DEMOGRAPHY of a BREEDING POPULATION of WHIMBREL (Numenius

DEMOGRAPHY OF A BREEDING POPULATION OF WHIMBREL (Numenius

phaeopus) NEAR CHURCHILL, MANITOBA, CANADA

A Thesis submitted to the Committee on Graduate Studies in Partial Fulfillment of the

Requirements for the Degree of Master of Science in the Faculty of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada

Environmental and Life Sciences Graduate Program

September 2014

ABSTRACT

Demography of a Breeding Population of Whimbrel (Numenius phaeopus) Near Churchill, Manitoba, Canada

Johanna Perz

I used a GIS raster layer of an area in the Churchill, Manitoba region to investigate the effect of breeding habitat on demography and density of Whimbrel from

2010 through 2013. Program MARK was used to quantify adult and daily nest survival.

Apparent annual survival of 0.73 ± 0.06 SE (95% CI = 0.60-0.83) did not significantly differ between sexes or habitats and was lower than expected based on longevity records and estimates for other large-bodied shorebirds. Nest success, corrected for exposure days, was highly variable, ranging from a low of 3% (95% CI = 0-12%) in 2011 to a high of 71% (95% CI = 54-83%) in 2013. The highest rate of nest survival occurred in the spring with the warmest mean temperature. I developed a generalized linear model

(GLM) with a negative-binomial distribution from random plots that were surveyed for abundance to extrapolate a local breeding population size of 410 ± 230 SE and density of

3.2 birds/km2 ± 1.8 SE. The result of my study suggests that other aspects of habitat not captured by the land cover categories may be more important to population dynamics.

Keywords: curlew, apparent survival, nest-site fidelity, nest success, program MARK,

nest initiation, abundance, density, Earth Observation for Sustainable Development

(EOSD) land cover map

Acknowledgements

I feel incredibly fortunate to have the experiences I gained throughout my education and this project in particular. I am ever grateful that my supervisor, Dr. Erica

Nol, provided me with the opportunity to carry out research on Whimbrel in the equally majestic landscape of subarctic Churchill and for her confidence in my abilities. My success would not have been possible without the shared knowledge, guidance, advice, and time of my supervisor and committee members, Drs. Ken Abraham and Jeff

Bowman. I am grateful to have had the opportunity to meet and work with talented and amazing people in the field, particularly Andy Johnson, Devin Turner, Christine

Anderson, Danielle Gough, and Mary Heung. Data provided by Nathan Senner (2010-

2011) and Kate Ballantyne (2007-2008) were instrumental for my survival and nest success analyses. This project received appreciated financial support from an Ontario

Graduate Scholarship, the Northern Scientific Training Program, the Northern Research

Fund Grant of the Churchill Northern Studies Centre, the Manomet Center for

Conservation Sciences, and the Natural Sciences and Engineering Research Council of

Canada. I am also thankful for funding provided by Calidris, which allowed me to participate in the Western Hemisphere Shorebird Group Meeting in Santa Marta,

Colombia and appreciate the extent of conservation endeavours. I also extend my gratitude to Arctic Shorebird Demographics Network and the staff of Churchill Northern

Studies Centre and Trent University for logistical support. In particular, my knowledge of

GIS benefited from the expertise of Tracy Armstrong. Of course, the love and support of friends and family is valued for without, achieving my goals would have been an overwhelming challenge.

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Table of Contents

Abstract ...... ii Acknowledgements ...... iii List of Figures ...... vi List of Tables ...... viii List of Appendices ...... x Chapter 1: General Introduction ...... 1 Threats to Shorebird Populations ...... 1 Species and Area of Study ...... 4 Thesis Objectives ...... 7 Chapter 2: Apparent Adult Survival and Nest-Site Fidelity of Whimbrel (Numenius phaeopus) Near Churchill, Manitoba, Canada ...... 11 Abstract ...... 11 Introduction ...... 13 Methods ...... 17 Study Area and Field Methods ...... 17 Apparent Adult Survival ...... 19 Sensitivity Analysis ...... 20 Nest-site Fidelity...... 21 Results ...... 23 Apparent Adult Survival ...... 24 Simulated Survival Adding Live Encounters ...... 25 Nest-site Fidelity...... 25 Discussion ...... 26 Chapter 3: Factors Affecting Variation in Whimbrel Nesting Success Near Churchill, Manitoba, Canada ...... 39 Abstract ...... 39 Introduction ...... 41 Methods ...... 45 Study Area and Nest Monitoring ...... 45 Individual Variation in Nest Success...... 47

Nest Initiation ...... 49 Inter-annual Variation in Nest Success ...... 50 Results ...... 52 Nest Survival Models ...... 53 Effect of Nest Initiation Date...... 53 Annual Variation in Nest Success ...... 54 Discussion ...... 54 Individual Variation in Nest Success...... 54 Inter-annual Variation in Nest Success ...... 56 Chapter 4: Abundance and Distribution of Whimbrel Breeding Near Churchill, Manitoba ...... 68 Abstract ...... 68 Introduction ...... 70 Methods ...... 72 Study Area ...... 72 Whimbrel Survey ...... 73 Modeling Abundance ...... 75 Distance to Nearest Neighbor ...... 77 Results ...... 78 Population Size and Density ...... 79 Distance to Nearest Neighbor ...... 80 Discussion ...... 81 Evaluation of Land Cover Map and Population Size Estimate ...... 81 Distance to Nearest Neighbor and Nest Spacing ...... 84 Chapter 5 General Conclusion ...... 94 Literature Cited ...... 99

List of Figures

Figure 1.1 Distribution of Whimbrel in the Western Hemisphere (source: Cornell Lab of Ornithology http://www.allaboutbirds.org/guide/whimbrel/lifehistory)...... 9

Figure 1.2 Map of the Churchill region, Manitoba, Canada. Courtesy of Peter Kershaw (modified, Ballantyne 2009)...... 10

Figure 2.1 Earth Observation for Sustainable Development (EOSD) preliminary land cover map of the study area in the Churchill, Manitoba region (http://mli2.gov.mb.ca/landuse/index.html). Classification of Landsat 7 images, 1999- 2001, received limited analyst intervention. Nine regularly visited sites (outlined in red) covered an area of approximately 10 km2...... 31

Figure 2.2 Frequency of inter-annual dispersal distance (distance between consecutive nest attempts in different years) of marked Whimbrel breeding near Churchill, Manitoba, Canada ( 27 nests). Twenty-six of 72 (36%) banded individuals were never re- sighted...... 32

Figure 2.3 The mean and standard error of distance moved to nest in subsequent year by males of unknown mate fidelity and those belonging to reunited pairs ( 19) and females of unknown mate fidelity and those divorced and re-paired ( 8) near Churchill, Manitoba, Canada, 2010-2013...... 33

Figure 2.4 The mean and standard error of distance moved to nest in subsequent year by individuals banded at nests of herb wetland-dominated territories ( 18) and individuals banded at nests of territories dominated by other land cover categories ( 9) near Churchill, Manitoba, Canada, 2010-2013...... 34

Figure 3.1 Frequency distribution of nest initiation date for Whimbrel occupying territories of differing habitat near Churchill, Manitoba, Canada, 2010-2013 ( 103). Data were pooled across years and territory habitat was defined by dominant land cover category of an EOSD land cover map in a 250 m radius around the nest...... 61

Figure 3.2 Annual nest survival estimates (DSR25) of Whimbrel in the Churchill Manitoba region against mean spring temperatures (°C) during the pre-laying and early incubation period (Mar 20-June 10). Error bars represent 95% confidence intervals...... 62

Figure 4.1 Study area was arbitrary defined around nine study sites (indicated by stars) within approximately 2 km of road access south and east of Churchill, Manitoba, Canada.

Land cover categories were defined by the EOSD land cover map, created from Landsat 7 images, 1999-2001, with limited analyst intervention...... 87

Figure 4.2 Frequency distribution of number of breeding Whimbrels observed per 36 ha plot by rapid surveyors ( 33), 10 of which were also repeatedly searched by intensive surveyors in the Churchill, Manitoba region, 2013...... 88

Figure 4.3 Proportion of 600 by 600 m rapid survey plots ( = 23) containing herb wetland habitat, as defined by an EOSD preliminary land cover map, poorly predicted breeding Whimbrel abundance in the Churchill, Manitoba region, 2013...... 89

Figure 4.4 Mean distance to nearest neighbor of nests in herb wetland-dominated territories or territories dominated by another land cover category of an EOSD land cover map during each breeding season from 2010 through 2013 in the Churchill region, Manitoba, Canada. Territory was delineated as a 250 m radius around the nest ( 152)...... 90

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List of Tables

Table 2.1 The number of consecutive annual nest attempts ( ), mean inter-annual dispersal distance (m), and standard error (SE) for banded Whimbrel in the Churchill, Manitoba region, 2010-2013...... 35

Table 2.2 Observed return of Whimbrel, marked as adults, to the study area in the year following banding and in any year subsequent to banding near Churchill, Manitoba, Canada, 2010-2013...... 36

Table 2.3 Cormack-Jolly-Seber candidate models for estimates of apparent survival ( ) and recapture probability ( ) for Whimbrel adults breeding near Churchill, Manitoba, Canada, 2010-2013 ( 72). Quasi-Akaike’s Information Criterion for small sample sizes (QAICc), differences in QAICc from the best fit model QAICc (∆QAICc), model weight ( ), and number of parameters (K) are provided...... 37

Table 2.4 Model-averaged estimates of substantially supported models (∆QAICc < 2) for apparent survival ( ), encounter probabilities ( ), standard error (SE), lower 95% confidence limit (95% LCL), and upper 95% confidence limit (95% UCL) for Whimbrel adults near Churchill, Manitoba, Canada from 2010-2013 ( 72)...... 38

Table 2.5 Top model estimates of apparent survival ( ), encounter probabilities ( ), standard error (SE), lower 95% confidence limit (95% LCL), and upper 95% confidence limit (95% UCL) for Whimbrel adults near Churchill, Manitoba, Canada from 2010-2013 ( 72)...... 38

Table 3.1 Dates that nest searching commenced, first nest was found, and last nest hatched for Whimbrel over four breeding seasons (2010-2013) in the Churchill, Manitoba region...... 63

Table 3.2 Total nests monitored, nest fate, and nest success estimates (apparent and DSR25) for Whimbrel near Churchill, Manitoba, Canada, 2010-2013. Nests considered successful if at least one chick hatched...... 64

Table 3.3 Global, null, and supported candidate models (∆AICc < 4) for daily nest survival estimates of Whimbrels breeding near Churchill, Manitoba, Canada, 2010-2013

( 150). Akaike’s Information Criterion for small sample sizes (AICc), differences in

AICc (∆AICc) from the best fit model AICc, model weight ( ), and number of parameters (K) are provided...... 65

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Table 3.4 Covariate beta estimates from Program MARK for substantially supported models (i.e., ∆AICc < 2, Table 3.3) of daily survival rates of Whimbrel nests near Churchill, Manitoba, Canada, 2010-2013 ( 150)...... 66

Table 3.5 Beta ( ) estimates, standard error (SE), value, and adjusted coefficient of determination ( ) for winter (Dec 1-March 30) and early spring (May 20-June 10) weather predictors of annual nest success (%) of Whimbrel near Churchill, Manitoba, Canada, 2007-2013 ( 6)...... 67

Table 4.1 Composition (%) of land cover types on all 36 ha plots ( ) sampled for breeding Whimbrel (2013) and of 26,000 ha study area near Churchill, Manitoba, Canada based on a preliminary EOSD land cover map derived from Landsat images, 1999-2001...... 91

Table 4.2 Candidate negative-binomial models of breeding Whimbrel abundance, near Churchill, Manitoba, 2013 ( 23). Model predictors included only physical and biological habitat variables measured from ground surveys at random, rapidly surveyed plots. Akaike’s Information Criterion for small sample sizes (AICc), differences in AICc

(∆AICc) from the best fit model AICc, model weight ( ), log likelihood, and number of parameters (K) are provided for supported models (∆AICc < 4)...... 92

Table 4.3 Beta estimates (estimate), standard error (SE), and value ( ) from top negative-binomial model of breeding Whimbrel abundance on random rapid survey plots near Churchill, Manitoba, Canada, 2013 ( 23)...... 93

List of Appendices

Appendix 1. NAD83 decimal degree coordinates of Whimbrel nest-sites located in the Churchill area, Manitoba, Canada, 2010-2013...... 114

Appendix 2a. Cormack-Jolly-Seber candidate models for estimates of apparent survival ( ) and recapture probability ( ) from data modified to demonstrate the effects of moderate nonbreeding season mortality on a population of Whimbrel breeding near Churchill, Manitoba, Canada, 2010-2013 ( 72). Quasi-Akaike’s Information Criterion for small sample sizes (QAICc), differences in QAICc (∆QAICc) from the best fit model QAICc, model weight ( ), and number of parameters (K) are provided...... 118

Appendix 2b. Cormack-Jolly-Seber candidate models for estimates of apparent survival ( ) and recapture probability ( ) from data modified to demonstrate the effects of high nonbreeding season mortality on a population of Whimbrel breeding near Churchill, Manitoba, Canada, 2010-2013 ( 72). Quasi-Akaike’s Information Criterion for small sample sizes (QAICc), differences in QAICc (∆QAICc) from the best fit model QAICc, model weight ( ), and number of parameters (K) are provided...... 119

Appendix 3. EOSD land cover map class legend ...... 120

Chapter 1: General Introduction

Threats to Shorebird Populations

Overall, shorebirds (Order: Charadriiformes) worldwide are declining since the number of species with decreasing population trends is greater than those that are stable or increasing (IWSG 2003, Wetlands International 2012). In North America, declines in populations are particularly evident for shorebirds that breed at high latitudes. These species in Canada, have declined by 60% since the 1970s (NABCI 2012). Accurately estimating population sizes of species on their northern breeding grounds is particularly challenging because shorebirds are dispersed across the vast and remote landscape

(Morrison et al. 2006, NABCI 2012). Population size estimates and trends are usually obtained from counts or surveys made during migration or on nonbreeding grounds where species tend to amass at higher densities, facilitating their enumeration (Andres et al. 2009, Watts and Truitt 2011). Declining numbers may also result from shifts in distribution or migratory behaviour (e.g., reduced length of stay on stopover or staging areas because of increased risk of predation, Bart et al. 2007, Ross et al. 2012). It is difficult to distinguish between population change and altered migration or distribution; however, declining populations for some species of shorebirds is probable because of independent evidence of decline from different locations (e.g., Churchill, Jehl and Lin

2001; along the Atlantic coast, Bart et al. 2007; and southern Ontario, Ross et al. 2012).

