Apparent Survival of Two Trans-Equatorial Migrant Seabirds Breeding in Canada

APPARENT SURVIVAL OF TWO TRANS-EQUATORIAL MIGRANT SEABIRDS BREEDING IN CANADA

DANIELLE T. FIFE B. Sc. (Hons.) University of Guelph, 2010

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

Acadia University Spring Convocation 2015

The examining committee for the thesis was:

______Dr. Nelson J. O’Driscoll, Chair

______Dr. Karel A. Allard, External Reader

______Dr. Mike J. W. Stokesbury, Internal Reader

______Dr. Mark L. Mallory, Supervisor

______Dr. Dave Shutler, Supervisor

______Dr. Steve W. Mockford, Head of Department

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

I, Danielle T. Fife, 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.

______Author

______Supervisor

______Date

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TABLE OF CONTENTS

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... ix

Symbols and Abbreviations ...... xi

Abstract ...... xiii

Acknowledgments ...... xiv

Chapter 1 – General Introduction ...... 1

Seabirds and the marine environment ...... 1

Seabird life history traits ...... 3

Monitoring seabird populations ...... 4

Study species: Leach’s Storm-Petrel and Sabine’s Gull ...... 10

Objectives ...... 12

Chapter 2 – Apparent survival of adult Leach’s Storm-Petrels (Oceanodroma leucorhoa) breeding on Bon Portage Island, Nova Scotia ...... 15

Abstract ...... 15

Introduction ...... 16

Methods ...... 19

Study site ...... 19

Capture-mark-recapture of adults ...... 20

Survival analyses ...... 21

Model selection ...... 22

Goodness-of-fit ...... 23

Results ...... 23

Survival analyses ...... 24

Discussion ...... 25

Effects of VHF tags on survival...... 27

Effects of gulls on survival ...... 29

Other factors affecting survival ...... 31

Conclusions ...... 32

Chapter 3 – Anomalous climate conditions reduce survival of an Arctic trans-equatorial migrant seabird ...... 40

Abstract ...... 40

Introduction ...... 41

Methods ...... 44

Study Site ...... 44

Capturing and Resighting Adults ...... 45

Survival Analyses ...... 46

Effect of Climate Variability on Survival ...... 48

Goodness-of-fit ...... 49

Results ...... 50

Goodness-of-fit ...... 50

Apparent Survival on Nasaruvaalik Island ...... 50

Combined Analysis and Effect of Climate variability ...... 51

Discussion ...... 52

Apparent Survival on Nasaruvaalik Island ...... 52

Effect of Climate Variability on Survival ...... 53

Survival and Breeding Biology of Sabine’s Gulls ...... 56

Conclusions ...... 59

Chapter 4 – General Discussion ...... 71

Importance of survival analyses to conservation and management ...... 71

Primary findings and future research ...... 73

Conservation and management actions ...... 75

Literature Cited ...... 77

LIST OF TABLES

Table 2.1. Reduced m-array showing when and how many adult Leach’s Storm-Petrels were recaptured for the first time after initial release. Note ‘# released’ at a particular occasion (‘Year released’) includes storm-petrels newly banded and those that were previously banded and recaptured at that occasion. Storm-petrels separated into those nesting in plots with (‘gulls’; 10 plots) and without (‘no gulls’; 2 plots) breeding Herring Gulls and those with (‘VHF’) and without (‘no VHF’) VHF tags (sample size of storm-petrels at initial capture in parentheses).

Numbers of storm-petrels from each group were pooled for the initial analysis...... 33

Table 2.2. Model selection results testing for influence of gull presence and VHF tags on apparent survival (ϕ) and encounter (p) probabilities of adult Leach’s Storm-Petrels on Bon

Portage Island. Only models with QAICc weights ≥ 0.01 are included in this table. Note that two out of 12 plots had 3–4 pairs of nesting Herring Gulls...... 35

Table 2.3. Apparent survival (ϕ) estimates for Leach's Storm-Petrels from other colonies, and for other tubenose species. Metrics of variability are given if reported in the literature...... 36

Table 3.1. Descriptions of climate indices used in this study: The principal-component-based

North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), and Tropical/Northern

Hemisphere pattern (TNH)...... 61

Table 3.2. Pearson correlations of selected climate indices based on mean winter values from

1952–2014. Bolded values are significant (P < 0.05)...... 63

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Table 3.3. Reduced m-array summarizing encounter history data for adult Sabine’s Gulls (n =

84) banded on Nasaruvaalik Island from 2007–2013. Note the number of birds ‘released’ in a particular year includes those newly banded and those resighted in that year...... 64

Table 3.4. Reduced m-array summarizing encounter history data for adult Sabine’s Gulls (n =

43) banded at East Bay from 1998–2002. Note number of birds ‘released’ in a particular year includes those newly banded and those resighted in that year...... 65

Table 3.5. Model selection results from the Nasaruvaalik Island and combined (East Bay +

Nasaruvaalik Island) survival analyses, where ϕ is apparent survival and p is resighting probability. Note pstudy in the combined analysis denotes differing structure for resighting probabilities for each colony. For East Bay, resighting is constant from 1998–2001, and different in 2002 and for Nasaruvaalik Island, resighting is constant from 2007–2013...... 66

Table 3.6. Adult survival estimates (± SE) from capture-mark-recapture studies of other gulls and terns...... 67

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LIST OF FIGURES

Figure 1.1. Example of a three-occasion (year) capture-mark-recapture study, where apparent survival probabilities (ϕi) are estimated for intervals between each occasion and recapture probabilities (pi) are estimated at each occasion. Note that it is not possible to estimate ϕ and p separately in the final occasion, and p cannot be estimated for the first occasion because animals have not been previously released and recaptured...... 14

Figure 2.1. Global breeding and marine (non-breeding and foraging) distributions of Leach's

Storm-Petrels. Inset map shows location of the study site, Bon Portage Island, Nova Scotia

Canada...... 38

Figure 2.2. Model-averaged annual survival estimates (±SE) from 2009 to 2013 for adult

Leach’s Storm-Petrels nesting in plots with or without Herring Gulls and with or without VHF tags. Note, the particularly large SEs in 2009-10 are due to small sample sizes of storm-petrels banded in 2009...... 39

Figure 3.1. Locations of the study sites, Nasaruvaalik Island and East Bay Migratory Bird

Sanctuary, in Nunavut, Canada...... 68

Figure 3.2. Index values from 1998–2013 for (A) the principal-component-based North Atlantic

Oscillation Index (NAO) (B) Tropical/Northern Hemisphere pattern (TNH) and (C) Southern

Oscillation Index (SOI). Mean winter (Dec–Feb for Tropical/Northern Hemisphere pattern,

Dec–Mar for rest) index values used. Note each value includes December of the previous year.

...... 69

Figure 3.3 Annual apparent survival of adult Sabine’s Gulls at East Bay (1999–2001) and

Nasaruvaalik Island (2007–2013) in relation to winter (Dec–Feb) Tropical/Northern Hemisphere pattern...... 70

SYMBOLS AND ABBREVIATIONS

ϕ Apparent survival probability p Recapture or resighting probability

σ2 Time-dependent process variation

AIC Akaike’s Information Criterion

AICc Akaike’s Information Criterion corrected for small sample size

ĉ Overdipersion parameter

CMR Capture-mark-recapture

CJS Cormack-Jolly-Seber model

GLS Global Location Sensing/geolocators

JS Jolly-Seber model

K-L Kullback-Leibler

Lϕ Expectation of future life

MEI Multivariate El Niño Index

NAO North Atlantic Oscillation

NOAA National Oceanic and Atmospheric Administration

NOI Northern Oscillation Index

NPI North Pacific Oscillation

PDO Pacific Decadal Index

SOI Southern Oscillation Index

SST Sea Surface Temperature

TNH Tropical/Northern Hemisphere pattern

QAIC Quasi-Akaike’s Information Criterion

VHF Very High Frequency

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ABSTRACT

Long-lived species, such as seabirds, typically have slow reproductive rates, so that even slight reductions in adult survival could lead to substantial population declines. Adult survival is therefore an important monitoring tool, used to assess health of seabird populations. Using

Program MARK, I estimated annual apparent survival of adults of two seabird species breeding in Canada: Leach’s Storm-Petrel (Oceanodroma leucorhoa), on Bon Portage Island, Nova

Scotia; and Sabine’s Gull (Xema sabini) on Nasaruvaalik Island, Nunavut. In addition, I examined potential variables influencing adult survival for both species. For Leach’s Storm-

Petrels, I found evidence of positive effects of VHF tag attachment; however, this was likely due to sampling bias and/or limited data. I also found preliminary evidence of Herring Gulls (Larus argentatus) having negative effects, but further years of data are needed to improve confidence in this result. Regardless, adult survival of Leach’s Storm-Petrels at this colony was low compared to estimates for similar species (e.g., > 0.90 for many albatrosses and petrels), but similar to recent estimates for other Leach’s Storm-Petrel colonies in Atlantic Canada. For

Sabine’s Gulls on Nasaruvaalik Island, survival was high and generally constant (0.90 ± 0.03).

However, after combining data for that colony with data for a low Arctic colony, I detected an influence of anomalous climate conditions, with reduced survival in a year with a high, positive value for the Tropical Northern Hemisphere pattern. This is a teleconnection pattern that has anomalies centred over the Gulf of Alaska, Hudson Bay, and Mexico. For both species, additional data are needed from respective study sites to refine current estimates and to help identify and address factors that negatively affect their survival.

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ACKNOWLEDGMENTS

Most importantly, I thank Dave Shutler and Mark Mallory for their ongoing advice, understanding, sense of humour, and support. I couldn’t have asked for better supervisors! I also thank Greg Robertson (survival analysis/Program MARK guru) on my thesis advisory committee, for his patience and guidance throughout the writing process and for putting up with my multiple ‘final’ and ‘really final this time’ versions of my analyses.

This thesis would not have been possible without the dedication of Ingrid Pollet and

Shanti Davis (and those who helped them in the field), who generously provided me with CMR data for Leach’s Storm-Petrels and Sabine’s Gulls, respectively. Countless hours went into capturing, banding and resighting/recapturing birds (and even fending off polar bears and angry terns), which is much appreciated!

Thank you to my lab mates, honorary lab mates, and friends at Acadia for making my time here ridiculously fun and memorable. This experience would not have been the same without you guys.

Thank you to my defense committee members, Steve Mockford, Nelson O’Driscoll, and especially Karel Allard (external reader) and Mike Stokesbury (internal reader) for providing valuable feedback on my thesis and insights that will ultimately make me a better seabird biologist. I am also grateful to those who provided advice, whether it was life, motivational, or academic, in particular Trevor Avery and Rob Ronconi, and to those that provided additional information or data that enhanced my thesis, in particular Iain Stenhouse, Grant Gilchrist, Sabina

Wilhelm, and April Hedd.

Finally, I thank the following for providing financial and/or logistical support for my research: Nunavut General Monitoring Program, Polar Continental Shelf Program, Environment

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Canada, Natural Sciences and Engineering Council, Nova Scotia Habitat Conservation Fund,

Canadian Wildlife Federation, and Acadia University.

Chapter 1 – General Introduction

Seabirds and the marine environment

There are roughly 350 bird species that rely, to varying degrees, on the world’s marine environments for the greater part of their annual cycles (Croxall et al. 2012). As a group, seabirds possess a variety of foraging strategies, feeding at multiple trophic levels, different parts of the water column, and across broad geographic ranges (Mallory et al. 2010). During the breeding season, many seabirds are central place foragers, confined to nesting sites on land between intermittent foraging trips (Gaston 2004). However, during the non-breeding season, many are found almost exclusively at sea, foraging in coastal upwelling areas that are associated with high productivity (e.g., Pollet et al. 2014a, 2015, Davis 2015). Their roles as apex predators in marine food webs and their propensity to form dense breeding colonies, which allows sampling of relatively large numbers of individuals, makes many seabird species effective and important bioindicators of marine ecosystem health (Mallory et al. 2010).). Thus, studies of seabird demographics, behaviour, and physiology have been used to monitor environmental variables such as climate variability (Diamond and Devlin 2003), abundance and distribution of prey, (Einoder 2009, Cury et al. 2011), and marine contaminants, respectively (Goodale et al.

2008).

Over the last century, the world’s oceans and coastal environments have undergone dramatic changes, which have largely been a result of increasing human industrialization, and over-exploitation of marine resources (Halpern et al. 2008, Grémillet and Boulinier 2009).

Increasing sea surface temperatures (SST) associated with greenhouse gas emissions, depletion of many of the world’s fish stocks by commercial fisheries, and ongoing input of potentially toxic chemicals into marine waters have all put considerable pressure on marine ecosystems

(Halpern et al. 2008, Pinsky et al. 2011, Tyrrell 2011). As a consequence, seabirds are currently regarded as more threatened and rapidly declining than comparable groups of birds, with populations of almost half of seabird species currently believed or known to be in decline

(Croxall et al. 2012). Of these, pelagic species (e.g., tubenoses, the taxon that includes albatrosses and petrels) are more threatened than coastal species (e.g., gulls), possibly because pelagic species tend to have smaller clutch sizes than coastal species, and thus are slower to recover from increased adult mortality as direct or indirect results of human disturbances

(Croxall et al. 2012). Some gull species have also benefited from access to human refuse (e.g., fisheries discards, landfills), although with relatively recent reforms to waste management protocols, and fisheries moratoria, these food sources are no longer reliable (Cotter et al. 2012).

Consequently, some gull species have experienced population declines and/or have altered their diets through exploitation of alternative prey, including small birds (e.g., storm-petrels;

Stenhouse and Montevecchi 1999).

Seabird population declines are seldom attributable to a single factor, but rather a combination of local and regional threats that can vary among breeding, migratory, and winter habitats (Gaston 2011). At breeding colonies, the greatest threat according to Croxall et al.

