ANNUAL SPATIAL ECOLOGY OF HERRING GULLS (LARUS ARGENTATUS) IN EASTERN NORTH AMERICA

by

Christine M. Anderson B. Sc. Trent University 2013

Thesis Submitted in partial fulfillment of the requirements for the Degree of Master of Science (Biology)

Acadia University Spring Graduation 2017

© by Christine M. Anderson, 2017 ii

This thesis by Christine M. Anderson was defended successfully in an oral examination on 25 April 2017.

The examining committee for the thesis was:

This thesis is accepted in its present form by the Division of Research and Graduate Studies as satisfying the thesis requirements of the degree Master of Science (Biology).

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I, Christine M. Anderson, grant permission to the University Librarian at Acadia University to reproduce, loan or distribute copies of my thesis in microform, paper, or electronic formats on a non-profit basis. I, however, retain the copyright in my thesis.

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Table of Contents

Table of Contents ------iv List of Tables ------v List of Figures ------vi Abstract ------ix Acknowledgements------x Chapter 1 – General introduction and methods ------1 Background ------1 Herring Gull ------4 General Methods ------10 Objectives ------14 Chapter 2 – Are Herring Gulls residents or migrants? ------21 Introduction------21 Validating methods with simulated data ------24 Applying methods to Herring Gull data ------27 Conclusion ------29 Chapter 3 – Do short and long distance migrants use different migration strategies? 35 Introduction------35 Methods ------38 Results ------41 Discussion ------44 Chapter 4 – Does winter habitat use differ by population? ------65 Introduction------65 Methods ------67 Results ------70 Discussion ------73 Chapter 5 – General Discussion ------87 Findings ------87 Applications and future directions ------88 References ------93 Appendix: R code for Chapter 2 simulations ------115 v

List of Tables

Table 1-1: Adult Herring Gull survival estimates found in previous studies. ------16 Table 1-2: Details of tracking device deployment for collecting Herring Gull movement data used in this thesis. ------17 Table 3-1: Migration characteristics of Herring Gulls tracked from Nunavut (n = 8 [autumn], 1 [spring]), Newfoundland (n = 6, 9), Sable Island (n = 17, 11), and the Bay of Fundy (n = 11, 10). All summary statistics are presented as mean ± SD (range). Range and SD are absent for all Nunavut spring summaries because only one individual was tracked for the full migration period. ------56 Table 3-2: Model averaged parameter estimates (β, 95% confidence intervals) and cumulative Akaike weights (Σ ωi) for generalized linear mixed models examining the effects of population and season on the migration characteristics for Herring Gulls tracked from Nunavut (NU; n = 8 autumn, 1 spring), Newfoundland (NL; n = 6, 9), Bay of Fundy (BF, n = 11; 10) and Sable Island (SI; n = 17, 11) during autumn (A) and spring (S). Individual is included as a random effect in all models except for directness. Estimates of conditional r2 (fixed effect only) and marginal r2 (fixed and random effects) are presented for the global model of each candidate set. Population and season are both categorical variables; the intercept is the predicted value for Herring Gull from Nunavut in the autumn, which then acts as the reference level for the other parameter estimates.------58 Table 3-3: Arrival and departure dates for autumn and spring migrations of Herring Gulls tracked from Nunavut (n = 8, 1), Newfoundland (n = 6, 9), Sable Island (n = 17, 11), Bay of Fundy (n = 11, 10). Colony departure and arrival dates of Great Lakes Herring Gulls (n = 10, 5) included for comparison. All summary statistics are presented as mean ± SD (range). ------59

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List of Figures

Figure 1-1: Herring Gull (Larus argentatus) distribution in North America (BirdLife International and NatureServe 2014). Breeding distribution is shaded in yellow, year-round distribution is shaded in blue, wintering distribution is shaded in red, and migration distribution is hatched grey. ------18 Figure 1-2: Schematic of state-space model structure, adapted from Jonsen et al (2013). The process model evolves through time as a random walk, starting at x1 and ending at xn. At times t1, t2, …., tn, measurements y1, y2, …, yn are taken as described by the observation model. An arrow from one variable (for example x1) to a subsequent variable (for example x2) indicates that x2 is expressed in terms of the conditional probability distribution of x2 given x1. Note that in the process model, all observations of x are dependent on x at the previous time step. Observations of y are independent of one another, and are dependent on x at the same time step. ------19 Figure 2-1: An example of a) random movement by resident population and b) directed movement by a migrating population, simulated for n = 10 individuals. ------31 Figure 2-2: Coulson and Brazendale’s (1968) method for differentiating between migrants and residents requires all available non-breeding locations to be grouped into distance zones extending outwards from the breeding colony. Demonstrated for Herring Gulls breeding in the Great Lakes. Distance zones are at 100 km intervals, extending to a maximum of 1500 km ------32 Figure 2-3: Top panels show the relationship between distance from the breeding colony and the proportion of locations in that distance zone or greater for one simulation of a resident or migrant population (n = 10). Simulated data are displayed in grey, fitted logarithmic function is displayed in red. Bottom panels show quantile plots of the r2 value for 1000 simulations of each sample size for resident and migrant populations.------33 Figure 2-4: The relationship between distance from the breeding colony and the proportion of locations in that distance zone or greater for Herring Gulls breeding in the Great Lakes (n = 8), the Bay of Fundy (n = 10), Sable Island (n = 8), Newfoundland (n = 3) and Nunavut (n = 8). Observed data are displayed in grey, fitted logarithmic function is displayed in red. ------34 Figure 3-1: Scatterplot illustrating variation in a) directness, b) overall migration speed, and c) total stopover days between populations and seasons for Herring Gulls tracked from Nunavut, Newfoundland, Sable Island, and the Bay of Fundy. Herring Gulls breeding in Nunavut are represented in yellow, Herring Gulls breeding in Atlantic Canada are represented in red. ------60 vii

Figure 3-2: Map of migration routes for Herring Gulls breeding in Nunavut. Stopover days are represented in yellow, and travel days are represented in blue. Breeding site on Southampton Island, NU is indicated by a pink triangle. ------61 Figure 3-3: Map of migration routes for Herring Gulls breeding in Atlantic Canada. Stopover days are represented in yellow, and travel days are represented in blue. Breeding sites are indicated by a pink triangle, from north to south: Newfoundland (3 sites), Sable (1 site), and Bay of Fundy (2 sites). ------62 Figure 3-4: Map of stopover sites used by Herring Gulls in Nunavut. Stopover days are represented in yellow, and travel days are represented in blue. ------63 Figure 3-5: Schematic of meta-population migration patterns, adapted from Newton (2008). Leapfrog migration occurs when populations reverse their latitudinal sequence between seasons. Chain migration occurs when populations maintain their latitudinal sequence between seasons. ------64 Figure 4-1: Winter daily locations for Herring Gulls tracked from Nunavut (n = 8), and winter home ranges calculated as minimum convex polygon.------80 Figure 4-2: Winter daily locations for Herring Gulls tracked from Atlantic Canada (n = 21), and winter home ranges calculated as minimum convex polygon. From north to south, birds from Newfoundland are indicated in medium grey, birds from Sable Island are indicated in dark grey, and birds from the Bay of Fundy are indicated in light grey. ------81 Figure 4-3: Winter daily locations for Herring Gulls tracked from the Great Lakes (n = 8), and winter home ranges calculated as minimum convex polygon. ------82 Figure 4-4: a) Histogram and b) boxplots of individual home range area for each population. Home range area is calculated as minimum convex polygons for Herring Gulls from Atlantic Canada (red, n = 21), the Arctic (yellow, n = 8), and the Great Lakes (blue, n = 8). ------83 Figure 4-5: a) Proportion of marine, natural land, cropland, urban, and freshwater habitats available within the winter home range of Herring Gull populations from the Arctic, the Great Lakes, and Atlantic Canada; b) Proportion of time spent in each habitat by individual Herring Gulls. Boxes represent the 95% confidence interval of the median individual habitat use for each population, acquired through 1000-fold bootstrapping; c) The ratio of available habitat within the population’s home range to the proportion of individual habitat use. Values close to one indicate the birds are using this habitat as often as would be expected to occur randomly. Values lower or higher than one indicate non-random use, respectively avoidance or preference for a certain type of habitat. ------84 Figure 4-6: Individual values for the first two principal components from Principal Components Analysis of the proportion of time individual Herring Gulls spent in marine, natural land, cropland, urban, and freshwater habitats. Grey polygons viii enclose all individual who spent the most time in marine, natural land, and urban habitats. Individuals who spent the most time in cropland and freshwater habitats are marked with a white dot and the initial of their respective habitat type. Large triangles indicate where habitat availability within the population’s winter home range falls within the principal components analysis. ------85

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Abstract

Ecological studies on birds tend to be biased towards the breeding season, yet understanding the full annual cycle of an organism is critical for understanding how effects carry over between the seasons. The aim of this thesis is to gain a more complete picture of Herring Gull movements outside the breeding season. By examining the migration and wintering patterns of Herring Gulls from five different sites across their breeding range in eastern North America, I evaluate the variability in the behaviour of this highly flexible species, both among individuals and populations. My research reveals for the first time that Herring Gulls breeding in the Arctic migrate long distances to spend the winter in the Gulf of Mexico. I confirm that Herring Gulls from the Great Lakes disperse and do not truly migrate. Regardless of the distance they travelled, Herring Gulls tended to migrate at a seemingly relaxed pace, with many stopovers and indirect routes.

Each population of Herring Gulls had strong migratory connectivity between their breeding and wintering areas, meaning that environmental stressors in one area can potentially have strong population-specific effects. This finding gives a particular significance to the differences I observed in winter habitat use among populations. I found that Herring Gulls from the Arctic spent the majority of the winter in marine habitats, while those from the Great Lakes and Atlantic Canada used a wider variety of habitats. However, only in the Atlantic populations did a large proportion of individuals spend most of their time in urban habitats. Previous studies have found that Herring Gulls in Atlantic Canada have lower survival rates than those in the Great Lakes and the Arctic, which suggests that there may be a link between urban habitat use during the winter and poor adult survival. x

Acknowledgements

Mark Mallory, thank you from the bottom of my heart. During the past couple of years, I can’t count the number of times I’ve paused for a moment to fully appreciate how lucky I am to have had you as a supervisor, usually after one of your unassuming acts of integrity, thoughtfulness or humour. A friend once aptly described you as a “unicorn supervisor” – a fantastic, mythological being that most people can’t believe exists.

Grant Gilchrist, thank you for your enthusiasm and mentoring. I really value your big-picture thinking. You’ve played a significant role in sparking my imagination and making my research a rich and meaningful endeavour. The East Bay Island camp is a truly special place – thank you for being the glue that holds it all together.

I’m very grateful to Rob Ronconi, Daniel Clark, Chip Weseloh, Kate Shlepr, and

Grant Gilchrist for providing me with their hard-won tracking data, which made this project possible, along with the countless field technicians who deployed tracking devices. Thank you to Mitacs, Baffinland Iron Mines, NSERC, and Acadia University for their financial support, and to ACENET for their superb technical support. I would like to extend my thanks to Greg Robertson for his input when I was planning my project, particularly for proposing the idea of looking at winter habitat use, and to Rob Ronconi for his advice on state-space models. I would also like to thank Phil Taylor for his guidance in programming and statistics, as well as Anna Redden for always being keen to give me guidance and perspective. Special thanks to the East Bay field crews for bringing research to life, and for shamelessly loving both ducks and butter.

Most importantly, I would like to express my gratitude to the community of people that sustains me. Thank you to my friends and roommates who have filled my life xi with laughter, encouragement and insight. Thank you to my sister Emily and to my Mom and Dad for your steadfast encouragement and support. I’m so happy to have shared these adventures with you all – here’s to many more.

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Chapter 1 – General introduction and methods

Background

The spatial ecology of migratory birds is complex. Migratory birds are well suited to exploit seasonal changes in weather, food availability, predation and competition because they are capable of movement across large geographic scales (Webster et al.

2002). In the northern hemisphere, migratory birds typically spend the winter at a more southerly latitude, taking advantage of less stressful temperatures and greater food availability. They return to breed at more northerly latitudes to take advantage of seasonal abundances of food and breeding habitats, and reduced predation risk (McKinnon et al.

2010). Their seasonal habitats are often separated by long distances, each having a unique set of risks and rewards which require birds to adapt their behaviour to suit.

Linking the spatially discrete parts of avian annual cycles is particularly useful for gaining perspective on the ways observed population dynamics are shaped by events throughout the entire year (Morales et al. 2010). The migratory connectivity of a population is the degree to which phases of their annual cycles are geographically linked

(Boulet and Norris 2006). Migratory connectivity is strong when the majority of individuals in an area converge on the same area after migrating. On the contrary, migratory connectivity is weak when individuals in one area migrate to multiple different areas. The structure of these migratory networks have important consequences.

Populations with strong migratory connectivity are more sensitive to local environmental disruptions. For example, all three populations of the Red Knot rufa subspecies (Calidris canutus rufa) converge on Delaware Bay, USA during their spring migration, feasting on 2 the eggs of spawning horseshoe crabs (Limulidae). Red Knots seem to be struggling to gain adequate energy reserves at this critical staging site due to overfishing of horseshoe crabs. Red Knot rufa numbers have declined dramatically, and have been listed as an

Endangered species in Canada (Baker et al. 2004).

For individual birds, events during one season will often carry over to influence their success in following seasons (Norris and Marra 2007). Behaviour is motivated by an individual’s past history and their stores of energy and nutrients (Harrison et al. 2011).

Cumulatively, these individual behaviours can translate into population level patterns.

Historically, studying the annual cycle of birds at an individual level was not feasible, simply because following individuals across their annual cycle was impractical.

Large scale spatial ecology was necessarily population-based, largely through banding studies. Tracking technology has revolutionized the study of animal movement patterns, permitting us to follow individuals across large distances and time-periods (Meyburg and

Meyburg 2009). Tracking data do not have the spatial bias of bird banding, which is constrained by the distribution of humans to observe and/or recover banded birds. These technologies can also reveal bird movements with a much finer-grained resolution than bird banding. Telemetry and bird banding are best regarded as complementary programs, as banding studies can offer a much greater sample size for population level studies, while telemetry can offer the precise details of individual movement (Fiedler 2009).

Remote tracking is particularly useful for studying seabirds; our understanding of seabird spatial ecology is poor compared to other groups of birds because their largely marine distribution overlaps very little with our own (Robertson et al. 2012). Telemetry data are 3 adding new detail to our understanding of natural history, and inspiring new questions about the evolutionary and ecological mechanisms that shape movement and migration.

Detailed knowledge of annual spatial ecology is also proving to be a critical tool for policy and conservation planning. Preserving networks of critical habitat protects species across their entire range; examples include the Western Hemisphere Shorebird

Reserve Network, American National Wildlife Refuge System and Canada’s Migratory

Bird Sanctuaries (WHSRN 2017; USFWS 2017; ECCC 2017). Areas where seabirds congregate in high densities can also identify productive ocean habitats when developing

Marine Protected Areas (Ronconi et al. 2012). Understanding the factors influencing migratory individuals throughout their annual cycle is essential for creating robust ecological models to predict responses to environmental change (Webster et al. 2002).

In this thesis, my goal is to expand our knowledge of the annual spatial ecology of

Herring Gulls (Larus argentatus) across their eastern North American range. This information will be useful both for the management of Herring Gulls, but also to give

Herring Gulls new context as model study organism. In the past, Herring Gulls have been used as a case study subject for pioneering research studies because they are robust, they are abundant and widespread, and they use a diverse range of foods and habitat. Niko

Tinbergen won a Nobel Prize for his research on the causes of individual and social behaviour patterns, using Herring Gulls as a model (Tinbergen 1954). Environment and

Climate Change Canada has used Herring Gull eggs to monitor environmental contaminants since the 1970s (Hebert 2000). Herring Gulls have the most extensive banding dataset of any seabird in North America, which has stimulated a great quantity and quality of publications on their movement patterns (Gaston et al. 2008). However, 4 these publications are strongly biased towards to the breeding season, leaving conspicuous gaps in our understanding of their annual cycle.