Many shorebirds traverse long distances between nonbreeding grounds in Central and South America and breeding grounds in the arctic and subarctic where they are thought to capitalize on the superabundance of invertebrates typical of the brief summer

for rearing young (Schekkerman et al. 2003). Shorebirds also migrate to escape high levels of predation (McKinnon et al. 2010) and parasite loads typical of lower latitudes

(Piersma 1997). Migratory species rely on a network of habitats throughout the hemisphere; thus, factors at any stage in their annual life-cycle may be contributing to decline.

Many shorebirds are likely negatively affected by loss and degradation of nonbreeding habitat. Major cities and associated development follow the shoreline because historically, colonization of the Western Hemisphere began as settlements near bodies of water for ease of access. Human activity is also concentrated along the coast because of recreation opportunities and proximity to coastal resources. Shorebirds are particularly susceptible during migration because many species congregate at a small number of stopover or staging sites to rest and refuel (Warnock 2010). Reduced number of intertidal habitats and disturbance to foraging or roosting birds may negatively affect survival by interfering with the ability of birds to accumulate sufficient fat reserves to fuel their northward or southward journeys (Lafferty 2001, Baker et al. 2004, Peters and

Otis 2007, Trulio and Sokale 2008). Other hazards during migration and/or on nonbreeding grounds include hunting, contaminants, and increased frequency and severity of extreme weather events due to climate change (Wilke and Johnston-González

2010, NABCI 2012).

Although threats of habitat loss and degradation are prominent along migratory routes and in nonbreeding areas, impacts of climate change are expected to be pronounced on shorebird breeding grounds (Johannessen et al. 2004, Barber et al. 2008,

Serreze and Barry 2011). Climate change may reduce habitat availability for subarctic

and arctic breeding shorebirds from the drying of wetlands and encroachment of trees and shrubs onto formerly open habitat (Ballantyne and Nol 2011). The effects of climate change may also be indirect, involving biotic interactions. For example, the collapse of small mammal population cycles (Ims et al. 2008) may negatively affect shorebird productivity because nests lost to predators have been shown, in a number of studies

(e.g., Bêty et al. 2001), to increase in years of low primary prey abundance. Furthermore, shorebird productivity may be negatively affected by the loss of synchrony between peaks in food availability and nesting or chick-rearing periods (Tulp and Schekkerman

2008, McKinnon et al. 2012). As sea ice extent decreases, the North is becoming more developed due to increased accessibility (e.g., Mackenzie Gas Project, the Mary River

Project, and De Beers Snap Lake Diamond Mine). Increased natural resource exploration and extraction may destroy habitats, disturb nesting birds, and increase risk of environmental contamination (Pirie et al. 2009, Liebezeit et al. 2009).

Life-history characteristics of shorebirds may limit population growth of declining species. Many shorebirds are long-lived and impacts on adult survival can substantially alter population size (Hitchcock and Gratto-Trevor 1997). Furthermore, shorebirds have a low reproductive potential (Sandercock 2003) and so it is difficult to reverse past declines and recover populations rapidly (Brown et al. 2001, Blomqvist et al.

2010). In summary, shorebirds as a group are of particular conservation concern, owing to intrinsic factors and their dependence on a number of wetland and coastal habitats

(Donaldson et al. 2000, Brown et al. 2001, Bart et al. 2007). Effective protection of shorebird habitat thus requires coordinated action from several locations that are separated by vast distances and involve a number of jurisdictions (Brown et al. 2001).

Species and Area of Study

Belonging to the family Scolopacidae within the order Charadriiformes,

Whimbrel, Numeius phaeopus, is a large (mass 310-493 g) shorebird with the long, decurved bill characteristic of members belonging to the genus Numenius, commonly known as curlews (Skeel and Mallory 1996). Sexes of the species appear similar, although females tend to be slightly larger than males (Skeel 1982). Whimbrel is distributed across the globe breeding in open habitats of northern regions (Skeel and

Mallory 1996). The North American subspecies, N. p. hudsonicus, is thought to consist of two disjunct breeding populations: an eastern population west and south of the Hudson

Bay and a western population in Alaska and north-western Canada (Figure 1.1, Skeel and

Mallory 1996).

Based on counts made during migration, Whimbrel, particularly the eastern breeding population, is one of many shorebirds in the Western Hemisphere with a declining population trend (Brown et al. 2001, Morrison et al. 2006, Watts and Truitt

2011, Andres et al. 2012a). Consequently, Whimbrel has been designated as a species of high conservation concern by both Canadian and American Shorebird Conservation Plans

(Donaldson et al. 2000, Brown et al. 2001, USSCP 2004). The species was assigned to this status because of relatively low abundance and declining population trends

(Donaldson et al. 2000, Brown et al. 2001, USSCP 2004). As a result of the designation of high conservation concern, a Whimbrel Conservation Plan has been produced (Wilke and Johnston-González 2010).

The North American estimate of 80,000 individuals consists of approximately equal numbers of Whimbrel from the eastern and western populations (Andres et al.

2012a). These estimates assume that the eastern and western populations maintain separate migratory pathways along the Atlantic and Pacific coast respectively (Skeel and

Mallory 1996). The validity of the assumption is now in question following evidence from several Whimbrel fitted with satellite transmitters that suggested a portion of the western population overlaps with the eastern population during spring migration (Watts et al. 2008, F. Smith pers. comm). This finding suggests that the use of peak counts made during migration along the Atlantic coast may overestimate the size of the eastern breeding population, although more work is needed to assess the relative importance of the Atlantic region to the western breeding population (Watts et al. 2008).

Understanding factors affecting migratory populations is complicated because these animals rely on geographically disparate locations to complete their annual cycle

(Norris 2005). Whimbrel is understudied, which further confounds assessment of the source of decline; therefore, current knowledge is insufficient to recommend actions that would effectively combat threats (Wilke and Johnston-González 2010). Whimbrel is likely susceptible to the same factors negatively affecting all shorebirds, such as nonbreeding habitat loss and degradation, disturbance, contaminants and pollution, hunting, and the effects of climate change (Wilke and Johnston-González 2010).

Declining population trends of Whimbrel is disconcerting because there are a number of closely related species that are considered at risk. For example, Eskimo Curlew

(Numenius borealis) populations were decimated by overharvest, with the last confirmed record from a specimen collected in Barbados in 1963 (COSEWIC 2009, U.S. Fish and

Wildlife Service 2011). The Eskimo Curlew is listed as endangered by the Committee on the Status of Wildlife in Canada (COSEWIC) under the Species at Risk Act (SARA) but

the lack of recent and reliable sightings suggest that the designation of extirpated or possibly extinct is more appropriate (Morrison et al. 2006). Eskimo Curlew was once a very abundant species with a known breeding range entirely in Canada (Gill et al. 1998,

COSEWIC 2009, U.S. Fish and Wildlife Service 2011). Uncontrolled hunting during migration in the late 1800s led to dramatic decline and the species was almost extinct by

1900 (Gill et al. 1998, COSEWIC 2009, U.S. Fish and Wildlife Service 2011). In

Canada, another related species, Long-billed Curlew (Numenius americanus) is listed as special concern under SARA. According to both Canadian and American Shorebird

Conservation Plans, Long-billed Curlew is considered highly imperilled (Donaldson et al.

2000, Brown et al. 2001, USSCP 2004). The Status of Whimbrel has not yet been assessed under SARA but the species receives protection in North America from the

Migratory Birds Convention Act.

Whimbrel is ideal to study because the species is charismatic which may help promote shorebird conservation, and may be considered an umbrella species. Umbrella species are those that if protected, result in the protection of other species. Larsen &

Moldsvor (1992) demonstrated that Whimbrel breeding territories encompassed those of other shorebirds that nested closer than expected by chance. Other species may nest near

Whimbrel because they benefit from vigilance and conspicuous anti-predator behaviour of Whimbrel while avoiding costs of increased detection (Larsen and Moldsvor 1992).

Other species may also breed near Whimbrel because of similar nesting habitat requirements (Larsen and Moldsvor 1992). Regardless, the conservation of Whimbrel would likely extend to other nearby nesting species.

My research is based on data collected from the eastern breeding population of

North America near Churchill, Manitoba, Canada (58o 44' N, 94o 04' W) located on the western coast of the Hudson Bay near the mouth of the Churchill River (Figure 1.2). The climate of Churchill is subarctic, typified by brief mild summers and long cold winters.

Churchill sits at the transitional boundary between arctic tundra to the north and the boreal forest to the south. Churchill is also situated in the Hudson Bay Lowlands which cover 374,000 km2 or 3.7% of Canada, comprising the largest wetland in North America

(ESWG 1995, Abraham and Keddy 2005). The area attracts many nesting shorebirds and waterfowl because of numerous shallow lakes and ponds and extensive wetlands

(Abraham and Keddy 2005).

Thesis Objectives

My project attempts to satisfy some of the recommended actions for Whimbrel research on the breeding grounds as contained in the Whimbrel Conservation Plan (Wilke and Johnston-González 2010). The objectives of Chapter 2 are to 1) estimate Whimbrel apparent adult survival, for which there are no recent or reliable estimates, 2) assess dispersal because mark-recapture analyses do not distinguish between mortality and permanent emigration, and 3) compare apparent adult survival and dispersal between sexes and habitats. The goals of Chapter 3 are to 1) examine patterns of Whimbrel daily nest survival and 2) investigate the effect weather may have on inter-annual variation in

Whimbrel nest success through effects on predator-prey relationships or reproductive investment. In the final chapter, the objectives are to 1) identify breeding ground habitat variables that are associated with Whimbrel abundance, and 2) estimate the population size and density of the species in the Churchill region. By investigating factors that affect

demography and density of Whimbrel, improved understanding of the processes underlying population decline will contribute to direct future conservation efforts.

Figure 1.1 Distribution of Whimbrel in the Western Hemisphere (source: Cornell Lab of

Ornithology http://www.allaboutbirds.org/guide/whimbrel/lifehistory).

Figure 1.2 Map of the Churchill region, Manitoba, Canada. Courtesy of Peter Kershaw

(modified, Ballantyne 2009).

Chapter 2: Apparent Adult Survival and Nest-Site Fidelity of Whimbrel (Numenius phaeopus) Near Churchill, Manitoba, Canada

Abstract

Among shorebirds Whimbrel has especially low reproductive rates because individuals do not breed until their third year, lay at most four eggs, and the brief arctic summer limits pairs to raise a maximum of one brood per year (Skeel and Mallory 1996).

Whimbrel ground nests are also relatively exposed and subject to high levels of predation

(Jehl 1971, Ballantyne and Nol 2011). Although low reproductive potential may be offset by high adult survival rates, recent and reliable estimates for Whimbrel are lacking. An objective of this study was to estimate apparent or local survival of adults banded from

2010 through 2013 near subarctic Churchill, Manitoba. Nest-site fidelity was also quantified because mark-recapture analyses do not distinguish between mortality and permanent emigration. In 2013, the sample consisted of 72 banded adult Whimbrel in the study area and the probability that an individual survived from one year to the next was estimated at 0.73 ± 0.06 SE (95% CI = 0.60-0.83). The mean distance between consecutive nest attempts in different years was 342 m ± 157 SE; however, a female in

2013 nested over 4 km from the nest-site of the previous year. Neither apparent survival nor site fidelity differed significantly between sexes or habitats. The survival rate obtained from this study was lower than expected based on longevity records from North

America and Europe, the latter of which provides evidence that at least some individuals live for 16 (Klima et al. 2013) and 26 years respectively (Robinson and Clark 2012,

Klima et al. 2013). Also, higher estimates of adult survival have been obtained for related species of similar size (e.g., 0.85 for Bristle-thighed Curlews, Marks and Redmond

1996). If the survival estimate from this study is indicative of true survivorship, then

adult mortality during the nonbreeding season (e.g., extreme weather events, hunting) may be a factor in the decline of Whimbrel breeding in the eastern portion of the species’ range.

Introduction

Population trends suggest that Whimbrel (Numenius phaeopus) and other shorebirds are declining (Jehl and Lin 2001, Morrison et al. 2006, Bart et al. 2007, Watts and Truitt 2011, Andres et al. 2012a, Ross et al. 2012). Whimbrel has been assigned a status of high conservation concern by both Canadian and American Shorebird

Conservation Plans (Donaldson et al. 2000, Brown et al. 2001). Knowledge of demography can contribute to effective conservation because demographic models can show whether populations are growing or declining and may also indicate the stages of the annual cycle during which the species is most vulnerable (Hitchcock and Gratto-

Trevor 1997, Larson et al. 2002).

