Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Mancuso, Kristen A.
Doctor of Philosophy - PhD
University of British Columbia
Migratory songbirds are declining in North America, and a better understanding of their full annual cycle (breeding, migration, overwintering) is important to identify potential threats and to better implement conservation initiatives. How populations are connected through different phases of the annual cycle, termed migratory connectivity, is another important conservation metric. Additionally, the identification of potential threats that may affect population persistence is important to direct conservation efforts. One less-commonly studied threat is mercury, which is a toxic heavy metal that can negatively affect neurology, reproduction, and endocrinology. Moreover, mercury may also disrupt the hypothalamic-pituitary-adrenal axis and subsequent stress response. Here, I examine the movement ecology of two migratory songbirds – the Gray Catbird (Dumetella carolinensis) and the Yellow-breasted Chat (Icteria virens). I describe migration routes, overwintering locations, and migratory connectivity. Further, I examine if mercury toxicity and its associated effects on the stress response is a potential threat to Yellow-breasted Chat populations throughout their breeding range. To answer questions about migration ecology, I use GPS tags, geolocators, genetics, stable hydrogen isotopes, and bird banding encounter data. To answer questions about conservation physiology, I measured mercury and corticosterone levels in chat feathers. I found that breeding Gray Catbirds in British Columbia and Montana migrate eastward and then southward to overwintering locations in northeastern Mexico and Texas. Individuals from both populations overlapped during the overwintering period; a case of weak migratory connectivity. I found that Yellow-breasted Chats in British Columbia migrate south to overwintering locations in western Mexico. However, other breeding populations also appear to overwinter in western Mexico, suggesting weak migratory connectivity. From my physiology analyses, I found that mercury levels in feathers were relatively low, 0.30 ± 0.02 µg/g, and mostly considered background levels across the chat breeding range. There was no correlation between mercury and corticosterone levels, suggesting that mercury was not affecting the activity of the hypothalamus-pituitary-adrenal axis. The results of my research elucidate key details of the full annual cycle for Gray Catbirds and Yellow-breasted Chats and indicate little threat of mercury toxicity and subsequent endocrinology malfunction to Yellow-breasted Chats.
MIGRATION ECOLOGY OF THE YELLOW-BREASTED CHAT AND GRAY CATBIRD AND PHYSIOLOGY OF THE YELLOW-BREASTED CHAT by Kristen A. Mancuso B.Sc. (Hons), University of Guelph, 2010 M.Sc., Trent University 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2020 © Kristen A. Mancuso, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Migration ecology of the Yellow-breasted Chat and Gray Catbird and physiology of the Yellow-breasted Chat submitted by Kristen A. Mancuso in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Karen E. Hodges, Biology Department, Irving K. Barber School of Arts and Sciences Supervisor Dr. Christine A. Bishop, Environment and Climate Change Canada Co-Supervisor Dr. Adam Ford, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Jill Jankowski, Biodiversity Research Center, Faculty of Science University Examiner Dr. Matt Reudink, Department of Biological Sciences, Thompson Rivers University External Examiner Additional Committee Members include: Dr. Jason Pither, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. John D. Alexander, Klamath Bird Observatory Supervisory Committee Member iii Abstract Migratory songbirds are declining in North America, and a better understanding of their full annual cycle (breeding, migration, overwintering) is important to identify potential threats and to better implement conservation initiatives. How populations are connected through different phases of the annual cycle, termed migratory connectivity, is another important conservation metric. Additionally, the identification of potential threats that may affect population persistence is important to direct conservation efforts. One less-commonly studied threat is mercury, which is a toxic heavy metal that can negatively affect neurology, reproduction, and endocrinology. Moreover, mercury may also disrupt the hypothalamic-pituitary-adrenal axis and subsequent stress response. Here, I examine the movement ecology of two migratory songbirds – the Gray Catbird (Dumetella carolinensis) and the Yellow-breasted Chat (Icteria virens). I describe migration routes, overwintering locations, and migratory connectivity. Further, I examine if mercury toxicity and its associated effects on the stress response is a potential threat to Yellow-breasted Chat populations throughout their breeding range. To answer questions about migration ecology, I use GPS tags, geolocators, genetics, stable hydrogen isotopes, and bird banding encounter data. To answer questions about conservation physiology, I measured mercury and corticosterone levels in chat feathers. I found that breeding Gray Catbirds in British Columbia and Montana migrate eastward and then southward to overwintering locations in northeastern Mexico and Texas. Individuals from both populations overlapped during the overwintering period; a case of weak migratory connectivity. I found that Yellow-breasted Chats in British Columbia migrate south to overwintering locations in western Mexico. However, other breeding populations also appear to overwinter in western Mexico, suggesting weak migratory connectivity. From my physiology analyses, I found that mercury levels in feathers were relatively low, 0.30 ± 0.02 µg/g, and mostly considered background levels across the chat breeding range. There was no correlation between mercury and corticosterone levels, iv suggesting that mercury was not affecting the activity of the hypothalamus-pituitary-adrenal axis. The results of my research elucidate key details of the full annual cycle for Gray Catbirds and Yellow-breasted Chats and indicate little threat of mercury toxicity and subsequent endocrinology malfunction to Yellow-breasted Chats. v Lay Summary For songbirds, details about migration routes and overwintering areas are unknown for most populations but are relevant for conservation efforts. I tracked the Gray Catbird and Yellow-breasted Chat to describe their migration routes and overwintering areas. I found that Gray Catbirds in British Columbia and Montana flew east and then south to overwintering sites in northeastern Mexico and Texas. I found that Yellow-breasted Chats in British Columbia flew south to western Mexico. I also measured a potentially harmful chemical (mercury) in the feathers of breeding Yellow-breasted Chats. I found a low risk of mercury toxicity across the breeding range. I also tested to see if mercury was affecting the stress response in chats, but I did not find any evidence to support this idea. My research contributes to a better understanding of the full life cycle of these two songbird species, plus I rule out mercury as a likely threat to chats. vi Preface Manuscripts of chapters 2, 3, and 4 will be sent for publication and recognizing the contribution of co-authors. All co-authors and committee members have reviewed each chapter of my thesis. The agreed list of co-authors from the manuscript based on chapter 2 include: Kristen A. Mancuso, Megan A. Fylling, Karen E. Hodges, Michael B. Lancaster, Christine A. Bishop, Katharine R. Stone. The agreed list of co-authors from the manuscript based on chapter 3 include: Kristen A. Mancuso, Karen E. Hodges, John D. Alexander, Manuel Grosselet, A. Michael Bezener, Luis Morales, Sarahy C. Martinez, Jessica Castellanos-Labarcena, Michael A. Russello, Sarah M. Rockwell, Matthias E. Bieber, Christine A. Bishop8 The agreed list of co-authors from the manuscript based on chapter 4 include: Kristen Mancuso, Karen E. Hodges, Manuel Grosselet, John Elliott, John D. Alexander4, Christine A. Bishop. Field Efforts: With the help of field technicians, biologists, and volunteers, I collected Yellow-breasted Chat feather samples from study sites in the South Okanagan, British Columbia, Northern California, USA, Nayarit, Mexico, and Chiapas, Mexico. Feather samples from Veracruz, Mexico were collected by Manuel Grosselet of Tierra de Aves. Feather samples from Oregon were collected by biologists at Klamath Bird Observatory. Likewise, long-term monitoring efforts of colour-banded birds of Gray Catbirds and Yellow-breasted Chats in the south Okanagan Valley, British Columbia, Canada began before my arrival to the project. I continued to assist with colour-banding and resighting efforts from 2016 – 2019. Long-term monitoring efforts of colour-banded Gray Catbirds in the Bitterroot River Valley of Montana, USA were conducted by biologists with MPG Ranch and the University of Montana. Geolocators on Yellow-breasted Chats had previously been deployed and retrieved in the south Okanagan Valley, British vii Columbia, Canada by biologists with Environment and Climate Change Canada. Geolocators on Yellow-breasted Chats had been previously deployed and retrieved in Trinity River area, California, USA by biologists with Klamath Bird Observatory. I assisted in the retrieval of geolocators at both sites, plus deploying and retrieving GPS tags on Yellow-breasted Chats and Gray Catbirds in the south Okanagan Valley. All geolocators and GPS tags deployed and retrieved on Gray Catbirds in the Bitterroot River Valley, Montana, USA, were done by biologists with MPG Ranch and the University of Montana. They shared their geolocator and GPS data for analyses. Lab Work: Genetic analyses of Yellow-breasted Chat feathers were completed by Dr. Michael Russello and Jessica Castellanos-Labarcena at the University of British Columbia Okanagan. Michael and Jessica wrote the bulk of the genetic methods, and results, and provided the figures and data for the corresponding appendix. I provided the “unknown” feather samples for genetic analyses by subsampling a few mm of the calamus. Stable hydrogen isotope analyses were outsourced to the Laboratory of Stable Isotope Science at the University of Western Ontario, London, Ontario, Canada after I had washed and prepared the samples at the University of British Columbia, Okanagan. Mercury analyses were outsourced to the National Wildlife Research Centre in Ottawa, Ontario, Canada. The samples were washed and prepped at the facility. I completed the corticosterone analyses at the University of British Columbia Okanagan after being trained on how to use the ELISA kits from Jared Maida, a recent M.Sc. graduate from Thompson Rivers University who had experience using the kits. Data Analyses I performed all data analyses except for the apparent annual survivorship analyses on Gray Catbirds in Montana – biologists at the University of Montana conducted these analyses in viii part because I did not have their survivorship data. I created all figures, maps, and tables and wrote the dissertation and corresponding manuscripts. Permits: Research on birds was approved by the University of British Columbia Okanagan Animal Care Committee (A16-0079). Federal bird banding permits (10761L,10761Q, 10761R, 23206) were acquired for all bird banding activities. Corticosterone lab work occurring at the University of British Columbia Okanagan was approved by the Biosafety Committee and occurred under biosafety protocol and certificate B18-0185. All appropriate permits and permission for land accessed were obtained for fieldwork. ix Table of Contents Abstract .................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ...................................................................................................................................... vi Table of Contents ..................................................................................................................... ix List of Tables .......................................................................................................................... xiii List of Figures ......................................................................................................................... xv Acknowledgements ............................................................................................................... xx Chapter 1: Introduction ........................................................................................................... 1 1.1 Why Birds Are Important ........................................................................................... 1 1.2 Songbird Migration .................................................................................................... 2 1.3 Threats to Songbirds ................................................................................................ 3 1.4 Monitoring Programs ................................................................................................ 6 1.5 Migratory Connectivity .............................................................................................. 8 1.6 Advanced Tracking Methods ...................................................................................10 1.7 Conservation Initiatives ............................................................................................11 1.8 Collaborations ..........................................................................................................12 1.9 Study Species ..........................................................................................................16 1.10 Data Chapters .........................................................................................................18 Chapter 2: Migration ecology of western Gray Catbirds.........................................................22 2.1 Introduction ..............................................................................................................22 2.2 Methods ...................................................................................................................26 2.2.1 Study Areas .......................................................................................................26 2.2.2 Tracking Devices ...............................................................................................26 2.2.3 Repeat Tracking ................................................................................................27 x 2.2.4 Analyses of Tracking Devices ............................................................................27 2.2.5 eBird Data ..........................................................................................................29 2.2.6 Migratory Connectivity .......................................................................................29 2.2.7 Apparent Annual Survivorship ............................................................................30 2.3 Results ....................................................................................................................32 2.3.1 Migration Routes and Stopover Sites .................................................................32 2.3.2 Overwintering Sites ............................................................................................34 2.3.3 Multiple Years of Tracking ..................................................................................34 2.3.4 Migratory Connectivity .......................................................................................35 2.3.5 Apparent Annual Survivorship ............................................................................35 2.4 Discussion ...............................................................................................................36 Chapter 3: Migration and overwintering ecology of the Yellow-breasted Chat .......................57 3.1 Introduction ..............................................................................................................57 3.2 Methods ...................................................................................................................61 3.2.1 Study Areas .......................................................................................................61 3.2.2 Bird Captures and Handling ...............................................................................61 3.2.3 Apparent Annual Survivorship and Overwintering Return Rate ..........................63 3.2.4 Migration and Overwintering Sites .....................................................................64 3.2.5 Breeding Origin of Overwintering and Migrating Birds ........................................66 3.2.6 Stable Hydrogen Isotope Analyses ....................................................................67 3.2.7 Chat Movements from Bird Banding Data ..........................................................71 3.3 Results ....................................................................................................................71 3.3.1 Migration and Overwintering Sites of Breeding Chats ........................................71 3.3.2 Apparent Annual Survivorship and Overwintering Return Rates ........................73 3.3.3 Inferred Breeding Origin from Overwintering and Migrating Chats .....................73 xi 3.3.4 Bird Banding Data ..............................................................................................74 3.4 Discussion ...............................................................................................................74 Chapter 4: Mercury toxicity risk and corticosterone levels in the Yellow-breasted Chat ........91 4.1 Introduction ..............................................................................................................91 4.2 Methods ...................................................................................................................96 4.2.1 Study Areas .......................................................................................................96 4.2.2 Bird Handling and Feather Collection .................................................................97 4.2.3 Moult Strategy of Chats......................................................................................98 4.2.4 Breeding Assignment of Overwintering Birds .....................................................99 4.2.5 Mercury Analyses ............................................................................................ 100 4.2.6 Corticosterone Extraction and Assay ............................................................... 101 4.2.7 Mercury Risk .................................................................................................... 103 4.2.8 Statistical Analysis ........................................................................................... 104 4.3 Results .................................................................................................................. 106 4.3.1 Mercury ............................................................................................................ 107 4.3.2 Corticosterone ................................................................................................. 108 4.3.3 Mercury and Corticosterone Combined ............................................................ 109 4.4 Discussion ............................................................................................................. 109 Chapter 5: Conclusion ........................................................................................................ 128 5.1 Conservation Implications ...................................................................................... 131 5.2 Conservation Physiology ....................................................................................... 134 5.3 Closing Remarks ................................................................................................... 136 Bibliography .......................................................................................................................... 138 Appendices ........................................................................................................................... 166 ...................................................................................................... 166 xii ...................................................................................................... 168 ...................................................................................................... 177 ...................................................................................................... 188 ...................................................................................................... 192 ....................................................................................................... 196 ...................................................................................................... 197 ...................................................................................................... 205 ........................................................................................................ 214 ....................................................................................................... 216 xiii List of Tables Table 2.1. Models for the apparent annual survivorship of Gray Catbirds (Dumetella carolinensis) in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA using Gray Catbirds. Cormack-Jolly-Seber models were used, which includes annual survivorship (ϕ) and detection probability (p). TSM is a time-since-marking approach whereby the first encounter is separated from separate encounters to factor in young and transient individuals. Intercept only models are designated by (.). ......................................45 Table 2.2. Parameter estimates for top two apparent annual survivorship models for Gray Catbirds (Dumetella carolinensis) in the in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA. Cormack-Jolly-Seber models were used, which includes annual survivorship (ϕ) and detection probability (p). Intercept only models are designated by (.). ................................................................................................................46 Table 2.3 – Summary of GPS tags attached to Gray Catbirds (Dumetella carolinensis) in the South Okanagan Valley, British Columbia, Canada and in the Bitterroot River Valley, Montana, USA with an emphasis on migration. Year is year deployed. The fall depart date represents the last fix at the breeding site and the arrival date represents the first fix at the overwintering site. The spring depart date represents the last fix at the overwintering site and the arrival date represents the first fix back at the breeding site. Locations (Loc.) are abbreviated as Nuevo Leon (NLE), San Luis Potosi (SLP), Tamaulipas (TAM), Veracruz (VER), Hidalgo (HID), Puebla (PUE), and Texas (TX). .............................................................................................................47 Table 2.4. Details of overwintering sites obtained from GPS tags placed on Gray Catbirds (Dumetella carolinensis) in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA. Abbreviations include location (Loc), length of stay (Days), the distance between current and previous overwintering site (KM). Locations are abbreviated as Nuevo Leon (NLE), San Luis Potosi (SLP), Tamaulipas (TAM), Veracruz (VER), Hidalgo (HID), Puebla (PUE), and Texas (TX). .........................................................................48 Table 3.1. Yellow-breasted Chat (Icteria virens) study areas. Some sites had multiple subsites within the area. The sample size (N) for geolocators and GPS tags refers to the number deployed. Dashes indicate no data. ..........................................................................................81 Table 3.2. GPS Tag Results. Lotek PinPoint GPS tags were attached to male Yellow-breasted Chats (Icteria virens) breeding in the South Okanagan Valley of British Columbia, Canada. Fall depart refers to the last point at the breeding site, and winter arrive refers to the first point on the overwintering site. All winter sites are within Mexico. Final point is the date of the last fix. Dashes indicate no data. ...........................................................................................................81 Table 3.3. Feather stable hydrogen isotope (δ2Hf) summary. Feathers were collected from Yellow-breasted Chats (Icteria virens) from 3 different sites in Mexico. Chats were sampled in Nayarit in the overwintering stage and are of the western subspecies. Chats were sampled in xiv Veracruz and Chiapas during migration and are of the eastern subspecies. Stable hydrogen isotope values are reported per thousand (‰) standardized to Vienna Standard Mean Ocean Water. The bold bins have the highest percentage of chats from each site. Dashes indicate no data. ..........................................................................................................................................82 Table 4.1. Yellow-breasted Chat (Icteria virens) study site locations where feather samples were for corticosterone (Cort), mercury (Hg), and hydrogen stable isotope analyses (δ2Hf). The breeding location of chats sampled during migration and overwintering was inferred using stable hydrogen isotopes. .................................................................................................................. 118 Table 4.2. Models compared to predict mercury in Yellow-breasted Chat (Icteria virens) feathers. K is the number of parameters. AICc is Akaike’s Information Criterion adjusted small sample sites. The total sample size is 150. The response variable, mercury (μg/g) in Yellow-breasted Chat feathers, was loge transformed to improve model fit. ........................................ 119 Table 4.3. Comparing models to explain corticosterone in Yellow-breasted Chat (Icteria virens) feathers. K is the number of parameters. AICc is Akaike’s Information Criterion adjusted small sample sites. The total sample size is 196. Note that the response variable is corticosterone (pg/mm) in Yellow-breasted Chat feathers was transformed to the power of -0.7 to improve model fit. ................................................................................................................................. 119 Table 4.4. Weighted parameter estimates for mercury concentrations in Yellow-breasted Chats (Icteria virens) based on all models using the subset method. The 95% confidence intervals that do not include 0 are in bold. .................................................................................................... 120 Table 4.5. Weighted parameter estimates for corticosterone concentrations in Yellow-breasted Chats (Icteria virens) based on all models using the subset method. The 95% confidence intervals that do not include 0 are in bold. ............................................................................... 120 xv List of Figures Figure 1.1. Schematic illustration depicting strong and weak migratory connectivity. Circles represent distinct songbird populations, and circles of the same shade represent the same stage of the annual cycle. Arrows indicate the direction of bird movements. In scenario A, I see strong migratory connectivity as birds from different populations never mix in either stage of the annual cycle. In contrast, in scenario B, I see weak migratory connectivity in that individuals from one population spread out and overlap with individuals from other populations.................20 Figure 1.2. Range map of the Gray Catbird (IUCN 2016a). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. ................................................................20 Figure 1.3. Range map of the Yellow-breasted Chat (IUCN 2016b). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. ...........................................21 Figure 2.1. Southward migration path from GPS tags retrieved from Gray Catbirds (Dumetella carolinensis) breeding in the South Okanagan Valley, British Columbia, Canada, and in the Bitterroot River Valley, Montana, USA. Black tracks are from GPS tags retrieved from the Bitterroot River Valley (n=12) and gray tracks are from GPS tags retrieved from the South Okanagan Valley (n=6). Gray Catbird range map provided by IUCN (2016) and World Terrain Base Map provided by ESRI, USGS, and NOAA within ArcMap 10.6.1. Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. ...........................................49 Figure 2.2. Elevation profile of Gray Catbirds (Dumetella carolinensis) navigating across the Rocky Mountain Range during fall migration. Points and tracks are from GPS tags on catbirds breeding in the South Okanagan Valley, British Columbia, Canada, and in the Bitterroot River Valley, Montana, USA. The elevation map was created in ArcMap 10.7.1 using the 30 arc-second digital elevation model of North America (ESRI 2019). The GCS WGS 1984 coordinate system was used. .....................................................................................................................50 Figure 2.3. Stopover sites where Gray Catbirds (Dumetella carolinensis) spent at least one day at the location during migration. Stopover sites identified using GPS tags. Crosses represent birds tagged in the South Okanagan Valley, British Columbia, Canada, and exes represent birds tagged in the Bitterroot River Valley, Montana, USA. Stars represent study site locations. All sites are from fall migration, except for the asterisk in Texas, which was a spring stopover site for a Gray Catbird that bred in the Bitterroot River Valley. Points are scaled relative to the amount of time spent at a site and range between 1 and 16 days. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. ...........................................................................................................51 Figure 2.4. Overwintering points as determined from GPS tags retrieved from Gray Catbirds (Dumetella carolinensis) breeding in the South Okanagan Valley, British Columbia, Canada (squares) and the Bitterroot River Valley, Montana, USA (circles). The size of the symbol represents how long the bird stayed at the site, ranging from 4 to 169 days. Most birds had xvi more than one overwintering site. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. .............52 Figure 2.5. Geolocator overwintering location estimates from Gray Catbirds (Dumetella carolinensis) breeding in the Bitterroot River Valley, Montana, USA. Each polygon represents the 50% Kernel Density Estimates from locations obtained between November 15 – March 1 from 2014-2018. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. ...........................................53 Figure 2.6. A comparison of southward (black) and northward migration (gray) tracks from 5 different Gray Catbirds (Dumetella carolinensis) marked in their breeding site in the Bitterroot River Valley, Montana, USA. Migration routes determined from GPS tags. All birds were heading north by May 10, except for catbird with GPS 41820, who headed north by May 3. Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. .............54 Figure 2.7. Repeat tracking of 3 separate Gray Catbirds (Dumetella carolinensis) given different tracking devices in different years. Tracking devices were deployed in the Bitterroot River Valley, Montana, USA. The year that the track was recorded is shown. Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. Geolocator abbreviated as Geo. ..........................................................................................................................................54 Figure 2.8. Weekly abundance of Gray Catbirds (Dumetella carolinensis) by state between 1900 – 2019 using citizen science observation records from eBird (2017). Abundance includes the number of catbirds reported on all checklists (including checklists without catbirds). Catbirds have low abundance in states within the Pacific Flyway with no peaks during migration (top panel). In the Central Flyway, peaks in abundance during migration within each state trace the geographic movement of birds across the landscape in the same manner as indicated by GPS tags (bottom panel). ..................................................................................................................55 Figure 2.9. Boxplot of pairwise distances of Gray Catbirds (Dumetella carolinensis) in the breeding and overwintering grounds separately by site. Each box displays the first quartile, median, and third quartile. Whiskers show first and third quartiles plus 1.5 times the interquartile range. Points beyond the whiskers are outliers. The sample size of pairwise distances for the Bitterroot River Valley, Montana, USA is 66 and for the South Okanagan Valley, British Columbia, Canada is 15. Distances were determined from locations identified using GPS tags. When multiple overwintering sites were used, only the first site was included for analysis. .......56 Figure 3.1. Yellow-breasted Chat (Icteria virens) study sites. The stars symbolize breeding sites, the square symbolizes the overwintering site, and the triangles symbolize migration sites. The eastern (I.v. virens) and western (I.v. auricollis) subspecies breeding ranges are illustrated along with the species’ non-breeding wintering range (IUCN 2016). Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. ..........................................83 xvii Figure 3.2. Fall migration routes of Yellow-breasted Chats (Icteria virens). Five male chats were tracked using Lotek PinPoint GPS units from breeding sites in the South Okanagan Valley of British Columbia, Canada to overwintering sites in Sinaloa and Nayarit, Mexico. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. ..................84 Figure 3.3. Western Yellow-breasted Chat (Icteria virens) fall migration stopover site. One Yellow-breasted Chat tracked with a Lotek GPS PinPoint from its breeding site in British Columbia, Canada, stopped during fall migration in Idaho, USA for at least 8 days between September 14 - 22, 2017. The cross marks the stopover site. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. ..........................................85 Figure 3.4. Overwintering areas of Yellow-breasted Chats (Icteria virens) that bred in British Columbia. Asterisks denote overwintering locations as determined from tracking chats with GPS tags. The square is the Nayarit study site. Satellite imagery of overwintering locations provided for visualizing habitat. Protected areas of Mexico provided by the United Nations Environment World Conservation Monitoring Centre (UnEP-WCMC 2020). Overwintering range provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. ....................................................................................................................86 Figure 3.5. Overwintering longitudes of Yellow-breasted Chats (Icteria virens) inferred from geolocators. Geolocators were attached to breeding Yellow-breasted Chats in the South Okanagan Valley of British Columbia, Canada and in northern California, USA. Points represent median longitudinal estimates from Nov 15 – Mar 1 and lines represent interquartile range. Note that line placement is in an increasing eastward longitudinal order and that latitudes are arbitrary within the mapped IUCN (2016) overwintering range as latitudinal estimates from my geolocators were inaccurate. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. .............................................................................................87 Figure 3.6. Hydrogen stable isotope values in Yellow-breasted Chat (Icteria virens) feathers, (δ2Hf). Chats were sampled in Chiapas, Mexico during fall migration from September to October in 2018 (n=45). Chats were sampled in Nayarit, Mexico during the overwintering period in 2018 (n=40). Chats were sampled in Veracruz, Mexico during fall migration in March, May, and late August to early November in 2014 (n=125). Stable hydrogen isotope values are reported per thousand (‰) standardized to Vienna Standard Mean Ocean Water. Each point represents an individual chat hydrogen isotopic signature. Errors bars represent mean and 95% confidence intervals. ...................................................................................................................................88 Figure 3.7. Yellow-breasted Chat (Icteria virens) breeding origins from δ2Hf samples. The outlined polygon for Nayarit is the western subspecies range (IUCN 2016). The outlined polygon for Veracruz and Chiapas is the eastern subspecies range (IUCN 2016). Group assignment estimates are shown for the least depleted 10‰ bin (left), the 10‰ bin containing the most birds (center and outlined in black), and the most depleted 10‰ bin (right). Feather samples from Nayarit are from overwintering chats. Feather samples from Veracruz and Chiapas are from migrating chats. .........................................................................................................................89 xviii Figure 3.8. Band recovery data for Yellow-breasted Chats (Icteria virens). Encounters at least 40 km apart from 1920 – 2019 (North American Banding Program 2019) are connected with straight lines to visualize range-wide movements (IUCN 2016). Spring dates are between April 1 – May 14. Breeding dates are between May 15 – August 30. Fall dates are between September 1 – October 30. Winter dates are between January 1 – February 28. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. ...............................90 Figure 4.1. Study areas for Yellow-breasted Chats (Icteria virens). The breeding ranges of the eastern (I.v. virens) and western (I.v. auricollis) subspecies are shown. Star symbols represent birds sampled from study areas in the breeding season, the square represents birds sampled from a study area in the overwintering season, and triangles represent birds sampled from study areas during migration. Map made in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator projection and coordinate system. ............................................................................ 121 Figure 4.2. Average mercury concentrations in Yellow-breasted Chats (Icteria virens) in their breeding range. Pentagons are sites where chats were sampled during the breeding season. Circles are breeding sites inferred from stable hydrogen isotope analyses, and a single point was used to represent each group assignment, but assignment probabilities spanned larger areas. Sample sizes are indicated beside each point. The breeding range of the Yellow-breasted Chat is shown in stippled gray in the background (IUCN 2016). Map made in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator projection and coordinate system. Note that while higher mercury concentrations are shown as darker shades of red, all levels are still considered low risk of mercury toxicity. ................................................................................... 