Herring Gull

Systematics

The Herring Gull is a large, white-headed gull, common across fresh and saltwater coastal habitats of North America and Eurasia (Pierotti and Good 1994). It belongs to a complex of more than 20 similar gull species, distributed across a circumpolar Holarctic breeding range (Sternkopf et al. 2010). Herring Gulls regularly hybridize with other species, including Glaucous-winged (Larus glaucescens) and Lesser

Black-backed (Larus fuscus) gulls (Pierotti and Good 1994). In 2007, the British

Ornithological Union elected to split the Herring Gull into two separate species, the

American Herring Gull (Larus smithsonianus) and the European Herring Gull (Larus argentatus), based on mitochondrial DNA evidence, but the American Ornithological

Union voted to not follow suit (Sangster et al. 2002; American Ornithologists Union

2007). Discussion of Herring Gulls in my thesis refers only to the North American population unless stated otherwise. DNA fingerprinting and banding studies suggest

Herring Gulls from the Great Lakes and Atlantic Canada are separate populations, with little immigration between them (Weseloh 1984; Yauk et al. 2000).

Distribution

Herring Gull breeding populations are most concentrated in the Great Lakes, Gulf of St. Lawrence and the east coast from Newfoundland to North Carolina (Cotter et al. 5

2012). The northern boundary of the Herring Gull’s breeding range in Canada reaches the northern Yukon, Northwest Territories and Nunavut mainland, as well as Southampton

Island, Foxe Basin and the southern portion of Baffin Island (Pierotti and Good 1994).

Most Herring Gulls spend the winter along the Atlantic, Pacific, and Gulf coasts, as well as in the Great Lakes and the Mississippi River Valley. The northern boundary of their wintering distribution follows a -12°C thermocline (Pierotti and Good 1994). Throughout the year, Herring Gulls are found in the greatest concentrations near waterways, coasts and cities (Gaston et al. 2008).

Resident and Migrant Movement

Banding data on Herring Gulls from the Great Lakes and Atlantic colonies are among the richest datasets available for any seabird, which has allowed for a very thorough analysis of their general movement patterns (Gaston et al. 2008). Populations from the Great Lakes and the northeastern United States show a partial migration pattern where distance travelled is inversely related to age (Moore 1976). Juvenile birds migrate longer distances to the Gulf Coast, Mexico, and Central America, where they reside from

November to March. Immature birds migrate successively shorter distances as they mature, and adults disperse in the area of the breeding colony, typically within 500 km

(Moore 1976; Gabrey 1996). Adults from Atlantic Canada undertake short migrations, and are most often resighted in New England and New York (Gaston et al. 2008).

Banding data have been used to determine the overall pattern of movement and connectivity between breeding and wintering locations, but details about timing, precise routes, and individual variation are unknown. 6

The low density Herring Gull populations found in the Arctic and inland boreal habitats are not regularly monitored, and little is known about their population status, movement patterns and life history. Relatively little banding has been done; there are only two published records of Herring Gulls banded in the North American Arctic, which were resighted in Niagara Falls, NY and Texas (Allard et al. 2006). These unmonitored populations were suspected to migrate to winter in Atlantic Canada (Gross 1940; Gaston et al. 2008). The origin of individuals wintering in Newfoundland and the Bay of Fundy are unknown, but there is considerable certainty that they are not of local origin, nor from any of the well-studied populations (Gaston et al. 2008).

Habitat Use

As a generalist species, Herring Gulls are very flexible in their breeding and feeding habitat requirements. The dramatic increase in Herring Gull populations during the 1900s can be attributed to their ability to exploit human-altered habitats in terrestrial, coastal, and marine environments (Belant et al. 1983). Herring Gulls forage in a variety of different habitats such as open ocean, the intertidal zone, sandy beaches, and mudflats, as well as anthropogenic habitats such as dumps, fields, picnic sites, and fish processing plants (Pierotti and Annett 1991).

Herring Gulls can be found breeding in large colonies of up to several thousand pairs, on the edge of other seabird colonies, or solitarily. They tend to choose breeding habitats on islands free of terrestrial predators, and on dry, well-drained substrates such as rock, sand, meadows, forests, and occasionally urban areas (Pierotti and Good 1994).

The variation in their nesting habitat choice between colonial and solitary sites suggests 7

Herring Gulls may use several distinct life history strategies (Allard et al. 2006).

Generally, Herring Gulls have the highest reproductive success when feeding on fish and intertidal organisms, and lower reproductive success when feeding on refuse or other seabirds (Pierotti 1982; Pierotti and Annett 1991). Changes in Herring Gull breeding habitat preferences are often a response to changes in food availability. Between 1979 and 1999, Herring Gulls in Witless Bay, Newfoundland shifted their breeding habitat preference from rocky habitats, associated with a diet specialized on intertidal organisms, to meadow habitats, associated with a diet specialized on petrels (Robertson et al. 2001).

Delays in the arrival of capelin (Mallotus villosus), a primary source of food for chick- rearing appear to have caused a shift to lower quality habitat and food sources, such as petrels and refuse (Robertson et al. 2001).

During the non-breeding season, Herring Gulls spend most of their time in foraging habitats, mainly offshore areas, coastlines and refuse dumps (Pierotti and Good

1994). The offshore distribution of Herring Gulls is highly influenced by commercial fishing activity, from which they scavenge for discards (Powers 1983; Gjerdrum and

Bolduc 2016). At night, Herring Gulls often congregate in roosting habitats near water

(Schreiber 1967). No detailed studies have been done on Herring Gull habitat use during migration and wintering.

Diet

Chicks of other species can be an important food source for Herring Gulls during chick-rearing, and as a result their breeding sites are often found in proximity to other species of birds (Pierotti and Good 1994). On the breeding grounds, diet plays a large 8 role in determining reproductive success. Pierotti and Annett (1991) found Herring Gulls specializing on intertidal prey had only 2.5% addled eggs, while those specializing on petrels or refuse produced 11.3% and 22.2% addled eggs respectively. This may be due to nutritional deficiencies (e.g. calcium) or elevated contaminants (e.g. mercury). Herring

Gulls may change their feeding habits over the course of the breeding season, using terrestrial prey most heavily during the chick-rearing period (Washburn et al. 2013).

Gulls are income breeders, meaning their diet on the breeding grounds is more important for egg development than their diet on the wintering grounds (Drent and Daan 1980).

Herring Gulls as a species are generalists, but individuals tend to specialize in a particular foraging strategy and diet (McCleery and Sibly 1986). However, some individuals adjust their diet between seasons and between years as they learn and adapt to environmental changes (Pierotti and Annett 1991). For example, fish are the preferred food source for Herring Gulls in the Great Lakes, but birds, invertebrates, mollusks and refuse are readily substituted when fish are unavailable (Ewins et al. 1994). Arctic breeding gulls are unlikely to have access to human refuse during the breeding season, and therefore are more likely to rely on natural prey (Gilchrist and Robertson 1999).

Population Dynamics

Over the past century, Herring Gull populations have fluctuated greatly in North

America. In the late 1800s, Herring Gulls bred only as far south as Maine, their population sizes were very small, and their distribution was geographically restricted due to human exploitation of eggs for food and feathers for the millinery (hat making) trade

(Pierotti and Good 1994). This trend reversed quickly after the implementation of the 9

Migratory Birds Convention in 1916. Herring Gull numbers increased rapidly for 50 years, aided by abundant food supplies from dumps and commercial fisheries waste

(Anderson et al. 2016). Their breeding range now extends 1600 km farther south into

North Carolina (Pierotti and Good 1994). Populations stabilized in the 1970-80s, and have since shown decreasing trends in Ontario, Quebec, New Brunswick, Nova Scotia, and Newfoundland, likely due do fisheries collapses, closure of open landfills and high contaminant loads (Wilhelm et al. 2016; Cotter et al. 2012; Hebert 1998; ECCC 2011).

Herring Gulls are a long-lived species, typically starting to breed around the age of four to five and living up to 20 years (Pierotti and Good 1994). In long-lived species, adult survival rates are one of the most important life-history traits in determining population trends, as their reproductive rates are relatively low (Lebreton et al. 1992).

Breeding adult Herring Gulls have high survival rates, but with notable differences between populations (Table 1-1). Although these differences in survival rates appear small, they translate to substantial differences in life expectancy and number of breeding seasons. While an average adult will have 19 breeding seasons at 95% survival, the number of breeding seasons is reduced to 10 at 90% survival, and six at 85% survival

(Coulson and Butterfield 1986). Robertson et al. (2016a) have suggested lower survival rates in some Herring Gull populations may be linked to lower habitat quality during migration and/or wintering, but information is lacking on non-breeding habitat use for all

North American populations.

Coulson and Butterfield (1986) estimated that in a non-migratory population of

Herring Gulls, 50% of mortality occurred during the breeding season, and 50% was distributed throughout the rest of the year, although we might expect different trends in a 10 migratory population. Major sources of Herring Gull mortality include injuries, contaminants, fishing equipment, lethal control, conflict with other gulls and predators

(Pierotti and Good 1994).

Conservation and management

Herring Gulls are interesting from a conservation perspective; species in which we have seen recoveries and resilience provide an interesting counterpoint to the conservation focus on decline and fragility. Although their modern reputation is often an overabundant pest species, Herring Gulls were once considered rare by renowned naturalist John James Audubon (Pierotti and Good 1994). The subsequent increase in

Herring Gull population size led state agencies to cull and actively manage gull populations to open nesting habitat for other seabirds, and to prevent interference with human facilities such as dumps, reservoirs, and airports (Anderson et al. 2016). However, declines in the abundance of Herring Gulls since the 1980s have been reported fairly consistently across their range (Ronconi et al. 2016; Mittelhauser et al. 2016; Washburn et al. 2016; Wilhelm et al. 2016). Herring Gulls have been red-listed in Britain’s “Birds of Conservation Concern,” and identified as a common species in steep decline by the

American “State of the Birds 2014” (Eaton et al. 2015; NABCI 2014).

General Methods

The studies I present in this thesis follow Herring Gulls from five distinct areas across eastern North America. Herring Gulls were tracked using telemetry devices, which periodically recorded their locations over time. However, the tracking devices used at 11 each site varied substantially in their accuracy and sampling frequency. I first processed the data from each site to create the standardized dataset of Herring Gull movement which is used in the following chapters.

Tracking

Tracking devices were deployed on Herring Gulls at one site in the eastern

Canadian Arctic (Southampton Island, NU), at four sites in the Atlantic region (Kent

Island, NB; Brier Island, NS; Sable Island, NS; and within 75 km of the Quabbin

Reservoir, MA), and at two sites in Ontario (Agawa Rocks, Lake Superior; and Double

Island, Lake Huron; Table 1-2). I combined the data from Kent Island and Brier Island to represent the Bay of Fundy region due to their close proximity. In Massachusetts, devices were deployed in the winter and birds were subsequently tracked to their breeding locations in Newfoundland and Ontario. Wintering birds were captured using a Coda net launcher hidden under a pickup truck. Bait was placed in front of the net, and the launcher was detonated from inside the truck’s cab (Clark et al. 2014). All other devices were deployed during the incubation period at breeding locations. Breeding birds were captured using a self-triggering wire mesh drop trap over their nest (Mills and Ryder

1979). At the Massachusetts and Ontario sites, devices were attached using variations of a chest harness, with the transmitter resting on the upper back, secured with loops around the wings and joined at the chest (Morris et al. 1981). At the Nunavut, Bay of Fundy and

Sable Island sites, devices were attached using a leg loop harness, with the transmitter resting on the lower back and secured with loops around the bird’s legs (Mallory and

Gilbert 2008). 12

Birds were equipped either with Ecotone devices, which archive GPS data internally and transmit data to a base station at the breeding site, or with platform terminal transmitters (PTTs), which derive location data from either GPS or Doppler shifts and transmit through the Argos satellite system (Argos 2016). Doppler-derived data are collated and processed by Argos, and categorized into location error classes (LC 3, 2,

1, 0) which have an estimated error radius associated with the location based on at least four transmissions (<150 m, 150-300 m, 350-1000 m, >1000 m), or location classes (LC

A, B, Z) which have no estimated error radius because fewer than four transmissions were received (3, 2, <2). Data from GPS were considered to have a fixed location class F, with an error radius of 0m. Note that although this is common practice, recent studies have shown GPS data can have an error radius of up to 20 m depending on habitat and animal movement (Frair et al. 2010). Tracking devices weighed 11.5 g to 30 g (<3% of average Herring Gull body mass), and were programmed with a variety of duty cycles.

After removing tracks of insufficient length, I obtained tracks from 37 individuals, lasting between 57 and 1670 days.

Analysis of movement data

Tracking data (Argos Doppler data in particular) are recorded at irregular time intervals, and are known often to be less precise than the given location error estimates

(Vincent et al. 2002). The data I collected from different sites varied greatly in their sampling frequency, which in turn can strongly influence the interpretation of movement metrics such as distance and directness (Tanferna et al. 2012). To compensate for these issues, I used Bayesian hierarchical switching state-space models to estimate locations at 13 regular 24 hr intervals (Jonsen et al. 2013; Jonsen et al. 2015). State-space models estimate the most probable movement path of an individual using two components

(Figure 1-2). First, the process model describes the movement path of an individual as a first-difference correlated random walk, switching between two behavioural states

(migratory and non-migratory) that dictate the distributions of speed and turning angles between locations. Second, the observation model relates the observed data points to the animal’s unobserved location from the process model. The observation model characterizes measurement error by using independently verified data from Vincent et al.

(2002) to determine the distribution of each ARGOS location error class. Fitting all individuals within a population within the same state-space model improves the accuracy of location estimates. Additional details about the general parameterization of these models are described in Jonsen et al. (2005).

Prior to modelling, I removed duplicate locations and applied a speed filter of 200 km/hr to remove outlier locations from each dataset, enabling faster and more accurate estimates (Freitas et al. 2008; Freitas 2012). Based on run lengths suggested by Raftery and Lewis (1991) diagnostics of model test-runs, I fit state-space models to the dataset using two chains of 400,000 Monte Carlo Markov Chain (MCMC) samples. I discarded the first 50,000 samples as a burn-in, and retained only every 50th sample of the remaining 350,000 samples to reduce autocorrelation. I checked the parameter estimates from the remaining 7000 samples for convergence by examining: (1) trace plots of model parameters for good mixing and stationary chains, (2) autocorrelation plots for independence between locations, and (3) density plots, Gelman and Rubin (1992) diagnostics, and Geweke (1992) diagnostics for evidence that posterior distributions were 14 unimodal. Diagnostics tests are found in the R package CODA (Plummer et al. 2006). I also visually compared the modeled locations to the observed locations. I removed locations that were not modeled within one day of an observed location point, as the state-space model tended to provide biologically unrealistic estimates during large data gaps.

A position was categorized as a travel day if either (1) the bird moved more than

0.3° of latitude in the same direction for two of three days in a sliding window, or (2) the bird moved more than 75 km in a single day. If the position did not meet either of these criteria, it was categorized as a stopover day. Each track was divided into breeding, autumn migration, wintering, and spring migration periods. Autumn migration was considered to start on the first day of a period of travel moving beyond a 200 km radius from their breeding colony, and was considered to end when a period of travel finished within the individual’s wintering latitude. Similarly, spring migration was considered to start on the first day of a period of travel departed the individual’s wintering latitude, and was considered to end when a period of travel finished within a 200 km radius of their breeding colony.

All maps are displayed using a North America Lambert Conformal Conic projection. Statistical analyses were performed in R version 3 (R Core Team 2017).

Means are reported throughout the text ± standard deviation (SD).

Objectives

The main objective of my thesis is to understand how Herring Gulls move during the non-breeding period. I focus on the migration and wintering periods because 15 information about these phases of the annual cycle is scarce compared to the breeding season. The dataset I compiled presents a unique research opportunity to compare the non-breeding movements of Herring Gulls breeding in three very distinct climatic regions, allowing us to observe Herring Gull movement on a continental scale right to the northern limits of their breeding range. This information on Herring Gulls from the Arctic is rare, as conducting research in the north is difficult and expensive. In the following chapters, I assess the diversity of Herring Gull movement, both between individuals and between populations. By studying individual movement paths, we can observe how events and behaviours are connected through time. By comparing populations, we can gain insights into how movement patterns are influenced by differences in environmental conditions across a large geographic range. These studies represent an important step towards a more holistic understanding of the Herring Gull’s full annual cycle.