(2012) is invasive species, with costs to adult and juvenile seabirds because of high predation rates or competition for resources. This is especially true for burrow-nesting species (e.g., petrels; McChesney and Tershy 1998, Cuthbert et al. 2013) that nest on offshore islands to avoid mammalian predators. These species are not equipped to contend with invasions of some native or introduced species, such as rodents (Cuthbert et al. 2013). Other threats on land include human disturbance and habitat destruction (Croxall 2012, Trathan et al. 2015). At sea, threats include entanglement in fishing gear (i.e., bycatch; Weimerskirch and Jouventin 1987, Tuck et

2 al. 2011, Barbraud et al. 2012), reductions in prey availability resulting from overharvesting by commercial fisheries or as indirect consequences of extreme climate (Ottersen et al. 2001, Cury et al. 2011, Cohen et al. 2014), contact with oil and natural gas residues from both catastrophic spills and chronic leakage, collisions with offshore oil and gas platforms (Ronconi et al. 2014), high levels of marine contaminants (Fort et al. 2014), and ingestion of plastic debris (Provencher et al. 2014a). Some links between threats and reduced survival are less direct; for instance, high concentrations of certain contaminants may cause deterioration of an animal’s body condition and increase their susceptibility to certain pathogens (e.g., nematodes in eiders; Fisk et al. 2005).

Seabird life history traits

Most seabirds spend the majority of their lives at sea, typically only returning to land to breed during a relatively short window in a given year. Combined with their ability to travel great distances for both migration and foraging, this has led to a suite of behavioural and life history characteristics that Gaston (2004) described as ‘The Seabird Syndrome’. The majority of species form long-term monogamous pair bonds and are highly colonial. Colonies of some species can be very dense, consisting of millions of breeding pairs in a relatively small area (Sklepkovych and Montevecchi 1989, Egevang et al. 2003). Similar to other birds groups, nearly all seabird species show high breeding site fidelity, returning to the same nesting locations to breed. Natal philopatry (i.e., the tendency for individuals to return to their place of origin to breed;

Greenwood and Harvey 1982) tends to be much lower, although this varies both among and within species (Frederiksen 1999, Schreiber and Burger 2002, Gaston 2004).

Relative to other bird groups, seabirds typically have much slower reproductive rates; they have delayed sexual maturity, lay a single clutch each breeding season, and have small

3 clutch sizes. Slow reproductive rates enable long life spans, which in turn, provide some flexibility in their investment in annual reproductive output (Schreiber and Burger 2002, Gaston et al. 2004). For example, some seabirds skip breeding in years with extreme climate events and/or reduced prey availability, so that they are able to maintain their own health (Croxall and

Rothbury 1991, Cubaynes et al. 2010). Thus, seabirds, and other long-lived species, tend to have high adult survival rates that are fairly resilient to natural environmental variability (Wooller et al. 1992). However, this resilience is sustainable only up to a certain threshold and even small reductions in adult survival can have substantial effects on populations that have slow reproductive rates and are thus slow to recover from abnormally high adult mortality (Wooller et al. 1992).

Monitoring seabird populations

Seabird populations are monitored for a variety of reasons, whether it is to assess the status of species that are at risk or the effectiveness of recovery programs, to establish sustainable harvest levels for game species (e.g. eiders; Gilliland et al. 2009), or to diagnose environmental or anthropogenic stressors linked to population declines and which demographic mechanisms (e.g., vital rates such as survival or reproduction) that potential stressors are acting through (Witmer

2005). For some species, little is known regarding their population status or dynamics, so establishing baseline values of vital rates is adequate incentive to monitor their populations

(Stenhouse and Robertson 2005, Lewison et al. 2012). This enables researchers to track changes in populations over time and to predict how populations might respond to future perturbations

(Pollock et al. 2002). More importantly, it provides a framework for appropriate actions to be taken to prevent or minimize effects of perturbations (Baillie and Schaub 2009).

Often, population abundance and density are the first population parameters to be measured; however, these can be quite labour-intensive and costly to obtain (Witmer 2005).

Further diagnostics such as reproductive and survival rates are then needed to determine underlying mechanisms for changes, if any, in population abundance. Although several years of recaptures are required to produce reliable inter-annual survival rates, estimates tend to be more precise than those of abundance. Moreover, adult survival is considered to be the most sensitive parameter influencing populations of long-lived species; thus, low or variable adult survival indicates that a population is under stress.

Capture-Mark-Recapture

Capture-mark-recapture (CMR) techniques (Lebreton et al. 1992, Baillie and Schaub

2009) have long been used in seabird monitoring studies to obtain estimates for a variety of population parameters, including abundance and distribution, reproductive success, recruitment, dispersal, and adult survival (Jolly 1965, Hudson 1966, Coulson and Horobin 1976, Hatch 1987,

Croxall et al. 1990, Spendelow et al. 1995). In CMR studies, individuals are marked with a unique identifier (e.g., a uniquely numbered metallic leg band or a plastic wing tag) and released back into a population, with ‘encounters’ (i.e., recaptures or resightings) recorded on subsequent occasions. From this, an encounter history is produced for each marked animal, consisting of a string of 1s (for encountered) and 0s (for not encountered), that allows estimation and comparison of population parameters of interest (Lebreton et al. 1992).

Trapping and handling techniques are important when conducting CMR studies because they can have behavioural and demographic consequences. For example, depending on the type of trap used and what they experience at a particular trapping occasion, animals may avoid or be

5 attracted to subsequent recaptures (i.e., become ‘trap shy’ or ‘trap happy’, respectively; Pradel et al. 2005), or may even abandon a study site altogether, which can produce estimates that are biased (Lebreton et al. 1992, Sandland and Kirkwood 1981). Moreover, bands might impede an animal’s mobility, making them susceptible to predation or reducing their foraging efficiency

(e.g., Reed et al. 2005). Ideally, both trapping and banding should be conducted in a way that prevents harm to animals and is as minimally invasive as possible.

To further reduce error and bias in parameter estimates, CMR studies require well- defined study sites and sampling periods. In general, the types of parameters of interest determine how these boundaries are defined and demographic models tend to be developed based on whether they describe open or closed populations. Open population models allow populations to change with birth, recruitment, death, and emigration over the course of a study

(Pollock and Alpizar-Jara 2005). Studies of open populations are usually long-term, sometimes spanning multiple generations, and with enough time between sampling occasions to allow for such changes to occur. Conversely, closed population models assume that there are no gains or losses to populations within the study period and that sampling is almost instantaneous (Chao and Huggins 2005). Because most populations observed in nature are highly likely to change through time, models based on open populations are more appropriate for multi-year survival studies.

Estimating apparent survival and recapture probabilities

The two demographic parameters central to this thesis were apparent survival and encounter probabilities. Apparent survival (ϕ) is the probability that an individual marked at a certain occasion (e.g., year) survives to the next occasion and remains in the study population

(Lebreton et al. 1992). It is estimated for each interval between occasions over the duration of a study (Fig. 1.1). However, because there is no way of knowing whether an individual survived unless it was actually recaptured, or resighted at subsequent occasions, modern CMR models incorporate estimation of the encounter parameter (p), which is the probability of being recaptured or resighted at each occasion (Lebreton et al. 1992) (Fig. 1.1). The term ‘apparent survival’ is used because individuals that permanently emigrated from a study site cannot be distinguished from those that died (Pollock 1990, Pollock and Alpizar-Jara 2005). For this reason, apparent survival estimates tend to be biased below genuine survival (Gilroy et al. 2012,

Kendall et al. 2013).

Early demographic studies often only reported a populations’ annual return rate (i.e., the proportion of marked individuals returning to a study site in subsequent years after initial marking; Cooch and White 2014), which is really the product of apparent survival and encounter probabilities (ϕp). In the final occasion of a multi-year CMR study only a return rate can be estimated because without recaptures from the following year, p cannot be estimated. Similarly, p is not estimable for the initial year of a study because animals have not yet been released and recaptured. Thus, a minimum of three years is required to obtain a single survival (and encounter) estimate and at least seven years are needed to begin to detect annual variability or relationships with individual or environmental variables. As more years of recaptures/resightings are obtained, precision of estimates improves (Lebreton et al. 1992).

In the mid-1960s, two prominent models were developed for demographic analyses of marked animals in open populations: the Jolly-Seber (JS) and Cormack-Jolly-Seber (CJS) models (Cormack 1964, Jolly 1965, Seber 1965). Both use maximum-likelihood estimation to estimate ϕ and p that vary over time and are based on encounters of live individuals (Pollock and

Alpizar-Jara 2005). The JS model also takes the ratio of marked to unmarked individuals and uses that to estimate population size. In general, estimates of survival are less affected by heterogeneity in catchability and therefore tend to be more robust to violations of model assumptions than are estimates of population size, depending on the nature of the study and the species being monitored (Pollock 1982, Lebreton et al. 1992).

The CJS model was the global model used in this thesis and has the following general assumptions (paraphrased from Pollock and Alpizar-Jara 2005 and Cooch and White 2014):

i) All animals caught at time t have an equal chance of being recaptured/resighted at time

t+1;

ii) All animals captured at time t have an equal chance of surviving from t to t+1;

iii) No bands are lost or missed, and are recorded correctly; and

iv) Sampling is instantaneous and animals are released immediately.

Since initial development of the JS and CJS models, advances have been made towards refining their flexibility and accuracy. Greater computational power and development of software has allowed for reduced models that constrain either ϕ or p to be constant each year and for models to incorporate effects of age, sex, and location (Lebreton et al., 1992, Amsptrup et al.

2005), as well as individual (e.g., morphometrics; Horswill et al. 2014) and environmental (e.g., climate variables; Sandvik et al. 2005) covariates. Furthermore, more recent models, such as the

Barker model (Barker 1997) make it possible to estimate ‘true’ survival, along with additional parameters such as site fidelity and emigration, but this requires resightings from locations outside a study site and recoveries of dead individuals (Gilroy et al. 2012, Kendall et al. 2013).

With advances in field methods, this approach is becoming more prevalent (e.g., Robertson et al.

2015). However, goodness-of-fit testing has not yet been adequately developed for the Barker model (Pradel et al. 2005).

Model Selection

Survival analyses typically employ an information-theoretic approach to selecting among multiple models in a pre-defined candidate set. In this framework, a ‘best’ model can be identified and all other models in the set ranked and weighted in relation to it. In many cases, this is accomplished using Akaike’s Information Criterion (AIC; equation 1), which is based on an estimate of the relative distance (known as Kullback-Leibler distance) between a fitted

(candidate) model and a ‘true’ unknown model that generated the data (Burnham and Anderson

2002). Logically, the best-supported model will have the lowest AIC value (or shortest K-L distance). Generally this is the most parsimonious model with the least possible number of parameters, and therefore least amount of variance, but one that is still representative of the data

(i.e., is a good fit) (Cooch and White 2014). Maximum-likelihood estimates of ϕ and p are provided by the best-supported model, or are averaged and weighted by model support (AIC weights) if there are multiple equally-supported top models. As a general rule, any model with a

ΔAIC ≤ 2 is considered to have similar support and models with ΔAIC ≥ 7 have no support

(Burnham and Anderson 2002). For each model,

AIC = -2ln ( ̂ data) 2 (1)

9 where L is the model likelihood based on the parameters being estimated and the data, and K is the number of parameters (Cooch and White 2014). Adjustments can be made to equation 1 to account for small sample sizes and overdispersion of CMR data (Cooch and White 2014).

Program MARK

Program MARK (White and Burnham 1999) is a comprehensive computer application used to analyze data from marked individuals, based on encounter histories. It provides survival and recapture probabilities for a set of candidate models that can be built and ranked in MARK based on the data. A growing number of studies use MARK (e.g., Bertram et al. 2000,

Jenouvrier et al. 2005, Sandvik et al. 2005, Stenhouse and Robertson 2005, Allard et al. 2006,

Horswill et al. 2014), because it integrates methods used by many of the other well-known modelling packages used for these types of analyses, such as SURGE (Pradel and Lebreton

1993) and it has a user-friendly interface.

Study species: Leach’s Storm-Petrel and Sabine’s Gull

Leach’s Storm-Petrel (Oceanodroma leucorhoa) and Sabine’s Gull (Xema sabini) are both long- distance trans-equatorial migrants that breed in the Northern Hemisphere and travel thousands of kilometers to their winter habitats in tropical and subtropical waters of the Southern Hemisphere during the non-breeding season (Stenhouse et al. 2012, Pollet et al. 2014a, 2015, Davis 2015).

Leach’s Storm-Petrels (> 10 million pairs globally) breed on islands throughout the Northern

Hemisphere and are fairly evenly divided between the Atlantic and Pacific Oceans (Huntington et al. 1996). Sabine’s Gulls (~ 330,000 to 700,000 individuals) breed throughout the Arctic, with

10 the distribution of colonies being somewhat patchy (Day et al. 2001, Wetlands International

2006).

Both species are pelagic surface feeders in general, with zooplankton making up the bulk of their diet during the non-breeding season. However, during the breeding season, Leach’s

Storm-Petrels feed primarily on small fish (e.g., myctophids), whereas Sabine’s Gulls feed almost exclusively on aquatic insects and marine invertebrates (Huntington et al. 1996, Day et al.

2001). Their foraging strategies also differ with respect to distances travelled and time spent away from the nest. During the breeding season, Leach’s Storm-Petrels may spend up to a week at sea, travelling more than 2000 kilometers to access nutrient-rich upwellings associated with continental slopes, which is impressive considering their small size (~ 50 g); these distances are much farther than previously believed (Pollet et al. 2014b, 2015). Sabine’s Gulls, on the other hand, remain at or stay relatively close to their colony to forage (Day et al. 2001). Sabine’s

Gulls are considered unique among gulls in that they are quite small (~ 140–210 g), phylogenetically distinct, and display certain characteristics that are more similar to terns (e.g., male feeds whole prey to female during courtship) and shorebirds (e.g., foraging on mudflats) than to other gulls (Day et al. 2001).

Leach’s Storm-Petrels nest inside burrows dug in soil, or in crevices in rocky areas

(Huntington et al. 1996). A clutch consists of a single egg and once a chick hatches, it remains in its burrow until the end of the breeding season when it is ready to fledge. Parents take turns feeding their chick energy-rich oil that they store in their stomachs and regurgitate directly into chicks’ throats. Chicks becomes quite fat (even doubling the mass of adults) over the summer and must lose enough weight to be able to achieve flight at the time of fledging (Huntington et al. 1996). Sabine’s Gulls build simple nests above ground in open areas and will lay a single

11 clutch with 2–3 eggs in a given breeding season (Day et al. 2001). Their chicks leave nests almost immediately after hatching and remain hidden in nearby vegetation. They are capable of foraging on small insects and crustaceans themselves, although their parents also stay nearby and feed their young regurgitated food for the remainder of the breeding season (Day et al. 2001).