122 Figure 4.3. Conditioning plots of the top model explaining feather mercury in Yellow-breasted Chats. The model shown is: Feather Mercury ~ Age + Range Position, where feather mercury has been loge transformed. The upper plot shows the conditioning plot for age while the lower plot shows the conditioning plot for range position. Each plot displays partial residuals (black dots), prediction lines (blue line), and 95% confidence bands (gray band). ............................. 123 Figure 4.4. Mercury concentrations in Yellow-breasted Chat (Icteria virens) feathers. Untransformed data and means are shown. The right outer tail feather was used. Range position is divided by subspecies (Western, W and Eastern E) and position within range (North, N; Central, C; and Southern, S). Sample sizes are W.N=22, W.C.=42, W.S=14, E.N.=15, E.C = 26, E.S.=31. Age includes the first moult cycle (n=97) and the definitive moult cycle (n=53). Sexes are female (F, n=38), male (M, n=94), and unknown (U, n=18). The total sample size is 150. ......................................................................................................................................... 124 Figure 4.5. Conditioning plots of the top model explaining feather corticosterone in Yellow-breasted Chats. The top model shown is: Feather Corticosterone ~ Subspecies, where feather corticosterone has been transformed to the power of -0.7. The plot displays partial residuals (black dots), prediction lines (blue line), and 95% confidence bands (gray band). ................... 125 xix Figure 4.6. Corticosterone concentrations in Yellow-breasted Chat (Icteria virens) feathers. Untransformed data and means are shown. The right outer tail feather was used. Range position is divided by subspecies (Western, W and Eastern E) and position within range (North, N; Central, C; and Southern, S). Sample sizes are W.N=23, W.C.=31, W.S.=12, E.N.=21, E.C = 64, E.S.=45. Age includes the first moult cycle (n=148) and the definitive moult cycle (n=48). The total sample size is 196. Note that one outlier with corticosterone concentration of 35.53 pg/mm is not visible................................................................................................................. 126 Figure 4.7. Correlation between mercury and corticosterone in Yellow-breasted Chat (Icteria virens) feathers. Feathers represent breeding sites throughout North America (N = 127). Both variables were transformed to meet assumptions of normality. Pearson r = -0.054, df = 125, p = 0.54. ........................................................................................................................................ 127 xx Acknowledgements The biggest acknowledgement goes to all of the birds who made this research possible. Every bird who endured being tricked, captured, handled, banded, photographed, given a tracking device, and sampled for this research project deserves more than I can ever give. Thank you to all of the collaborators who made this large-scale project come to fruition plus all the landowners who granted access to your land to conduct this research. Thanks to the Penticton Indian Band for granting us access to your unceded lands to study the birds. I am appreciative of all the work and effort put forth by the many volunteers, field technicians, and biologists (both past and present) on behalf of this collaborative research effort. I am especially grateful to Matthias Bieber, Michael Bezener, and Macgregor Aubertin-Young for remaining patient, positive, and encouraging despite working through ungodly hours, extreme heat, poison ivy, and the worst mosquitoes in the South Okanagan Valley. My field seasons were made undoubtedly more fun with all of you. I am incredibly grateful for the financial support from Sioux Valley Dakota Nation which was immensely helpful in reducing the financial stress associated with pursuing a Ph.D. Likewise, I was fortunate to receive additional funding from the University of British Columbia Okanagan, Environment and Climate Change Canada, NSERC, Indspire, and the National Indian Brotherhood Trust Fund. I would like to give thanks to my supervisors, Dr. Karen E. Hodges and Dr. Christine A. Bishop for their guidance and support throughout my Ph.D. Thank you to my supervisory committee members: Dr. Adam Ford, Dr. Jason Pither, and Dr. John Alexander for their advice and feedback on my project and dissertation. Special thanks to my friends and family for their unwavering love and support throughout the whole Ph.D. process, especially Morgan for his endless love, encouragement, and patience. 1 Chapter 1: Introduction My dissertation examines the migration ecology of two migratory songbird species, the Yellow-breasted Chat (Icteria virens) and the Gray Catbird (Dumetella carolinensis). Additionally, I probe deeper into the conservation physiology of the Yellow-breasted Chat to address if environmental and physiological threats may be of concern to breeding populations. This research is not only valuable for advancing the natural history knowledge of these two species but also tests some of the general tenets of songbird migration theory and environmental toxicology. Concerningly, we are in an unprecedented time of global loss of biodiversity, including the loss of songbirds (Dirzo et al. 2014). Songbirds face multiple threats spanning their full annual cycle, and therefore, conservation monitoring and research programs are crucial to identify, address, and reverse songbird declines. Moreover, baseline data is crucial to evaluate the stability of populations and to ensure common species remain abundant for future generations. Collaborations at multiple scales and new technologies are paving the way to advancing our understanding of the full annual cycle of songbirds and my dissertation embodies these elements. Here, I describe (1) why songbirds are important, (2) songbird migration, (3) threats to songbirds, (4) monitoring programs, (5) migratory connectivity, (6) advanced tracking methods, (7) conservation initiatives, (8) my study species, (9) international collaborations, and lastly (10) the research conducted in each data chapter in this dissertation. 1.1 Why Birds Are Important Songbirds are valuable to humans for environmental, economic, cultural, aesthetic, and recreational values (Wenny et al. 2011, Whelan et al. 2015). Songbirds provide important ecosystem services such as consuming large quantities of insects that could otherwise damage agricultural and natural systems (Wenny et al. 2011, Sekercioglu et al. 2016). Likewise, songbirds disperse seeds through consuming fruit and caching seeds (Hougner et al. 2006, 2 Whelan et al. 2015). Songbirds are culturally significant, symbolizing spiritual values and their feathers are used in ceremony (Tidemann and Gosler 2010). Although controversial and sometimes illegal, capturing, trading, and selling songbirds has deep cultural roots and provides a source of income and livelihood (Roldán-Clarà et al. 2017). Additionally, birdwatching and associated ecotourism can have substantial economic and health benefits (Glowinski 2008, Clucas et al. 2015, Callaghan et al. 2018). Lastly, songbirds provide aesthetic value and have influenced the works of photographers, artists, writers, and poets alike (Boardman 2016). As such, it is of great importance to ensure that songbirds remain on the landscape for current and future generations. 1.2 Songbird Migration In North America, over 350 species of birds migrate between Canada, U.S.A., and Mexico annually (North American Bird Conservation Initiative 2016). The life cycle of migratory songbirds is generally broken down into four distinct phases: breeding, fall migration, overwintering, and spring migration (Faaborg et al. 2010). Whether songbird migration originated from temperate populations (northern home theory) or tropical populations (southern home theory) remains debated (Rappole and Jones 2002, Salewski and Bruderer 2007, Winger et al. 2014). Regardless, the evolution of songbird migration is maintained by the fitness benefits of food resources, reproductive opportunities, and decreased competition that occurs in temperate regions (Rappole 1995, Rappole and Jones 2002, Mila et al. 2006). As environmental conditions deteriorate later in the year in temperate regions, survival is higher if birds overwinter in more southerly areas during the non-breeding season, further selecting for migratory individuals (Rappole 1995, Salewski and Bruderer 2007). Therefore, the maintenance of annual migration cycles of songbirds between temperate and tropical areas is driven by underlying natural selection for increased survival and fitness(Rappole 1995, Salewski and Bruderer 2007). 3 1.3 Threats to Songbirds Many species of North American songbirds are declining (North American Bird Conservation Initiative 2016, Sauer et al. 2017). Concern over songbird decline has been echoed for decades (Jones 1986, Askins 1995, Keller and Yahner 2006), but recent estimates of the extent of the decline in North America is staggering – a net loss of 2.5 billion (nearly 1 in 5 birds) since 1970 including losses of the most abundant birds (Rosenberg et al. 2019). Factors contributing to the decline of songbirds include loss and fragmentation of habitat, direct mortality from anthropogenic sources, environmental contamination, and global climate change (Birdlife International 2018a, Rosenberg et al. 2019). The loss and fragmentation of habitat is a major threat to songbirds (Taylor and Stutchbury 2016, Rushing et al. 2016, Birdlife International 2018a). On a global scale, natural areas have been destroyed through agriculture, forestry, resource extraction, and urban development (Laurance 2010, Venter et al. 2016, Watson et al. 2018). What little natural habitat remains is often fragmented or degraded (Laurance 2010). Fragmentation increases the proportion of edges and associated edge effects, including easier access by predators (Valentine et al. 2019). Predators such as raccoons (Procyon lotor), crows (Corvus brachyrhynchos), ravens (Corvus corax), and coyotes (Canis latrans) thrive in fragmented and urban habitats where they commonly prey upon songbirds and their nests (Robinson and Wilcove 1994, Valentine et al. 2019). Indeed, songbirds nesting in fragmented areas have lower nest success than those in intact natural areas (Paton 1994). Forest fragmentation also allows for increased rates of nest parasitism by Brown-headed Cowbirds (Molothrus ater, Paton 1994, Robinson and Wilcove 1994, Cox et al. 2012). In the tropics and subtropics, deforestation is resulting in the loss of nesting habitat for endemic species and overwintering habitat for migrant birds (Keller and Yahner 2006, Taylor and Stutchbury 2016). Of concern, the loss of forested habitat on the overwintering grounds is 4 projected to increase until at least 2050 (La Sorte et al. 2017). Carry over-effects of reduced overwintering habitat quality can result in declining breeding populations (Rockwell et al. 2012, Rushing et al. 2016). However, the conversion of tropical forests to eco-friendly agricultural options, such as shade-grown coffee, provides high-quality habitat for some overwintering songbirds (Bakermans et al. 2009, González-Prieto 2018). Anthropogenic sources of songbird mortality and injury include collisions with human-made structures and the introduction of non-native and invasive species. Collisions (most notable during migration) with windows, wind turbines, transmission lines, and other buildings, can result in songbird death or injury (Klem Jr. 2009, Calvert et al. 2013, Brabant and Vanermen 2015, Santos et al. 2017, Hudecki and Finegan 2018). These anthropogenic features on the landscape add obstacles to the airspace in ways that songbirds have not evolved with and therefore it is inherently difficult for birds to learn to avoid these obstacles (Davy et al. 2017). Also, humans have contributed to the spread of non-native species on the landscape, such as rats (Rattus spp.) and domestic cats (Felis catus). These non-native species prey upon songbird nests, nestlings, and adults, contributing to species’ declines and extinctions (Doherty et al. 2016, Loss and Marra 2017). In the U.S. alone, cats are estimated to kill between 1.3 and 4.0 billion birds annually (Loss et al. 2013), and in Canada, cats are estimated to kill 100 to 350 million birds annually (Blancher 2013, Calvert et al. 2013). Moreover, the mere presence of house cats can induce sublethal effects to songbirds via reduced provisioning rates and increased third-party nest depredation events (Bonnington et al. 2013). Environmental contamination can also threaten certain songbird populations, although it is generally not considered a major threat compared to the other sources mentioned above (Rosenberg et al. 2019). Songbirds can be exposed to deleterious substances in the environment and as a result incur negative effects. Pesticides and chemical pollutants, such as DDT, polychlorinated biphenyls, dieldrin, organophosphates, and neonicotinoids can impair 5 reproduction, behaviour, and migration (Wurster Jr. et al. 1965, Risebrough 1986, Hallmann et al. 2014, Eng et al. 2014, 2017, 2019). Heavy metals, such as lead, cadmium, and mercury can negatively affect neurology, immune response, behaviour, body condition, and reproduction in birds (Scheuhammer 1987, Snoeijs et al. 2004, Jackson et al. 2011a, Beyer et al. 2013). Of the heavy metals, mercury is the most well-studied in birds (Jackson et al. 2015, Ackerman et al. 2016, Fuchsman et al. 2017, Heddle et al. 2019). While mercury is a naturally occurring element, industrialization has drastically increased the amount of mercury released into the environment (Driscoll et al. 2007, 2013). Bacteria transform ionic mercury into the bioavailable form known as methylmercury, which is toxic to birds and other wildlife (Driscoll et al. 2007). Songbirds become exposed to mercury by consuming invertebrates: aquatic invertebrates and those of higher trophic levels such spiders pose the greatest risk (Rimmer et al. 2005, Cristol et al. 2008, Evers et al. 2012). In songbirds, mercury is a threat because toxicity affects reproduction, physiology, and neurology (Whitney and Cristol 2017). Another metric of conservation physiology linked to mercury is the hormone corticosterone - the main stress hormone in songbirds (Romero 2004). There is some evidence that mercury alters the normal function of the hypothalamus-pituitary-axis and subsequently the release of corticosterone (Wada et al. 2009, Herring et al. 2012, Moore et al. 2014). When the stress response is activated, a cascade of biochemical reactions ensue, ultimately resulting in increased corticosterone levels in the blood (Wingfield 2013a). This stress response results in increased energy mobilization and the cessation of less critical life processes to overcome the stressor at hand and to promote survival (Sapolsky et al. 2000). Therefore, any malfunction in the stress response, either in the form of hyperactivity (i.e. abnormally high levels of corticosterone) or hypo-activity (i.e. abnormally low levels of corticosterone) results in a bird that is unable to adequately cope with life’s stressors. Chronically elevated levels of corticosterone have negative effects such as weakened immune systems, hypertension, neuronal cell death, 6 and memory loss (Busch and Hayward 2009). Similarly, a suppressed stress response may result in negative fitness consequences (Kleist et al. 2018). Additionally, climate change is an exacerbating factor that compounds ongoing threats to songbirds (North American Bird Conservation Initiative 2010, Rushing et al. 2016). It is estimated that 30% of threatened passerine species have already experienced the negative effects of climate change (Pacifici et al. 2017). Earlier springs have resulted in a mismatch between the timing of reproduction and peak food sources for migrant songbirds, ultimately lowering reproductive success and leading to population declines (Both et al. 2006; Jones and Cresswell 2010, but see Dunn et al. 2011). As the global climate changes, shifts in plant communities including the increased spread of invasive species alter habitat suitability for migrant songbirds (Robinson et al. 2009), which can result in local extinctions (Martin 2007). Wetlands are especially sensitive habitats that are predicted to experience drastic changes due to altered hydrology, plant communities, insect abundance, and nutrient cycling, which will impact birds that rely on these ecosystems (Martin 2007, North American Bird Conservation Initiative 2010, Perry et al. 2012, Capon et al. 2013). Additionally, increased occurrences of unpredictable and severe weather events, such as storms, may be associated with reduced annual survival of migrant songbirds (Huang et al. 2017). 1.4 Monitoring Programs In light of the many threats songbirds face throughout their full annual cycle, a variety of monitoring programs have been employed to identify population trends for North American songbirds. Perhaps the most well-known survey is the Breeding Bird Survey. The Breeding Bird Survey was created in 1966 in response to a lack of broad-scale songbird population estimates (Hudson et al. 2017). The survey involves roadside stops where an observer takes note of all birds heard and seen during a three minute period with surveys occurring between May and July (Hudson et al. 2017). The results of these surveys have been instrumental in assessing 7 songbird population trends and identifying species at risk (Hudson et al. 2017, Sauer et al. 2017, Rosenberg et al. 2019). Another monitoring effort aimed at understanding vital rates of songbirds is the Monitoring Avian Productivity and Survivorship program (MAPS, DeSante 1992). The MAPS program began in 1989 and operates within Canada and the United States. Substantially more effort is required for the MAPS program compared to the Breeding Bird Survey, but much more detailed demographic information can be obtained by using this program (Saracco et al. 2005). Apparent annual survivorship is a vital rate that can be used to assess what proportion of individuals are surviving migration and the overwintering period and returning to breeding sites in subsequent years. Annual survivorship is a key demographic rate that is valuable for effective conservation efforts because it can indicate population health, habitat quality, and life cycle stages at risk (Desante et al. 2005, Saracco et al. 2008). Survivorship is sensitive to short-term and local environmental changes and, therefore, can help elucidate the underlying mechanisms underlying changes in population size (Desante et al. 2005, Saracco et al. 2008). The MAPS protocol involves setting up a minimum of 5 mist-nets once every 10 days during the breeding season for at least 5 sessions (DeSante et al. 2020). Mist nets are common tools used to catch songbirds and are made up of fine, nearly invisible, mesh stretched between two poles; unsuspecting songbirds get trapped and tangled when they fly into the net. The vital rates of songbirds resulting from this program on both a temporal and spatial scale are publicly available at vitalratesofnorthamericanlandbirds.org. The complement of MAPS is the Monitoreo de Sobreviviencia Invernal (MoSI) program in Mexico, which involves five monthly overwinter mist-netting pulses from November to the end of March (Desante and Saracco 2009). Each monitoring pulse occurs over 2-3 days (Desante and Saracco 2009). Like MAPS, MoSI aims to quantify vital rates during the non-breeding period for neotropical migrant birds (Desante et al. 2005, Saracco et al. 2005). 8 Furthermore, one of the newest initiatives to monitor songbirds is eBird (Callaghan and Gawlik 2015, eBird 2017, Walker and Taylor 2017). eBird is an online citizen science platform where users from all over the world can submit bird observations in the form of checklists (eBird 2017). eBird also allows users to submit historical observation records, therefore the breadth of eBird data is massive as it spans both past and current bird sightings on a global scale. This rapidly growing citizen-science initiative is the largest in the world (eBird 2017). These observations allow for a better understanding of songbird distribution, migration timing, habitat use, movement patterns, abundance, and population trends (eBird 2017, Walker and Taylor 2017). 1.5 Migratory Connectivity To fully understand songbird ecology it is imperative to know the movement, spread, and connectedness of bird populations across their full annual cycle (Faaborg et al. 2010, Marra et al. 2015). The various ways in which migratory populations are connected in different phases of the annual cycle has led to specific terminology describing these relationships. Populations with “strong migratory connectivity” show little geographic overlap as most individuals from different populations do not co-occur (Webster et al. 2002; Boulet and Norris 2006, Figure 1.1). Populations with “weak migratory connectivity” are geographically linked as individuals from different populations overlap with one another (Webster et al. 2002, Boulet and Norris 2006). Intermediate levels of migratory connectivity are also possible (Webster et al. 2002). Moreover, strong migratory connectivity can be further categorized based on the movement of individuals between populations (Boulet and Norris 2006). Some species exhibit chain migration, where individuals at the northern part of their breeding range migrate to the northern part of their overwintering range, and individuals at the southern part of their breeding range migrate to the southern part of their overwintering range (Boulet and Norris 2006, Norris et al. 2006). Parallel migration occurs when the migration route of two 9 populations follow similar trajectories but are separated by distance (Boulet and Norris 2006). Leap-frog migration occurs where those at the most northern part of the breeding range overwinter at the most southern part of their overwintering range (Kelly et al. 2002, Newton 2006, Haché et al. 2017). Understanding the connections between populations and migration patterns is important for conservation and management initiatives (Martin et al. 2007, Faaborg et al. 2010, Marra et al. 2015). For example, protecting habitat for a species with strong migratory connectivity will be more straightforward than a population that exhibits weak migratory connectivity (Webster et al. 2002). A large hurdle to describing to the full annual cycle and migratory connectivity is the difficulty in tracking individual birds across large geographic areas. The longest ongoing effort since the 1920s in North America to mark and track songbirds is through bird banding (U.S. Geological Survey 2020), where birds are given an aluminum leg band with a unique number. Unfortunately, the return rate of banded songbirds is only 0.5% because most birds banded are never recovered at locations beyond where they were originally captured (Brewer et al. 2006). Moreover, the vast majority of recaptures occur during migration or on the breeding grounds, and not on the overwintering grounds. Nevertheless, banding data is crucial for estimating survivorship. Calculating apparent annual survivorship is laborious because it requires long-term efforts marking songbirds over many years to tease apart detectability from survivorship estimates. Detectability accounts for the fact that some birds may survive to the following year but are not recaptured (Allen et al. 2002). In some studies, birds are given auxiliary markers such as coloured leg bands that allow birds to be identified visually to confirm that a bird has survived a certain period without the need to be recaptured. The additional effort to colour-band birds and resight birds can improve the accuracy of survivorship estimates beyond standard capture-recapture techniques with numbered aluminum bands (Martin et al. 2017). Therefore, 10 the continued banding of songbirds is valuable in its own right, however, the sample size of re-encounters for tracking individual birds across large geographic areas remains low. 1.6 Advanced Tracking Methods Technological advances in the 21st century have vastly improved methods to track songbirds (Stutchbury et al. 2009, Knight and Norris 2016). Tracking individual birds through space and time is now possible with the use of geolocators (McKinnon et al. 2013), global positioning system (GPS) tags (Fraser et al. 2017), and MOTUS wildlife tracking system tags (Brown and Taylor 2017). Geolocators constantly record ambient light levels and these light levels can be used to infer approximate geographic position (Wilson et al. 1992). GPS tags use satellites to obtain geographic coordinates with high precision at pre-selected times and dates (Lotek Wireless Inc. 2020). Signals from MOTUS tags are received by MOTUS towers (Brown and Taylor 2017). Arrays of MOTUS tower networks can indicate bird movement across the landscape and detect the presence or absence of a bird in a specific area (Taylor et al. 2017). Additional new techniques used to understand bird movement include the examination of intrinsic biochemical signatures of tissues, such as feathers (Chabot et al. 2012, Hobson et al. 2015b, Choi et al. 2020). The use of feathers as intrinsic biomarkers is possible because (1) feathers incorporate the chemical signature of their environment during growth, and (2) for most species I know when and during what stage of the annual cycle feathers are moulted (Pyle 1997). The most commonly used chemical signatures to examine range-wide movements are stable hydrogen isotopes (Hobson et al. 2015b, Toews et al. 2017, Choi et al. 2020). Stable hydrogen isotopes in precipitation vary latitudinally across North America and ultimately become incorporated into feathers (Hobson et al. 2014). Therefore, one can compare the hydrogen isotopic signature of a feather to known calibrated values across the landscape to infer where the feather was grown (Hobson et al. 2014). This method is particularly useful for sampling feathers from birds on the wintering grounds and linking these birds to their breeding location. A 11 second intrinsic signature that can be used to link birds across vast distances is genetics, if a genetic signature varies geographically (Lovette et al. 2004). For example, in some songbird species, the underlying genetic structure in mitochondrial DNA results in different haplotypes among breeding populations that vary geographically (Lovette et al. 2004). Therefore, identifying which haplotype an individual belongs to can be used to assign individuals of unknown origin during the overwintering or breeding life stages to a specific breeding population (Ruegg and Smith 2002, Lovette et al. 2004). However, these advanced techniques do not negate the use of traditional fieldwork methods. Advanced methods reliant on assumptions and models should be field-verified and models can be further improved by the collection of additional data (Elbroch et al. 2018, Ríos-Saldaña et al. 2018). Detailed field notes consisting of sketches, observations, maps, and ideas are at the root of ecology and natural history. Knowledge of the biology of the study species including understanding habitat associations, recognizing songs and calls, and knowing bird behaviour is crucial for obtaining reliable data for songbird conservation biology (Elbroch et al. 2018). Metrics of return rates and survivorship (through bird banding) are needed to design field studies and assess whether deploying individual tracking devices would be worthwhile because in most cases tracking devices need to be retrieved in a subsequent year. 1.7 Conservation Initiatives As the full annual cycle of many songbirds span multiple political boundaries, international collaboration is valuable for forming partnerships to direct bird conservation, monitoring, and research efforts (Boulet and Norris 2006, Nevins et al. 2009, Faaborg et al. 2010). A better understanding of potential threats and limiting factors requires understanding songbird ecology through each stage of the annual cycle (breeding, migration, overwintering). As most research and monitoring on migrant songbirds is focused on the breeding grounds (Sauer et al. 2017), ongoing efforts to better understand the overwintering and migration stages 12 of North American breeding songbirds entails forming partnerships outside of Canada and the United States. Fieldwork spanning multiple political boundaries poses many challenges but recruiting local biologists who know the field sites, culture, language, and bird ecology can remedy some issues and form strong partnerships to advance bird conservation. Several bird conservation initiatives exist in North America that aim to bridge the political and geographic gap in understanding and conserving migratory songbirds. Two exemplary ones include the North American Bird Conservation Initiative and Partners in Flight. The North American Bird Conservation Initiative (2020) is a partnership between Canada, the United States, and Mexico that began in 1999 to conserve birds and their habitats. Similarly, Partners in Flight (2020) is an initiative that was formed in 1990 to keep common birds common and to reduce the declines of songbirds at risk (Alexander 2011, Rosenberg et al. 2016,). The ideology behind this approach is to ensure songbirds remain abundant to support ecosystem function and healthy habitats (Rosenberg et al. 2016). Songbirds are indicators of ecosystem health and therefore monitoring common songbirds can enable scientists to detect a decline that may further signal a disruption to the ecosystem (Rosenberg et al. 2016). Partners in Flight consists of a network of 150 partner organizations and supports all aspects of songbird conservation, using a science-based approach to address bird populations, notably the conservation of birds across their full annual cycle (Rosenberg et al. 2016, Partners in Flight 2020). In conjunction with these broad-scale bird monitoring initiatives, local and regional programs play a further role in songbird conservation. Often, local initiatives allow for long-term population monitoring and research-specific goals to be undertaken. My Ph.D. dissertation takes advantage of collaborations and initiatives at various scales. 1.8 Collaborations My Ph.D. dissertation is founded upon international partnerships spanning Canada, the United States, and Mexico and involved efforts and funding from government, non-government 13 organizations, universities, and local unaffiliated biologists and volunteers. My Ph.D. research also builds upon data collected before I joined the project in 2016. A summary of the collaborative efforts and how they fit into my research are elaborated on for clarity. • Environment and Climate Change Canada (Delta, British Columbia, Canada): Dr. Christine Bishop, Rene McKibbin, Andrew Huang from Environment and Climate Change Canada and Tim Forrester from Simon Fraser University have been monitoring and researching riparian songbirds in the south Okanagan Valley of British Columbia, Canada, for many years (Morgan et al. 2007, 2017; Mckibbin and Bishop 2008; McKibbin and Bishop 2012; Forrester 2015; Huang et al. 2017; Forrester et al. 2017). Specifically, Yellow-breasted Chats have been monitored since 2001, with datasets involving detailed territory mapping and a large colour-banded population. Geolocators were deployed on Yellow-breasted Chats in 2013-2014. Likewise, Gray Catbirds have been monitored since 2002, with a large colour-banded population. Geolocators were deployed on Gray Catbirds in 2014 – 2015. • Barry Lancaster, unaffiliated biologist, (Oliver, British Columbia, Canada): Barry has been banding birds along the Okanagan River in Oliver, British Columbia, Canada for many years and has colour-banded hundreds of Gray Catbirds for my research project. Barry’s dataset of colour-banded Gray Catbirds and associated recaptures were added to the larger dataset to estimate survivorship for Gray Catbirds in the South Okanagan Valley. • The En’owkin Centre (Penticton, British Columbia, Canada): Michael Bezener, Richard Armstrong, and Harron Hall collaborated with Environment and Climate Change Canada on Gray Catbirds and Yellow-breasted Chat research on Penticton Indian Band land. All aspects of Yellow-breasted Chat and Gray Catbird monitoring and research occurred at 14 their study site, including deploying geolocators, GPS tags, colour-banding birds, and collecting feather samples. • Klamath Bird Observatory (Ashland, Oregon, USA): I collaborated with Dr. Sarah Rockwell and Dr. John Alexander from Klamath Bird Observatory who operate the Klamath Monitoring Network. The Klamath Monitoring Network is extensive and operates in collaboration with various land management stakeholders (Alexander 2011). For example, comprehensive monitoring has occurred in southern Oregon and northern California with methods including count surveys, mist-netting, colour-banding, and territory monitoring (Alexander et al. 2004, Rockwell et al. 2017, Rockwell and Stephens 2018). Geolocators were deployed on Yellow-breasted Chats in California along the Trinity River in 2014 – 2016 for my project. Likewise, feather samples from Yellow-breasted Chats were collected for my project in California and Oregon for genetic, stable isotope, mercury, and corticosterone analyses. • University of British Columbia Okanagan (Kelowna, British Columbia, Canada): My research was based out of this institution under my supervisor Dr. Karen Hodges, but I also had additional collaborators at this university. The development of novel mitochondrial DNA genetic assays was performed by Dr. Michael Russello and Jessica Castellanos-Labarcena. This approach allowed us to assign to Yellow-breasted Chat feather samples from Mexico to the eastern or western subspecies. • MPG Ranch (Florence, Montana, USA): MPG Ranch serves as a large conservation property purchased in 2009 in the Bitterroot River Valley of western Montana, USA (MPG Ranch 2020). Research at MPG Ranch focuses on restoration, conservation, and wildlife ecology ( Sawaya et al. 2017; Mummey et al. 2018; Proffitt et al. 2019). • University of Montana (Missoula, Montana, USA): Megan Fylling at the University of Montana worked with Dr. Kate Stone at the MPG Ranch to study Gray Catbirds. Gray 15 Catbirds were tracked using geolocators and GPS tags at MPG Ranch. Similarly, Gray Catbird populations have been monitored through colour-banding and MAPS efforts. Therefore, the collaboration between MPG Ranch and the University of Montana on Gray Catbirds broadened the scope of my Gray Catbird research and was added as a separate study population. • San Pancho Bird Observatory (San Francisco, Nayarit, Mexico): A pilot overwintering bird monitoring effort began in San Francisco, Nayarit, Mexico in 2017 in partnership with Dr. Christine Bishop from Environment and Climate Change Canada, Dr. John Alexander from Klamath Bird Observatory, Dr. Sarahy Contereras from the Universidad de Guadalajara-CuCostaSur (Autlán, Jalisco, Mexico) and Luis Morales from San Pancho Bird Observatory. In the first few years of the study, mist-nets were haphazardly set up to capture Yellow-breasted Chats. Yellow-breasted Chats were colour-banded and feathers were taken for my project for genetic, stable hydrogen isotope, mercury, and corticosterone analyses. Ongoing efforts have continued here and a MoSI station now occurs at this site. I used this bird banding data set for the return rate calculations for Yellow-breasted Chats on their overwintering sites. • Tierra de Aves (Minatitlán, Veracruz, Mexico): Bird bander Manuel Grosselet with Tierra de Aves was instrumental in collecting Yellow-breasted Chat feathers for my project. Manuel collected hundreds of feathers from migrating chats during bird banding efforts in Veracruz, Mexico in 2014 – 2015. Additionally, Manuel operated another bird banding migration station in Chiapas, Mexico in 2018, where I joined him and his team to collect additional Yellow-breasted Chat simples. These feather samples were used for genetic, hydrogen isotope, mercury, and corticosterone analyses. 16 1.9 Study Species The two focal species in my dissertation are the Gray Catbird and the Yellow-breasted Chat. The Gray Catbird is a 37-gram migrant songbird that is considered of least conservation concern globally (Sibley 2014, Birdlife International 2016). The Gray Catbird is a common species that has been increasing slightly in their breeding range at 0.29% per year between 1993 – 2015, as indicated by Breeding Bird Surveys (Sauer et al. 2017). Taxonomically, the Gray Catbird is in the Mimidae family, which is an understudied family in North America (England and Laudenslayer 1993, Alberta Sustainable Resource Development 2004, Cavitt and Haas 2004, Farnsworth et al. 2011, but see Reynolds 1999 for Sage Thrasher [Oreoscoptes montanus]). Gray Catbirds occupy scrubby, dense, early successional edge, and riparian habitats (Smith et al. 2020). Their breeding range occurs primarily in the central and eastern United States, and southern Canada, but western populations extend into Montana and Idaho (Figure 1.2, IUCN 2016a). The overwintering range of Gray Catbirds includes Florida, southeastern Texas, southeastern Louisiana, eastern Mexico, and Central America (IUCN 2016a). The migration ecology and migratory connectivity of Gray Catbirds have been studied in detail only in the eastern portion of their breeding range (Ryder et al. 2011); populations breeding in the mid-Atlantic overwinter in Cuba and Florida, and populations breeding in the Midwest overwinter in Central America (Ryder et al. 2011). The migration ecology of western populations remains to be explored in depth. The only study linking birds in the western portion of their range used stable hydrogen isotope analyses of overwintering catbirds in Tamaulipas, Mexico, and found that these catbirds originated from the northwestern portion of their breeding range (Hobson et al. 2014). However, beyond that one study, migration routes, apparent annual survivorship, and migratory connectivity for western populations of catbirds are unknown. A better understanding of migratory connectivity in this species is desirable for management efforts (DeSante et al. 2015). 