In Chapter one, I review relevant background information, present the methods and analyses that are the foundation for the subsequent chapters of my thesis, and summarize my thesis objectives. In Chapter two, I present a brief methodological paper. I describe an adapted technique for using underlying movement processes to clarify whether populations are residents or migrants during the non-breeding season, and test this technique on Herring Gull tracking data. In Chapter three, I compare the migration strategies of populations of Herring Gulls that are short distance migrants to those that are long distance migrants. In Chapter four, I outline the wintering home ranges of these

Herring Gull populations, and explore whether there may be differences in their choice of winter habitat. In Chapter 5, I summarize the results of my thesis, discuss their implications, and present future directions for research. 16

Table 1-1: Adult Herring Gull survival estimates found in previous studies.

Location Survival Estimate ± SE Source Nunavut 0.87 ± 0.03 Allard et al. 2006 Nova Scotia 0.82 ± 0.07 Freeman and Morgan 1992 Newfoundland 0.83 ± 0.04 Robertson et al. 2016a Ontario 0.91 ± 0.02 Breton et al. 2008 17

Table 1-2: Details of tracking device deployment for collecting Herring Gull movement data used in this thesis. Local Breeding Deployment Years of Type of n Size Duty Cycle Attachment Capture Population Location Tracking Device Method Method Nunavut Southampton Island, 2008, Doppler 16 18g 10 hr on, Leg loop harness Drop Trap Nunavut 2013-2015 PTT 24 hr off (Mallory and (Mills and Gilbert 2008) Ryder 1979)

Newfoundland Within 75 km of 2009-2013 Doppler 9 11-30g 8 hr on, Chest harness Net Launcher (Witless Bay, Quabbin Reservoir, PTT, 18 hr off; (Morris et al. (Clark et al. Little Bay Island, Massachusetts GPS PTT 6 locations 1981) 2014) Little Green Island) per day

Sable Island Sable Island, NS 2012-2016 GPS PTT 8 22g 8 locations per Leg loop harness Drop Trap day (Mallory and (Mills and Gilbert 2008) Ryder 1979)

Bay of Fundy Kent Island, NB; 2009-2010, Doppler 12 17-18g 6 locations per Leg loop harness Drop Trap Brier Island, NS 2014-2015 PTT, day (Mallory and (Mills and GPS Gilbert 2008) Ryder 1979) logger

Great Lakes Double Island, 1999-2001, Doppler 12 20-30g 8 hr on, Chest harness Drop Trap Lake Huron; 2008-2009 PTT 72 hr off (Morris et al. (Mills and Agawa Rocks, 1981) Ryder 1979) Lake Superior; Within 75 km of Quabbin Reservoir, Massachusetts

18

Figure 1-1: Herring Gull (Larus argentatus) distribution in North America (BirdLife International and NatureServe 2014). Breeding distribution is shaded in yellow, year-round distribution is shaded in blue, wintering distribution is shaded in red, and migration distribution is hatched grey.

19

Figure 1-2: Schematic of state-space model structure, adapted from Jonsen et al (2013). The process model evolves through time as a random walk, starting at x1 and ending at xn. At times t1, t2, …., tn, measurements y1, y2, …, yn are taken as described by the observation model. An arrow from one variable (for example x1) to a subsequent variable (for example x2) indicates that x2 is expressed in terms of the conditional probability distribution of x2 given x1. Note that in the process model, all observations of x are dependent on x at the previous time step. Observations of y are independent of one another, and are dependent on x at the same time step.

21

Chapter 2 – Are Herring Gulls residents or migrants?

Introduction

Bird migration is most commonly defined as the regular seasonal movements between breeding areas and resting or wintering areas (Berthold 2001). Yet, it is not always easy to identify a bird population as migrant or resident. Many species display a broad spectrum of behaviours, and it can be difficult to make objective measurements of their movement behaviours without the appropriate conceptual framework. Resident bird populations still move during the non-breeding season, traveling in response to habitat quality, changing food availability, competition and predation pressure (Paradis et al.

1998). Migrants and residents both navigate away from and return to their breeding area.

The one defining difference between migrants and residents is in how their wintering area is selected (Coulson and Brazendale 1968). For migrants, the distance and direction to their winter home range is at least partially inherited. By contrast, the winter home range of residents is the result of random movements in the locality of their breeding site. These movements may be random in relation to distance and habitat selection, but still biased in a particular direction due to factors such as topography and climate (Thomson 1964).

Collectively, the outward similarities of these movement patterns makes it difficult to distinguish between migration and residency when there is some geographical overlap between the breeding and wintering areas (Coulson and Brazendale 1968).

The majority of Herring Gulls in North America appear to be migratory. Herring

Gulls breeding at northern and inland sites must be migratory, as the wintering range of 22

Herring Gulls does not extend into those areas (Pierotti and Good 1994). Herring Gulls breeding in Atlantic Canada typically migrate to the northeastern United States (Gross

1940; Threlfall 1978; Gaston et al. 2008). However, the movement patterns of Herring

Gulls breeding in the Great Lakes have been described inconsistently in scientific publications. The movement patterns seen in Herring Gull banding data from the Great

Lakes has been described as migrant behaviour in some papers (Hebert 1998), and resident behaviour in others, albeit with a directional bias along shorelines and river systems (Moore 1976; Gabrey 1996). Herring Gulls in the Great Lakes remain in the

Great Lakes basin year-round, but do have a tendency to move from the upper Great

Lakes to the lower Great Lakes (Gabrey 1996). Juvenile Herring Gulls from the Great

Lakes migrate long distances, and moving successively shorter distances each year as they mature (Moore 1976). This gradient in distance travelled over their lifetime obscures any potential transition from migrant to resident behaviour. This pattern could be interpreted as a migration, or a directional bias in resident foraging movements.

Coulson and Brazendale (1968) developed a method which uses bird banding data to differentiate between migrant and resident populations. They posited that resident individuals move randomly with respect to distance, a simple mathematical relationship can describe the progressively smaller number of bands recovered at increasing distance from the breeding colony. The distribution of sightings from a migrating population could not be modeled in such a way; predicting the extent and position of a specific wintering area would require a more complicated mathematical description. Coulson and

Brazendale (1968) grouped band recoveries according to equal distance zones extending from the breeding colony. If the relationship between the proportion of recoveries at or 23 beyond each distance zone is plotted against distance, a logarithmic relationship is expected for a population. A logarithmic pattern arises when a constant proportion of birds move beyond each distance zone, as would theoretically be expected in a randomly

j travelling population. This relationship can be formulated as pj = r , where j is the total number of distance zones, pj is the proportion of birds moving beyond the outer limit of zone j, and r is a constant rate of displacement (Coulson and Brazendale 1968). A migrating population would be poorly described by such a function. This method has been used to clarify the movement patterns in a range of bird species with diverse migration strategies: Great Cormorants (Phalacrocorax carbo; Coulson and Brazendale

1968), Greenfinches (Chloris chloris; Boddy and Sellers 1983), Common Murres (Uria aalge; Birkhead 1974), Adriatic Yellow-legged Gulls (Larus michahellis; Kralj et al.

2014), Western Gulls (L. occidentalis; Coulter 1975), and Herring Gulls in Europe

(Parsons and Duncan 1978; Coulson and Butterfield 1985).

Bird movement is now frequently studied with tracking data derived from telemetry devices rather than banding data (Bridge et al. 2011). One fundamental difference between these two types of data is their unit of focus. Banding data typically contains very few locations for a large sample of individuals within a population, whereas tracking data contain many locations for a much smaller sample of the population. In this paper, I first simulate tracking data from migrant and resident populations to verify if

Coulson and Brazendale’s (1968) method can detect the same patterns in tracking data. I then apply their method to clarify if Herring Gulls breeding in the Great Lakes should be considered migrants or residents outside the breeding season.

24

Validating methods with simulated data

Coulson and Brazendale’s (1968) method for differentiating between migrant and resident populations has previously been used only on bird banding data. I simulated tracking data from migrant and resident populations to evaluate what patterns should be expected when using this method on tracking data, and how reliably migrant and resident behaviour can be distinguished from one another. Parameters for these simulated data were chosen to emulate time period and total movement displacement seen in populations with these types of unclear movement patterns (e.g. Coulson and Brazendale 1968;

Butterfield et al. 1983; Gabrey 1996 )

In all simulated tracks, one step of a random walk represented a daily location observation (Codling et al. 2008). Migrating individuals were simulated using a combination of three biased correlated random walks to simulate autumn migration, winter, and spring migration. First, an autumn migration simulated movement from the breeding point of origin towards a migration destination, randomly selected from a 100 km x 100 km box, 500 km south of the breeding area. Second, a winter period of movement was biased around that destination, continuing until a total of 200 days had elapsed since the individual departed the breeding site. Third, a spring migration was biased to return to the breeding point of origin. Resident individuals were simulated using a combination of two correlated random walks. The first did not have a target destination, and continued until 200 days had elapsed since the bird departed the breeding site. The second was biased to return to the breeding point of origin (Figure 2-1). I ran 1000 simulations each for sample sizes of n = 5, 10, 20, 40 and 80 individuals for both migrant 25 and resident populations. I was particularly interested in testing if the different behaviours could be identified at the small sample sizes common in many tracking studies.

I then applied an adapted version of Coulson and Brazendale’s (1968) method to these simulations. All non-breeding locations were grouped into equal distance zones at

50 km intervals extending outwards from the breeding colony (e.g. Figure 2-2). I fit a linear model of the relationship between distance from the breeding colony and the log- transformed proportion of locations in that distance zone or greater. I used r2 as a measure of how well the data were fit by the logarithmic function, with the expectation that the fit would be better (i.e., more variance in the pattern explained) for a resident population than for a migrant population (Saunders et al. 2012).

A logarithmic function fit well to the simulated resident populations, and comparatively poorly to the simulated migrating populations. For example, with a sample size of 10 tracked individuals, resident populations had a median r2 value of 0.90, while migrating populations had a median with a r2 value of 0.32 (Figure 2-3). At each sample size, the median r2 value for resident populations was at least double that of migrating populations. At smaller sample sizes, r2 was more strongly influenced by individuals with

2 atypical behaviour, and therefore I saw greater variation in the expected value of r

(Figure 2-3). There was a small risk of a resident population being misidentified as migrant population when fewer than 20 individuals were tracked, but it is unlikely a migrant population would be misidentified as a resident population (Figure 2-3). 26

When plotting the observed proportion of locations beyond each distance zone, the shape of the curve can also be used to differentiate between migrants and residents.

Migrant populations tended to show a characteristic ‘step pattern’, while resident populations were smoother (Figure 2-3). This same characteristic has been used to differentiate between resident and migrant populations in banding studies (Kralj et al.

2014).

These simulations are crude simplifications, as real populations have much greater complexity in their movement behaviour. When modeling the proportion of locations beyond each distance zone in a real population, intermediate values of r2 could represent a situation in which individuals in the same populations exhibit radically different behaviours. For example, the population could be partially migratory, with only a subset of individuals in the population migrating (Chapman et al. 2011). The population could also have a differential migration pattern, with diverse migration strategies between sexes or age classes (Cristol et al. 1999). In the case of intermediate values of r2, individual movement patterns within the population should be scrutinized closely, as it may be necessary to identify multiple movement patterns within the same population.

Other characteristics such as the extent of overlap between the wintering and breeding areas, and the size of wintering range can be combined with Coulson and Brazendale’s

(1968) method when judging how a population's non-breeding movement should be characterized.

The methods of this study share some similarities to the model-driven approach for differentiating between migration and dispersal developed by Bunnefeld et al. (2011). 27

Their approach involved fitting a series of models describing different annual movement patterns to the net squared displacement (NSD) of an individual or population. The NSD approach is very comprehensive, and the authors proposed a unified framework that could be applied to any animal movement data. However, the NSD approach was developed using concepts from mammal movement literature. Both the migrant and resident populations simulated in this study would be classified as migrating under their framework because both return to the same breeding site each year. The NSD approach could potentially be refined to differentiate between random and directed movement during the non-breeding season.

Applying methods to Herring Gull data

I applied this adaptation of Coulson and Brazendale’s (1968) method (see above) to discriminate between migrant and resident behaviour in Herring Gulls from the Great

Lakes (n = 8), the Bay of Fundy (n = 10), Sable Island (n = 8), Newfoundland (n = 3) and

Nunavut (n = 8; Chapter 1). I applied the same methodology described above, using 100 km intervals between distance zones (Figure 2-2). I used a one sample Kolmogorov-

Smirnov (K-S) test in addition to r2 to assess the goodness of fit of a logarithmic model to the data.

The relationship between distance and the proportion of locations beyond each distance zone was poorly fit by a logarithmic function for each of the Arctic and Atlantic populations (r2 = 0.48 - 0.63). The observed relationships both showed a step pattern, as predicted given their known migratory behaviour (Figure 2-4). The Great Lakes 28 population had a smooth relationship that was fit well by a logarithmic function (r2 =

0.9), suggesting this population not undertaking a true migration, and should be considered residents.

An r2 value of 0.63 for the Sable Island population does not provide clear support for either a resident or migrant pattern. However, the winter home range of the Sable

Island population appears to be focused in a narrow area which is geographically distinct from its breeding area (Chapter 4), and birds traveled to this area with few stopovers

(Chapter 3). When all of these characteristics are considered, Herring Gulls from Sable

Island should be considered migratory. The difference in r2 value between the Sable

Island and the other resident populations indicates there might be some behavioural complexities unique to this population, which is unsurprising given the flexibility of gull movement patterns (Klaassen et al. 2012).

I performed a K-S test for each population to compare the proportion of points beyond each distance zone category to a reference exponential distribution. I used these tests to statistically evaluate if there appeared to be a constant proportion of birds move beyond each distance zone, which would suggest a resident population. The results largely agreed with the findings above: the Herring Gull populations from Nunavut (D =

0.40, p < 0.001), Newfoundland (D = 0.45, p = 0.002) and Sable Island (D = 0.37, p =

0.018), appeared to be migratory, and the population from the Great Lakes appeared to be residents (D = 0.31, p = 0.11). Interestingly, a K-S test did not reject the hypothesis that the Bay of Fundy was drawn from an exponential distribution (D = 0.28, p = 0.24). 29

Upon closer inspection, Herring Gulls tracked from the Bay of Fundy had highly variable non-breeding behaviour. Two individuals remained in Nova Scotia during the winter. Two individuals made stops of 3-4 months in Maine before continuing further south. The remaining six individuals migrated directly to and from their wintering areas around Chesapeake Bay with few or no stopovers in-between. The migratory behaviour of gulls from a single breeding site can be quite variable in some cases (Shamoun-

Baranes et al. 2017), and their movement behaviour can be challenging to categorize.

Note that goodness of fit statistics like the K-S test should be used with caution in this type of analysis. Because sample size is directly linked to the size of the number of distance zones in which the total migrating is divided, the absolute value of the p value is artificial. For example, had I chosen to use 50 km instead of 100 km distance zones, the

K-S test would arrive at p > 0.05 for all populations. The K-S test can still be informative for comparing between populations, as the relative distance between their p values will remain constant regardless of the number of distance zones. In this case, the p values of the Great Lakes and Bay of Fundy populations were consistently an order of magnitude larger than for the Sable Island, Newfoundland and Nunavut populations.

Conclusion

In this chapter, I demonstrated how Coulson and Brazendale’s (1968) method for differentiating between migrant and resident populations can be applied to telemetry data, with a few adaptations to facilitate interpretation, particularly among bird populations with variable movement strategies among individuals (e.g. Bay of Fundy Herring Gulls).

This technique is useful when conducting studies of migratory behaviour. In ambiguous 30 cases, it can be difficult to characterize when the standard milestones of the migratory period are taking place. My analyses confirm the population of Herring Gulls breeding in the Great Lakes is best described as a resident population, and therefore does not have a migratory period analogous to those of the Nunavut and Atlantic Canada populations.

31

Figure 2-1: An example of a) random movement by resident population and b) directed movement by a migrating population, simulated for n = 10 individuals.

32

Figure 2-2: Coulson and Brazendale’s (1968) method for differentiating between migrants and residents requires all available non-breeding locations to be grouped into distance zones extending outwards from the breeding colony. Demonstrated for Herring Gulls breeding in the Great Lakes. Distance zones are at 100 km intervals, extending to a maximum of 1500 km

. 33

Figure 2-3: Top panels show the relationship between distance from the breeding colony and the proportion of locations in that distance zone or greater for one simulation of a resident or migrant population (n = 10). Simulated data are displayed in grey, fitted logarithmic function is displayed in red. Bottom panels show quantile plots of the r2 value for 1000 simulations of each sample size for resident and migrant populations.