The reproductive potential of shorebirds is typically low due to a number of lifehistory characteristics (Sandercock 2003). The onset of maturity is delayed for curlews,

Numenius spp. (Skeel and Mallory 1996, Marks et al. 2002), and other shorebirds (e.g., godwits, Limosa spp., McCaffery and Gill 2001, Walker et al. 2011; American

Oystercatcher, Haematopus palliates, American Oystercatcher Working Group et al.

2012) with breeding attempts not made until at least 2 years old. Most arctic and subarctic breeding shorebirds are not capable of raising more than one brood during the brief summer before southward migration commences (Sandercock 2003). Shorebird chicks are precocial, and so clutches contain relatively large but few eggs laid in scrapes made on the ground (Ross 1979). Furthermore, the nests of shorebirds may be subject to high levels of predation (Ricklefs 1969). Whimbrel nests are relatively exposed, lacking concealing vegetative cover and so are particularly depredated by predators among the shorebird species breeding in the Churchill, Manitoba region (Jehl 1971, Ballantyne and

Nol 2011). The few eggs that do hatch result in chicks that survive to fledging

(McCaffery 1996). Yet if a population is stable, then it is necessarily true that low reproductive potential typical of many shorebirds is counteracted by relatively high adult survival (Sandercock 2003). Lower than expected adult survival may suggest that the source of decline occurs during migration or on the nonbreeding grounds. Factors potentially affecting Whimbrel survival, particularly during migration, an energy-taxing segment of the annual life-cycle (Piersma et al. 2000, Sillett and Holmes 2002), are habitat loss and degradation (Lafferty 2001, Baker et al. 2004), hunting (Fowlie 2011,

Watts 2013), contaminants (e.g., oil spills, Henkel et al. 2012), and the effects of climate change (e.g., rise in sea level, Galbraith et al. 2002). Deviations from typical survival rates have the potential to substantially affect population size of long-lived organisms

(Hitchcock and Gratto-Trevor 1997, Baker et al. 2004) and are thus particularly important to monitor to ensure conservation of a declining species (Sandercock 2003).

There is a lack of a recent and reliable estimate of Whimbrel adult survival (Wilke and Johnston-González 2010). Previous studies, conducted prior to the popularity of mark-recapture analyses, quantified the return rate for the species, which is the proportion of marked individuals that return to the study area in either the following or some future year (Sandercock 2003). From the Northern Isles of Shetland, Grant (1991) reported a return rate of 89% for breeding adults. Return rates of Whimbrel from a study conducted in Churchill in the late 1970s varied with habitat quality and were as high as 99% for the hummock-bog habitat but as low as 26% for sedge-meadow and heath-tundra habitats combined (Skeel 1983).

Mark-recapture analyses produce estimates for apparent or local survival probability, , and encounter probability, (Cooch and White 2012). Apparent survival was defined as the probability that a bird alive in breeding season i survived until the following breeding season i + 1 and returned to the study area. Apparent survival estimates produced from mark-recapture models more accurately represent true survival than annual return rates because encounter probability accounts for failure to detect a marked bird (Cooch and White 2012). However, estimates of apparent survival produced by mark-recapture analyses not only reflect the probability that an individual survives, but also the probability that the bird returns to breed in the same study area in subsequent years (Sandercock 2003). For this reason, differences in apparent survival within or between species may indicate differences in survival, site fidelity, or both. Moreover, distinction between mortality and permanent emigration may be especially obscured when survival analyses are based on data from studies conducted at small geographic scales (Roodbergen et al. 2008). Therefore a measure of nest-site fidelity is a valuable complement to local studies on survival because low nest-site fidelity is likely to be associated with increased probability of temporary or permanent movements from the study area, resulting in lower re-sighting probability and/or apparent survival

(Roodbergen et al. 2008).

Survival and/or site fidelity may depend on sex or habitat. Sex-dependent apparent survival may suggest that survivorship differs between males and females if, for example, costly egg production (Brunton 1988) compromises the females’ ability to accumulate sufficient fat stores for southward (post-breeding) migration. Conversely, differing rates of apparent survival between the sexes may reflect a greater tendency of

males to return to former breeding territories (Clarke et al. 1997, Flynn et al. 1999,

Johnson et al. 2010, Lishman et al. 2010). Apparent survival may also substantially differ among habitats. For example, apparent adult survival of Black-tailed Godwits (Limosa limosa) was significantly higher in high-quality habitat on both the breeding grounds of the European population in the Netherlands (0.93 ± 0.03 SE vs. 0.81 ± 0.04 SE,

Roodbergen et al. 2008) and the wintering sites of the Icelandic population in the United

Kingdom (0.94 ± 0.02 SE vs. 0.87 ± 0.02 SE, Gill et al. 2001). Similarly, based on banding and re-sighting data from 1978-1983 in coastal Virginia, USA, American

Oystercatchers nesting in salt marshes exhibited substantially higher apparent survival

(0.94 ± 0.03 SE, 95% CI = 0.85-0.98) than birds nesting on beaches (0.81 ± 0.06 SE,

95% CI = 0.67-0.90) (Nol et al. 2012). Differences in apparent survival estimates across habitats may indicate true differential survival if individuals occupying high-quality habitat are in better condition (Norris et al. 2004) or individuals are exposed to a higher risk of predation in one habitat over another (Nol et al. 2012). Differences in apparent survival between habitats may also result from differences in emigration rates

(Roodbergen et al. 2008). Apparent adult survival for Black-tailed Godwits was higher in the site with high breeding pair density and nest-site fidelity indicating either high-quality habitat or fewer vacancies forcing individuals to remain close to former territories

(Roodbergen et al. 2008). Site fidelity has also been shown, in a number of studies, to have a positive relationship with habitat quality. In a number of species, including but not limited to Black-tailed Godwits, Piping Plovers (Charadrius melodus), Prothonotary

Warblers (Protonotaria citrea), and Eastern Phoebes (Sayornis phoebe), birds tend to return to high-quality breeding sites, measured by whether they successfully nested there

in the previous year, whereas individuals are more likely to relocate following failed breeding attempts in low-quality habitat (Groen 1993, Beheler et al. 2003, Hoover 2003,

Cohen et al. 2006).

In this study, I estimated survival for Whimbrel adults in the Churchill region in northern Manitoba, Canada based on live encounters (primarily re-sighting) from 2010 through 2013. Nest-site fidelity was estimated using distances between nests of the same individuals in successive years (Roodbergen et al. 2008, Johnson et al. 2010). Both apparent survival and site fidelity were contrasted between the sexes and habitat categories. I predicted that apparent survival and nest-site fidelity would be higher in males than females, because of reproduction costs in females (Brunton 1988) and because males tend to return to former breeding territories at a higher rate than females, as reported for other shorebirds (Flynn et al. 1999, Johnson et al. 2010, Lishman et al.

2010). I also predicted, based on the results from the earlier study in the Churchill region

(Skeel 1983), that apparent survival and nest-site fidelity would vary as a function of breeding habitat quality.

Methods

Study Area and Field Methods

My field assistants and I regularly searched nine breeding sites (Figure 2.1) in a total area of approximately 10 km2 south and east of Churchill, Manitoba, Canada (58o

44' N, 94o 04' W). Churchill is situated in a transitional zone between boreal forest and arctic tundra, on the west coast of Hudson Bay in northern Manitoba at the mouth of the

Churchill River. Between 2010 and 2013, suitable habitat in the nine sites was repeatedly searched throughout the breeding season (early June-late July) for banded birds and nests,

using binoculars and spotting scopes. Sites were chosen based on accessibility (within approximately 2 km of road access) and knowledge of the locations of former territories

(Ballantyne 2009). Banding of adult Whimbrel occurred in every year of the study period. Whimbrel adults were captured on the nest during the incubation period using a bownet. Eggs were temporarily replaced with painted, hard-boiled chicken or wooden eggs during capture to avoid damage by the captured adult or net. Captured Whimbrel were measured, weighed, and marked with a unique alphanumeric engraved flag over a blue color band above the left tibio-tarsal joint and a government-issued metal identification band above the right tibio-tarsal joint. Sex was determined in the field from the combination of mass and length of bill, wing, tail, and tarsus, with females larger and heavier (Skeel 1982, Skeel and Mallory 1996). As this method has not been confirmed using molecular markers, there may have been some sexing errors.

All habitat analyses were conducted in ArcGIS 10.1 (ERSI 2012) using an Earth

Observation for Sustainable Development (EOSD) preliminary land cover map (Figure

2.1) obtained online from the Manitoba Land Initiative

(http://mli2.gov.mb.ca/landuse/index.html). The EOSD land cover map partitioned the study area into the following general habitats types: water, rock/rubble, exposed land, bryoids, treed wetlands, shrub wetlands, herb wetlands, dense coniferous forest, open coniferous forest, and sparse coniferous forest. The EOSD land cover maps were created using Landsat 7 images from between 1999 and 2001 and data were classified with limited analyst intervention. The resolution of the images was 30 m. I investigated the effect of habitat on survival and nest-site fidelity at the territory scale. Territories were delineated by placing a 250 m buffer around nests where the individual was initially

banded. A territory scale of 250 m was chosen based on previously reported densities and nest spacing (Skeel 1983, Ballantyne and Nol 2011). Although the shape of territories is unlikely to be a perfect circle, I assumed that a radius of 250 m centered at the nest approximated territory. The dominant composition of each territory was determined from the proportion of each habitat classified in the EOSD land cover map, using the Intersect

Polygons with Raster GME tool (Geospatial Modeling Environment Version 7.2.1, Beyer

2012).

Apparent Adult Survival

Observations of the 72 adults banded from 2010 to 2013 were conducted over the breeding seasons of 2011 to 2013. Annual return rates were calculated as percentage of birds that returned to the study area in the first year subsequent to the year they were banded. Overall return rates were defined as the percentage of birds that returned to the study area in any year following the year they were banded. Data were also analyzed using the Cormack-Jolly-Seber model for live encounters (CJS, Cormack 1964; Jolly

1965; Seber 1965) and using Program Mark 6.2 (White and Burnham 1999). A global model of and included sex- and time-dependence because these parameters have been shown to vary between males and females and across years in many shorebird species (Sandercock 2003). The global model also included habitat-dependent survival and encounter probabilities because habitats may differ in quality and a habitat effect on apparent survival has been observed in other large shorebirds (Roodbergen et al. 2008,

Nol et al. 2012). The global model was tested for over-dispersion by calculating a variance inflation factor (ĉ) using two methods: the bootstrap goodness-of-fit ĉ and the median ĉ (Cooch and White 2012). The bootstrap goodness-of-fit procedure calculated ĉ

as the observed deviance or ĉ divided by the mean expected deviance or ĉ from the simulated (n = 1000 replicates) bootstrap distribution (Burnham and Anderson 2002).

Using the median ĉ procedure, a logistic regression was used to estimate ĉ of simulated (n

= 1000 replicates) Whimbrel survival data (Cooch and White 2012). Models with ĉ ≤ 3 indicate the structure adequately fits the data (Lebreton et al. 1992). Reduced models representing all possible combinations that included a time-dependent encounter rate were tested because effort to re-sight Whimbrel became a stronger focus in 2012 and

2013. I assessed the fit of competing models using an information-theoretic approach

(Burnham and Anderson 2002). Because the data were over-dispersed (ĉ > 1), I used quasi-Akaike’s Information Criterion, corrected for small sample size (QAICc), for model selection (Lebreton et al. 1992). Models within two QAICc units of the model with the lowest QAICc value (∆QAICc < 2) were considered to be substantially supported or equally parsimonious. I evaluated the relative fit of a particular candidate model using

QAICc weights and the ratio of weights between sequentially ranked models.

Sensitivity Analysis

Given that survival probability was lower than expected compared to rates calculated from other studies of related species (Marks and Redmond 1996, Roodbergen et al. 2008, Nol et al. 2012), I modified the encounter history file to determine how sensitive the estimate was if 2 and 5 individuals were lost to mortality outside of the breeding season. The only evidence of adult mortality on the breeding grounds over four breeding seasons was the remains of a male Whimbrel (unbanded mate of a marked female) that were found approximately 5 m from a nest (12WHIM32) in 2012. From the close proximity of the remains to the nest and because the nest was depredated in a 3-day

interval between nest checks, the cause was likely predation by Red Fox (Vulpes vulpes), which had been observed in the site earlier in the season. Therefore, the rate of adult mortality on the breeding ground was assumed to be minimal. The encounter history file was modified adding live encounters, i.e., re-sights or recaptures, of two and five banded individuals that were hypothetically removed from the population over the period of study. With this approach, I simulated scenarios in which neither moderate nor high mortality affected adult survival for the population of Whimbrel breeding in the Churchill region. This proportion of the population (2 and 5 of 72 adults), considering years marked, that I assumed did not return to the study area because of nonbreeding season mortality was reasonable considering 6% of Whimbrel trips tracked with satellite transmitters by American researchers were affected by hunting on the Caribbean island of

Guadeloupe (Fowlie 2011, Watts et al. 2011, Sorenson 2013). Since the grouping factors of sex and habitat did not significantly affect survival calculated from the unmodified dataset, they were removed for the simulations. I randomly selected two and five individuals from the marked population that were assigned an additional live encounter; however, I ensured that the additional live encounter did not occur in a year where the individual was simply not detected (e.g., I did not select an individual with an encounter history of 1001, since the bird would have been alive in the 0 years).

