82 juvenile, and 65 adult sooty fox sparrows were fitted with a numbered metal and 1-3 coloured plastic bands to facilitate re-sighting. A smaller number of distinctly marked fox sparrows (1989, or ‘naturally’ identifiable by a unique distribution of white feathers from 1982 – 1987) originally confirmed year-round residence in this population, but were not observed in sufficient detail to use in analyses presented here.

2.2.2 Estimation of survival and fecundity

I thus used birds marked from 2010 – 2016 to estimate juvenile annual survival (Sj) and adult annual survival (Sa) using recapture and re-sighting data and program MARK (version 8.x). When doing so, individuals banded outside the formal re-sighting period (May 1 –June 30) in year t to t+1 were entered into the survival encounter history as if they were observed in the re-sighting period in year t+1. ‘Juveniles’ include birds banded as nestlings (~4-8 days-old), fledglings (~12- 24 days-old), or independent young (<76 days-old). Our initial analyses indicated no difference in overwinter survival estimates whether nestlings and older young were treated separately or pooled, I therefore pooled these groups to increase sample size and simplify our population model by estimating survival for single period (nestling to recruit: ‘juvenile survival’). Candidate models were developed using combinations of the most common determinants of survival (age in two classes: juvenile or adult, variation by year of observation, or simply held constant); the ‘logit’ link function was used to test all models. Model goodness-of-fit (GOF) was examined using a bootstrap GOF test in MARK on the most parameterized model.

I estimated life history traits and fecundity based on 54 nests observed from nest building through fledging. For nests found with nestlings (n = 36), I estimated hatch date and the date the first egg of the clutch was laid (DFE) by back-dating nestling age in days using traits well-established for song sparrows (e.g., feather development and body size Smith et al. 2006, Germain et al. 2016) and assuming a 14-day incubation period (Ryan 1974). Detailed observations of two sooty fox sparrow nests found prior to laying and followed closely thereafter confirmed that nesting phenology and development in sooty fox sparrows is nearly

8 identical to that observed in song sparrows on Mandarte Island (Smith et al. 2006). I used t- tests to quantify differences in mean clutch size between the sooty fox sparrows I observed versus values reported for migratory fox sparrows (e.g., Threlfall and Blacquiere 1982, Rogers 1994). A lack of demographic data from other populations prevented more detailed comparisons.

To compare the timing of nest initiation and number of broods produced by sooty fox sparrows in different years, I standardized all sooty fox sparrow DFEs by the median date of first nests initiated by song sparrows in the same year (e.g., Wilson and Arcese 2006). This standardization helped to account for annual variation in climate, given that sooty fox sparrows were monitored opportunistically, and more often early in the breeding season when their nests were easier to find and effort required to monitor song sparrows slightly less. I quantitatively assessed the similarity of breeding timing in fox and song sparrows by running an Anderson-Darling test in program R (version 3.3.3, package kSamples, Scholz and Zhu 2015) to test whether the shapes of the continuous distributions of sooty fox sparrow and song sparrow breeding times differed significantly (using 10,000 resampling permutations).

Annual fecundity (F) was estimated by multiplying the mean observed clutch size (CS,

Nnests = 49), the estimated number of broods initiated annually (NB), and the fraction of young surviving to independence from parental care (Sn) as: F = CS × NB × Sn. Sn was estimated following Mayfield (1961) as a probability of nest success (Nnests = 23), and following Shaffer (2004) using a small sample of nests observed over the entire nesting cycle. Because these methods yielded very similar estimates of daily survival, but very few nests were observed over the entire nesting cycle, I report Mayfield estimates here. These estimates are robust to the assumption of constant mortality based on the simple predator community present on Mandarte Is. and detailed results obtained in the sympatric song sparrow population (Johnson and Shaffer 1990, Arcese et al. 1992, Smith et al. 2006).

2.2.3 Stochastic estimation of population growth

I next estimated expected population growth rate as: λexp = Sa + (Sj × F) by creating a stochastic distribution of λexp, simulating over the observed standard deviations of component estimates of Sa, Sj, CS, and Sn (Walters 1986). The stochastic simulation was necessary to incorporate error and account for the opportunistic collection of data, which varied in detail and amount between years (Bartlett 1960). I adopted a deterministic estimate of NB equal to 1.5, based on a comparison of song sparrow and fox sparrow DFEs (see Results). To accurately reflect the potential for environmental variation in survival due to climate—as opposed to sampling error—I used standard deviations for Sa and Sj estimated over 42 years for the sympatric song sparrow population (cf Arcese et al. 1992, Smith et al. 2006).

2.3 Results 2.3.1 Clutch size and nest success

Clutch size in 49 nests found prior to fledging was 2.82 ± 0.44 (mean ± SD) and nearly identical to our estimate based on all 13 nests found prior to hatching (2.85 ± 0.38). Eight nests were found with eggs that did not hatch due to abandonment or infertility (4 of 9 eggs opened after broods fledged showed no development, 5 contained embryos in arrested development, cf Taylor et al. 2010). Of all 49 nests, 10 had a CS of 2 eggs, 38 had 3 eggs, and 1 nest had 4 eggs. Mean clutch size in our resident fox sparrow population is significantly smaller than clutches in migratory populations; sooty fox sparrows in Alaska averaged 4.11 ± 0.33 (n = 9, Rogers 1994; t= 8.35, p < 0.0001), while red fox sparrows in Newfoundland had a mean clutch size 3.24 ± 0.60 (n = 34, Threlfall and Blacquiere 1982; t = 3.68, p < 0.0004).

I estimated the probability of success to independence using Mayfield calculations for nest success (Mayfield 1961). With a subset of 23 nests visited multiple times during incubation and nestling stages, I calculated a daily survival rate of 98.5%. Given a total nesting period of 26 days (from laying to fledge, see Methods) the overall probability of nest success Sn = 0.68 ± 0.02.

2.3.2 Number of annual nests Researchers observed two successful nesting attempts in a given season by two pairs of color- banded sooty fox sparrows, and a third by a pair composed of a banded male and unbanded female. In each case the first egg of the first nest was laid in April (14th, 19th, and 23rd), followed by 30, 36, and 48 days respectively until the first egg of the subsequent clutch was laid. To approximate the breeding period and estimate the number of broods initiated annually, I standardized initiation dates (DFEs) of all nests by the median DFE observed in the sympatric song sparrow population (see Methods). Pooling data in this way suggests two apparent peaks in laying, implying that at least some sooty fox sparrows initiated multiple nests annually (Figure 2.1). The second peak of sooty fox sparrow nests observable in Figure 2.1 is unlikely to reflect re-nesting following failures given that only 3 failures were recorded at all, and because the period between peaks suggests a ~40 day interval between the initiation of nests, also seen in the sympatric song sparrow population. The qualitative alignment of the distributions of fox and song sparrow nest initiation dates is consistent with the idea that each species responded similarly to annual variation in environmental conditions, despite a quantitative comparison of the distributions indicating that the samples are not drawn from the same distribution (Anderson-Darling test value = 4.01, p < 0.01). This statistical difference may arise because the sooty fox sparrow breeding season is shorter than observed in the sympatric song sparrow population, or due to low monitoring effort in sooty fox sparrows as compared to song sparrows (see Methods). The earliest and latest DFE recorded for sooty fox sparrows suggests a breeding season of ~86 days, spanning March 24 – June 18 (Figure 2.1).

Figure 2.1 Comparison of sooty fox sparrow and song sparrow lay dates.

Lay dates (i.e., date of first egg, DFE) are standardized between years by the song sparrow median DFE for females’ first broods in a year. Medium grey areas indicate overlap of the two distributions. The double peak in fox sparrow lay dates indicates at least some portion of the population is double brooding, as song sparrows are known to do. 2.3.3 Survival and population growth rate To estimate juvenile and adult overwinter survival, I summarized encounter histories (re- sighting and re-capture data) for 65 juveniles and 120 adults and used a variety of model structures in program MARK to estimate survival and re-sighting probabilities varying year (t), age class (a = 1 or 2), or held constant (.) (Table 2.1). A fully parameterized model, Φ(a/2 – t/t) p(t), showed no over-dispersion (bootstrapped GOF test c ̂ = 1.15). The best fitting model based on AICc, Φ(a) p(t), estimated survival by age class and re-sighting probability by year, as expected given that re-sighting effort varied annually (see Methods; mean re-sighting probability = 0.50 ± 0.08). Overall, survival estimates from our best model were both relatively high and precise (Sa = 0.69 ± 0.05 SE, and Sj = 0.32 ± 0.06).

Table 2.1 Model selection results examining variation in sooty fox sparrow annual survival

Model AICc Δ AICc Model likelihood No. Par.