17 The Yellow-breasted Chat is a 25-gram neotropical migrant and like the catbird, also inhabits and nests in dense, scrubby areas often in proximity to riparian habitats (Sibley 2014, Eckerle and Thompson 2020). Yellow-breasted Chats were originally misclassified as a member of the Parulidae (warbler) family, but recently were reclassified to their own family, the Icteriidae family (Barker et al. 2015, Chesser et al. 2017). There are two subspecies of Yellow-breasted Chat (Pyle 1997). The eastern subspecies (I.v. virens) breeds in the eastern USA and southern Ontario (Figure 1.3, IUCN 2016b). The western subspecies (I.v. auricollis) breeds in the western USA and southern British Columbia, Alberta, and Saskatchewan, Canada (IUCN 2016a). Like the Gray Catbird, the Yellow-breasted Chat is also considered globally stable and of least concern (Birdlife International 2018b). Unlike the catbird, several Canadian populations are federally listed as endangered (Environment and Climate Change Canada 2016, 2019). Notably, the population breeding in British Columbia is endangered (Environment and Climate Change Canada 2016). Low population sizes are due to the historical loss of riparian habitat in part due to channelization of the Okanagan River (Environment and Climate Change Canada 2016). This population occurs in the South Okanagan Valley, Similkameen Valley, and eastern British Columbia (Environment and Climate Change Canada 2016). The recovery strategy for this species in British Columbia outlines the need to understand threats during migration and on the overwintering grounds (Environment and Climate Change Canada 2016). Therefore, detailed knowledge of the migration routes and overwintering locations for this population is desired. Likewise, given the affinity of chats for riparian habitats, they are potentially at risk of elevated mercury exposure, which could also influence their stress-response system. Therefore, probing deeper into Yellow-breasted Chat physiology is warranted, especially for the endangered British Columbia population. 18 1.10 Data Chapters Tying everything together, my Ph.D. research aims to better understand the full annual cycle of two songbird species, the Yellow-breasted Chat and the Gray Catbird. Additionally, I aim to address on a range-wide scale to what extent breeding Yellow-breasted Chat populations are at risk of mercury toxicity and evaluate their corticosterone levels. To undertake this research, I use a combination of traditional field methods combined with the latest technology. I incorporate data from local, regional, and international monitoring efforts. For the second chapter of my dissertation, I examine the full annual cycle of Gray Catbirds from two breeding populations in the western portion of their range. The objectives of this research are to (1) describe migration routes; (2) identify overwintering locations; (3) assess migratory connectivity; and (4) compare apparent annual survivorship for both populations. My two focal Gray Catbird populations are in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA. I track individual birds using new technologies: light-level geolocators and GPS tags. Additionally, I plot eBird abundance data to trace the movement of Catbirds across the landscape during migration. I also use long-term banding and resighting datasets to assess survivorship. For the third chapter of my dissertation, I examine the migration ecology of Yellow-breasted Chats across multiple populations. The research objectives are to (1) determine migration routes, overwintering locations, and survivorship for endangered chats breeding in British Columbia, Canada; (2) identify overwintering locations for chats breeding in California, USA; (3) infer breeding locations and return rates for overwintering chats in Nayarit, Mexico; and (4) infer breeding locations for chats migrating through Chiapas and Veracruz, Mexico. To track birds, I used new technologies on breeding birds, including geolocators and GPS tags. To better understand habitat use and to assess whether chats overwinter in protected areas, I overlay satellite imagery and the protected areas of Mexico onto maps of 19 overwintering locations. I also use biochemical signatures of feathers (stable hydrogen isotopes, and genetics) to infer breeding locations from overwintering and migrating chats. I use bird banding encounter data to further examine range-wide movement patterns. Moreover, I examine survivorship and return rates using datasets of colour-banded populations. In the fourth chapter of my dissertation, I examine mercury and corticosterone levels in the feathers of Yellow-breasted Chats on a range-wide scale. The research objectives were to: (1) visualize and identify areas of high risk of mercury toxicity; (2) examine how age, sex, subspecies, or range position affect mercury levels on a range-wide scale; (3) examine how age, sex, subspecies and range position affect corticosterone on a range-wide scale; and (4) assess if mercury and corticosterone are correlated. Mercury encapsulated in feathers represents recent dietary exposure as well as the body burden mobilized from other organs and tissues (Scheuhammer 1987, Thompson et al. 1991, Evers et al. 2005). Similarly, corticosterone encapsulated in feathers represents levels of the hormone during the period of feather growth (Bortolotti et al. 2008). In Yellow-breasted Chats, feathers are grown on the breeding grown before fall migration (Pyle 1997, pers. obs.), thus, using feathers allows for a broad examination of conservation physiology across the chat breeding range. The results of genetics and stable hydrogen isotope analyses from chapter 3 are used again for assignment locations for a range-wide picture of corticosterone and mercury. In the fifth chapter of my dissertation, I summarize the outcomes of the research objectives for each data chapter. I describe how the conservation strategies incorporating full annual cycle habitat use vary between Gray Catbirds and Yellow-breasted Chats based on my results. I comment specifically on how my research is valuable for the conservation and recovery of the endangered population of Yellow-breasted Chats breeding in the South Okanagan Valley of British Columbia, Canada. Lastly, I outline the conceptual advances of the results of my research in the fields of migration ecology and conservation physiology. 20 Figure 1.1. Schematic illustration depicting strong and weak migratory connectivity. Circles represent distinct songbird populations, and circles of the same shade represent the same stage of the annual cycle. Arrows indicate the direction of bird movements. In scenario A, there is strong migratory connectivity as birds from different populations never mix in either stage of the annual cycle. In contrast, in scenario B, there is weak migratory connectivity in that individuals from one population spread out and overlap with individuals from other populations. Figure 1.2. Range map of the Gray Catbird (IUCN 2016a). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 21 Figure 1.3. Range map of the Yellow-breasted Chat (IUCN 2016b). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 22 Chapter 2: Migration ecology of western Gray Catbirds 2.1 Introduction The decline of North American songbirds is an ecological issue of concern in the Anthropocene (Sauer et al. 2017, Rosenberg et al. 2019). There are major knowledge gaps in the ecology of most migratory bird species, with deficits evident in the overwintering grounds and migration because most research is focused on breeding birds (Lovejoy 2007, Carlisle et al. 2009, Marra et al. 2015, Sauer et al. 2017). The link through space and time between breeding and overwintering periods is important to address, in part because the effects in one season can influence events in another season, such as habitat quality influencing body condition and migration timing (Marra and Holberton 1998, Marra et al. 2015). The full annual cycle of migrant songbirds includes low-movement phases during breeding and non-breeding, as well as a high-movement phase during spring and fall migration. To identify potential threats throughout the full annual cycle, it is important to understand where, when, and how long a bird is present at each stage of the cycle (Webster et al. 2002, Faaborg et al. 2010). Therefore, we must identify migration routes, stopover sites, and overwintering locations to direct conservation efforts. Addressing gaps in understanding the full annual cycle and survivorship of songbirds should not be limited to only those species that are already at risk of extinction (Marra et al. 2015). Indeed, keeping common species common is a mantra among bird conservation groups such as Partners in Flight and the Montana Bird Conservation Partnership (Rosenberg et al. 2016). Common species are often more feasible to study than species already at low numbers. Moreover, it is valuable to understand the ecology of common species to be better equipped if a decline does occur. 23 The degree to which different populations of the same species are connected to each other is central to our ability to manage them. Migratory connectivity refers to how populations are geographically linked to one another through different phases of the annual cycle (Webster et al. 2002). Populations with strong migratory connectivity show little geographic overlap at any time of the year, as individuals from different populations do not co-occur (Webster et al. 2002, Boulet and Norris 2006). Conversely, populations with weak migratory connectivity are geographically linked as individuals from different breeding populations intermingle with one overwinter (Webster et al. 2002). An understanding of the strength of migratory connectivity is important for the conservation and management of songbirds and in understanding the evolution of migratory behaviour (Boulet and Norris 2006). The degree and type of migratory connectivity between populations influences the extent to which a threat might impact a species (González-Prieto et al. 2017, Finch et al. 2017). For example, a threat on the overwintering grounds will have a greater effect on species with strong migratory connectivity as an entire breeding population may be affected, whereas the effect may be diffused among multiple breeding populations if a species is characterized by weak migratory connectivity (Webster et al. 2002). However, it may be more challenging to conserve all sites used across the annual cycle for populations with weak migratory connectivity. Geographic barriers such as mountains may influence migratory behaviour and drive genetic divergence resulting in separate species or subspecies (Milot et al. 2000, Boulèt et al. 2006). In North America, the Rocky Mountains span from northern British Columbia to New Mexico for 4,800 km with peaks exceeding 4,000 meters above sea level (Marston and Eardley 2019). The movement of birds in North America between breeding and overwintering areas generally runs north-south and the Rocky Mountains act as a natural barrier separating the Pacific and Central Flyway (Newton 2006). Most bird species breeding west of the mountains migrate using the Pacific Flyway, whereas most birds east of the mountains follow the Central 24 Flyway (Vermeer 1977, Toews et al. 2017, Knight et al. 2018, Lisovski et al. 2019), but, some bird species do not follow either of these migratory patterns (Delmore et al. 2012, Hobson et al. 2015a). myNotably, the use of geolocators and GPS tags to track birds has shown that members of the Thrush family, Veery (Catharus fuscescens) and Swainson’s Thrush (C. ustulatus), breeding in central and coastal British Columbia cross over the Rocky Mountains to migrate to South America using the Central Flyway (Delmore et al. 2012, Hobson et al. 2015a, Kardynal and Hobson 2017). Survivorship is crucial to understanding to what extent individuals are surviving migration and winter and returning to breed, and often requires monitoring marked individuals over many years. Survivorship is a key demographic rate that is valuable for effective conservation efforts as it can indicate the health of a population, life cycle stages at risk, and identify high-quality habitat(DeSante et al. 2005, Saracco et al. 2008). Additionally, survivorship is sensitive to short-term and local environmental changes and, therefore, and can help elucidate the underlying mechanisms to changes in population size(DeSante et al. 2005, Saracco et al. 2008) Birds of the Mimidae family are generally understudied in North America (England and Laudenslayer Jr. 1993, Blouin 2004, Farnsworth et al. 2011, Cavitt and Haas 2014 but see Reynolds et al. 1999 for Sage Thrasher [Oreoscoptes montanus]). The only species within the Mimidae family whose migratory behaviour has been studied in detail is the Gray Catbird (Dumetella carolinensis) but only in the eastern portion of its extensive North American range (Ryder et al. 2011). Gray Catbirds are not of conservation concern globally or locally (Siddle 2015, Marks et al. 2016, Birdlife International 2020), and estimated gains of 748 542 breeding individuals have occurred between 1970 – 2017 (Rosenberg et al. 2019). A better understanding of migratory connectivity for Gray Catbirds is important for successful management efforts (DeSante et al. 2015). The breeding range of Gray Catbirds is predominantly concentrated throughout eastern and central North America, but populations also 25 occur as far west as British Columbia, Montana, and Idaho (IUCN 2016b). The known overwintering range of Gray Catbirds includes Florida, southeastern Texas, southeastern Louisiana, eastern Mexico, and Central America (IUCN 2016b). In the eastern and central portion of the breeding range, Ryder et al. (2011) found catbirds breeding in the mid-Atlantic overwintered in Cuba and Florida, and those breeding in the Midwest overwintered in Central America. The migration ecology of western Gray Catbirds, including how they travel through or around the Rocky Mountain Range, remains to be explored. Phillips et al. (1964) postulated that western Gray Catbirds may migrate east before heading south, implying that these birds have to cross the Rocky Mountains. Similarly, Hobson et al.'s (2014) hydrogen isotope analyses of catbirds linked overwintering birds in Mexico to inferred breeding sites located in the northwestern part of their breeding range but could not illuminate details about migration behaviour and routes. In this study, I examined the migration behaviour and estimated annual survivorship of catbirds breeding west of and within the Rocky Mountain Range. My goals were to: (1) track migration routes; (2) identify stopover sites; (3) determine overwintering sites; (4) assess migratory connectivity; and (5) compare apparent survivorship for Gray Catbirds breeding in western Montana, USA and southern British Columbia, Canada. If these two breeding populations exhibit strong migratory connectivity, I would expect these two separate breeding populations to have non-overlapping wintering sites. Conversely, if these two breeding populations exhibit weak migratory connectivity, I would expect the two populations to have some overlap on the wintering grounds. My research will also expand current life history information to help ensure this species remains abundant on the landscape. 26 2.2 Methods 2.2.1 Study Areas I examined breeding Gray Catbirds in two locations: (1) the South Okanagan Valley, British Columbia, Canada (49.200° N, 119.552° W), and (2) the Bitterroot River Valley, Montana, USA (46.668° N, 114.023° W). These two study areas are 495 km apart, with the South Okanagan Valley population occurring west of the Rocky Mountains and the Bitterroot River Valley population occurring within the Rocky Mountains. Catbirds are locally abundant in both study sites. 2.2.2 Tracking Devices Gray Catbirds were captured in mist-nets passively or with the aid of call-playback in the breeding seasons from 2013 to 2018. Each bird was given a standard USGS band and a unique combination of colour-bands to aid in future identification. Birds were aged and sexed according to Pyle (1997) Adult male and female catbirds were outfitted with an M-Series light-level geolocator, Lotek, Newmarket, Canada (mass between 0.3 – 1.0g, hereafter “geolocator”) or PinPoint GPS tag, Lotek, Newmarket, Canada, (mass 1.0g, hereafter “GPS tag”, (British Antarctic Survey and Biotrack 2012, Lotek Wireless Inc. 2020). Tracking devices were fitted onto each bird by creating a leg-loop harness made of stretch magic string. Both kinds of devices were retrieved in subsequent breeding seasons by targeting individuals identified by their unique colour-band combination. Captured birds had the harness and device removed, and each bird was released unharmed. I deployed 39 geolocators and 21 GPS tags in the South Okanagan Valley, and 32 geolocators and 52 GPS devices in the Bitterroot River Valley. The average accuracy (SD) of the M-Series geolocator is 185 (115) km, but many factors may affect the accuracy of geolocators, including shading, clouds, sensor degradation, and artificial lights (Biotrack Limited 2013). The shrubby riparian habitat inhabited by Gray 27 Catbirds may be an additional source of shade and further limit geolocator accuracy. The accuracy of GPS tags varies depending on the number of satellites available during a scheduled fix, however, in all cases should be better than 300 m (Lotek Wireless Inc. 2018). I tested the GPS tags before deployment in the South Okanagan Valley at a stationary location and found that 90% of the points were within 100 m of the test location, and many within a few meters. GPS tags were pre-programmed to obtain GPS coordinates at specific dates, which varied by location and year but generally were one to five days apart during migration and 10 - 30 days apart during winter. 2.2.3 Repeat Tracking Three Bitterroot River Valley birds were tracked in multiple years with separate devices. One female catbird was given a geolocator in 2012, another geolocator in 2013, and a GPS tag in 2014. One male catbird was given a geolocator in 2013 and a GPS tag in 2014. Another male catbird was given a GPS tag in 2016 and another GPS tag in 2017. 2.2.4 Analyses of Tracking Devices Geolocator files (.lig) were downloaded and light data were processed using R (v 3.5.1; R Core Team 2018). Methods for the analyses of the geolocators followed Lisovski et al. (2020). I used the GeoLight (v. 2.0.1; Lisovski and Hahn 2012), TwGeos (v.0.1.2; Lisovski et al. 2016), and adehabitatHR (v. 0.4.16; Calenge 2006) packages. Twilights were determined using the preprocessLight function in the TwGeos package using a threshold value of 0.5 because there did not appear to be any nighttime light interference. Twilights were edited using the twilightEdit function from the TwGeos package (with window set to 4, outlier mins 45, stationary.mins 25). Overwintering dates were defined as November 15 – March 1 to avoid the equinox by 3 weeks and to include times when birds would be on their wintering grounds (Smith et al. 2020). The Hill-Ekstrom method was used to determine the sun elevation angle during winter months using 28 the findHEZenith function from the TwGeos package (Hill and Braun 2001, Ekstrom 2004). The Hill-Ekstrom method determines the correct sun elevation angle for stationary periods at unknown locations by determining a sun elevation angle that minimizes variance in latitude estimates (Lisovski et al. 2012). Winter latitude and longitude estimates were calculated using the coord function of Geolight. To summarize the overwintering areas used by Gray Catbirds, 50% Kernel Density estimates were calculated from coordinates using the kernelUD function from the adehabitatHR package. A shapefile compatible with ArcMap 10.7.1 (ESRI 2019) was created by using the getverticeshr function. The location coordinates from the GPS tags were vetted by removing points that had low accuracy, defined as those with dilution of precision values greater than 20 (Lotek Wireless Inc. 2018). The remaining points were plotted using ArcMap 10.7.1. Points were connected to create migration tracks and to calculate distances. Stopover sites were identified when 2 or more consecutive fixes occurred at the same location during migration in September and early October. Overwintering locations were defined as the first stationary period of consecutive fixes in the known overwintering range. However, for the older generation GPS tags where 8 or fewer fixes were obtained, I assume that the points occurring from mid-October onwards correspond to overwintering locations, as most GPS-tagged birds from both sites had reached their overwintering sites by early October. The results of the geolocators and GPS tags were overlaid on Gray Catbird range maps (IUCN 2016b) using ArcMap 10.7.1. To visualize travel routes around or within the Rocky Mountains, tracks and points were overlaid on a digital elevation model (DEM in ArcMap 10.7.1. The specific DEM was the 30 arc-second DEM of North America (ESRI 2019). To better contextualize habitat and elevation at stopover sites and overwintering sites, I overlaid satellite imagery using ArcMap 10.7.1 for each catbird during stopovers and in the non-breeding period. 29 2.2.5 eBird Data To assess general trends of peaks in relative abundance across states during migration, I obtained eBird weekly Gray Catbird abundance data. eBird is a citizen science initiative to which users can submit observations of birds from all over the world (Sullivan et al. 2009). I used eBird to examine weekly Gray Catbird abundance between 1900 - 2019 for states in the Pacific Flyway (Washington, Idaho, Oregon, California, Utah, and Nevada) and states of interest within the Central Flyway (Montana, Wyoming, Nebraska, Kansas, Oklahoma, Arkansas, and Texas. Data were downloaded on November 21- 22, 2019. Abundance was defined as the average number of individuals reported on all checklists during a given week over all years (including those checklists without Gray Catbirds). Abundance data for each week for each state was plotted on an overlapping line graph separately for the Pacific and Central Flyway. 2.2.6 Migratory Connectivity Migratory connectivity was quantified with a Mantel’s correlation (rM) between breeding and overwintering locations (Mantel 1967). I compared the geographic distances between all individuals on the breeding ground to the geographic distances between all individuals on the overwintering grounds. All catbirds with GPS tags from both sites were included in the matrices. A significant positive correlation in the distances between breeding individuals and overwintering individuals suggests strong migratory connectivity (Ambrosini et al. 2009). I used the coordinates of the first overwintering site for consistency in all analyses. Matrices were created using the r.dist.earth function of the fields package (v.9.8.6; Nychka et al. 2017). in R. The Mantel correlation coefficient was calculated using the mantel.rtest function of the ade4 package (v1.7.13; Dray and Dufour 2007). A P-value associated with the correlation coefficient was calculated based on 9,999 random permutations. To further examine the geographic spread of individuals, pairwise distances between all individuals within each site were calculated separately for breeding and overwintering locations. 30 The Mantel’s correlation coefficient was a better fit than the migratory connectivity metric proposed by (Cohen et al. 2018) for my data for 2 main reasons: (1) GPS coordinates provide high accuracy in location estimates and therefore uncertainty is negligible; and (2) the migratory connectivity metric requires the user to predefine clusters or groups of observations, and such data were not easily identifiable for my partially overlapping winter locations. 2.2.7 Apparent Annual Survivorship I included both recaptured and resighted birds in my apparent annual survivorship analyses. In the South Okanagan Valley, the data set spanned 2015 to 2019. Bird banding involved 2 separate efforts, (1) intermittent target and passive mist-netting in June-August in conjunction with deploying and retrieving tracking devices; and (2) a standardized approach once a week between May 1-September 15 using 10 nets in a fixed location for 6 hours. Passive and target netting involved setting up 1-8 nets from before sunrise to early afternoon at the latest. Resighting effort and target netting to identify colour-banded individuals occurred intermittently (~1-3 times a week) between May-August where 1-3 biologists searched for birds using binoculars and confirmed colour-band combinations using primarily high-zoom digital cameras but on occasion only with binoculars. Effort was increased during the last year of the study in 2019, where at least 5 days a week, catbirds were resighted or recaptured from the end of May to early July. In the Bitterroot River Valley, the dataset spanned 2006 to 2018 and included additional banding data from nearby sites. Additional nearby sites included 8 Monitoring Avian Productivity and Survivorship Protocol sites (MAPS, DeSante 1992) plus one more site where banding occurred intermittently for educational purposes (~5 times). The MAPS protocol involves setting up 10 mist nets for 6 hours a day spread out for 7 sample periods during the breeding season (DeSante et al. 2020). Effort in the Bitterroot River Valley also included MAPS banding from 2011-2018. Resighting effort occurred between 2012-2018 and included 1-3 technicians 31 working most weekdays between May 25-July 15 from sunrise to approximately 11:30 AM to resight and target net colour-banded catbirds. Apparent annual survivorship of Gray Catbirds in the South Okanagan Valley and the Bitterroot River Valley were calculated using RMark (v. 2.2.7; Laake 2013) in R. Cormack-Jolly-Seber models (Cormack 1964, Schwarz 2001) were used, which includes the apparent annual survivorship (ϕ) and detection probability (p) as model parameters. Assumptions of Cormack-Jolly-Seber models are that (1) every bird has the same probability of being recaptured/resighted at the next sampling period; (2) every bird has the same probability of surviving to the next sample period; (3) Colour-bands are not lost or missed, and; (4) sampling time is short (or instantaneous) relative to the interval in between sampling times. To assess the goodness-of-fit of my data to the assumptions of Cormack-Jolly-Seber models I used the program RELEASE via RMark using the function release.gof. Goodness-of-fit was assessed by examining the fit of the global model. Because I was comparing the fit of multiple models with varying parameters, I used the model with the highest number of parameters as the global model, which contained sex as a covariate. In order to specifically test assumptions (1) and (2), time was included as a covariate in the global model. There was no evidence of a lack of fit of the global model to Cormack-Jolly-Seber model assumptions (χ2 = 27.35, df = 25, P-value = 0.34). Birds of all ages (except nestlings) were used in the analyses. Sex was included as a covariate for both survivorship and detection. To account for potential local differences between sites within each study area, site was included as a covariate. Because the first encounter is likely to include young or transient birds who are more likely to permanently leave the study area through death or immigration, a time-since-marking approach was included that separates the first encounter from subsequent encounters (Pradel et al. 1997, Allen et al. 2002). Multiple models that contained different combinations of covariates were compared using an Akaike 32 Information Criteria (AIC) approach adjusted for small sample sizes (Table 1, Burnham and Anderson 2002). I considered models with ΔAICc values of less than 2 to have substantial support (Burnham and Anderson 2002). I also calculated AICc weight and considered any model with a weight of greater than 0.90 as a clear top model (Burnham and Anderson 2002). For all analyses, mean and standard error are reported. 2.3 Results In the South Okanagan Valley, the return rate of birds with geolocators was 8 out of 39 (20.5%), and 7 geolocators were retrieved. The return rate of catbirds with GPS tags was 6 out of 20 (30.0%), excluding one deceased catbird found in the same year of deployment. All 6 GPS tags were retrieved. I experienced poor performance in the geolocator technology. One geolocator had light data for only 2 months, but all other geolocators contained data for the full year, resulting in a partial device failure rate of 1 in 7 (14.3%). All GPS tags contained data but fewer than the maximum number of 80 fixes. Light levels recorded on the geolocators were low and inconsistent, making the determinations on sunrises and sunsets spurious (Appendix A). All location estimates were severely outside known overwintering distributions (Appendix A). In the Bitterroot River Valley, the return rate of birds with geolocators was 10 out of 32 (31.3%), and 6 geolocators were retrieved. The return rate of catbirds with GPS tags was 18 out of 52 (34.6%), and 14 GPS tags were retrieved. All geolocators contained usable data, but 2 GPS tags did not contain usable data. Therefore, the device failure rate for GPS tags was 2 out of 14 (14.3%). 2.3.1 Migration Routes and Stopover Sites The migration route revealed from GPS tags showed catbirds from both locations heading east across the Rocky Mountain Range, then south towards overwintering areas in Mexico and Texas (Figure 2.1). While navigating the Rocky Mountain Range, Gray Catbirds from the South Okanagan Valley traveled through low elevation corridors (Figure 2.2). The 33 timing of fixes for Bitterroot River Valley Gray Catbirds began too far east of the Rocky Mountain Range to assess their specific paths through the mountains. In 5 cases from the Bitterroot River Valley, I have partial or full spring migration. These birds followed the same general routes during fall migration as spring migration (Figure 2.3). The speed of spring and fall migration were similar (t = 1.2, df = 10, P-value = 0.25, 95% CI: -4.6, 15.9; Table 2.1). No spring migration route location data was available from catbirds that had bred in the South Okanagan Valley due to battery exhaustion of the GPS tags. From eBird data, abundance is low or zero across states within the Pacific Flyway with no peaks evident during migration. In contrast, abundance peaks during spring and fall migration across the states spanning the migration route of Gray Catbirds (Montana, Wyoming, Nebraska, Kansas, Oklahoma, Arkansas, Texas). The timing of each peak in each state generally traces the migration route as determined from GPS tags (Figure 2.4). Fifteen fall migration stopover sites were identified for 6 South Okanagan Valley Gray Catbirds and 5 fall migration stopover sites for 5 Bitterroot River Valley Gray Catbirds (Figure 2.3). One spring stopover site was identified in Texas from one Bitterroot River Valley Gray Catbird (Figure 2.3). None of the tagged Gray Catbirds stopped at the same stopover site, and the closest sites were 24.7 km apart. Stopover sites for South Okanagan Valley Gray Catbirds were in Montana, Kansas, Oklahoma, Missouri, and Arkansas (Figure 2.3). Stopover sites for Bitterroot River Valley Gray Catbirds were in Kansas, Oklahoma, and Arkansas. The amount of time spent at each stopover site ranged between a minimum of 1 to 16 days. From satellite imagery, apparent habitat type at stopovers generally including patches of trees or shrubby habitat, often within 500 m to water (Appendix B). The exception to the pattern of riparian habitat use was one stopover in an urban environment. The elevation of stopover sites varied widely, from 74 to 1060 meters above sea level. 34 2.3.2 Overwintering Sites Gray Catbirds from both capture locations overwintered in overlapping areas in Texas and Mexico. Gray Catbirds breeding in the South Okanagan Valley overwintered in Texas, Tamaulipas, San Luis Potosi, Hidalgo, and Nuevo Leon, Mexico (Figure 2.4 and Table 2.4). Gray Catbirds that bred in the Bitterroot River Valley overwintered in Texas, San Luis Potosi, Tamaulipas, Veracruz, Puebla, and Hidalgo. Tamaulipas was the most used site by birds from both study areas. South Okanagan Valley Gray Catbirds traveled an average of 4048 ± 193 km to their overwintering sites, and Bitterroot River Valley Gray Catbirds traveled 3604 ± 76 km to their overwintering sites. Overwintering location estimates from birds with geolocators were less precise than GPS tags but also encompassed San Luis Potosi, Tamaulipas, and Veracruz (Figure 2.6). One exception is a Gray Catbirds with a geolocator whose overwintering estimate was centered over Oaxaca, Mexico, and the Pacific Ocean, although this overwintering location appears suspect (Figure 2.7). Several Gray Catbirds from both breeding populations used more than one overwintering location, with up to four distinct overwintering sites documented (Table 2.4). Each subsequent overwintering location was farther south than the previous one, with the exception of one Gray Catbird who moved slightly farther north for his second overwintering site, then south for the subsequent sites. Distances between overwintering sites ranged from 5 to 353 km. The length of stay at any one overwintering site ranged from 17 to 169 days. Overwinter, Gray Catbirds occupied natural areas with dense vegetative cover, often in association with edges or riparian habitats, at elevations of 9-678 m above sea level (Appendix C). 2.3.3 Multiple Years of Tracking Three Gray Catbirds were tracked in multiple years from the Bitterroot River Valley. One female Gray Catbird had similar overwintering locations in northeastern Mexico in two winters based on one GPS tag and one geolocator, but another geolocator showed third winter estimate 35 of southern Mexico and the Pacific Ocean (Figure 2.7). This geolocator overwintering estimate over southern Mexico and the Pacific Ocean appears questionable. One male Gray Catbird had an overwintering location of eastern Mexico based on geolocator data, and then Texas in another year based on GPS data. A different male Gray Catbirds overwintered in northeastern Mexico in both years based on GPS tag data. 2.3.4 Migratory Connectivity There was no significant correlation between the distances between individual birds in the breeding grounds and the distances between individual birds within the overwintering grounds (rM = 0.013, P-value = 0.41), indicating weak migratory connectivity. Additionally, the pairwise distances in the overwintering grounds (South Okanagan Valley: 347.2 ± 68.3 km, n = 15, Bitterroot River Valley: 255.8 ± 34.7 km, n = 66) were greater than the pairwise distances in the breeding grounds (South Okanagan Valley: 0.74 ± 0.11 km, n=15, Bitterroot River Valley: 0.02 ± 0.002 km, n = 66, Figure 2.9). 2.3.5 Apparent Annual Survivorship The Bitterroot River Valley dataset for apparent annual survivorship contained 741 birds, including hatch-year birds. The top model had a weight of 93% and no other models had ΔAICc values of <2. , suggesting that this model was clearly the best. Therefore, parameter estimates for this model alone are reported. The apparent annual survivorship was greater for males (ϕ = 0.43 ± 0.04) than females (ϕ = 0.34 ± 0.05, Table 2.1, Table 2.2). The South Okanagan Valley dataset for apparent annual survivorship contained 537 birds, including hatch-year birds. The top model had a weight of 98% and no other models had ΔAICc values of <2. Therefore, parameter estimates for this model alone are reported. The top model for apparent annual survivorship in the South Okanagan Valley included sex as a covariate for both survivorship and detection probability (Table 2.1). Males had greater detection 36 probability (p = 0.72 ± 0.07) than females (p = 0.25 ± 0.07) and birds of unknown sex (p = 0.35) but males and females had similar survivorship (ϕ = 0.64 ± 0.05 and 0.61 ± 0.06, respectively, Table 2.2). 2.4 Discussion Based on breeding locations occurring west of and within the Rocky Mountains, I expected the Gray Catbirds in my study to follow the Pacific Flyway. Instead, both South Okanagan Valley and Bitterroot River Valley Gray Catbird populations crossed the continental divide and followed the Central Flyway to overwintering sites. The Rocky Mountains did not appear to be a significant barrier to Gray Catbirds, as all individuals I tracked crossed the mountainous barrier during both fall and spring migration. Catbirds followed low-elevation corridors, as indicated by the GPS fixes from South Okanagan Valley birds within the Rocky Mountain Range. eBird sightings in September within the Rocky Mountain Range confirm the idea that Gray Catbirds travel within the valleys, although this could be an artefact of the greater accessibility of low elevation birdwatching opportunities by citizen scientists (eBird 2017). By examining eBird trends, I see that peaks in abundances across the landscape match the western Gray Catbird general migration route (as determined from GPS tags)from Montana through to Texas, confirming that many Gray Catbirds are stopping over or passing through these areas at the same time. The migration route for western Gray Catbirds may reflect the evolution of migration in these populations during historical range expansion, similar to Veeries nesting in the South Okanagan Valley (Kardynal and Hobson 2017). As is postulated for Veeries, Gray Catbirds likely originated in eastern North America and then slowly expanded their range northward and then westward as the continental glaciers receded (Kardynal and Hobson 2017). The absence of this species in most of the Pacific Northwest, such as coastal Washington, Oregon, and California, is consistent with this hypothesis. The relicts of historical range expansions are 37 recapitulated in migration and can be observed in other species whose breeding and overwintering grounds are continents apart, such as Northern Wheatear (Oenanthe Oenanthe; Bairlein et al. 2012), Bar-tailed Godwit (Limosa lapponica; Battley et al. 2012), and Blackpoll Warbler (Setophaga striata, DeLuca et al. 2019). In British Columbia, ancestral routes similar to what I have found in the western Gray Catbird have also been observed in Swainson’s Thrushes (Ruegg and Smith 2002, Delmore et al. 2012), but my study is the first documented case in the Mimidae family. Ancestral relicts of range expansion may also work in conjunction with ecological limitations to explain why the migratory routes I documented are indirect and farther east than I expected (Newton 2006). Catbirds are habitat generalists that prefer shrubby edges and riparian habitats (Nickell 1965, Smith et al. 2020). Riparian habitats are especially important sites for migrants at stopovers by providing both food and water (Carlisle et al. 2009) and support most bird species during some portion of their life cycle (Strong and Bock 1990, Naiman and Decamps 1997). Though migrating along the edge of the open Great Plains ecoregion, Gray Catbirds in my study did not stop over until reaching sites within the Temperate Forest region of North America, replete with woody vegetation and riparian areas (Commission for Environmental Cooperation 1997). The Great Plains ecoregion is characterized by relatively flat topography, semi-arid climate, grasslands with little forest, and high winds (Commission for Environmental Cooperation 1997). Consequently, stopover sites in the Great Plains region are potentially less abundant or of lower quality than those within the Temperate Forest Region, and therefore, a route that allows more in the Temperate Forest Region may be evolutionarily favourable. The Temperate Forest ecoregion is more typical of the Gray Catbird’s habitat preferences, being characterized by streams, lakes, wetlands, forests and a temperate climate (Commission for Environmental Cooperation 1997). 