Nest-site Fidelity

Nest ownership was established by observing birds flush off the nest or return to incubate, or during capture attempts at the nest. Nest locations were recorded with a

Global Positioning System (GPS) in NAD83 UTM or decimal degree coordinates and monitored until fate could be determined (e.g., hatch, predation, abandonment, etc.).

Since some Whimbrel re-nested following egg predation, distance between a marked individual’s last nest of one year and its first nest of the subsequent year was used as a measure of nest-site fidelity and is hereafter, also referred to as inter-annual dispersal distance (Flynn et al. 1999, Roodbergen et al. 2008, Johnson et al. 2010, Lishman et al.

2010). Inter-annual dispersal distance was severely right-skewed for some levels of each factor (i.e., sex and habitat) but the distribution of log-transformed data was normal and homogeneity of variances was retained across groups. A two-factor analysis of variance was used to test whether inter-annual dispersal distance depended on sex or habitat

(Johnson et al. 2010, Lishman et al. 2010).

Although Whimbrel may be faithful to its previous mate across years and both sexes incubate (Skeel and Mallory 1996), often only a single parent was banded and mate fidelity was unknown; therefore, nests were categorized as either representing typical male or female behaviour (Table 2.1). In birds with monogamous breeding systems, males typically establish and defend territories; thus, if divorce occurs, then it is the female that disperses farther (Clarke et al. 1997, Flynn et al. 1999, Lishman et al. 2010).

Therefore, I predicted the distance between nests in successive years would be smaller in males than in females. Similarly, pairs that are faithful to one another tend to return to former breeding territories (Flynn et al. 1999). For the 11 nests that belonged to a known reunited pair, only the male was included in the analysis because a smaller inter-annual dispersal distance was similarly expected in males of unknown mate fidelity (Table 2.1).

For the 3 nests that were incubated by a divorced individual (re-pair), the returning birds were females (Table 2.1). In addition to small sample sizes for some years, the scope of this study was not concerned with annual variation in nest-site fidelity and so I combined

data following a one-way analysis of variance that ensured inter-annual dispersal distance did not significantly differ across years ( = 0.756, = 0.481). The total number of distance measurements was 27 from 22 individuals. Difference among individuals was not expected to substantially contribute to the variance (Lishman et al. 2010); therefore, I did not include an individual’s identity as a random factor in any analyses. Statistical analyses were carried out in program R (version 3.0.1) and a significance level α of 0.05 was used.

Results

Seventy-two adult (37 males and 35 females) Whimbrel were marked in the first 3 of 4 years of study. Most banded Whimbrel (46 individuals) were captured at nests in territories dominated by herb wetland. Most of the remaining marked individuals (22) were trapped at nests in territories dominated by shrub wetland. The territories of four individuals were covered mostly by water; however, the second most dominant habitat type was shrub wetland. Minimum return rates (proportion of individuals that returned to the study sites in the year following banding) varied annually with detection rate but were generally lower in females than males (Table 2.2). The mean number of re-sighted

Whimbrel per year, combining sexes, was 50% (Table 2.2). Over the four-year study period, 46 of 72 (64% ) marked individuals were observed in at least one year subsequent to banding (Table 2.2), a proportion that is higher than the previous report for the

Churchill region of 43% (22 of 51 banded individuals were re-sighted in at least one year subsequent to banding during a four-year study, Skeel 1983).

Apparent Adult Survival

Goodness-of-fit tests detected over-dispersion indicating the data were more variable than explained by the global model. The ĉ estimate indicated the global model was a satisfactory starting point (ĉ ≤ 3) and I used the more conservative variance inflation factor estimate (parametric bootstrap goodness-of-fit ĉ = 1.887) to adjust AICc

(QAICc) and continued with model selection (Lebreton et al. 1992). Model selection based on QAICc indicated that the best-fit model included a constant survival probability and an encounter probability that varied annually (Table 2.3). Candidate models with less than two ∆QAICc units included main effects of sex and territory habitat on survival rates

(Table 2.3). All other substantially supported candidate models retained a time-dependent encounter rate (Table 2.3). The only other supported model (2 < ∆QAICc < 4) included main effects of time on both survival and encounter probability. The confidence intervals for estimates produced from model-averaging (Burnham and Anderson 2002) of equally parsimonious models largely overlapped (Table 2.4). Thus, although survival rates were marginally higher in males than females (0.74 vs. 0.73) and in birds that occupied herb wetland-dominated territories than territories dominated by another habitat (0.73 vs.

0.72), differences were not statistically significant. The top model was also associated with a relatively large Akaike weight of 0.40 and was favored over the second most supported model by a factor of 2.5 (Table 2.3). Annual apparent survival of Whimbrel breeding near Churchill, estimated by the top model, was 0.73 ± 0.06 SE, 95% CI = 0.60-

0.83 (Table 2.5).

Simulated Survival Adding Live Encounters

Goodness-of-fit tests indicated that both datasets simulating effects of moderate or high nonbreeding season mortality were, like the original, over-dispersed. Because model structure adequately fit the data for both scenarios (ĉ ≤ 3), I used the more conservative variance inflation factor estimate for each to adjust AICc (QAICc) and continued with model selection (Lebreton et al. 1992). The ĉ obtained from the parametric bootstrap goodness-of-fit for the moderate and high mortality simulation was 1.567 and 2.054 respectively. In both cases the top model was the same as that produced from the unmodified re-sighting data, with a constant survival and time-dependent encounter or reporting rate. Furthermore, in both the moderate and high mortality scenario, the most supported model was substantially favored over the second most supported model by a factor of 2.5 and 3 respectively. When re-sights of two and five individuals were added, apparent adult survival increased to 0.77 ± 0.08 SE (95% CI = 0.57-0.90) and 0.81 ± 0.07

SE (95% CI = 0.63-0.91) respectively.

Nest-site Fidelity

At least one marked adult was identified in consecutive years for 27 nests from

2010-2013. Combining sexes and only one measure per pair, consecutive nest attempts in different years were, on average, separated by a distance of 342 m ± 157 SE (range = 31 m-4.3 km, Figure 2.2). The individual dispersing the greatest distance was a female observed nesting 4.3 km away from her 2012 nest location in 2013 (Figure 2.2). One male also dispersed 1 km between nesting attempts. Twenty-six marked adults (36%) were not observed in the study area in any year subsequent to banding and these birds may have dispersed farther. The mean inter-annual dispersal distance was higher in

females (688 m ± 139 SE, = 8) than males (196 m ± 107 SE, = 19); however, this difference was not significant ( = 0.29, = 0.60, Fig. 2.3). Inter-annual distance between consecutive nesting attempts for nests in herb wetland-dominated territories ( =

18) and territories dominated by another habitat type ( = 9) were 180 m ± 122 SE and

665 m ± 223 SE respectively (Figure 2.4). The distance between consecutive nesting attempts in different years was also not significantly affected by habitat ( = 1.49, =

0.23).

Discussion

The survival rates obtained from this study were lower than expected based on estimates obtained for shorebirds of similar size using similar methods. Apparent survival of adult Bristle-thighed Curlew Numenius tahitiensis (mass 310-800g, Marks et al. 2002) using the Jolly-Seber model was estimated at 0.85, 95% CI = 0.77-0.92 (Marks and

Redmond 1996). Apparent adult survival of Black-tailed Godwits (mass 258-356g, Groen and Yurlov 1999) varied with habitat and was 0.81-0.93 for the European population

Limosa limosa limosa using Burnham's joint live and dead encounters model

(Roodbergen et al. 2008) and 0.87-0.94 for the Icelandic population L. l. islandica with the CJS method (Gill et al. 2001). It is assumed that Whimbrel first breed in their third year (Skeel and Mallory 1996) but using the survival rate obtained from this study, mean life expectancy of Whimbrel in Churchill is only 3.2 years (- ; Seber 1982).

Longevity records from North America and Europe also suggest that survival should be higher. A Whimbrel recaptured on June 25, 2012 in my Churchill study area was originally banded as an adult thirteen years earlier; thus, this bird may have been at least

16 years of age (Klima et al. 2013). The European Whimbrel longevity record from Great

Britain, Shetlands is 24 years (Robinson and Clark 2012) but this bird was also captured as a breeding adult and may have been 26 years of age (Klima et al. 2013).

Apparent survival rates obtained on the breeding grounds will accurately represent true survival only if breeding site fidelity is high (Sandercock 2003). If this is not the case, then survival and permanent emigration may be confounded resulting in underestimated survival rates (Roodbergen et al. 2008). Although most individuals exhibited high nest-site fidelity (74% of nests of known individuals in consecutive years were within 200 m), the male (who reunited with a former mate) that moved over 1 km and the female that dispersed over 4 km provide evidence that significant movement can occur. Apparent survival may be low because the size of our study area was too small to capture Whimbrel dispersal. Only areas within 2 km of road access were repeatedly surveyed because of difficult terrain and time restrictions. At the same time, no banded birds that went undetected in the survey were encountered in the surrounding area

(approximately 20 km2). Therefore, because I do not know to what extent such large movements occur, Whimbrel breeding in the Churchill region may experience either a high rate of mortality and/or emigration.

Local populations within a species’ spatially restricted distribution may be subject to differing physical and biological conditions (McKinnon et al. 2010, Lishman et al.

2010). Indeed, colonization beyond the range limit is thought to be inhibited when birth and immigration are insufficient to compensate for demographic losses (Bridle and Vines

2007). Therefore, high emigration and/or low survival may be typical for marginal populations of Whimbrel breeding near the tree line. Species distributional limits are not necessarily static, and may advance or recede in response to the influence of other factors

(Hitch and Leberg 2007). Evidence of drying and subsequent shrub and tree encroachment from aerial photographs may suggest that the distribution of Whimbrel in

Churchill has shifted northward since the 1960s (Ballantyne 2009). If climate change is affecting Churchill habitats, then it may be contributing to elevated emigration rates of

Whimbrel from the study area.

Apparent survival estimates obtained from this study may be indicative of true survival. If this is the case, higher than normal rates of adult morality during the nonbreeding season may be a factor in the decline of the eastern breeding population of

Whimbrel. When I simulated a scenario in which the Churchill population was not affected by high mortality, apparent survival increased by 6% and resembled survival estimates obtained for other large-bodied shorebirds (e.g., Marks and Redmond 1996).

Furthermore, when re-sights of two and five individuals were simulated, the confidence intervals for the apparent survival estimates overlapped with those obtained for other large-bodied shorebirds (Marks and Redmond 1996, Roodbergen et al. 2008, Nol et al.

2012).

Sources of mortality during the nonbreeding season include extreme weather events, hunting, and loss of quality habitat and disturbance on wintering grounds or stopover sites. Whimbrel, along with a number of migratory birds breeding in eastern

North America, often undertake long-distance flights over the Atlantic ocean, during which landing may not be possible when unfavorable weather is encountered (Butler

2000, Smith et al. 2010a). Storms encountered during travel to nonbreeding grounds may expose Whimbrel of the eastern breeding population to more risks than those breeding in the western portion of the species’ range. These direct and indirect negative effects of bad

weather on migratory species may be amplified with the increased frequency and severity of extreme weather events predicted with climate change (IPCC 2007). Greater declines in the abundance of Neotropical songbirds in the eastern portion of the range than in the western portion of the range may be related to differences in migration, with species from eastern populations that undertake long-distance, transoceanic flights more susceptible to adverse weather-related mortality (Butler 2000). The 2-3 month-long fall migration of

Whimbrel does begin to overlap with months (September-October) of peak storm activity in the Atlantic (Skeel and Mallory 1996). Direct storm-induced mortality may occur, although is less likely for this robust species than for small passerines (Newton 2007).

Nonetheless, diminished energy reserves of Whimbrel forced to migrate during unfavorable weather may affect survival and/or reproduction (Calvert et al. 2009).

Furthermore, when recuperating depleted energy reserves or avoiding adverse weather by landing, Whimbrel may be exposed to other risks such as collisions with manmade structures or predation (Newton 2007). Hunting, too, has a long tradition in some

Caribbean countries, particularly Guadeloupe, Martinique, Barbados, Puerto Rico, and

Trinidad and Tobago, as well as in countries along the northern coast of South America

(Fowlie 2011, Watts 2013). Hunters take advantage of the large concentration of birds attracted to islands in the Caribbean region while seeking refuge from storms encountered during migration (Fowlie 2011, Watts et al. 2011).

With a lack of specific information, it is impossible to assess the effect hunting has on population decline but it may be significant. High hunting pressure is suggested by the September 2011 incident where two of 17 Whimbrel tracked with satellite transmitters by the Center for Conservation Biology were shot by hunters within hours of

arriving on Guadeloupe after encountering storm systems (Fowlie 2011, McClain 2013).

Because the attachment of tracking devices can negatively affect birds in a number of ways (e.g., increased energy expenditure), these birds may have been more likely to break from migration when a storm was encountered, increasing their vulnerability to hunting (Barron et al. 2010). Hunting mortality occurring in the Caribbean and along the northern coast of South America needs to be further investigated to determine whether

Whimbrel or other shorebird populations are significantly affected.

Figure 2.1 Earth Observation for Sustainable Development (EOSD) preliminary land cover map of the study area in the Churchill, Manitoba region

(http://mli2.gov.mb.ca/landuse/index.html). Classification of Landsat 7 images, 1999-

2001, received limited analyst intervention. Nine regularly visited sites (outlined in red) covered an area of approximately 10 km2.