34

Figure 2-4: The relationship between distance from the breeding colony and the proportion of locations in that distance zone or greater for Herring Gulls breeding in the Great Lakes (n = 8), the Bay of Fundy (n = 10), Sable Island (n = 8), Newfoundland (n = 3) and Nunavut (n = 8). Observed data are displayed in grey, fitted logarithmic function is displayed in red. 35

Chapter 3 – Do short and long distance migrants use different

migration strategies?

Introduction

Understanding where birds migrate and what strategies they use is critical to understanding their annual cycle, and ultimately contributes to their sound conservation and management (Berthold 2001). Migration is fundamentally different from the breeding and wintering periods. It is a highly dynamic phase in which birds may travel great distances, often in relatively short periods of time, and have an elevated risk of mortality when doing so (Klaassen et al. 2014). I define ‘migration strategy’ as the choices birds make about when to migrate, what routes to take, and when and where to stop. Migration strategies have evolved to maximize fitness in seasonal environments

(Alerstam et al. 2003), and differences in migration strategy between populations can indicate how the various factors influencing migration decisions vary across the geographic range of a single species (Fifield et al. 2014; Ramos et al. 2015; van Toor et al. 2017).

Migratory decisions are complex. A bird’s internal state, its physical capacity for motion, its navigational abilities, and its external environment are perpetually evolving and interacting to shape its optimal movement path (Nathan et al. 2008). Within an optimization framework, the majority of birds are thought to hinge their migration strategy on minimizing the amount of time spent on migration, although an alternative approach for some birds may be to minimize energy cost or predation risk during migration instead (Alerstam and Hedenström 1998). 36

As omnivorous foraging generalists, gulls can take advantage of many different types of food, and therefore can use many different terrestrial, freshwater, and marine habitats

(Pierotti and Annett 1991). Therefore, their choice of migratory route is much less constrained by the need to target specific habitats compared with foraging specialists. As flight generalists, gulls can use a wide range of flight modes: flapping flight, thermal soaring, ridge soaring, dynamic soaring (Shamoun-Baranes et al. 2016). This flexibility permits them to have long migration travel days and fewer restrictions on terrain and weather conditions. Gulls also travel with lower fuel loads, as they can use a fly-and- forage migration strategy and feed along the way (Klaassen et al. 2012).

Rich datasets of Herring Gull band resightings, dating back to the 1920s, have revealed that the non-breeding movements of Herring Gulls differ between geographically distinct populations in North America. Adult Herring Gulls breeding in the Great Lakes are considered a resident population, remaining in the region throughout the winter concentrating around Lake Erie and Lake Ontario (Moore 1976; also see

Chapter 2). By contrast, Herring Gulls breeding in Atlantic Canada are known to winter between Maine and North Carolina (Gross 1940). The migratory routes and destinations of Herring Gulls breeding in the less densely populated parts of their range, including northern, central, and western North America, are currently unknown because very few birds have been banded in these areas (Gaston et al. 2008). There are two published records of Herring Gulls banded on Southampton Island, NU, which were resighted in

Niagara Falls, NY and Texas (Allard et al. 2006). The majority of Herring Gulls wintering in Nova Scotia and Newfoundland don’t appear to be of local breeding origin, and it has been speculated they must come from northern breeding populations (Gross 37

1940; Gaston et al. 2008). While banding studies have been instrumental to our understanding of where populations of birds move throughout the year, they are inadequate for studying how individuals travel. By studying the migration movements of wide-ranging species like Herring Gulls at an individual level, we can gain a greater understanding of how their great ecological flexibility may influence their migration strategies.

Gulls have received minimal attention in tracking studies, however recent studies of

Lesser Black-backed Gulls (Larus fuscus) in Europe suggest their choice of migration strategy and the currency they optimize may differ between short and long distance migrants. Klaassen et al. (2012) predicted that as both foraging and flight generalists,

Lesser Black-backed Gulls migrating short distances would be well suited for a rapid, time-minimizing migration strategy, with high overall migration speeds, few and short stopovers, and direct routes. Instead, they found a population of short distance migrants travelled at low overall migration speeds, made many and long stopovers, and used indirect routes. They proposed that gulls migrating short distances use an energy- minimizing strategy. In contrast, Bustnes et al. (2013) found a population of Lesser

Black-backed Gulls migrating long distances and crossing the poor habitat of the Sahara

Desert used a migration strategy that was rapid and direct, aligning with the predicted characteristics of a time-minimizing migration strategy.

Here, I examine the migratory movements of Herring Gulls from four local breeding populations in eastern North America to study the variation in their migration strategies. The three populations breeding in Atlantic Canada (Newfoundland, Sable

Island, Bay of Fundy) have previously been identified as short distance migrants, while 38 the population breeding in the Arctic are revealed to be long distance migrants. My objectives were: (1) to identify the migration routes of these Herring Gull populations, particularly for the Arctic-breeding Herring Gulls whose non-breeding distribution was previously unknown, (2) to characterize the migration strategies of these populations, and

(3) to test for differences in migration strategy between short and long distance migrants.

Given the similarities in the ecological niches of Herring Gulls and Lesser Black-backed

Gulls (Liebers et al. 2004), I predicted Herring Gulls migrating short distances would travel at low overall migration speeds, make many and long stopovers, and take less direct routes. Conversely, I predicted Herring Gulls migrating long distances would travel at high overall migration speeds, make few and short stopovers, and take more direct routes to their final destinations.

Methods

Details on study sites, remote tracking of gulls, and data processing can be found in

Chapter 1.

For this analysis, I used 74 full migration tracks, representing eight gulls from

Nunavut (eight autumn tracks, one spring track), three gulls from Newfoundland (six autumn, nine spring), eight gulls from the Bay of Fundy (11 autumn, 10 spring) and eight gulls from Sable Island (17 autumn, 12 spring). I also included individuals with incomplete migration tracks when assessing migratory routes. Two birds tracked from

Brier Island were excluded from migration analyses because they did not migrate. 39

The duration of each migration was the total number of days between departure and arrival at breeding and wintering sites. This period was further divided into travel days and stopover days (See Chapter 1 methods). The total migration distance was calculated only for travel days, as the distance traveled during stopover days varied greatly depending on the length of stopovers. Travel speed was the total migration distance divided by the number travel days, while the overall migration speed was the total migration distance divided by the total duration of migration. Directness was the percent difference between an individual’s total migration distance and the shortest possible route between their starting and ending location, calculated as the great circle route (shortest distance between two points on the surface of a sphere) using the Vincenty ellipsoid method (Hijmans 2016). The number of stops was calculated as the number of unique stopover sites used by the individual, while stopover length was calculated as the number of days spent at each of these sites.

The above metrics were modeled using sets of candidate generalized linear mixed models (GLMM), using Gaussian (migration speed, travel speed, distance), gamma

(directness), negative binomial (duration, travel days), or zero-inflated negative binomial

(stopover days, number of stops, stopover length) distributions, and their respective canonical link functions (Skaug et al. 2016; Bates et al. 2015). Distributional assumptions about the data were checked through graphical analysis of scaled residual plots (Hartig

2016). Population was included as a fixed effect to assess if migration strategy differed between populations. Season was included as a fixed effect to assess if populations altered their migration strategy between their spring and autumn migrations. Because gulls have a high degree of individual variation, and some individuals were tracked for 40 greater lengths of time than others, I included individual as a random effect to control for these differences (n = 27), except in the model for directness, where variation by individual did not explain any of the observed variance. I also considered controlling for inter-annual variation as a random effect, but variation by year was also uninformative. I excluded the interaction between population and season from the model sets. It improved model fit only marginally in one of nine models, and therefore I excluded it to facilitate model averaging and interpretation (Grueber et al. 2011). I would like to note that only one track was available for spring migration in the Nunavut population, therefore my analysis cannot detect seasonal variation in this population. Variance of random effects was not reported because I determined through simulations that the low sample size of spring migration from Nunavut (n=1) made the random effect parameter unreliable; this did not appear to be an issue when estimating the fixed effects.

I used an information-theoretic approach to model selection, comparing candidate models within each set using AICc (Burnham and Anderson 2002). I elected to use conditional model averaging (Barton 2016), as the evidence ratios of the Akaike weights from the first to the second ranked model in each set (ω1 / ω2) were typically around 3, suggesting a fair amount of model selection uncertainty (Burnham and Anderson 2002).

Cumulative Akaike weights (Σ ωi) are presented as an index of relative variable importance. I infer strong support for a difference between populations or seasons when

95% confidence intervals excluded zero (Grueber et al. 2011). I assessed the explanatory power of these models with conditional r2 (fixed effect only) and marginal r2 (fixed and random effects; Nakagawa and Schielzeth 2013; Lefcheck 2016).

41

Results

Spatial patterns

The mean great circle distance between the breeding sites and wintering sites of

Herring Gulls was 3940 ± 3704 km from the Nunavut colony, 1389 ± 1200 km from the

Newfoundland colonies, 1219 ± 1024 km from the Sable Island colonies, and 903 ± km from the Bay of Fundy colonies. Herring Gulls from Nunavut traveled much greater distances and took much less direct routes than the Atlantic Herring Gulls (Table 3-1).

Gulls from Nunavut traveled 6921 ±1603 km, 76% longer than their respective great circle routes, while collectively gulls from Atlantic Canada traveled 1491 ±487 km, only

29% longer than their respective great circle routes (Table 3-1, Figure 3-1a). For all populations, the distance traveled and the directness of their route remained consistent between their spring and autumn migration (Table 3-2).

Herring Gulls breeding in Nunavut used two distinct autumn migration routes with equal frequency (Figure 3-2). Five individuals migrated using a strictly coastal route, passing east through Hudson Strait, south along the coast of Labrador, and then followed the Atlantic coast to their wintering range offshore from Louisiana, Texas, and Mexico

(Figure 3-2; also see Chapter 1). By contrast, six individuals migrated due south from their breeding colony through Hudson Bay, crossed over land to the Atlantic Coast between the St. Lawrence River and Chesapeake Bay, and then followed a coastal route to the same wintering range. Flights from Hudson Bay to the Atlantic coast often covered

2000km or more in 3 days. The single individual from Nunavut tracked for a full spring migration followed a similar route to its autumn migration, returning up the Atlantic

Coast before crossing over land to Hudson Bay. All Herring Gulls breeding in Atlantic 42

Canada migrated along the Atlantic coast to their coastal wintering range between

Chesapeake Bay and Cape Cod (Figure 3-3).

Temporal patterns

The migration of Herring Gulls from Nunavut took a mean of 71 days in the autumn and 58 days in the spring. Herring Gulls from Atlantic Canada tended to depart earlier and migrate in less time: they took a mean of 28 days in the autumn, and 9 days in the spring (Table 3-1, Table 3-2, Table 3-3). The migrations of birds breeding in Nunavut and Newfoundland were longer in duration than the migrations of birds breeding in the

Bay of Fundy or on Sable Island (Table 3-2). For all populations of Herring Gulls, the total duration of their spring migration was shorter than their autumn migration (Table

3-1), meaning the overall speed of their migration was much faster in the spring (248 ±

98 km/day) compared to the autumn (151 ± 102 km/day). However, model selection indicated little support for an appreciable difference in overall migration speed between populations, as the confidence intervals for all four populations overlapped considerably

(Table 3-2, Figure 3-1b).

On average, Herring Gulls from Nunavut completed their migrations over the course of 22 ± 15 and 29 travel days, in the autumn and spring respectively, travelling at speeds of 397 and 270 km/day (Table 3-1). In contrast, Herring Gulls from the Atlantic populations completed their migrations in fewer travel days, taking a mean of 8 ± 3 days in the autumn and 6 ± 3 days in the spring, and travelled at speeds of 231 ± 67 and 280 ±

87 km/day (Table 3-1). Model selection identified season as an important predictor of travel speed and number of travel days, with Herring Gulls travelling at slightly higher 43 speeds in the spring than in the autumn, and therefore required slightly fewer travel days

(Table 3-2).

Stopovers

Many of the Herring Gulls breeding in Nunavut made prolonged stopovers in

Hudson Strait and Foxe Channel at the beginning of their migration, with 10 of 14 tracked individuals stopping for anywhere from 19 to 101 days (Figure 3-4). On the remainder of their route, migratory stopovers were not concentrated in any particular area, but were spread across the Atlantic coast from Newfoundland to Florida (Figure

3-2). However, four of eight birds with full migration tracks made at least one stopover on the Atlantic coast between Chesapeake Bay and Cape Cod, the same area covered by the Atlantic Canada populations’ wintering ranges (Figure 3-2;Figure 3-3). The only non- coastal stopover site used by Herring Gulls breeding in Nunavut was on the St. Lawrence

River. All of the stopovers made by Herring Gulls breeding in Atlantic Canada were likewise spread across their migratory route, with no particular stopover site attracting a large proportion of individuals (Figure 3-3).

Season was consistently an important predictor of stopover behaviour. All

Herring Gull populations stopped for a greater number of days during their autumn migration than in the spring (Figure 3-1c). Moreover, both the number of stops Herring

Gulls made during migration and the mean length of their stopovers were both greater in the autumn than in the spring. (Table 3-2). There was a high degree of individual variation in stopover tendencies within each population (Table 3-1). Overall, 50% of the recorded Herring Gull migrations were direct, with no stopovers. However, in the 44 seasonal movements of a single population, the proportion of individuals who did not make any stopovers ranged from 0% to 100%. In all populations, Herring Gulls made fewer stopovers and were more likely to make direct flights in the spring compared to the autumn (Table 3-1). Between populations, only gulls from Sable Island differed considerably from the other populations, making fewer stops while migrating, stopping for shorter periods of time, and therefore stopping for fewer total days than the other populations (Table 3-2). Interestingly, no birds tracked from Sable Island made stopovers on their spring migration.

Discussion

In this study, I compared the migratory movements of four Herring Gull populations breeding at one site in the Arctic and three sites in Atlantic Canada. Within these four populations, Herring Gulls showed a great deal of individual variation, but in general the migration strategies of short and long distance migrants had some surprisingly similarities. Herring Gulls clearly altered their migration strategies between seasons.

Population migration strategies

The migration strategies used by short distance migrants breeding in Atlantic Canada and long distance migrants breeding in the Arctic shared more similarities than I had predicted. The overall speeds of their migrations, which encompasses both stopover and travel periods, were not significantly different. Likewise, there was not a clear difference between the stopover behavior of the Arctic and Atlantic breeding populations. 45

Compared to the Atlantic population, Herring Gulls breeding in the Arctic used migration routes that were actually less direct, not more.

Given that overall migration speed was comparable between short and long distance migrants, it is not surprising that there was also no clear difference in stopover behaviour.

A number of recent studies have found differences in overall migration speed within a population are mostly driven by differences in the extent to which birds use stopovers during migration, while travel behaviour tends to be more consistent (Nilsson et al. 2013;

Klaassen et al. 2012; Kölzsch and Gerhard 2016). There were some differences in stopover behaviour between the four populations, but these differences were not clearly linked to the distance of their migration. For example, there was a cline in the total number of stopover days, with individuals breeding in Nunavut and Newfoundland using the most stopover days, the Bay of Fundy being intermediate, and those breeding on

Sable Island using the fewest.

Although Herring Gulls made extensive use of stopovers, for the most part individuals did not congregate in specific staging areas, but rather individuals stopped throughout their migratory routes. As foraging generalists, large gulls can find food in a diversity of habitats, and are therefore much less restricted in their selection of stopover sites than species with specialized foraging habits. For example, the smaller Sabine’s

Gull (Xema sabini) feeds on a narrower range of marine organisms in the non-breeding season; individuals converge on staging sites at reliable coastal upwellings to find these food resources (Davis et al. 2016). The one exception to this pattern was the concentrated use of Hudson Strait and the Foxe Channel by Arctic-breeding Herring Gulls. These birds spent a mean of 40 days at stopover sites in Nunavut, presumably taking advantage of 46 good foraging conditions, and then departed, perhaps as the formation of sea ice cut off access to their marine food resources. These prolonged stopovers in the Arctic could help

Arctic-breeding Herring Gulls prepare for long distance flights to their next stopover sites; these flights often covered 2000 km or more in 3 days (Figure 3-2). As changing arctic sea ice dynamics cause sea ice to form later and cover less total area (Stroeve et al.

2012), Herring Gulls may be able to remain in the Arctic for longer periods of time prior to migration. A similar pattern of dispersal near the breeding area prior to long distance migration has also been observed in Finnish Herring Gulls (Kilpi and Saurola 1983).