Objectives

Adult survival estimation is a key diagnostic tool used in monitoring populations of long-lived species and for identifying sources of population declines. In light of declines observed for many populations of seabirds, the primary objective of this thesis was to estimate adult survival and recapture probabilities for two seabird species breeding in Canada: Leach’s Storm-Petrels from Bon Portage Island, Nova Scotia (Chapter 2) and Sabine’s Gulls from Nasaruvaalik Island,

Nunavut (Chapter 3). In the last few decades, populations of Leach’s Storm-Petrels have declined at many colonies (e.g., Robertson et al. 2006a, Newson et al. 2008) and some have even collapsed completely (e.g., Seal Island, Nova Scotia; Huntington et al. 1996). Demographic trends for Sabine’s Gulls are poorly understood due to the remoteness of their breeding colonies

(Stenhouse and Robertson 2005, Mallory et al. 2012).

Apparent survival and encounter probabilities were estimated using Program MARK, using an information-theoretic approach to model selection. Furthermore, I investigated whether adult survival was influenced by predation at the breeding colony, or attachment of Very High

Frequency (VHF) radio tags for Leach’s Storm-Petrels (i.e., differences in survival among groups), and winter climate variability for Sabine’s Gulls (i.e., differences in survival among years). For both species, I expected that survival probabilities would be high (> 0.80 for

Sabine’s Gulls and > 0.90 for Leach’s Storm-Petrels), given their long life-spans and estimates

12 reported for similar species (see tables 2.3 and 3.6). For Leach’s Storm-Petrels, I predicted that survival would be lower for birds nesting in plots occupied by Herring Gulls, based on other studies showing high predation rates of storm-petrels by avian predators (e.g., Stenhouse and

Montevecchi 1999, Miles 2010) and that VHF tags would not have an effect on survival, based on evidence that attachment of geolocators (Global Location Sensing; GLS) did not affect return rates (Pollet et al. 2014a). Results of this study will be useful in providing a baseline context required to assess health of Leach’s Storm-Petrels and Sabine’s Gulls at specific colonies.

Moreover, information from this study can be used to infer overall conditions of their associated marine environments and provide insights into factors that might influence survival of other sympatric species that are rare or at risk.

Figure 1.1. Example of a three-occasion (year) capture-mark-recapture study, where apparent survival probabilities (ϕi) are estimated for intervals between each occasion and encounter probabilities (pi) are estimated at each occasion. Note that it is not possible to estimate ϕ and p separately in the final occasion, and p cannot be estimated for the first occasion because animals have not been previously released and re-encountered.

Chapter 2 – Apparent survival of adult Leach’s Storm-Petrels (Oceanodroma leucorhoa) breeding on Bon Portage Island, Nova Scotia

ABSTRACT

Populations of Leach’s Storm-Petrels (Oceanodroma leucorhoa; hereafter ‘storm-petrel’), one of the most widespread procellariiform seabirds in the world, appear to be declining in many parts of their breeding range. As part of a regional effort to assess status of storm-petrel colonies in eastern North America, I estimated apparent survival and recapture rates from 2009–2014 for adults on Bon Portage Island (43° 28' N, 65° 44' W), located off the southwestern coast of Nova

Scotia, Canada. This ~1.5-km2 island is recognized globally as an Important Bird Area, supporting roughly 50,000 (95% CI: 37,541 to 58,944) pairs of storm-petrels during the breeding season. Constant annual survival estimated for this colony was low (0.79 ± 0.05) compared to estimates for other procellariiforms (e.g., > 0.90 for many albatrosses and petrels). Storm-petrels that were fitted with Very High Frequency (VHF) radio tags had an average of 0.11 ± 0.05 (95%

CI: 0.01 to 0.21) higher survival probabilities than those that were not, possibly because VHF tags were attached to known, established breeders. Alternatively, the apparent difference may have resulted from a small sample size (and thus high error) for birds with VHF tags. There was weak evidence that survival was reduced by an average of 0.07 ± 0.04 for storm-petrels in study plots that were occupied by Herring Gulls (Larus argentatus) and their chicks; however this result was not statistically significant (95% CI: -0.15 to 0.02). Other than predation at breeding colonies, potential threats to storm-petrels include encounters with off-shore oil and natural gas platforms, heavy metal contamination, and changing climatic conditions in both their breeding and wintering ranges. Low survival estimates are an early indication that this important colony

15 may be under stress. However, further work is needed to determine if the colony is indeed declining and if so, to determine causes(s) of the decline so that they may be addressed.

INTRODUCTION

For long-lived species such as seabirds, adult survival is one of the most important demographic traits for maintaining viable populations and should remain relatively high and constant despite natural environmental variability (Clobert and Lebreton 1991, Erikstad et al. 1998, Sæther and

Bakke 2000, Cubaynes et al. 2010). Therefore, variability in seabird population size is often associated with reproductive output, because breeding adults are able to reallocate investment from reproductive effort to self-maintenance when conditions (e.g., weather, foraging) are poor

(Croxall and Rothbury 1991, Wooller et al. 1992, Erikstad et al. 1998, Cubaynes et al. 2010).

Conversely, slow reproductive rates and high breeding site fidelity of seabirds mean that their populations are very sensitive to even minor changes in adult survival; consistently low or highly variable survival rates are signs that a population is under stress and at a greater risk of rapid decline (Wooller et al. 1992). Proper management of long-lived seabirds should therefore prioritize ongoing monitoring of adult survival in conjunction with other population parameters such as reproductive parameters, and abundance.

Seabirds are currently more threatened worldwide than comparable groups of birds, with several species declining at increasing rates (Croxall et al. 2012). Reasons for these declines include an intricate combination of both regional and local-scale threats, largely brought on by human encroachment and increased industrial activities. At sea, major threats to seabirds include entanglement in fishing gear (i.e., bycatch, Weimerskirch and Jouventin 1987), reductions in prey availability (e.g., from competition with commercial fisheries (Wanless et al. 1998), and as

16 consequences of increased climate variability (Otterson et al. 2001, Durant et al. 2004), activities associated with oil and natural gas exploration (e.g., collisions with platforms and contact with discharged residues from chronic and catastrophic spills; Zabala et al. 2010, Ronconi et al.

2014), high concentrations of marine contaminants (e.g., mercury; Scheuhammer et al. 2007,

Fort et al. 2014), and ingestion of plastic debris (Provencher et al. 2014a, Fife et al. 2015). At breeding colonies, threats include loss of habitat, and predation pressure, especially from invasive species (Trathan et al. 2015, McChesney and Tershy 1998, Croxall et al. 2012). An understanding of how these and other threats relate to adult survival (e.g., Hovinen et al. 2014) is critical for assessing conservation needs of different seabird species.

Despite the urgency of acquiring annual survival estimates of seabirds given their conservation status, long-term data are often limited due to logistical constraints involved with monitoring highly pelagic birds that nest in remote locations (Gaston 2011). Leach’s Storm-

Petrel (Oceanodroma leucorhoa, hereafter ‘storm-petrel’) is a small (~ 45-g), long-lived seabird that nests in burrows on offshore islands. Birds from Atlantic colonies winter at sea off of eastern

South America and southwestern Africa (Huntington et al. 1996, Pollet et al. 2014a, 2015). It is the most abundant tubenose species (Order: Procellariiformes) breeding in the Northern

Hemisphere, with > 10 million breeding pairs, nearly a third of which are found within a single colony on Baccalieu Island, Newfoundland (Fig.2.1; Sklepkovych and Montevecchi 1989,

Huntington et al. 1996, Robertson et al. 2006a). Adults forage at sea, sometimes for several days, feeding on a variety of pelagic zooplankton, crustaceans, and small fish (Hedd and

Montevecchi 2006, Hedd et al. 2009, Pollet et al. 2014b, 2015). Recent tracking during the breeding season of storm-petrel adults from two colonies in Nova Scotia (Bon Portage Island and

Country Island) revealed that foraging storm-petrels travel as far as 2600 km in single foraging

17 trips of 5–7 days to access nutrient-rich waters beyond the continental shelf (Pollet et al. 2014b).

Thus, the vast migratory and foraging ranges covered by storm-petrels expose them, at varying spatial and temporal scales, to a variety of threats throughout the year.

Populations of storm-petrels from other colonies have recently shown steep declines

(Robertson et al. 2006a, Newson et al. 2008) and even complete collapses (e.g., at Seal Island,

Nova Scotia, Canada due to introductions of predators, including rats, dogs and cats; Huntington et al. 1996). Up until the early 2000s, large colonies of storm-petrels in the northwest Atlantic were relatively stable, whereas smaller colonies were in decline (Robertson et al. 2006a). Since

2000, however, the large colony at Great Island, Newfoundland has declined by nearly 55%

(Canadian Wildlife Service, unpublished data), possibly due to the high number of predatory

Great Black-Backed Gulls (Larus marinus) and Herring Gulls (L. argentatus) breeding on the island. Similarly, Great Skua (Stercorarius skua) predation has contributed to a 54% decline of a colony on St. Kilda, Scotland, the largest colony in the United Kingdom (Newson et al. 2008).

Preliminary analyses of a 2013 population census of the huge Baccalieu Island colony suggest that burrow densities were highest in fern and grass/herb habitats and were within the range of densities previously reported in the 1980s, whereas densities in forest habitat were much lower compared to earlier reports. However, further analyses (e.g., changes in densities relative to changes in habitat types) are required to determine the extent of these changes (Canadian

Wildlife Service, unpublished data). On Bon Portage Island, Nova Scotia, a population census in 2001 estimated that about 50,000 (95% CI: 37,541 to 58,944) pairs of storm-petrels bred on the island (D. Shutler, unpublished data; see Oxley 1999 for similar census methods). However, because of uncertainty related to large sampling error (± ~ 10,000), it is difficult to assess actual population size, or moreover, detect trends with successive censuses.

In light of declines at multiple colonies, the aim of this study was to estimate survival and recapture probabilities for a population of storm-petrels breeding on Bon Portage Island, from

2009 to 2014. Given apparent effects that gulls and other predators have had at other colonies, I compared survival rates between plots that were occupied by Herring Gulls and their chicks and those that were not. I predicted that storm-petrels nesting in plots with gulls would have lower survival or recapture probabilities than those nesting in plots without gulls.

Another factor that I considered was whether attachment of Very High Frequency (VHF) radio tags influenced survival and/or recapture rates. Nearly 10% (n = 83) of the storm-petrels used in this study received a VHF tag for a separate study to monitor storm-petrel activity around the colony (I. L. Pollet, unpublished data). Although VHF tags used on birds are generally small

(Burger and Schaffer 2008), they could affect return rates by impeding, for example, mobility

(Reynolds et al. 2004). Moreover, extended handling time required for attaching the devices could cause additional stress and deter adults from returning to their burrows. Because return rates of storm-petrels at this colony were not influenced by attachment of geolocators (Global

Location Sensing; GLS), which were heavier than VHF tags (I. L. Pollet, unpublished data,

Pollet et al. 2014a), I predicted that VHF tags would not have an effect on survival or recapture probabilities.

METHODS

Study site

Bon Portage Island (43° 28' N, 65° 44' W) lies ~ 3 km off the southwestern shore of Nova Scotia

(Fig. 2.1). The ~ 0.5- × 3-km island is dominated by white spruce (Picea glauca), black spruce

(P. mariana), and balsam fir (Abies balsamea). However, much of the softwood on the island

19 has died due to invasion by the eastern hemlock looper moth (Lambdina fiscellaria fiscellaria), and periodic fires, allowing space for various hardwood trees, as well as small shrubs (personal observation).

Storm-petrels were monitored in twelve 12- × 12-m quadrats, spaced ~ 10 m apart and situated primarily within forested areas. However, two of the plots are in more open habitat with only a few standing dead trees. These two plots have each year been occupied by ~3–4 pairs of

Herring Gulls and their chicks (I. L. Pollet, personal observation). Each plot contained an average of 29.1 (range: ~ 15–53) uniquely numbered marked burrows.

Capture-mark-recapture of adults

All procedures were approved by the Acadia University Animal Care Committee. Banding of adults began in 2009 and continued until 2014. However, early in the study, several adults were banded in burrows outside the current study area. These burrows were not monitored as routinely as those within the study area; thus to reduce bias in survival estimates, these burrows were not included in the present analysis. The sample size of birds within current plots from

2009 is therefore smaller than that of 2010 onwards.

From ~ June to September, burrows were monitored weekly (but no more than twice during incubation; Pollet et al. 2014b) for nesting adults, which were extracted from burrows by hand. Adults were caught opportunistically, or with one-way traps placed at burrow entrances.

At initial capture, adults were banded with a numbered Canadian Wildlife Service stainless steel leg band. From 2010 to 2013, a random subset of active breeders (n = 83) received VHF tags, which were attached to their backs with tape and glue. VHF tags weighed 0.29 g (< 0.7 % of average adult body mass) and measured 10 × 4 × 2 mm, with an external aerial measuring 180 ×

0.2 mm (I. L. Pollet, unpublished data). These birds were distinguished in the analysis from those that did not receive a VHF tag in any year. In 2012 and 2013, a smaller proportion of birds

(also active breeders; n = 13 in 2012 and n = 22 in 2013) received GLS, which, were affixed to their backs either with tape and glue, or with sutures. Including attachment material, GLS weighed 1.3 g (< 3% of the average mass of adult storm-petrels) and measured 21.9 × 7.9 × 3.8 mm (Pollet et al. 2014a). Chicks of storm-petrels with back-sutured GLS had lower growth rates compared to those of controls, but there was no difference in return rates, mass, or fledging success of storm-petrels with and without GLS (see Pollet et al. 2014a, b, 2015). Therefore I did not consider GLS effects, nor did I exclude birds with GLS from the analysis. Accurate sex and age data are difficult and expensive to obtain for storm-petrels and were not included in this study. However, I recognize that these factors, in addition to attachment of GLS, could have influenced survival (e.g., Robertson et al. 2006b, Sanz-Aguilar et al. 2009).