12 10

8 6

Frequency 4 male 2 female 0

Inter-annual dispersal distance category

Figure 2.2 Frequency of inter-annual dispersal distance (distance between consecutive nest attempts in different years) of marked Whimbrel breeding near Churchill, Manitoba,

Canada ( 27 nests). Twenty-six of 72 (36%) banded individuals were never re- sighted.

1400 1200

1000

800 annual annual dispersal

- 600

distance distance (m) 400

200 Mean Mean inter 0 male female Sex

Churchill, Manitoba, Canada, 2010-2013.

1200

1000

800

600

annual annual dispersal -

distance distance (m) 400

200 Mean Mean inter 0 herb wetland other Territory habitat

9) near Churchill, Manitoba, Canada, 2010-2013.

Table 2.1 The number of consecutive annual nest attempts ( ), mean inter-annual dispersal distance (m), and standard error (SE) for banded Whimbrel in the Churchill,

Manitoba region, 2010-2013.

Levels of factor Mean inter-annual SE

dispersal distance

(m)

Year

2010-2011 3 108 27

2011-2012 5 132 31

2012-2013 19 434 221

Mate fidelity

Male (unknown) 8 196 50

Male (Reunited pair) 11

Female (unknown) 5 688 519

Female (Re-pair) 3

Territory habitat

Herb wetland 18 180 52

Other 9 665 457

Overall 27 342 157

Table 2.2 Observed return of Whimbrel, marked as adults, to the study area in the year following banding and in any year subsequent to banding near Churchill, Manitoba,

Canada, 2010-2013.

Percent re-sighted

Years Male Female Both sexes

2010-2011 35.2 (6/17) 30.8 (4/13) 33.3 (10/30)

2011-2012 60.0 (3/5) 33.3 (2/6) 45.5 (5/11)

2012-2013 73.3 (11/15) 68.8 (11/16) 71.0 (22/31)

Overall (2010-2013) 70.3 (26/37) 57.1 (20/35) 63.9 (46/72)

Mean (2010-2013) 56.2 44.3 49.9

Table 2.3 Cormack-Jolly-Seber candidate models for estimates of apparent survival ( ) and recapture probability ( ) for Whimbrel adult s breeding near Churchill, Manitoba,

Canada, 2010-2013 ( 72). Quasi-Akaike’s Information Criterion for small sample sizes (QAICc), differences in QAICc from the best fit model QAICc (∆QAICc), model weight ( ), and number of parameters (K) are provided.

a b Model QAICc ∆QAICc K

190.06 0.00 0.40 4

191.92 1.85 0.16 5

191.99 1.92 0.15 5

192.08 2.02 0.15 6

223.39 33.32 0.00 20 a Model factors included: s = male or female, h = herb wetland or other, t = annual variation, c = constant b Corrected Quasi-Akaike’s Information Criterion, where estimated ĉ = 1.877

Parameter Estimate SE Lower 95% CI Upper 95% CI

male 0.74 0.09 0.54 0.87

female 0.73 0.08 0.55 0.86

herb 0.73 0.08 0.55 0.86

other 0.72 0.09 0.51 0.87

2011 0.44 0.11 0.24 0.66

2012 0.83 0.09 0.58 0.94

2013 0.96 152.42 0.00 1.00

( 72).

Parameter Estimate SE 95% LCL 95% UCL

constant 0.73 0.06 0.60 0.83

2011 0.44 0.11 0.25 0.65

2012 0.83 0.09 0.59 0.94

2013 0.99 0.12 0.00 1.00

Chapter 3: Factors Affecting Variation in Whimbrel Nesting Success Near Churchill, Manitoba, Canada Abstract

I investigated whether temporal or habitat variables affected daily survival rate

(DSR) of 157 nests of Whimbrel monitored over four breeding seasons (2010 through

2013). Because year was the only variable that had a significant effect on daily nest survival, I examined if weather and a weather proxy for predator pressure explained inter- annual variation in nest success. To increase the sample size of years, I combined my data with recent estimates of Whimbrel nest success near Churchill (2007-2008,

Ballantyne 2009; Ballantyne & Nol 2011) that were derived using similar methods.

Winter conditions (mild mean temperatures and deeper snow) conducive to small mammal survival, the primary prey of mammalian nest predators, were predicted to have a positive effect on annual nest success by reducing predator pressure on Whimbrel eggs.

Mild early spring conditions that facilitate invertebrate prey availability and/or reduce energetic demands associated with Whimbrel reproduction were also expected to increase annual nest success. Nest success, corrected for exposure days, varied substantially over the four years of study from 3% (95% CI = 0-12%) in 2011 to 71% (95% CI = 54-83%) in 2013. Nest success estimates from breeding seasons of 2010-2013 were consistent with recent reports (Pirie 2008, Ballantyne 2009, Ballantyne and Nol 2011), although lower than previous values (Jehl 1971, Skeel 1983) for Whimbrel near Churchill. Although no weather variables explained variation in annual nest success, mean temperature for the pre-laying and laying period (May 20-June 10) of 2013 was considerably warmer than other years; nest success in 2013 was the highest over the 6 years of study. I recommend

monitoring this population for a longer time-series to increase the power to elucidate contributing factors to inter-annual variation in Whimbrel nest success.

Introduction

The life history strategy of shorebirds is characterized by high survivorship and long lifespan but low annual fecundity (e.g., small clutch size). Actual reproduction is further depressed by high nest failure, owing primarily to predation (Jehl 1971, Martin

1995, Sandercock 2003). Nesting success of Whimbrel, although variable (Jehl 1971,

Skeel 1983, Grant 1991, Pirie 2008, Ballantyne and Nol 2011), is typically low relative to other shorebirds breeding in the Churchill, Manitoba region (e.g., apparent nest success, combining years, for American Golden Plover Pluvialis dominica was 71%, Byrkjedal

1989; Semipalmated Plovers Charadrius semipalmatus 66%, Badzinski 2000;

Semipalmated Sandpiper Calidris pusilla 80%, Jehl 2006; Dunlin Calidris alpina 81%,

L. Koloski et al. unpubl. data). Nest success is therefore unlikely to be constant and may vary both within and among years (Grant et al. 2005). Annual variation in reproductive parameters of many subarctic and arctic breeding birds can be considerable, particularly in recent decades (Skinner et al. 1998). Factors that contribute to variation in reproduction include annual fluctuations in weather (Nol et al. 1997, Skinner et al. 1998,

Smith et al. 2010b, Iles et al. 2013) and predator pressure (Summers and Underhill 1987,

Underhill et al. 1993, Summers et al. 1998, Bêty et al. 2001, Gauthier et al. 2004, Smith et al. 2007, Reiter and Andersen 2011, Iles et al. 2013).

In years when shorebirds are affected by high predator pressure, loss of eggs and young can contribute to reduced recruitment and limit population growth (Meltofte et al.

2007a). Nest predation by arctic and subarctic mammals including, but not limited to

Arctic Fox (Vulpes lagopus), is important but depredation events by this suite of nest predators is often not directly observed by researchers (Liebezeit and Zack 2008,

McKinnon and Bêty 2009). The primary food source of meso-mammalian nest predators in arctic and subarctic ecosystems is small mammals (Smith and Foster 1957, Gauthier and Berteaux 2011, Reiter and Andersen 2011), particularly lemmings (Dicrostonyx spp.,

Lemmus spp.) and voles (Clethrionomys spp., Phenacomys spp., Microtus spp.). Small mammal populations are cyclic, characterized by peaks and troughs in abundance, often with the cycles synchronized across more than one species and geographical area (Krebs et al. 2002, Gauthier and Berteaux 2011). In years of low small mammal availability, predators increase feeding on alternative prey such as ground-nesting birds and bird eggs

(Summers and Underhill 1987, Underhill et al. 1993, Summers et al. 1998, Bêty et al.

2001, Gauthier et al. 2004, Smith et al. 2007, Reiter and Andersen 2011, Iles et al. 2013).

Therefore, ground-nesting birds are predicted to experience reduced reproductive success following peaks in small mammal abundance, particularly when predators remain abundant (Summers and Underhill 1987, Underhill et al. 1993, Summers et al. 1998, Bêty et al. 2001, Gauthier et al. 2004, Smith et al. 2007, Reiter and Andersen 2011, Iles et al.

2013).

Annual fluctuations in weather can influence reproduction of birds breeding at high latitudes. For example, spring conditions (e.g., warm and dry) that allow for early- nest initiation were associated with increased nest survival of Common Eiders (Iles et al.

2013). Unfavorable weather during early spring may have direct or indirect negative consequences on reproduction. Direct nest failure due to adverse weather may occur by abandonment (Meltofte et al. 2007a). Challenging weather conditions also exacerbate the already substantial energetic demands required to maintain internal body temperature of arctic breeding shorebirds (Piersma et al. 2003). In an effort to recoup energy losses,

birds may take more frequent incubation breaks and thereby increase the likelihood of predation (Smith et al. 2010b). Additionally, snow-covered ground or flooding may limit availability of nest-sites and increase the risk of nest loss when predators restrict their search to exposed ground (Byrkjedal 1980). Shorebirds are “income breeders”, i.e., dependent on resources acquired primarily from the breeding ground for egg formation

(Meltofte et al. 2007a). Therefore, adverse weather may compromise the ability to produce and incubate eggs leading to delayed nest initiation (Smith et al. 2010b) or reduced size of eggs or clutch (Nol et al. 1997).

Early nest initiation is expected to coincide with early arrival on the breeding grounds. Annual weather fluctuations may also influence timing of migration by altering the schedule of individuals already underway or by affecting conditions prior to departure

(Senner 2012). Timing of migration may also vary at the individual scale (Gunnarsson et al. 2006). Between-individual variation in the timing of arrival on the breeding grounds and subsequent nest initiation is expected to have consequences on fitness (Sandercock et al. 1999, Vergara et al. 2007). Birds arriving early on the breeding grounds may benefit from unobstructed access to high-quality territories and mates, which may contribute to reproductive success (Lozano et al. 1996, Sandercock et al. 1999, Kokko 1999). Also individuals that arrive on the breeding grounds and initiate nests early may be able to lay a second clutch following a failed nesting attempt in time for chick development to coincide with peak insect emergence (Meltofte et al. 2007b, Colwell 2010). At the same time, individuals arriving too early may risk unfavorable weather and low food availability, which may negatively impact fitness (Bêty et al. 2004). A factor that may be contributing to between-individual variation in migration timing may be habitat-quality at

any stage of the annual life-cycle (Norris et al. 2004, Gunnarsson et al. 2006). Norris et al. (2004) found that winter habitat quality of American Redstart, Setophaga ruticilla, influenced arrival date on the breeding grounds, which in turn affected variables associated with reproduction such as number of young fledged. Similarly, the population of Black-tailed Godwits (Limosa limosa islandica) in Iceland has been increasing and

Gunnarsson et al. (2006) demonstrated individuals occupying recently colonized, low- quality breeding and wintering sites arrived later than those from high-quality sites. To summarize, selection to arrive early may be more pronounced in certain habitats because of reproductive gains or high population density and competition (Lozano et al. 1996,

Sandercock et al. 1999, Kokko 1999) or carry-over effects from the nonbreeding season

(Norris et al. 2004).

The objective of this chapter was to explore the interplay between nest initiation as an indicator of timing of arrival to the breeding grounds, type of territory habitat, and nest success. I hypothesized that nests initiated early in the breeding season would be by individuals that were first to arrive on the breeding grounds (those who might gain access to high-quality territories and/or mates). The expected outcome would be that nests initiated early in the breeding season would be in high-quality habitat and more successful. Another objective of this chapter was to investigate variables that might contribute to annual variation in Whimbrel nest success. I predicted that annual nest success of the Churchill region population of Whimbrel would be higher when weather was favorable to small mammals (McKinnon et al. 2013) and/or egg formation and incubation (Meltofte et al. 2007b, Iles et al. 2013). Specifically, I predicted that annual

population reproductive success would positively respond to increases in both winter snow cover and early breeding season temperatures.

Methods

Study Area and Nest Monitoring

Nests were located by observing adults incubating nests, flushing from or returning to nests, and searching appropriate habitat in nine study sites, covering a total area of about 10 km2 south and east of subarctic Churchill, Manitoba, Canada (58o 44'

N, 94o 04' W). Study sites were within 2 km of roads and areas searched were consistent between years. Nests were occasionally found outside study sites in traditionally occupied open, mostly treeless habitat (Skeel and Mallory 1996). Nest locations were determined in NAD83 UTM or decimal degree coordinates using a Global Position

System (GPS) unit. Nests were monitored at least once per week during the breeding season (from the beginning of June to end of July, Table 3.1) from 2010 through 2013 until fate was determined. Nest initiation was defined as the day when the first egg was laid while the start (day 0) of incubation (clutch completion date) was defined as the day when the last egg was laid. If a nest was discovered during laying, I assumed one day for each egg until the clutch was complete (Skeel and Mallory 1996). For nests found during incubation, eggs were floated to estimate age and I back-calculated nest initiation dates

(Liebezeit et al. 2007). Since shorebirds hatch precocial young, nests were considered successful if at least 1 egg hatched (Mayfield 1975). Hatching was confirmed by observing chicks in or near nests, parental behaviour, and/or egg shell evidence (Mabee

1997). Failed nests were those in which eggs were lost prior to the estimated hatching date (predation), eggs were left unattended by adults for prolonged periods and parents

were absent (abandonment), or nest cups were empty but a long interval had transpired such that I could not confidently assign the nest any category (unknown). Removing unknown fates can result in biasing nest success estimates; therefore, nests with unknown fates ( = 3 nests or 1.9% of all nests) were treated as failures in nest success estimates

(Manolis et al. 2000).