Both the Atlantic and Arctic Herring Gulls generally followed the Atlantic coast during their migrations. As a result of this pattern, their respective migration routes were

29% and 76% longer than the shortest possible route between their breeding and wintering areas. Herring Gulls showed a clear preference for marine coastal habitats, despite the fact that they are capable of acquiring food from freshwater habitats and anthropogenic sources like landfills, urban areas, and farm fields (Pierotti and Good

1994). Gulls likely prefer these coastal habitats both because they contain predictable sources of food, and because coastal topography creates opportunities for energetically efficient soaring (Hedenstrom 1993). Interestingly, half of the Herring Gulls tracked from

Nunavut made a major overland crossing of more than 2000 km between Hudson Bay and the Atlantic Ocean. The six birds that made this crossing traveled at high speeds; four birds made no stops, and two made short stops at major bodies of freshwater in Québec

(St Lawrence River, Lac St. Jean, La Grande River).

Herring Gulls migrating in North America from these four breeding populations generally followed the coast, regardless of their migration distance. Lesser Black-backed 47

Gulls from the Netherlands similarly followed the coast, using indirect routes, and migrating at a leisurely pace with many stopovers. By contrast, Lesser Black-backed

Gulls from Norway appear to use more direct routes and fewer stopovers, travelling at higher overall migration speeds (Bustnes et al. 2013). However, this population of Lesser

Black-backed Gulls makes two long overland crossings: one across mainland Europe to reach stopover sites in the Mediterranean Sea, and one across the Sahara Desert to reach their wintering sites in East Africa. Habitat may be an important driver of migration strategy in gulls; it would be interesting to look more explicitly at how the migration strategies of gulls are influenced by habitat characteristics at multiple spatial scales

(Buler et al. 2007).

One case where long distance migrants did appear to have notably different behaviour from short distance migrants was in their travel behaviour. Herring Gulls from Nunavut had markedly higher speeds on travel days than the birds from Atlantic Canada (Table

3-1). However, with only one location per day, travel speed is best considered a measure of net displacement distance during a day of travel rather than an accurate estimate of their speed (Tanferna et al. 2012). It is possible Herring Gulls from Nunavut are truly flying at faster speeds in order to cover greater distance, but they may also simply be travelling in a more direct path to cover the distance more efficiently. Lesser Black-

Backed Gulls tracked with a high location sampling rate appear to cover similar total distances on a daily basis, regardless of if they were residents or short distance migrants.

However, the movements of migrants were more targeted towards a goal destination, and therefore showed a greater net displacement (Shamoun-Baranes et al. 2017). In either 48 case, it appears Herring Gulls migrating long distances from Nunavut are more efficient and cover larger distances on travel days.

When stopovers are factored in, there was evidently no difference in the overall speed of migration between populations. Rather, there was a substantial amount of individual variation in overall migration speed within each population. This makes sense given the flexibility of Herring Gulls as generalists. Species with more specialized diets seem to have less individual variation in their migration strategies, stemming from constraints on their foraging and stopover locations (Beatty et al. 2013; Piersma 2007; Fraser et al.

2013).

When compared to the closely related Lesser Black-backed Gull, the overall autumn migration speed of Herring Gulls in eastern North America (151 km/day) appears to fall between that of short distance migrants (44 km/day; Klaassen et al. 2012) and long distance migrants (371 km/day; Bustnes et al. 2013). Similar to Lesser Black-backed

Gulls that are short distance migrants, Herring Gulls from both the Arctic and Atlantic

Canada used indirect routes and extended stopover periods. These findings support the idea that gulls may minimize energy spent on migration rather than the time spent on migration (Hedenstrom and Alerstam 1997). They may be doing this by waiting at stopover sites for favourable wind conditions to reduce the costs of flight (Harel et al.

2016). As generalists with diverse diets and foraging strategies, Herring Gulls could combine fly-and-forage migration with stopovers to greatly reduce the energy cost of transporting fuel (Strandberg and Alerstam 2007). Strandberg et al. (2009) suggested a shift in the balance of selection pressures for speed and duration of migration occurs between short and long distance migrants. They proposed some maximum threshold of 49 migration duration; short distance migrants can afford the time to be more selective of weather conditions, while long distance migrants cannot. However, this does not seem to apply to Herring Gulls migrating long distances. In the case of Lesser Black-Backed

Gulls migrating long distances (Bustnes et al. 2013), their apparent time-minimizing strategy may a result of poor habitat quality as they migrate over arid deserts rather than migration distance.

Seasonal migration strategies

Seasonal differences in Herring Gull migration strategies can be largely attributed to differences in their stopover behaviour. The duration of their spring migration was shorter than in autumn, but the routes they took and the number of days they actively travelled were consistent between seasons. However, Herring Gulls made fewer stopovers in the spring, including a greater proportion of individuals who made no stops.

Those stopovers that did occur were shorter on average. It is generally more common for birds to migrate faster in the spring than in the autumn (Nilsson et al. 2013). Birds may adopt different decision rules between seasons in response to environmental conditions like food availability and wind conditions which vary seasonally (Kölzsch and Gerhard

2016). Also, Herring Gulls are territorial in their breeding colonies, and therefore have an incentive to arrive early to their breeding site to compete for territories. They are not territorial on their wintering grounds, which eliminates this incentive to arrive before conspecifics (Pierotti and Good 1994).

Similarly, Lesser Black-Backed Gulls travelling short distances migrate faster during their spring migration due to a reduced number of stopover days (98 km/day spring vs 44 50 km/day autumn; Klaassen et al. 2012). However, Lesser Black-Backed Gulls travelling long distance migrations actually migrate more slowly during their spring migration, using a greater number of stopover days than during their autumn migration (191 km/day spring vs 371 km/day autumn; Bustnes et al. 2013). My analysis was limited in its capacity to detect these types of interactions between the effect of population and season because only one track was available for Arctic-breeding Herring Gulls in the spring.

With more such data, it would be possible to test if Herring Gulls breeding in Nunavut may also migrate more slowly in the spring than in the autumn. Migrants are typically thought to follow a wave of ecological productivity as spring advances northwards (La

Sorte et al. 2014). Herring Gulls wintering in the Arctic spend their winters at great distances from their breeding sites, and therefore may need to complete their migration in a series of stages as they wait for phenological cues to progress farther north. Premature arrival to the breeding site can be costly to birds breeding in the Arctic, as snow and ice can prevent access to both terrestrial and marine food sources (Bêty et al. 2004). Herring

Gulls breeding in Atlantic Canada spend their winters in closer proximity to their breeding sites, within the same climate regime, and therefore may be able to better detect when the timing is right for a quick spring migration (see further discussion below).

Species migration pattern

This study revealed the previously unknown, non-breeding movements of Herring

Gulls from the Arctic. Herring Gulls breeding in Nunavut migrate longer distances than was previously suspected, traveling an average of 6921 km to their wintering grounds in the Gulf of Mexico. Their migration is around four times the distance of Herring Gulls 51 breeding in Atlantic Canada. It is still not clear where Herring Gulls wintering in Atlantic

Canada are breeding. They could potentially be coming from lower density portions of their range where few birds have been banded, including Labrador, the St. Lawrence

River, or inland areas. It is also possible a small proportion of Atlantic Canada birds are residents. Two Herring Gulls tracked from Brier Island, NS did not migrate. During the breeding season, Herring Gulls from Brier Island are known to scavenge food from mink farms in southern Nova Scotia (K. Shlepr, unpublished data). These two individuals appeared to remain in the vicinity of the mink farms for the entirety of the winter. It seems likely some of the Herring Gulls wintering in Atlantic Canada are local breeders which take advantage of anthropogenic food subsidies such as these. Partially migratory bird populations are relatively common, and the proportions of migrants vs residents in a population will shift in response to environmental conditions (Berthold 2001).

In light of the long distance migration undertaken by Herring Gulls breeding in the

Arctic, my results indicate for the first time that Herring Gulls in North America exhibit a

‘leapfrog migration pattern’ (Newton 2008). The latitudinal sequence of Herring Gulls breeding in the Arctic, Atlantic Canada, and the Great Lakes is reversed during the winter, with the northernmost breeding population becoming the southernmost wintering population (Figure 3-5). This is in contrast to a ‘chain migration pattern’, wherein breeding populations along a latitudinal gradient migrate similar distances, maintaining the same latitudinal positions during the winter relative to each other (reviewed in

Newton 2008). Lesser Black-backed Gulls in Europe show a similar leapfrog migration pattern (Hallgrimsson et al. 2012). Leapfrog migration has been observed in a wide 52 variety of bird families, including waders, waterfowl, seabirds, raptors, and passerines

(Newton 2008).

Why would the northern breeding population not simply stop migrating at the northern edge of their wintering range? There are three main hypotheses as to why species have evolved leapfrog migration patterns. The competition hypothesis proposes it is beneficial to minimize migration distance, and that short distance migrants are dominant, competitively excluding long distance migrants from the preferred wintering sites (Pienkowski et al. 1985). In some cases, this hypothesis has been supported by differences in body size between populations (Holmgren and Lundberg 1993). This is not true for Herring Gulls; there are no differences in body size between Herring Gulls from the Arctic and the Atlantic populations, and only slight differences in morphology between these populations and the Great Lakes Herring Gulls (Robertson et al. 2016b).

However, Herring Gulls from Atlantic Canada may exclude northern Herring Gulls from their wintering sites simply by prior occupancy. Herring Gulls breeding in Nunavut typically departed their breeding site more than a month after Herring Gulls breeding in

Atlantic Canada, and thus gulls breeding in the Arctic may choose to bypass the eastern

United States and Atlantic Canada to reduce conspecific competition. An estimated

60,000 to 70,000 Herring Gulls breed in Atlantic Canada (Wilhelm et al. 2016), while perhaps only 4000 Herring Gulls breed in the Arctic (Pierotti and Good 1994). It is worth noting that Adult Herring Gulls from Nunavut would be competing with juvenile Herring

Gulls from both Atlantic Canada and the Great Lakes, which are also known to winter in the Gulf of Mexico (Gabrey 1996; Gross 1940; Moore 1976). 53

The spring predictability hypothesis suggests birds breeding in temperate areas benefit from wintering in nearby areas in the same climate regime as their breeding site because they can then respond directly to environmental cues signalling the advancement of spring and the development of suitable breeding conditions. Alerstam and Hogstedt

(1980) proposed the advancement of spring in the Arctic is more consistent than in temperate regions, and therefore Arctic-breeding birds can migrate to distant wintering areas, and return at the optimal time principally using their internal circannual clock. This hypothesis has been criticized because there is no evidence the advancement of spring is any more predictable in the Arctic than other climate zones (Pienkowski et al. 1985).

However, I think the spring predictability hypothesis could still be plausible without the assumption of a more predictable spring in the Arctic. Atlantic-breeding Herring Gulls could conceivably improve their ability to detect the advancement of spring at their breeding site by wintering in the same climate regime. In contrast, Arctic Herring Gulls must spend the winter outside the climate regime of their breeding area given that the northern edge of Herring Gull winter distribution in North America is well removed from polar tundra zone (Pierotti and Good 1994; Kottek et al. 2006). Therefore, regardless of which wintering area they select, gulls breeding in the Arctic could not gain this advantage in predicting the arrival of spring. Other factors like competition and food availability would be more relevant, and may make a more distant wintering area the optimal choice.

The time allocation hypothesis similarly suggests different breeding populations independently optimize their wintering location, based on the optimal time of arrival at their breeding sites (Bell 1996; 1997). This hypothesis proposes that because Herring 54

Gulls breeding in the Arctic migrate relatively late in the spring, their Gulf Coast wintering sites are optimal because they have time to take advantage of the increase in the spring abundance in food availability before they migrate. Herring Gulls breeding in

Atlantic Canada would have to leave southern wintering areas before this spring bounty, and therefore would gain no such advantage from undertaking a longer migration.

To date, these hypotheses about the cause of leap-frog migration patterns have not been directly tested against one another in a single study. One way to do this could be to compare the phenology and abundance of food resources at both breeding and wintering sites. How strong is the competition for food on the wintering grounds? How accurately do birds time their arrival around food abundance? Is migration departure triggered by some threshold of food abundance? These questions must be addressed to better understand what conditions explain the patterns of annual distribution I observed in eastern North American Herring Gull breeding populations, and more broadly, what conditions lead to leapfrog and chain migration. In the past 20 years, these meta- population scale spatial patterns have been clarified for a large number of species, using a combination of remote tracking, banding, stable isotopes and genetic techniques. The time is ripe for a meta-analysis of the species level spatial ecology of migratory birds, to understand how common these patterns truly are, and how geographic and phylogenetic factors affect their occurrence.

Conclusion

As generalists, Herring Gulls are very flexible in their movement behaviour. They may remain in their breeding area as residents, or migrate long distances of up to 10,000 55 km one-way to their wintering area. A great many features of their individual migration strategies seem negotiable, yet when we move to a larger scale to compare populations, a more consistent pattern emerges. Herring Gulls tend to migrate at a low overall migration speed with frequent stopovers. Their preference for coastal habitats compels them to follow rather indirect routes, regardless of whether they are short or long distance migrants. This picture of their migratory strategy suggests Herring Gulls are optimizing energy spent on migration rather than time. 56

Table 3-1: Migration characteristics of Herring Gulls tracked from Nunavut (n = 8 [autumn], 1 [spring]), Newfoundland (n = 6, 9), Sable Island (n = 17, 11), and the Bay of Fundy (n = 11, 10). All summary statistics are presented as mean ± SD (range). Range and SD are absent for all Nunavut spring summaries because only one individual was tracked for the full migration period.

Autumn Spring mean ± SD (range) mean ± SD (range) Duration Nunavut 70.5 ±48.2 (12-153) 58.0 (days) Newfoundland 38.3 ±13.1 (20-53) 13.8 ±18.7 (4-63) Sable Island 13.5 ±15.9 (4-63) 4.3 ±2.2 (2-10) Bay of Fundy 58.9 ±71.3 (2-177) 6.1 ±5.5 (1-20)

Migration Nunavut 179 ±148 (46-460) 135 Speed Newfoundland 56 ±24 (34-88) 224 ±96 (42-337) (km/day) Sable Island 201 ±80 (29-307) 419 ±145 (222-682) Bay of Fundy 148 ±162 (14-491) 312 ±206 (126-815)

Travel Nunavut 397 ±108 (193-563) 270 speed Newfoundland 213 ±71 (136-322) 269 ±89 (183-497) (km/day) Sable Island 239 ±66 (160-409) 321 ±83 (190-455) Bay of Fundy 229 ±72 (157-390) 241 ±74 (158-407)

Travel Nunavut 6921 ±1603 (5520-10154) 7202 Distance Newfoundland 1562 ±164 (1417-1862) 1662 ±265 (1260-2028) (km) Sable Island 1704 ±596 (1078-2991) 1596 ±490 (1234-2988) Bay of Fundy 1123 ±270 (698-1578) 1209 ±463 (713-2364)

Directness Nunavut 1.76 ±0.4 (1.38-2.55) 1.80 Newfoundland 1.14 ±0.1 (1.07-1.25) 1.18 ±0.1 (1.00-1.38) Sable Island 1.40 ±0.5 (1.00-2.48) 1.30 ±0.4 (1.01-2.33) Bay of Fundy 1.22 ±0.2 (1.00-1.53) 1.36 ±0.3 (1.06-2.19)

57

Table 3-1 continued: Migration characteristics of Herring Gulls tracked from Nunavut (n = 8 [autumn], 1 [spring]), Newfoundland (n = 6, 9), Sable Island (n = 17, 11), and the Bay of Fundy (n = 11, 10). All summary statistics are presented as mean ± SD (range), except for the proportion of birds from each site using stopovers. Range and SD are absent for all Nunavut spring summaries because only one individual was tracked for the full migration period. Range is not presented for Sable Island stopover summaries because all individuals made direct migrations, therefore there was no variation.