Survival analyses

Data were collected using traditional capture-mark-recapture (CMR) methods and analysed using

Program MARK (Lebreton et al. 1992, White and Burnham 1999). Apparent survival (ϕ) was estimated as the probability of surviving from one sampling occasion (i.e., year) to the next.

Estimates of ‘apparent’ survival are generally biased below ‘true’ survival because mortality and emigration are confounded without additional information on, for example, dead recoveries or recaptures from outside the study area (Kendall et al. 2013). Encounter probability (p) was estimated as the probability of being recaptured at each occasion (Lebreton et al. 1992).

Encounter histories displaying initial captures and recaptures for each bird were generated using the RMark (Laake 2014) package in R 3.2.1 (R Core Team 2014).

Storm-petrels were separated in the encounter history file into four groups based on whether or not they were nesting in plots with gulls and whether or not they received a VHF tag at any point in the study (i.e., if a bird received a VHF tag in any year, it treated in the analysis as having a VHF tag in all years). The global model used in the analysis was the Cormack-Jolly-

Seber model (CJS; Cormack 1964, Jolly 1965, Seber 1965), an open-population model where ϕ and p vary over time (Lebreton et al. 1992), but I included additional parameters for the gull and

VHF effects (ϕgulls*VHF*time, pgulls*VHF*time).

Model selection

Akaike’s Information Criterion adjusted for small sample size (AICc) was used to select the best model, where the lower the AICc value, the better-supported a particular model is. In this information-theoretic approach, models with a ΔAICc (i.e., the difference between the highest supported model and each of the other models) ≤ 2 are considered to have similar support within a candidate model set (Burnham and Anderson 2002). Due to complexity of the global model in relation to the limited data available for this study, the candidate model set did not include all possible reduced parameterizations of the global model (e.g., only simple interactive models were included). Models were first reduced according to support for p submodels, and then according to ϕ submodels. If multiple models were given similar support, model-averaging was used to produce annual estimates that were based on the full candidate set of models and weighted by the overall support for each model (note that the sum of AICc weights across the model set = 1; Burnham and Anderson 2002). Variance component analysis of the global model provided an average survival estimate, and an estimate of the amount of time-dependent process variation (i.e., the difference between the total variance and the sampling variance; σ2) in the data

(Burnham et al. 1987). Expectation of further life (± SE) was calculated using the equation

-1 ϕ = -ln (ϕ), based on the survival rate estimated from variance component analysis (i.e., ϕ adjusted to account for sampling error).

Goodness-of-fit

I tested for violations of CJS assumptions (Chapter 1, Lebreton et al. 1992, Pollock and Alpizar-

Jara 2005) in the global model using program RELEASE, a built-in component of MARK. The two primary tests implemented in RELEASE essentially look for evidence of heterogeneity in survival and recapture rates (TEST 3 and TEST 2, respectively) among individuals (e.g., due to transiency or trap effects; Pradel et al. 2005, Pradel et al. 1997). The variance inflation factor (ĉ) was estimated using the median ĉ method, derived from the global model (Cooch and White

2014). If an adjustment to ĉ was made, model selection was based on the Quasi-Akaike’s

Information Criterion (QAICc), which assigns further penalty for the number of parameters in a model (Anderson et al. 1994).

RESULTS

In total, 709 adults were banded from 2009 to 2014, with a total of 860 recaptures during that time. Table 2.1 is a reduced m-array showing when birds (separated into groups based on gull presence and VHF tags) were seen again for the first time after release. For example, of the 283 individuals (in total) released in 2010, 38% were seen again for the first time in 2011 and 54% were seen at least once in subsequent years. A small number of individuals banded in 2009 (n =

18 across all groups) explains the large confidence intervals surrounding ϕs for 2009–2010.

I did not find significant sources of heterogeneity in recapture and survival probabilities

2 considered together (TEST 2 TEST 3: χ 34 = 21.1, p = 0.96), or when recapture and survival

2 2 were considered separately (TEST 2: χ 11 = 13.3, p = 0.27, TEST 3: χ 23 = 7.8, p = > 0.99). In accordance with results from RELEASE, overdispersion was minimal (ĉ =1.11), but was adjusted in the model selection.

Survival analyses

Models with a VHF effect on recapture probability were well-supported (sum of QAIC weights =

0.89); therefore I modelled survival probability with recapture constrained only by the VHF variable (Table 2.2). The model with the lowest QAIC value (ϕVHF*time, pVHF) suggests that compared to storm-petrels that were not fitted with a VHF tag, those with a VHF tag had higher survival in the first three occasions, but lower survival in the last two occasions of the study.

However, this model estimated survival poorly (i.e., > 0.99) for birds with VHF tags in the first interval (2009–10). The model with the next lowest QAIC value (ϕgulls + VHF + time, pVHF) had similar support (ΔQAIC < 2) and suggests that there was a negative effect of gulls and a positive effect of VHF tags on survival over time (Table 2.2).

In general, there was high support for models containing a VHF effect on survival (sum of QAICc weights = 0.91 across the model set that included a VHF effect on ϕ), but because there was similar support for more than one model and some evidence of a gull effect on ϕ (sum of QAICc weights = 0.45 across models containing a gull effect on ϕ), I model-averaged estimates of both ϕ and p (Table 2.2, Fig. 2.2). Model-averaged survival probabilities were highest in all years for birds fitted with VHF tags, regardless of whether or not they were in plots with gulls. However, within both VHF subgroups (i.e., birds with and without VHF tags),

24 survival was reduced in birds nesting in plots with gulls (Fig. 2.2). Model-averaged recapture rates were higher for birds with compared to those without VHF tags (p = 0.68 ± 0.06, 95% CI:

0.49 to 0.61 and p = 0.55 ± 0.03, 85% CI: 0.55 to 0.78, respectively); however confidence intervals of these estimates overlapped.

I calculated model-averaged effect sizes, and their associated SEs, of both the gull and

VHF tag variables using models that contained single variable or additive effects only; models with interactions were excluded from this process to avoid confounding effects of multiple variables. Within the selected models, mean effect sizes and SEs for each variable were calculated and then weighted by respective QAIC weights (rescaled to one) and transformed to the probability scale. Based on this, presence of gulls reduced ϕ by 0.07 ± 0.04, although this result was not statistically significant (95% CI: -0.15 to 0.02), whereas attachment of VHF tags increased ϕ by 0.11 ± 0.05 (95% CI: 0.01 to 0.21). Return rates (± SE) for the final year were as follows: 0.44 ± 0.03 for birds without VHF tags and in plots without gulls, 0.36 ± 0.09 for birds with VHF tags and in plots without gulls, 0.38 ± 0.08 for birds without VHF and in plots with gulls, and 0.57 ± 0.20 for birds with VHF and in plots with gulls.

Mean survival estimated from variance component analysis was 0.79 ± 0.05 and expectation of further life was 4.24 ± 1.14 yr, based on this estimate. Variance-component analysis also indicated that there was significant time-dependent process variance (σ2 = 0.02,

95% CI: 0.01 to 0.08).

DISCUSSION

Mean annual survival of adult storm-petrels on Bon Portage Island was low compared to that of other tubenose species (typically survival for this group is > 0.90; Hamer et al. 2002), and even

25 to earlier estimates from another storm-petrel colony (Morse and Buchheister 1977, Table 2.3).

There was also evidence that survival rates varied annually. Survival of adult storm-petrels from a colony on Gull Island, Newfoundland also varied over time and was similarly low, on average

~ 0.80 from 2003 to 2013 (A. Hedd, unpublished data). Similarly, on Kent Island, New

Brunswick, mean survival was ~ 0.79 from 1953–1994 (Huntington et al. 1996). Seabirds are usually characterized as having high adult survival probabilities that should be quite resilient to environmental variability. Maintenance of high survival rates is possible, in part because they can reduce reproductive effort, or even skip breeding in unfavorable years to focus instead on self-preservation (Wooller et al. 1992, Cubaynes et al. 2010, Zabala et al. 2010). However, given their slow reproductive rates, even minor reductions in adult survival can have pronounced effects on seabird populations, which are slow to recover after episodes of high adult mortality

(Wooller et al. 1992). Moreover, typically high rates of breeding site fidelity (i.e., probability of returning to the same breeding location) among seabirds, (Schreiber and Burger 2002) mean that they are unlikely to relinquish breeding sites even if conditions are poor in multiple years.

Therefore, losses to the population accrued as consequences of reduced survival and/or skipped breeding are likely to persist with ongoing environmental perturbations (see Gaston 2011 and references therein).

Something to be cognisant of in survival and other CMR studies is investigator disturbance, which can influence reproductive success and potentially lead to nest abandonment

(Blackmer et al. 2004, Carey 2009, but see O’Dwyer 2006, Fiske et al. 2013). Burrows on Bon

Portage Island were visited only twice during incubation, which should not lead to abandonment

(Blackmer et al. 2004). Once chicks have hatched, burrows were monitored ~ weekly, to quantify chick growth rates and other parameters associated with breeding success. During this

26 time, adults are rarely present in burrows during the day; thus disturbance of adults was minimal

(I. L. Pollet, personal observation). In cases where certain adults had yet to be recaptured in a particular breeding season, one-way traps were placed at their burrow entrances, which may have influenced their willingness to return to those burrows in subsequent years. As a rough gauge of the effects of repeated handling of storm-petrels on Bon Portage Island, a burrow-switching rate of ~ 8.1% per year was calculated and almost all moves were within < 1 m of the old burrow (I.

L. Pollet, unpublished data). I cannot completely exclude the possibility that birds simply emigrated to another location, either outside of the study plots on Bon Portage Island or even to other islands. However, reported breeding site fidelity of storm-petrels is high (0.92–0.96;

Morse and Buchheister 1979, Huntington et al. 1996); thus bias in survival rates due to emigration should be minimal.

Effects of VHF tags on survival

The burden of carrying tracking devices can be energetically costly, even if they are small compared to the size of a bird. For example, corticosterone levels were elevated and tail growth reduced in Thin-Billed Prions (Pachyptila belcheri) fitted with GLS that were < 1% of their body mass (Quillfeldt et al. 2012). These sorts of responses can, in turn, have consequences for survival (Reynolds et al. 2004). Surprisingly, I found that storm-petrels with VHF tags had ~

10% higher survival than those without tags. This might be partially due to an influence of VHF tags on recapture rates. There appears to be strong evidence that much of the variation in recapture rates was explained by VHF effects; birds with tags may have been monitored more frequently with the use of telemetry and thus had higher recapture rates than those without tags.

Any improvements to recapture rates tend to yield more precise and reliable survival estimates

27 and, in fact, telemetry is sometimes used in CMR studies to increase detectability of animals

(Murray 2006); however, both the short duration of this study and the small sample size for storm-petrels with VHF tags (and for all storm-petrels in 2009) may have produced biased (i.e., unrealistically high), and poorly estimated survival rates for storm-petrels with VHF tags (e.g.,

Naef-Daenzer and Grüebler 2014). Hence, it is unclear whether survival was indeed higher because of better recapture rates for storm-petrels with VHF tags or as a consequence of limited data.

Storm-petrels were selected for VHF attachment to monitor reproductive-related activity around the colony. Therefore, it is also plausible that these birds had higher survival because they were established, successful breeders, whereas the rest of the sample would have included a mix of established breeders, recent recruits, and other adults. The latter would be a better representation of the general population; thus inferences regarding temporal variability and influence of covariates should be drawn from the majority of birds, which were not tagged.

Regardless, it seems unlikely that attachment of tracking devices explains the low survival on

Bon Portage Island, especially given that other colonies appear to have similarly low survival rates.

I did not investigate effects of GLS in the current analysis because it would have required additional statistical power to be able to include another variable in the models. Moreover, GLS were deployed in later years of the study (n = 13 in 2012 and 22 in 2013) and thus would not have contributed to the particularly low survival estimates during 2010–2011 and 2011–2012.

Nevertheless, although GLS did not affect return rates of birds (Pollet et al. 2014a, 2015), effects of GLS on survival is something that should be explored further in subsequent analyses.

Effects of gulls on survival

High predation rates could account for low survival at certain colonies where abundance of avian and/or mammalian predators is high (e.g., Stenhouse and Montevecchi 1999, Miles 2010). By nesting on offshore islands, storm-petrels can avoid mammalian predators to some extent, although both wild and domestic animals introduced to islands have had devastating impacts

(McChesney and Tershy 1998, de León et al. 2006, Pollard 2008). Avian predators are not excluded from offshore islands, however, and often nest near or in storm-petrel colonies. At St.

Kilda Island, Scotland, the storm-petrel population has declined by 54% in less than 10 years, with growing populations of Great Skuas that kill ~ 21,000 adult storm-petrels per year (Newson et al. 2008, Miles 2010). With such a high number of adults being killed, the colony should be declining at a faster rate, but it is possible that it is being supplied with new breeders from larger colonies (e.g., from Iceland; Bicknell et al. 2012, Bicknell 2013a).

Similar increases in predation of storm-petrels by gulls have been reported as well

(Stenhouse and Montevecchi 1999). In the 1970s and 1980s most gull populations increased in response to high food availability (e.g., human refuse and forage fish stock; Cotter et al. 2012).

However, in the last couple of decades there have been significant reforms to handling of fisheries discards, and closures or changes to landfills (Cotter et al. 2012). This, in addition to moratoria on several major fish stocks in the North Atlantic, has led to shifts in gull (and skua;

Bicknell et al. 2013b) diets, with increased predation of storm-petrels and other small seabirds

(Stenhouse and Montevecchi 1999). On Great Island, in Newfoundland, the storm-petrel population remained relatively stable during the 1990s, despite an average of ~ 49,000 storm- petrels killed per year by Great Black-Backed and Herring Gulls (Stenhouse et al. 2000,

Robertson et al. 2006a). However, the population has since plummeted by ~ 55% (Canadian

Wildlife Service, unpublished data). Predation pressure on Great Island has apparently become too high to sustain the breeding population of storm-petrels, even with possible recruitment of new breeders from nearby Baccalieu Island. Interestingly, Baccalieu Island does not have a population of breeding gulls, nor does it suffer from introduced or invasive mammalian predators, factors which have possibly contributed to the large size and apparent stability of the storm-petrel population on the island.