All habitat analyses were conducted in ArcGIS 10.1 (ERSI 2012) using an Earth

Observation for Sustainable Development (EOSD) preliminary land cover map obtained online from the Manitoba Land Initiative (http://mli2.gov.mb.ca/landuse/index.html). The

EOSD land cover map partitioned the study area into the following general habitats types: water, rock/rubble, exposed land, bryoids, treed wetlands, shrub wetlands, herb wetlands, dense coniferous forest, open coniferous forest, and sparse coniferous forest.

The EOSD land cover maps were created using Landsat 7 images from between 1999 and

2001 and classified with limited analyst intervention. Since the resolution of the images was 30 m, I included the effect of territory habitat on daily nest survival and initiation date. Territories were delineated by placing a 250 m buffer around nests based on previously reported densities and nest spacing (Skeel 1983, Ballantyne and Nol 2011).

Although the shape of territories is unlikely a perfect circle, I assumed that a radius of

250 m centered at the nest approximated a territory. Composition and predominant land cover of each territory was determined using the Intersect Polygons with Raster GME tool (Geospatial Modeling Environment Version 7.2.1, Beyer 2012). Distance to nearest neighbor was determined for each breeding season using the Distances among Points

GME tool (Geospatial Modeling Environment Version 7.2.1, Beyer 2012) in ArcMap

10.1 (ERSI 2012).

Individual Variation in Nest Success

Apparent nest success was calculated for each year using the number of successful nests divided by total number of nests. Program MARK (Dinsmore et al.

2002) was used to estimate daily survival rate (DSR) of nests. Daily survival rate is the probability that a nest will survive a single day (Dinsmore et al. 2002). Nest survival is the probability that a nest will survive until completion, which is the day on which the first egg hatches for precocial shorebird chicks (Dinsmore et al. 2002). Nest survival is determined by raising DSR to incubation period (i.e., from last egg laid until first egg hatched), which for Whimbrels is 25 days. Although the term nest success is more appropriately applied to individual birds (Dinsmore et al. 2002), I used it interchangeably with nest survival hereafter. The mean incubation period for the 6 nests that were found during laying and survived until hatch was 25 days, consistent with literature (Skeel and

Mallory 1996). This value was used in calculations of nest success.

A total of 157 Whimbrel nests were found over four nesting seasons (2010-2013).

One nest (wh11u) was not monitored following discovery and was excluded from summary statistics on nest fate and calculations of nest success. Six nests were under observation for only one day because they were found on either the day of hatching or while being depredated. Since the number of exposure days for these six nests was zero, they were not included in Program MARK nest survival estimates (Cooch and White

2012). These nests were, however, included in estimates of apparent nest success. In

2013, there were two nests (1.3% of all nests) that contained unviable eggs. Since the nest persisted and parents continued to incubate until the expected hatch date, nests were considered successful in MARK estimates (Colwell et al. 2011). For situations where

hatch date was unknown, I used estimated hatch date. If hatch date was not reliably estimated or a nest hatched prior to or following estimated hatch, I used the last date that it was observed active to avoid positively biasing the number of exposure days

(Dinsmore et al. 2002). I observed a lower percentage (68%) of the usual four egg clutches than the previously reported rate of 78% (Skeel and Mallory 1996). The percentage of nests with 3, 2, and 1 egg clutches was 21%, 6%, and 5% respectively.

While nests with fewer than 4 eggs may have lost egg(s) to predation, a few nests found during the laying period were never observed to contain more than 2 or 3 eggs.

Therefore, I did not assume that the bird was still laying or that nest was partially depredated if it contained less than 4 eggs on multiple visits.

Program MARK was also used to develop 24 a priori candidate models that examined effects of temporal variation across and within breeding seasons and habitat on daily nest survival. Support of models was compared using Akaike’s Information

Criterion corrected for small sample size (AICc) (Burnham and Anderson 2002). The temporal variables included were year, a linear within-year time trend, and a quadratic within-year time trend. I used day of breeding season (linear time trend) to determine whether daily nest survival decreased as the nesting season progressed (Dinsmore et al.

2002, Cooch and White 2012), which reflected my expectation that nests initiated early were more successful (Grant et al. 2005). A quadratic time trend was also fitted to allow for a bimodal pattern of nest survival, which might occur if there were a substantial number of birds that re-nested midsummer (Dinsmore et al. 2002, Cooch and White

2012). Models with a quadratic effect of date were polynomial and included both linear and quadratic terms (Grant et al. 2005). Although there were few known re-nests, many

nests were by unmarked individuals and there were a large number of nests that contained less than 4 eggs, which may indicate that the nest represented an additional attempt

(Colwell 2010). Since more pairs occupied herb wetland-dominated territories (110) than territories that were dominated by other land cover categories (46), I included the proportion of the former habitat as a covariate. Finally, distance to nearest neighbor was included to reflect the expectation that population density and habitat quality correspond to each other (Skeel 1983, Gunnarsson et al. 2005, Johnson 2007, Pérot and Villard

2009). The first day of the breeding season was standardized to the earliest day for which a nest was estimated to have been initiated (May 29), a few days prior to when searching commenced. All days were numbered sequentially afterwards.

Nest Initiation

Date of nest initiation was standardized as a deviation from mean initiation date of the year nest was found, since there was a weak positive relationship between mean nest initiation date and mean temperature during early spring ( = -1.11 ± 0.51, =

4.73, = 0.10). Unless the parent was banded, it was difficult to determine whether a nest represented a pairs’ first attempt. I excluded the five nests that were known to represent at least a pairs’ second attempt (13WHIM27, 13WHIM38, 13WHIM53, 12WHIM43, and wh14u) but included two possible re-nests. I included the two possible re-nests because parents were not banded and suspicion arose in the field based on proximity to a failed nest, which is unreliable since distances to nearest neighbor are variable and can often be less than 100 m (see Chapter 3). The initiation date of 49 nests was either unknown or unreliable because eggs were never floated or consistency in methods was uncertain.

These nests were excluded from further analysis, leaving a sample size of 103 nests. To

evaluate differences in breeding phenology between habitats, I compared mean initiation date using a one-tailed two-sample t-test because individuals occupying herb wetland- dominated territories were predicted to arrive and initiate nests earlier, assuming that herb wetland is higher quality habitat. Assumptions of normality and homogeneity of variance were met.

Inter-annual Variation in Nest Success

I combined my 4 years (2010-2013) of nest survival estimates with data from

2007-2008 (Ballantyne 2009, Ballantyne and Nol 2011), which were also based on a 25- day incubation period but were calculated using logistic-exposure models (Shaffer 2004).

The logistic-exposure method gives results that are identical or similar to those obtained with the nest survival model in Program MARK (Shaffer 2004).

Annual variation in nest success was large so I examined local meteorological variables that potentially contributed to this variation. Weather variables were obtained online (http://climate.weatheroffice.gc.ca) from the Environment Canada weather station in Churchill, 5-20 km from our sites. A correlation analysis, variance inflation factor, and tolerance were used to identify collinearity among weather variables (Quinn and Keough

2002). Mean early spring (May 20-June10) temperature was correlated with mean winter

(December 1-March 31) temperatures and total winter precipitation over two years prior to the breeding season. For this reason, and to avoid over-parameterization, general linear models were developed to explain annual variation in nest survival (%) with either winter or spring variables as predictors. Residuals conformed to expectations of a normal distribution and homoscedasticity.

Studies have shown that for small mammals, winter reproduction under the snow may be associated with the population growth phase (Gauthier and Berteaux 2011). I hypothesized that the effect of prey-switching by mammalian predators on annual nest success of Whimbrel would be revealed when related to winter weather variables, if certain conditions are associated with increased small mammal abundance. Based on the distribution of winter nests, small mammals prefer deep snow cover because of warmer ground temperatures (Duchesne et al. 2011, Bilodeau et al. 2013). Because daily snowfall

(cm) and mean depth of snow on the ground (cm) were often missing from the

Environment Canada weather data, in a few instances for the whole time period under investigation, I used total precipitation (water equivalent of total snowfall in mm) instead

(S1). I also included mean winter temperature (°C), WT, and total precipitation over two winters prior to the breeding season (S2). For the second model, I included mean temperature (°C) (ST) of early spring (May 20-June 10) because it may impact egg formation and incubation after arrival on the breeding grounds (Meltofte et al. 2007b, Iles et al. 2013). Total early spring precipitation was also included (RN). The date interval used to represent early spring was expected to capture annual fluctuations in weather during the pre-laying and early incubation periods based on knowledge of Whimbrel northbound migration. Numbers of Whimbrel peak in early to mid-May at a number of stopover sites along the Gulf and Atlantic coasts (Dinsmore et al. 1998, Dodd and Spinks

2001). Furthermore, birds fly via Great Lakes prior to arrival in the eastern arctic with peaks on or about May 24, varying only by 4 days between 1976 and 2005 (Wilke and

Johnston-González 2010). Statistical analysis of nest initiation and annual variation in

nest success was carried out in program R (version 3.0.1) and a significance level α of

0.05 was used.

Results

A total of 156 Whimbrel nests were monitored over four years (2010-2013) of which 110 (70.5%) were located in herb wetland-dominated territories and 46 (29.5%) were located in territories dominated by another habitat category. Non-herb wetland territories were dominated primarily by shrub wetland (76%), water (15%), exposed land

(7%), and open coniferous forest (2%). In territories dominated by water, the second most dominant habitat type was usually shrub wetland (6 of 7 nests).

Regardless of the measure, nest success varied considerably among years (Table

3.2). Apparent nest success ranged from 17.6% in 2011 to 73.7% in 2013 (Table 3.2).

Averaged across all years, apparent annual nesting success was 42.5% ± 12.4 SE, and overall 48.1% hatched. The fate of only 1 nest was not determined in 2010 (< 1% of nests combining years). Predation by mammalian or avian predators was the most common cause of nest failure, with eggs disappearing from 73 nests (46.8%), combining predation and unknown fate categories. The other causes of failure were abandonment (6 nests or

3.8%) and unviable eggs (2 nests or 1.3%). The only predation events witnessed were those on eggs by Common Raven (Corvus corax) and Parasitic Jaeger (Stercorarius parasiticus). However, a cached egg with a pip hole and adult remains were observed near different nests in 2012, suggesting predation by either Red Fox (Vulpes vulpes) or

Arctic Fox (Vulpes lagopus). Other possible predators of eggs and young that were often observed in the study area included Herring Gull (Larus argentatus), Northern Harrier

(Circus cyaneus), Rough-legged Hawk (Buteo lagopus), Sandhill Crane (Grus

canadensis), Short-eared Owl (Asio flammeus), Merlin (Falco columbarius), and Polar

Bears (Ursus maritimus).

Nest Survival Models

Of the nests that were monitored, 3 were found during hatch and 3 were found after nest failure. Therefore, 150 nests were used for Program MARK nest success estimates and nest survival analysis. Program MARK nest success estimates ranged from

2.5% (95% CI = 0.0-11.9%) in 2011 to 70.8% (95% CI = 53.6-82.6%) in 2013.

Combining years, the overall DSR estimate was 0.957 (95% CI = 0.946-0.966), corresponding to a nesting success of 33.2% (95% CI = 25.1-41.6%). Mean DSR was

0.933 (95% CI = 0.896-0.958), corresponding to a nesting success of 17.7% (95% CI =

6.4-33.9%). There was a strong annual effect on DSR as year appeared in every supported model (Table 3.3). Including the main effects of other variables in addition to annual effects improved fit over the null model (Table 3.3); however these additional variables were uninformative (Arnold 2010). Competitive models indicated weak evidence that nest survival varied with proportion of herb wetland in territory, day of the breeding season, and distance to nearest neighbor; however, the 95% CI of the beta coefficient for all variables, except year, included zero (Table 3.4).

Effect of Nest Initiation Date

Mean nest initiation date had a non-significant negative relationship with population nest success ( = -5.82 ± 3.70, = 2.47, = 0.19). Earliest mean nest initiation date was June 8 in 2013 ( 48), the breeding season of high nesting success. Mean nest initiation was June 11 in 2010 ( 8) and 2012 ( 44). Mean nest initiation date was June 15 in 2011 ( 3). Nest initiation of Whimbrels occupying

territories dominated by different land cover categories was compared to determine whether certain habitats were used first. Of the nests included in these analyses, 68 were by pairs occupying herb wetland-dominated territories while 35 were by pairs occupying territories dominated by another habitat category. Mean nest initiation in herb wetland- dominated territories was slightly earlier (June 9) than nests in territories dominated by another habitat type (June 11), pooling data across years (Figure 3.1). On average, nest initiation of Whimbrel occupying territories dominated by herb wetland was slightly earlier than the mean initiation date ( ̅ = - 0.07 days ± 0.11 SE, = 68). In contrast, individuals occupying territories dominated by another land cover category generally initiated nests later than the mean initiation date ( ̅ = 0.14 days ± 0.18 SE, = 35).

Difference in nest initiation between habitats was not statistically significant (one-tailed two-sample t-test assuming equal variance = -1.06, = 0.15).

Annual Variation in Nest Success

No winter ( = 0.50, = 0.72) or spring ( = 2.13, = 0.27) weather variables significantly affected nest success (Table 3.5). At the same time, the breeding season of 2013 with substantially higher nest survival corresponded to the warmest mean spring temperature (Figure 3.2).