38 Stopover or staging sites are locations where migratory birds stop to rest and acquire energy on long journeys (Warnock 2010). That all Gray Catbirds used different stopover sites poses a challenge for conservation, as the protection of many small riparian areas throughout the migration route may be necessary to support current Gray Catbird’s populations. My data are supported by observations from eBird checklists during migration where in many cases the number of Gray Catbirds reported is 1, highlighting the ecology of migrating Gray Catbirds are generally travelling solo or in small flocks during migration(eBird 2017, Smith et al. 2020). Montana, Oklahoma, and Arkansas provided the highest number of apparent stopover sites and these states might be suitable locations to set up networks of Motus Wildlife Tracking System towers to further examine migrant movement (Taylor et al. 2017, McKinnon and Love 2018). I found that spring migration routes closely matched fall migration routes, so it is possible that the same or similar stopover sites are used during both fall and spring migration. The examination of stopover site fidelity would be useful to better understand to what extent catbirds are flexible in their stopover site use. A good habitat conservation model may be to preserve riparian areas at various elevations, as catbirds appear to breed, stopover, and overwinter at a wide variety of elevations, including over 1,000 m above sea level. Similarly, conserving a network of riparian habitats across the landscape is a valuable conservation approach for not just Gray Catbird’s, but other wildlife, as it allows for landscape-level connectivity and movement corridors. Of additional conservation concern, predicted future global climate change may degrade and reduce riparian habitat, especially at lower elevations, through changes in hydrological regime, the timing of peak flows, and increasing anthropogenic pressures (Martin 2007, Perry et al. 2012, Capon et al. 2013). Global climate change is predicted to increase drought onset, length, and severity (Trenberth et al. 2014). It is thus important to ensure not only that suitable riparian stopover sites persist into the future, but to also monitor the quality of the sites over time because climate change is predicted to reduce insect abundance in riparian habitats, which 39 could be detrimental to migrant insectivorous birds (Perry et al. 2012). Given the precise nature of GPS tag locations, ground-truthing stopover sites and identifying key habitat features may aid in understanding site selection to complement the habitat information determined from aerial photographs. One caveat is that my ability to detect a stopover site was limited by the length of time between GPS tag fixes (which varied between one and five days). Therefore, it is likely that more stopover sites were used by Gray Catbirds that I was unable to detect. Unexpectedly, Gray Catbirds appear to use multiple overwintering sites and moved farther south as the winter and spring season progressed. In recent years, other studies tracking songbirds have revealed that winter movements may not be uncommon. Prothonotary Warblers (Protonotaria citrea), Veeries, and Swainson’s Thrushes use more than one overwintering site(Heckscher et al. 2011, Cormier et al. 2013, Wolfe and Johnson 2015, Kardynal and Hobson 2017). Some songbird species such as the Black-headed Grosbeak (Pheucticus melanocephalus), Bullock’s Oriole (Icterus bullockii), and Lazuli Bunting (Passerina amoena) use distinct sites for moulting, a phenomenon known as moult migration, but moult migration is an unlikely explanation for Gray Catbirds because they complete their pre-basic moult prior to fall migration and do not moult flight feathers during their limited pre-alternate moult in the winter (Young 1991, Pyle 1997, Siegel et al. 2016). Moult migrants also tend to stay for an extended period of a couple of months or more during fall migration rather than using multiple sites over the course of the non-breeding period that I describe. Plausible explanations for overwintering movements may be that birds follow a shift in seasonal arthropod or plant resources, but further research is needed to determine exact causes. IA better understanding of overwintering habitat use and how it may vary by age and sex is a priority for future Gray Catbird research (Smith et al. 2020). From repeat tracking of birds, I found that some Gray Catbirds return to the same wintering area in subsequent years, while other Gray Catbirds go to different overwintering 40 locations. One Gray Catbird I tracked with two GPS tags in two different years showed that the bird used similar migration routes, but the smaller number of fixes in one year was a limiting factor to track the migration route in fine detail. Repeat tracking of individual songbirds over multiple years is rare. One study on Wood Thrushes (Hylocichla mustelina) found that birds tracked in multiple years do not always use the same route and one bird used a different overwintering location (Stanley et al. 2012). My geolocator results are confounded by large errors in precision. Average errors in latitude of 201 km, and as high as 390 km, occur in forested habitats (Fudickar et al. 2012). For the individual with two geolocators worn in consecutive years, longitude estimates were similar year to year, but the latitudinal estimates were potentially erroneous due to differences in sun elevation angles during calculation (sun elevation estimates were -3.5˚, and -1.75˚ for the two separate years of geolocator tracking). Banding birds in the non-breeding season can elucidate trends on overwintering site fidelity in the absence of repeat tracked birds with geolocators or GPS tags. One overwinter study found that 21 out of 90 (23%) banded catbirds returned in a subsequent year to their overwintering site in Florida (Somershoe et al. 2009), but I advise caution on inferring whether these trends on eastern catbirds apply to western catbirds. That Gray Catbirds use multiple overwintering sites and vary in their degree of overwintering site fidelity question important assumptions of overwintering monitoring programs, such as Monitoreo de Sobreviviencia Invernal (MoSI), which uses standardized mist-netting efforts during the overwintering period to assess overwintering survivorship (Desante et al. 2005). The Gray Catbird is one target species of the MoSI program (DeSante 1992) (Desante et al. 2009). The MoSI protocol involves five monthly overwinter mist-netting pulses from November to the end of March where each pulse occurs over 2-3 days (Desante et al. 2009). Therefore, the local population of Gray Catbirds present at a station may vary from pulse to pulse throughout the overwintering period, which means the probability of recapture is not the 41 same across the entire overwintering period. It is likely that overwintering survivorship estimates will be underestimated for Gray Catbirds due to their overwintering movements. A better understanding of songbird overwintering movement in other species is crucial to assessing the accuracy of vital rate metrics derived from MoSI data. I found weak migratory connectivity for the two Gray Catbird populations I examined, with Bitterroot River Valley and South Okanagan Valley Gray Catbirds overwintering in similar areas across northeastern Mexico. Birds that bred close to one another dispersed up to 1,000 km apart during the overwintering period. One important caveat is that the scale of the study can distort how migratory connectivity is perceived – if I consider both breeding sites from the South Okanagan Valley and the Bitterroot River Valley as one population, and the span of Texas to Veracruz as one overwintering population, it would be perceived as strong migratory connectivity. The lack of precision from geolocators can also potentially confound interpretations of overwintering locations as the spread of individuals cannot be determined as accurately as GPS tags. Therefore, studies that rely only on geolocators should ensure that study sites are separated by distances much greater than the estimates of error for geolocators. By comparison, Ryder et al. (2011) found strong migratory connectivity for eastern and central populations of Gray Catbirds. Broadly speaking, the results of my study and previous studies on migration patterns of Gray Catbirds result in a comprehensive picture of population connectivity across their range. Gray Catbirds breeding in the west overwinter in eastern Mexico (my study and Hobson et al. 2014), Catbirds breeding in the mid-west overwinter in Central America (Ryder et al. 2011), Catbirds breeding near the Great Lakes overwinter in Guatemala (Hobson et al. 2014), and Catbirds breeding in the Atlantic overwinter in Cuba and the Caribbean Islands (Ryder et al. 2011). Establishing demographic rates for species, such as apparent annual survivorship, are important for identifying populations at risk of decline and informing conservation efforts 42 (Desante et al. 2005, Saracco et al. 2009). The apparent annual survivorship estimate was higher for Gray Catbirds in the South Okanagan Valley than in the Bitterroot River Valley. The estimates for Gray Catbirds in the South Okanagan Valley of 0.61 ± 0.06 for females and 0.64 ± 0.05 for males exceed reported adult average survivorship values in the Northern Rockies bird conservation region (which spans both study areas) of 0.54 ± 0.02 between 1992 – 2006 (Desante et al. 2015). For comparison, apparent annual adult survivorship for other common songbirds in the Northern Rockies bird conservation region includes the Yellow Warbler (Setophaga petechia) with values of 0.57 ± 0.02, Black-headed Grosbeak (Pheucticus melanocephalus) with values of 0.59 ± 0.02, Lazuli Bunting (Passerina amoena) with values of 0.51 ± 0.02, Song Sparrow (Melospiza melodia) with values of 0.50 ± 0.02, and Western Wood-Pewee (Contopus sordidulus) with values of 0.53 ± 0.03 (Desante et al. 2015). Likewise, apparent annual survivorship values of male Yellow-breasted Chats (Icteria virens) in the same South Okanagan Valley study area from 2013 – 2019 were 0.50 ± 0.04 (Chapter 3) and 0.65 ± 0.07 between 2001 – 2006 (adults, McKibbin and Bishop 2012). Why the apparent annual survivorship of Bitterroot River Valley Gray Catbirds with values of 0.30 ± 0.05 for females and 0.41 ± 0.04 for males is substantially lower than other songbirds in the region and Gray Catbirds in the South Okanagan Valley study area is unclear. Both study areas had resighting efforts, which when combined with recapture methods, improves apparent annual survivorship estimates (Martin et al. 2017). Several possibilities exist that could explain these low values, such as mortality during migration or the non-breeding period, but more likely, I think it is an artefact of lower effort in the Bitterroot River Valley compared to the South Okanagan Valley. Additionally, lower habitat quality due to cattle grazing, the presence of agriculture, or other abiotic factors may play a part in lower apparent annual survivorship values in the Bitterroot River Valley. Future research should examine the drivers of lower survivorship values in the Bitterroot River Valley (Desante et al. 2015). 43 I experienced partial and complete device failure for both types of transmitters used in this study. I experienced a 14% device failure rate for geolocators in the South Okanagan Valley, which is similar to partial device failures of 12% reported elsewhere for geolocators (Bridge et al. 2013). My device failure rate of 14% for the GPS tags in the Bitterroot River Valley is similar to the 10% reported not functioning properly by Hallworth and Marra (2015) but is lower than the 33% failure rate reported by Pederson et al. (2019). Geolocators retrieved from the South Okanagan Valley had a high degree of shading after leaving the breeding grounds, resulting in low-quality data and inaccurate overwinter location estimates (Appendix A). Other studies mention geolocator shading (e.g. Cormier et al. 2013, Salewski et al. 2013), but there is no standardized method to quantify shading to which I can compare my data. I hypothesize that during the pre-basic moult, newly grown interscapular feathers may have obscured the light sensor in the device, reducing the ability to capture daylight. Given that the manufacturer was the same, it is unclear why the quality of the data varied between the two study sites, especially since that the aerial imagery of stopover and overwintering sites as determined by GPS tags showed that general habitat types were similar. Differences in data quality could potentially be attributed to manufacturing inconsistencies. Methods for attaching the devices to the birds were similar between sites, however, I cannot rule out minor differences caused by different field technicians as a factor. In contrast, the GPS tags worked well in both sites, as the antenna is longer and projects further than the geolocator light stalk, plus GPS tags offer much more precise location estimates compared to geolocators. For future studies, I would suggest using GPS tags over geolocators. From a conservation perspective, the detail I now have on the full annual life cycle of Gray Catbirds may help in ensuring the species remains a common species on the landscape well into the future. Preserving key areas of suitable habitat in the central and southern portions of Tamaulipas would benefit both Bitterroot River Valley and South Okanagan Valley Gray 44 Catbirds, as Tamaulipas is a key overwintering area for western Catbirds as found in my study, plus found by Hobson et al. (2014). Additionally, proactively protecting and monitoring riparian areas along their migration route and stopover areas may allow us to detect early changes related to the predicted effects of climate change impacting the persistence and quality of riparian habitats (Perry et al. 2012, Capon et al. 2013). Moreover, should a population decline occur in the future, baseline information on the survivorship and migratory connectivity of these populations will be valuable for conservation efforts (Wilson et al. 2018). In conclusion, my study is the first to examine the migration behaviour and apparent annual survivorship of Gray Catbirds breeding in western North America. Birds from both populations migrated eastward then southward towards overwintering in northeastern Mexico and Texas, following a putative ancestral route. Unexpectedly, Gray Catbirds used multiple overwintering sites during the non-breeding season, which may have important consequences in assumptions for overwintering monitoring programs, such as MoSI. I found that Gray Catbirds from the two separate breeding populations overlapped in the winter, a case of weak migratory connectivity. 45 Table 2.1. Models for the apparent annual survivorship of Gray Catbirds (Dumetella carolinensis) in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA using Gray Catbirds. Cormack-Jolly-Seber models were used, which includes annual survivorship (ϕ) and detection probability (p). TSM is a time-since-marking approach whereby the first encounter is separated from separate encounters to factor in young and transient individuals. Intercept only models are designated by (.). Model Parameters AICc ΔAIC Weight Deviance Bitterroot River Valley (n = 741) ϕ(sex)p(site) 5 496.4 0.00 0.93 159.9 ϕ (sex)p(.) 4 503.0 6.56 0.04 130.5 ϕ (site)p(sex) 5 503.7 7.27 0.02 167.2 ϕ (sex)p(sex) 6 506.8 10.36 0.01 130.2 ϕ (.)p(sex) 4 511.3 14.91 0.00 138.9 ϕ (TSM)p(sex) 5 513.3 16.94 0.00 138.9 ϕ(TSM)p(.) 3 552.4 56.00 0.00 65.2 ϕ(site)p(.) 3 596.4 100.00 0.00 124.3 ϕ(.)p(site) 3 596.5 100.14 0.00 124.4 Φ(site)p(site) 4 598.2 101.76 0.00 124.0 Φ(TSM)p(site) 4 598.6 102.17 0.00 124.4 Φ(.)p(.) 2 600.7 104.20 0.00 115.4 South Okanagan Valley (n = 537) ϕ(sex)p(sex) 6 709.2 0.00 0.98 119.2 ϕ(sex)p(.) 4 718.1 8.91 0.01 132.1 ϕ (sex)p(site) 5 719.4 10.16 0.01 164.3 ϕ (site)p(sex) 5 724.0 14.82 0.00 169.0 ϕ (.)p(sex) 4 724.2 15.00 0.00 138.2 ϕ (TSM)p(sex) 5 726.1 16.91 0.00 138.1 ϕ (TSM)p(.) 3 813.4 104.24 0.00 51.1 ϕ (TSM)p(site) 4 815.4 106.22 0.00 76.4 ϕ (.)p(.) 2 836.7 127.48 0.00 76.4 ϕ (site)p(.) 3 838.5 129.34 0.00 101.6 ϕ (.)p(site) 3 838.6 129.45 0.00 101.7 ϕ (site)p(site) 4 839.7 130.48 0.00 100.7 46 Table 2.2. Parameter estimates for top two apparent annual survivorship models for Gray Catbirds (Dumetella carolinensis) in the in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA. Cormack-Jolly-Seber models were used, which includes annual survivorship (ϕ) and detection probability (p). Intercept only models are designated by (.). Model Parameter Group Estimate Standard Error Bitterroot River Valley ϕ(sex)p(site) ϕ Female 0.34 0.05 ϕ(sex)p(site) ϕ Male 0.43 0.04 ϕ(sex)p(site) ϕ Unknown 0.06 0.01 ϕ(sex)p(site) p MPG Ranch 0.77 0.07 ϕ(sex)p(site) p Other 0.34 0.10 South Okanagan Valley ϕ (sex)p(sex) ϕ Female 0.61 0.06 ϕ (sex)p(sex) ϕ Male 0.64 0.05 ϕ (sex)p(sex) ϕ Unknown 0.20 0.20 ϕ (sex)p(sex) p Female 0.25 0.07 ϕ (sex)p(sex) p Male 0.72 0.07 ϕ (sex)p(sex) p Unknown 0.35 0.13 47 Table 2.3. Summary of GPS tags attached to Gray Catbirds (Dumetella carolinensis) in the South Okanagan Valley, British Columbia, Canada and in the Bitterroot River Valley, Montana, USA with an emphasis on migration. Year is year GPS tag deployed. The fall depart date represents the last fix at the breeding site and the arrival date represents the first fix at the overwintering site. The spring depart date represents the last fix at the overwintering site and the arrival date represents the first fix back at the breeding site. Locations (Loc.) are abbreviated as Nuevo Leon (NLE), San Luis Potosi (SLP), Tamaulipas (TAM), Veracruz (VER), Hidalgo (HID), Puebla (PUE), and Texas (TX). Fall Spring Tag Bird Sex Year Fix Loc. Depart Arrive Length (Days) Distance (km) Speed (km/day) Depart Arrive Length (Days) Distance (km) Speed (km/day) South Okanagan Valley 42000 YRGX M 2017 23 NLE Sep 6 Oct 12 36 3905 108 42005 YROX M 2017 53 TX Sep 10 Oct 12 32 3236 101 48964 BBRX M 2018 46 SLP Sep 10 Oct 16 36 4686 130 48961 BBXB M 2018 48 TAM Sep 12 Oct 14 32 4098 128 48965 BBYX M 2018 51 TAM, SLP, HID Sep 16 Oct 22 36 4171 116 48963 BBXD F 2018 28 TAM* Sep 6 Oct 16 40 4192 105 Mean ± SE 35 ± 1.2 4048 ± 193.5 115 ± 4.9 Bitterroot River Valley 251 LLBkA F 2014 3 TAM - Dec 7 - 3235 - 262 DBkAR M 2014 4 TX - Dec 7 - 2746 - 245 RAWW M 2014 2 TAM - Jan 7 - 3507 - 40958 LRYA M 2016 8 TAM, HID - Oct 18 - 3072 - 48126 LRYA M 2017 38 TAM, HID Sep 14 Oct 19 35 3390 97 Apr 26 May 31 35 3501 100 48120 ODAS M 2017 41 TAM, VER, HID Sep 9 Oct 14 33 3358 102 May 3 May 31 28 3424 122 48121 OOAS F 2017 22 VER Sep 4 Oct 29 55 3911 71 48123 DOGA 2017 36 TAM, HID Sep 9 Oct 14 35 3579 102 Apr 26 May 24 28 3764 134 48124 GOAS F 2017 40 TAM, VER Sep 19 Oct 19 30 3407 114 May 3 May 31 28 3677 131 48131 WWAS M 2017 36 TAM, SLP, VER Sep 9 Oct 14 35 4032 115 48133 RLGA F 2017 37 TAM, PUE Sep 9 Oct 14 35 3401 97 May 3 - - - - 48134 ADBkW M 2017 37 VER, HID Sep 9 Oct 4 25 3760 150 Mean ± SE** 35 ± 3.2 3604 ± 76.2 119 ± 7.8 29 ± 2 3591 ± 78 122 ± 7.5 *Last fix on October 16, assuming overwintering site but could potentially still be on migration. **Mean (± Standard Error) but not including length or speed for units with less than 10 fixes. 48 Table 2.4. Details of overwintering sites obtained from GPS tags placed on Gray Catbirds (Dumetella carolinensis) in the South Okanagan Valley, British Columbia, Canada and the Bitterroot River Valley, Montana, USA. Abbreviations include location (Loc), length of stay (Days), the distance between current and previous overwintering site (KM). Locations are abbreviated as Nuevo Leon (NLE), San Luis Potosi (SLP), Tamaulipas (TAM), Veracruz (VER), Hidalgo (HID), Puebla (PUE), and Texas (TX). Winter Site 1 Winter Site 2 Winter Site 3 Winter Site 4 Tag Dates Loc Days Dates Loc Days KM Dates Loc Days KM Dates Loc Days KM South Okanagan Valley 42000 Oct 12-Nov 1 NLE 20 42005 Oct 12-Mar 9 TX 158 48964 Oct 16-Mar 27 SLP 162 Mar 31-Apr 10 SLP 10 109 48961 Oct 14-Apr 2 TAM 169 48965 Oct 22-Oct 26 TAM 4 Dec 1-Feb 1 TAM 62 32 Mar 1-Mar 17 SLP 16 178 Mar 19-Apr 20 HID 32 67 48963 Oct 16 TAM Bitterroot River Valley 251 Dec 7-Jan 7 TAM 30 262 Dec 7-Feb 7 TX 62 245 Jan 7 TAM 40958 Oct 18-Jan 5 TAM 79 Feb 4 TAM 19 Mar 1 HID 278 48126+ Nov 1-Nov 21 TAM 20 Dec 1-Feb 19 TAM 80 5 Mar 15-Apr 26 HID 42 275 48120 Oct 14-Nov 11 TAM 28 Dec 1-Feb 19 TAM 81 114 Mar 8-Apr 19 VER 41 234 Apr 26-May 3 VER 7 8 48121 Oct 29-Dec 21 VER 53 Dec 31 VER 6 48123 Oct 14-Mar 1 TAM 138 Mar 15-Apr 26 HID 42 353 48124 Oct 19-Mar 1 TAM 133 Mar 8-May 3 VER 56 313 48131 Oct 14-Jan 20 TAM 98 Feb 19-Mar 8 SLP 17 168 Mar 22-Apr 26 VER 35 79 48133 Oct 14-Feb 19 TAM 128 Mar 29-May 3 PUE 63 275 48134 Oct 14-Mar 29 VER 176 Apr 5-May 3 HID 28 119 +This bird moved around in Tamaulipas between Oct 19 and Nov 1.49 Figure 2.1. Southward migration path from GPS tags retrieved from Gray Catbirds (Dumetella carolinensis) breeding in the South Okanagan Valley, British Columbia, Canada, and in the Bitterroot River Valley, Montana, USA. Black tracks are from GPS tags retrieved from the Bitterroot River Valley (n=12) and gray tracks are from GPS tags retrieved from the South Okanagan Valley (n=6). Gray Catbird range map provided by IUCN (2016) and World Terrain Base Map provided by ESRI, USGS, and NOAA within ArcMap 10.6.1. Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 50 Figure 2.2. Elevation profile of Gray Catbirds (Dumetella carolinensis) navigating across the Rocky Mountain Range during fall migration. Points and tracks are from GPS tags on catbirds breeding in the South Okanagan Valley, British Columbia, Canada, and in the Bitterroot River Valley, Montana, USA. The elevation map was created in ArcMap 10.7.1 using the 30 arc-second digital elevation model of North America (ESRI 2019). The GCS WGS 1984 coordinate system was used. 51 Figure 2.3. Stopover sites where Gray Catbirds (Dumetella carolinensis) spent at least one day at the location during migration. Stopover sites identified using GPS tags. Crosses represent birds tagged in the South Okanagan Valley, British Columbia, Canada, and exes represent birds tagged in the Bitterroot River Valley, Montana, USA. Stars represent study site locations. All sites are from fall migration, except for the asterisk in Texas, which was a spring stopover site for a Gray Catbird that bred in the Bitterroot River Valley. Points are scaled relative to the amount of time spent at a site and range between 1 and 16 days. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 52 Figure 2.4. Overwintering points as determined from GPS tags retrieved from Gray Catbirds (Dumetella carolinensis) breeding in the South Okanagan Valley, British Columbia, Canada (squares) and the Bitterroot River Valley, Montana, USA (circles). The size of the symbol represents how long the bird stayed at the site, ranging from 4 to 169 days. Most birds had more than one overwintering site. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 53 Figure 2.5. Geolocator overwintering location estimates from Gray Catbirds (Dumetella carolinensis) breeding in the Bitterroot River Valley, Montana, USA. Each polygon represents the 50% Kernel Density Estimates from locations obtained between November 15 – March 1 from 2014-2018. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 54 Figure 2.6. A comparison of southward (black) and northward migration (gray) tracks from 5 different Gray Catbirds (Dumetella carolinensis) marked in their breeding site in the Bitterroot River Valley, Montana, USA. Migration routes determined from GPS tags. All birds were heading north by May 10, except for catbird with GPS 41820, who headed north by May 3. Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. Figure 2.7. Repeat tracking of 3 separate Gray Catbirds (Dumetella carolinensis) given different tracking devices in different years. Tracking devices were deployed in the Bitterroot River Valley, Montana, USA. The year that the track was recorded is shown. Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. Geolocator abbreviated as Geo. 55 Figure 2.8. Weekly abundance of Gray Catbirds (Dumetella carolinensis) by state between 1900 – 2019 using citizen science observation records from eBird (2017). Abundance includes the number of catbirds reported on all checklists (including checklists without catbirds). Catbirds have low abundance in states within the Pacific Flyway with no peaks during migration (top panel). In the Central Flyway, peaks in abundance during migration within each state trace the geographic movement of birds across the landscape in the same manner as indicated by GPS tags (bottom panel). 56 Figure 2.9. Boxplot of pairwise distances of Gray Catbirds (Dumetella carolinensis) in the breeding and overwintering grounds separately by site. Each box displays the first quartile, median, and third quartile. Whiskers show first and third quartiles plus 1.5 times the interquartile range. Points beyond the whiskers are outliers. The sample size of pairwise distances for the Bitterroot River Valley, Montana, USA is 66 and for the South Okanagan Valley, British Columbia, Canada is 15. Distances were determined from locations identified using GPS tags. When multiple overwintering sites were used, only the first site was included for analysis. 57 Chapter 3: Migration and overwintering ecology of the Yellow-breasted Chat 3.1 Introduction An alarming ecological issue in the Anthropocene is the decline of biodiversity (Dirzo et al. 2014, Ceballos et al. 2017). Songbirds are no exception, as many North American songbirds have experienced drastic declines over the last 50 years (Sauer et al. 2017, Rosenberg et al. 2019). To enact conservation measures to protect and recover songbirds, a better understanding of their ecology spanning their full annual cycle is crucial (Hutto 1989, Marra et al. 2015). The full annual cycle of songbirds can be broken down into four phases: breeding, fall migration, overwintering, and spring migration (Faaborg et al. 2010). As most songbird research is biased towards the breeding grounds, there are large gaps in knowledge regarding the migration and overwintering phases of the annual cycle (Marra et al. 2015). Specifically, the links between the phases of the annual cycle generally remain unknown (Marra et al. 2019). Notable exceptions include Kirtland’s Warbler (Setophaga kirtlandii, Sykes 1998, Cooper et al. 2017) and Barn Swallows (Hirundo rustica, Garcia-Perez and Hobson 2014, Hobson et al. 2015). Understanding the full annual cycle is important because events occurring in one phase may influence events occurring at another phase, termed carry-over effects (Marra et al. 2015). For example, overwintering habitat quality can influence body condition (Black-throated Blue Warblers, Setophaga caerulescens, Bearhop et al. 2004) and arrival to breeding sites (American Redstarts, Marra et al. 1998 Kirtland’s Warbler, Rockwell et al. 2012), which in turn, may affect access to high-quality territories (American Redstarts, Marra et al. 1998) and result in earlier laying dates, larger clutches, and subsequently greater reproductive success (American Redstarts Norris et al. 2004, Reudink et al. 2009; Pied Flycatchers, Ficedula hypoleuca, Goodenough et al. 2017). However, in other species, carry-over effects do not seem 58 to occur (Wood Thrush, Hylocichla mustelina, McKinnon et al. 2015, Red-backed Shrikes, Lanius collurio, Pedersen et al. 2016). Understanding the full annual cycle has been a challenge due to an inability to track small songbirds outside of a single season (Holmes 2007). Banding and recovery of songbirds has been ongoing for over 100 years; however, band recovery data are limited and fail to encompass broad geographic areas (Brewer et al. 2006). Fortunately, advances in technology in the 21st century have vastly improved the ways to study birds across their full annual cycle (McKinnon and Love 2018). External trackers such as light-level archival geolocators and GPS tags (McKinnon et al. 2013, Hallworth and Marra 2015), as well as intrinsic markers such as DNA and hydrogen stable isotopes (Hobson and Wassenaar 1997, Lovette et al. 2004, Toews et al. 2017), can be used to link breeding, migrating, and overwintering phases of the annual cycle. In particular, understanding whether breeding populations disperse widely throughout the overwintering range or whether populations are constrained to small geographic areas requires an understanding of migratory connectivity. Migratory connectivity describes how populations are linked between different phases of the annual cycle and is most often used when comparing breeding and overwintering life phases (Boulet and Norris 2006). Migrants with strong migratory connectivity generally have minimal geographic spread within the breeding or overwintering season and individuals from other populations do not overlap (Boulet and Norris 2006). Migrants with weak migratory connectivity use a wider geographic area and are more likely to overlap with individuals from other populations (Newton 2006, Finch et al. 2017). Understanding migratory connectivity is useful for predicting how threats may impact a discrete population and provide essential information needed to guide strategic recovery planning to achieve the maximum return on investment in efforts to conserve a population (Boulet and Norris 2006, Martin et al. 2007, Sheehy et al. 2010, Finch et al. 2017). For example, the loss of 59 critical overwintering habitat for populations with strong migratory connectivity has the potential to extirpate an entire population (Webster et al. 2002). In contrast, in the same scenario with populations with weak migratory connectivity, not all individuals rely on overwintering habitat in that region, and therefore, a diffuse effect among multiple populations may ensue (Finch et al. 2017, Marra et al. 2019). The Yellow-breasted Chat (Icteria virens, hereafter, chat) is a neotropical migrant whose breeding range encompasses southern Canada and the United States (Figure 1, (IUCN 2016a). Their overwintering range spans coastal Mexico and Central America (IUCN 2016a). Specific links between breeding and overwintering populations are not clear (IUCN 2016a, Eckerle and Thompson 2020). The chat consists of two subspecies – eastern (I.v. virens) and western (I.v. auricollis), which occupy distinct breeding ranges (Smith et al. 2005). The eastern subspecies breeds in southern Ontario and the eastern United States, while the western subspecies breeds in western and central Canada, the western United States, and northwestern Mexico. The chat is phylogenetically distinct as it is the only representative of the Icteriidae family, which, in itself, warrants the conservation of this unique species (Jetz et al. 2014, Barker et al. 2015, Chesser et al. 2017). While at a global scale the chat is considered of least concern (Birdlife International 2018b), reductions in occurrence relative to historic levels have been documented in Oregon (Sharp 1985), Nevada (Klebenow and Oakleaf 1984), British Columbia (Environment and Climate Change Canada 2016), Ontario (Environment and Climate Change Canada 2019), and California (Shuford and Gardali 2008). Estimates from the North American breeding bird survey show chats declined at 0.3% per year between 1993 – 2015 (Sauer et al. 2017). A net loss of just under 4 million breeding chats has occurred between 1970 – 2017 (Rosenberg et al. 2019). Of additional concern, certain edge populations are at risk - in Connecticut, chats are endangered (State of Connecticut 2015), in California chats are special concern (Shuford and 60 Gardali 2008), and in New York chats are special concern with a proposed uplisting to threatened (New York State 2019). In Canada, populations in British Columbia and Ontario are both federally listed as endangered (Environment and Climate Change Canada 2016, 2019). A lack of understanding of migration and overwintering locations is described in the recovery strategy for this species in British Columbia, Canada, and therefore, filling these knowledge gaps is a priority for the conservation of chats (Environment and Climate Change Canada 2016). Given the geographic location of the breeding population of chats in British Columbia, I hypothesize that this population will migrate using the Pacific Flyway. The Pacific Flyway is the westernmost flyway in North America and supports north-south movements of birds situated west of the Rocky Mountain Range (Boere and Stroud 2006). Determining whether at-risk populations overwinter in areas that already have some degree of protection is important for assessing population persistence. Therefore, I aimed to better understand the full annual life cycle of the chat using a descriptive approach by examining specific focal populations in the breeding, migrating, and overwintering grounds. I had four main objectives: (1) For chats breeding in British Columbia, Canada, I calculate survivorship, describe migration routes, and discover overwintering locations by studying a colour-banded population, deploying GPS tags and light-level geolocators. (2) For chats breeding in California, USA, I describe overwintering locations using light-level geolocators. (3) For chats overwintering in Nayarit, Mexico, I calculate return rates and infer breeding locations by studying a colour-banded population and using stable hydrogen isotopes and genetics. (4) For chats migrating through Veracruz and Chiapas, Mexico, I infer breeding origins using stable hydrogen isotopes and genetics. 61 Taken together, these research objectives allow us to better understand the full annual cycle of the Yellow-breasted Chat on a range-wide scale. 3.2 Methods 3.2.1 Study Areas I studied chats in 5 study areas at different phases of the annual life cycle (Figure 3.1). Breeding chats were studied in southern British Columbia and northern California. Breeding habitat of Yellow-breasted Chats generally consists of dense, shrubby areas often in association with riparian habitats (Eckerle and Thompson 2020). Study sites in British Columbia were adjacent to the channelized south Okanagan River (McKibbin and Bishop 2010). In northern California, sites were located along the Trinity River (Rockwell and Stephens 2018). Migrating chats were studied in Veracruz and Chiapas, Mexico. The Veracruz study site is located at the northern end of the Isthmus of Tehuantepec and consists of a marshy area with a mix of shrubs and trees < 5 m tall. The Chiapas site is located within the northern end of the La Encrucijada Biosphere Reserve on the Pacific coast of Mexico (Gahbauer et al. 2016). The Chiapas site consisted of lagoons and wetlands with interspersed shrubs and grasses. Overwintering chats were studied in San Francisco, Bahia de Banderas, Nayarit, Mexico and surrounding areas where habitat consisted of tropical semi-deciduous forests and sites located near forest edges and riparian areas (Table 3.1). 3.2.2 Bird Captures and Handling Yellow-breasted Chats were captured in mist-nets passively and using call playback, often paired with a decoy. Birds were banded, aged, and sexed following criteria in Pyle (1997). In British Columbia, California, and Nayarit, birds were also given a unique combination of colour bands to aid in resighting. Feathers were taken during banding for genetic and hydrogen isotopic analyses. One right outer rectrix and one right inner secondary feather (S4-S6) were 62 collected from each bird, except in California where no secondaries were collected, but instead, two outer rectrices were taken. Feathers were collected from breeding chats in May-July, 2018 in British Columbia, and in May-June, 2018 in California. In Veracruz, feathers were collected from chats in March, May, August-November 2014 and March 2015. In Chiapas, feathers were collected from chats during September-October 2018. I assume that birds sampled in Veracruz and Chiapas were all migrating. In Nayarit, feathers were collected in January-February 2017-2019 and I assume that the sampled chats were overwintering. At the breeding sites, adult male birds were equipped with tracking devices. Devices were attached to the back of birds using a leg-loop harness (Rappole and Tipton 1991) with Stretch MagicTM string melted closed using a soldering iron. Harness size and fit were based on mass equations from Naef-Daenzer (2007). Yellow-breasted Chats have high site fidelity, which facilitated re-sighting and recapturing efforts to retrieve deployed tracking devices (McKibbin and Bishop 2012). In subsequent years, I returned to breeding territories where tracking devices were deployed and nearby breeding territories to retrieve as many devices as possible. I used GPS tags to identify migration routes and stopovers for chats breeding in British Columbia. Fourteen GPS pinpoint units were deployed on adult males in 2016 and 2017 in British Columbia (Table 3.1). I used 1.0-gram GPS-10 and GPS-10 Swift devices (hereafter, GPS tags, Lotek Wireless Inc. 2020). GPS tags deployed in 2016 could acquire up to 10 fixes (Lotek Wireless Inc. 2020) and I scheduled fixes to occur 10-30 days apart, with fixes during anticipated migration scheduled for 10-15 days apart. GPS tags deployed in 2017 could acquire up to 80 fixes (Lotek Wireless Inc. 2020). Tags deployed in 2017 were scheduled to obtain fixes every 4 days. Therefore, to capture stopover locations during migration, a chat would have had to remain in the same spot for at least 10-15 days for GPS tags deployed in 2016, and for at least 4 days for GPS tags deployed in 2017. 63 I deployed 37 Biotrack light-level geolocators (hereafter, geolocators) on chats in British Columbia during 2013-2014, and 49 geolocators on chats in California during 2014-2016 (Table 1). I tagged mostly adult males, but also two hatch-year females, one hatch-year male, and one adult female. Geolocators record ambient light levels throughout the day and are used to infer location (McKinnon et al. 2013). 