Despite the small sample size and short duration of the study on Bon Portage Island, an effect of gull presence was indicated. Model-averaged effect size of gulls suggests that presence of gulls reduced survival of storm-petrels by 7%. Although confidence limits of this effect did include zero, a 7% decline in survival would have biologically significant consequences for a population (e.g., reduce breeding lifespan; Wooller et al. 1992). I refer to an effect of gull

‘presence’ rather than gull ‘predation’ because it is not clear whether this reduction in survival would be due to direct mortality from gulls, or from storm-petrels abandoning the study site.

Some Great-Horned Owl (Bubo virginianus) pairs also breed on Bon Portage Island and examination of their pellets revealed their diet consists almost exclusively of storm-petrels (I. L.

Pollet, personal observation). Whether they pose a significant threat to the storm-petrels there remains unknown. Once again, additional years of data are needed to properly investigate influences of gulls and other predators on storm-petrel survival. It would be useful for future studies to include quantities of different species suspected to be depredating storm-petrels at this colony, as well as estimations of the number of storm-petrels killed annually by each predator species.

Alternatively, survival might be lower in plots occupied by gulls because the habitat of those plots is noticeably more open than the other plots, perhaps making it less suitable for

30 nesting. Storm-petrels prefer to burrow in forested, as opposed to more exposed habitats, in part because soil may be less compact, and thus ideal for digging burrows, in forested areas

(Stenhouse and Montevecchi 2000). Moreover, maintenance of proper humidity levels required for chick development may be easier to accomplish in habitats with better cover (Ricklefs et al.

1980, Huntington et al. 1996).

Other factors affecting survival

At a regional scale, the large distance between breeding and non-breeding habitats (Pollet et al.

2014a, 2015) and highly pelagic behaviour (Pollet et al. 2014b) means that survival of storm- petrels is most-likely influenced by a combination of factors, which are challenging to tease apart. Aside from predation, some of the main threats to storm-petrels are high levels of marine contaminants (e.g., mercury; Goodale et al. 2008, Bond and Diamond 2009), collision with oil and natural gas platforms (Ronconi et al. 2014), ingestion of plastic debris (Bond and Lavers

2013), and adverse climatic conditions (Boersma and Groom 1993, Soldatini et al. 2014).

Moreover, recent tracking data during the breeding season showed that storm-petrels from two nearby colonies (Bon Portage Island, and Country Island, Nova Scotia: 45° 06' N, 61° 32' W) forage in very different areas of the northwest Atlantic and generally ingest different prey types

(Pollet et al. 2014b). Therefore, factors affecting survival of storm-petrels might be manifested in different ways even at breeding colonies that are relatively close to each other. When looking at survival estimates for only storm-petrels in the ‘no gull, no VHF’ group (Fig. 2.2) survival is quite low, even without the effect of gull predation. Thus, there are clearly other processes linked to their survival that have not been addressed by this study. Future survival analyses of

31 storm-petrels from Bon Portage Island and other colonies should examine environmental variables, such as climate variability and food availability, as influences on survival.

Conclusions

While it is clear that more years of data are needed to refine survival and recapture estimates for storm-petrels on Bon Portage Island, there are three main points that can be taken from this study: 1) that VHF tags did not appear to have a negative effect on survival; 2) that losses due to avian predators might be widespread across several breeding colonies, and 3) that annual survival estimates were low, even after accounting for VHF tags and effects of nesting gulls.

This study contributes to evidence that populations of storm-petrels could potentially be facing global declines. Consistently low survival over the last few years is cause for concern and warrants further attention. Storm-petrels are a widespread, yet cryptic, species and dramatic population declines could go unnoticed unless they are routinely monitored throughout their range (Lormee et al. 2012). Therefore, concerted efforts by researchers working at different colonies may be critical for determining factors associated with storm-petrel survival and identifying ways to mitigate their overall impact on the health of storm-petrel populations.

Table 2.1. Reduced m-array showing when and how many adult Leach’s Storm-Petrels were recaptured for the first time after initial release. Note ‘# released’ at a particular occasion (‘Year released’) includes storm-petrels newly banded and those that were previously banded and recaptured at that occasion. Storm-petrels separated into those nesting in plots with (‘gulls’; 10 plots) and without (‘no gulls’; 2 plots) Herring Gulls and those with (‘VHF’) and without (‘no

VHF’) VHF tags (sample size of storm-petrels at initial capture in parentheses). Numbers of storm-petrels from each group were pooled for the initial analysis.

Year released # released # recaptured for first time after release

2010 2011 2012 2013 2014 Total no gulls, no VHF (n = 515)

2009 11 6 2 0 0 0 8

2010 188 63 20 6 7 96

2011 145 50 16 7 73

2012 150 75 16 91

2013 259 114 114

no gulls, VHF (n = 69)

2009 1 1 0 0 0 0 1

2010 41 24 6 1 0 31

2011 39 22 6 0 28

2012 35 20 4 24

2013 33 12 12

33 gulls, no VHF (n = 111)

2009 5 2 0 0 0 0 2

2010 46 15 1 1 2 19

2011 38 12 4 0 16

2012 23 8 4 12

2013 42 16 16

gulls, VHF (n= 14)

2009 1 0 0 1 0 0 1

2010 8 6 1 0 0 7

2011 7 3 1 1 5

2012 5 2 0 2

2013 7 4 4

Table 2.2. Model selection results testing for influence of gull presence and VHF tags on apparent survival (ϕ) and encounter (p) probabilities of adult Leach’s Storm-Petrels on Bon

Portage Island. Only models with QAICc weights ≥ 0.01 are included in this table. Note that two out of 12 plots were occupied by 3–4 pairs of Herring Gulls and their chicks.

QAICc Model ΔQAICc Likelihood K Deviance weights

ϕVHF*time, pVHF 0.00 0. 35 1.00 12 115.75

ϕgulls + VHF + time, pVHF 1.27 0.18 0.53 9 123.14

ϕgulls + VHF, pVHF 2.14 0.12 0.34 5 132.12

ϕVHF + time, pVHF 2.30 0.11 0.32 8 126.20

ϕgulls*VHF, pVHF 2.59 0.09 0.27 6 130.55

ϕVHF, pVHF 3.54 0.06 0.17 4 135.54

ϕgulls, pVHF 4.79 0.03 0.09 4 136.80

ϕgulls + time, pVHF 5.41 0.02 0.07 8 129.32

ϕ, pVHF 6.07 0.02 0.05 3 140.09

ϕtime, pVHF 6.38 0.01 0.04 7 132.32

Table 2.3. Apparent survival (ϕ) estimates for Leach's Storm-Petrels from other colonies, and for other tubenose species. Metrics of

variability are given if reported in the literature.

Species Location Years ϕ Reference

Northern Fulmar (Fulmarus glacialis) Semidi Island, Alaska 1976–1981 0.97 Hatch (1987)

Southern Fulmar (F. glacialoides) Adélie Land, Antarctica 1964–2002 0.92 ± 0.01 Jenouvrier et al. (2003)

Cape Petrel (Daption capense) Signy Island, South 1947–1961 0.94–0.95 Hudson (1966)

Orkney Islands

Snow Petrel ( Pagodroma nivea) Adélie Land, Antarctica 1967–1988 0.93 ± 0.03 Chastel et al. (1993) 36

Black Petrel (Procellaria parkinsoni) Aotea Island, New 1964–2014 0.94 ± 0.02 Bell et al. (unpublished report)

Zealand SE

Blue Petrel (Halobaena caerulea) Kerguelen Islands 1986–2002 0.87 ± 0.01 Connan et al. (2008)

Bulwer’s Petrel (Bulweria bulwerii) Great Salvage Island, 1984–1987 0.95 ± 0.47 Mougin (1989) in Megysi and

Portugal SD O’Daniel (1997)

Hawaiian Petrel Haleakala National Park, 1979–1981 0.93 Simons (1984) in Simons and

(Pterodroma sandwichensis) Maui Hodges (1998)

Sooty Shearwater (Puffinus griseus) South Island, New Zealand 1992–2005 0.92 Clucas et al. (2008)

European Storm-Petrel (Hydrobates Aketx Islet, Gulf of Biscay 1990–2006 0.82–0.90 Zabala et al. (2010)

pelagicus)

Leach’s Storm-Petrel (Oceanodroma Matinicus Rock, Maine 1963–1970 0.94* Morse and Buchheister (1977)

leucorhoa)

Gull Island, 2003–2013 ~ 0.80 A. Hedd (unpublished data)

Newfoundland 37

Bon Portage Island, Nova 2009–2014 0.79 This study

Scotia

Wilson’s Storm-Petrel (Oceanites Signey Island, Antarctica 1959–1962 0.91 Beck and Brown (1972)

oceanicus)

*Corrected from 0.79 for incomplete censusing methods (Huntington et al. 1996).

Figure 2.1. Global breeding and marine (non-breeding and foraging) distributions of Leach's

Storm-Petrels. Inset map shows location of the study site, Bon Portage Island, Nova Scotia

Canada (map adapted from BirdLife International 2015).

Figure 2.2. Model-averaged annual survival estimates (±SE) from 2009 to 2013 for adult

Leach’s Storm-Petrels nesting in plots with or without Herring Gulls and with or without VHF tags. Note that the particularly large SEs in 2009–10 are due to small sample sizes of storm- petrels banded in 2009.

Chapter 3 – Anomalous climate conditions reduce survival of an Arctic trans- equatorial migrant seabird

ABSTRACT

Extreme shifts in climate can negatively affect adult survival directly (through physiological demands) and indirectly (through changes in prey availability), which can have significant consequences for populations of seabirds and other long-lived species with slow reproductive rates. Trans-equatorial migrants such as Sabine’s Gull (Xema sabini) are exposed to a variety of climate regimes throughout their travels and thus broad-scale climate indices, such as the North

Atlantic Oscillation and Southern Oscillation Index, may explain some variability in their annual survival probabilities. Program MARK was used to estimate apparent survival and resighting probabilities from 2007–2013 for adult Sabine’s Gulls breeding at a high Arctic colony.

Additionally, influences of climate variability on Sabine’s Gull survival were examined by incorporating three climate indices in an analysis using data for the high Arctic colony combined with those collected from a low Arctic colony from 1998–2002. Mean ± SE apparent survival estimated for Sabine’s Gulls at the high Arctic colony was constant at 0.90 ± 0.03 and was similar to that previously reported for the low Arctic colony. From the combined analysis, I found a negative relationship between survival and the Tropical/Northern Hemisphere pattern, an atmospheric mode that modulates the extent and location of the Pacific jet stream. Two key findings were highlighted: 1) Sabine’s Gull survival was generally high and constant over time, but adults may not be able to maintain high rates during years when climate is extreme; and 2)

Arctic seabirds may be influenced by weather systems that are thousands of kilometers away from their breeding grounds. Results from this study raise concerns regarding the persistence of

Sabine’s Gull and other Arctic seabirds in the face of increasing climate instability and severity.

INTRODUCTION

Overwhelming evidence is accumulating of negative effects of increasing climate variability on various aspects of seabird ecology (Durant et al. 2004, Grosbois et al. 2008, Dorresteijn et al.

2012, Sydeman et al. 2012, Jenouvrier 2013). For many seabirds breeding in the Arctic, foraging efficiency and timing of reproduction are heavily dependent on optimal breeding conditions

(e.g., sea ice dynamics) within short breeding windows (Gaston 2011). Thus, these species are particularly vulnerable to consequences of changing climate, such as increasing sea surface temperatures, and greater severity and unpredictability of environmental conditions in this region

(Walsh et al. 2011, Gilg et al. 2012). Effects of increased climate variability may be exacerbated by other stressors affecting Arctic seabirds, such as increased shipping activity (Humphries and

Huettmann 2014), increased levels of marine contaminants (Fort et al. 2014, Provencher et al.

2014b), and commercial fisheries in Arctic waters (Gaston 2011, Croxall et al. 2012).

Consequently, studies of Arctic seabird demographics are important to assess the extent to which environmental and anthropogenic stressors affect populations.

In addition to relying on optimal conditions during breeding seasons, Arctic seabirds that undergo trans-equatorial migrations are subject to a variety of climate regimes over the thousands of kilometers travelled to and from their wintering areas in the Southern Hemisphere

(e.g., Egevang et al. 2010, Stenhouse et al. 2012, Gilg et al. 2013). Therefore, although these species avoid harsh Arctic winters (Gilg et al. 2012), they are exposed to a variety of local and broad-scale climatic conditions that are dependent on both season and region (Grosbois et al.

2008). Consequently, studies examining influences of climate on ecological processes often

require indices that capture an array of climate variables (Ottersen et al. 2001, Stenseth et al.

2003, Grosbois et al. 2008).

Indices of atmospheric patterns, known as teleconnections, are typically used as proxies of broad-scale climate variability (Stenseth et al. 2003, Grosbois et al. 2008). Teleconnections are essentially fluctuations in atmospheric circulation, which are linked among distant geographic regions and modulate opposing anomalous climate conditions in separate parts of the globe. For example, warmer, wetter conditions in one region may correspond to cooler, drier conditions in another (Stenseth et al. 2003). Some well-documented indices include the North

Atlantic Oscillation (NAO), and Southern Oscillation Index (SOI), which have been associated with climate variability in the North Atlantic and southern Pacific Oceans, respectively

(Trenberth 1984, Hurrell 1995, Stenseth et al. 2003).

Both NAO and SOI have been linked to various aspects of seabird ecology, such as reproductive phenology and success (Lynch et al. 2012, Sandvik et al. 2012), and recruitment

(Crespin et al. 2006). Annual survival of adult seabirds is thought to be robust to temporal environmental variability, especially for Arctic species that are adapted to highly seasonal conditions (e.g., Julien et al. 2013). This is largely because of their typically long life spans that enable them to reduce investment in, for example, reproductive effort in the interest of self- maintenance when conditions are poor (Wooller et al. 1992, Cubaynes et al. 2010, Zabala et al.