Discussion

Individual Variation in Nest Success

Nest success was not explained by land cover at the territory scale, as defined by the GIS layer. Similarly, nest success of Whimbrels in the Churchill region was not predicted by microhabitat (nest-site) and mesohabitat (presumed territory) characteristics

in a previous study (Ballantyne and Nol 2011). Estimates of DSR (0.917-0.986) and corresponding nest success (2.5-70.8%) obtained in this study were highly variable but overall (DSR = 0.957, nest success = 33.2%) and mean (DSR = 0.933, nest success =

17.7%) were within the range of previous reports. Recent nest success estimates from

Churchill were 14-26% (Ballantyne and Nol 2011). Similarly, combining 2006-2007 data from the Mackenzie Delta region, DSR of 0.932-0.985 and corresponding nest success of

18.6-69.2% varied with habitat and were estimated using the similar Mayfield method and an incubation period of 24 days (Pirie 2008). DSR and corresponding nest success, also calculated using the Mayfield method and an incubation period of 24 days, was higher in Churchill for the combined breeding seasons of 1973 and 1974 at 0.974-0.994 and 53.7-86.1%, depending on habitat (Skeel 1983). Raising DSR estimates to a 25-day incubation period instead of a 24-day incubation period decreases nest success by almost

2%.

Hatching success is generally high for birds breeding in the Churchill region, and

Whimbrel is one of a few exceptions with most eggs being lost to predation (Jehl 1971).

Neither nest initiation date nor predominant land cover of territory significantly predicted inter-individual variation in nest success. Northern breeding shorebirds that occupy a relatively homogenous landscape that lacks structural complexity are unlikely most limited by suitable nest-sites and territories; thus reproductive success of these birds may be determined by factors other than physical habitat characteristics (Smith et al. 2007).

Habitat could contribute to nest fate if habitats differed in other aspects, such as predator communities (Smith et al. 2007). Another source of between-individual variation in nest success may be parental behaviour. Incubation recess and restless movements (e.g., egg

rolling, nest maintenance, preening) of incubating parent birds were shown to increase the risk of nest predation (Smith et al. 2012). This is because conspicuous behaviours, especially for a large bird, could disclose nest location to predators that use visual cues

(Smith et al. 2012). Furthermore, more time spent away from the eggs while foraging may increase their likelihood of being detected by a predator and followed back to the nest location (Smith et al. 2012). Whimbrel are the largest shorebirds in Churchill and anti-predator behaviour involves alarm calling, chasing in the air, and distraction displays

(Skeel and Mallory 1996). The variation in this behaviour and its implications on nest fate could be a topic of future studies.

Inter-annual Variation in Nest Success

The increase phase of small mammal populations is thought to be associated with winter reproduction, which may be more favorable in deep, less dense snow conditions because of warmer, less variable ground temperatures (Gauthier and Berteaux 2011,

Duchesne et al. 2011, Bilodeau et al. 2013). I predicted that winter reproduction of small mammals would correspond to winters with greater mean temperatures and total precipitation and consequently, relieve shorebirds of high mammalian predator pressure in the ensuing spring and summer. I may have failed to show a significant effect of winter

(Dec 1-March 31) weather on inter-annual variation in Whimbrel nest success because total winter precipitation may not adequately represent snow conditions relevant to small mammals. During the winter, total precipitation (mm) represents the water equivalent of snowfall, which depends on snow density (Roebber and Bruening 2003). Snow density can be affected by atmospheric conditions and surface processes that affect size and shape of ice crystals; for example, wind moving snow causes ice crystals to fracture,

resulting in compaction (Roebber and Bruening 2003). Consequently, total precipitation may be the same in different years yet one year may be characterized by deeper, less dense snow, preferred by small mammals. Furthermore, if some precipitation fell as rain or freezing rain, then small mammals would be negatively affected (Kausrud et al. 2008).

Much of the arctic receives less snowfall than depths (60-120 cm) that can increase the probability of small mammal occurrence (Duchesne et al. 2011). However, if it is redistributed by the wind, it can be substantially deeper where it is trapped by heterogeneous topography and vegetation (Gauthier and Berteaux 2011, Duchesne et al.

2011). Snow depth, which is the mean accumulated snow on the ground measured at several points that appear representative of the immediate area, is an alternative measurement that could have been used, but for which missing data were extensive or absent for a number of years. Similarly, data on total snowfall, which is the amount of frozen precipitation, would be valuable. Alternatively, density-dependent factors such as food supply and predator abundance may be more important than winter weather conditions in regulating the population dynamics of small mammals (Oksanen and

Oksanen 1992, Turchin et al. 2000).

While none of the winter weather variables adequately explained inter-annual variation in Whimbrel nest success, further study would be a worthwhile endeavour because most eggs are lost to predation and, as demonstrated by the results of this study, nest failure can be substantial in some years. Furthermore, predator-prey relationships may be altered by changing temperature and precipitation regimes associated with climate change, which has occurred in arctic regions at an accelerated rate compared with lower latitudes (Houghton et al. 2001). Recent patterns of change in small mammal

abundance in other parts of the arctic differs substantially from previous records with less regular, dampened, or no longer existent cyclic population dynamics (Gauthier and

Berteaux 2011). Since successful winter reproduction under the snow is thought to be responsible for the occurrence of a peak in small mammal abundance, increased frequencies of freeze-thaw cycles during the winter and its influence on snow conditions have been invoked as a possible cause for altering patterns of small mammal population dynamics (Ims et al. 2008).

Nesting success of shorebirds and other ground-nesting waterfowl may be negatively affected by the presence of an increased number of geese through apparent competition (Iles et al. 2013, McKinnon et al. 2013). Increasing numbers of geese and their nests may maintain high populations of mammalian predators in years of low small mammal abundance and may increase numbers of avian predators (Iles et al. 2013).

Shorebirds, particularly those nesting in areas of high goose density, may be more susceptible to predators than geese which aggressively defend their nests (McKinnon et al. 2013). The indirect effect of Canada Goose (Branta canadensis), perhaps through enhancing the number of nest predators in the Churchill region, is unknown, but if similar increases have occurred as with Snow Geese in nearby La Perouse Bay (Jefferies et al.

2004), comparable results may be expected (Sammler et al. 2008).

If future shorebird researchers in Churchill undertake an investigation on the importance of predator-prey population dynamics on annual variation in nest success, I would recommend gauging predator pressure using a different method than what I employed here. Collared Lemming (Dicrostonyx richardsoni), a lemming species in the study area (Roth 2002), are known to exhibit cyclic population dynamics (Scott 1993).

Arctic fox pelt harvest data (Iles et al. 2013), daily observations of predators or lemmings

(Smith et al. 2007, 2010b), lemming trapping (Bêty et al. 2001, McKinnon et al. 2013), and lemming nest surveys could be used as improvements over the measure of winter weather that I used here.

Early spring (May 20-June 10) weather also did not adequately explain annual variation in nest success. Of the weather variables investigated, increasing mean early spring temperatures did have a positive effect on nest success that approached significance. The breeding season of 2013 had substantially higher nest success (71% vs. mean of 19% for 2007-2012) and a considerably warmer spring than other years of the study (6°C vs. mean of 2.2°C for 2007-2012). Shorebirds may benefit from warm temperatures during pre-laying and laying periods because most shorebirds are “income breeders”, i.e., use resources acquired from invertebrate prey on the breeding ground for egg formation (Meltofte et al. 2007a). Warmer temperatures promote snowmelt and insect activity, which may allow breeding birds to accumulate sufficient resources to initiate nests early (Meltofte et al. 2007b). There was some evidence of a negative relationship between mean initiation dates and annual nest success. Nest success may be higher when there is an earlier start to the breeding season because of a longer period after loss of the initial clutch in which birds can re-lay (Meltofte et al. 2007b).

Furthermore, mild spring temperatures may reduce energy loss to maintain body temperature (Williamson et al. 2006) and with decreased activity around nest, location may be less likely revealed to predators (Smith et al. 2007, 2012). Perhaps continued monitoring of Whimbrel nests and an increasing sample size of years may yield a significant pattern for future studies. Studies that have found significant effects of annual

fluctuations in weather on avian reproduction have often included more than 10 years of nest monitoring data (Skinner et al. 1998, Hötker and Segebade 2000, Smith et al. 2010b,

Iles et al. 2013).

9 8

6 5 4 Herb wetland

Number nests Number of 3 Other 2 1 0

Initiation date

Figure 3.1 Frequency distribution of nest initiation date for Whimbrel occupying territories of differing habitat near Churchill, Manitoba, Canada, 2010-2013 ( 103).

Data were pooled across years and territory habitat was defined by dominant land cover category of an EOSD land cover map in a 250 m radius around the nest.

70 2013

40 2010

Nest survival (%) survival Nest 30 2007 20 2008 10 2012 2011 0 0 1 2 3 4 5 6 7 Mean spring temperature (°C)

Figure 3.2 Annual nest survival estimates (DSR25) of Whimbrel in the Churchill

Manitoba region against mean spring temperatures (°C) during the pre-laying and early incubation period (Mar 20-June 10). Error bars represent 95% confidence intervals.

Table 3.1 Dates that nest searching commenced, first nest was found, and last nest hatched for Whimbrel over four breeding seasons (2010-2013) in the Churchill, Manitoba region.

Year Start of nest search First nest found Last nest hatched

2010 June 5 June 8 July 13

2011 June 1 June 14 July 15

2012 June 2 June 4 July 18

2013 June 1 June 4 July 18

Table 3.2 Total nests monitored, nest fate, and nest success estimates (apparent and

DSR25) for Whimbrel near Churchill, Manitoba, Canada, 2010-2013. Nests considered successful if at least one chick hatched.

2010 2011 2012 2013 Overall

Total number of nests 30 17 52 57 156

( )

Successful ( ) 15 3 15 42 75

Predation ( , %) 11 (36.7) 12 (70.6) 36 (69.2) 11 (19.3) 70 (44.9)

Abandonment ( , %) 1 (3.3) 2 (11.8) 1 (1.9) 2 (3.5) 6 (3.8)

Unviablea ( , %) 0 0 0 2 (3.5) 2 (1.3)

Unknownb ( , %) 3 (10.0) 0 0 0 3 (1.9)

Apparent success (%) 50.0 17.6 28.8 73.7 48.1

DSRc (95% CI) 0.965 0.863 0.917 0.986 0.957 (0.942-0.980) (0.780-0.919) (0.887-0.940) (0.975-0.992) (0.946- 0.966)d Nest successc, DSR25 41.4 2.5 11.6 70.8 33.2 (%, 95% CI) (22.2-59.9) (0.2-11.9) (5.0-21.4) (53.6-82.6) (25.1-41.6)d a Unviable eggs persisted until expected hatch and were considered successful in MARK estimates of nest survival. b Unknown nests were classified as failed for nest success estimates. c Estimates obtained from program MARK. The fate of 6 nests were determined the day of discovery and excluded from analysis. d used constant DSR model for overall estimates.

Table 3.3 Global, null, and supported candidate models (∆AICc < 4) for daily nest survival estimates of Whimbrels breeding near Churchill, Manitoba, Canada, 2010-2013

( 150). Akaike’s Information Criterion for small sample sizes (AICc), differences in

AICc (∆AICc) from the best fit model AICc, model weight ( ), and number of parameters (K) are provided.

a Model AICc ∆AICc K

275.09 0.00 0.17 4

275.47 0.39 0.14 5

275.70 0.61 0.12 5

275.81 0.73 0.12 5

275.98 0.90 0.11 6

276.27 1.18 0.09 6

276.84 1.76 0.07 6

277.24 2.15 0.06 7

277.71 2.62 0.04 6

277.98 2.90 0.04 7

278.28 3.20 0.03 7

279.26 4.18 0.02 8

319.06 43.98 0.00 1 a Model factors/covariates included: year = annual variation, % herb = proportion of territory covered by herb wetland, T = linear time trend, TT = quadratic time trend, NN = distance to nearest neighbor

Table 3.4 Covariate beta estimates from Program MARK for substantially supported models (i.e., ∆AICc < 2, Table 3.3) of daily survival rates of Whimbrel nests near

Churchill, Manitoba, Canada, 2010-2013 ( 150).

Covariate and model Beta estimate SE 95% LCI 95% UCI

% herb

Model 2 0.541 0.421 -0.285 1.367

Model 5 0.559 0.421 -0.267 1.384

Model 7 0.440 0.440 -0.422 1.302

Linear time trend

Model 3 0.017 0.014 -0.011 0.045

Model 5 0.017 0.014 -0.011 0.046

Model 6 0.018 0.015 -0.010 0.047

Distance to nearest nest

Model 4 -0.0002 0.0001 -0.0004 0.0001

Model 5 -0.0002 0.0001 -0.0004 0.0001

Model 7 -0.0001 0.0001 -0.0004 0.0002

Canada, 2007-2013 ( 6).