AICc Weight Deviance

Φ(a) p(t) 376.965 0.000 0.98065 1.0000 8 65.741

Φ(a/2-t/t) p(t) 384.854 7.889 0.01899 0.0194 16 55.814

Φ(.) p(t) 392.899 15.934 0.00034 0.0003 7 83.815

Φ(t) p(t) 398.288 21.323 0.00002 0.0000 11 80.530

Φ(t) p(.) 422.184 45.219 0.00000 0.0000 5 117.327

Φ(a) p(.) 434.354 57.389 0.00000 0.0000 3 133.652

Φ(.) p(.) 449.510 72.545 0.00000 0.0000 2 150.859

I next estimated population growth as a stochastic distribution derived by Monte Carlo simulation (see Methods). Specifically, I used juvenile and adult survival rates estimated above

(Sa = 0.69 and Sj = 0.32), paired with standard deviations for those estimates from song sparrows (e.g., SD Sa = ± 0.16, Sj = ± 0.18; see Methods) to more accurately reflect the potential for annual variation in the environment to affect survival. Using the above values and estimated fecundity for sooty fox sparrows allowed us to estimate population growth rate as: λexp = Sa + (Sj × F), replicated 10,000 times. To do so, I calculated fecundity (F) as the product of the mean number of broods per year (NB = 1.5 ± 0.01), clutch size (CS = 2.82 ± 0.44), and the probability of survival to independence (Sn = 0.68 ± 0.02). The resulting distribution included 86.2% of

10,000 estimates of λexp > 1, and a mean λexp of 1.61 ± 0.57 (SD, Figure 2.2), suggesting a potential for rapid growth.

Figure 2.2 The distribution of stochastically estimated population growth rates (λ)

Approximately 86% of estimates have a λ greater than 1 (i.e., a growing population), and mean λ = 1.61 ± 0.57 (SD).

2.4 Discussion Our results support earlier accounts which suggest that sooty fox sparrows established resident populations in coastal regions of the Pacific Northwest after ~1950, in contrast to the migratory habits of all other fox sparrow populations studied to date (Threlfall and Blacquiere 1982, Rogers 1994, Weckstein et al. 2002). Sooty fox sparrows colonized Mandarte Is. in 1975 and have since resided there year-round. Females in this population laid on average 2.82 eggs in multiple nests annually; fewer than reported for migratory fox sparrows in Newfoundland (3.24, Threlfall and Blacquiere 1982) or Alaska (4.11, Rogers 1994). Sooty fox sparrows on Mandarte Is. also initiated breeding earlier and produced more broods annually than migrants (Figure 2.1; Threlfall and Blacquiere 1982, Weckstein et al. 2002), exhibiting a breeding season ~86 days long (March-July) versus ~63 days (May 1 - July 2) among migrants in Alaska (Rogers 1994). Although 36 nests on Mandarte Is. were found at the nestling stage, mean clutch sizes estimated from 13 nests observed at all stages were similar (2.82 vs. 2.85, respectively). These observations support our expectation that clutch size would decline and brood number

14 increase with the adoption of a residential versus migratory lifestyle (cf Sandercock and Jaramillo 2002, Gillis et al. 2008, Bruderer and Salewski 2009).

Our estimates of demographic vital rates for sooty fox sparrows on Mandarte Is. suggest the potential for rapid population growth (λexp = 1.61, Figure 2.2). In contrast, censuses on Mandarte indicate sooty fox sparrows grew from one to 14 pairs from 1975 – 1983, and to 30 pairs by 2010, reflecting slower realized growth (Johnson et al. 2018). The difference in estimated versus realized population growth rates may indicate that sooty fox sparrows often emigrate from Mandarte Is., making the population a source of potential colonists within the region.

2.5 Conclusion Given that sooty fox sparrows were previously known only as migrants (Swarth 1920, Weckstein et al. 2002), and that Vancouver Island represents the northern-most wintering range of sooty fox sparrows (Swarth 1920, Bell 1997, Weckstein et al. 2002), it is possible that newly established sooty fox sparrow populations in our study area have responded to favourable changes in the environment that enhance individual fitness. If severe winter weather sometimes limits sooty fox sparrow populations—as shown for song sparrows on Mandarte Is. (Arcese et al. 1992, Smith et al. 2006)—a century of climate warming in the Georgia Basin may have relaxed those limits sufficiently to allow for growing populations of non-migratory sparrows of multiple species (Arcese, P. and Norris, R. unpub data). The demographic vital rates I report here imply a continued expansion of the sooty fox sparrow in the Georgia Basin in future.

3. Climate warming and the establishment of resident populations in a former obligate migrant

3.1 Introduction Theory suggests that spatial and/or temporal variation in the costs and benefits of migration can lead to trade-offs that drive the evolution of partial migration. Factors that may influence these tradeoffs include variation in climate, seasonality, or other environmental gradients (Chapman et al. 2011, Reid et al. 2018). Such trade-offs are evident in many partial migrant complexes, wherein some individuals or populations of a species migrate while others do not, often as a function of one or more temporal or spatial environmental gradients capable of affecting individual or population fitness (Reid et al. 2018). Given climate warming, we might therefore expect that in some obligate migrant species, individuals or populations may instead remain resident if over-wintering areas become more suitable as breeding habitat, or areas within the historic breeding ranges warm sufficiently to facilitate overwinter survival (Chapman et al. 2011, Reid et al. 2018). In this paper I test the hypothesis that climate warming has facilitated the evolution of resident populations within a former obligate migrant.

Several recent studies link the establishment of resident populations from migratory ancestors to changes in climate or habitat that influence the cost and benefits of migration (Møller et al. 2014, Plummer et al. 2015, Shephard et al. 2015). Factors favouring residency over migration include climate driven improvements in habitat quality and warmer winters, as well as direct and indirect human influence of supplemental feeding to birds remaining in their breeding areas overwinter, such as from backyard feeders or refuse sites (Zuckerberg et al. 2010, Møller et al. 2014, Plummer et al. 2015, Shephard et al. 2015). However, whereas existing case studies document the loss of winter migratory behaviour, it is plausible that climate warming could also facilitate the loss of migration to historical breeding areas if resident phenotypes which remained in wintering areas achieved a fitness advantage in the breeding season as a consequence (Reid et al. 2018). For example, climate warming in late winter could act directly to extend the breeding season and allow residents time to produce more broods annually. Climate warming might also act indirectly to favour residency of migration due to

16 changes in competitor or predator communities affecting survival or reproduction. To date, however, there are no empirical examples wherein resident populations establish from migrants in the historic winter range.

Climate warming is a well-documented driver of earlier, longer breeding seasons (Tryjanowski et al. 2013, Kluen et al. 2017, Pudalov et al. 2017) and can increase fecundity by advancing laying date and increasing the number of broods a pair can raise annually (Wilson and Arcese 2003, Griebeler et al. 2010, Townsend et al. 2013, Pincheira-Donoso and Hunt 2017, Tarwater and Arcese 2018). For example, resident song sparrows in western North America enjoy both higher annual survival and more broods per season than their migratory counterparts, highlighting the costs of migration and benefits to those capable of remaining resident (Arcese et al. 2002, Sandercock and Jaramillo 2002). In both resident and migratory species, late winter and early spring are frequently the critical time period mediating breeding initiation (Saino et al. 2003, Marra et al. 2005, Kelly et al. 2016, Drake and Martin 2018).

Sooty fox sparrows (Passerella unalaschcensis J.F. Gmelin, 1789, also known as Passerella iliaca, unalaschcensis B. Merrem, 1786) represent a new partial migrant, after the establishment of year-round resident populations in the Georgia Basin of western North America after ~1975. Prior to 1950, all sooty fox sparrows were known as obligate migrants and were common in winter along coastal and lowland habitats in southwestern BC and northwest WA (Swarth 1920, Munro and Cowan 1947). After the first resident population was reported on Mandarte Is., BC, in 1975, additional resident populations were later reported in the Gulf and San Juan Islands of British Columbia (BC) and Washington (WA) (Campbell et al. 2001, Wahl et al. 2005, Visty et al. 2018). Swarth (1920) and Bell (1997) hypothesized that sooty fox sparrows were represented by six putative races, wherein the ‘fuliginosa’ race in coastal southern BC and northern WA was recognized as short-distance altitudinal migrants to montane breeding habitats in the Cascade Mountains. Five other races wintering south through California were said to ‘leap-frog’ over the range occupied by fuliginosa to breed in northern BC and coastal Alaska (Swarth 1920, Bell 1997). However, recent geolocator data suggest that the ranges of these putative races overlap, with most individuals throughout the winter range migrating to the same breeding sites in Alaska (Fraser et al. 2018). Other genetic and phenotypic studies also

17 indicate that the ‘races’ are not diagnosable (Zink 1994, Pyle and Howell 1997, Fraser et al. 2018). Sooty fox sparrows in coastal BC and WA now represent a ‘breeding partial migrant’ complex, wherein migrant and resident phenotypes co-exist only in the wintering area (Chapman et al. 2011, Reid et al. 2018).

In support of the hypothesis that climate warming has facilitated the establishment of resident sooty fox sparrow populations in coastal areas of the Pacific Northwest, Visty et al. (2018) showed that resident sooty fox sparrows on Mandarte Is. bred up to 20 days longer and produced twice as many broods as migrants, while also expressing high annual adult survival

(Sa= 0.68) and the potential for rapid population growth (λest ≈ 1.6). I used knowledge of breeding initiation and duration for the resident song sparrow population on Mandarte Is., which has been studied in detail for 45 years and which expresses a life history very similar to fox sparrows, to consider the pre-breeding period climate niche in sooty fox sparrows of the Pacific Northwest (Johnson et al. 2018, Visty et al. 2018). I tested whether climate warming during the pre-breeding period has expanded the climate niche for breeding sooty fox sparrows in coastal southern BC and WA by first characterizing the pre-breeding climate niche of resident sooty fox sparrow populations and mapping their distribution in our study area. I then tested whether similar climatic conditions prevailed in any places within our study area one hundred years ago, at a time when all sooty fox sparrows were said to be migratory (Swarth 1920). Last, I asked if the emergence of the pre-breeding climate niche currently occupied by resident populations corresponds to the first reports of sooty fox sparrows breeding at coastal locations in BC and WA.