2010). However, growing evidence suggests that climate also affects adult survival of seabirds, either as a direct result of extreme weather (Frederiksen et al. 2008, Mallory et al. 2009), or, more likely, as an indirect result of changes in abundance and distribution of prey (Grosbois and

Thompson 2005, Sandvik et al. 2005, Hovinen et al. 2014). For example, during El Niño years

(associated with negative SOI), low atmospheric pressure in the eastern Pacific reduces the

southeast trade winds in that region. The nutrient-rich, cold water upwelling of the Humboldt

Current off of Peru diminishes or ceases completely and is replaced by warm, southward flowing waters. This results in poor phytoplankton production and subsequent poor productivity higher in the food web in top predators such as seabirds (Barber and Chavez 1983). Opposite conditions typically prevail during La Niña years, with a highly productive upwelling and good feeding conditions (Boersma 1998). Variability in foraging conditions would be reflected in body condition and thus survival rates of seabirds foraging in affected areas (Harding et al.

2011).

Sabine’s Gull (Xema sabini) is a small (~200-g), Holarctic gull that breeds along the

Arctic coasts of North America, Greenland, Spitsbergen, and Siberia, primarily on small islands and in wetland areas (Day et al. 2001). It is the only Arctic-breeding gull to undergo trans- equatorial migration, travelling more than 28,000 km (Stenhouse et al. 2012, Davis et al. 2015) to highly productive upwelling zones off the coasts of Peru and southern Africa, where they spend the winter. Their long-distance migratory behaviour makes Sabine’s Gull a particularly interesting, yet challenging, species on which to study effects of climate variability because of the broad range of climate regimes they encounter during the year.

Limited census data for Sabine’s Gulls suggest that their numbers are stable or increasing in parts of their breeding range (e.g., Stenhouse 2003 in Johnston and Pepper 2009, Mallory et al.

2012), but declining in others (Raven and Dickson 2006, Johnston and Pepper 2009). For example >50% declines in Sabine’s Gull densities were observed for colonies on Prince Charles

Island, Nunavut, and Victoria Island, Northwest Territories over ~10-year periods (Gaston et al.

1986, Raven and Dickson 2006, Johnston and Pepper 2009). High breeding site fidelity of

Sabine’s Gulls (Stenhouse and Robertson 2005) suggests that these observations did not result

from dispersal to different breeding locations, but rather that colonies experienced true population declines. To date, the only survival estimate reported for Sabine’s Gulls is from a low Arctic colony at East Bay Migratory Bird Sanctuary (Southampton Island, Nunavut) and was constant and relatively high at 0.89 ± 0.02 from 1998—2002 (Stenhouse and Robertson 2005).

However, the short duration of that study precluded the ability to detect temporal variability in survival, or assess influences of environmental variables.

To further our understanding of Sabine’s Gull population dynamics, apparent survival and resighting probabilities were estimated for a high Arctic colony of Sabine’s Gulls, and compared to the low Arctic colony at East Bay. Furthermore, data for both colonies were combined to test for an influence of climate variability on apparent survival of adult Sabine’s

Gulls breeding in the Canadian Arctic. This was accomplished using three broad-scale climate indices (Table 3.1) as proxies for climate variability. Slow reproductive rates and generally low breeding dispersal of long-lived seabirds mean that even minor changes in adult survival due to extreme or changing climatic conditions could have significant consequences for their populations (Wooller et al. 1992, Frederiksen et al. 2008, Gilg et al. 2012). Thus, reduced or highly variable adult survival may indicate that a population is under stress and should be identified as an immediate conservation concern.

METHODS

Study Site

Part of this study was carried out on Nasaruvaalik Island (75° 49’ N, 96° 18’ W; Fig. 3.1A), one of several islands found within Queen’s Channel, Nunavut. This area is considered important breeding and foraging habitat for many marine birds, largely due to the occurrence of polynyas,

which are areas of persistent open water surrounded by sea ice (Mallory and Gilchrist 2003).

The sparse landscape of the 3- ×1-km gravel island consists of old beach ridges and scattered patches of purple saxifrage (Saxifraga oppositifolia), moss, and lichen (Mallory et al. 2012).

Sabine’s Gulls (16–31 pairs, over eight years of monitoring) nest within Arctic Tern (Sterna paradisaea) colonies (~ 600 pairs in total) at either end of the island (Mallory and Gilchrist

2003, Mallory et al. 2012, Davis 2015).

To obtain a longer time series, Nasaruvaalik Island data were combined with previously published data from a colony at East Bay Migratory Bird Sanctuary (64° 01’ N, 81° 47’ W; Fig.

3.1B), Southampton Island, Nunavut. Southampton Island is much larger than Nasaruvaalik

Island and can essentially be considered a mainland site, of which the East Bay sanctuary covers approximately 1200 km2. This area is located within the low Arctic; however, it is influenced by deep, cold waters of the Foxe Channel and shows many physical and ecological similarities to the high Arctic (e.g., sea ice cover persisting into July, and near-freezing temperatures throughout the summer; Stenhouse et al. 2001). The study site is situated in low-lying, wetland tundra that contains several brackish and freshwater ponds and is more densely vegetated than

Nasaruvaalik Island. Nesting Sabine’s Gulls are much more dispersed on this island and may be found among or separate from Arctic Terns (for a more detailed description of the East Bay study, see Stenhouse and Robertson 2005).

Capturing and Resighting Adults

Banding of Sabine’s Gull adults on Nasaruvaalik Island began in 2007, with additional adults banded in each successive year until 2012. No new adults were banded in 2013 due to breeding failure at the colony (see Discussion). Adults were captured during incubation (starting about

late June to early July for ~20–23 days) using a modified bow-net trap (Salyer 1962). Traps were set daily for 1–2 h until both members of a nesting pair were captured and banded with a numbered US Fish and Wildlife Service aluminum band, as well as a unique combination of

Darvic color bands (typically three) on their tarsi for easy identification from a distance.

Subsequent resightings of adults were obtained each year until 2013. Resightings were recorded daily by two or three observers from about mid-June to mid-August during the rest of the breeding season in the year a gull was captured, and in subsequent years, either from viewing blinds (~150 m away from the colony), or by searching for nests on foot. Typically 4–5 hours were spent each day (weather permitting) scanning Sabine’s Gulls around the island to determine their unique color band combination and thus confirm resightings.

Similar methods were employed at East Bay from 1998–2002. After 2002, however, banding and resighting effort at East Bay has been intermittent (< 10 days per season), and following a series of preliminary analyses, data after 2002 were considered too sparse to be used in this study. Therefore, the analysis takes into account the gap (2003–2006) from the end of the

Stenhouse and Robertson (2005) study, to the beginning of the Nasaruvaalik Island study.

Survival Analyses

Apparent survival (ϕ), i.e., true survival confounded by permanent emigration (White and

Burnham 1999), and encounter (p) probabilities were estimated using traditional capture-mark- recapture (CMR) methods in Program MARK (hereafter ‘MARK’; White and Burnham 1999).

Encounter histories displaying initial captures and resightings for each bird were generated using the RMark (Laake 2014) package in R 3.2.1 (R Core Team 2014). To account for the time gap between studies in the combined analysis, all years outside the study period for each colony were

treated as ‘zero’ within the encounter history for each individual (i.e., every individual received a

‘zero’ each year from 2003–2013 for East Bay, and from 1998–2006 for Nasaruvaalik Island).

All unobserved island-year combinations were assigned to a single dummy ϕ parameter, with the associated p fixed to zero. The dummy ϕ and respective fixed p were not counted as estimable parameters during model selection.

The global model used for Nasaruvaalik Island, as well as the previous East Bay study, was the Cormack-Jolly-Seber model (CJS; Cormack 1964, Jolly 1965, Seber 1965), an open- population model, where apparent survival and recapture probabilities vary over time (Lebreton et al. 1992). A preliminary analysis of the Nasaruvaalik Island data was conducted to determine the underlying model structure of encounter probability to be used in the combined analysis, whereas model structure of resighting probability for East Bay was retained from the best model selected in Stenhouse and Robertson (2005). Once the best resighting parameterization was determined, it was denoted as pstudy for this analysis.

For analysis of both sites combined, the starting model included the interactive effect of time and colony on survival (i.e., ϕcolony*time), a model for which the amount of time-dependent process variation (σ2) in survival probabilities for each colony using variance component analysis can be determined (Burnham et al. 1987). Expectation of further life was calculated

-1 using the equation Lϕ = -ln (ϕ), based on the constant survival probability estimated from variance component analysis (i.e., ϕ adjusted to account for sampling error).

An information-theoretic approach to model selection was employed, using Akaike’s

Information Criterion adjusted for small sample size (AICc), to choose the best overall model.

Models with a ΔAICc value ≥ 2 are considered to have little to no support within a candidate model set (Burnham and Anderson 2002).

Effect of Climate Variability on Survival

Effect of climate variability on survival was assessed by incorporating broad-scale climate indices as covariates in the models. A comprehensive list of indices can be found through the

National Oceanographic and Atmospheric Administration (NOAA; available at http://www.esrl.noaa.gov/psd/data/climateindices/list/). Several climate indices represent global atmospheric patterns, and selecting those that may be relevant to the biology of particular species of interest can be challenging, particularly for those that migrate long distances (Grosbois et al.

2008).

Recent geolocator (Global Location Sensing; GLS) data from adult Sabine’s Gulls tagged at Nasaruvaalik Island show that the majority of the gulls (n = 25) migrated west towards Alaska, and then followed the Pacific coast south to wintering grounds off of Peru (Davis 2015).

However, two tagged gulls nesting sympatrically with the other tagged gulls crossed the Atlantic

Ocean and wintered off of South Africa, similar to gulls from Greenland (Stenhouse et al. 2012,

Davis 2015). Tracking data have not yet been obtained for Sabine’s Gulls at East Bay, but it is possible they follow the same route as Nasaruvaalik Island gulls (M. L. Mallory, personal communication). Considering their more eastern location, however, they could potentially migrate to Atlantic waters as well. It should be noted that I did not examine effects of GLS attachment on survival of birds in this study. This was primarily because data were too sparse to separate birds into groups and would therefore produce poor estimates of survival and resighting probabilities. However, I recognize that GLS may have influenced either parameter (Quillfeldt et al. 2012, but see Discussion).

Climate indices were selected based primarily on where Sabine’s Gulls overwinter and their general route taken during migration (Fig. 3.1B), the latter of which is often a time of significant stress and high mortality (Newton 2007). Because multi-collinearity among covariates can interfere with parameter estimation and model selection results (Grosbois et al.

2008), Pearson correlations were evaluated to further reduce the number of indices considered by excluding indices which were strongly correlated (arbitrarily r > 0.35) to multiple other indices in the set (Table 3.2). Ultimately, given the large non-breeding distribution of Sabine’s Gulls spanning two ocean basins, three indices were tested (details in Table 3.1): the principal component-based North Atlantic Oscillation (NAO; Fig. 3.2A), Tropical/Northern Hemisphere pattern (TNH; Fig. 3.2B), and Southern Oscillation Index (SOI; Fig. 3.2C). For each index, winter values were used (Dec–Mar for all except TNH, for which only Dec–Feb data were available). Results are presented ± SE unless otherwise indicated.

Goodness-of-fit

Violations of CJS assumptions (Lebreton et al. 1992, Nichols 2005) were based on the global model by using program RELEASE, a built-in component of MARK. TEST 2 and TEST 3, the two primary tests in RELEASE, look for evidence of heterogeneity among individuals (e.g., due to transiency or trap effects; Pradel et al. 2005, Pradel et al. 1997) in recapture and survival probabilities, respectively. The variance inflation factor (ĉ) was based on the global model and estimated using either the median ĉ method or with the parametric bootstrap, using 1000 simulations and taking the ratio of observed to expected deviance (Anderson et al. 1994, White et al. 2001). If an adjustment to ĉ was made, model selection was based on Quasi-Akaike

Information Criterion (QAICc), which assigns further penalties for the number of parameters in a model (Anderson et al. 1994).

RESULTS

Goodness-of-fit

There was no major heterogeneity found among individuals in recapture or survival probabilities

2 for Nasaruvaalik Island (TEST 2 + TEST 3: χ 12 = 20.1, P = 0.06). However, upon examination of individual test components, there was potential heterogeneity in the timing of bird sightings in subsequent years as a function of when they were first captured; i.e., resighting intervals may

2 have been longer for some birds due to trap effects (TEST 2: χ 3 = 10.8, P = 0.01) (Lebreton et al. 1992). This effect was entirely driven by a single cell in the final year of the study, so possible trap effects do not appear to have been pervasive throughout the study. In Stenhouse

2 and Robertson (2005), results from RELEASE (TEST 2 + TEST 3: χ 5 < 0.1, P > 0.99) and an estimated ĉ of 0.49 using the bootstrap method both indicated underdispersion (i.e., less variation than expected based on the global model) of the East Bay data. Because the median ĉ method did not perform well for the combined-site analysis, the bootstrap method was used and model selection criteria and parameter estimates were adjusted by a variance inflation factor (ĉ = 1.34) calculated from the global model ϕcolony*time, pstudy.

Apparent Survival on Nasaruvaalik Island

Eighty-four adult Sabine’s Gulls were banded on Nasaruvaalik Island from 2007–2012, with 188 resightings until 2013. Table 3.3 is a reduced m-array showing when birds were seen again for the first time after release (a reduced m-array is also presented for East Bay in Table 3.4).

The null model (ϕ, p) was best-supported for Nasaruvaalik Island, suggesting annual survival probabilities of 0.90 ± 0.03 (95% CI: 0.83 to 0.95) and resighting probabilities of 0.85 ±

0.04 (95% CI: 0.76 to 0.91) that were constant over time (Table 3.5). This model was ~5 times better-supported (based on QAICc weights) than the model with time-dependent survival, indicating that either there was little variability in gull survival from 2007–2013, or that variability was not detected due to the relatively small sample size and few years of data.

Combined Analysis and Effect of Climate Variability

In the combined analysis, a colony effect on survival was not detected (ΔQAICc > 2; Table 3.5).