Autumn Spring mean ± SD (range) mean ± SD (range) Proportion Nunavut 0.88 1.00 using Newfoundland 1.00 0.44 stopovers Sable Island 0.47 0.00 Bay of Fundy 0.64 0.30

# of Stops Nunavut 3.4 ±2.9 (0-9) 7.0 Newfoundland 2.2 ±1.0 (1-4) 1.0 ±1.7 (0-5) Sable Island 0.5 ±0.6 (0-2) 0.0 ±0.0 Bay of Fundy 1.0 ±1.0 (0-3) 0.3 ±0.5 (0-1)

Mean Nunavut 13.5 ±16.1 (1-59) 4.4 ±2.2 (2-8) Stopover Newfoundland 10.6 ±10.1 (2-37) 7.6 ±5.0 (3-15) Length Sable Island 11.2 ±14.7 (2-43) 0.0 ±0.0 (days) Bay of Fundy 41.5 ±49.9 (2-129) 3.7 ±1.2 (3-5)

# of Nunavut 41.1 ±38.6 (0-117) 30.0 Stopover Newfoundland 23.0 ±14.2 (8-43) 7.6 ±16.9 (0-52) days Sable Island 5.1 ±11.4 (0-43) 0.0 ±0.0 (days) Bay of Fundy 41.5 ±54.5 (0-136) 1.1 ±1.9 (0-5)

# of Nunavut 22.0 ±15.1 (11-58) 29.0 Travel Newfoundland 9.7 ±2.9 (6-13) 7.2 ±2.8 (3-12) days Sable Island 7.7 ±3.0 (3-17) 5.3 ±2.2 (3-11) (days) Bay of Fundy 7.1 ±3.4 (3-12) 5.9 ±3.9 (2-16)

58

Table 3-2: Model averaged parameter estimates (β, 95% confidence intervals) and cumulative Akaike weights (Σ ωi) for generalized linear mixed models examining the effects of population and season on the migration characteristics for Herring Gulls tracked from Nunavut (NU; n = 8 autumn, 1 spring), Newfoundland (NL; n = 6, 9), Bay of Fundy (BF, n = 11; 10) and Sable Island (SI; n = 17, 11) during autumn (A) and spring (S). Individual is included as a random effect in all models except for directness. Estimates of conditional r2 (fixed effect only) and marginal r2 (fixed and random effects) are presented for the global model of each candidate set. Population and season are both categorical variables; the intercept is the predicted value for Herring Gull from Nunavut in the autumn, which then acts as the reference level for the other parameter estimates.

59

Table 3-3: Arrival and departure dates for autumn and spring migrations of Herring Gulls tracked from Nunavut (n = 8, 1), Newfoundland (n = 6, 9), Sable Island (n = 17, 11), Bay of Fundy (n = 11, 10). Colony departure and arrival dates of Great Lakes Herring Gulls (n = 10, 5) included for comparison. All summary statistics are presented as mean ± SD (range).

Autumn Departure Date Wintering Arrival Date mean ± SD (range) mean ± SD (range) Nunavut Sep 18 ±29 (Aug 19 - Oct 27) Nov 27 ±32 (Oct 29 - Jan 19) Newfoundland Oct 04 ±29 (Sep 09 - Nov 24) Nov 11 ±27 (Oct 14 - Dec 21) Sable Island Sep 09 ±45 (Jun 21 - Nov 01) Sep 22 ±52 (Jul 07 - Dec 30) Bay of Fundy Aug 28 ±56 (Jul 04 - Nov 27) Oct 26 ±64 (Jul 13 - Dec 28) Great Lakes Aug 17 ±31 (Jul 30 - Nov 13) - - -

Spring Departure Date Breeding Arrival Date mean ± SD (range) mean ± SD (range) Nunavut Apr 24 Jun 21 Newfoundland Apr 03 ±7 (Mar 18 - Apr 10) Apr 17 ±13 (Apr 03 - May 20) Sable Island Mar 26 ±9 (Mar 17 - Apr 14) Mar 31 ±10 (Mar 20 - Apr 20) Bay of Fundy Mar 27 ±12 (Mar 10 - Apr 11) Apr 02 ±11 (Mar 19 - Apr 17) Great Lakes - - - Apr 17 ±14 (Apr 01 - May 11)

60

Figure 3-1: Scatterplot illustrating variation in a) directness, b) overall migration speed, and c) total stopover days between populations and seasons for Herring Gulls tracked from Nunavut, Newfoundland, Sable Island, and the Bay of Fundy. Herring Gulls breeding in Nunavut are represented in yellow, Herring Gulls breeding in Atlantic Canada are represented in red. 61

Figure 3-2: Map of migration routes for Herring Gulls breeding in Nunavut. Stopover days are represented in yellow, and travel days are represented in blue. Breeding site on Southampton Island, NU is indicated by a pink triangle.

62

Figure 3-3: Map of migration routes for Herring Gulls breeding in Atlantic Canada. Stopover days are represented in yellow, and travel days are represented in blue. Breeding sites are indicated by a pink triangle, from north to south: Newfoundland (3 sites), Sable (1 site), and Bay of Fundy (2 sites).

63

Figure 3-4: Map of stopover sites used by Herring Gulls in Nunavut. Stopover days are represented in yellow, and travel days are represented in blue.

64

Figure 3-5: Schematic of meta-population migration patterns, adapted from Newton (2008). Leapfrog migration occurs when populations reverse their latitudinal sequence between seasons. Chain migration occurs when populations maintain their latitudinal sequence between seasons. 65

Chapter 4 – Does winter habitat use differ by population?

Introduction

Habitat quality can have strong carryover effects between seasons in wildlife populations. Among birds for example, the quality of winter habitat can influence their physical condition, which in turn can affect their migration schedule, survival during migration, and subsequent reproductive success (Marra et al. 1998). At a population level, mortality in the non-breeding season can partly determine population density in the following season, thereby affecting density dependent processes (Norris and Marra

2007). By tracking birds across geographically separated phases of their annual cycle, it may be possible to study how events and conditions in one season carry over to affect individuals and/or populations in subsequent seasons (Webster and Marra 2005).

The consequences of carryover effects can be mediated by the degree of connection between populations at different phases of the annual cycle, also known as migratory connectivity (Webster and Marra 2005). In populations with strong connectivity, birds breeding in the same area largely winter in the same area, while populations with weak connectivity spend the winter in a wide range of locations

(Webster et al. 2002). The geographic structure of a migratory network can have a significant influence on population dynamics (Taylor and Norris 2010). Populations with strong migratory connectivity are more sensitive to localized environmental changes; circumstances within a limited wintering area can affect a large proportion of the breeding population (Macdonald et al. 2012; Stanley et al. 2014). Breeding populations with weak connectivity are less likely to be affected by local wintering conditions, as the 66 effects will be buffered by individuals who spent the winter elsewhere (Fraser et al.

2012). Habitat change in wintering areas has been identified as a major cause of bird population declines, and conservation actions are more effective when migratory connectivity is taken into account (Martin et al. 2007).

Many gull species are migratory. Gulls have generalist diets and use a wide range of habitats throughout the year (Washburn et al. 2013). Research on Herring Gull habitat use has focused almost exclusively on birds in Newfoundland during the breeding season

(Robertson et al. 2001; Pierotti 1982; Rodway and Regehr 1999; Pierotti 1987; Pierotti and Annett 1991; Belant et al. 1983; Bond et al. 2016), but much less is known about their habitat preferences during the winter. Outside the breeding season, Herring Gulls are frequently observed offshore from Chesapeake Bay to Newfoundland, aggregating around fishing vessels (Powers 1983; Gjerdrum and Bolduc 2016). Herring Gulls are also abundant in urban areas, resulting in regular conflicts with people (Belant 1997).

However, it is not clear how Herring Gulls budget their time between different habitats during the winter, and how winter habitat use differs between individuals and populations.

Survival rates may vary among Herring Gulls populations in eastern North

America. Estimates of apparent adult survival in Atlantic Canada have ranged from 0.80 to 0.83 (Kadlec 1976; Freeman and Morgan 1992; Robertson et al. 2016a), while estimates from the Arctic and the Great Lakes were considerably higher at 0.87 and 0.91, respectively (Allard et al. 2006; Breton et al. 2008). It has been speculated that lower survival rates in the Atlantic Canada populations may be due, in part, to differences between their wintering home ranges. Adult Herring Gulls from Atlantic Canada are most 67 often resighted in New England and New York (Gaston et al. 2008), a heavily populated and industrialized region (Sanderson et al. 2002). This has prompted greater active management of gulls through shooting and disturbance at locations including airports, landfills, and industrial buildings where gulls nest on roofs (Belant 1997). Survival rates may also be linked to food availability within certain habitats. Previous studies have found the majority of Herring Gulls specialize in a specific diet and habitat type (Davis

1975; McCleery and Sibly 1986; Pierotti and Annett 1987). Declines in Herring Gull abundance have been linked to declines in fish availability (Breton et al. 2008; Wilhelm et al. 2016).

In this study, I analyze the wintering distribution of Herring Gulls breeding in

Atlantic Canada, the Great Lakes and the Arctic to explore the following questions: (1) what is the extent of migratory connectivity between Herring Gull populations in eastern

North America? and (2) are there differences in habitat selection between these populations during the the winter, at both individual and regional scales, that could possibly influence their survival? Given that survival rates of the Atlantic population are lower than the Arctic and Great Lakes populations, I predicted Herring Gulls breeding in

Atlantic Canada would use anthropogenic habitats more frequently than the other populations, as these habitats likely incur higher risk of mortality.

Methods

Details on study sites, remote tracking of gulls, and data processing can be found in

Chapter 1. 68

For this analysis, I used Herring Gull locations from each individual’s annual wintering period, generally between October and March (see Table 3-3). I excluded annual wintering periods with less than 30 days of tracking data. The data I used represented eight tracks from the Arctic (eight individuals), 37 from Atlantic Canada (21 individuals), and eight from the Great Lakes (eight individuals), for a total for 6628 individual location observations. I calculated the extent and size of home range at both a population and individual scale. Annual winter home range was calculated using minimum convex polygons, defined by the smallest convex set of points containing 95% of the given data (Calenge 2006). I used a linear mixed model to assess if individual home range size differed by population, including individual as a random effect to account for birds tracked over multiple years. The p values were obtained by using a

Wald chi-square test to compare this model to a null model with only the random effect.

I obtained habitat data from the 2010 North American Land Cover database

(Latifovic et al. 2012). In this database, remote sensing data from Moderate Resolution

Imaging Spectroradiometer (Justice et al. 1998) have been categorized into 19 land cover classes at a 250 m spatial resolution. I used ArcGIS to simplify the dataset into five land cover classes. Two aquatic habitats, “Marine” and “Freshwater”, were both originally classified as water. I separated the two using the ocean coastline feature from the 2010

North America Environmental Atlas Bathymetry dataset (Commission for Environmental

Cooperation 2010). I retained two anthropogenic land cover classes from the original dataset classifications: “Urban” was defined as areas containing ≥ 30% urban constructed materials for human activities such as buildings and roads, and “Cropland” was defined as areas dominated by intensively managed crops, with crop vegetation accounting for > 69

20% of total vegetation. The remaining 16 land classes were grouped as a single “Natural

Land” land cover class, which included forests, shrublands, grasslands, wetlands and barren lands.

I extracted the proportion of each of the five habitat types within each Herring

Gull breeding population’s winter home range polygons to indicate habitat selection on a regional spatial scale. I also extracted the habitat type associated with each location of an individual within their annual winter home range, and calculated the proportion of locations within each habitat type to indicate individual habitat use. I used a chi-square test to examine if the three populations were using the habitats in different proportions. I also used chi-square tests to examine if habitat was being used in proportion to its availability for each population. Following the definition of habitat preference in Aarts et al. (2008), I assessed the ratio of individual habitat use to the availability of habitat within their population’s winter home range as an index of habitat preference. In this case, values close to one indicated birds were using this habitat as often as would be expected to occur randomly. Values lower or higher than one indicated non-random use, respectively avoidance or preference for a certain type of habitat (van Toor et al. 2017).

To summarize how individuals budgeted their time between the five habitat types, I used principal components analysis (PCA) with Aitchison compositional scaling to account for proportional data (Aitchison 1982). I used a Wald chi-square test on linear mixed models to assess if the first two principal components differed by population, with individual controlled for as a random effect.

70

Results

Winter home range

Each of the three breeding populations were tracked to a distinct winter home range. Herring Gulls that bred in Nunavut spent the majority of the winter offshore from

Louisiana, Texas and Mexico (Figure 4-1). Two individuals went as far as Mexico, and one individual spent several months in Florida before its tracking device ceased functioning during an offshore flight near Bermuda. Herring Gulls that bred in Atlantic

Canada wintered in the northeastern United States. Within the Atlantic region, gulls from each of the three breeding sites used somewhat different wintering areas, although the areas did overlap: birds from Newfoundland concentrated around Cape Cod, birds from

Sable Island concentrated around New Jersey, and birds from the Bay of Fundy concentrated around Delaware Bay (Figure 4-2). Two individuals from the Bay of Fundy population did not migrate, spending the entire winter in Nova Scotia within 50 km of their breeding site. Herring Gulls that bred in the Great Lakes spent the majority of the winter in the Great Lakes basin, although individual birds spent time in Massachusetts, the Ottawa Valley, Ontario, and the Gaspé Peninsula, Quebec (Figure 4-3).

The Great Lakes population had a winter home range size of 830,000 km2, larger than the populations from Atlantic Canada and Nunavut, with respective home range sizes of 600,000 km2 and 570,00 km2. There was a small degree of overlap between the winter home ranges of the Great Lakes and Atlantic populations, neither of which overlapped with the home range of the Nunavut population wintering in the Gulf of

Mexico. Individual home range size had a strong left skew: 50 winter tracks had home range of 100 km2 to 150,00 km2 , while three individuals (two from Nunavut and one 71 from the Great Lakes) had home ranges of 375,000 km2 to 450,000 km2 (overall median =

7,900 km2; Figure 4-4a). Individual home range size varied by population (LMM, χ2 =

41.1, p < 0.001), as Herring Gulls from Atlantic Canada had home ranges which were smaller and less variable than Herring Gulls either from Nunavut and the Great Lakes

(Figure 4-4b).

Habitat use

The composition of available habitat was distinctly different in each population’s winter home range (Figure 4-5a). The winter home range of Herring Gulls from Nunavut was dominated by marine habitat (87%), with minimal amounts of each other habitat type

(<6%). For Herring Gulls from Atlantic Canada, their winter home range was largely composed of marine (53%) and natural land habitats (31%). In the Great Lakes, there was a higher proportion of terrestrial habitats: natural land was the most available habitat

(52%), followed by cropland (19%). The main aquatic habitat for birds originating in

Great Lakes was freshwater (25%) instead of marine.

2 Individual habitat use was significantly different between populations (χ 8 =

1834, p < 0.001, Figure 4-5b). Individual Herring Gulls from Nunavut spent most of their time in marine habitats (median 82%). They appeared to use both coastal and pelagic habitats; locations in marine habitats were a mean of 43km offshore, up to a maximum of

286 km. Birds from Atlantic Canada largely used marine habitats close to shore, with a mean distance of 8km from the coast. Herring Gulls from Atlantic Canada and the Great

Lakes tended to split their time between several habitats: Atlantic birds regularly used marine (17%), urban (30%) and natural land (30%) habitats, while Great Lakes birds 72 consistently used freshwater (23%), urban (11%), cropland (17%) and natural land (34%) habitats.

2 2 Herring Gulls from Nunavut (χ 4 = 86, p < 0.001), Atlantic Canada (χ 4 = 8427, p

2 < 0.001), and the Great Lakes (χ 4 = 205, p < 0.001) each used habitat differently than would be expected based on habitat availability within their respective home range

(Figure 4-5c). A large proportion of individuals from all three populations used urban habitats more frequently than would be expected relative to their availability. However, among birds from the Arctic population, the opposite was also true: half of the birds were never tracked to urban areas. Herring Gulls from Nunavut and Atlantic Canada also preferentially used the small amount of freshwater habitats available in their home ranges, although still infrequently compared to other habitat types. Throughout the winter, birds from Atlantic Canada tended to use cropland and, surprisingly, marine habitats less frequently than would be expected given their availability.

Principal conponents analysis

Individual Herring Gulls predominantly used either marine, natural land or urban habitat, and individual time budgets in these habitat types differed by breeding population

(Figure 4-6). When I assessed individual habitat preferences using PCA, the first principal component explained 66% of the variance in habitat use, and represented strong loadings which contrasted the influence of marine (+ 0.65) and urban (-0.75) habitats.