3.2 Methods I took three main steps to test the connection between pre-breeding climate and emerging resident populations of sooty fox sparrows. I first established a study region with year-round presence of sooty fox sparrows by identifying known breeding populations in southwest coastal BC and WA, based on personal observations and point count data, consultation with local experts, and eBird observations over the course of the year (eBird 2012). Second, I characterized the pre-breeding climate niche of newly resident sooty fox sparrows by modelling the climate niche with point count data and current (2005-2015) monthly climate from late

18 winter to early spring. Third, I tested whether the pre-breeding climate niche is new and could explain the settling of resident sooty fox sparrows in the last century by mapping resident sooty fox sparrows’ climate niche under historical climate during a period when sooty fox sparrows were obligate migrants (1911-1920, Swarth 1920).

3.2.1 Establishing locations with year-round presence Our study area (49° 53' 38”N x 127° 05' 55”W to 47° 03’ 04”N x 121 14’ 34”W) was chosen to encompass the region wherein sooty fox sparrows were expected to have become resident, based on their year-round presence as recorded in eBird (eBird 2012) and 30 years of casual surveys, mist-netting and color-banding in fox sparrow populations resident in the Southern Gulf Islands, BC (Visty et al. 2018, P. Arcese pers. obs.). This area includes parts of two biogeoclimatic zones, the Coastal Douglas Fir (CDF; southeast Vancouver Island, a narrow coastal band of mainland BC and WA, and San Juan and Gulf Islands) and a portion of the Coastal Western Hemlock (wet hypermaritime subzone; CWHwh, including much of Vancouver Island, coastal BC and Olympic Peninsula, WA). These zones are known for mild coastal weather patterns and high rainfall from November to April, supporting abundant coniferous and mixed deciduous forests (Meidinger and Pojar 1991). The high diversity of habitat elements and species in these two zones make them high priority for conservation and monitoring with climate change (Meidinger and Pojar 1991).

I assembled 1,915 point counts across 858 sites; 1,770 were collected between April and July 2005-2011 in the Georgia Basin, BC, in a separate study to map bird species distribution in the region (Schuster et al. 2014). An additional 145 point count sites were surveyed in June of 2017 to expand the original survey area to include mainland southwestern BC, western Vancouver Island, BC, and the Olympic Peninsula, WA (Figure 3.1). Point counts took place from 05:00h to 12:00h, lasted 10 min, and recorded all birds detected within a 50m radius (Schuster et al. 2014).

Figure 3.1 Map of study area comprising southwest coastal British Columbia, Canada and northwest Washington, USA.

Inset of surveys through the southern Gulf Islands, BC. Point count sites shown in black (sites comprise 5-25 point counts within two kilometers).

3.2.2 Characterizing the pre-breeding climate niche of resident sooty fox sparrows I used the random forest approach to model the pre-breeding climate niche in sooty fox sparrows. Climate niche models are a powerful way to predict changes in species distribution through time, and random forest models are considered one of the most statistically sound methods for modelling climate niches (Elith et al. 2008, Wang et al. 2012a, Anderson 2013, Laube et al. 2015, Walther and van Niekerk 2015). Random forest models are robust to correlated predictor variables and can model complex variable associations without the assumptions made by many linear models (Breiman 2001, Cutler et al. 2007, Dormann et al. 2013). I also attempted to model regional occupancy with climate using a hierarchical Bayesian model approach to account for detection error and spatial autocorrelation in our data, but the inclusion of many correlated climate predictors created conflicting model variables (see Appendix A).

Climate variables for our model were obtained through ClimateWNA (version 5.50, Wang et al. 2016), a program which generates high-resolution climate data utilizing historical weather station data by extracting and downscaling PRISM (Daly et al. 2008) and ANUSPLIN

(Hutchinson 1991) monthly data, resulting in scale-free point data (Wang et al. 2012b). I chose climate variables from monthly records for January through March during 2005-2015 to represent pre-breeding climate in our models. I selected seven (of 14 possible) climate variables which were found in previous research to be related to breeding initiation or success in song sparrows (Wilson and Arcese 2003, Crombie and Arcese 2018): Average Temperature (°C), Maximum Temperature (°C), Minimum Temperature (°C), Precipitation (mm), Degree Days < 0°C, Degree Days > 5 °C, and Number of Frost Free Days. I removed the remaining seven climate variables due to lack of justifiable biological significance to the birds, or because there was no connection to regional climate during these months (i.e., Degree Days Above/Below 18°C, Solar Radiation, Hargreaves Evaporation and Moisture Deficit, Relative Humidity, Precipitation as Snow).

I built the random forest model using the randomForest package in R (version 3.4.1, R Development Core Team, Liaw and Wiener 2002). Random forest models produce many classification trees (collectively creating a ‘forest’), aggregating the results over all trees. Each decision tree is constructed using a bootstrap sample of the input data (i.e., a random sample with replacement) and the forest (‘bagged’ dataset) is complete when ~64% of the original observations are present; the remaining observations comprise the ‘out-of-bag’ (OOB) sample. The observations in the OOB sample are then classified (presence or absence) by the trees grown from the bagged dataset, and a prediction error is created (OOB error rate, % of incorrectly classified samples). The random forest model’s accuracy was verified by minimizing the OOB error rate, optimizing the number of decision trees at 500 and leaving the number of predictors at each node at two (Breiman 2001). The final model OOB error rate primarily held type two errors (false negatives); this is typical for an unbalanced dataset, with greater representation of absences than presences. I attempted to reduce the type two error rate by balancing the selected sample size of presences and absences in model training, increasing the probability that each decision tree would encounter a positive occurrence and thus improve the positive prediction precision. However, the balanced sampling model increased the rate of type one errors (false positives) to such a degree that the model’s OOB error rate doubled, and the predicted sooty fox sparrow presence in the model increased to an improbable level. Therefore,

I did not alter the sampling balance in our final model, erring on the side of under- representation in predicted sooty fox sparrow presence. I cross-validated the model using the R package rfUtilities (Evans and Murphy 2018).

Given the lack of estimates for sampling detection error in random forest modelling, I could not use repeat observations and therefore counted ‘presence’ at all sites which had one or more fox sparrows detected in any point count at that location. I used this model to map predicted sooty fox sparrow presence (i.e., climate niche) by obtaining model predictions with our current climate variables at gridded points across a regional surface (250m resolution), and then importing the output into ArcMap (version 10.5, ESRI 2017).

3.2.3 Testing historical presence of the pre-breeding climate niche The final step of our analysis was to use the random forest model trained above to predict sooty fox sparrow presence historically when all populations were migratory, with climate data from 1911-1920 (Swarth 1920). A major strength of random forest models is the ability to predict species’ presence in climate periods beyond the present, since forests trained on presence/absence data can create predictions based on any climate data it is given (Wang et al. 2012a). I used the random forest model to predict occupancy at the same regional resolution (250m) using historical climate data from 1911-1920 (ClimateWNA; Wang et al. 2016).

3.3 Results I used the random forest model first to characterize the current pre-breeding climate niche of resident sooty fox sparrows, and second to estimate the effect of climate warming on the extent of that niche over the last century. I then asked whether patterns of establishment in resident sooty fox sparrows populations matched our predicted occupancy given long-term historical change in the pre-breeding climate niche.

3.3.1 Pre-breeding climate niche of resident sooty fox sparrows The random forest model identified aspects of current pre-breeding climate that best predicted the occurrence of resident populations of sooty fox sparrows, emphasizing primarily January and February conditions, with both precipitation and temperature variables among the five

22 most influential on model accuracy (Table 3.1). Random forest predictions had an OOB error rate of 8.28% overall, but cross-validation gave an error rate of only 4.67% ± 1.93e-5.

Table 3.1 Top five most important climate variables ranked in the random forest model.

Mean Decrease in Variable Accuracy (%) January Precipitation 18.62 February Precipitation 16.71 January Max Temp 14.08 March Degree Days >5°C 13.18 January Avg Temp 12.98

Mean Decrease in Accuracy estimates each variable’s contribution to the accuracy of decision tree classification. Complete list of variables ranked by importance is in Appendix B, Table B.1

3.3.2 Testing historical presence of the pre-breeding climate niche Maps of sooty fox sparrow occurrence based on climate averages for 2005-2015 and 1911-1920 (Figure 3.2) illustrate several key differences in climate and predicted distribution of sooty fox sparrows over time. First, three areas in the current climate map appear particularly suited to resident populations of sooty fox sparrows: a) southwest coast of Vancouver Island (probability of occurrence, Pocc >50%), b) west coast of the Olympic Peninsula (Pocc >50%), and c) the

Georgia Basin lowlands and Gulf and San Juan Islands (Pocc ≈40%). Although each area currently hosts resident populations of sooty fox sparrows, the subspecies is not yet widespread within its predicted climate niche (Figure 3.2). In contrast, our historical map suggests that sooty fox sparrows were less than half as likely to occur in the study region in 1911-1920 (Pocc ≤ 25%). However, I also observed that sites predicted to have the highest probabilities of occurrence in 1911-1920 match those in which sooty fox sparrows have subsequently settled, and also include the locations in which they were first recorded to breed within the region (Figure 3.2). Two of three early records of nesting sooty fox sparrow occurred near Tofino, BC, a modern ‘hotspot’ for resident sooty fox sparrows.