3.2.3 Apparent Annual Survivorship and Overwintering Return Rate To ensure that my use of geolocators and GPS tags were not a hindrance to the endangered British Columbian population of chats, I compared the apparent annual survivorship of birds with tracking devices to birds without. Between May and August of 2013-2019, chats were resighted by field technicians visiting previously identified chat territories to determine if a male was present. If a chat was detected, I used binoculars and high-zoom cameras to identify whether the chat was colour-banded or unbanded. If no male chat was heard or seen within 10 minutes of entering a known territory, 30 seconds of call-playback was broadcast to elicit a territorial response from a male, followed by 2 minutes of silence, another 30 seconds of call-playback, and a final 2 minutes of silence. In addition to previously identified territories, many new chat territories were identified each year when males were heard singing in suitable habitat and these chats were also identified by colour-band combination or lack thereof. Target netting using 1 to 4 mist nets in a territory also occurred to continue colour-banding efforts and to retrieve tracking devices. The dataset used for my apparent annual survivorship analyses for Yellow-breasted Chats included adult colour-banded males between 2013 – 2019. For geolocator and GPS birds, I calculated survival from when the tracking device was attached to the bird. I examined the return rates of birds to the overwintering site in Nayarit, Mexico. Here I banded chats in 2017, 2018, and 2019. Mist nets were placed haphazardly in areas with dense, scrubby vegetation and moved throughout the morning to maximize the number of Yellow-64 breasted Chats caught. In 2017, mist-netting occurred over 14 days in January and February. In 2018, mist-netting occurred over 16 days in January. In 2019, a standard mist-netting effort known as MoSI (Monitoreo de Sobreviviencia Invernal, Desante et al. 2005) occurred in Nayarit in 2019. This effort involved 12 mist nets open for 5 hours for 2 days each month in January-May and November-December. Apparent annual survivorship was calculated using the RMark package (v.2.2.7; Laake 2013 within v 3.5.1; R Core Team 2018) using Cormack-Jolly-Seber models (Cormack 1964, Schwarz 2001). The parameters included intercepts for apparent annual survivorship (ϕ) and detection probability (p). This model was previously determined to be the best model for the Yellow-breasted Chat population in the British Columbia study area and allows for direct comparison (McKibbin and Bishop 2012). Assumptions of Cormack-Jolly-Seber models are that (1) every bird has the same probability of being recaptured/resighted at the next sampling period; (2) every bird has the same probability of surviving to the next sample period; (3) Colour-bands are not lost or missed; (4) sampling time is short (or instantaneous) relative to the interval in between sampling times. To assess the goodness-of-fit of my data to the assumptions of Cormack-Jolly-Seber models I used the program RELEASE via RMark using the function release.gof. In order to specifically test assumptions (1) and (2), time was included as a covariate in the global model. I found no significant deviation of my global model to Cormack-Jolly-Seber model assumptions for the dataset of adult male chats without colour bands (χ 2 = 15.88, df = 9, P-value = 0.07) or for the dataset of adult male chats with GPS tags or geolocators (χ 2 = 9.05, df = 8, P-value = 0.34). 3.2.4 Migration and Overwintering Sites Overwintering sites for chats breeding in British Columbia were identified using geolocators and GPS tags. Overwintering sites for chats breeding in California were identified using geolocators. For high-precision overwintering locations obtained from GPS tags, I used 65 satellite imagery to visualize the sites and plotted protected areas to assess if birds overwintered in protected areas (UNEP-WCMC 2020). Light level geolocators were analyzed using the Geolight package (v2.0.1; Lisovski and Hahn 2012) in R following methods by Lisovski et al. (2020). I determined twilights using the preprocessLight function from the TwGeos package (v0.1.2; Lisovski et al. 2016) with a threshold of 0.5. To remove unlikely Twilight estimates, I used the twilightEdit function from the TwGeos package (window set to 4, outlier minutes set to 45, and stationary minutes set to 25). I constrained overwintering dates to Nov 15 – March 1 to capture the time when chats are overwintering and to avoid the equinox. I used the findHEZenith function from the TwGeos package, which uses the Hill-Ekstrom method, to determine the most likely sun elevation angle from Nov 15 – Mar 1 (Hill and Braun 2001, Ekstrom 2004). Twilights were converted to latitude and longitude estimates using the coord function in Geolight. Geolocators generally have much higher error estimates for latitude than longitude, especially in forested habitats (Fudickar et al. 2012, Biotrack Limited 2013). To visualize overwintering locations, I plotted the median, 1st quartile, and 3rd quartile longitude values from my geolocators but constrained to the known overwintering range reported by IUCN (2016) as latitudinal estimates were outside the known wintering range. For GPS PinPoint units retrieved from chats, latitude and longitude data were downloaded directly. I vetted location estimates by removing those considered to have a low accuracy (dilution of precision values greater than 20, Lotek Wireless Inc. 2018). Stopover sites were defined as any location with consecutive fixes during migration. Migration routes and stopovers were plotted on range maps (IUCN 2016a) using ArcMap 10.7.1 (ESRI 2019). For GPS tags, I plotted overwintering fixes as well as satellite imagery of each overwintering bird to visualize habitat. I added a layer of the protected areas of Mexico (UNEP-WCMC 2020) to assess whether chats overwintered in protected areas. 66 3.2.5 Breeding Origin of Overwintering and Migrating Birds To determine the breeding origin from chats sampled in their overwintering and migration sites, I used a combination of genetics and stable hydrogen isotopes. Previous genetic analyses based on the ATPase gene in the mitochondrial genome identified 18 unique haplotypes within 34 chats sampled from across their North American distribution, including both I. v. auricollis and I. v. virens (Lovette et al. 2004). Importantly, the authors reconstructed two distinct haplotype groups that were geographically concordant with the breeding distribution of the two subspecies, revealing several single nucleotide polymorphisms (SNPs) that were diagnostic of western and eastern chats. Here, I designed and validated novel TaqMan® genotyping assays targeting diagnostic SNPs in the mitochondrial ATPase gene to rapidly and cost-effectively identify eastern and western chats sampled in their overwintering range. Taqman® genotyping assays detect the two possible variants at a SNP within a target sequence using two fluorescent probes in each reaction. I used previously published mtDNA ATPase sequences from the reference dataset compiled by Lovette et al. (2004) to design two genotyping assays (Iv_ATPase_SNP200; Iv_ATPase_SNP778) using the Custom TaqMan® Assay Design Tool provided by Life Technologies. I initially validated the diagnostic ability of the assays by genotyping 79 reference samples of known locality and subspecies origin across the North American range of I. virens as part of the Bird Genoscape Project (www.birdgenoscape.org): 39 samples of eastern I.v. virens subspecies (Indiana, Kentucky, Alabama, Missouri) and 40 samples for the western I.v auricollis subspecies (Montana, California, Oregon, British Columbia). I then used the Taqman® genotyping assays to identify the subspecies of 211 samples, including 38 from Chiapas, 19 from Nayarit and 154 from Veracruz (Table 1). DNA was extracted from the calamus of the outer rectrix feather. Extractions were performed using 100 μL of 5% Chelex®-100 (Bio-Rad laboratories, CA, USA) in ddH20, 5 uL of 67 proteinase K, and 3 μl of DTT and incubated for 4 hours at 56° C. A volume ranging from 80 to 90 μL of DNA extract was removed from the Chelex resin for further analysis. Initial tests revealed that feather DNA concentrations were too low to genotype the extracts directly. Consequently, I employed a nested approach by first using polymerase chain reaction to amplify a 1018 nucleotide region of the mtDNA genome spanning the ATPase 8 and ATPase 6 genes along with the complete tRNA-lysine and a short flanking region of the cytochrome oxidase II and III genes using previously published external primers (ATP9947H and ATP8929L; Hunt et al. 2001). Samples were amplified in a total volume of 12.5 µL containing: 6.9 µL of ddH2O, 1.25 of dNTPs 2 µM (KAPPA Biosystems), 1.25 µL of forward and reverse primer (10 µM), 1.25 µL of 10X Buffer with MgCl2 (Applied Biosystems), 0.1 µL of AmpliTaq Gold DNA polymerase 5U/µL (Applied Biosystems) and 1 µL of DNA. Cycling conditions were as follows: an initial denaturation at 95 °C for 10 minutes followed by 35 cycles of 95 °C for 30 s, 50 °C for 30 s, and 72 °C for 60 s, and a final extension at 72 °C for 7 min. Genotyping reactions were then performed in a 6 μL final volume, consisting of 2.50 μL of TaqMan Universal PCR Master Mix, 1.25 μL of ddH2O, 0.25 μL of 20X SNP genotyping assay mix and 2 μL of the PCR product with the amplified ATPase fragment. Non-template controls for each assay were included in each experiment. Reactions were performed on the ViiA™ 7 Real-Time PCR System (Applied Biosystems) with cycling conditions of 60°C for 30 s, 95°C for 10 minutes followed by 40 cycles of 95° C for 15 s and 60° C for 1 min, and a final step of 60° C for 30 seconds. Genotype calls were performed with the Applied Biosystems® TaqMan® Genotyper™ Software. 3.2.6 Stable Hydrogen Isotope Analyses Stable hydrogen isotope analyses were used to infer breeding location from chats sampled on their overwintering grounds in Nayarit (n=40), and migrating chats in Veracruz (n=125) and Chiapas (n=45, Table 3.1). The chemical signature of feathers represents 68 conditions in the breeding grounds, as chats complete their pre-basic moult before fall migration (Pyle 1997, pers. obs). Feathers of known origin for calibration purposes were collected from breeding chats in British Columbia (n=15) and California (n=15). Additional chat feathers were provided by Klamath Bird Observatory from two breeding sites in Oregon in 2018 for calibration (n=10). One inner secondary feather from each chat was used for stable hydrogen isotope analyses, except for chats sampled in Oregon where one rectrix was used instead of a secondary feather. The feathers were prepared following methods from Bontempo et al. (2014). Each feather was washed three times in a 2:1 solution of diethyl ether:methanol by sonicating for 1 minute. The feathers were air-dried in a fume hood overnight. Once dry, the rachis was removed using scissors or a razor blade and the remaining vane was placed in a glass scintillation vial with forceps. The vane was cut up into the finest powder possible within the glass vial using stainless-steel scissors. An anti-static gun was used to help prevent the feather bits from sticking to the vial. Stable hydrogen isotope analyses were performed by the Laboratory of Stable Isotope Science at the University of Western Ontario, London, Ontario, Canada. Feathers were analyzed using a Thermo Scientific High-Temperature Conversion Elemental Analyzer (TC/EA) plus a continuous flow Thermo Scientific Delta V Plus isotope ratio mass spectrometer. Samples and standards were held at room temperature for at least 72 hours to allow for hydrogen isotope equilibration, where the exchangeable hydrogen in the sample and standards equilibrated to the conditions of the atmospheric water vapour. Approximately 0.3-0.4 mg of sample were loaded into silver capsules then into a Zero Bank Autosampler. Once in the unit, the sample was purged using dry helium at 90 ml per minute for 7 minutes to remove any moisture. The samples were then dropped automatically into the TC/EA reactor column. The internal design of the TC/EA follows the design of Dr. Christine France at the Museum 69 Conservation Institute, Smithsonian Institute, whereby chromium powder (99.5% purity, 100 mesh size), quartz chips, and quartz wool spacers are added to achieve the correct height. The TC/EA allows samples to undergo pyrolysis by heating to 1120° C. Gas separation occurs in a gas chromatography column heated to 90° C and packed with a 0.6 M molecular sieve. The hydrogen isotope compositions were calibrated using two standard reference materials: (1) Caribou Hoof Standard, and (2) Kudu Horn Standard based on the updated values from Soto et al. (2017). Keratin powder was used for monitoring drift and correction. Final hydrogen isotope values in feathers (δ2Hf) are presented in parts per thousand (‰) normalized to Vienna Standard Mean Ocean Water. Hydrogen isotope values in precipitation vary latitudinally across the landscape, and isotopic signatures found within animal tissues reflect landscape variation and, therefore, can be used to infer where the animal tissue was grown (Hobson and Wassenaar 1997, Hobson et al. 2012). I used the package IsoriX (v0.8.2; Courtiol et al. 2019) in R to create a North America precipitation isoscape. This precipitation isoscape was subsequently calibrated to reflect the hydrogen isotopic signature in chat feathers to infer the geographic location of origin from my chat samples. IsoriX is superior to simple map lookup approaches because it incorporates the underlying variation in isotope values spatially and temporally (Courtiol et al. 2019). To create the precipitation isoscape, Canada, the United States of America, and Mexico precipitation values for δ2H were downloaded on December 7, 2019, from the Global Network of Isotopes in Precipitation (IAEA/WMO 2019). The dataset included monthly values for each station between 1953 to 2019. A base North American precipitation isoscape was fitted using the function isofit in IsoriX. Isofit creates two interrelated geostatistical models. The first model is a residual dispersion fit model which describes the variance across months in precipitation hydrogen isotopic values over geographic areas and is fitted using a gamma generalized linear mixed-effects model (Courtiol et al. 2019). The second model is a mean fit model that describes 70 how the mean precipitation hydrogen isotopic values vary geographically and is fit using a linear mixed-effects model (Courtiol et al. 2019). Fixed factors include elevation and latitude and random factors include station ID and the Matérn autocorrelation of the latitude + longitude. The two models are interrelated in that the predicted values of the residual dispersion model define the residual variance of the mean model (Courtiol et al. 2019). Further details of the background geostatistical modeling approach used by IsoriX can be found in Courtiol and Rousset (2017) and Courtiol et al. (2019). Visual examination of the diagnostic model plots suggested a good model fit to the data (Appendix D). This North American precipitation isoscape was calibrated to reflect δ2Hf values for chats by using 47 samples of known origin location, with 38 prepared ourselves plus 9 rectrix samples from known locations from Hobson et al. (2012, Appendix E). The 9 values from Hobson et al. (2012) were measured using old calibration values of standard reference materials, and I updated the values accordingly using the following equation provided in Soto et al. (2017): δ2Hf = 10.774 + 0.852 * old data. Calibration was completed using the calibfit function within IsoriX, which fits a linear mixed-effects model that accounts for the uncertainty associated with making predictions from isoscapes by factoring in the variance and covariance associated with predictions across geographic space (Courtiol et al. 2019). The linear mixed effect model includes the mean source value (from the precipitation hydrogen isotopic geostatistical model) as a fixed effect and also includes site (from calibration samples) as a random effect (Courtiol et al. 2019, Appendix E). To infer geographic location using stable hydrogen isotope results, samples of unknown origin were divided into 10‰ bins spanning the data range, which resulted in 9 bins. The purpose of the bins was to group individuals with similar isotopic signature together for summarizing the area of origin using the isofind function of IsoriX. I used the mask option of 71 IsoriX to constrain the geographic assignment area to the known breeding range (IUCN 2016a), separately for eastern and western subspecies as determined genetically. 3.2.7 Chat Movements from Bird Banding Data Bird banding data were used to complement my methods to assess movements of chats across North America. Bird band recovery data for chats were requested on September 18, 2019 from the Canadian Bird Banding Office, Canadian Wildlife Service, Ottawa, Canada. Recoveries were sorted by the distance between original banding location and encounter location, and only those points separated by at least 40 km were retained to visualize long-distance movement patterns. I excluded observations <40 km using professional judgment where a natural break in the data occurred and because movements shorter than 40 km were not meaningful in the context of understanding range-wide movement patterns and connecting individuals through different phases of the annual cycle (Appendix F). Banding locations and recovery locations were connected with straight lines and overlaid on range maps (IUCN 2016a) using ArcMap 10.7.1. 3.3 Results 3.3.1 Migration and Overwintering Sites of Breeding Chats Six of 14 (42.9%) GPS tags were retrieved from chats in British Columbia (Table 3.2). Of the 6 tags, 5 contained data, collecting between 4 and 52 points (Table 3.2). The remaining tag was sent to Lotek after I was unable to retrieve any data, and Lotek also was unable to retrieve data. No spring migration tracks were obtained due to battery exhaustion. Chats breeding in British Columbia migrated south in early September through the western United States (Washington, Oregon, Idaho, Nevada, Utah, and Arizona) then into western Mexico (Sonora, Chihuahua, Sinaloa, Nayarit, Figure 3.2). The migration track of these birds follows the Pacific Flyway, which is the westernmost of the four flyways in North America. Due to the timing 72 between fixes, I did not obtain much data on stopover sites. I identified one stopover site in Idaho, where a chat remained for a minimum of 8 days between September 14 – 22 (Figure 3.3). The stopover site was a narrow riparian strip surrounded by a human-modified landscape. More stopover sites were likely used but I was unable to detect them. Chats arrived at their overwintering grounds in Sinaloa or Nayarit in October (Table 3.1). One chat had a single and final overwintering point on December 7 in Chihuahua after 12 consistent stationary points between October 4 – November 29 in Sinaloa. However, the dilution of precision value was 11.3, which is considered fair (Lotek Wireless Inc. 2018), but the altitude estimate for this Chihuahua point is 9,872 m, suggesting the point is likely erroneous. Therefore, I consider the main overwintering location for this specific chat to be Sinaloa. Satellite imagery of overwintering locations shows birds resided in a mix of undisturbed and disturbed landscapes (Figure 3.4). Two chats overwintered in protected areas; one chat overwintered in the Biosphere Reserve Marismas Nacionales Nayarit and one overwintered in a natural resource protected area, Cuenca Alimentadora del Distrito Nacional de Riege 043 Estado de Nayarit (UNEP-WCMC 2020). I retrieved 17 geolocators from chats in British Columbia and 8 from chats in California. Of these, only 6 from British Columbia and 6 from California contained usable light data. The remaining 13 geolocators were unusable due to battery exhaustion, insufficient light data, or device failure for unknown reasons. From the 12 geolocators with light data, heavy shading was apparent and ultimately resulted in latitude estimates outside the known overwintering range. Therefore, to maximize the value of these 12 geolocators, I based estimates on longitude only and constrained latitudes to the known overwintering range (IUCN 2016a). Longitudinal estimates from light-level geolocators, which are determined from local noon and midnight, generally are more accurate than latitudinal estimates, which are determined from day length (Lisovski et al. 2012). I plotted the median longitude and interquartile range of longitudes of 73 these geolocators, which resulted in longitudinal estimates corresponding generally to the western and central portion of their Mexico overwintering range (Figure 5). However, even with these measures, 1 of the 12 tags resulted in a longitude estimate in the Pacific Ocean, with no overlap of Mexico. 3.3.2 Apparent Annual Survivorship and Overwintering Return Rates I did not find any evidence of tracking devices hindering the survivorship of chats. The apparent annual survivorship (± SE) of birds with tracking devices between 2013 – 2019 was 0.62 ± 0.05 (n= 50). The apparent annual survivorship of birds without tracking devices between 2013 – 2019 was 0.55 ± 0.04 (n=137). At the chat overwintering site in Nayarit, 47 chats were banded between 2017 – early 2019. Of those, 4 were recaptured in a subsequent winter between 2018 and the end of 2019, resulting in a return rate of 8.5%. 3.3.3 Inferred Breeding Origin from Overwintering and Migrating Chats I genotyped 76 of 79 reference samples at both SNP assays; two of the remaining individuals were successfully genotyped at one assay, while one sample from California did not amplify. All genotyped reference individuals were accurately assigned to their geographic subspecies of origin (Appendix G). All field-collected samples were genotyped successfully at both SNP assays, except three Veracruz samples whose assignments were based on one SNP assay. All samples from Nayarit were assigned to the western I.v. auricollis subspecies. All samples from Chiapas were assigned to the eastern I.v. virens subspecies. The DNA from 11 of the 154 Veracruz samples did not amplify, but of the 143 samples that did amplify, all were assigned to the eastern I.v. virens subspecies (Appendix G). The linear mixed-effects model calibrating the feather isoscape from the precipitation isoscape included a slope of 0.60 ± 0.08 and an intercept of -17.97 ± 7.72. The δ2Hf values for chats sampled in Nayarit ranged between -87.4‰ and -10.7‰, with the -30 to -40‰ bin 74 containing 32.5% of the birds (Figure 3.7, Table 3.3). This bin corresponded to birds originating from the southern and western portion of their breeding range. The δ2Hf values for chats sampled in Chiapas ranged between -90.4‰ to -20.1‰ with the -30 to -40‰ bin containing 33.3% of the birds (Figure 3.7, Table 3.3). Origin estimates for this bin correspond to birds originating from the central portion of their breeding range. The δ2Hf values for chats sampled in Veracruz ranged between -58.2 and -25.5‰, with the -40 to -50‰ bin containing 56.0% (Figure 3.7, Table 3.3). This bin corresponds to birds originating from the southern and central portion of the breeding range. 3.3.4 Bird Banding Data From 1920 – 2019, there were 134 band recovery records for chats, with most records consisting of short-distance movements (Figure 3.8, North American Banding Program 2019). Of these records, only 16 were at least 40 km from the original banding record. For the western subspecies, banding encounters linked individuals generally north to south at the very western edge of their range within the Pacific Flyway (Figure 3.9). A few individuals in the northeastern part of their breeding range were linked to Veracruz, Mexico, and the remainder of the observations were short east-west movements within the breeding range. 3.4 Discussion My research has described more detailed ecology about the full annual cycle of the Yellow-breasted Chats during breeding, fall migration, and the non-breeding period, on a range-wide scale. The endangered population of Chats breeding in British Columbia migrated via the Pacific Flyway. Overwintering locations for the British Columbia population were in western Mexico in Sinaloa and Nayarit, with two of five chats overwintering within protected areas. Geolocators from chats breeding in British Columbia and California provided broad longitudinal overwintering estimates and suggested chats were overwintering in central and western Mexico. 75 My combined isotope and genetic results from chat feathers from Nayarit showed western chats from multiple breeding latitudes share this overwintering location, with most birds originating from the southern and western portion of their breeding range. Similarly, isotope and genetic data from migrating chats in Veracruz and Chiapas suggest eastern chats from multiple breeding latitudes pass through these sites. In Veracruz, most chats originated from the central portion of their breeding range, while in Chiapas, most chats originated from the central and southern parts of their breeding range. The fall migration route for British Columbia chats followed the Pacific Flyway, with birds flying over Washington, Oregon, Idaho, Nevada, Utah, and Arizona. This route is common for songbirds breeding west of the Rocky Mountain Range (Hutto 1989, Brewer et al. 2006). The bird banding recovery data, while limited, also supports these north-south movements for the western subspecies. I detected one stopover site in Idaho, which consisted of a small strip of riparian habitat located within a heavily modified landscape, highlighting that even small areas of suitable habitat are valuable for migrating chats. However, given my small sample sizes and my inability to detect more stopover sites, more research is necessary to characterize stopover sites used by this chat population. Chats with GPS tags that bred in the south Okanagan Valley overwintered in Nayarit and Sinaloa, Mexico. Geolocators provided little resolution for overwintering sites but suggest that chats overwintered in western and central Mexico when constrained to latitudes of known wintering ranges. For geolocator estimates that spanned both the Pacific and Gulf coasts of Mexico, it is more likely that these birds overwintered on the Pacific coast, as my genetic results on chats, previous genetic work on chats (Lovette et al. 2004), and GPS tag results all showed western chats occurred exclusively along the Pacific coast of Mexico. Satellite imagery suggests the chats used a range of overwintering habitats. Some Chats overwintered in areas of undisturbed sub-deciduous tropical forests, while others 76 overwintered in a matrix of habitats disturbed by agriculture and rural dwellings. Ground-truthing the sites to characterize habitat further would be valuable. Previous documentation of chats in their overwintering range in Mexico suggest chats prefer secondary growth forest with canopy heights less than 10 m (Hutto 1989, Rappole et al. 1998). One chat overwintered in the Biosphere Reserve Marismas Nacionales Nayarit, which is a 2000 km2 wetland area of international importance designated in 1995 (UNEP-WCMC 2020). However, aerial imagery suggests the presence of agriculture at this site. The second chat overwintered in a natural resource protected area, Cuenca Alimentadora del Distrito Nacional de Riege 043 Estado de Nayarit, which consists of a conglomerate of smaller areas encompassing 23,290 km2 designated in 2002 (UNEP-WCMC 2020). The site appears to be a subtropical forest without evidence of human disturbance. My study sites in Nayarit also consisted of a mix of natural areas and working landscapes with agriculture and cattle grazing, further evidence that overwintering western chats are using both natural and human-modified landscapes. Outside of these protected areas, chats overwintering in western Mexico may be at risk of habitat loss, fragmentation, or degradation due to conversion for agriculture, grazing, and human development (Challenger 2019, Valdez 2019). Tropical deciduous forests along the Pacific coast of Mexico are hotspots of biodiversity and are predicted to be at high risk of habitat loss due to land conversion and high pressure of human footprint (Venter et al. 2016, Wilson et al. 2019). The expansion or addition of new protected areas in Nayarit and Sinaloa would further protect this population, as well as the many other neotropical migrants that overwinter in western Mexico. Higher sample sizes would be beneficial to better assess what proportion of the British Columbian chat population overwinters in protected areas. Chats breeding in California also appears to overwinter in western Mexico. Unfortunately, the poor quality geolocator data failed to provide resolution meaningful enough to identify specific overwintering locations of chats. The high levels of shading present on the 77 geolocators could be attributed to the chat’s preference for dense, shrubby vegetation (Eckerle and Thompson 2020) or could arise if back feathers obscured the sensor. Exact causes of low- quality data are unclear but similar issues with geolocators were encountered with Gray Catbirds (Dumetella carolinensis) tagged in the same British Columbia study area, who also occupy dense, shrubby vegetation (Chapter 2). Large errors in assignments have been noted elsewhere, including errors in latitude as high as 365 km in tropical forested habitats (McKinnon et al. 2013). In contrast, geolocators worked well in the same British Columbia site for Veeries (Catharus fuscescens) where they were used to identify migration routes and overwintering areas in South America (Kardynal and Hobson 2017). To better understand the migration and overwintering locations of chats breeding in California and to compare locations to that of British Columbia chats, it would be best to use GPS tags. It is encouraging that the use of tracking devices did not affect the apparent annual survivorship for chats. These results are in congruence with other reviews that find the use of tracking devices generally does not negatively affect the survivorship for most songbirds, except highly aerial species (Bridge et al. 2013, Morganti et al. 2017). Survivorship estimates of 0.62 for birds with tracking devices are similar to the survivorship estimate of 0.65 for males in the study area from 2001 – 2006 (McKibbin and Bishop 2012) and higher than my 0.50 estimate for chats without tracking devices. The lower survivorship estimates of birds without tracking devices is likely a reflection of reduced resighting effort (Martin et al. 2017); I prioritized fieldwork effort on territories of chats with devices. The apparent annual survival for chats in my study area was similar to the estimate for resident adult chats within the Northern Rockies bird conservation region of 0.61 ± 0.04 between 1992 – 2006 (DeSante et al. 2015). My overwintering banding efforts in Nayarit show that some chats return to the same overwintering area in subsequent years. Other banding efforts in the overwintering grounds have similarly shown chats recaptured in subsequent years at the original capture site in 78 Mexico, Belize, Panama, and El Salvador (Ely et al. 1977, Loftin 1977). Continued efforts for monitoring overwinter survival (e.g. by continued efforts such as MoSI are valuable to improve sample sizes for estimating overwintering site fidelity. Colour-banding and monitoring overwintering birds could improve survival estimates (Martin et al. 2017), but unfortunately, chats are difficult to detect and resight. My attempts to resight colour-banded chats during the overwintering period in Nayarit were unsuccessful, even with the use of call-playback surveys. The 5 chats tagged with GPS tags in British Columbia overwintered within a few hundred kilometers of each other. However, the isotope results indicate birds from multiple populations overlap during the non-breeding season, suggesting weak migratory connectivity. Chats from multiple breeding latitudes overwinter in Nayarit, where most birds originate from the southern and western portion of their breeding range, but 10% of the birds originated from the northern edge of their breeding range (Table 3.3). Additionally, geolocator results from chats breeding in California also suggest these birds overwintered in western Mexico. Therefore, it appears birds from both the northern and southern portions of their breeding range overwinter in Sinaloa and Nayarit. It would be valuable to deploy more GPS tags on the endangered British Columbia population as well as other populations across the chat range to better understand their full annual cycle, including specific overwintering locations. Additionally, I only tracked male chats, and it would be interesting to see whether any sex-based differences exist in overwintering areas for chats breeding in British Columbia, as habitat segregation between sexes has been identified in the overwintering grounds for other songbirds (Lynch et al. 1985, Parrish and Sherry 1994). Similarly, my stable hydrogen isotope results from migrating birds in Veracruz and Chiapas also suggest birds from multiple different breeding latitudes migrate through these sites. Birds originating from a wide range of latitudes migrated through Chiapas compared to Veracruz. However, the eastern chat breeding range spans a smaller geographic area than the 79 western; therefore, assignment estimates cover a larger portion of their breeding range, preventing precise origin estimates. For example, group assignments for birds within the -30‰ to -40‰ δ2Hf bin covers the majority of the breeding range except the very northern and southern edges. The limitation of stable hydrogen isotopes assigning individuals to broad geographic areas has been noted elsewhere (Hobson and Wassenaar 1997, Langin et al. 2009). Nonetheless, bird banding recovery data link birds from the northeastern portion of their breeding range to Veracruz, reiterating this site is used by birds from various parts of their breeding range. My method of SNP genotyping using mitochondrial DNA to determine eastern and western subspecies was novel and worked successfully. Therefore, this is another useful technique for examining range-wide movements for species where underlying mitochondrial DNA differences exist. I found no co-occurrence of the eastern and western subspecies in Mexico and my results are similar to Lovette et al.'s (2004). I expand the known overwintering range southward for eastern chats with my Chiapas site. Combining my results with Lovette et al.'s (2004), it appears that the separation between the eastern and western chat occurrences in Mexico is between coastal Chiapas and coastal Oaxaca. From a conservation perspective, the riparian habitats the chats use for breeding, stopover, and overwinter are at risk of loss and degradation due to climate change. Specifically, increased droughts and changes in hydrology, compounded by an increased water demand for human needs, will be detrimental to wetlands and riparian habitat (Perry et al. 2012). The preservation and restoration of suitable riparian habitat would benefit this species, especially in areas where historical declines have occurred in the breeding range (Eckerle and Thompson 2020). In my British Columbia study area, one restoration technique that increased abundance of chats over 10 years involved the exclusion of cattle from riparian areas (Forrester et al. 2017). Additionally, a study examining recently restored habitats involving vegetation removal 80 during channel reconstruction and subsequent replanting in California highlight the importance of maintaining structural diversity and shrubs for chat nesting habitat by retaining remnant patches of mature vegetation (Rockwell and Stephens 2018). Incorporating restoration techniques that maintain vegetative structure and complexity such as dense thickets and shrubs could benefit the chats if implemented in the overwintering range as well. The protection of migration sites would be valuable for chats to maintain landscape connectivity as chats pass between breeding and overwintering phases each year. Chats originating from multiple breeding latitudes used migration sites in Veracruz and Chiapas. The Chiapas site is currently a protected area, however, better enforcement of regulations to keep the area natural would be beneficial as I observed habitat modification, cattle grazing, and pesticide use within La Encrucijada Biosphere Reserve. Like many migratory songbirds, the preservation of suitable habitat for chats is a challenge due to birds crossing multiple socio-political boundaries within which resources, political views, and cultural attitudes towards songbirds vary widely (Roldán-Clarà et al. 2017, Ortego-Santos et al. 2019). Continued international partnerships and collaborations (e.g. this study, Partners in Flight), and the inclusion of diverse stakeholders, notably Indigenous stakeholders, are beneficial moving forward to protect and conserve songbirds (Nevins et al. 2009, Ortego-Santos et al. 2019). 81 Table 3.1. Yellow-breasted Chat (Icteria virens) study areas. Some sites had multiple subsites within the area. The sample size (N) for geolocators and GPS tags refers to the number deployed. Dashes indicate no data. Province/ State Site Name Lat. Long. Elevation (m) Stage N Geolocators GPS Feathers (genetics) Feathers (isotopes) British Columbia, CA South Okanagan Valley 49.094 -119.537 300 Breeding 37 14 - - California, US Trinity River 40.695 -122.854 500 Breeding 49 - - - Nayarit, MX San Pancho (San Francisco) 20.905 -105.398 33 Overwinter - - 19 40 Chiapas, MX La Encrucijada Biosphere Reserve 15.552 -93.206 3 Migration - - 38 45 Veracruz, MX Santa Alejandrina Bird Observatory 18.002 -94.588 4 Migration - - 154 125 Table 3.2. GPS Tag Results. Lotek PinPoint GPS tags were attached to male Yellow-breasted Chats (Icteria virens) breeding in the South Okanagan Valley of British Columbia, Canada. Fall depart refers to the last point at the breeding site, and winter arrive refers to the first point on the overwintering site. All winter sites are within Mexico. Final point is the date of the last fix. Dashes indicate no data. Tag Bird Year No. Fixes Fall Depart Winter Arrive Duration (days) Distance (km) Winter Site Final Point 40734 XW:DD 2016 4 - Oct 5 - 3170 Sinaloa Oct 20 40735 YD:XY 2016 4 - Oct 5* - 3330 Nayarit Oct 5 40739 LX:DL 2016 3 - Oct 5* - 2919 Sinaloa Oct 5 41998 XG:GY 2017 31 Sep 6 Oct 4 29 3145 Sinaloa Dec 7 42001 GX:YW 2017 52 Sep 6 Oct 20 44 3296 Sinaloa May 21 * Final fix, therefore, unknown if bird went farther south. 82 Table 3.3. Feather stable hydrogen isotope (δ2Hf) summary. Feathers were collected from Yellow-breasted Chats (Icteria virens) from 3 different sites in Mexico. Chats were sampled in Nayarit in the overwintering stage and are of the western subspecies. Chats were sampled in Veracruz and Chiapas during migration and are of the eastern subspecies. Stable hydrogen isotope values are reported per thousand (‰) standardized to Vienna Standard Mean Ocean Water. The bold bins have the highest percentage of chats from each site. Dashes indicate no data. Nayarit Chiapas Veracruz δ2Hf (‰) Mean ± SE N % Mean ± SE N % Mean ± SE N % -10 < -20 -15.4 ± 1.4 3 7.5 - - - - - -20 < -30 -28.0 ± 0.5 2 5.0 -27.1 ± 0.9 12 26.7 -28.0 ± 0.9 4 3.2 -30 < -40 -35.8 ± 0.2 13 32.5 -34.6 ± 1.0 15 33.3 -36.4 ± 0.5 32 25.6 -40 < -50 -45.0 ± 0.2 11 27.5 -43.0 ± 0.4 10 22.2 -44.1 ± 0.3 70 56.0 -50 < -60 -52.9 ± 0.3 3 7.5 -52.9 ± 1.2 6 13.3 -52.9 ± 0.5 19 15.2 -60 < -70 -61.58 ± 0.6 3 7.5 - - - - - - -70 < -80 -72.5 1 2.5 -71.4 1 2.2 - - - -80 < -90 -84.8 ± 0.7 4 10 - - - - - - -90 < -100 - - - -90.4 1 2.2 - - - Total -45.4 ± 0.5 40 100 -39.1 ± 1.9 45 100 -43.0 ± 0.6 125 100 83 Figure 3.1. Yellow-breasted Chat (Icteria virens) study sites. The stars symbolize breeding sites, the square symbolizes the overwintering site, and the triangles symbolize migration sites. The eastern (I.v. virens) and western (I.v. auricollis) subspecies breeding ranges are illustrated along with the species’ non-breeding wintering range (IUCN 2016). Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. 84 Figure 3.2. Fall migration routes of Yellow-breasted Chats (Icteria virens). Five male chats were tracked using Lotek PinPoint GPS units from breeding sites in the South Okanagan Valley of British Columbia, Canada to overwintering sites in Sinaloa and Nayarit, Mexico. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. 85 Figure 3.3. Western Yellow-breasted Chat (Icteria virens) fall migration stopover site. One Yellow-breasted Chat tracked with a Lotek GPS PinPoint from its breeding site in British Columbia, Canada, stopped during fall migration in Idaho, USA for at least 8 days between September 14 - 22, 2017. The cross marks the stopover site. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. 86 Figure 3.4. Overwintering areas of Yellow-breasted Chats (Icteria virens) that bred in British Columbia. Asterisks denote overwintering locations as determined from tracking chats with GPS tags. The square is the Nayarit study site. Satellite imagery of overwintering locations provided for visualizing habitat. Protected areas of Mexico provided by the United Nations Environment World Conservation Monitoring Centre (UnEP-WCMC 2020). Overwintering range provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. 87 Figure 3.5. Overwintering longitudes of Yellow-breasted Chats (Icteria virens) inferred from geolocators. Geolocators were attached to breeding Yellow-breasted Chats in the South Okanagan Valley of British Columbia, Canada and in northern California, USA. Points represent median longitudinal estimates from Nov 15 – Mar 1 and lines represent interquartile range. Note that line placement is in an increasing eastward longitudinal order and that latitudes are arbitrary within the mapped IUCN (2016) overwintering range as latitudinal estimates from my geolocators were inaccurate. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. 88 Figure 3.6. Hydrogen stable isotope values in Yellow-breasted Chat (Icteria virens) feathers, (δ2Hf). Chats were sampled in Chiapas, Mexico during fall migration from September to October in 2018 (n=45). Chats were sampled in Nayarit, Mexico during the overwintering period in 2018 (n=40). Chats were sampled in Veracruz, Mexico during fall migration in March, May, and late August to early November in 2014 (n=125). Stable hydrogen isotope values are reported per thousand (‰) standardized to Vienna Standard Mean Ocean Water. Each point represents an individual chat hydrogen isotopic signature. Errors bars represent mean and 95% confidence intervals. 89 Figure 3.7. Yellow-breasted Chat (Icteria virens) breeding origins from δ2Hf samples. The outlined polygon for Nayarit is the western subspecies range (IUCN 2016). The outlined polygon for Veracruz and Chiapas is the eastern subspecies range (IUCN 2016). Group assignment estimates are shown for the least depleted 10‰ bin (left), the 10‰ bin containing the most birds (center and outlined in black), and the most depleted 10‰ bin (right). Feather samples from Nayarit are from overwintering chats. Feather samples from Veracruz and Chiapas are from migrating chats. 90 Figure 3.8. Band recovery data for Yellow-breasted Chats (Icteria virens). Encounters at least 40 km apart from 1920 – 2019 (North American Banding Program 2019) are connected with straight lines to visualize range-wide movements (IUCN 2016). Spring dates are between April 1 – May 14. Breeding dates are between May 15 – August 30. Fall dates are between September 1 – October 30. Winter dates are between January 1 – February 28. Map created in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator coordinate system. 91 Chapter 4: Mercury toxicity risk and corticosterone levels in the Yellow-breasted Chat 4.1 Introduction Mercury is an environmental contaminant of concern to wildlife because it can negatively affect reproduction, neurology, immunology, and behaviour (Wolfe et al. 1998, Scheuhammer et al. 2007, Seewagen 2010). Although this heavy metal occurs naturally in the environment, anthropogenic activities have increased levels dramatically (Driscoll et al. 2007, 2013), and these increases are reflected within wildlife at some locations (Thompson 1989; Bond et al. 2015; Perkins et al. 2019, but see Elliott and Elliott 2016). Globally, approximately 600 metric tonnes of mercury is deposited into the atmosphere annually through emissions from coal-fired electrical generation, chlorine production, and waste incineration (Driscoll et al. 2007). The effects of contamination may be evident in wildlife up to 50 years later after the point source of contamination has ceased operation (Jackson et al. 2011b). Mercury from both anthropogenic sources (e.g. chlor-alkali plants, coal burning, waste incineration) and naturally occurring sources (e.g. volcanic activity, forest fires) can travel vast distances in the atmosphere before settling in soil and waterways (Driscoll et al. 2013). Eventually, mercury in waterways gets transported downstream and settles in the sediments of aquatic and wetland habitats (Cristol et al. 2008, Driscoll et al. 2013). Here, under anoxic conditions, inorganic mercury becomes transformed by methylating bacteria into its most biohazardous form, methylmercury, and readily moves through food webs (Driscoll et al. 2007, 2013, Cristol et al. 2008). Therefore, most research on mercury toxicity has focused on species associated with aquatic habitats, primarily piscivorous aquatic birds (Scheuhammer et al. 2007, Carravieri et al. 2014, Jackson et al. 2016) and mammals (Hitchcock et al. 2017, Ljungvall et al. 2017). However, recent evidence suggests that songbirds, especially those that use riparian, aquatic, and wetland habitats, may 92 also be at risk of mercury exposure through consumption of carnivorous and aquatic invertebrates (Rimmer et al. 2005, Brasso and Cristol 2008, Edmonds et al. 2010, Evers et al. 2012, Jackson et al. 2015, Ackerman et al. 2016, Pacyna et al. 2017). The identification of songbird populations at risk of mercury toxicity is important because multiple sublethal effects may affect the persistence of a population. For example, mercury exposure can negatively affect reproduction through altered nest-building (Chin et al. 2017), reduced courtship behaviour (Heddle et al. 2019), smaller egg size, smaller clutches (Brasso and Cristol 2008, but see Heddle et al. 2019), and lowered nest success through hatchling mortality (Scheuhammer 1987, Custer et al. 2007, Jackson et al. 2011a, Heddle et al. 2019). Early developmental exposure to mercury can also affect survival, reproduction, behaviour, and neuroanatomical development (Yu et al. 2017, Paris et al. 2018, Heddle et al. 2019). Moreover, mercury may negatively affect migration and overwinter survival (Ma et al. 2018), which may cause further declines in breeding populations. Additionally, mercury reduces body condition and negatively affects the immune function in birds, therefore, individuals may be less equipped to fend off diseases and parasites (Whitney and Cristol 2017, Ackerman et al. 2019). The majority of studies on songbirds and mercury are either lab dosing studies (Finley et al. 1979, Maddux et al. 2015, Yu et al. 2017, Chin et al. 2017) or field studies focused on a specific point source of contamination (Brasso and Cristol 2008, Hothem et al. 2008, Jackson et al. 2011b, Fuchsman et al. 2017, Howie et al. 2018). Field studies examining mercury levels in free-living birds without a source of contamination are usually limited in geographic scope (Keller et al. 2014, Stenhouse et al. 2019). A few notable exceptions include a comprehensive study of blood mercury levels in 102 songbird species in eastern North America where they found that invertivores had greater mercury levels than omnivores, wetland invertivores had higher levels of mercury than upland invertivores, and adults had higher mercury levels than juveniles (Jackson et al. 2015). A study on Nelson’s Sparrow (Ammodramus nelsoni) across 93 Ontario, North Dakota, and North Carolina found mercury levels in blood were higher on the breeding grounds compared to the overwintering grounds (Winder and Emslie 2011). Likewise, a study on Bicknell’s Thrushes (Catharus bicknelli) spanned northeastern North America, including Canada, the United States, Hispaniola, and Cuba, and found overall blood mercury concentrations were greater on the overwintering grounds than the breeding grounds (Rimmer et al. 2005). Lastly, a study on Rusty Blackbirds (Euphagus carolinus) spanned the eastern United States and Alaska and found concentrations to be highest in the eastern breeding range compared to the western breeding range, plus higher in the breeding range than the overwintering range (Edmonds et al. 2010). In birds, the risk of mercury toxicity may vary by diet, age, and sex. Because mercury bioaccumulates, songbirds that consume arthropods at higher trophic levels, including spiders, can accumulate very high levels of mercury (Rimmer et al. 2005, Keller et al. 2014). Some studies show older individuals are at greater risk of mercury toxicity because it accumulates in the organs of the body (Rimmer et al. 2005, Warner et al. 2012, Jackson et al. 2015).In contrast, a study on Snow Petrels (Pagodroma nivea) found older individuals had lower levels of mercury than younger individuals and these age-related differences may be attributed to foraging niche (Tartu et al. 2014). Likewise, in Great Skuas (Catharacta skua), adults had higher mercury concentrations than chicks, but once adulthood was reached, mercury concentrations did not vary between adults of different ages (Thompson et al. 1991). Males may have higher mercury levels than females because females can depurate some mercury through egg-laying (Scheuhammer 1987, Rimmer et al. 2005, Robinson et al. 2012). However, a study on Snow Petrels found that females had higher levels than males, but these differences may be due to sex-based foraging preferences (Tartu et al. 2015). Likewise, a study on Great Skuas found no differences in feather mercury between males and females (Thompson et al. 1991). 94 Of additional concern, high levels of mercury can disrupt the stress response system. The stress response involves the release of glucocorticoids into the bloodstream via adrenal glands through the hypothalamus-pituitary-adrenal axis (Siegel 1980, Wingfield 2013b). In songbirds, the main glucocorticoid is corticosterone (Siegel 1980, Romero 2004). There has been some controversy on the usefulness of this hormone for conservation physiology, in particular, as an indication of stress in an organism (Romero 2004, Dickens and Romero 2013, MacDougall-Shackleton et al. 2019). As this hormone also relates to metabolism, baseline levels are always present in the body but become elevated in response to a stressor (Siegel 1980, Romero 2004, MacDougall-Shackleton et al. 2019). The elevation of glucocorticoids prepares the body for survival mode and temporarily suspends less critical processes (Sapolsky et al. 2000). This hormonal cascade is adaptive for an animal to overcome unexpected obstacles it encounters in the wild and is highly conserved among vertebrates (Wingfield et al. 1998, Romero 2004). Malfunctions of the normal stress response system is maladaptive, with effects including weakened immune systems, hypertension, neuronal cell death, memory loss, and negative fitness consequences (Busch and Hayward 2009, Kleist et al. 2018). A study on Tree Swallows (Tachycineta bicolor) found a complex relationship between mercury and corticosterone on nestlings that varied with age: stress-induced corticosterone levels were elevated in mercury-contaminated sites relative to reference sites for nestlings aged 3-6 and 7-12 days old, whereas nestlings aged 13 – 18 days had suppressed corticosterone levels in contaminated sites (Wada et al. 2009). Another study on Tree Swallows in contaminated sites found a negative correlation between blood mercury and blood baseline corticosterone, but found no relationship between blood mercury and stress-induced corticosterone (Franceschini et al. 2009). A lab dosing study on Zebra Finches (Taeniopygia guttata) showed a suppressed stress response with increasing blood mercury levels and yet no effect on baseline corticosterone levels (Moore et al. 2014). However, studies on Snow Petrels 95 (Tartu et al. 2015), Tree Swallows (Wada et al. 2009; Franceschini et al. 2009), Forster’s Terns (Sterna forsteri; Herring et al. 2012), and Zebra Finches (Moore et al. 2014) have shown a reduced stress response with increasing mercury. Several factors influence the activity of the hypothalamus-pituitary-adrenal axis. Specifically, older birds may have a lowered stress response compared to younger birds as a life history trade-off to maximize reproduction later in life (Heidinger et al. 2006, 2010, Boves et al. 2016). Moreover, corticosterone levels vary by sex as a function of caregiving responsibilities for the offspring, where the primary caregiver has a lowered stress response than the less-invested sex (Wingfield et al. 1995, O’Reilly and Wingfield 2001). Differences in corticosterone levels vary between subspecies, which may be due to underlying differences in life-history strategies (Romero et al. 2005, Angelier et al. 2011). In addition to the factors mentioned above, naturally occurring stressors that free-living birds encounter in their environment include food shortages, predators, anthropogenic disturbances, competition, and bad weather events, all of which can also affect the stress response and levels of corticosterone (Sapolsky et al. 2000, Lobato et al. 2010, Ouyang et al. 2012, Kleist et al. 2018). Therefore, corticosterone levels are expected to be variable for free-living birds under natural conditions due to these biotic and abiotic factors that are difficult to control or account for, but here I examine if trends related to range position are present. Here, I examine trends in mercury and corticosterone across the breeding range of Yellow-breasted Chat (Icteria virens, hereafter chat). The scale at which I examine these two chemicals is unprecedented and I am not aware of any studies that have measured these two chemicals in chats. The chat is a neotropical migrant songbird whose habitat consists of dense, shrubby thickets in or near riparian habitats – a habitat zone generally believed to be a hotspot for mercury (Eckerle and Thompson 2020). Additionally, given that mercury can transcend aquatic-terrestrial interfaces via invertebrate vectors, the Yellow-breasted Chat may be exposed 96 to mercury through the consumption of invertebrates in these riparian habitats. The diet of the Yellow-breasted Chat consists of terrestrial invertebrates (ants, wasps, spiders, beetles, leafhoppers) and also plant matter (Yard et al. 2004). Probing deeper into the conservation physiology for this species may prove valuable for conservation efforts, as several populations are of conservation concern (British Columbia, Ontario, Environment and Climate Change Canada 2016, 2019; California, Shuford and Gardali 2008; Connecticut, State of Connecticut 2015; New York, New York State 2019). My study had multiple goals. First, I measured mercury concentrations across the breeding range and examined mercury toxicity risk based on published benchmarks. Second, I examined how mercury levels vary with age, sex, range position, and subspecies on a broad geographic scale. The specific hypotheses I tested with this second goal were: (i) females have lower levels of mercury than males, and (ii) older individuals have higher mercury levels than younger birds. Third, I examined how corticosterone levels vary with age, sex, subspecies, and range position on a broad geographic scale as an exploratory investigation. Fourth, I tested the hypothesis that mercury suppresses the stress response system, thereby resulting in a negative correlation between mercury and corticosterone. 4.2 Methods 4.2.1 Study Areas I sampled chats in seven study areas. Four areas were known breeding locations in British Columbia, Canada, and California and Oregon, USA (Table 4.1, Figure 4.1). The remaining three study areas were in Mexico in Nayarit, Chiapas, and Veracruz, where chats were sampled during migration and the non-breeding period. Breeding location was inferred from Mexico sites using hydrogen stable isotope analyses and genetics (Chapter 3, Figure 3.6, Figure 3.7). 97 All breeding sites were in riparian areas. The British Columbia study area was along the channelized South Okanagan River (McKibbin and Bishop 2010). The California study area was along the Trinity River (Rockwell and Stephens 2018). The WIIM Oregon study area was along the Rogue River, while the TOPS Oregon study area was along the Klamath River (Rockwell et al. 2017). The Nayarit, Mexico study area consisted of openings and edges of semi-deciduous tropical forests. The Chiapas study site was located within the La Encrujijada Biosphere Reserve near the Pacific Ocean within a large open wetland surrounded by small patches of trees and tall grasses. The Veracruz, Mexico study site was adjacent to a wetland area and surrounded by shrubs and short trees (Gahbauer et al. 2016). 4.2.2 Bird Handling and Feather Collection In British Columbia and California, chats were captured and sampled during the breeding period between May and August of 2017 and 2018. In Oregon, chats were captured and sampled in 2018 between May and July but two birds were sampled in September. In Veracruz, chats were sampled in March, May, August to November 2014, and March 2015, and I assume that these were migrating chats. In Chiapas, chats were sampled in September and October of 2018, and I assume these were fall migrating chats. In Nayarit, chats were sampled in January and February 2018 and 2019, and I assume these were overwintering chats. Chats were caught in mist nets often with the aid of call-playback and a taxidermied decoy. Birds were removed from nets, sexed according to (Pyle 1997), and placed into age categories defined by Wolfe et al. (2010), then banded. Young birds and birds that could not clearly be distinguished as male or female were classified as unknown sex. The right outer rectrix feather was collected for corticosterone analyses. The left outer rectrix feather was collected for mercury analyses. One secondary feather (S1-S6) was collected for hydrogen stable isotope analysis (the same samples from Chapter 3). Birds were released after a short handling time. 98 Mercury encapsulated in the feather represents recent dietary exposure and body burden mobilized from other organs and tissues during the period of feather growth (Furness et al. 1986, Scheuhammer 1987, Thompson et al. 1991, Evers et al. 2005). Similarly, corticosterone encapsulated in feathers represents hormonal levels during the period of feather growth (Bortolotti et al. 2008). 4.2.3 Moult Strategy of Chats Knowing when and where a feather was grown has important implications for my interpretation of the exposure of chats to mercury and corticosterone. The age classification of birds I used relates to their moult cycle. The first set of feathers grown are juvenal feathers, which are grown in the nest and are referred to as the first cycle juvenal plumage (Wolfe et al. 2010). Then, putatively on the breeding grounds and prior to fall migration, chats undergo their incomplete preformative moult (Wolfe et al. 2010). This incomplete preformative moult in chats includes the outer primaries, inner secondaries, tertials, and greater coverts (Pyle 1997). (Pyle 1997) states that most chats during their preformative moult only replace at most the two inner rectrices, but Grosselet et al. (2014) found that most eastern chats replace their entire tail during the preformative moult. Therefore, feathers collected from eastern chats in their formative plumage represent environmental conditions on the breeding grounds where they grew these feathers before fall migration. However, in western chats, it is unknown to what extent chats replace their tail feathers during their preformative moult; it appears that some do replace tail feathers but likely many do not (pers. comm from Matthias Bieber; Pyle 1997). Therefore, feathers collected from western chats in their formative plumage represent either natal conditions grown in the nest (if feathers are juvenal) or the breeding grounds before fall migration (if feathers are formative). Chats do not undergo a pre-alternate moult on the overwintering grounds. Thus, flight feathers replaced prior to fall migration are kept until the following fall, when the chat moults 99 again. The next moult a chat undergoes is its prebasic moult, which again putatively occurs on the breeding grounds prior to fall migration (Pyle 1997, pers. obs.). This moult is complete, where all flight and body feathers are replaced, and the associated plumage is the definitive basic plumage (Wolfe et al. 2010). Therefore, feathers collected from both eastern and western chats in their definitive basic plumage represent conditions on the breeding grounds prior to fall migration (Pyle 1997). 4.2.4 Breeding Assignment of Overwintering Birds To determine the breeding origin of chats sampled in Mexico, I examined hydrogen stable isotope values in feathers (Chapter 3). Hydrogen stable isotope values in precipitation vary latitudinally across the landscape and this chemical signature becomes incorporated into animal tissues (Hobson and Wassenaar 2008). Therefore, by examining the hydrogen stable isotope contents of animal tissue (feathers in this case), I can infer where the tissue was grown (Hobson and Wassenaar 1997). In brief, feathers were washed in a solvent to remove any impurities, air dried, and cut into small pieces. Feathers were sent to the Laboratory of Stable Isotope Science at the University of Western Ontario, London, Ontario, Canada for analyses. Stable hydrogen isotope composition was determined using an elemental analyzer where samples undergo pyrolysis by heating to 1120˚C. Samples were calibrated using standard reference materials (Soto et al. 2017). The standard notation of stable hydrogen isotopes (δ2H) is parts per thousand (‰) relative to Vienna Standard Mean Ocean Water. A subscript F is included when referring to feathers, or P when referring to precipitation hydrogen isotope values. A precipitation isoscape for North America was created using publicly available δ2Hp values in rainwater from the Global Network of Isotopes in Precipitation (IAEA/WMO 2019). The package IsoriX (v0.8.2; Courtiol et al. 2019) in R (v3.5.1.; R Core Team 2018) was used to 100 create the precipitation isoscape and then calibrated using Yellow-breasted Chat feather samples of known breeding origin to reflect the values found in chat feathers (Chapter 3). To estimate the breeding origin of chats, δ2Hf values were placed into 10‰ bins and a group location was estimated using the isofind function of IsoriX. Group location estimates were exported as a raster file to create maps in ArcMap 10.7.1 (ESRI 2019). Location estimates were constrained to breeding ranges based on subspecies identification previously determined using genetics (Chapter 3). The western subspecies (I.v. auricollis) breeds in the western United States, southwestern Canada, and northwestern Mexico (Figure 1, IUCN 2016; Eckerle and Thompson 2020). The eastern subspecies (I.v. virens) breeds in the eastern United States and the very southern tip of Ontario, Canada (IUCN 2016a, Eckerle and Thompson 2020). I showed genetically that chats in Chiapas and Veracruz were of the eastern subspecies while chats from Nayarit were of the western subspecies (Chapter 3). 4.2.5 Mercury Analyses I measured total mercury concentration in 150 chat feathers (Table 4.1). Samples were analyzed by the National Wildlife Research Centre in Ottawa, Ontario, Canada. Feathers were washed with acetone, then a solution of 0.25% Triton X-100, then twice in ultrapure water before being air-dried for ≥ 48 h. Cleaned samples were freeze-dried and ground into a homogenous powder. Samples were then placed into the autosampler on nickel boats. Samples were decomposed under a continuous flow of ultra-pure oxygen within a quartz catalytic tube. The products of combustion were subsequently carried to the catalytic furnace for oxidation. Halogens, nitrogen oxides, and sulfur oxides become trapped and the remaining products were carried further to a gold amalgamator that targets mercury. The system was flushed with oxygen to remove any unwanted by-products and the amalgamator was heated to release mercury vapour. The mercury vapour was carried via the oxygen flow through absorbance cells within an atomic absorption spectrophotometer. The absorbance was measured at 253.7 nm. Sample 101 values for mercury are reported in µg/g dry mass. As greater than 95% of the mercury present in feathers is methylmercury, total mercury content represents methylmercury concentration (Thompson et al. 1990), which is the organic and biohazardous form of mercury. A minimum of four standard reference materials were tested throughout the day. The average recovery (± SE) of mercury from standard reference materials was 99.3 ± 0.51% across 8 days and 55 tests. The method detection limit was determined using 10 consecutive analyses of a standard reference material containing a low concentration of mercury. The method detection limit as determined on August 12, 2019 based on five mg of SRM Oyster Tissue 1566b was 0.024 ng. Reported method detection limits were adjusted for each sample based on mass and varied between 0.003 – 0.018 µg/g. 4.2.6 Corticosterone Extraction and Assay I followed standard feather corticosterone extraction methods for 197 chat feathers (Bortolotti et al. 2008, Fairhurst et al. 2015, Harris et al. 2016). Each feather was washed with ~1% dilute soap to remove any surface contaminants and then rinsed with ultrapure water. The feathers were air-dried overnight in a fume hood. The calamus was discarded and the length of the feather was measured with a clear ruler to the nearest millimeter. The feather was cut into small pieces using fine scissors within a glass test tube and 10 ml of 99.9% HPLC grade methanol was added. Test tubes were covered and sonicated in a water bath at room temperature for 30 minutes and then placed in a heated shaking water bath at 50°C overnight. The feather pieces were separated from the methanol solution via vacuum filtration into a new glass test tube. A 1.0 L vacuum filtration flask was filled with aquarium gravel to ensure the test tube was at the appropriate height. A glass funnel plugged with marine PolyWoolTM as a filter was fit snug to the filtration funnel using a rubber sleeve. The test tube, feather remnants, and filter were washed twice with 2.5 ml of methanol to capture any remaining corticosterone. The methanol solution was evaporated in a fume hood under air and the extract residues were 102 reconstituted with 300 µl of assay buffer and vortexed 3 times at room temperature. The samples were then placed in 1.5 ml centrifuge tubes and centrifuged for ~5 minutes to pelletize any remaining debris or larger molecules. A volume of 100 µl of the supernatant was pipetted into an Enzyme-Linked Immunosorbent Assay plate for each sample in duplicate. Corticosterone was assayed using Enzo Life Sciences Enzyme-Linked Immunosorbent Assay kit no. ADI-900-097. This is a competitive binding immunoassay kit specific to the hormone corticosterone that uses a polyclonal sheep corticosterone antibody and a 96 well plate coated with donkey anti-sheep immunoglobulin G. The reported cross-reactivity of these kits include 28.6% deoxycorticosterone, 1.7% progesterone, 0.13% testosterone, 0.28% tetrahydrocorticosterone, 0.18% aldosterone, 0.046% cortisol, <0.03% pregnenolone, <0.03% β-estradiol, <0.03% cortisone, and <0.03% 11-dehydrocorticosterone acetate (Enzo Life Sciences Inc. 2018). The reported sensitivity of the kit is 26.99 pg/mL based on two standard deviations from the blank reading using 16 replicates (Enzo Life Sciences Inc. 2018). I followed the kit instructions to determine corticosterone concentrations, culminating in reading the optical density using a microplate reader set to 405 nm (Enzo Life Sciences Inc. 2018). Samples were run in duplicate and averaged during reporting. Three samples of approximately 50% binding with a concentration of 600 pg/mL were added randomly to each plate to assess for inter-assay variability. The inter-assay coefficient of variation was calculated as the standard deviation of the plate means divided by the mean of the means for the 8 plates. The intra-assay coefficient of variation was calculated as the overall average of the standard deviation divided by the mean for each duplicate sample for all samples for 8 plates. To determine the corticosterone concentration of samples in the plate, the average optical density for the blank cells was subtracted from the optical density for all samples. A 4-parameter logistic model was used to create a standard curve with known corticosterone concentrations of 20000, 4000, 800, 132, and 32 pg/mL. The drc package (v. 3.2.1; Ritz et al. 103 2015) in program R was used to create the standard curve, and the unknowns were interpolated along the standard curve using the ED function (Appendix H). The concentration of corticosterone within each feather was standardized to feather length and reported in values of pg/mm. 4.2.7 Mercury Risk To examine the risk of mercury toxicity to chats, I compared my mercury values to known blood toxicity benchmarks provided by Ackerman et al. (2016) based on a broad examination of studies on multiple bird species: < 0.2 µg/g background levels; 0.2 – 1.0 µg/g low risk; 1.0 – 3.0 µg/g moderate risk; 3.0 – 4.0 µg/g higher risk; > 4.0 µg/g severe risk. These values are similar to reproductive effect thresholds for small birds of 2.1 – 4.2 km/kg summarized from a meta-analysis by Fuchsman et al. (2017). However, as Ackerman et al.'s (2016) rankings are based on blood mercury levels (wet mass), I transformed my feather mercury concentrations to equivalent blood mercury concentrations. I used two different transfer equations, and therefore, had two sets of blood mercury equivalents to assess against these benchmarks. The first transfer equation was from Eagles-Smith et al. (2008), who reviewed mercury levels across many tissue types within the same individuals for multiple seabird species. I used the transfer function reported for breast and head feathers because this equation was used to adjust between feathers and blood in Ackerman et al. (2016). The equation was: Ln(blood mercury) = ln(feather mercury)*0.675 – 1.673. My second transfer function was from Jackson et al. (2011a) for Carolina Wrens (Thyrothorus ludovicianus), which may be more relevant to chats because Carolina Wrens are insectivorous songbirds that inhabit riparian zones. Jackson et al. (2011a) also used outer tail feathers as my study did. Their equation is: Tail feather mercury = 3.388*blood mercury + 0.64Using the results from these two transfer functions, I calculated the percent of total samples that fell within each risk category. 104 4.2.8 Statistical Analysis To determine which factors influence mercury and corticosterone levels in chats, I used an information-theoretic approach comparing the fit of multiple models using Akaike’s Information Criterion adjusted for small sample sizes (AICc, Burnham and Anderson 2002). All values are reported as mean ± standard error unless otherwise noted. Both mercury and corticosterone were transformed before analyses to improve normality and model fit as both variables were highly positively skewed. Mercury was loge transformed. Corticosterone was transformed to the power of -0.7 based on a Tukey’s Ladder of Powers transformation to produce a more normal distribution. Transformed variables were assessed with a Shapiro-Wilk’s test to verify normality. For mercury samples (n=150), I tested 12 models containing different combinations of the parameters age, sex, subspecies, and range location (Table 4.2). Age included two categories: first cycle and definitive cycle. Most birds in their first cycle were in their formative plumage, but I also included 4 western chats that were currently moulting into their formative plumage and 1 western chat in its juvenal plumage. Sample sizes were too small for chats in their juvenal plumage to have their own age category. Two chats in unknown plumage were lumped into the definite cycle age group. Age was included to test the hypothesis that older birds accumulate more mercury than younger birds due to bioaccumulation (Evers et al. 2005, Warner et al. 2012, Jackson et al. 2015). Sex included male, female, and unknown and was included to test the hypothesis that females have lower levels of mercury than males due to egg-laying (Scheuhammer 1987, Robinson et al. 2012). Subspecies was used as a way to compare exposure levels between eastern and western North America as well as examine any differences between subspecies. Range location was included to examine if differences in mercury exposure occurred in different geographic areas. The range classes were chosen to reflect the broad geographic assignments from isotopes while maintaining adequate sample 105 sizes for each category. Range location included six categories based on subspecies and general position within the breeding range. The western north range included samples from British Columbia, and δ2Hf values ≤ -80‰ for the western subspecies (Appendix I). The western central range location included samples from California, Oregon, and δ2Hf values from -50 > -80‰ for the western subspecies. The western southern range location included δ2Hf values -10 > -50‰ for the western subspecies. The eastern north range included δ2Hf values ≤ -50‰ for eastern subspecies. The eastern central range included δ2Hf values -40 > -50‰ for the eastern subspecies. The eastern southern range included δ2Hf values -20 > -40‰ for the eastern subspecies. An interaction between subspecies and age was included to account for the differences in preformative moult between subspecies, as described earlier. All factors were fixed because I was specifically interested in examining the effects of each factor level of each predictor on the response variable. For corticosterone samples (n=196), models were conducted in the same way as for mercury. Minor differences for the age category for corticosterone samples was that the first cycle category contained one western chat in juvenal plumage and one western chat moulting into its formative plumage. Two chats in unknown plumage were lumped into the definite cycle age category. Age, sex, subspecies, range location, and the interaction between age and subspecies were included as model parameters. I tested twelve exploratory models with different combinations of these predictors (Table 4.3). Linear models were created using the lme4 package (v.1.1.21; Bates et al. 2015) in R. I inspected plots of the model residuals versus fitted values for the full model and for the top models to verify that the assumptions of linear models were met. The fit of each model was compared using the MuMIn package (v1.43.15; Barton 2019). I report ΔAIC values and relative weights for each model (Burnham and Anderson 2002). Conditional plots of the top model were implemented using the visreg package (v2.6.0; Breheny and Burchett 2017). If the top model 106 had a weight of less than 0.90, this suggested model uncertainty, and parameter estimates were identified using a model averaging approach (Burnham and Anderson 2002) (Burnham and Anderson 2002). I computed the average model parameter estimates and 95% confidence intervals by factoring in the relative weight of the models using the model.avg function in MuMln. Standardization of the predictors was not necessary before calculating parameter estimates as all predictors were categorical (Gelman 2008). Parameter estimates shown are from the subset method, where only those models containing the parameters are used in estimation (Barton 2019). I also estimated parameters using the full method, where all models are used in estimation, and results were similar (Appendix J). While my statistical approach used transformed values of corticosterone and mercury as indicated to meet model assumptions, I also present original values for visualization. To examine whether there was a relationship between mercury and corticosterone (n=127 birds where both were sampled), I ran a correlation analysis using the mosaic package (v.1.5.0; Pruim et al. 2017) in R for all birds, plus separately for birds in different age classes. To meet the assumption of bivariate normality, mercury values were loge transformed and corticosterone values were inversely transformed before analyses. Shapiro-Wilk’s tests were conducted on the transformed variables to confirm normality. 4.3 Results Stable hydrogen isotope values spanned between -17.85‰ and -90.38‰ (Appendix 9). The most depleted bins represent samples originating from the northern part of the chat’s breeding range, and the least depleted bins represent samples originating from the southern part of their breeding range. 107 4.3.1 Mercury The average mercury levels in feathers across all chats was 0.30 ± 0.02 µg/g. No samples fell below the method limit of detection, therefore, all 150 samples were measured successfully. The two transfer functions converting feather mercury to the equivalent blood levels resulted in the same number of chats falling within each mercury risk category. Almost all (98.6%) chats tested had mercury levels considered background (< 0.2 μg/g blood equivalent). Only two chats (1.3%) were in the low mercury toxicity risk category of 0.2 - 1 μg/g (blood equivalent), one of which originated from the Oregon I study area, and the other in the southwestern region (δ2Hf: -30 < –40‰, Appendix 9). No samples fell within the moderate, high, or severe risk categories for mercury toxicity. The transfer function from Eagles-Smith et al. (2008) was more biologically relevant than the transfer function from Jackson et al. (2011a), as the Jackson et al. (2011a) equation resulted in 139 out of 150 (92.7%) samples with negative values. My isotopic assignments from overwintering sites combined with known breeding sites provided a wide coverage of the chat breeding range (Figure 4.2, Appendix I). However, geographic assignments from stable hydrogen isotopes are to broad geographic areas and therefore, I cannot say with high precision where exposure occurred. The two sites with the highest average concentrations occurred just south of the approximate center of the breeding range of the western subspecies (Figure 4.2). The sample size varied widely between breeding sites, with several sites represented by only 1 sample and so average values representing an entire area should be interpreted with caution. Mercury levels in chat feathers were best explained by a top model that included age and range position as predictors (Table 4.4, Figure 4.3. This model explained little variance in mercury levels in chats as the adjusted R2 value for this model was 0.13. The weight of the top model was 75.4% suggesting that there is some uncertainty about model selection, therefore, all 108 models were factored in for parameter estimation. Mercury levels were lowest for chats in their definitive moult cycle (parameter estimate: -0.44 ± 12 relative to first cycle moult; 95% CI -0.67 - -0.21, Table 4.4). Mercury levels were lowest in the eastern subspecies’ northern (parameter estimate: -0.43 ± 0.22; 95% CI -0.86 - -0.01) and central range locations (parameter estimate: -0.46 ± 0.19; 95% CI -0.82- -0.09) relative to the northwestern range position (Table 4.4). No differences were found between ages or sexes as these parameter estimates included 0 (Figure 4.4). 