The second principal component explained 28% of the variance, contrasting marine (+

0.55) and urban (+ 0.41) from natural land (-0.71) habitats. Individuals from each of the three populations differed in their habitat use time budgets (PC1: χ2 = 26.4, p < 0.001; 73

PC2: χ2 = 34.3, p < 0.001). Post-hoc Tukey tests showed the Nunavut population differed from the Atlantic and Great Lakes populations in their use of marine habitats (PC1), and that all three populations differed from each other in their use of urban and natural land habitats (PC2).

PCA highlighted that all individuals tracked from Nunavut spent the majority of their time in marine habitats, while individuals from the Atlantic Canada and Great Lakes populations used a variety of habitat types and had greater diversity in their habitat use time budgets. Half of the individuals tracked from the Great Lakes were observed most frequently in natural land habitats, but the remaining individuals spent more time in urban, freshwater or agricultural habitats.

Virtually all Herring Gulls that used urban habitats most frequently originated from the Atlantic Canada population. Out of 37 tracks, 14 used urban habitats most frequently, 16 used natural land habitats most frequently, and only one used freshwater habitats most frequently. Despite the fact that overall Atlantic Herring Gulls used marine habitats less than expected, six of 37 tracks predominantly used marine habitats.

Consequently, Atlantic Canada was the only population in which most tracked birds did not predominantly use the most widely available habitat.

Discussion

In this study, I compared the wintering distribution of Herring Gulls from the

Arctic, Atlantic Canada, and the Great Lakes. Herring Gulls in eastern North America exhibit strong migratory connectivity, with populations consistently migrating to their own discrete wintering area. The composition of available habitats was distinctly 74 different in each population’s wintering home range. At an individual scale, Herring

Gulls from Nunavut concentrated to winter in marine habitats. Herring Gulls from

Atlantic Canada and the Great Lakes used a wider variety of winter habitats, with high variation in how individuals budgeted their time between habitat types.

A majority of individual Herring Gulls spent the winter within a home range of less than 10,000 km2. This was consistent with reports that individual Herring Gulls have high site fidelity between years (Clark et al. 2016) and often specialize in specific feeding locations (Davis 1975). However, two individuals from Nunavut and one individual from the Great Lakes had winter home ranges greater than 375,000 km2, almost 50 times larger than the overall median of 7,900 km2. Individuals having a home range size substantially larger than average are also known as “floaters” (Winker 1998). This concept has been more widely applied during the breeding season (Lenda et al. 2012), but has also occasionally been applied to the winter period (mainly in passerines, reviewed in Brown and Long 2007). It is not clear what factors lead to floater behaviour during the winter. In some cases, floaters may be individuals who arrive later, or who are less socially dominant, and move extensively to find territorial vacancies in high quality habitat

(Winker 1998), but this unlikely for Herring Gulls as they are not territorial during the winter. A more likely explanation may be that floater behaviour is an alternative strategy which allows individuals to take advantage of ephemeral food supplies (Brown and Long

2007).

Irrespective of floaters, individuals from the Atlantic population had consistentely smaller individual hame ranges than individuals from the Arctic and Great Lakes populations (Figure 4-4). This compression in territory size may be a density dependent 75 effect of competition in a heavily populated area, or it may be due to differences in habitat and resource availability. A study conducted at a finer geographic scale would be better suited to test if there is a relationship between home range size and habitat use in

Herring Gulls. For example, Santangeli (2012) found for Boreal Owls (Aegolius funereus), habitat size was correlated with habitat type, and food supplementation did not alter home range size.

All three populations showed a preference for aquatic habitats. This is not surprising, as fish and intertidal organisms are typically the most common source of food for Herring Gulls (Fox et al. 1990; Ewins et al. 1994; Steenweg et al. 2011). However,

Herring Gulls from Nunavut primarily used marine habitats during the winter, whereas the other populations appeared to be more flexible in their habitat use. In marine habitats,

Herring Gulls from Nunavut were observed across the full extent of the continental shelf, while those from Atlantic Canada largely stayed close to shore.

Large gulls opportunistically exploit any locally abundant food source (Ewins et al. 1994), and the terrestrial sources of food which Herring Gulls usually eat (mammals, bird eggs and chicks, invertebrates, garbage) should be available in the Gulf of Mexico region. Herring Gulls from Nunavut may be more specialized in exploiting marine habitats, particularly if individual specialization in habitats and prey types carries over between seasons. Herring Gulls from Atlantic Canada and the Great Lakes would have access to urban, cropland, natural land, and aquatic habitats in both their breeding and wintering areas. In contrast, as remote Arctic breeders, Herring Gulls in Nunavut do not have access to anthropogenic habitats during their breeding season, and the available natural land habitats would be dramatically different from those they would encounter in 76 their wintering range. It would be interesting to assess the habitat use of Herring Gulls from Nunavut during the breeding and post-breeding periods to observe if they have the same affinity for marine habitats. Habitat preferences can be a driver of population distribution patterns (Ramos et al. 2015). However, recent translocation experiments suggest differential habitat use between gull populations is likely due to their flexibility as generalists, and does not indicate local genetic adaptation (van Toor et al. 2017).

Although there was evidence from each population that gulls preferentially selected urban habitats, only the Atlantic population had a substantial proportion of individuals that spent more time in urban areas than in any other habitat. This finding is consistent with the hypothesis that the lower survival rates of Herring Gulls from Atlantic

Canada compared to other populations in eastern North America could be associated with differences in their winter habitat use (Robertson et al. 2016a). However, mechanisms by which habitat may negatively influence the survival of Herring Gulls during the winter are uncertain. Herring Gulls in urban areas may rely more heavily on anthropogenic food, such as direct handouts from humans or garbage from landfills (Belant et al. 1983; Clark et al. 2015), and refuse is generally thought to be a poor quality food. Gulls specializing in refuse have lower reproductive success (Pierotti and Annett 1987). Nonetheless, gulls specializing on refuse may have an advantage in the long term if it is a dependable and consistently available source of energy (Auman et al. 2017).

The differences I observed in habitat use between populations may not necessarily correspond to differences in diet. Although habitat selection is correlated with the diet of

Herring Gulls during the breeding season (Pierotti and Annett 1991; Robertson et al.

2001), this same relationship has never been quantified for the wintering period. In 77

Herring Gulls sampled from culls at a New York City airport, marine organisms were found in the stomachs of more than 60% of individual birds, and made up more than 50% of food volume (Washburn 2013). It is possible Herring Gulls on the Atlantic coast may be able to forage efficiently in marine environments, potentially by taking advantage of fishing vessels (Pierotti and Good 1994), and thus the time in urban areas may reflect time spent loafing rather than foraging. Generally, the diet of Herring Gulls in New York

City appears similar to diet of Herring Gulls in the Great Lakes and Atlantic Canada

(Washburn et al. 2013; Ewins et al. 1994; Hebert et al. 1999; Pierotti and Annett 1991), although there have been few studies of gull diets during the winter.

Another mechanism that may decrease survival of Herring Gulls in urban habitats is direct mortality from human influences. In urban and industrial areas, Herring Gulls may be harassed and shot as part of active management programs, particularly at airports

(Belant 1997). For example, the large scale culling of gulls appears to have played a significant role in the decline of Herring Gull populations across the UK during the last

45 years (Coulson 2015). In eastern North America, the U.S. Fish and Wildlife Service has developed Potential Biological Removal models which suggest annual take of more than 16,725 (95% CI = 7,788 – 25,397) Herring Gulls would lead to population declines in the region spanning Delaware Bay to Nova Scotia (USDA 2010). Between 2010 and

2013, the average annual take of Herring Gulls was 4,445 in the northeastern United

States (USDA 2015). However, more than 80% of gulls are lethally taken during the period from September to March (USDA 2010). Gull control programs in the United

States may have a disproportionate effect on the Herring Gulls breeding in Atlantic

Canada which move into this region during this same period of the autumn and winter. 78

To get a better sense of the impact of culling on gull populations North America, a review similar to Coulson’s (2015) evaluation of the roles landfills and culling have played in the population dynamics of British Herring Gulls would be beneficial. It would also be interesting to confirm the cause of mortality for tracked individuals (Klaassen et al. 2014), allowing the relationship between habitat use and survival rates to be studied more directly.

Herring Gulls in eastern North America have a high degree of migratory connectivity between their breeding and wintering ranges. This pattern suggests that carryover effects between the winter and breeding season can be population specific if they only occur within the extent of one population’s home range. For example, the contaminant burden of Herring Gulls is dependent, at least in part, on where birds migrate to and what they eat during the winter (Hebert 1998; Burgess et al. 2013). Herring Gull eggs from the Great Lakes are known to have higher organochlorine burdens than eggs from Atlantic Canada and the Arctic (Lavoie 2010). The patterns can be attributed to specific environments for each population since they are geographically separated for their whole annual cycle. An example of a more acute environmental change would be the Deepwater Horizon oil platform explosion (Montevecchi et al. 2012). This event only had the potential to affect adult Herring Gulls from Nunavut, as the effects of the oil spill were essentially limited to this population’s winter home range. Toxicity from oils spills can have both lethal and sublethal effects which carryover to influence birds during migration and breeding periods, both at an individual and population level (Henkel et al.

2012). 79

In addition to demonstrating strong migratory connectivity, my results provide new insights into winter habitat use by Herring Gulls in eastern North America. Herring

Gulls from Atlantic Canada and the Great Lakes used a diverse set of habitats during the winter, particularly urban, natural land, and aquatic habitats. By contrast, Herring Gulls from Nunavut spent the vast majority of their time in marine environment of the Gulf of

Mexico. All three populations showed preferential use of urban habitats to some extent, but only in the Atlantic Canada population did a substantial number of individuals use urban areas most frequently. These conclusions provide support for the hypothesis that lower survival rates for Herring Gulls in eastern North America may be related to characteristics of their wintering areas, suggesting it would be worthwhile to investigate both the foraging ecology and causes of direct mortality for Herring Gulls during the non- breeding season.

80

Figure 4-1: Winter daily locations for Herring Gulls tracked from Nunavut (n = 8), and winter home ranges calculated as minimum convex polygon.

81

Figure 4-2: Winter daily locations for Herring Gulls tracked from Atlantic Canada (n = 21), and winter home ranges calculated as minimum convex polygon. From north to south, birds from Newfoundland are indicated in medium grey, birds from Sable Island are indicated in dark grey, and birds from the Bay of Fundy are indicated in light grey.

82

Figure 4-3: Winter daily locations for Herring Gulls tracked from the Great Lakes (n = 8), and winter home ranges calculated as minimum convex polygon. 83

Figure 4-4: a) Histogram and b) boxplots of individual home range area for each population. Home range area is calculated as minimum convex polygons for Herring Gulls from Atlantic Canada (red, n = 21), the Arctic (yellow, n = 8), and the Great Lakes (blue, n = 8).

84

Figure 4-5: a) Proportion of marine, natural land, cropland, urban, and freshwater habitats available within the winter home range of Herring Gull populations from the Arctic, the Great Lakes, and Atlantic Canada; b) Proportion of time spent in each habitat by individual Herring Gulls. Boxes represent the 95% confidence interval of the median individual habitat use for each population, acquired through 1000-fold bootstrapping; c) The ratio of available habitat within the population’s home range to the proportion of individual habitat use. Values close to one indicate the birds are using this habitat as often as would be expected to occur randomly. Values lower or higher than one indicate non-random use, respectively avoidance or preference for a certain type of habitat. 85

Figure 4-6: Individual values for the first two principal components from Principal Components Analysis of the proportion of time individual Herring Gulls spent in marine, natural land, cropland, urban, and freshwater habitats. Grey polygons enclose all individual who spent the most time in marine, natural land, and urban habitats. Individuals who spent the most time in cropland and freshwater habitats are marked with a white dot and the initial of their respective habitat type. Large triangles indicate where habitat availability within the population’s winter home range falls within the principal components analysis.

87

Chapter 5 – General Discussion

Findings

The main objective of my thesis is to understand how Herring Gulls move during the non-breeding period. I compared Herring Gulls from five different breeding sites in three ecologically diverse regions to capture the flexibility of these notorious generalists.

My results showed that at a species scale, Herring Gulls had a leapfrog migration pattern with strong migratory connectivity. Birds from the Great Lakes dispersed rather than truly migrating, birds from Atlantic Canada migrated short distances, and Herring

Gulls from the Arctic migrated long distances to the Gulf of Mexico. Migratory behaviour was highly variable among individuals, but overall, both short and long distance migrants tended to migrate at low overall migration speeds along indirect coastal routes, making frequent stopovers. In the context of optimal migration theory, this seems to suggest they are minimizing energy spent on migration rather than time.

I found that winter habitat use of Herring Gulls varied quite starkly among breeding populations. The strong preference for marine habitats I observed in Herring

Gulls was remarkable, as Herring Gull behaviour is generally much more variable even within a population. Herring Gulls from the Great Lakes and Atlantic Canada used a wider variety of habitats, a result that agrees with previous studies of their habitat use during the breeding season. However, only the Atlantic Canada population had a large proportion of individuals spending the majority of their time in urban habitats, which could contribute to their lower survival rates.

88

Applications and future directions

Although the distinct phases of the annual cycle may be separated in time and space, they are fundamentally linked through the lives of individual organisms. The results of my thesis provide a foundation for research into how the phases of the annual cycle are interconnected. Given the differences I found in non-breeding movement and habitat use between Herring Gull populations, it is likely that these differences carry over to influence fitness and life history trade-offs in the breeding season.

The correlation I observed between winter habitat use and survival rates seems to be the most obvious instance where the non-breeding experiences of Herring Gulls may be carrying over between seasons to affect their population dynamics. Winter habitat quality may be directly affecting Herring Gull survival during the winter, but also may affect the timing of migration and breeding (Woodworth et al. 2016). The reproductive fitness of an organism is also highly dependent on the nutrition, energy reserves and body condition, which carry over between seasons (Harrison et al. 2011). For Herring Gulls, it would be challenging to disentangle the effects of winter habitat quality on annual survival and reproduction from the effects of reduced fisheries discards and changing waste management practices in their breeding areas (Wilhelm et al. 2016, Pons and Migot

1995). Tracking individuals for their full annual cycle, or even multiple annual cycles, provides the best opportunities to fully assess the interaction of events between seasons

(O’Connor et al. 2014).

Our understanding of population dynamics can be improved by developing a more holistic view of the annual cycle, incorporating interactions between seasons

(Norris and Marra 2007). This information will become increasingly relevant if 89 widespread downward trends in Herring Gull populations persist (Cotter et al. 2012,

Nisbet et al. 2013). With distribution and demographic information available from the entire annual cycle, advanced planning tools can be used to optimize the use of limited resources for management and conservation (Sheehy et al. 2010).

The strong migratory connectivity I observed within Herring Gull populations has practical implications for monitoring the transfer of disease. As both migrants and opportunistic scavengers, gulls can play a significant role in spreading wildlife diseases such as avian cholera between habitats (Wille et al. 2016). Information on bird migration can also be important for human public health. In response to lethal avian influenza outbreaks in Southeast Asia, Brown-headed Gulls (Larus brunnicephalus) have been identified as a potential vector for spreading the virus (Ratanakorn et al. 2012). Herring

Gulls have been identified as carriers of Salmonella, and likely transmit the bacteria to sheep and cattle (Coulson et al. 1983). Herring Gulls are more likely to be infected with

Salmonella when feeding at landfill sites (Monaghan et al. 1985), which underscores the value of understanding their habitat use patterns and feeding ecology. Gulls can pose a risk to human health, particularly if they congregate near water supplies and reservoirs

(Clark 2014).

Migratory connectivity also has significance for programs using Herring Gull eggs to monitor environmental contaminants. Migratory birds such as Herring Gulls may act as a biovector for contaminant transport in the environment (Blais et al. 2007).

Organochlorine concentrations in Herring Gull eggs appear to be influenced by annual shifts in migration patterns and winter distribution within the Great Lakes (Hebert 1998).

Given the strong migratory connectivity I observed within Herring Gull populations, it 90 would be useful to test for relationships between winter habitat use and contaminant burdens in other populations of Herring Gulls as well. The Atlantic Canada breeding population in particular may be subject to higher levels of pollution because of their contact with urban environments year-round. Each of the Herring Gull populations tracked in this thesis spend the winter in more heavily populated areas in comparison to their breeding areas, suggesting the wintering areas I identified are likely the sites of much of their contaminant exposure. By tracking seabirds, it is possible to trace the sources of marine pollution that are having significant impacts on birds (Ito et al. 2013).