Figure 3.2 Current and historical probability of sooty fox sparrow occupancy predicted by random forest models

top: Predicted occurrence (Pocc) modelled with monthly climate variables from January-March 2005- 2015 (250m resolution). Crosses indicate recorded breeding sooty fox sparrow presence in point counts, diamonds are known breeding locations confirmed via literature and personal correspondence (eBird Northwest team 2016, I. Cruickshank personal communication). bottom: Pocc modelled with monthly climate variables from January-March 1911-1920 (250m resolution). Stars indicate first recorded sooty fox sparrow nests in the region, 1930-35 (Munro and Cowan 1947).

3.4 Discussion I developed a random forest model to map the pre-breeding climate niche for sooty fox sparrows, and then predicted the historic and current distribution of breeding populations that have established in mild, coastal sites in the Pacific Northwest of North America during the last century. Historic and current predictions of the sooty fox sparrow pre-breeding climate niche indicate the highest probabilities of occurrence in warm coastal areas, and despite Pocc not exceeding 25% historically, a suitable niche has dramatically expanded over many near-shore areas (Pocc > 50%). Two of three early breeding records of sooty fox sparrows match sites with the highest Pocc in the historic pre-breeding climate niche, and currently support resident breeding populations of sooty fox sparrows. However, our results further suggest that the climate niche in which resident populations have become established exists over a relatively large area, implying that resident populations of sooty fox sparrows appear likely to spread within the region in future, or that their distribution is limited by factors other than the pre- breeding climate niche I modeled.

3.4.1 The current sooty fox sparrow pre-breeding climate niche The current pre-breeding climate niche for sooty fox sparrows established in our model emphasizes conditions in January and February (Table 3.1), suggesting that resident populations of sooty fox sparrows are more likely to occur in areas experiencing relatively warm, dry conditions during late winter. This late winter period precedes the early onset of breeding in resident sooty fox sparrows, which occurs a month earlier than populations of migratory fox sparrows breeding further north (e.g., on Mandarte Is. vs in Alaska; Ryan 1974, Visty et al. 2018), and is also a highly influential period affecting breeding phenology of resident song sparrows in the region (Smith et al. 2006, Wilson and Arcese 2003, Tarwater and Arcese 2018). Although migratory birds typically alter breeding and migration timing based on cues later in spring (Greives et al. 2016, Chmura et al. 2017), conditions in late winter often determine the onset of breeding in year-round residents (Wingfield 2008, Brommer et al. 2008, Williams et al. 2015), especially those that breed in early spring as sooty fox sparrows do (Drake and Martin 2018). This difference in breeding phenology could potentially facilitate greater divergence

25 between migrants and residents, and possibly even increase the likelihood of persistence of both migratory and resident populations (Griswold et al. 2010, Reid et al. 2018).

The extent of the current pre-breeding climate niche I estimated for sooty fox sparrows exceeded their known breeding distribution (Figure 3.2), suggesting that resident populations of sooty fox sparrows may expand their distribution in the future if not limited by other factors. For example, although I did not model aspects of habitat other than climate, sooty fox sparrows are known to require dense shrub as breeding habitat (Bent 1958, Weckstein et al. 2002). Some areas within our study region may therefore lack elements to support resident populations of sooty fox sparrows—despite a favorable climate niche—given urban, agricultural, and semi- natural areas lack suitable shrub habitats (e.g., greater Vancouver, Seattle, and Puget Sound regions). This limitation is particularly noticeable on Vancouver Island and larger Gulf islands, where overabundant deer populations eliminate shrub cover and reduce the occurrence of breeding sooty fox sparrows significantly (Martin et al. 2011, Arcese et al. 2014). Thus, a refined model including site-level factors would likely predict sooty fox sparrow distribution more precisely.

3.4.2 Expansion of the pre-breeding climate niche and establishment of resident populations Our results support the hypothesis that the pre-breeding climate niche for sooty fox sparrows expanded in coastal lowlands of the Georgia Basin and western Vancouver Island over the last century (Figure 3.2). They also imply that while the subspecies was unlikely to have bred in the region historically, recently established resident populations have the potential for rapid population growth in mild, near-shore habitats throughout the region (Visty et al. 2018, Figure 3.2). Within our study area, the coastal lowland regions have seen the largest increases of average temperature in the last century, with mean temperatures in February rising 3-4°C since 1910 (Appendix B, Figure B.1), vastly exceeding increases known to measurably affect the onset, duration, and success of breeding in songbirds (Gordo and Doi 2012, Pearce-Higgins et al. 2015, Marrot et al. 2018).

Given that few species track climate niches in real time, due to barriers to or limits on dispersal or population growth unrelated to climate (e.g., Morin and Thuiller 2009, Johnson et al. 2018), I expect resident populations of sooty fox sparrows to increase regionally in future. Citizen science surveys (National Audubon Society 2010, Hearne 2015) already suggest increasing numbers of sooty fox sparrows in our study region over time, consistent with empirical demonstration of high population growth rates in resident populations of resident sooty fox sparrows (λest ≈ 1.6, Visty et al. 2018).

Other factors likely influence individual fitness in ways that may affect migratory behavior of sooty fox sparrows, especially considering that climate and habitat structure are both changing globally with different potential effects across the sooty fox sparrow range. Given sufficient climate warming in Alaskan breeding areas of migrant populations, year-round residents might be expected to establish in the future (e.g., Duputié et al. 2012). Intrinsic factors similar to those seen in other partial migrant complexes may also drive migration patterns, such as if dominant, larger bodied individuals are able to remain on territories year- round while subordinate individuals ‘make the best of a bad job’ by migrating (Adriaensen and Dhondt 1990, Belthoff and Gauthreaux 1991, Gillis et al. 2008, Grayson et al. 2011). Swarth (1920) noted the fuliginosa sooty fox sparrows in the Pacific Northwest to be the largest race of the subspecies, but contemporary work has not been able to find diagnostic differences across the range (Pyle and Howell 1997, Fraser et al. 2018). Establishment of resident populations may depend on a range of additional poorly known intrinsic factors related to phenotype or genotype (Ronce 2007, Duputié et al. 2012).

3.5 Conclusion The establishment of resident populations of sooty fox sparrows in southwestern BC and northwestern WA appear to represent the first case of an obligate migrant abandoning migration in favor of residing year-round within the wintering area (Chapman et al. 2011, Reid et al. 2018). The occurrence of resident populations of sooty fox sparrows are largely consistent with predictions from our pre-breeding climate niche models, supporting our hypothesis that climate warming over the last century has led to milder conditions, favoring early breeding and the production of multiple broods annually. It is plausible that subsequent increases in

27 reproductive success and survival (by avoiding the cost of migration) now favours year-round residency in some locales. These advantages likely led to a novel complex of partial migrant birds, wherein overwinter migrants co-occur with residents, but breed in separate ranges and consequently express different life histories.

4. Conclusion

4.1 Summary of key findings My goal was to describe and assess changes in resident sooty fox sparrow demography and distribution after their recent shift from migratory to resident life history. I first studied a single population of resident sooty fox sparrows and found residents breed over a month earlier, for twenty days longer, and with multiple broods but fewer eggs than migratory sooty fox sparrows (Visty et al. 2018, Rogers 1994, Threlfall and Blacquiere 1982). I used this apparent fecundity benefit of residency to shape my hypothesis that climate amelioration in the pre-breeding season is a key driver enabling resident sooty fox sparrows to settle in the area, allowing them to breed earlier and longer than their migrant counterparts. I mapped the regional sooty fox sparrow pre-breeding climate niche and found that warmer, drier late winter climate particularly predicts resident occupancy. I also mapped the probability of sooty fox sparrow occurrence historically, at a time when sooty fox sparrows were considered to be obligate migrants, and found that the pre-breeding climate niche was largely absent historically. Overall, my case study provides the first evidence of a formerly obligate migrant bird species establishing resident populations in their historical wintering grounds. Although previous case studies have focused on climate warming improving winter habitat and survival, my results suggest that climate warming can also alter fecundity and breeding niches. Such cases allow us to test theory on the interdependence of demography and migration strategy, and therefore better predict species distributions and abundance as migratory patterns change on a global scale.

4.2 Implications Demographic rates estimated for the population of sooty fox sparrows that have established on

Mandarte Is. after 1975 indicate the potential for very rapid population growth (λest ≈ 1.6). However, a mismatch between the potential and observed growth rate of the population suggests that a potentially high fraction of offspring hatched on Mandarte Is. disperse to other habitats in the area (Visty et al. 2018). In contrast, surveys of the region suggest that sooty fox sparrows remain rare or absent on most islands where deer are overabundant, likely due to

29 reduced shrubby habitats occupied by sooty fox sparrows (Martin et al. 2011). Populations of resident sooty fox sparrows elsewhere within areas surveyed occurred primarily in a localized, mild coastal climate niche pockets (Figure 3.2). Established sooty fox sparrow populations are clustered in only a few regional places thus far, with the high estimated population growth indicating good potential to disperse given appropriate habitat and time. In future, I expect continued colonization of sooty fox sparrows along mild, coastal, densely shrubby areas in the region. Dispersal rate and extent of sooty fox sparrows in their climate niche will most likely be limited by shrub habitat, whether due to deer presence or human altered rural, suburban, and urban habitats. However, given the high predicted population growth, current populations should continue expanding even where dispersal is limited.