Variance component analysis of apparent survival estimates from the global model (ϕcolony*time,

2 pstudy) indicated that process variance (σ = 0.001, 95% CI: -0.001 to 0.015) accounted for only a small proportion of the total annual variance (process + sampling variance) in the data. Taking into account sampling error, mean annual survival for both colonies was 0.92 ± 0.02. Based on this estimate, expectation of future life, Lϕ, was 12.0 ± 3.6 yr.

Of the three climate indices tested, the model with TNH had the highest support and was the best model overall, with ~17.5 times more support than the next highest model (Table 3.5).

This model describes a negative relationship between TNH and survival (β = -1.08 ± 0.39, 95%

CI: -1.84 to -0.32), implying that annual survival could be influenced by variability in climate, with strong, positive TNH years corresponding to lower survival probability (Fig. 3.3).

DISCUSSION

Apparent Survival on Nasaruvaalik Island

This study presents the first estimate of apparent survival for a high Arctic colony of breeding

Sabine’s Gulls; however, there was no evidence to suggest that there was a difference in survival between the high Arctic colony on Nasaruvaalik Island and the low Arctic colony at East Bay

(see Stenhouse and Robertson 2005). There are no published estimates of survival or recapture reported for Sabine’s Gulls elsewhere in their range. Ecological studies of Arctic species are difficult to undertake due to the logistical constraints of working in remote locations. Only in the last couple of decades have we been able to acquire data of sufficient duration to estimate trends in population demographics for species breeding in this region (Gaston et al. 2009a, Gaston

2011).

A constant annual survival of ~90% is suitable for maintaining a healthy gull population, and is even in the upper range of estimates for similar-sized seabirds, such as other gulls and terns (Table 3.6). High and steady adult survival probabilities are characteristic of long-lived seabirds, which are able to reduce investment in other aspects of their life history (e.g., reproductive effort) to focus instead on maintaining their own health when environmental conditions are poor (Wooller et al. 1992).

Although survival was generally constant over time, it is interesting that there was a particularly low estimate for Nasaruvaalik Island gulls during the 2008–2009 interval (Fig. 3.3).

Because of the relatively short duration of this study, it is difficult to detect temporal variability in survival, so that models with fewer parameters (e.g., constant models) tend to be better- supported (Anderson et al. 1994). Furthermore, many seabird species often skip breeding for one or multiple years in a row, sometimes in response to unfavorable conditions leading up to the

breeding season (e.g., Zabala et al. 2010, Goutte et al. 2014). In Cormack-Jolly-Seber models, permanent emigration is indistinguishable from mortality, along with some amount of temporary emigration unless the birds are encountered again (Lebreton et al. 1992). However, most temporary emigration should be detected if recapture rates are high, especially if it is early in a study, with more years available to re-encounter birds. Nonetheless, additional years of data should help refine current estimates and provide higher statistical power to detect variability in and/or covariates influencing survival of Sabine’s Gulls.

It should be noted that three of the birds in this study received GLS in 2008, but each bird returned in the following year (i.e., there was a 100% return rate for birds with GLS). Therefore, the low survival rate during 2008–2009 would not have been a result of GLS attachment in 2008.

The majority of the GLS were deployed in 2010 (n = 23 birds, one of which was previously tagged in 2008) and 2011 (n = 21 birds, 10 of which were previously tagged in 2010) and there were no differences in return rates or nest success between tagged and untagged birds in any year

(Davis 2015).

Effect of Climate Variability on Survival

Despite the limited duration of this study, there was evidence for a negative effect of TNH on apparent survival of Sabine’s Gulls. TNH modulates the strength and eastward flow of the

Pacific jet stream and its signal is more pronounced in El Niño and La Niña years (Mo and

Livezey 1986, Livezey and Mo 1987, Barnston et al. 1991). During winter, a positive TNH phase is associated with colder temperatures in northwestern North America (Budikova and

Nkemdirim 2005) and is also linked to higher than average winter precipitation throughout

central and eastern subtropical North Pacific regions, and in some Arctic regions (Washington et al. 2000, NOAA 2014a).

Although direct mortality due to extreme weather events does occur (Frederiksen et al.

2008, Mallory et al. 2009), effects of regional climate variability on seabird population dynamics tend to manifest via changes in food availability (Durant et al. 2004, Sandvik et al. 2005, Gaston

2011, Hovinen et al. 2014). A reliable food source leading up to the breeding season is important for maintaining body condition, and thus influences adult survival and reproductive effort in the coming summer (Monaghan et al. 1989, Oro et al. 2004, Davis et al. 2006, Harding et al. 2011). Limited information on Sabine’s Gull diet during winter and migration suggests that they rely heavily on various zooplankton species (e.g., amphipods) during those periods

(Duffy 1983, Day et al. 2001). Consequences of high, positive TNH (e.g., high winter precipitation and/or storm activity, changes in SST in the North Pacific) may have reduced access to zooplankton and/or fish stocks (see Vincent et al. 2002, Dorresteijin et al. 2012) at some point when adult Sabine’s Gulls were migrating back from Peru to Arctic breeding sites.

However, given the relationship between La Niñas (such as in 2008–2009) and high upwelling productivity, it is not clear how cool conditions in the Pacific could have led to reduced food availability. Perhaps low survival was not a result of poor winter food availability, but rather cooler-than-average temperatures at the breeding site, which would have prolonged snow and ice cover in the spring. This, in turn, may have reduced or delayed access to nesting sites and foraging areas. During the 2009 breeding season, average temperatures at Nasaruvaalik Island were indeed 2 to 4°C cooler than in other years of the study. Nonetheless, more detailed studies of Sabine’s Gull diet are needed to properly assess how anomalous climate conditions affect

availability of their primary prey sources, and thus their body condition and survival throughout the year.

Spatial variability in climate regimes throughout the range of habitats encountered by long-distance migratory species presents obvious challenges for investigating influences of climate on adult survival (Grosbois et al. 2008). Climate systems across such large areas are governed by interdependent atmospheric fluctuations; thus multiple indices and other factors

(e.g., time of year, small-scale weather variables; Sandvik et al. 2005, Lavers et al. 2008) likely contribute to overall effects of climate on Sabine’s Gull survival. Potential variation in migratory routes among, and even within, colonies should also be acknowledged. For example,

Sabine’s Gulls returning north to Nasaruvaalik Island from their wintering habitat off the coast of

Peru follow three routes: all Pacific coastal, Pacific coastal plus a terrestrial “short cut” across

Alaska, or Pacific coastal plus a major terrestrial route across Canada to Hudson Bay and then north (Davis 2015). Exposure to food supplies, predation and weather conditions should differ markedly on these routes. Gulls from the East Bay colony are thought to winter in the Pacific; however, this is yet to be verified with tracking data. The fact that no colony effect was detected in annual survival estimates might suggest birds from both colonies were wintering in the same location, but it could also simply mean that they were exposed to similarly ‘good’ conditions during the non-breeding season, which facilitated high survival rates. Thus, more tracking work is needed to determine the proportion of gulls going to each location so we can be more confident in selecting climate indices to test.

Oro (2014) reviewed studies showing effects of climate on seabirds, with a particular emphasis on adult survival. The review, first and foremost, underscored just how extensive the current literature linking survival to climate variability is becoming; more than 77% of the

studies (covering 36 different species) found a relationship between adult survival and climate.

Second, many of the studies showed relatively linear relationships between climate and survival

(e.g., Sandvik et al. 2005, Jenouvrier et al. 2009). However, other patterns emerged that were similar to what was found here for Sabine’s Gulls. For example, two colonies of Cassin’s

Auklets (Ptychoramphus aleuticus) had the lowest survival rates during an El Niño event in

1997/98 (Bertram et al. 2005, Morrison et al. 2011). Lavers et al. (2008) found adult Razorbills

(Alca torda) at one colony maintained high survival rates in relation to Labrador Current SSTs, except in one year (also 1997/98) that was particularly high (also see Jones et al. 2007).

Likewise, Sabine’s Gull adults had generally high, steady survival rates which dropped when

TNH was both high and positive in 2008/09 (a weak La Niña year; NOAA 2014b). These sorts of patterns are not surprising because adults of long-lived species can maintain survival rates in relation to climate variability, but apparently only up to a certain threshold (Wooller et al. 1992).

Annual Variability in Sabine’s Gull Breeding Biology

Generally for seabirds, variability in population size tends to depend more on reproductive rates than on adult survival, in part because their long life span enables some flexibility in annual reproductive effort (Wooller et al. 1992). Similar to other colonial nesting seabirds, Sabine’s

Gulls have strong fidelity to their breeding grounds, even if they experienced breeding failure in previous years (Stenhouse and Robertson 2005). This is possibly because nesting success is more related to variable environmental conditions than nest habitat quality (Stenhouse and

Robertson 2005). Within breeding habitats, climate variability influences predator abundance, access to food, breeding phenology, and hatching and fledging rates, among other components of

reproductive success, as well as survival of both adults and juveniles (Ottersen et al. 2001,

Dorresteijn et al. 2012, Lynch et al. 2012, Sandvik et al. 2012).

Mallory et al. (2012) investigated reproductive biology of Sabine’s Gulls at Nasaruvaalik

Island and found that both reproductive effort (clutch size) and hatching success were below average in 2009, possibly due to consistently cold conditions on the field site that year. In contrast, the 2010 breeding season was much warmer and wetter than any other year, and was also poor in terms of hatching success and chick mortality, but the authors concluded that this was due to a particularly high abundance of predators on the island during that breeding season.

Whether or not an interaction between climate variables and predator abundance affected survival and/or resighting probabilities of Sabine’s Gulls at Nasaruvaalik Island is unclear, although adult survival was quite high at the colony during the 2010–2011 interval (Fig 3.3).

Compared to East Bay, reproductive success at Nasarvuaalik Island was generally higher. This could be attributable to Nasaruvaalik Island being a comparatively smaller island with lower predation pressure (e.g., fewer arctic foxes, Vulpes lagopus, and large gulls) than East Bay, and the close proximity of Nasaruvaalik Island to polynyas, which offer a reliable food source to birds during the breeding season (Stenhouse et al. 2001, Mallory et al. 2012).

On Nasaruvaalik Island, Sabine’s Gulls nest sympatrically with a larger colony of ~600 pairs of Arctic Terns, possibly to gain protective advantages of tern mobbing behavior (Young and Titman 1986). This is generally the case on small islands (e.g., Levermann and Tøttrup

2007), whereas on some large island or mainland colonies, Sabine’s Gulls also nest separately from terns (Day et al. 2001, Mallory and Gilchrist 2003, Stenhouse et al. 2005, Mallory et al.

2012). Interestingly, in 2013 and 2014, reproductive effort of Sabine’s Gulls on Nasaruvaalik

Island was effectively zero (one nesting attempt in two years; M. L. Mallory, personal

observation), coinciding with abandonment of the island by terns in those years. Adults of both species stayed on, or close to the colony for the remainder of the breeding season, but did not initiate nests (M. L. Mallory, personal observation). In 2006, Levermann and Tøttrup (2007) observed similar behaviour in Arctic Terns and Sabine’s Gulls on an island off northeastern

Greenland. Delayed sea ice melt gave mammalian predators extended access to the island during a critical period at the beginning of the breeding season when birds of both species decide whether or not to initiate a clutch. Both species maintained their pair bonds and other breeding behaviours even after breeding had failed, so it seems that the birds remained in the area to invest in future breeding success (Levermann and Tøttrup 2007). There was a complete breeding failure of terns on Nasaruvaalik Island in 2010 as well, but adults abandoned the colony by mid-

August, after chicks had hatched. Immediately (and atypically), the Sabine’s Gulls moved their chicks to offshore ice to finish rearing (Mallory et al. 2012).

A preliminary survival analysis conducted on CMR data for the terns at this colony indicated annual survival probability was 0.98–1.0 from 2007–2012, values which seem unrealistically high, possibly due to low resighting effort, and these should serve only as coarse estimates. Nonetheless, the preliminary analysis for Arctic Terns did not indicate lower survival in 2008/2009 as occurred in Sabine’s Gulls, suggesting that differences in annual survival may not have been attributable to local colony effects. Like the Sabine’s Gull, the Arctic Tern is a long distance, trans-equatorial migrant, travelling thousands of kilometers to its wintering area in the Southern Hemisphere (Egevang et al. 2010). However, Arctic Terns spend winter at edges of pack ice off Antarctica (Hatch et al. 2002, Egevang et al. 2010) and thus are exposed to different winter climate and foraging conditions than those experienced by Sabine’s Gulls. Therefore, the apparent difference in patterns of annual survival for terns and gulls at the colony were likely due

to different environmental factors affecting survival of each species during the non-breeding season.

Conclusions

For long-distance migrants like Sabine’s Gulls, climate variability can be experienced across their breeding, migratory, and winter habitats (Grosbois et al. 2008). Thus, for this study, selection of climate indices was based on Sabine’s Gull tracking data throughout their annual cycle. Although the model with TNH was best-supported among the given set of models, it is possible that a different index, or a suite of indices and/or local climate variables, better explains variability in Sabine’s Gull survival estimates. However, testing effects of interactions requires high statistical power that is not well-supported by the present data. I also wanted to avoid finding spurious results by taking an “all possible models” approach (as described by Anderson et al. 2001). Nevertheless, findings from this study add to growing evidence that variability in climate influences survival of long-lived seabirds (e.g., Grosbois and Thompson 2005, Sandvik et al. 2005, Lavers et al. 2008, Dorresteijn et al. 2012, Sydeman et al. 2012, Genovart et al. 2013,

Hovinen et al. 2014).

Seabirds are often viewed as indicators of environmental change because of their tendency to forage over large regions and at a range of trophic levels (Mallory et al. 2010).

Arctic seabirds in particular, have recently experienced a variety of drastic changes, both directly and indirectly related to climate (Gaston 2011, Gilg et al. 2012). Reductions in survival may occur in years where weather conditions are more severe, despite the relative resilience of seabirds to natural, environmental variability (Sandvik et al. 2005, Fredericksen et al. 2008,

Genovart et al. 2013). Furthermore, with such slow reproductive rates, subsequent recovery

from high adult mortality may be slow for seabird colonies. Therefore, if years of low survival occur frequently, risks of dramatic population declines are high. Although results from this study should be considered preliminary, they nonetheless raise concerns regarding the persistence of Sabine’s Gull, and other Arctic seabirds, in the face of increasing climate instability and severity. Thus, long-term monitoring of adult survival and other population parameters at multiple colonies are critical to understanding and mitigating impacts (e.g., initiatives to improve prey availability; Cury et al. 2011) of such changes on entire ecosystems.