By linking an individual’s behaviours during the winter, migration and breeding seasons, it is possible to gain a more accurate sense of their fitness implications.

Explanations for the evolution of leapfrog migration often invoke a situation whereby long distance migrants are in some way less competitive than short distance migrants, and are forced to spend more time on a migration which we have perceived as being very energetically costly. However, a recent study examining the cumulative distances moved by Lesser Black-backed Gulls over the course of their full annual cycle found short distance migrants cumulatively traveled an equivalent amount to long distance migrants

(Shamoun-Baranes et al. 2017). It would be valuable to simultaneously test the multiple competing hypothesis about the factors promoting leapfrog migration using annual tracking data. This type of study has the potential to clarify some of the assumptions we currently make about the costs and benefits of migration. Herring Gulls would be an ideal candidate for this type of study, as their leapfrog migration pattern is very distinct, and there is a large body of literature on their ecology to provide necessary context. 91

Herring Gulls are recognized for the variability of their behaviour and their ability to adapt to diverse environments. My thesis provides new insights on how these traits are manifested during the non-breeding season. By comparing Herring Gull movement and habitat use from across the full breadth of their range in eastern North America, I show the full extent of their flexibility, but also the degrees of consistency in their behaviour.

93

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Appendix: R code for Chapter 2 simulations

#All code was written in R version 3.3.1 (2016-06-21)

#Load Packages options(scipen = 999) library(lubridate) library(plyr) library(dplyr) library(ggplot2) library(reshape2) library(adehabitatLT) library(ggmap) library(rworldmap) library(CircStats) library(sp) library(rgeos) library(maptools) library(boot)

# -Defines wintering area that the birds will migrate to. Units are arbitrary # -winter function randomly selects a spot within the defines wintering area # -sim.mig function simulates a track the migrates from breeding are (0,0) to randomly generates wintering area, eventually returning to the point of origin. See Chapter 2 for more detailed description.

#define wintering area. winter function represents individual bird tagret within area N = -450 S = -550 W = -50 E = 50 B = c(0,0)

#winter function, chooses a location in wintering area that bird will target winter <- function(North, South, East, West){ lon = runif(1, South, North) lat = runif(1, West, East) return(c(lat,lon)) }

#fuction that simulates one migration track to random random spot in winter area bounds sim.mig <- function(North = 0, South = 0, East = 0, West = 0, breed = c(0, 0)){

116

#SL = step lengths, TA = turning angles, h = target destination #SL is arbitrary. sample appropriate number of points to get daily location after

h <- winter(North, South, East, West) nsteps <- 150 SL <- rexp(nsteps,0.1) TA <- x <- y <- rep(0,nsteps+1) for(i in 2:(nsteps+1)){ TA[i] <- rwrpcauchy(1,atan2((h[2]-y[i-1]),(h[1]-x[i-1])),0.5) x[i] <- x[i-1] + SL[i-1]*cos(TA[i]) y[i] <- y[i-1] + SL[i-1]*sin(TA[i]) }

loc <- data.frame(cbind(x,y)) coordinates(loc) <- ~x+y

#find first point whin 10km of target #target is h, but as spatial points

target <- data.frame(x = h[1], y = h[2]) coordinates(target) <- ~x+y

dist <- gDistance(target, loc, byid = TRUE)

arrive <- ifelse(any(dist < 10), min((which(dist < 10))), which.min(dist))

#Subsample to create fall migration track

locf <- loc[1:arrive] locf <- locf[seq(1, length(locf), 2)]

row.names(locf) <- seq(1, length(locf), 1)

#create winter track

wstart <- as.data.frame(locf[length(locf)])

nsteps <- 200-length(locf) SL <- rexp(nsteps,0.1) TA <- x <- y <- rep(0,nsteps+1) x[1] <- wstart[1,1] y[1] <- wstart[1,2] for(i in 2:(nsteps+1)){ TA[i] <- rwrpcauchy(1,atan2((h[2]-y[i-1]),(h[1]-x[i-1])),0.5) x[i] <- x[i-1] + SL[i-1]*cos(TA[i]) y[i] <- y[i-1] + SL[i-1]*sin(TA[i]) }

locw <- data.frame(cbind(x,y)) 117

coordinates(locw) <- ~x+y

row.names(locw) <- seq((length(locf)+1), by = 1, length.out = length(locw))

#create spring track

sstart <- as.data.frame(locw[length(locw)]) h <- breed

nsteps <- 150 SL <- rexp(nsteps,0.1) TA <- x <- y <- rep(0,nsteps+1) x[1] <- sstart[1,1] y[1] <- sstart[1,2] for(i in 2:(nsteps+1)){ TA[i] <- rwrpcauchy(1,atan2((h[2]-y[i-1]),(h[1]-x[i-1])),0.5) x[i] <- x[i-1] + SL[i-1]*cos(TA[i]) y[i] <- y[i-1] + SL[i-1]*sin(TA[i]) }

loc <- data.frame(cbind(x,y)) coordinates(loc) <- ~x+y

#find first point whin 10km of target #target is h, but as spatial points

target <- data.frame(x = h[1], y = h[2]) coordinates(target) <- ~x+y

dist <- gDistance(target, loc, byid = TRUE)

arrive <- ifelse(any(dist < 10), min((which(dist < 10))), which.min(dist))

#Subsample to create spring migration track

locs <- loc[1:arrive] locs <- rbind(locs[seq(1, length(locs), 6)], target)

row.names(locs) <- seq((length(locf)+length(locw)+1), by = 1, length.out = length(locs))

#full non-breeding track

locnb <- rbind(locf, locw, locs)

return(locnb) 118

}

# -sim.disp simulates a track that disperses randomly away from the breeding area (0,0), eventually returning to the point of origin. See thesis for more details.

#Define Breeding area B = c(0,0)

#fuction that simulates one migration track with winter random walk and then return to colony sim.disp <- function(breed = c(0, 0)){

#simulate winter dispersal (resident population)

h <- breed nsteps <- 200 SL <- rexp(nsteps,0.08)

TA <- rwrpcauchy(nsteps,0,0.5) TA <- ifelse(TA<=pi,TA,TA-2*pi) ATA <- cumsum(TA)

# Get movement path x <- cumsum(SL*cos(ATA)) y <- cumsum(SL*sin(ATA)) x[1] <- 0 y[1] <- 0 locd <- data.frame(cbind(x,y)) coordinates(locd) <- ~x+y

#simulate spring return

sstart <- as.data.frame(locd[length(locd)]) h <- breed

nsteps <- 150 SL <- rexp(nsteps,0.1) TA <- x <- y <- rep(0,nsteps+1) x[1] <- sstart[1,1] y[1] <- sstart[1,2] for(i in 2:(nsteps+1)){ TA[i] <- rwrpcauchy(1,atan2((h[2]-y[i-1]),(h[1]-x[i-1])),0.5) x[i] <- x[i-1] + SL[i-1]*cos(TA[i]) 119

y[i] <- y[i-1] + SL[i-1]*sin(TA[i]) }

loc <- data.frame(cbind(x,y)) coordinates(loc) <- ~x+y

#find first point whin 10km of target #target is h, but as spatial points

target <- data.frame(x = h[1], y = h[2]) coordinates(target) <- ~x+y

dist <- gDistance(target, loc, byid = TRUE)

arrive <- ifelse(any(dist < 10), min((which(dist < 10))), which.min(dist))

#Subsample to create spring migration track

locs <- loc[1:arrive] locs <- rbind(locs[seq(1, length(locs), 6)], target)

row.names(locs) <- seq((length(locd)+1), by = 1, length.out = length(locs))

#full non-breeding track

locnb <- rbind(locd, locs)

return(locnb)

}

# -plot.sim plots simulated tracks, connecting locations and highlighting the origin # -max.dist calculates the maximum distance between the simulated track and origin

#plotting function

120

plot.sim <- function(sim, B){ plot(sim, pch=20) plot(as(sim,"SpatialLines"),add=TRUE) points(B[1],B[2],col="red",pch="*", cex=3) }

#max distance function max.dist <- function(sim, B){ target <- data.frame(x = B[1], y = B[2]) coordinates(target) <- ~x+y dist <- gDistance(target, sim, byid = TRUE) return(max(dist)) }

#Migratory population

set.seed(321) mig.pop <- sim.mig(N, S, E, W, B) id <- as.data.frame(rep(1, length(mig.pop))) colnames(id) <- "id" mig.pop <- SpatialPointsDataFrame(mig.pop, id) for(i in 2:100){ bird <- sim.mig(N, S, E, W, B) id <- as.data.frame(rep(i, length(bird))) colnames(id) <- "id" bird <- SpatialPointsDataFrame(bird, id) mig.pop <- rbind(mig.pop, bird) } plot.sim(mig.pop, B)

#Dispersing population (resident)

disp.pop <- sim.disp(B) id <- as.data.frame(rep(1, length(disp.pop))) colnames(id) <- "id" disp.pop <- SpatialPointsDataFrame(disp.pop, id) for(i in 2:100){ bird <- sim.disp(B) 121

id <- as.data.frame(rep(i, length(bird))) colnames(id) <- "id" bird <- SpatialPointsDataFrame(bird, id) disp.pop <- rbind(disp.pop, bird) } plot.sim(disp.pop, B)

#load simulated data sim <- readRDS("simulation_bootstrap_data.rds") sim$type[sim$type == "mig.pop"] <- "Migrating" sim$type[sim$type == "disp.pop"] <- "Dispersing"

#add the summary code in here, it can still be useful (in rough scrips) difs50 <- c(summary$`50%`[1]-summary$`50%`[2], summary$`50%`[3]-summary$`50%`[4], summary$`50%`[5]-summary$`50%`[6], summary$`50%`[7]-summary$`50%`[8], summary$`50%`[9]-summary$`50%`[10])

###quantile boxplots

sim$type[sim$type == "Migrating"] <- "Migration" sim$type[sim$type == "Dispersing"] <- "Dispersal"

box.sim <- ggplot(sim, aes(x = as.factor(n), y = r2)) + stat_boxplot(outlier.shape = NA) + facet_grid(~type) + scale_y_continuous(limits = c(0,1), breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1)) + labs(x = "Number of individuals tracked", y = bquote('r'^2)) + theme_bw() + theme_bw() + theme(legend.position = "none", panel.spacing = unit(2, "lines"), axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), axis.title.x = element_text(size = 14, margin = margin(10, 0, 0, 0)), axis.title.y = element_text(size = 14, margin = margin(0, 10, 0, 0)), strip.text.x = element_text(size = 14)) 122

box.sim

# -summarize number of points in each bin distance for each population # -models exponential curve fit to bin distances # -plot exponential curve with data to see fit ####################################################################### #########################

#create bins bins <- seq(50, 5000, 50)

#import data SSMseason <- readRDS(file = "data- clean/outputMAY16/SSM18MAY16full.rds") SSMnb <- SSMseason[SSMseason$season != "breeding", c("pop", "id", "distcolony")]

#remove the tracks that don't move in the winter due to human influence SSMnb <- SSMnb[SSMnb$id != 6 & SSMnb$id != 1,] pops <- unique(SSMnb$pop)

#keep only the birds that tracks include winter

#SSMseason$id <- as.character(as.numeric(SSMseason$id)) winterid <- unique(SSMseason$id[SSMseason$season == "winter"]) #remove <- c(1,6,4) winterid <- winterid[! winterid %in% remove]

SSMw <- SSMseason[SSMseason$season != "breeding", c("pop", "id", "distcolony")] SSMw <- SSMw[SSMw$id %in% winterid,]

SSM.maxw <- summarize(group_by(SSMw, pop, id), max.dist = max(distcolony)) SSM.maxw2 <- summarize(group_by(SSM.maxw, pop), mean = mean(max.dist), sd = sd(max.dist)) ggplot(SSM.maxw2[SSM.maxw2$pop != "NU",], aes(x = mean, y = sd, colour = pop)) + geom_point()

#create a list that has the bin proportions for each populations blank <- rep(0, length(bins)) pop.bins <- rep(list(blank), length(pops)) names(pop.bins) <- pops 123

for(j in 1:length(pops)){ x.bins <- rep(0, length(bins)) for(i in 1:length(bins)){ x.bins[i] <- (sum(SSMw$distcolony[SSMw$pop == pops[j]] >= bins[i])/nrow(SSMw[SSMw$pop == pops[j],])) } pop.bins[[pops[j]]] <- x.bins }

#remove 0s rm0 <- function(x){ x <- x[x != 0] } pop.rm0 <- lapply(pop.bins, rm0)

#fit exponential curve exp.lm <- function(x) { elm <- lm(log(x) ~ bins[1:length(x)]) return(elm) }

pop.exp.lm <- lapply(pop.rm0, exp.lm)

#predicted values for plotting exponential fit curves binval2 <- seq(1, max(bins), 1) exp.predictor <- function(z){ ep <- exp(predict(z, data.frame(bins=binval2, x = nrow(z$model)))) return(ep) } pop.exp.pred <- lapply(pop.exp.lm, exp.predictor)

#put predicted values into dataframe pop.exp.predx <- lapply(pop.exp.pred, function(x) cbind(x = binval2, y = x)) list.names <- names(pop.exp.predx) lns <- sapply(pop.exp.predx, nrow) pop.exp.pred.df <- as.data.frame(do.call("rbind", pop.exp.predx)) pop.exp.pred.df$pop <- rep(list.names, lns)

#put real values into dataframe (pop.bins) pop.binsx <- lapply(pop.bins, function(x) cbind(x = bins, y = x)) list.names <- names(pop.binsx) lns <- sapply(pop.binsx, nrow) 124 pop.bins.df <- as.data.frame(do.call("rbind", pop.binsx)) pop.bins.df$pop <- rep(list.names, lns)

#combine predicted and real values for plotting merger <- pop.bins.df merger$type <- "Data" merger2 <- pop.exp.pred.df merger2$type <- "Fitted Exponential Curve"

merged <- rbind(merger2, merger)

#get r2 for each pop getr2 <- function(z){ zsum <- summary(z) get <- zsum$r.squared return(get) }

pop.exp.r2 <- lapply(pop.exp.lm, getr2) pop.exp.r2.df <- melt(as.data.frame(pop.exp.r2)) colnames(pop.exp.r2.df) <- c("pop", "r2") pop.exp.r2.df$r2 <- signif(pop.exp.r2.df$r2, digits = 2)

#Create Label names for pops pop.names <- list('BF' = "Bay of Fundy", 'MA' = "Newfoundland", 'NU' = "Nunavut", 'ON' = "Great Lakes", 'SA' = "Sable Island") pop.labeller <- function(value){ return(pop.names[[value]]) }

#Label names for plots merged$pop.lab <- 0 for(i in 1:nrow(merged)){ x <- merged$pop[i] merged$pop.lab[i] <- pop.labeller(x) } merged$pop.lab <- as.factor(merged$pop.lab) 125

#label names for r2 pop.exp.r2.df$pop.lab <- 0 for(i in 1:nrow(pop.exp.r2.df)){ x <- pop.exp.r2.df$pop[i] pop.exp.r2.df$pop.lab[i] <- pop.labeller(x) } pop.exp.r2.df$pop.lab <- as.factor(pop.exp.r2.df$pop.lab) merged2 <- merged merged2$pop.lab <- factor(merged$pop.lab, levels = c("Bay of Fundy", "Newfoundland", "Sable Island", "Nunavut", "Great Lakes"))

DATAPLOT <- ggplot(merged2, aes(x = x, y = y)) + geom_ribbon(data = merged2[merged2$type == "Data",], aes(ymin = 0, ymax = y)) + geom_line(data = merged2[merged2$type == "Fitted Exponential Curve",], colour = "red") + scale_x_continuous(expand = c(0,0), limits = c(50, 4999)) + scale_y_continuous(expand = c(0,0), limits = c(0,1), breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1)) + geom_text(data = pop.exp.r2.df, aes(x = 4000, y = 0.9, label=paste("R^2:", r2, sep=" ")), inherit.aes=FALSE, parse = TRUE, show.legend = FALSE) + facet_wrap(~pop.lab) + labs(x = "Distance", y = "Proportion of locations beyond Distance") + theme_bw() + theme(legend.position = "none", panel.spacing = unit(2, "lines"), axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12), axis.title.x = element_text(size = 14, margin = margin(10, 0, 0, 0)), axis.title.y = element_text(size = 14, margin = margin(0, 10, 0, 0)), strip.text.x = element_text(size = 14))

DATAPLOT