Empirical examples of the formation and maintenance of partial migrant frameworks are still scarce, despite the fact that partial migrant complexes are now recognized as being equally as common as classic ‘full’ migration patterns (Boyle and Conway 2007, Reid et al. 2018). Many species are predicted to alter and lose their migration tendencies (Wilcove and Wikelski 2008), likely bringing many new examples of partial migrant species. These changes may vastly alter community composition and dynamics in the coming decades. For example, sooty fox sparrows resident on Mandarte Is. overlap in diet and seed preference almost perfectly with the long-established resident song sparrows, creating winter food competition which is driving an ongoing population decline for the behaviourally subordinate song sparrows (Johnson et al. 2018). Additional studies implicate changes in migratory patterns for altering herbivore and insectivore dynamics, including mismatches in food availability that may affect species’ persistence (Cohen et al. 2015, Ramos-Robles et al. 2016). As studying partial migrant complexes becomes easier with improved technology to track individuals through space and time, we can better document losses of migration and community-level effects (Berthold 1999, Wikelski et al. 2007, Boyle 2008). Testing current theory and documenting known changes, though complicated, provides a framework for what is possible and offers a clearer path to exploring the environmental drivers of migration versus residency.

4.3 Caveats and suggestions for future study Violations of some assumptions made in this study regarding the extent of sooty fox sparrow populations may alter the results I presented here. I compiled the most complete list of resident populations of sooty fox sparrows possible by convening with experts, eBird, and using several years of regional point count data (see Methods 3.2.1), but additional targeted surveys of the extent of our study area, including Vancouver Island, the San Juan islands, and the Olympic peninsula, may reveal more populations that have not been documented, especially considering that newly colonizing populations are likely small and therefore less likely to be detected initially. A broader distribution of resident populations of sooty fox sparrows may mean that the climate niche represented in my models is too limited. Additionally, sooty fox sparrow distribution through time has been primarily pieced together through historical literature which contains very little explicit documentation of populations; Munro and Cowan (1947) record the three known breeding records in the area, located on Vancouver Island, prior to 1975 (Figure 3.2). Although resident populations of sooty fox sparrows may have simply lacked historical documentation, the abundance of winter records in the area indicates that the species was recorded with some attention and assumed to be migrant based on surrounding breeding records through Alaska and the Olympic mountains (Swarth 1920, Munro and Cowan 1947, Campbell et al. 2001). Additional evidence confirms that sooty fox sparrows were not breeding on Mandarte Is. when researchers were present from 1960-63, but colonized in 1975 (see Methods 2.2.1). I have done my best to assess the historical literature and provide evidence in changes between historical and current sooty fox sparrow distribution.

Further study should focus on demography and assessing the extent of the partial migrant complex in sooty fox sparrows. Demographic rates have hardly been assessed in any fox sparrow populations prior to my study, with previous accounts providing limited estimates of reproductive success (e.g., Threlfall and Blacquiere 1982 give some breeding biology, Rogers 1994 gives clutch size) and overwinter survival (Sandercock and Jaramillo 2002 estimated survival from n= 6 migratory individuals). A demographic study across multiple migrant and resident populations would allow rigorous testing of the drivers and differences between the two life history strategies. Additionally, distribution studies could assess whether sooty fox

31 sparrows also exist in a partial migrant complex elsewhere in their range along the west coast of the US. Although there is not currently a signal of year-round presence on eBird, many places throughout the California wintering range of sooty fox sparrows have a similar or warmer pre- breeding climate to our study area. By documenting other resident populations, we can learn more about the factors determining residency and potential test additional reasons for migration such as genetics, habitat, or interspecific competition.

4.4 Conclusion Here I’ve presented support for my prediction that newly established populations of resident sooty fox sparrows enjoy an earlier, longer breeding season and thereby gain a fecundity benefit from remaining resident, and based on these findings, explored climate warming in the pre-breeding season as a driver of loss of migration. The ongoing, rapid, change in climate and land use—particularly in the Georgia Basin study region—creates a pertinent need for further conclusive studies on changes in animal migration, distribution, and life history. Such cases will provide more evidence for theoretical frameworks regarding drivers of migration and demographic rates, as well as elucidate the consequences of these shifts on the avian community as a whole.

References

Adriaensen, F. and Dhondt, A.A. (1990). Population dynamics and partial migration of the European Robin (Erithacus rubecula) in different habitats. Journal of Animal Ecology 59, 1077-1090.

Anderson, R.P. (2013). A framework for using niche models to estimate impacts of climate change on species distributions. Annals of the New York Academy of Sciences 1297(1), 8-28.

Arcese, P., Smith, J.N.M., Hochachka, W., Rodgers, C. and Ludwig, D. (1992). Stability, regulation and the determination of abundance in an insular song sparrow population. Ecology 73, 805-822.

Arcese, P., Sogge, M.K., Marr, A.B. and Patten, M.A. (2002). Song Sparrow (Melospiza melodia), version 2.0. In: A.F. Poole, and F.B. Gill, eds., The Birds of North America. Ithica, NY: Cornell Lab of Ornithology.

Arcese, P., Schuster, R., Campbell, L., Barber, A. and Martin, T.G. (2014). Deer density and plant palatability predict shrub cover, richness, diversity and aboriginal food value in a North American archipelago. Diversity and Distributions 20, 1368-1378.

Austin, M.A., Buffett, D.A., Nicolson, D.J., Scudder, G.G.E. and Stevens, V. (2008). Taking Nature’s Pulse: The Status of Biodiversity in British Columbia. Victoria, BC: Biodiversity BC. Available: www.biodiversitybc.org.

Baron, N. and Acorn, J. (1997). Birds of coastal British Columbia. Edmonton, AB: Lone Pine Publishing.

Bartlett, M.S. (1960). Stochastic population models in ecology and epidemiology. London, UK: Metheun Publishing.

Beever, E.A., Hall, L.E., Varner, J., Loosen, A.E., Dunham, J.B., Gahl, M.K., Smith, F.A. and Lawler, J.J. (2017). Behavioral flexibility as a mechanism for coping with climate change. Frontiers of Ecology and Environment 15, 299–308.

Bell, C.P. (1997). Leap-frog migration in the fox sparrow: minimizing the cost of spring migration. The Condor 99, 470-477.

Belthoff, J.R. and Gauthreaux, S.A. Jr. (1991). Partial migration and differential winter distribution of House Finches in the eastern United States. The Condor 93, 374-382.

Bent, A.C. (1958). Life histories of North American blackbirds, orioles, tanagers, and allies. Order: Passeriformes; families: Ploceidae, Icteridae, and Thraupidae. Washington, DC: Smithsonian Institution.

Bendire, C.E. (1889). Notes on the general habits, nests and eggs of the genus Passerella. Auk 6, 107-116.

Berthold, P. (1999). A comprehensive theory for the evolution, control and adaptability of avian migration. Ostrich 70, 1–11.

Boyle, W.A. (2008). Partial migration in birds: tests of three hypotheses in a tropical lekking frugivore. Journal of Animal Ecology 77, 1122–1128.

Boyle, W.A. and Conway, C.J. (2007). Why migrate? A test of the evolutionary precursor hypothesis. American Naturalist 169, 344–359.

Breiman, L. (2001). Random forests. Machine Learning 45, 5–32.

Brommer, J.E., Rattiste, K. and Wilson, A.J. (2008). Exploring plasticity in the wild: laying date— temperature reaction norms in the common gull Larus canus. Proc. R. Soc. B 275, 687-693.

Bruderer, B. and Salewski, V. (2009). Lower annual fecundity in long-distance migrants than in less migratory birds of temperate Europe. Journal of Ornithology 150, 281-286.

Campbell, R.W., Dawe, N.K., McTaggart-Cowan, I., Cooper, J.M., Kaiser, G.W. and Stewart, A.C. (2001). Birds of British Columbia Vol. 4: Wood-warblers through old world sparrows. Vancouver, BC: University of British Columbia Press.

Cannings, R.J. (2018). Birds of British Columbia and the Pacific Northwest: A complete guide. Vancouver, BC: Heritage Publishing.

Chapman, B.B., Bronmark, C., Nilsson, J. and Hansson, L. (2011). The ecology and evolution of partial migration. Oikos 120(12), 1764-1775.

Chesser, R., Burns, K., Cicero, C., Dunn, J., Kratter, A., Lovette, I., Rasmussen, P., Remsen, J., Rising, J., Stotz, D. and Winker, K. (2016). Fifty-seventh supplement to the American Ornithologists' Union check-list of North American birds. Auk 133, 544-560.

Chmura, H.E., Meddle, S.L., Wingfield, J.C. and Hahn, T.P. (2017). Effects of a social cue on reproductive development and pre-alternate molt in seasonally breeding migrant and resident female songbirds (Zonotrichia leuchophrys). Journal of Experimental Biology 220, 2947-2956.