Table 3.1. Descriptions of climate indices used in this study: The principal-component-based North Atlantic Oscillation (NAO),

Southern Oscillation Index (SOI), and Tropical/Northern Hemisphere pattern (TNH).

Index Description Major effects Literature cited Data source

NAO Atmospheric pressure Positive phase related to Hurrell (1995), https://climatedataguide.ucar.e

difference between warmer waters, and more Ottersen et al. (2001) du/climate-data/hurrell-north-

the Azores and severe storms crossing atlantic-oscillation-nao-index-

Iceland North Atlantic to Europe pc-based

TNH Derived from a Positive phase related to Mo and Livezey http://www.cpc.ncep.noaa.gov/

61 rotated principal colder temperatures in (1986), Barnston and data/teledoc/tnh.shtml

component analysis of western North America Livezey (1987),

normalized 500-hPa and above-average Barnston et al.

height anomalies precipitation over central (1991), NOAA

and eastern subtropical (2014a)

Pacific.

SOI Atmospheric pressure Negative and positive Trenberth (1984) http://www.cpc.ncep.noaa.gov/

differences between values related to warm (El data/indices/soi

Tahiti and Darwin, Niño) and cold (La Niña)

Australia eastern tropical Pacific

waters, respectively

Table 3.2. Pearson correlations of selected climate indices based on mean winter values from

1952–2014. Bolded values are significant (P < 0.05).

Climate variable* Label MEI NAO NPI PDO NOI TNH

Multivariate El Nino Index MEI

North Atlantic Oscillation NAO 0.01

North Pacific Index NPI -0.53 0.02

Pacific Decadal Oscillation PDO 0.56 -0.05 -0.77

Northern Oscillation Index NOI -0.76 0.06 0.60 -0.47

Tropical/Northern Hemisphere Index TNH -0.36 0.31 0.32 -0.07 0.59

Southern Oscillation Index SOI -0.95 -0.01 0.55 -0.54 0.74 0.34

* Climate data were retrieved from https://climatedataguide.ucar.edu/climate-data and http://www.esrl.noaa.gov/psd/data/climateindices/list/.

Table 3.3. Reduced m-array summarizing encounter history data for adult Sabine’s Gulls (n =

84) banded on Nasaruvaalik Island from 2007–2013. Note the number of birds ‘released’ in a particular year includes those newly banded and those resighted in that year.

Year released # released # resighted for first time after release

2008 2009 2010 2011 2012 2013 Total

2007 9 8 0 0 0 0 0 8

2008 35 24 2 0 0 0 26

2009 33 27 3 2 0 32

2010 33 26 2 4 32

2011 51 43 1 44

2012 60 46 46

Table 3.4. Reduced m-array summarizing encounter history data for adult Sabine’s Gulls (n =

43) banded at East Bay from 1998–2002. Note number of birds ‘released’ in a particular year includes those newly banded and those resighted in that year.

Year released # released # resighted for first time after release

1999 2000 2001 2002 Total

1998 26 21 2 0 0 27

1999 34 30 1 0 31

2000 32 25 1 26

2001 30 18 29

Table 3.5. Model selection results from the Nasaruvaalik Island and combined (East Bay +

Model Δ QAICc QAICc weight Model likelihood K QDev

Nasaruvaalik Island (ĉ = 1.67)

ϕ, p 0.00 0.82 1.00 2 57.63

ϕtime, p 3.28 0.16 0.19 7 50.44

ϕ, ptime 7.93 0.02 0.02 7 55.09

ϕtime, ptime 8.84 0.01 0.01 11 47.27

East Bay Nasaruvaalik Island (ĉ = 1.34)

ϕTNH, pstudy 0.00 0.89 1.00 5 70.60

ϕ, pstudy 5.72 0.05 0.06 4 78.38

ϕNAO, pstudy 7.50 0.02 0.02 5 78.11

ϕcolony, pstudy 7.70 0.02 0.02 5 78.30

ϕSOI, pstudy 7.78 0.02 0.02 5 78.38

ϕcolony*t, pstudy 12.51 0.00 0.00 12 68.39

Table 3.6. Adult survival estimates (± SE) from capture-mark-recapture studies of other gulls and terns.

Species Study location Estimate Source

Black-legged Kittiwake, Alaska, USA 0.92 ± 0.02 McDonough and

Rissa tridactyla Erwin (2003) in Hatch

et al. (2009)

Herring Gull, Larus Nunavut, Canada 0.87 ± 0.03 Allard et al. (2006) argentatus

Ivory Gull, Pagophila Nunavut, Canada *0.86 ± 0.04 Stenhouse et al. eburnea (2004)

Glaucous Gull, Larus Nunavut, Canada 0.84 ± 0.03 Gaston et al. (2009b) hyperboreus Nunavut, Canada 0.86 ± 0.05 Allard et al. (2010)

Thayer’s Gull, Larus Nunavut, Canada 0.81 ± 0.05 Allard et al. (2010) thayeri

Arctic Tern, Sterna Maine, USA 0.70–0.96 Devlin et al. (2008) paradisaea

* Estimate based on recovery data.

Figure 3.1. Locations of the study sites, Nasaruvaalik Island and East Bay Migratory Bird

Sanctuary, in Nunavut, Canada.

Figure 3.2. Index values from 1998–2013 for (A) the principal-component-based North Atlantic

Oscillation Index (NAO) (B) Tropical/Northern Hemisphere pattern (TNH) and (C) Southern

Oscillation Index (SOI). Mean winter (Dec–Feb for Tropical/Northern Hemisphere pattern,

Dec–Mar for rest) index values used. Note each value includes December of the previous year.

Figure 3.3 Annual apparent survival of adult Sabine’s Gulls at East Bay (1999–2001) and

Nasaruvaalik Island (2007–2013) in relation to winter (Dec–Feb) Tropical/Northern Hemisphere pattern.

Chapter 4 – General Discussion

This thesis provided survival probabilities for two seabird species breeding in Canada: Leach’s

Storm-Petrels in Nova Scotia and Sabine’s Gulls in Nunavut. While these species are phylogenetically distinct, they both rely heavily on marine environments and are exposed to threats that are common to most seabirds (Huntington et al. 1996, Day et al. 2001, Croxall et al.

2012). Moreover, they are both long-distance, trans-equatorial migrants and therefore environmental conditions that they experience throughout their ranges may be quite diverse

(Stenhouse et al. 2012, Pollet et al. 2014a, 2015). Anthropogenic and natural environmental variability influences demographic rates underlying population dynamics. For long-lived seabirds, populations are immediately most sensitive to changes in adult survival, whereas consequences of changes in reproductive success and recruitment are manifested over longer time periods (Lewison et al. 2012). For both Leach’s Storm-Petrels and Sabine’s Gulls, my aim was to begin to delineate the importance of specific environmental variables to adult survival, which could ultimately be applied to their conservation and management.

Importance of survival analyses to conservation and management

Survival estimation is one of the main methods used by wildlife managers and conservationists to monitor bird populations (e.g., Weimerskirch and Jouventin 1987, Sandvik et al. 2005,

Stenhouse and Robertson 2005, Allard et al. 2010, Lewison et al. 2012, Julien e al. 2013).

Estimates of population abundance and distribution are useful for identifying coarse changes in populations (e.g., declines); however, further diagnostic tools are needed to assess the relative importance of underlying demographic parameters associated with changes. These are typically

components of fitness such as reproductive success and adult survival, as well as other processes including dispersal and recruitment (Croxall et al. 1990, Green 1995).

Survival analyses are especially valuable for species that are at risk and/or in decline

(e.g., Stenhouse et al. 2004). If a population is suspected or known to be in decline, adult survival is often the first parameter to be investigated and in many cases is easier to obtain and often more precise than measures of abundance (G. J. Robertson, personal communication).

Furthermore, for some species that are difficult to access (e.g., burrow-nesting species such as storm-petrels; Sydeman et al. 1998) it is easier to monitor populations through adult survival than it is to census population size because relatively fewer individuals are needed to provide estimates with a reasonable level of confidence. To some extent, survival analyses for certain species can also be used to indicate perturbations affecting survival of sympatric species that are vulnerable or difficult to monitor (Wiens et al. 2008). Information from survival analyses

(usually based on recovery data) are also regularly applied to regulation of certain harvested species that are actively managed to determine sustainable harvest levels (e.g., for geese, and some ducks; Menu et al. 2002, Gilliland et al. 2009). In general, being able to diagnose which life history traits are under pressure allows us to identify potential threats associated with population changes, and to prioritize when and where to focus conservation efforts.

In monitoring seabird populations a central goal is to ensure that variability in population size remains below a certain threshold level. When populations, especially those that are small or already under considerable strain from multiple stressors, are pushed beyond these limits (e.g., in years with extreme weather events), they are at a higher risk of extirpation or extinction (Karr

1982, Lewison et al. 2012).

Primary findings and future research

For Leach’s Storm-Petrels, preliminary evidence from this study suggests that Herring Gull presence had a negative effect on survival at the breeding site. Mortality due to avian predation has been observed at many other storm-petrel colonies (e.g., Stenhouse and Montevecchi 1999,

Miles 2010). Therefore, it is plausible that predation contributed to low survival of storm-petrels on Bon Portage Island, particularly given the growing gull population there (I. L. Pollet, personal observation). VHF tags appeared to have a positive effect on survival, although the magnitude of this effect may have been inflated due to having relatively few years of data and small sample sizes in the first year of capture. The effect may have also resulted from sampling bias, because birds selected for VHF attachment were likely established breeders. Regardless, survival probabilities were low, even for birds in plots without gulls, so it appears that there were factors other than just gull predation affecting survival during the study period. As it stands, considering age of first reproduction for storm-petrels is 4–5 years and only one egg is laid each breeding season (Huntington et al. 1996), the estimates I have presented are below the threshold for maintaining a viable storm-petrel population; survival rates for most tubenoses should be ≥ 90%

(Table 2.4).

Sabine’s Gull survival was high and generally consistent over the study period, which is what I expected for a long-lived species. However, I did detect an effect of anomalous climate conditions, with one year having particularly reduced survival in relation to a high, positive value for Tropical/Northern Hemisphere pattern. What is most noteworthy is the pattern of consistently high survival in most years, which dropped noticeably in one year with a particularly high TNH value. Similar patterns have been observed in other seabird species (e.g.,

Lavers et al. 2008), which supports the idea that there is a limit to the robustness of adult survival

of long-lived species with respect to severity of climate conditions. Another important point emphasized here is that although Sabine’s Gulls breed in the Arctic, they migrate to the Southern

Hemisphere in the non-breeding season, thus reinforcing the need to take into account variables acting at multiple stages in their annual cycles, and to consider these variables both spatially and temporally (Grosbois et al. 2008, Genovart et al. 2013, Jenouvrier et al. 2005).

A possible next step for Sabine’s Gull research would be to further explore how climate drives their annual survival throughout the year (i.e., test climate indices from multiple seasons), to pinpoint when and where effects of climate on survival are most important (e.g., Sandvik et al.

2005). Another important question about Sabine’s Gulls (and Arctic Terns) at Nasaruvaalik

Island is why have they forgone breeding in recent years? An investigation into environmental conditions (e.g., predation rates on adults, local weather, and food availability) during and leading up to the breeding season, with a focus on survival of breeding adults, may help to explain this.

Future research for both species should incorporate information from tracking technologies, which continue to improve in terms of precision, durability, miniaturization, and cost. For example, foraging ranges of storm-petrels reported by Pollet et al. (2014b) allow for more informed predictions regarding how food availability in specific regions might affect life history parameters such as adult survival. Undoubtedly, climate plays a role in foraging ecology and ultimately reproductive and survival rates of storm-petrels as well.

Because of the short durations of the data sets for both Leach’s Storm-Petrels and

Sabine’s Gulls, my thesis only provided a snapshot of long-term temporal dynamics in adult survival for both species, but in particular for storm-petrels. Thus, it is difficult to ascertain whether the patterns in temporal variability (or lack thereof) described by the analyses would

persist over longer time scales. In short-term survival studies, temporal variability tends to be masked and models with fewer parameters (e.g., constant models) are better supported

(Anderson et al. 1994); thus true mechanisms underlying survival might be overlooked or not captured fully. The fact that a climate signal affecting survival was detected for Sabine’s Gulls despite the limited data lends further support to the hypothesis that winter climate is an important variable influencing demographics of this species. Nonetheless, it would be beneficial to extend the current studies for additional years, particularly because changes in climate tend to occur over long time periods (i.e., decades or more) (Jenouvrier 2013).

Conservation and management actions

Croxall et al. (2012) stated that, generally for seabirds, priorities in conservation and management are: 1) control or eradication of invasive species; 2) improving quality and increasing area of protected habitat; and 3) improvements to and effective enforcement of legislation and regulation. Leach’s Storm-Petrels might benefit from plans to manage the gull population on Bon Portage Island. Alternatively, there has been some success (i.e., improvements to survival and reproductive rates) with implementation of nest boxes at a colony of Mediterranean Storm-Petrels (Hydrobates pelagicus), attributed to providing nesting habitat that afforded protection from predatory gulls (Libois et al. 2014).

Climate is obviously much less easy to control and future climate change is unavoidable.

One approach might be to minimize pressure on already stressed populations by identifying other factors that are affecting adult survival or other life history parameters and managing those. For example, this could include initiatives to try and maintain abundance of prey species above

certain thresholds (e.g., Cury et al. 2011). This would ensure that food sources are sufficient for maintenance of adult body condition and proper chick development.

Whereas some seabird species are able to buffer changes in their environment by altering their behaviours (e.g., Grémillet et al. 2012), there are limits to how much variability populations can withstand (e.g., Cohen et al. 2014). It is therefore critical to continue monitoring demographic rates of seabirds in relation to environmental conditions so that appropriate actions are taken to reduce impacts of a multitude of current and future threats to their survival.

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