4.3.2 Corticosterone Corticosterone concentrations were on average 3.68 ± 0.23 pg/mm. One sample was slightly outside the range of the standard curve (i.e. >20,000 pg/ml maximum) and therefore, the corticosterone concentration could not be calculated with confidence. Field notes show this bird had an unusual dark gray skin tone and may have had an underlying health issue; I omitted this sample, and my final sample size was 196. The inter-assay coefficient of variation was 11.3% and the intra-assay coefficient of variation was 3.2%. No samples fell below the level of sensitivity. The top model for corticosterone included subspecies as a predictor with a weight of 46.8% (Table 4.3, Figure 4.5). The adjusted R2 value of 0.091 indicates this model explained very little of the variance in feather corticosterone. Two other models were within 2 ΔAICc units of the top model and included age + subspecies, and the interaction between age and subspecies as predictors and had model weights of 19.8% and 18.7%, respectively (Table 4.3). Regardless, the low weight of the top model indicates there is some degree of model uncertainty, and therefore, I factored in all models for parameter estimates. Corticosterone levels were lower than the western subspecies (parameter estimate: -0.09 ± 0.02 relative to the eastern subspecies; 95% CI: -0.14 - -0.05) and accordingly, for all range positions of the eastern subspecies (Table 4.5, Figure 4.6). Despite age being a parameter in the 2nd and 3rd top models, 109 differences in corticosterone levels between the different age classes of chats were not statistically meaningful as the 95% confidence interval estimates included 0. No differences were detected in corticosterone levels between sexes (Figure 4.6). 4.3.3 Mercury and Corticosterone Combined I had 127 samples where both mercury and corticosterone were measured on the same individual. There was no correlation between mercury and corticosterone in chat feather samples (Figure 4.7, r = -0.054, df = 125, 95% CI: -0.223, 0.121, P-value = 0.54). When the correlation analysis was repeated separately for birds in different age classes, still no correlation was detected (first cycle birds: r = -0.103, df = 82 , 95% CI: -0.310, 0.114, P-value = 0.35; definitive cycle birds: r = -0.037, df = 41, 95% CI: -0.333, 0.267, P-value = 0.82). 4.4 Discussion I examined mercury concentrations in Yellow-breasted Chats across their breeding range and found no evidence that this species is at risk of mercury toxicity despite their affinity for riparian habitats. My feather samples collected from chats represented birds from across the breeding range, including the eastern and western subspecies. Mercury levels were lowest in the northeast and east-central portion of their breeding range. Across all sites, mercury in chat feathers reflected only background levels, except for 2 out of 150 which were low risk of mercury toxicity (Ackerman et al. 2016). In contrast to my hypotheses, mercury was similar between males and females, and mercury levels were higher in younger birds than older birds. Levels of corticosterone were higher in the eastern subspecies than the western subspecies, but no differences existed between chats of different ages or sexes. Lastly, my hypothesis of mercury suppressing the stress response system in chat was not supported as mercury and corticosterone were not correlated. 110 Mercury levels in chat feathers were relatively low, 0.30 ± 0.02 μg/g. At this mean mercury concentration, chat populations are unlikely to experience negative toxicity effects, because those generally occur at higher exposures (Ackerman et al. 2016). The two chats that fell within the low-risk category for blood levels (0.2-1.0 μg/g) were at the low end of this range with equivalent blood mercury levels less than 0.3 μg/g. These two chats are at minimal risk because most published effects within this category occur at blood mercury levels of 0.3 μg/g or greater (Ackerman et al. 2016). The only indication of a harmful effect of mercury at that level was reported in Lesser Scaup (Aythya affinis) experiencing some degree of oxidative stress at blood levels equivalent to 0.2 μg/g (Custer et al. 2000, Ackerman et al. 2016). Keller et al. (2014) had one sample from a chat in the southern Appalachians (secondary feather) with a value of 0.78 ug/g, which was higher than the overall average value of birds in their study (0.46 ug/g) and my study, but still within the range of values I detected. The average level of mercury in tail feathers for chats in my study is slightly lower than the 0.4 – 0.7 μg/g averages reported for other songbirds in terrestrial habitats without a known point source of contamination (Ma et al. 2018, Óvári et al. 2018, Stenhouse et al. 2019). Additionally, Jackson et al. (2015) had two blood samples from chats in contaminated sites in the Appalachian forests with a mean of 0.099 μg/g suggesting that even in contaminated sites, mercury toxicity risk is low for chats. I did not find evidence of chats being at risk of mercury toxicity, despite living in riparian habitats, which are often considered mercury hotspots. The association with riparian or wetland habitats alone does not necessarily always indicate high mercury risk (Brasso et al. 2020). Brasso et al. (2020) advised caution on the generalization that songbirds in wetland habitats are at greater risk of mercury bioaccumulation after conducting a literature review. Trophic level and diet are likely more important factors in mercury accumulation for chats. While chats consume both invertebrates and plants, it is likely that most invertebrates were of low trophic status or not of aquatic origin, food sources at low risk for mercury contamination. One study 111 found that less than 2% of the chat diet was aquatic insects (Yard et al. 2004). Omnivorous birds such as chat are considered low risk of mercury exposure and my study further supports this idea with quantitative results (Evers et al. 2005, Ackerman et al. 2016). While average levels of mercury in females were slightly lower than that for males, I found no statistical support for the hypothesis that female birds have lower levels than males. Mercury levels in chats are low and therefore, likely only an insignificant amount is depurated into eggs. Other studies on songbirds have also found no difference in mercury levels between sexes (Warner et al. 2012, Keller et al. 2014). It was unexpected that younger chats (in their first moult cycle) had higher levels of mercury than older chats (in their definitive moult cycle). Because mercury bioaccumulates (Evers 2018), I expected older birds to have the highest levels of mercury but my results do not support this hypothesis. Keller et al. (2014) found a similar result among a suite of songbird species in the Appalachians with younger birds having higher levels of mercury than older birds. Likewise, juvenile Coastal Plain Swamp Sparrows (Melospiza georgiana nigrescences) and Seaside Sparrows (Ammoddramous maritimus) had greater levels than adults and one idea to explain this result was that nestling birds generally have greater energetic demands compared to adults and therefore are consuming higher volumes of food (Warner et al. 2012). Similarly, (Ma et al. 2018) found juvenile White-throated Sparrows (Zonotrichia albicollis) and Swainson’s Thrushes (Catharus ustulatus) had greater mercury levels than adults and proposed that older birds may be eating more plant-based foods compared to younger birds. These age-related differences in energy demands and dietary partitioning may apply to chats as well. Feather samples from western chats in their first moult cycle putatively include birds where rectrices were grown in the nest (Pyle 1997), a time when most birds are fed a high-protein diet of insects from their parents for growth and development, and therefore may be exposed to higher levels of mercury. However, I have observed adult chats feeding their young berries as well as 112 invertebrates. Mercury levels in the feathers of in Aquatic Warblers (Acrocephalus paludicola) were also higher in younger birds than adults, but this result was attributed to differences in where the feathers were grown (breeding vs. overwintering grounds; Pacyna et al. 2017), a moult strategy that does not apply to chats (Pyle 1997). However, while my statistical analyses revealed sex-based differences in chat feather mercury, much variation in the data remains to be accounted for, suggesting other factors that we did not measure are important for predicting mercury. The differences between sexes overall are slight and are not of toxicological concern as all mercury samples fell within the background or low toxicological risk categories. Levels of mercury in Yellow-breasted Chats were lower in the eastern chat range, specifically within the northern and central regions, contradicting the general idea that mercury levels are higher in the east (Evers et al. 1998, 2007, Driscoll et al. 2007, Edmonds et al. 2010, Jackson et al. 2015). While my geographic assignments from hydrogen stable isotopes preclude precise estimates of each chat’s origin, the highest levels in my study generally occurred in the southwestern portion of the breeding range for the western chat, and mercury hotspots have been identified in this area (Ackerman et al. 2016). Mercury levels across the United States between 2013-2017 were highest in the central and western regions (National Atmospheric Deposition Program 2020). Hotspots of mercury exist in California due to the gold rush and mercury mining (Commission for Environmental Cooperation 1997, Rytuba 2000, Alpers et al. 2005). Likewise, agricultural wetlands in the Central Valley of California, notably for rice production, use hydrological regimes that further promote methylmercury production, more so than in naturally occurring wetlands (Windham-Myers et al. 2014). Elevated levels of mercury have been found in Cliff Swallows (Petrochelidon pyrrhonota) in the Cache Creek area of California, an area highly contaminated with mercury due to mining (Hothem et al. 2008). Western chats would likely pass through these more contaminated areas twice yearly during spring and fall migration (Chapter 3), which may be a factor for slightly elevated levels in certain 113 western populations. Regardless, overall mercury levels in chats in my study were of low concern for toxicity, despite small-scale differences in mercury levels between birds throughout their breeding range, and my modelling methods only captured a small portion of the variation in feather mercury levels of Yellow-breasted Chat Mercury levels in feathers generally correspond with the order of moult, with the first-moulted feathers containing the highest mercury levels (Furness et al. 1986). Most passerines replace their tail feathers about half-way during their moult beginning with the innermost tail feathers (Pyle 1997). Therefore, using tail feathers likely underestimates the true body burden of chat mercury concentrations, but these are common feathers used in analyses and therefore are suitable for comparison (Warner et al. 2012, Pacyna et al. 2017, Óvári et al. 2018). The tertiary feathers, which are moulted first s(Pyle 1997), would be the desired feather tract to sample to capture the highest levels of mercury in feathers. Other studies show the highest levels of mercury in the first primary feather, but tertiary feathers were not examined simultaneously (Furness et al. 1986, Warner et al. 2012). Regardless, my levels are so low that even if the true body burden of mercury is greater than I estimate, the toxicity risk for chats is unlikely to reach the zone for biological concern. The western subspecies of chat had higher corticosterone levels than the eastern subspecies. A lab study on Swamp Sparrows (Melospiza georgiana) found differences in the corticosterone stress response between two subspecies, which were potentially due to life-history effects and behavioural differences linked to aggression (Angelier et al. 2011). Whether eastern and western chats differ behaviourally is unknown. Similarly, stress-induced corticosterone levels varied between two subspecies of free-living Song Sparrows (Melospiza melodia) but these differences likely related to latitude and subsequent climate (Wingfield et al. 1995). While statistically I found a difference in corticosterone levels between subspecies, the magnitude of the differences between the eastern and western are slight and therefore are 114 unlikely to be meaningful in a biologically relevant context but would require further investigation such as body condition to better contextualize differences between subspecies (Rich and Romero 2005, Dickens and Romero 2013, Boves et al. 2016). Male and female chats had similar corticosterone levels. Differences in corticosterone between males and females may be related to parental care during the breeding season (O’Reilly and Wingfield 2001, Pérez et al. 2016). In chats, females make the nest and incubate the eggs, but both males and females provision the young (Eckerle and Thompson 2020). My samples may reflect similar parental investment, but it is also possible that any sex-based corticosterone effects are no longer present at the end of the breeding season during moult. Captive Zebra Finches also had similar baseline and stress-induced corticosterone levels in males and females (Moore et al. 2014). While my study explored if any consistent differences in corticosterone were found between age, sex, range position, and subspecies, the root cause and whether these differences translate into any biologically relevant endpoints (e.g. mass loss) requires more in-depth study. Corticosterone as a measure of stress physiology is complicated and controversial (Dickens and Romero 2013, Harris et al. 2017, MacDougall-Shackleton et al. 2019). As the main function of glucocorticoids relates to energy mobilization, levels of these hormones are not synonymous with stress (Busch and Hayward 2009, MacDougall-Shackleton et al. 2019). In some cases, chronic stress may present itself as elevated corticosterone levels if an organism fails to acclimate to the stressor, while in other cases prolonged stress can result in suppression of the activity hypothalamus-pituitary-adrenal axis, resulting in decreased corticosterone levels (Romero 2004, Rich and Romero 2005, Cyr and Romero 2007, Busch and Hayward 2009, Kleist et al. 2018). Yet in other cases, chronic stress may not affect the endocrine profile at all (Dickens and Romero 2013). 115 Additionally, levels of corticosterone vary drastically from species to species, and therefore, direct comparison of chats to other songbirds is not meaningful (Romero 2004). Better context of what corticosterone levels indicate may be revealed if baseline blood samples are taken, as well as stress-induced blood samples, on a repeated basis to assess if the hypothalamus-pituitary-adrenal axis is working properly and to ensure appropriate negative feedback occurs in a timely manner (Romero 2004). Measures of body condition are often useful as a covariate in studies examining stress, as a decrease in mass has been the only consistent finding associated with chronic stress (Rich and Romero 2005, Dickens and Romero 2013). In my study, incorporating body condition was not possible because relevant body condition would have needed to be assessed the year prior during moult and I did not have metrics of body scaling for birds at each site. The difficulty in predicting corticosterone is demonstrated by my top model having a very low R2 value and, therefore, explaining very little of the variation in corticosterone in Yellow-breasted Chats. Since the chats in my study were free-living wild birds, a multitude of other factors may influence corticosterone concentrations that I could not control, such as inclement weather (Wingfield et al. 1983, Ouyang et al. 2012), the presence of predators (Romero and Wingfield 2015), food abundance (Busch and Hayward 2009), injury (Sapolsky et al. 2000), human disturbance (Strasser and Heath 2013), and immune status (Busch and Hayward 2009), habitat quality (Marra and Holberton 1998), all of which vary in time and space. Quantifying such factors would be virtually impossible over such a large geographic area. I did not find any evidence that mercury affects corticosterone levels in chats. The true association between these chemicals is unclear as results are inconsistent between studies (Whitney and Cristol 2017). Two dosing studies on Zebra Finches found no increase in baseline corticosterone levels with increased mercury dose (Moore et al. 2014, Maddux et al. 2015). Likewise, in Turkey Vultures (Cathartes aura) no relationship was found between blood mercury 116 levels and fecal corticosterone levels (Herring et al. 2018). In contrast, a negative correlative relationship between the two chemicals has been found in Forster’s Terns (Sterna forsteri, Herring et al. 2012) and Tree Swallows (Franceschini et al. 2009), where high mercury levels correlate with low baseline corticosterone levels. The relationship between corticosterone and mercury may be related to the presence of other environmental contaminants. For example, in Common Ravens (Corvus corax) the response of corticosterone to mercury depended on lead levels (Herring et al. 2018): at low lead levels, a negative relationship between corticosterone and mercury existed; at intermediate lead levels, no relationship between corticosterone and mercury was present; and at high lead levels, a positive relationship between corticosterone and mercury existed. As mercury levels in chats were very low, it seems plausible that these background levels were not enough to elicit an abnormal hypothalamus-pituitary-adrenal axis response in either a positive or negative direction. As chats are migratory, mercury exposure and corticosterone levels likely varied at other times of the year that I was unable to capture in my study. As chats moult on the breeding grounds s(Pyle 1997), there are no feather tracts that would give us a picture of overwintering conditions. To better understand mercury and corticosterone levels throughout the annual cycle, sampling blood repeatedly (instead of feathers) across breeding, migrating, and overwintering phases would allow a comprehensive picture of mercury toxicity risk and hypothalamus-pituitary-adrenal axis function. Differences in mercury levels in birds between breeding and non-breeding sites have been documented elsewhere and would be a valuable avenue for future research (Nisbet et al. 2002, Edmonds et al. 2010, Winder and Emslie 2011, Townsend et al. 2013). In conclusion, my study was the first range-wide study to examine both mercury and corticosterone in a free-living terrestrial songbird. While it is generally thought that organisms associated with wetland or riparian habitat are at higher risk of mercury exposure, I find that in 117 the absence of a point source of contamination, chats are a low risk of mercury toxicity across their breeding range. 118 Table 4.1. Yellow-breasted Chat (Icteria virens) study site locations where feather samples were for corticosterone (Cort), mercury (Hg), and hydrogen stable isotope analyses (δ2Hf). The breeding location of chats sampled during migration and overwintering was inferred using stable hydrogen isotopes. Site Latitude Longitude Elevation (m) Years N Cort Hg δ2Hf Breeding South Okanagan Valley, British Columbia, CA 49.094 -119.537 300 2017, 2018 20 20 15a Trinity River, California, US 40.695 -122.854 500 2017, 2018 16 20 15a WIIM, Oregon I, US 42.490 -123.480 251 2018 8 13 7a TOPS, Oregon II, US 42.019 -122.140 905 2018 3 5 2a Migration Santa Alejandrina Bird Observatory, Veracruz, MX 18.002 -94.588 4 2014, 2015 100 36 36 La Encrucijada Biosphere Reserve, Chiapas, MX 15.552 -93.206 3 2018 30 36 36 Overwinter San Pancho Bird Observatory, Nayarit, MX 20.905 -105.398 33 2018, 2019 19 20 20 Total 196 150 92 aThese samples were collected only to calibrate the precipitation isoscape and are not included in the total. 119 Table 4.2. Models compared to predict mercury in Yellow-breasted Chat (Icteria virens) feathers. K is the number of parameters. AICc is Akaike’s Information Criterion adjusted small sample sites. The total sample size is 150. The response variable, mercury (μg/g) in Yellow-breasted Chat feathers, was loge transformed to improve model fit. Model K Log-Likelihood AICc Δ AICc Weight Age + Range Position 8 -137.00 291.03 0.000 0.754 Age + Sex + Range Position 10 -136.45 294.49 3.458 0.134 Age + Subspecies 4 -144.14 296.55 5.518 0.048 Subspecies*Age 5 -143.60 297.61 6.584 0.028 Age 3 -145.91 297.98 6.953 0.023 Age + Sex + Subspecies 6 -143.91 300.40 9.372 0.007 Sex + Age 5 -145.69 301.80 10.773 0.003 Intercept-only model 2 -150.27 304.62 13.587 0.001 Range Position 7 -145.11 305.00 13.970 0.001 Subspecies 3 -149.57 305.31 14.284 0.001 Sex 4 -149.95 308.18 17.154 0.000 Sex + Range Position 9 -144.47 308.23 17.199 0.000 Sex + Subspecies 5 -149.24 308.90 17.874 0.000 Table 4.3. Comparing models to explain corticosterone in Yellow-breasted Chat (Icteria virens) feathers. K is the number of parameters. AICc is Akaike’s Information Criterion adjusted small sample sites. The total sample size is 196. Note that the response variable is corticosterone (pg/mm) in Yellow-breasted Chat feathers was transformed to the power of -0.7 to improve model fit. Model K Log-Likelihood AICc Δ AICc Weight Subspecies 3 115.69 -225.26 0.000 0.455 Age + Subspecies 4 116.95 -223.58 1.672 0.197 Age * Subspecies 5 115.84 -223.48 1.782 0.187 Sex + Subspecies 5 116.02 -221.72 3.539 0.078 Age + Sex + Subspecies 6 116.19 -219.94 5.320 0.032 Range Position 7 117.26 -219.92 5.335 0.032 Age + Range Position 8 117.41 -218.05 7.208 0.012 Sex + Range Position 9 117.61 -216.26 8.995 0.005 Age + Sex + Range Position 10 117.77 -214.344 10.91 0.002 Intercept-only model 2 104.48 -204.9 20.36 0.000 Age 3 105.10 -204.08 21.18 0.000 Sex 4 105.49 -202.77 22.49 0.000 Sex + Age 5 105.85 -201.38 23.88 0.000 120 Table 4.4. Weighted parameter estimates for mercury concentrations in Yellow-breasted Chats (Icteria virens) based on all models using the subset method. The 95% confidence intervals that do not include 0 are in bold. Estimate Std. Error CI Lower CI Upper (Intercept)a -1.21 0.16 -1.52 -0.91 Age – Definitive Cycle -0.44 0.12 -0.67 -0.21 Range Position – West Central -0.03 0.17 -0.35 0.30 Range Position – West South 0.11 0.21 -0.30 0.53 Range Position – East North -0.43 0.22 -0.86 -0.01 Range Position - East Central -0.46 0.19 -0.82 -0.09 Range Position - East South 0.11 0.18 -0.24 0.45 Sex – Male 0.10 0.13 -0.14 0.35 Sex - Unknown 0.16 0.18 -0.20 0.51 Subspecies - Eastern -0.20 0.12 -0.41 0.01 Subspecies Eastern*Age - Definitive 0.23 0.23 -0.22 0.68 a The default values for categorical parameters are: Age = First Cycle, Subspecies =Western, Sex = Female, Range Position = Western – North. All parameter estimates are relative to the intercept. Table 4.5. Weighted parameter estimates for corticosterone concentrations in Yellow-breasted Chats (Icteria virens) based on all models using the subset method. The 95% confidence intervals that do not include 0 are in bold. Estimate Std. Error CI Lower CI Upper (Intercept)a -0.41 0.02 -0.45 -0.37 Subspecies - Eastern -0.19 0.02 -0.14 -0.05 Age – Definitive 0.00 0.03 -0.06 0.07 Subspecies – Eastern*Age – Definitive -0.07 0.05 -0.16 0.02 Sex - Male 0.01 0.02 -0.04 0.05 Sex - Unknown -0.03 0.04 -0.11 0.05 Range Position – West Central -0.02 0.04 -0.10 0.05 Range Position – West South 0.05 0.05 -0.04 0.15 Range Position – East North -0.09 0.04 -0.18 -0.02 Range Position – East Central -0.09 0.03 -0.17 -0.03 Range Position – East South -0.11 0.04 -0.18 -0.04 a The default values for categorical parameters are: Age = First Cycle, Subspecies = Western, Sex = Female, Range = West North. All parameter estimates are reported relative to the intercept. 121 Figure 4.1. Study areas for Yellow-breasted Chats (Icteria virens). The breeding ranges of the eastern (I.v. virens) and western (I.v. auricollis) subspecies are shown. Star symbols represent birds sampled from study areas in the breeding season, the square represents birds sampled from a study area in the overwintering season, and triangles represent birds sampled from study areas during migration. Map made in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator projection and coordinate system. 122 Figure 4.2. Average mercury concentrations in Yellow-breasted Chats (Icteria virens) in their breeding range. Pentagons are sites where chats were sampled during the breeding season. Circles are breeding sites inferred from stable hydrogen isotope analyses, and a single point was used to represent each group assignment, but assignment probabilities spanned larger areas. Sample sizes are indicated beside each point. The breeding range of the Yellow-breasted Chat is shown in stippled gray in the background (IUCN 2016). Map made in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator projection and coordinate system. Note that while higher mercury concentrations are shown as darker shades of red, all levels are still considered low risk of mercury toxicity. 123 Figure 4.3. Conditioning plots of the top model explaining feather mercury in Yellow-breasted Chats. The model shown is: Feather Mercury ~ Age + Range Position, where feather mercury has been loge transformed. The upper plot shows the conditioning plot for age while the lower plot shows the conditioning plot for range position. Each plot displays partial residuals (black dots), prediction lines (blue line), and 95% confidence bands (gray band). 124 Figure 4.4. Mercury concentrations in Yellow-breasted Chat (Icteria virens) feathers. Untransformed data and means are shown. The right outer tail feather was used. Range position is divided by subspecies (Western, W and Eastern E) and position within range (North, N; Central, C; and Southern, S). Sample sizes are W.N=22, W.C.=42, W.S=14, E.N.=15, E.C = 26, E.S.=31. Age includes the first moult cycle (n=97) and the definitive moult cycle (n=53). Sexes are female (F, n=38), male (M, n=94), and unknown (U, n=18). The total sample size is 150. 125 Figure 4.5. Conditioning plots of the top model explaining feather corticosterone in Yellow-breasted Chats. The top model shown is: Feather Corticosterone ~ Subspecies, where feather corticosterone has been transformed to the power of -0.7. The plot displays partial residuals (black dots), prediction lines (blue line), and 95% confidence bands (gray band). 126 Figure 4.6. Corticosterone concentrations in Yellow-breasted Chat (Icteria virens) feathers. Untransformed data and means are shown. The right outer tail feather was used. Range position is divided by subspecies (Western, W and Eastern E) and position within range (North, N; Central, C; and Southern, S). Sample sizes are W.N=23, W.C.=31, W.S.=12, E.N.=21, E.C = 64, E.S.=45. Age includes the first moult cycle (n=148) and the definitive moult cycle (n=48). The total sample size is 196. Note that one outlier with corticosterone concentration of 35.53 pg/mm is not visible.127 Figure 4.7. Correlation between mercury and corticosterone in Yellow-breasted Chat (Icteria virens) feathers. Feathers represent breeding sites throughout North America (N = 127). Both variables were transformed to meet assumptions of normality. Pearson r = -0.054, df = 125, p = 0.54.128 Chapter 5: Conclusion In my dissertation, I combined traditional methods (bird banding) and modern techniques (GPS tags, geolocators, hydrogen stable isotopes, genetics) to better understand the migration ecology of the Yellow-breasted Chat and Gray Catbird. I described the migration routes and identified overwintering locations, which further contributes to my understanding of the full annual cycle for these two species. I assessed to what extent populations overlapped with one another in the overwintering grounds to describe the migratory connectivity. Additionally, for Yellow-breasted Chats, I probed deeper into their physiology across the breeding range by examining levels of mercury and corticosterone in feathers. I assessed to what extent the levels of mercury present pose a toxicological risk and discovered whether age, sex, subspecies, or range position predict mercury on a range-wide scale. I examined whether mercury and corticosterone are correlated to test the idea that mercury affects the activity of the hypothalamus-pituitary-axis. Likewise, I assessed whether age, sex, subspecies, or range position predict corticosterone concentrations in Yellow-breasted Chats on a range-wide scale. In chapter 2, I determined the migration routes and overwintering locations for two western populations of Gray Catbirds. I found that both western populations migrated eastward across the Rocky Mountains and then southward to overwintering sites in northeastern Mexico and Texas. Stopover sites during migration were concentrated in Montana, Oklahoma, and Arkansas. Their long, indirect, and eastward migration is suggestive of an ancestral migration route and is less typical for birds breeding west of the Rocky Mountain Range (Newton 2006, Kardynal and Hobson 2017). This migration route is very similar to that of the Veery (Catharus fuscescens), which also breeds in the South Okanagan Valley of British Columbia (Kardynal and Hobson 2017). The migration route of the western Gray Catbird putatively recapitulates the range expansion of the species, where post-glaciation, individuals began their expansion from Mexico northward and then westward to their current breeding locations in British Columbia and 129 Montana (Mila et al. 2006). Individual Gray Catbirds from different breeding populations mixed on the overwintering grounds, a case of weak migratory connectivity. I found that apparent annual survivorship values were higher for catbirds in the South Okanagan Valley, British Columbia, Canada compared to the Bitterroot River Valley, Montana, USA, but further investigation is required to better understand why these differences occur and their ecological implications. In chapter 3, I described in detail the migration ecology of the Yellow-breasted Chat across their range. I found that the endangered population of Yellow-breasted Chats breeding in the South Okanagan Valley of British Columbia, Canada flew south via the Pacific Flyway to overwinter in western Mexico in the states of Nayarit and Sinaloa. I found that 2 out of 5 birds tracked with GPS tags overwintered in protected areas within Mexico. Chats breeding in northern California also likely overwintered in western Mexico, but exact locations were unknown due to the crude estimates obtained from geolocators. Genetic analyses of chats overwintering in Nayarit revealed all birds were of the western subspecies. Genetic analyses showed chats from Chiapas and Veracruz were all of the eastern subspecies. Stable hydrogen isotope analyses from feathers of chats overwintering in Nayarit showed a wide range of values, suggesting that birds breeding at various latitudes overwintered in this site. Stable hydrogen isotope analyses from feathers of chats migrating through Chiapas and Veracruz show a large spread of values, again indicating birds from multiple breeding latitudes migrated through these sites. Results from bird banding re-encounter data also linked birds in Veracruz, Mexico to the northeastern edge of their breeding range. Taken altogether, it appears that Yellow-breasted Chats in western Mexico have relatively low migratory connectivity, with birds from multiple breeding latitudes overwintering there. For the eastern subspecies, I found that birds from multiple breeding latitudes passed through these migration sites but overwintering locations remain unknown so migratory connectivity could not be fully assessed. 130 In chapter 4, I measured mercury in Yellow-breasted Chats to determine mercury toxicity risk based on published benchmarks (Ackerman et al. 2016). Mercury from both anthropogenic and naturally occurring sources travel through waterways and eventually get deposited in sediments of aquatic and wetland habitats (Cristol et al. 2008, Driscoll et al. 2013). Here, it becomes transformed by bacteria into its most biohazardous form, methylmercury, and readily moves through food webs (Cristol et al. 2008, Driscoll et al. 2013). Of concern, it is not just aquatic species that are at risk of elevated levels of mercury, as terrestrial species near contaminated point sources can also bioaccumulate hazardous levels of mercury through bioaccumulation (Cristol et al. 2008, Hothem et al. 2008, Jackson et al. 2011a). Therefore, Yellow-breasted Chats may be at risk of mercury exposure due to their affinity for riparian habitats (Perkins et al. 2019, Eckerle and Thompson 2020). I found that mercury levels in Yellow-breasted Chats were low across the breeding range, with levels primarily falling within the lowest possible toxicological risk category, which is considered background levels. Mercury levels were similar among sexes and subspecies. Mercury levels were higher in younger birds in their first moult cycle compared to birds in their definitive moult cycle. The biological explanation for this pattern is unclear but there may be dietary differences or differences in feeding locations between chats of different ages. For example, older birds could be consuming more plant matter and younger birds could be consuming more invertebrates overall or more carnivorous or aquatic invertebrates. Mercury levels varied with range position, with eastern birds in the central and northern parts of their breeding range having the lowest levels Corticosterone levels were similar between sexes and ages but varied between subspecies. Corticosterone values were lower in the eastern subspecies. The ultimate causes and consequences of these differences in corticosterone levels require further investigation. However, differences were of low magnitude and the biological relevance of these small 131 differences is unknown but unlikely to be concerning. More variation in corticosterone values was evident than in mercury values, and the variation in corticosterone would be expected given the natural stressors that would be encountered by free-living birds. I found no correlation of mercury and corticosterone when measured in the same individuals, suggesting that the low levels of mercury I detected in chats are unlikely to elicit a response from the hypothalamus-pituitary-adrenal axis. Taken altogether, despite Gray Catbirds and Yellow-breasted Chats having similar ecological niches and overlapping breeding habitat in the South Okanagan Valley, the two songbirds have different migration and overwintering strategies. The Yellow-breasted Chat follows the more typical route of birds breeding west of the Rocky Mountain Range, as they follow the Pacific Flyway directly south to their final overwintering sites. In contrast, Gray Catbirds cross into the Central Flyway following an eastward and then southward path to Mexico. Catbirds used multiple overwintering sites and progressively moved south during the overwintering period, whereas chats appeared to stay in one overwintering territory. Catbirds tracked in multiple years varied in whether they used the same overwintering locations in successive winters. Catbirds who shared one breeding site dispersed as much as 1,000 kilometers in the overwintering range, while chats dispersed about half that much. However, the sample size was lower for chats than catbirds, and stable isotope analyses revealed a large range of values from overwintering chats in Nayarit, Mexico, therefore quantifying the spread of chats from one population requires further validation. 5.1 Conservation Implications From a conservation and management perspective, conserving one breeding population of catbirds based on the populations I studied would be more challenging than conserving one population of chats for two reasons. For one, catbirds traveled further distances than chats and appeared to use multiple riparian stopover sites during migration. Therefore, to conserve 132 stopover sites for an entire population of catbirds, multiple riparian habitats would need to be protected, spanning large geographic areas. In contrast, chats traveled shorter distances following a direct route but only one stopover site was identified and it is not clear how many sites would benefit from protection. Secondly, catbirds used multiple overwintering sites, with my study detecting up to 5 sites used by one individual. Additionally, some catbirds used different overwintering sites in different years, although increasing the sample size for repeat-tracking individuals would better allow us to assess how common this phenomenon is. While Tamaulipas was the most commonly used overwintering Mexican state, Hidalgo, Veracruz, Nuevo Leon, and San Luis Potosi were also used. Additionally, Texas was used by two catbirds. Protecting overwintering areas used by all individuals in a population span multiple political boundaries. In contrast, Yellow-breasted Chats appear to defend overwintering territories rather than moving throughout winter. I also have evidence from colour-banding overwintering chats that at least some birds return to their overwintering territories in subsequent years. Therefore, less land would need to be protected to encompass all areas used by Yellow-breasted Chats from one population than Gray Catbirds. However, given that chats have high site fidelity to breeding sites, and at least some chats show fidelity to overwintering sites, the loss of critical habitat in either stage of the annual cycle means that chats might not readily adapt to seeking out new suitable habitat. One caveat is that my sample size is small for tagged chats and further verification of overwintering spread and site fidelity would be required to confirm this idea. The results of my research challenge the current understanding of songbird migration and overwintering ecology. From GPS-tracking of Gray Catbirds, I found an example of yet another species that breed west of the Rocky Mountains but fails to follow the Pacific Flyway and instead uses the Central Flyway (Ruegg and Smith 2002, Kardynal and Hobson 2017). Furthermore, it is generally assumed that songbirds remain in one overwintering territory. 