Cohen, E.B., Nemeth, Z., Zenzal, T.J. Jr., Paxton, K.L., Diehl, R., Paxton, E.H. and Moore, F.R. (2015). Spring resource phenology and timing of songbird migration across the Gulf of Mexico. In: E.M. Wood, and J.L. Kellerman, eds., Phenological Synchrony and Bird Migration, 1st edition. Boca Raton, FL: CRC Press/ Taylor & Francis Group.

Cox, G.W. (2010). Bird Migration and Global Change. Washington, DC: Island Press.

Crombie, M. and Arcese, P. (2018). Temporal variation in the effects of individual and environmental factors on nest success. Auk 135(2), 326-341.

Cutler, D.R., Edwards, T.C. Jr., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. and Lawler, J.J. (2007). Random Forests for Classification in Ecology. Ecology 88(11), 2783-2792.

Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. and Pasteris, P.P., 2008. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol 28, 2031-2064.

Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carre, G., Marquez, J.R.G., Gruber, B., Lafourcade, B., Leitao, P.J., Munkemuller, T., McClean, C., Osborne, P.E., Reineking, B., Schroder, B., Skidmore, A.K., Zurell, D. and Lautenbach, S. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46.

Drake, A. and Martin, K. (2018). Local temperatures predict breeding phenology but do not result in breeding synchrony among a community of resident cavity-nesting birds. Scientific Reports 8, 2756.

Drent, P.J., Van Tets, G.F., Tompa, F.S. and Vermeer, K. (1964). The breeding birds of Mandarte Island, British Columbia. Can. Field-Nat. 78, 208–263.

Duputié, A., Massol, F., Chuine, I., Kirkpatrick, M. and Ronce, O. (2012). How do genetic correlations affect species range shifts in a changing environment? Ecology Letters 15, 251- 259. eBird. (2012). eBird: An online database of bird distribution and abundance [web application]. Ithaca, NY: eBird. Available: http://www.ebird.org. [Accessed 20 February 2017]. eBird Northwest Team. (2016). Fox Sparrows—on of the Northwest’s more confusing species. [online] ebird.org. Available at: https://ebird.org/pnw/news/fox-sparrows-one-of-the- northwests-more-confusing-species/ [Accessed 10 August 2018].

Elith, J., Leathwick, J.R. and Hastie, T. (2008). A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813.

ESRI. (2017). ArcGIS Desktop: Release 10.5. Redlands, CA: Environmental Systems Research Institute.

Evans, J.S. and Murphy, M.A. (2018). rfUtilities, version 2.1-3. R package.

Feeley, K.J., Stroud, J.T. and Perez, T.M. (2017). Most ‘global’ reviews of species’ responses to climate change are not truly global. Diversity and Distributions 23(8), 967.

Fraser, K.C., Roberto-Charron, A., Cousens, B., Simmons, M., Nightingale, A., Shave, A.C., Cormier, R.L. and Humple, D.L. (2018). Classic pattern of leapfrog migration in Sooty Fox Sparrow (Passerella iliaca unalaschcensis) is not supported by direct migration tracking of individual birds. Auk 135(3), 572-582.

Garrett, K.L., Dunn, J.L. and Righter, R. (2000). Call notes and winter distribution in the fox sparrow complex. Birding 32, 412-417.

Germain, R.R., Wolak, M.E., Arcese, P., Losdat, S. and Reid, J.M. (2016). Direct and indirect genetic and fine-scale location effects on breeding date in song sparrows. J. Anim. Ecol. 85, 1613-1624.

Gillis, E.A., Green, D.J., Middleton, H.A. and Morrissey, C.A. (2008). Life history correlates of alternative migratory strategies in american dippers. Ecology 89, 1687-1695.

Gimona, A., Poggio, L., Polhill, J.G. and Castellazzi, M. (2015). Habitat networks and food security: Promoting species range shift under climate change depends on life history and the dynamics of land use choices. Landsc. Ecol. 30, 771-789.

Gordo, O. and Doi, H. (2012). Drivers of population variability in phenological responses to climate change in Japanese birds. Climate Research 54, 95-112.

Grayson, K.L., Bailey, L.L. and Wilbud, H.M. (2011). Life history benefits of residency in a partially migrating pond-breeding amphibian. Ecology 92, 1236-1246.

Greig, E.I., Wood, E.M. and Bonter, D.N. (2017). Winter range expansion of a hummingbird is associated with urbanization and supplementary feeding. Proc. R. Soc. B 284(1852), 256.

Greives, T.J., Fudickar, A.M., Atwell, J.W., Meddle, S.L. and Ketterson, E.D. (2016). Early spring sex differences in luteinizing hormone response to gonadotropin releasing hormone in co- occurring resident and migrant dark-eyed juncos (Junco hyemalis). General and Comparative Endocrinology 236, 17-23.

Griebeler, E.M., Caprano, T., and Bohning-Gaese, K. (2010). Evolution of avian clutch size along latitudinal gradients: do seasonality, nest predation or breeding season length matter? Journal of Evolutionary Biology 23, 888–901.

Griswold, C.K., Taylor, C.M. and Norris, R. (2010). The evolution of migration in a seasonal environment. Proceedings of the Royal Society B 277, 2711-2720.

Guiguet, C.J. (1983). The birds of British Columbia, 11: Sparrows and Finches. Victoria, BC: Dept. of Education, British Columbia Provincial Museum.

Hearne, M.E. (2015). Fox Sparrow. In: P.J.A Davidson, R.J. Cannings, A.R. Couturier, D. Lepage, and C.M. Di Corrado, eds., The Atlas of the Breeding Birds of British Columbia 2008-2012. Delta, BC: Bird Studies Canada.

http://www.birdatlas.bc.ca/accounts/speciesaccount.jsp?sp=FOSP&lang=en [Accessed on 20 December 2016]

Hsiung, A.C., Boyle, A.W., Cooper, R.J. and Chandler, R.B. (2018). Altitudinal migration: ecological drivers, knowledge gaps, and conservation implications. Biological Reviews.

Hudson, I.L. and Keatley, M.R. (2010). Phenological research: methods for environmental and climate change analysis. New York, NY: Springer Publishing.

Hutchinson, M.F. (1991). The application of thin plate smoothing splines to continent-wide data assimilation. In: J.D. Jasper JD, ed., BMRC Research Report No.27, Data Assimilation Systems. Melbourne: Bureau of Meteorology, pp. 104-113.

Jahn, A.E., Levey, D.J., Hostetler, J.A. and Mamani, A.M. (2010). Determinants of partial bird migration in the Amazon Basin. Journal of Animal Ecology 9, 983–992.

Jewell, K.J. and Arcese, P. (2008). Consequences of parasite invasion and land use on the spatial dynamics of host populations. Journal of Applied Ecology 45(4), 1180-1188.

Jewell, K.J., Arcese, P. and Gergel, S.E. (2007). Robust predictions of species distribution: Spatial habitat models for a brood parasite. Biological Conservation 140, 259-272.

Johnson, D.H. and Shaffer, T.L. (1990). Estimating nest success: When Mayfield wins. Auk 107, 595-600.

Johnson, K.M., Germain, R.R., Tarwater, C.E., Reid, J.M. and Arcese, P. (2018). Demographic consequences of invasion by a native, controphic competitor to an insular bird population. Oecologia 187(1), 155-165.

Kelly, J.F., Horton, K.G., Stepanian, P.M., de Beurs, K.M., Fagin, T., Bridge, E.S. and Chilson, P.B. (2016). Novel measures of continental-scale avian migration phenology related to proximate environmental cues. Ecosphere 7(9), e01434.

Kluen, E., Nousiainen, R. and Lehikoinen, A. (2017). Breeding phenological response to spring weather conditions in common Finnish birds: resident species respond stronger than migratory species. Journal of Avian Biology 48(5), 611-619.

La Sorte, F.A. and Thompson, F.R. (2007). Poleward shifts in winter ranges of North American birds. Ecology 88(7), 1803-1812.

La Sorte, F.A., Fink, D., Blancher, P.J., Rodewald, A.D., Ruiz-Gutierrez, V., Rosenberg, K.V., Hochachka, W.M., Verburg, P.H. and Kelling, S. (2017). Global change and the distributional dynamics of migratory bird populations wintering in Central America. Global Change Biology 23(12), 5284-5296.

Laube, I., Graham, C.H. and Böhning-Gaese, K. (2015). Niche availability in space and time: migration in Sylvia warblers. Journal of Biogeography 42(10), 1896-1906.

Leung, T.L.F. and Koprivnikar, J. (2016). Nematode parasite diversity in birds: the role of host ecology, life history, and migration. J. Anim. Ecol. 85(6), 1471-1480.

Liaw, A. and Wiener, M. (2002). Classification and Regression by randomForest, version 4.6-14. R News 2(3), 18--22.

MacLean, S.A. and Beissinger, S.R. (2017). Species’ traits as predictors of range shifts under contemporary climate change: a review and meta-analysis. Global Change Biology 23(10), 4904-4105.

Marra, P., Francis, C.M., Mulvihill, R.S. and Moore, F.R. (2005). The influence of climate on the timing and rate of spring bird migration. Oecologia 142(2), 307-315.

Marrot, P., Charmantier, A., Blondel, J. and Garant, D. (2018). Current spring warming as a driver of selection on reproductive timing in a wild passerine. J. Anim. Ecol. 87(3), 754-764.

Martin, T.G., Arcese, P., and Scheerder, N. (2011). Browsing down our natural heritage: Deer impacts on vegetation structure and songbird populations across and island archipelago. Biological Conservation 144(1), 459-469.

Mayfield, H. (1961). Nesting success calculated from exposure. Wilson Bulletin 73, 255-261.

McDermott, M.E. and DeGroote, L.W. (2017). Linking phenological events in migratory passerines with a changing climate: 50 years in the Laurel Highlands of Pennsylvania. PLoS One 12(4), e0174247.

Meidinger, D. and Pojar, J. (1991). Ecosystems of British Columbia. British Columbia Ministry of Forests.

Møller, A.P., Jokimaki, J., Piotr, S. and Tryjanowski, P. (2014). Loss of migration and urbanization in birds: a case study of the blackbird (Turdus merula). Oecologia 175, 1019-1027.

Morin, X. and Thuiller, W. (2009). Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology 90(5), 1301- 1313.

Munro, J.A. and Cowan, I.M. (1947). A review of the bird fauna of British Columbia. Victoria, BC: British Columbia Provincial museum, Department of Education.

National Audubon Society. (2010). The Christmas Bird Count Historical Results [online] christmasbirdcount.org. [Accessed on 20 December 2016]

Newson, S.E., Moran, N.J., Musgrove, A.J., Pearce-Higgins, J.W., Gillings, S., Atkinson, P.W., Miller, R., Grantham, M.J. and Baillie, S.R. (2016). Long-term changes in the migration phenology of UK breeding birds detected by large-scale citizen science recording schemes. Ibis 158(3), 481-495.

Pacifici, M., Foden, W., Visconti, P., Watson, J., Butchart, S., Kovacs, K., Scheffers, B., Hole, D., Martin, T., Akçakaya, H., Corlett, R., Huntley, B., Bickford, D., Carr, J., Hoffmann, A., Midgley, G., Pearce-Kelly, P., Pearson, R., Williams, S., Willis, S., Young, B. and Rondinini, C. (2015). Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215-225.

Pan, W., Sun, G. and Jin, Z. (2015). How demography-driven evolving networks impact epidemic transmission between communities. J. Theoretical Biology 382, 309-319.

Pearce-Higgins, J.W., Eglington, S.M., Martay, B., and Chamberlain, D.E. (2015). Drivers of climate change impacts on bird communities. J. Anim. Ecol. 84, 943-954.

Pincheira-Donoso, D. and Hunt, J. (2017). Fecundity selection theory: concepts and evidence. Biol. Rev. 92, 341-356.

Plummer, K.E., Siriwardena, G.M., Conway, G.J., Risely, K. and Toms, M.P. (2015). Is supplementary feeding in gardens a driver of evolutionary change in a migratory bird species? Global Change Biology 21(12), 4353-4363.

Princé, K. and Zuckerberg, B. (2014). Climate change in our backyards: the reshuffling of North America’s winter bird communities. Global Change Biology 21(2), 572-585.

Pudalov, N., Ziatek, S. and Jimenez, A.G. (2017). Birds in New York State have altered their migration timing and are experiencing different thermal regimes while breeding or on stopover from 2010 to 2015. International Journal of Zoology 2017, 2142075.

Pyle, P. and Howell, S.N.G. (1997). Identification Guide to North American Birds, Part 1: Columbidae to Ploceidae. Bolinas, CA: Slate Creek Press.

R Development Core Team. (2017). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

Ramos-Robles, M., Andresen, E. and Diaz-Castelazo, C. (2016). Temporal changes in the structure of a plant-frugivore network are influenced by bird migration and fruit availability. PeerJ 4, pe2048.

Reid, J.M., Travis, J.M.J., Daunt, F., Burthe, S.J., Wanless, S. and Dytham, C. (2018). Population and evolutionary dynamics in spatially structured seasonally varying environments. Biol. Rev. 93, 1578-1603.

Robillard, C.M., Coristine, L.E., Soares, R.N. and Kerr, J.T. (2015). Facilitating climate-change- induced range shifts across continental land-use barriers. Conservation Biology 29(6), 1586- 1595.

Rogers, C.M. (1994). Avian nest success, brood parasitism and edge-independent reproduction in an Alaskan wetland. J. Field Ornithol. 65, 433-440.

Rolshausen, G., Segelbacher, G., Hobson, K.A. and Schaefer, H.M. (2009). Contemporary evolution of reproductive isolation and phenotypic divergence in sympatry along a migratory divide. Curr. Biol. 19, 2097-2101.

Ronce, O. (2007). How does it feel to be like a rolling stone? Ten uestions about dispersal evolution. Annual Review of Ecology, Evolution, and Systematics 38, 231-253.

Ryan, A.G. (1974). An incubation period and a nestling period for the fox sparrow. Can. Field- Nat. 88, 230-231.

Saino, N., Szep, T., Romano, M., Rubolini, D., Spina, F. and Møller, A.P. (2003). Ecological conditions during winter predict arrival date at the breeding quarters in a trans-Saharan migratory bird. Ecology Letters 7(1), 21-25.

Sandercock, B.K. and Jaramillo, A. (2002). Annual survival rates of wintering sparrows: Assessing demographic consequences of migration. Auk 119, 149-165.

Sanderson, F.J., Donald, P.F., Pain, D.J., Burfield, I.J. and van Bommel, F.P.J. (2006). Long-term population declines in Afro-Palearctic migrant birds. Biol Conserv. 131, 93–105.

Scholz, F. and Zhu, A. (2015). kSamples: k-sample rank tests and their combinations, R package version 1.2-3.

Schuster, R., Martin, T.G. and Arcese, P. (2014). Bird Community conservation and Carbon Offsets in western North America. Plos One 9(6), e99292.

Shaffer, T.L. (2004). A unified approach to analyzing nest success. Auk 121, 526-540.

Shaw, A.K. (2016). Drivers of animal migration and implications in changing environments. Evolutionary Ecology 30(6), 999-1007.

Shephard, J.M., Rycken, S., Almalik, O., Struyf, K. and Van Erp-van der Kooij, L. (2015). Migration strategies revealed by satellite tracking among descendants of a population of European white stork (Ciconia ciconia) reintroduced to Belgium. Journal of Ornithology 156, 943-953.

Smith, J.N.M., Keller, L.F., Marr, A.B. and Arcese, P. (2006). Conservation and biology of small populations: The song sparrows of Mandarte Island. New York, NY: Oxford University Press.

Swarth, H.S. (1920). Revision of the avian genus Passerella with special reference to the distribution and migration of the races in California. Univ. Calif. Publ. Zool. 21, 75-224.

Tarwater, C.E. and Arcese, P. (2018). Individual fitness and the effects of a changing climate on the cessation and length of the breeding period using a 34-year study of a temperate songbird. Global Change Biology 24, 1212-1223.

Taylor, S.S., Sardell, R.J., Reid, J.M., Bucher, T., Taylor, N., Arcese, P. and Keller, L.F. (2010). Inbreeding coefficient and heterozygosity–fitness correlations in unhatched and hatched song sparrow nestmates. Mol. Ecol. 19, 4454–4461.

Taylor, C.M. and Stutchbury, B.J. (2016). Effects of breeding versus winter habitat loss and fragmentation on the population dynamics of a migratory songbird. Ecological Applications 26(2), 424-437.

Threlfall, W. and Blacquiere, J.R. (1982). Breeding biology of the fox sparrow in Newfoundland. J. Field Ornithol. 53, 235-239.

Tökölyi, J. and Barta, Z. (2011). Breeding phenology determines evolutionary transitions in migratory behaviour in finches and allies. Oikos 120, 184–193.

Tompa, F.S. (1963). Factors determining the numbers of song sparrows on Mandarte Island, British Columbia. PhD thesis. Department of Zoology, The University of British Columbia.

Townsend, A.K., Sillett, T.S., Lany, N.K., Kaiser, S.A., Rodenhouse, N.L., Webster, M.S. and Holmes, R.T. (2013). Warm springs, early lay dates, and double brooding in a North American migratory songbird, the Black-throated Blue Warbler. PLoS One 8(4), e59467.

Tryjanowski, P., Sparks, T.H., Kuzniak, S., Czechowskie, P. and Jerzak, L. (2013). Bird migration advances more strongly in urban environments. PLoS One 8(5), e63482.

Uriarte, M., Schwartz, N., Powers, J.S., Marin-Spiotta, E.M., Liao, W. and Werden, L.K. (2016). Impacts of climate variability on tree demography in second growth tropical forests: the importance of regional context for predicting successional trajectories. Biotropica 48(6), 780-797.

Visser, M.E. (2008). Keeping up with a warming world; assessing the rate of adaptation to climate change. Lond. B. Biol. Sci. 275, 649-659.

Visty, H., Wilson, S., Germain, R., Krippel, J. and Arcese, P. (2018). Demography of sooty fox sparrows following a shift from a migratory to resident life history. Canadian Journal of Zoology 96, 436-440.

Wahl, T.R., Tweit, B. and Mlodinow, S.G. (2005). Birds of Washington: Status and Distribution. Corvallis, OR: Oregon State University Press.

Walters, C.J. (1986). Adaptive management of renewable resources. New York, NY: Macmillan Publishing Company.