133 However, I found through the use of GPS tags that Gray Catbirds use multiple overwintering sites throughout the non-breeding period and also use different overwintering sites year to year. These findings violate the assumptions upon which overwintering survival estimates and monitoring programs are based upon and therefore likely have important conservation implications (Desante et al. 2005, Saracco et al. 2005). Specifically, overwintering survival estimates following the MoSI protocol will likely severely underestimate catbird survival as capture periods are brief and the chance of recapturing a catbird that moves around throughout the winter is much less than a bird that remains on territory during the entire non-breeding season. Yellow-breasted Chats, on the other hand, align with the assumptions of MoSI as chats appear to maintain overwintering territories in western Mexico and at least some chats show overwintering site fidelity. For the endangered population of Yellow-breasted Chats breeding in the South Okanagan Valley, I have identified key aspects of their ecology outlined in the recovery strategy (Environment and Climate Change Canada 2016). The identification of their migration route via the Pacific Flyway and overwintering locations in Nayarit and Sinaloa, Mexico will be of value for identifying critical habitat outside of the breeding range. Tracking more individuals, notably females, would help (1) determine the area that this endangered population uses over the non-breeding period, (2) further assess what percentage of the population overwinters in currently protected areas, and (3) determine if any sex-based differences in overwintering areas exist. I found no evidence of this population being exposed to mercury levels above background levels, and I found no evidence of corticosterone levels being any different from other populations. Therefore, sublethal effects from these two biochemicals are unlikely, and can be ruled out as potential threats to the British Columbia chat population at this time. Putting GPS tags on other endangered populations of Yellow-breasted Chats, such as the population in Ontario (Environment and Climate Change Canada 2019), would be a 134 worthwhile endeavor to compare migration ecology of eastern and western chats and to identify key overwintering areas for the endangered Ontario population. Similarly, putting GPS tags on western chats that breed in Alberta or Saskatchewan to assess migration routes and overwintering areas would provide a nice contrast between an at-risk population of western chats to a putatively not-at risk population of western chats. 5.2 Conservation Physiology Some studies show evidence of mercury affecting the stress-response system in birds (Herring et al. 2012, Moore et al. 2014, Soto et al. 2019), but II found no correlation between mercury and corticosterone. This result suggests that low levels of mercury did not influence the activity of the hypothalamus-pituitary-adrenal axis in chats. Additionally, the results of my research test current understanding of mercury risk in songbirds. Recent evidence has suggested that terrestrial songbirds, especially those in association with aquatic habitats, are at risk of mercury exposure at levels associated with toxicological effects (Edmonds et al. 2010, Winder and Emslie 2011, Evers et al. 2012). However, II found that levels of mercury in Yellow-breasted Chats across North America are low and pose little to no risk of mercury toxicity. Therefore, in the absence of a specific point-source of contamination, the chat is an example of a riparian songbird that is not exposed to concerning levels of mercury through its diet. Further evidence of the lack of negative effects of mercury toxicity for one population of chats (the South Okanagan Valley) include apparent annual survivorship values of 50%, an increasing population (Mancuso, unpublished data), and relatively high nest success rates of 57.8% for first broods and 69.2% for second broods (McKibbin and Bishop 2014). Therefore, I see no evidence of background levels of mercury affecting reproduction or survivorship of Yellow-breasted Chats, at least in the south Okanagan Valley population. From my research, it appears not all songbirds in riparian habitats whose diet includes insects are at risk of mercury exposure. A strength of my physiology study is that sample sizes 135 were large and spread out across North America. Further examination of other songbirds in the same habitat whose diet consists wholly of insects, such as the Yellow Warbler (Setophaga petechia), Bewick’s Wren (Thryomanes bewickii), or Willow Flycatcher (Empidonax traillii) would complement my study to assess multiple foraging guilds within riparian systems. One weakness of using feathers to examine conservation physiology is that feathers only capture a snapshot of time during the period of feather growth on the breeding grounds. It is possible that Yellow-breasted Chats were exposed to higher levels of mercury or that corticosterone levels varied during the migration or the non-breeding period, but I was unable to capture this signature. A complementary method to examine corticosterone and mercury would involve taking blood samples in breeding, migration, and overwintering sites to better understand conservation physiology over the full annual cycle. The results of my corticosterone analyses suggest that the activity of the hypothalamus-pituitary-adrenal axis across broad geographic areas does not vary by sex or age. I found differences in corticosterone between subspecies at a broad geographic scale, but whether these differences in corticosterone translate into differences in fitness requires further investigation. More outliers were present in the corticosterone analyses than mercury analyses, indicating that some individuals had high hypothalamic-pituitary-adrenal activity and presumably were exposed to a stressful situation during the period of feather growth, such as a predator attack. One drawback of the use of feathers is that the levels of corticosterone are averaged out over the period of feather growth and so specific comparisons between baseline physiology and stress-induced physiology are not possible. To get more detailed information about baseline and stress-induced levels of corticosterone in Yellow-breasted Chats, I suggest taking baseline blood samples upon first capture, followed by a restraint period of 30 minutes then a second blood sample, as this is the most commonly used method to examine hypothalamus-pituitary-136 adrenal activity and the stress response in birds (Rensel and Schoech 2011, Ouyang et al. 2012, Baugh et al. 2014). 5.3 Closing Remarks The international, local, and regional collaborations in my dissertation allowed for research questions that spanned from Canada to Mexico. These collaborations are valuable not only for advancing the field of conservation biology, but also bring diverse groups of people together for a common goal, which is yet another reason why songbirds are important. While both chats and catbirds are subject to the threats of habitat loss and fragmentation, mortality and injury from collisions and introduced species, toxic chemicals present in their environment, and a changing climate, my research at least rules out mercury and hypothalamic-pituitary-adrenal axis malfunction as significant threats to the health of chats. The detailed information I discovered on the full annual cycle for both species adds to the understanding of general habitat use and movement patterns throughout the year. These details provide starting points to focus efforts on reducing habitat loss in these locations. As climate change is expected to have a greater effect on riparian habitats, and both catbirds and chats rely on riparian habitats, monitoring the quality and diversity in such sites before irreversible changes are evident may enable early conservation action before degradation or loss occurs that would impact a multitude of songbird species. Therefore, efforts of conservation initiatives such as Partners in Flight and the North American Bird Conservation Initiative may be directed towards these goals. In addition, describing the migration routes of Yellow-breasted Chats and Gray Catbirds provide insight into the differences in evolutionary origins of migration in these two species. Catbirds putatively have eastern evolutionary roots and have slowly moved west, whereas the western chat likely originated from western evolutionary roots. Traditional methods, such as banding birds and field observations, plus the associated vital rates (e.g. survivorship, return rates) through specific monitoring programs (MAPS, MoSI) 137 are sprinkled throughout my dissertation. Therefore, these monitoring programs are worthwhile to inform future songbird conservation research. Newer technologies, such as GPS tags and geolocators varied tremendously in their success rate. GPS tags allowed for high-precision data of bird movement and locations at specific times. In contrast, geolocators, only provided crude estimates of location and did not work well for Yellow-breasted Chats or Gray Catbirds due to unusually high amounts of low quality, shaded light data. The use of genetics to separate eastern and western chats worked perfectly and would be a useful method for future work on this species. Stable hydrogen isotope analyses provided a general idea of where birds originated from but had large assignment areas, thus, a high degree of uncertainty exists in location estimates. On-the-ground validation of stable hydrogen isotope assignment areas would be valuable to confirm their accuracy. Likewise, collecting more samples for calibration of the hydrogen isotope isoscape would further refine location estimates. Assessing migratory connectivity across various populations is challenging in the absence of precise location estimates, and I encourage the use of more GPS tags to better understand the spatial distribution and overlap of birds within each population. There is still a lack of detailed information for female chats and year-to-year variation in migration routes and overwintering locations. 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The overwintering location estimates obtained from geolocators on catbirds in the South Okanagan Valley were substantially farther north than their known overwintering range. Shading affects location estimates (Lisovski et al. 2012) and likely explains my results. While the longitude estimates are robust to shading and are in line with the known overwintering range, the large error in latitudes (>600 km, Figure A2) provides little to no additional useful information, especially when compared to the precise overwintering estimates I have retrieved from GPS tags. At best, the longitudinal estimates suggest that South Okanagan Valley catbirds generally overwinter in the western part of their overwintering range. Figure A1. A comparison between high quality and low-quality light images from geolocators on Gray Catbirds (Dumetella carolinensis). The left image is from a geolocator on a catbird from the Bitterroot River Valley, Montana, USA, whereas the image on the right is from a geolocator on a Gray Catbird in the South Okanagan Valley, British Columbia, Canada. 167 Figure A2. Geolocator overwintering location estimates from Gray Catbirds (Dumetella carolinensis) breeding in the South Okanagan Valley, British Columbia, Canada. Study area indicated by the star. Each polygon represents 50% Kernel Density Estimates from predicted locations between November 15 – March 1. These estimates do not fall within the known wintering range of the species and do not corroborate the results from precise GPS tags. Gray Catbird range map provided by IUCN (2016). Map created in ArcMap 10.7.1 (ESRI 2019) using the GCS WGS 1984 coordinate system. 168 Aerial imagery of stopover sites used by Gray Catbirds (Dumetella carolinensis) from birds that were breeding in the south Okanagan Valley, British Columbia or the Bitterroot River Valley of western Montana. Note that all points are during fall migration, except for one point. 169 170 171 172 173 174 175 176 177 Aerial imagery of overwintering Gray Catbirds (Dumetella carolinensis) as determined via Lotek PinPoint GPS units that had bred in the South Okanagan Valley of British Columbia or the Bitterroot River Valley of Montana. 178 179 180 181 182 183 184 185 186 187 188 Geostatistical models and output for creating the North American precipitation isoscape for stable hydrogen isotopes using R package IsoriX (Courtiol et al. 2019). The mean_fit plot shows the relationship between the predicted isotope values and the observed isotope values for the mean fit model. The disp_fit plot shows the relationship between the predicted isotopic values and the observed isotopic values for the residual dispersion fit model. The spatial autocorrelation for both the mean fit model and the residual dispersion model are also shown and indicate strong spatial autocorrelation. 189 190 191 Model output: References: Courtiol, A., F. Rousset, M.-S. Rohwader, D. X. Soto, L. S. Lehnert, C. C. Voigt, and K. A. Hobson. 2019. Isoscape computation and inference of spatial origins with mixed models using the R package IsoriX. Pages 207–237 Tracking Animal Migration with Stable Isotopes. Second Edi. Academic Press, London, United Kingdom. 192 Calibration of the North American precipitation isoscape to reflect stable hydrogen isotope values in Yellow-breasted Chat feathers. Here I report the samples of known origin to calibrate the isoscape, the plot of the calibration model, and the model output. Table E1. Known origin Yellow-breasted Chat (Icteria virens) δ2Hf values. These values were used to calibrate a precipitation isoscape into a Yellow-breasted Chat feather isoscape. Age codes are after second year (ASY), second year (SY), hatch year (HY), or after hatch year (AHY). Dashes indicate no data. Data shown from Hobson et al. (2012) are transformed values based on the latest calibration methods from Soto et al. (2017) Province or State Longitude Latitude Elevation (m ASL) Sample ID Age Sex δ2Hf value (‰) Source BC -119.613 49.485 342 2561-00163 ASY M -93.5 This study BC -119.552 49.200 300 2561-00262 SY M -91.9 This study BC -119.541 49.306 329 2561-00230 SY M -92.3 This study BC -119.546 49.221 303 2561-00221 ASY M -80.4 This study BC -119.546 49.221 303 2561-00223 SY M -87.5 This study BC -119.544 49.096 286 2561-00217 ASY M -83.1 This study BC -119.551 49.221 303 2561-00119 ASY M -80.9 This study BC -119.544 49.096 286 2561-00219 ASY M -93.7 This study BC -119.544 49.096 286 2561-00215 ASY M -81.0 This study BC -119.544 49.096 286 2561-00220 ASY M -56.7 This study BC -119.551 49.221 303 2241-35320 SY F -88.1 This study BC -119.541 49.306 329 2241-35328 ASY M -88.4 This study BC -119.552 49.200 300 2691-79842 SY F -87.2 This study BC -119.613 49.485 342 2241-37991 ASY M -63.4 This study BC -119.551 49.221 303 2421-77218 ASY M -63.3 This study CA -122.854 40.695 534 1951-81015 SY M -61.6 This study CA -122.854 40.695 534 1951-81017 SY M -60.0 This study CA -122.854 40.695 534 2711-16061 SY M -59.1 This study CA -122.854 40.695 534 2711-16062 ASY M -63.5 This study CA -123.047 40.707 455 1951-81019 ASY M -80.0 This study CA -123.047 40.707 455 2711-16068 SY M -65.3 This study CA -123.028 40.683 462 2711-16058 SY M -67.3 This study CA -122.817 40.709 552 2711-16064 ASY M -65.9 This study CA -122.817 40.709 552 1951-81018 SY M -56.7 This study CA -122.854 40.695 534 2711-16060 SY F -87.5 This study CA -123.028 40.683 462 2711-16070 SY F -58.7 This study CA -122.854 40.695 534 2711-16063 SY F -64.5 This study CA -123.047 40.707 455 2711-16067 ASY F -57.9 This study CA -122.836 40.717 548 2711-16072 ASY M -68.6 This study CA -123.028 40.683 462 1951-81025 ASY F -63.6 This study OR -122.140 42.019 905 1801-51176 HY U -70.4 This study OR -122.140 42.019 905 2741-57607 HY U -76.7 This study OR -123.480 42.490 251 2471-12125 SY M -35.1 This study 193 Province or State Longitude Latitude Elevation (m ASL) Sample ID Age Sex δ2Hf value (‰) Source OR -123.480 42.490 251 2741-57719 HY U -65.0 This study OR -123.480 42.490 251 2741-57997 AHY F -63.4 This study OR -123.480 42.490 251 2741-57720 SY F -45.7 This study OR -123.480 42.490 251 2741-57993 SY F -53.6 This study OR -123.480 42.490 251 2741-57886 HY U -58.4 This study OR -123.480 42.490 251 2741-57994 SY M -65.1 This study OR -123.480 42.490 251 2741-57995 SY M -36.9 This study IN -85.458 38.966 259 454 - - -38.3 Hobson et al. 2012 IN -85.380 38.860 267 457 - - -44.1 Hobson et al. 2012 MI -86.519 33.762 80 455 - - -25.7 Hobson et al. 2012 MI -92.111 37.694 330 458 - - -38.0 Hobson et al. 2012 MO -92.180 37.779 255 456 - - -34.6 Hobson et al. 2012 MO -92.180 37.779 255 459 - - -38.6 Hobson et al. 2012 MO -92.180 37.779 255 460 - - -34.6 Hobson et al. 2012 MT -111.915 46.934 1155 461 - - -92.1 Hobson et al. 2012 OR -123.480 42.490 251 462 - - -62.7 Hobson et al. 2012 194 Figure E1. Calibration linear mixed-effects model of δ2Hf observed vs. δ2Hp predicted. The regression line and 95% confidence interval are displayed. This calibration model relates the hydrogen isotopic value in Yellow-breasted Chat (Icteria virens) feathers (δ2Hf) to the hydrogen isotopic value from precipitation (δ2Hp) in the environment. Isotope values are presented per thousand (‰) standardized to Vienna Standard Mean Ocean Water. The calibration samples used to fit the equation are from Table A5.1. The fit of this model was used to create the Yellow-breasted Chat feather isoscape, which was used to predict the origin of unknown samples. 195 Model Output: References: Hobson, K.A., S.L. Van Wilgenburg, L.W. Wassenaar, and K. Larson. 2012. Linking hydrogen (δ2H) isotopes in feathers and precipitation: sources of variation and consequences for assignment to isoscapes. PloS ONE 7(4):e35137. Soto, D.X., G. Koehler, L.I. Wassenaar, and K.A. Hobson. 2017. Re-evaluation of the hydrogen stable isotopic composition of keratin calibration standards for wildlife and forensic science applications. Rapid Communications in Mass Spectrometry. 31:1193-1203. 196 Figure F1. Histogram of distances associated with band recovery data for Yellow-breasted Chats (Icteria virens) between 1920 – 2019. The threshold distance of 40 km is shown with a dotted line, and all encounters greater than this were mapped to visualize movements. 197 Details of genetic assay assigning Yellow-breasted Chat (Icteria virens) samples to either the eastern (I.v. virens) or western (I.v. auricollis). Note that in Table A1, an additional 10 samples from California and 10 samples from British Columbia were run through the assay but the location was blind to the scientist running the assay. These 20 samples were correctly identified to the western subspecies, further validating the assay. Figure G1. Example amplification plot of 48 samples for assay Iv_ATPase_SNP778. Squares represent reference samples and negative control. Dots represent each individual analyzed. Blue symbols represent the eastern virens subspecies haplotype, while red symbols represent the western auricollis subspecies haplotype. 198 Table G1. Assay results assigning Yellow-breasted Chat (Icteria virens) samples to subspecies. Haplotype at each assay based on nucleotide character state (Iv_ATPase_SNP200 = A or C; Iv_ATPase_SNP778 = G or T), and genetic assignment to subspecies (auricollis or virens). Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 00N5434 Reference USA Oregon A C auricollis 00N5435 Reference USA Oregon A C auricollis 00N5436 Reference USA Oregon A C auricollis 00N5437 Reference USA Oregon A C auricollis 00N5438 Reference USA Oregon A C auricollis 00N5439 Reference USA Oregon A C auricollis 00N5440 Reference USA Oregon A C auricollis 01N7492 Reference USA Oregon A C auricollis 01N7493 Reference USA Oregon A C auricollis 01N7494 Reference USA Oregon A C auricollis 03N3464 Reference USA Montana A C auricollis 05N5857 Reference USA Montana A C auricollis 05N5934 Reference USA Montana A C auricollis 06N1697 Reference USA Montana A C auricollis 06N1698 Reference USA Montana A C auricollis 06N1699 Reference USA Montana A C auricollis 06N1700 Reference USA Montana A C auricollis 06N24258 Reference USA Montana A C auricollis 06N24262 Reference USA Montana A C auricollis 06N24272 Reference USA Montana A C auricollis 06N25258 Reference USA California A C auricollis 06N25259 Reference USA California A C auricollis 06N25280 Reference USA California A C auricollis 06N25287 Reference USA California A C auricollis 06N25288 Reference USA California A C auricollis 06N25293 Reference USA California A C auricollis 96N4049 Reference USA California A C auricollis 96N4051 Reference USA California A C auricollis 96N4055 Reference USA California A C auricollis 06N25272 Reference USA California ? ? ? 1321-53909 Reference Canada British Columbia A C auricollis 2241-35302 Reference Canada British Columbia A C auricollis 2241-3692 Reference Canada British Columbia A C auricollis 2241-36924 Reference Canada British Columbia A C auricollis 2241-36964 Reference Canada British Columbia A C auricollis 2241-36983 Reference Canada British Columbia A C auricollis 2241-37000 Reference Canada British Columbia A C auricollis 36922 Reference Canada British Columbia A C auricollis 36943 Reference Canada British Columbia A C auricollis 00N6050 Reference USA Missouri G T virens 00N6051 Reference USA Missouri G T virens 01N4013 Reference USA Missouri G T virens 01N4014 Reference USA Missouri G T virens 01N4017 Reference USA Missouri G T virens 199 01N4018 Reference USA Missouri G T virens Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 01N4025 Reference USA Missouri G T virens 01N4026 Reference USA Missouri G T virens 01N4027 Reference USA Missouri ? T virens 00N6049 Reference USA Missouri G T virens 04N7437 Reference USA Kentucky G T virens 04N7444 Reference USA Kentucky G T virens 04N7438 Reference USA Kentucky G T virens 04N7439 Reference USA Kentucky G T virens 04N7440 Reference USA Kentucky G T virens 04N7442 Reference USA Kentucky G T virens 05N4365 Reference USA Kentucky G T virens 05N4366 Reference USA Kentucky G T virens 04N7441 Reference USA Kentucky G ? virens 04N7443 Reference USA Kentucky G T virens 04N9209 Reference USA Indiana G T virens 04N9210 Reference USA Indiana G T virens 00N6578 Reference USA Indiana G T virens 06N23561 Reference USA Indiana G T virens 06N23597 Reference USA Indiana G T virens 07N23125 Reference USA Indiana G T virens 11N1079 Reference USA Indiana G T virens 6N23558 Reference USA Indiana G T virens 00N6579 Reference USA Indiana G T virens 02N2381 Reference USA Indiana G T virens 08N0536 Reference USA Alabama G T virens 10N11223 Reference USA Alabama G T virens 10N11224 Reference USA Alabama G T virens 13N1058 Reference USA Alabama G T virens 13N1059 Reference USA Alabama G T virens 13N1074 Reference USA Alabama G T virens 13N1075 Reference USA Alabama G T virens 13N1082 Reference USA Alabama G T virens 13N1083 Reference USA Alabama G T virens 14N0308 Reference USA Alabama G T virens 2241-37972 Unknown Canada British Columbia A C auricollis 2421-77218 Unknown Canada British Columbia A C auricollis 2541-98099 Unknown Canada British Columbia A C auricollis 2561-00119 Unknown Canada British Columbia A C auricollis 2561-00213 Unknown Canada British Columbia A C auricollis 2561-00215 Unknown Canada British Columbia A C auricollis 2561-00216 Unknown Canada British Columbia A C auricollis 2561-00230 Unknown Canada British Columbia A C auricollis 2561-00260 Unknown Canada British Columbia A C auricollis 2561-00262 Unknown Canada British Columbia A C auricollis 2711-16058 Unknown USA California A C auricollis 2711-16059 Unknown USA California A C auricollis 200 Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 2711-16065 Unknown USA California A C auricollis 2711-16066 Unknown USA California A C auricollis 2711-16068 Unknown USA California A C auricollis 2711-16069 Unknown USA California A C auricollis 1951-81015 Unknown USA California A C auricollis 1951-81019 Unknown USA California A C auricollis 1951-81026 Unknown USA California A C auricollis 1951-81023 Unknown USA California A C auricollis TDA-D0041563 Unknown Mexico Chiapas G T virens TDA-D0041565 Unknown Mexico Chiapas G T virens TDA-D0041576 Unknown Mexico Chiapas G T virens TDA-D0041565 Unknown Mexico Chiapas G T virens TDA-D0041577 Unknown Mexico Chiapas G T virens TDA-D0043180 Unknown Mexico Chiapas G T virens TDA-D0043330 Unknown Mexico Chiapas G T virens TDA-D0043331 Unknown Mexico Chiapas G T virens TDA-D0043341 Unknown Mexico Chiapas G T virens TDA-D0043345 Unknown Mexico Chiapas G T virens TDA-D0043350 Unknown Mexico Chiapas G T virens TDA-D0043351 Unknown Mexico Chiapas G T virens TDA-D0043352 Unknown Mexico Chiapas G T virens TDA-D0043374 Unknown Mexico Chiapas G T virens TDA-D0043512 Unknown Mexico Chiapas G T virens TDA-D0043514 Unknown Mexico Chiapas G T virens TDA-D0043537 Unknown Mexico Chiapas G T virens TDA-D0043538 Unknown Mexico Chiapas G T virens TDA-D0043541 Unknown Mexico Chiapas G T virens TDA-D0043339* Unknown Mexico Chiapas G T virens TDA-D0043582 Unknown Mexico Chiapas G T virens TDA-D0043568 Unknown Mexico Chiapas G T virens TDA-D0043584 Unknown Mexico Chiapas G T virens TDA-D0043585 Unknown Mexico Chiapas G T virens TDA-D0043587 Unknown Mexico Chiapas G T virens TDA-D0043589 Unknown Mexico Chiapas G T virens TDA-D0043597 Unknown Mexico Chiapas G T virens TDA-D0043598 Unknown Mexico Chiapas G T virens TDA-C0035271 Unknown Mexico Chiapas G T virens TDA-C0035272 Unknown Mexico Chiapas G T virens TDA-C0036008 Unknown Mexico Chiapas G T virens TDA-C0036046 Unknown Mexico Chiapas G T virens TDA-C0036086 Unknown Mexico Chiapas G T virens TDA-C0036099 Unknown Mexico Chiapas G T virens TDA-C0036047 Unknown Mexico Chiapas G T virens TDA-C0036102 Unknown Mexico Chiapas G T virens No Band Unknown Mexico Chiapas G T virens 2241-37935 Unknown Mexico Nayarit A C auricollis 2241-37936 Unknown Mexico Nayarit A C auricollis 201 Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 2241-37937 Unknown Mexico Nayarit A C auricollis 2241-37938 Unknown Mexico Nayarit A C auricollis 2241-37940 Unknown Mexico Nayarit A C auricollis 2241-37944 Unknown Mexico Nayarit A C auricollis 2241-37948 Unknown Mexico Nayarit A C auricollis 2241-37949 Unknown Mexico Nayarit A C auricollis 2241-37950 Unknown Mexico Nayarit A C auricollis 2241-37952 Unknown Mexico Nayarit A C auricollis 2241-37955 Unknown Mexico Nayarit A C auricollis 2241-37956 Unknown Mexico Nayarit A C auricollis 2241-37958 Unknown Mexico Nayarit A C auricollis 2561-00210 Unknown Mexico Nayarit A C auricollis 2561-00212 Unknown Mexico Nayarit A C auricollis 41105 Unknown Mexico Nayarit A C auricollis TDA-C0035412 Unknown Mexico Nayarit A C auricollis TDA-C0035413 Unknown Mexico Nayarit A C auricollis TDA-C0035414 Unknown Mexico Nayarit A C auricollis TDA-D0021882 Unknown Mexico Veracruz G T virens TDA-D0021887 Unknown Mexico Veracruz G T virens TDA-D0021889 Unknown Mexico Veracruz G T virens TDA-D0021894 Unknown Mexico Veracruz G T virens TDA-D0021910 Unknown Mexico Veracruz G T virens TDA-D0021976 Unknown Mexico Veracruz G T virens TDA-D0021979 Unknown Mexico Veracruz G T virens TDA-D0021984 Unknown Mexico Veracruz G T virens TDA-D0021987 Unknown Mexico Veracruz G T virens TDA-D0021992 Unknown Mexico Veracruz G T virens TDA-D00220004 Unknown Mexico Veracruz ? ? ? TDA-D0022030 Unknown Mexico Veracruz G T virens TDA-D0022043 Unknown Mexico Veracruz G T virens TDA-D0022047 Unknown Mexico Veracruz G T virens TDA-D0022061 Unknown Mexico Veracruz G T virens TDA-D0022074 Unknown Mexico Veracruz G T virens TDA-D0022076 Unknown Mexico Veracruz G T virens TDA-D0022160 Unknown Mexico Veracruz G T virens TDA-D0022233 Unknown Mexico Veracruz G T virens TDA-D0022239 Unknown Mexico Veracruz G T virens TDA-D0022241 Unknown Mexico Veracruz ? ? ? TDA-D0022294 Unknown Mexico Veracruz G T virens TDA-D0022300 Unknown Mexico Veracruz G T virens TDA-D0022323 Unknown Mexico Veracruz G T virens TDA-D0022422 Unknown Mexico Veracruz G T virens TDA-D0022600 Unknown Mexico Veracruz G T virens TDA-D0022860 Unknown Mexico Veracruz G T virens TDA-D0022987 Unknown Mexico Veracruz G T virens TDA-D0023061 Unknown Mexico Veracruz G T virens 202 Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 TDA-D0023065 Unknown Mexico Veracruz G T virens TDA-D0023336 Unknown Mexico Veracruz G T virens TDA-D0023474 Unknown Mexico Veracruz G T virens TDA-D0023475 Unknown Mexico Veracruz G T virens TDA-D0023476 Unknown Mexico Veracruz G T virens TDA-D0023479 Unknown Mexico Veracruz G T virens TDA-D0023537 Unknown Mexico Veracruz G T virens TDA-D0023539 Unknown Mexico Veracruz G T virens TDA-D0023550 Unknown Mexico Veracruz G ? virens TDA-D0023567 Unknown Mexico Veracruz G T virens TDA-D0023571 Unknown Mexico Veracruz G T virens TDA-D0023598 Unknown Mexico Veracruz G T virens TDA-D0024216 Unknown Mexico Veracruz G T virens TDA-D0024223 Unknown Mexico Veracruz G T virens TDA-D0024268 Unknown Mexico Veracruz G T virens TDA-D0024271 Unknown Mexico Veracruz G ? virens TDA-D0024281 Unknown Mexico Veracruz G T virens TDA-D0024285 Unknown Mexico Veracruz G T virens TDA-D0024291 Unknown Mexico Veracruz G T virens TDA-D0024700 Unknown Mexico Veracruz G T virens TDA-D0024701 Unknown Mexico Veracruz G T virens TDA-D0024703 Unknown Mexico Veracruz G T virens TDA-D0024710 Unknown Mexico Veracruz G T virens TDA-D0024983 Unknown Mexico Veracruz G T virens TDA-D0024991 Unknown Mexico Veracruz G T virens TDA-D0024992 Unknown Mexico Veracruz G T virens TDA-D0024993 Unknown Mexico Veracruz G T virens TDA-D0024997 Unknown Mexico Veracruz G T virens TDA-D0024998 Unknown Mexico Veracruz G T virens TDA-D0024999 Unknown Mexico Veracruz G T virens TDA-D0025028 Unknown Mexico Veracruz G T virens TDA-D0025029 Unknown Mexico Veracruz G T virens TDA-D0025036 Unknown Mexico Veracruz G T virens TDA-D0025048 Unknown Mexico Veracruz G T virens TDA-D0025056 Unknown Mexico Veracruz ? ? ? TDA-D0025200 Unknown Mexico Veracruz G T virens TDA-D0025256 Unknown Mexico Veracruz G T virens TDA-D0025258 Unknown Mexico Veracruz G T virens TDA-D0025260 Unknown Mexico Veracruz G T virens TDA-D0025264 Unknown Mexico Veracruz G T virens TDA-D0025268 Unknown Mexico Veracruz G T virens TDA-D0025271 Unknown Mexico Veracruz G T virens TDA-D0025335 Unknown Mexico Veracruz ? ? ? TDA-D0025339 Unknown Mexico Veracruz G T virens TDA-D0025399 Unknown Mexico Veracruz G T virens TDA-D0025411 Unknown Mexico Veracruz G T virens TDA-D0025412 Unknown Mexico Veracruz ? ? ? 203 Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 TDA-D0025420 Unknown Mexico Veracruz G T virens TDA-D0025423 Unknown Mexico Veracruz G T virens TDA-D0025425 Unknown Mexico Veracruz G T virens TDA-D0025428 Unknown Mexico Veracruz G T virens TDA-D0025497 Unknown Mexico Veracruz G T virens TDA-D0025509 Unknown Mexico Veracruz ? T virens TDA-D0025528 Unknown Mexico Veracruz G T virens TDA-D0025539 Unknown Mexico Veracruz G T virens TDA-D0025550 Unknown Mexico Veracruz G T virens TDA-D0025551 Unknown Mexico Veracruz G T virens TDA-D0025561 Unknown Mexico Veracruz G T virens TDA-D0025577 Unknown Mexico Veracruz G T virens TDA-D0025579 Unknown Mexico Veracruz G T virens TDA-D0025585 Unknown Mexico Veracruz G T virens TDA-D0025588 Unknown Mexico Veracruz G T virens TDA-D0025601 Unknown Mexico Veracruz ? ? ? TDA-D0025605 Unknown Mexico Veracruz G T virens TDA-D0025626 Unknown Mexico Veracruz G T virens TDA-D0025634 Unknown Mexico Veracruz G T virens TDA-D0025641 Unknown Mexico Veracruz G T virens TDA-D0025651 Unknown Mexico Veracruz ? ? ? TDA-D0025652 Unknown Mexico Veracruz G T virens TDA-D0025654 Unknown Mexico Veracruz G T virens TDA-D0025681 Unknown Mexico Veracruz G T virens TDA-D0025682 Unknown Mexico Veracruz G T virens TDA-D0025684 Unknown Mexico Veracruz G T virens TDA-D0025688 Unknown Mexico Veracruz G T virens TDA-D0025691 Unknown Mexico Veracruz ? ? ? TDA-D0025697 Unknown Mexico Veracruz G T virens TDA-D0025702 Unknown Mexico Veracruz G T virens TDA-D0025849 Unknown Mexico Veracruz G T virens TDA-D0025851 Unknown Mexico Veracruz ? ? ? TDA-D002589 Unknown Mexico Veracruz G T virens TDA-D0025919 Unknown Mexico Veracruz G T virens TDA-D0025937 Unknown Mexico Veracruz G T virens TDA-D0026008 Unknown Mexico Veracruz G T virens TDA-D0026010 Unknown Mexico Veracruz G T virens TDA-D0026087 Unknown Mexico Veracruz G T virens TDA-D0026088 Unknown Mexico Veracruz G T virens TDA-D0026152 Unknown Mexico Veracruz G T virens TDA-D0026155 Unknown Mexico Veracruz G T virens TDA-D0026156 Unknown Mexico Veracruz G T virens TDA-D0026183 Unknown Mexico Veracruz G T virens TDA-D0026194 Unknown Mexico Veracruz G T virens TDA-D0026199 Unknown Mexico Veracruz G T virens TDA-D0026201 Unknown Mexico Veracruz G T virens TDA-D0026210 Unknown Mexico Veracruz G T virens 204 Sample Type Country State/Province Haplotype Subspecies SNP200 SNP778 TDA-D0026219 Unknown Mexico Veracruz G T virens TDA-D0026237 Unknown Mexico Veracruz G T virens TDA-D0026241 Unknown Mexico Veracruz G T virens TDA-D0026243 Unknown Mexico Veracruz G T virens TDA-D0026253 Unknown Mexico Veracruz G T virens TDA-D0026260 Unknown Mexico Veracruz G T virens TDA-D0026261 Unknown Mexico Veracruz G T virens TDA-D0026306 Unknown Mexico Veracruz G T virens TDA-D0026335 Unknown Mexico Veracruz G T virens TDA-D0026589 Unknown Mexico Veracruz ? ? ? TDA-D0026649 Unknown Mexico Veracruz G T virens TDA-D0026802 Unknown Mexico Veracruz G T virens TDA-D0026901 Unknown Mexico Veracruz G T virens TDA-D0027279 Unknown Mexico Veracruz G T virens TDA-D0027399 Unknown Mexico Veracruz G T virens TDA-D0027470 Unknown Mexico Veracruz G T virens TDA-D0027476 Unknown Mexico Veracruz G T virens TDA-D0027477 Unknown Mexico Veracruz G T virens TDA-D0027797 Unknown Mexico Veracruz G T virens TDA-D0028580 Unknown Mexico Veracruz G T virens TDA-D0028585 Unknown Mexico Veracruz G T virens TDA-D0028606 Unknown Mexico Veracruz G T virens TDA-D0028609 Unknown Mexico Veracruz ? ? ? TDA-D0029553 Unknown Mexico Veracruz G T virens TDA-D0030303 Unknown Mexico Veracruz G T virens TDA-D0030329 Unknown Mexico Veracruz G T virens TDA-DOO30360 Unknown Mexico Veracruz G T virens TDA-D0030358 Unknown Mexico Veracruz G T virens TDA-D0025500 Unknown Mexico Veracruz G T virens TDA-D0022847 Unknown Mexico Veracruz G T virens TDA-D0041559 Unknown Mexico Chiapas G T virens 205 Four parameter logistic curves based on known standards were used to interpolate corticosterone levels in Yellow-breasted Chat feathers. A total of 8 Enzo Life Sciences Enzyme-Linked Immunosorbent assays plates for corticosterone (kit no. ADI-900-097) were used and the corresponding standard curves and parameter estimates are shown for each plate. The black dots in each graph are the known standards, and the blue triangles show the samples interpolated along the curve. The corticosterone concentration of the standards were 32, 160, 800, 4,000, and 20,000 pg/ml., The samples represent the mean corticosterone concentration (based on two samples) in 100 μl of sample. Corticosterone values reported in the thesis were standardized to feather length and reported in pg/mm. 206 207 208 209 210 211 212 213 214 Stable hydrogen isotope results from Yellow-breasted Chat feathers used in mercury and corticosterone analyses. Figure I1 – Spread of hydrogen isotope values (δ2Hf) for Yellow-breasted Chat (Icteria virens) feathers sampled in Mexico. Sample sizes were slightly different between those used for corticosterone (left) and mercury (right). Values for stable hydrogen isotope were divided into 10‰ bins and used to assign birds to approximate breeding origins. The hydrogen isotope sample size was 149 for corticosterone and 92 for mercury. 215 Figure I2 – Breeding origin estimates based on isotope values used in mercury analyses. Feathers from Yellow-breasted Chats (Icteria virens) were sampled on the wintering and migration sites and δ2Hf values were used to infer breeding origins. δ2Hf values were divided into 10‰ bins and a group estimate was made using the package IsoriX (v0.8.2; Courtiol et al. 2019) in R (v3.5.1; R Core Team 2018). The darkest areas represent the greatest probability that the samples originated from that area. The white cross represents point approximating each assignment bin and used for mercury risk mapping. Map made in ArcMap 10.7.1 (ESRI 2019) using a WGS 1984 Web Mercator projection and coordinate system. A single point from each group assignment, marked with a cross, was used in the creation of the mercury map (Figure 4.2 in the main chapter). Assignment estimates for corticosterone were similar and not shown. 216 Parameter estimates for modeling approach for examining mercury and corticosterone levels in Yellow-breasted Chat feathers. Estimates are based on a multi-model approach using the full method, where models without the parameter of interest are given a value of zero and factored into weighted parameter estimates. This full method approach is in contrast to the subset approach reported within the thesis, where only models containing the parameter of interest are factored into weighted parameter estimates. The parameters whose 95% confidence intervals exclude zero are the same between both the full and subset method. Table J1. Weighted parameter estimates for mercury concentrations in Yellow-breasted Chats (Icteria virens) based on all models using the full method. The 95% confidence intervals that do not include 0 are in bold. Estimate Std. Error CI Lower CI Upper (Intercept)a -1.21 0.16 -1.51 -0.91 Age – Definitive Cycle -0.44 0.12 -0.67 -0.21 Range Position – West Central -0.02 0.16 -0.33 0.29 Range Position – West South 0.10 0.20 -0.30 0.50 Range Position – East North -0.39 0.24 -0.87 -0.09 Range Position - East Central -0.41 0.23 -0.85 -0.04 Range Position - East South 0.10 0.17 -0.24 0.43 Sex – Male 0.02 0.06 -0.10 0.13 Sex - Unknown 0.02 0.08 -0.15 0.19 Subspecies - Eastern -0.02 0.07 -0.16 0.12 Subspecies Eastern*Age - Definitive 0.01 0.05 -0.10 0.11 a The default values for categorical parameters are: Age = First Cycle, Subspecies =Western, Sex = Female, Range Position = Western – North 217 Table J2. Weighted parameter estimates for corticosterone concentrations in Yellow-breasted Chats (Icteria virens) based on all models using the full method. The 95% confidence intervals that do not include 0 are in bold. Estimate Std. Error CI Lower CI Upper (Intercept)a -0.41 0.02 -0.45 -0.37 Subspecies - Eastern -0.09 0.03 -0.15 -0.03 Age – Definitive 0.00 0.02 -0.04 0.04 Subspecies – Eastern*Age – Definitive -0.01 0.04 -0.08 0.06 Sex - Male 0.00 0.01 -0.01 0.01 Sex - Unknown 0.00 0.02 -0.04 0.03 Range Position – West Central 0.00 0.01 -0.02 0.02 Range Position – West South 0.00 0.02 -0.03 0.03 Range Position – East North -0.01 0.02 -0.05 -0.04 Range Position – East Central -0.01 0.02 -0.05 -0.04 Range Position – East South -0.01 0.02 -0.05 -0.04 a The default values for categorical parameters are: Age = First Cycle, Subspecies = Western, Sex = Female, Range = West North.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Migration ecology of the yellow-breasted chat and gray catbird and physiology of the yellow-breasted chat
Text