Walther, B.A., and van Niekerk, A. (2015). Effects of climate change on species turnover and body mass frequency distributions of South African bird communities. African Journal of Ecology 53(1), 25-35.

Wang, T.L., Campbell, E.M., O’Neill, G.A. and Aitken, S.N. (2012a). Projecting future distributions of ecosystem climate niches: uncertainties and management applications. For. Ecol. Manage. 279, 128–140.

Wang, T., Hamann, A., Spittlehouse, D.L. and Murdock, T. (2012b). ClimateWNA—High- Resolution Spatial Climate Data for Western North America. Journal of Applied Meteorology and Climatology 51, 16-29.

Wang, T., Hamann, A., Spittlehouse, D. and Carroll, C. (2016). Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One 11(6), e0156720.

Weckstein, J.D., Kroodsma, D.E. and Faucett, R.C. (2002). Fox Sparrow (Passerella iliaca). In: P.G. Rodewald, ed., The Birds of North America. Ithica, NY: Cornell Lab of Ornithology.

Wikelski, M., Kays, R.W., Kasdin, N.J., Thorup, K., Smith, J.A. and Swenson, G.W. (2007). Going wild: what a global small-animal tracking system could do for experimental biologists. Journal of Experimental Biology 210, 181–186.

Wilcove, D.S. and Wikelski, M. (2008). Going, Going, Gone: Is Animal Migration Disappearing? PLoS Biol. 6(7), e188.

Williams, T.D., Bourgeon, S., Cornell, A., Ferguson, L., Fowler, M., Fronstin, R.B. and Love, O.P. (2015). Mid-winter temperatures, not spring temperatures, predict breeding phenology in the European starling Sturnus vulgaris. R. Soc. Open Sci 2, 140301.

Wilson, S. and Arcese, P. (2003). El Niño drives timing of breeding but not population growth in the song sparrow (Melospiza melodia). PNAS 100, 11139-11142.

Wilson, S. and Arcese, P. (2006). Nest depredation, brood parasitism, and reproductive variation in island populations of song sparrows (Melospiza melodia). Auk 123, 784-794.

Wingfield, J.C. (2008). Organization of vertebrate annual cycles: implications for control mechanisms. Philosophical Transactions of the Royal Society B 363, 425-441.

Winkler, D.W., Dunn, P.O. and McCulloch, C.E. (2002). Predicting the effects of climate change on avian life-history traits. Proc. Natl. Acad. Sci. U.S.A. 99, 13595-13599.

Zink, R.M. (1994). The geography of mitochondrial DNA variation, population structure, hybridization, and species limits in the fox sparrow (Passerella iliaca). Evolution 48, 96-111.

Zink, R.M. (2008). Microsatellite and mitochondrial DNA differentiation in the Fox Sparrow. Condor 110(3), 482-492.

Zink, R.M. and Weckstein, J.D. (2003). Recent evolutionary history of the fox sparrows (Genus: Passerella). Auk 120(2), 522-527.

Zuckerberg, B., Bonter, D.N., Hochachka, W.M., Koenig, W.D., DeGaetano, A.T. and Dickinson, J.L. (2010). Climate constraints on wintering bird distributions are modified by urbanization and weather. Journal of Animal Ecology 80(2), 403-413.

Appendices Appendix A: Hierarchical occupancy model

To assess sooty fox sparrow regional occupancy using climate, I created a Bayesian hierarchical model that includes estimates of detection error and spatial autocorrelation, two aspects which are not accounted for in the random forest method. I was unable to include this model in our analysis because of difficulty in modelling multiple correlated climate variables in this format.

The occupancy model used all climate variables (Methods 3.2.2). I also added two geographic variables, Distance to Ocean (m) and Elevation (m) (extracted from ArcMap version 10.5, ESRI 2017), to act as more general proxies for warm/dry climate, given that local sooty fox sparrows are hypothesized to remain in highly localized coastal pockets (eBird Northwest Team 2016). I also created a spatial contagion variable to account for the exceptional clustering of resident sooty fox sparrows using the z-score output of the Getis-Ord Gi* tool in ArcMap (optimized to a 1000m zone of indifference to incorporate closely associated breeding areas into hotspots, ESRI 2017). The Getis-Ord Gi* hotspot analysis statistically summarizes hotspot clusters of resident sooty fox occurrences; it was included to account for limited dispersal by resident sooty fox sparrows, given the little time since initial sites of establishment and temporal lag in occupying new habitat.

I assumed no variation in site occupancy by year (closed population) and did not estimate colonization or extinction rate, consistent with our regular but casual observations of breeding populations on Mandarte Is. and other islands from 1982 to present (e.g., Johnson et al. 2018, Visty et al. 2018). I scaled all covariates by their mean and standard deviation to avoid model convergence problems (Schuster et al. 2014). Although ultimately built in a Bayesian framework, I initiated occupancy model selection using the package unmarked in R (version 3.4.1). Occupancy models estimate occupancy given detection probability; the detection side of the model was not run through model selection, including just Date and Time. I used multi- model inference to model occurrence with all 10 possible covariates (7 climate variables, 2 geographic variables, and 1 spatial contagion variable) using a machine-learning covariate selection algorithm to create a candidate set of models based on the individual covariates’ AIC.

All candidate models with Δ AIC ≤ 2 from the top ranked model where then averaged (Burnham and Anderson 2002, Schuster and Arcese 2012).

Our final Bayesian model was built with R package R2OpenBUGS. I used the slopes and precision (1/SE2) from the covariates of the averaged occupancy model estimated in unmark as the center and range, respectively, for each variable’s prior in the final Bayesian model (Schuster et al. 2014; Table A.1). I assessed the final model for residual spatial autocorrelation using Moran’s I correlelograms (Schuster et al. 2014), where the null hypothesis of no spatial autocorrelation would give an I of zero, and positive or negative values indicate either positive or negative autocorrelation. I also evaluated the final model’s predictive performance using the Area Under the Curve (AUC), where a model with an AUC of 0.5 can categorically place a positive or negative occurrence with the same accuracy as chance, and a model with an AUC of 1.0 can perfectly categorize positive and negative occurrences (DeLong et al. 1988, Schuster et al. 2014).

Table A.1 Averaged occupancy model result from unmarked

Variable Estimate Std. Error z value Pr(>|z|) Precision p(Intercept) 0.78 1.50 0.52 0.60 0.45 p(Date) 5.92e-4 7.91e-3 0.07 0.94 15971.07 p(Time) -3.44 1.90 1.81 0.070 0.28 Psi(Intercept) -3.13 0.30 10.43 0 11.12 Psi(hotspot) 1.57 0.23 6.89 0 19.27 Psi(Jan degree days >5°C) 3.46 1.27 2.73 6.32e-3 0.62 Psi(Jan Avg Temp) -2.03 1.20 1.69 0.09 0.69 Psi(Feb # Frost Free Days) -0.95 0.81 1.17 0.24 1.53 Psi(Feb Min Temp) -0.38 0.72 0.53 0.59 1.95

Priors of Bayesian model were estimate (used as center) and precision (used as range).

Predictive performance of the occupancy model was excellent (AUC: 0.93 ± 0.05 95% CI). The spatial contagion variable (z-score of Getis-Ord Gi* Hotspot analysis) included in the occupancy model had moderate influence and a narrow CI (Table A.2); I found no need to account for residual spatial autocorrelation, given a maximum Moran’s I of 0.15. The detection side of the occupancy model included the intercept only (Table A.2).

Table A.2 Covariates included in the final Bayesian model.

Variable Estimate 2.5% 97.5% Std Dev p(Intercept) -0.38 -0.68 -0.08 0.15 p(Date) 0.00 -0.02 0.02 0.01 p(Time) -0.03 -0.26 0.21 0.12 Psi(Intercept) -3.17 -3.59 -2.78 0.21 Psi(hotspot) 1.62 1.32 1.94 0.16 Psi(Jan degree days >5°C) 3.39 1.91 4.89 0.76 Psi(Jan Avg Temp) -1.89 -3.36 -0.41 0.75 Psi(Feb # Frost Free Days) -1.13 -2.21 -0.06 0.55 Psi(Feb Min Temp) -0.28 -1.41 0.85 0.57

Appendix B: Additions to tables and maps Table B.1 Complete list ranking variables by importance in random forest model.

Mean Decrease Mean Decrease Variable Accuracy (%) Gini PPT01 18.622 13.6241 PPT02 16.7102 10.3848 Tmax01 14.084 11.145 DD5_03 13.1822 7.15715 Tave01 12.9885 7.45388 Tmin03 12.8989 4.79714 Tmin02 12.4849 5.64853 Tmin01 12.4597 5.53184 Tave03 12.0477 5.06103 NFFD01 11.5152 3.23664 DD_0_02 11.254 5.60238 DD5_01 11.1621 7.25469 DD_0_03 11.0747 2.39674 Tave02 10.7398 5.32867 PPT03 10.7008 13.354 DD_0_01 10.5266 5.65072 DD5_02 10.1968 6.43312 Tmax03 9.94054 6.39385 NFFD03 8.55804 2.45225 NFFD02 7.05431 2.59151

Mean decrease in accuracy indicates influence of the variable on accuracy of decision trees.

Figure B.1 Average temperature in February in 1900-1910 and 2005-2015.