Model Predictor a Estimate SE

Winter WT 1.36 6.18 0.85 -0.43

S1 -1.57 1.33 0.36

S2 1.23 1.33 0.45

Spring ST 12.19 6.1 0.14 0.31

RN 0.62 1.12 0.62 a Model predictors included: WT = mean winter (Dec 1-March 30) temperature, S1 = total winter precipitation, S2 = total precipitation over two winters, ST = mean early spring (May 20-June 10) temperature, RN = total spring precipitation

Chapter 4: Abundance and Distribution of Whimbrel Breeding Near Churchill, Manitoba Abstract

I used abundance and habitat relationships to estimate the breeding population size and density of Whimbrel in a 26,000 ha (260 km2) study area near Churchill,

Manitoba, Canada. The number of Whimbrel within 23 randomly surveyed plots was recorded in 2013 and adjusted for detection probability. I developed a generalized linear model (GLM) with a negative-binomial distribution of Whimbrel abundance per plot against proportion of suitable habitat and then applied it to the study area. The usefulness of a preliminary land cover map for predicting Whimbrel abundance based on habitat relationships was assessed from a model that instead incorporated additional physical and biological habitat variables obtained from ground surveys. Distance to nearest neighbor was determined from nests found in the breeding seasons of 2010-2013, contrasted between habitats, and compared with the results of the randomly surveyed plots. A large number of plots containing seemingly appropriate habitat were unoccupied. The importance of structural complexity and invertebrate prey availability during the egg- laying and incubation periods demonstrated that other factors not captured by the land cover map better predicted breeding Whimbrel abundance. The land cover map used may also be inaccurate or not describe habitat appropriate to breeding Whimbrel. Population size and density of breeding Whimbrel in the Churchill region were estimated at 410 individuals ± 230 SE and 3.2 birds/km2 ± 1.8 SE respectively, although proportions of different land cover categories did not adequately predict the number of breeding

Whimbrel. Distance to nearest neighbor was significantly lower in herb wetland than in

other occupied breeding habitats combined, which may suggest higher population density in the former.

Introduction

Arctic and subarctic breeding shorebirds are a dominant component of vertebrate biodiversity in these regions (Meltofte et al. 2007a). Many shorebirds in North America are thought to be declining, including Whimbrel (Morrison et al. 2006, Bart et al. 2007,

Andres et al. 2012a, Ross et al. 2012). Trends and sizes of shorebird populations breeding at high latitudes are difficult to monitor because of challenging weather conditions and terrain and because many species occur in remote areas, are dispersed widely across the vast landscape, and display cryptic behavior (Morrison et al. 2006, NABCI 2012). For this reason, surveying shorebirds is more effective during the nonbreeding season than on their breeding grounds (Morrison et al. 2006). However, estimates from the breeding grounds are valuable to improving our understanding of abundance and distribution of populations during a critical stage in their annual cycle (Bart and Earnst 2002).

Knowledge of breeding population size can provide baseline data which could be used to estimate trends following future surveys (Bart and Earnst 2002). Furthermore, monitoring population size and identifying habitat that supports a high population density are also essential to evaluate the effects of climate change, which is projected to cause significant loss of open habitats on shorebird breeding grounds through drying and subsequent tree and shrub encroachment (Ballantyne 2009). The objective of the Program for Regional and International Shorebird Monitoring (PRISM) is to produce population estimates for each species and subspecies of shorebirds breeding in the North American arctic; however, most of the eastern range of Whimbrel is not included in the arctic survey regions of PRISM (Bart and Smith 2012) and surveys focused on particular species may provide more accurate estimates (Morrison et al. 2006).

The pattern of organisms across a landscape often parallels non-randomly distributed resources or environmental conditions on which their survival and reproduction depend (Jones 2001). Habitats are selected when certain attributes maximize individual fitness (Gunnarsson et al. 2005, Pérot and Villard 2009, Gaillard et al. 2010).

In addition to individual performance, population density may indicate habitat quality

(Johnson 2007, Gaillard et al. 2010). Whimbrels may nest in loose aggregations (Skeel and Mallory 1996, Ballantyne and Nol 2011) implying some non-randomness and potential selection of habitats. Habitats that attract and support higher population densities may indicate quality, as determined from abundance of available resources such as nest-sites, food, and protection from predators and adverse weather (Skeel 1983, Bock and Jones 2004, Placyk Jr. and Harrington 2004, Pérot and Villard 2009, Gaillard et al.

2010). Furthermore, increased proximity to conspecifics may contribute to effective, joint predator defense (Skeel and Mallory 1996).

Studying patterns of occupation or abundance is an approach to identify variation in habitat quality for birds (Johnson 2007); however, heavily used or selected habitats may not necessarily confer high individual fitness if for example, density is inflated by surplus juveniles or inexperienced birds that were forced to disperse from high-quality habitat (Van Horne 1983). Furthermore, habitats modified by human activity may be

“ecological traps” that are unsuitable for reproduction but which nevertheless, attract a large number of birds (Anteau et al. 2012). High-quality habitats from the perspective of a land manager would consider both individual fitness and abundance and be defined as that which maximizes per capita reproduction and contribution to future populations

(Johnson 2007). Although individual fitness may or may not be highest in habitat with

greater population densities, identifying either high-quality habitat or habitat that supports large numbers of a declining species is important because limited funding requires prioritizing habitat for protection (Johnson 2007, De Wan et al. 2009). For example, Brown et al. (2007) conservatively estimated that the number of all shorebirds breeding in part of the Arctic National Wildlife Refuge, Alaska was large enough for the area to receive designation as a site of International Importance under both the Western

Hemisphere Shorebird Reserve Network and the Ramsar Convention, which may discourage development of petroleum reserves believed to occur there. The objective of this study was to identify habitat variables in the Churchill region that are important to determining occupancy and abundance of breeding Whimbrel and to estimate population size and density of this species in the study area to contribute to future monitoring efforts for this declining species.

Methods

Study Area

A preliminary Earth Observation for Sustainable Development (EOSD) land cover map that included the study area of the Churchill region was obtained from the

Manitoba Land Initiative (http://mli2.gov.mb.ca/landuse/index.html). The EOSD land cover maps were created using Landsat 7 images from between 1999 and 2001 and for which classification has had limited analyst intervention. The boundaries of the study area were arbitrarily defined around 9 study sites south of the Hudson Bay, encompassing an area of approximately 26,353 ha or 264 km2 (Figure 4.1). Nest locations were recorded with a Global Positioning System (GPS) and delineated within ArcMap 10.1

(ERSI 2012). Since the resolution of the images was 30 m, larger than the nest-site scale,

the smallest unit of habitat considered was territory, delineated by placing a 250 m buffer around nest. The land cover map used defined the following general habitats types: water, rock/rubble, exposed land, bryoids, treed wetlands, shrub wetlands, herb wetlands, dense coniferous forest, open coniferous forest, and sparse coniferous forest.

Whimbrel Survey

The Churchill region encompassed extensive areas that may support breeding

Whimbrel (Table 4.1) and abundance was determined for randomly selected plots containing a large proportion of appropriate land cover categories. Recent studies (Pirie et al. 2009, Ballantyne and Nol 2011) have shown that Whimbrel is a habitat generalist – individuals nest in both wet, sedge-dominated habitats and upland tundra dominated by lichen and dwarf shrubs, although at lower densities. During the breeding season,

Whimbrel do not use habitats that differ from the nest-site for foraging or resting (Pirie

2008). Appropriate habitat was determined from dominant land cover of territories of 157 nests found over four breeding seasons (2010-2013). I determined proportion of each habitat type within each territory using the Intersect Polygons with Raster tool

(Geospatial Modeling Environment Version 7.2.1, Beyer 2012) in ArcMap 10.1 (ERSI

2012). Nests placed in 250 m circular territories were dominated by the following habitats: herb wetland (112), shrub wetland (34), water (7), exposed land (3), and open coniferous forest (1). In water-dominated territories, the second most dominant land cover was typically shrub wetland or bryoids. Although nests were placed in territories dominated by exposed land or open coniferous forest in four instances (2.5% of nests), nest-sites did not typically include these habitats and I excluded these land cover categories as potential habitat (Ballantyne and Nol 2011).

A double sampling technique was followed to estimate abundance adjusted for detection, as outlined by Bart and Earnst (2002, 2005). Detection was determined from a large sample of plots surveyed a single time using a rapid method (rapid survey plots) and a subset of plots that were surveyed repeatedly throughout the breeding season

(intensive survey plots) (Bart and Earnst 2002, 2005). The detection ratio used to adjust abundance results was determined by dividing the mean number of birds estimated to occur on intensive survey plots using the rapid method by the mean number of birds actually present per plot in the subsample, determined by intensive surveyors (Brown et al. 2007). The rapid survey method employed was an area search in which surveyors covered approximately 10 ha per hour (Bart and Earnst 2002, 2005). Plots were located using coordinates of the corners and center and covered by walking between points in a

“W” pattern, using handheld GPS units to remain within boundaries and ensure the entire plot was covered. Each rapid survey plot was sampled for the number of breeding individuals whose territory centroid was within the plot. A territory in my study was defined as a well-bounded utilized area of an individual, which for Whimbrel can be quite large. Surveyors estimated the number of birds present using behavioral cues indicative of territory establishment or nesting during an approximately two-week period in June 2013 near the beginning of the breeding season (Brown et al. 2007, Stanley and

Skagen 2007). Rapid survey plots were visited once between 08:00 and 18:00 hours from

June 13 through June 29, 2013. Whimbrel is a monogamous species (Skeel and Mallory

1996) so the total number of adults on the plot was determined by assuming that each nest, probable nest, and territorial individual always represented a pair, at least some time during the season (Andres et al. 2012b). Intensive survey plots were visited repeatedly

throughout the breeding season of 2013 to locate all nests and territories of failed breeders (Brown et al. 2007). When determining abundance in intensive survey plots, I accounted for re-nests (Bart & Earnst 2002, 2005). The number of rapid and intensive plots surveyed was 33 and 10 respectively. Rapid surveyors had no knowledge of the numbers of Whimbrel present on each plot (Brown et al. 2007).

The study area was overlaid with a plot-sized grid (600 by 600 m or 36 ha). The size of plot was chosen because if Whimbrel is present, then at least one pair is expected per plot, based on minimum densities reported in the Churchill region (Skeel 1983,

Ballantyne 2009). Each plot was then assigned to its predominant land cover type. The number of rapid survey plots representing each land cover type was determined by whether the habitat was expected to support a high or low abundance of Whimbrel (Table

4.1). Sampling of plots was limited to those that were contained mostly within 2 km of road access and were at least 600 m apart to ensure independence.

Modeling Abundance

Abundance of Whimbrel per plot was modeled against the proportion of herb wetland and the combined proportion of shrub wetland and bryoids. This model was developed to extrapolate results of rapid surveys to estimate population size and density of Whimbrel breeding near Churchill (Bart and Earnst 2005). Rapid surveys of intensive survey plots were excluded from the model because they were not randomly selected

(Brown et al. 2007). I followed a conservative modeling approach outlined in Bart and

Earnst (2005) in which few variables with an expected positive effect on abundance entered the model. I determined the proportion of each habitat that would support breeding Whimbrel within each plot using Intersect Polygons with Raster GME tool

(Geospatial Modeling Environment Version 7.2.1, Beyer 2012) in ArcGIS 10.1 (ERSI

2012). My abundance data did not conform to the equal mean and variance assumption of a Poisson distribution (mean = 1.70, variance = 7.58). The Poisson model did not adequately fit the data because of a large number of zeros, i.e., Whimbrel was absent from many plots (White and Bennetts 1996, Martin et al. 2005). Presence of zero inflation can result from an ecological effect for example, demographic processes or poor habitat quality (disturbances or habitat characteristics not captured by land cover map)

(Martin et al. 2005). A generalized linear model (GLM) with a negative-binomial distribution adequately fit the data and predictors were not highly correlated.

Population density was

Where ̂ was an estimate of density if rapid surveys covered the entire study area and ̂ was the detection ratio (Brown et al. 2007). The numerator was obtained by applying the regression model to the study area, which was treated as a single plot (Bart and Earnst 2005). Variance of population density was determined using the standard formula for the estimated variance of a ratio (Cochran 1977). Estimated population size

̂ was then determined by multiplying population density by the area of suitable habitat in the study area and the variance of ̂ was estimated as ̂( ̂) ̂ (Brown et al.

2007).

To evaluate the utility of the land cover map as a tool in estimating Whimbrel population size and density, abundance was modeled against physical and biological habitat variables not captured by the land cover categories. In order to minimize disturbance to nesting birds, line transects were established and surveyed for habitat at

the end of the breeding season. I recorded observations at a point every 3 m along four 60 m transects, each of which was randomly placed within a quadrant of the plot. As predictors, I included the total number of individual woody plants (trees and shrubs) >

0.5 m high (VEG). I also included the total number of hummocks < 0.5 m high

(HUMMOCK) as an index of structural complexity. The variable representing structural complexity did not include hummocks greater than 0.5 m high because landscape features that exceeded the height of Whimbrel were thought to hinder detection of predators. An index of predator pressure was determined from the maximum number of predators observed at the plot (PREDATOR). Finally, an index of invertebrate availability at the plot was obtained during egg-laying and incubation (EARLY) and chick-rearing (LATE) periods by determining mean biomass from three pitfall traps separated by 25 m placed within the plot where most easily accessed. Samples were collected following a week, oven dried for 48 hours at 70°C, and then weighed to the nearest mg on an electronic balance (Ganihar 1997, Sample et al. 2013). Selection of the best-fit model was done using an information-theoretic approach (Burnham and Anderson 2002). Relative fit of the models was assessed by the number of AICc (Akaike’s Information Criterion, corrected for small sample size) units from the model with the lowest AICc value

(∆AICc), Akaike weights ( ), and the ratio of weights between sequentially ranked models.

Distance to Nearest Neighbor

Distance to nearest neighbor was determined for all nests and each breeding season using the Distances among Points GME tool (Geospatial Modeling Environment

Version 7.2.1, Beyer 2012) in ArcMap 10.1 (ERSI 2012). I ensured that the nearest

neighbor represented the nest of a different pair for the 10 nests that were known to represent one of multiple nesting attempts. Whimbrel has been reported to nest with no conspecifics nearby (Skeel 1983, Ballantyne and Nol 2011) but five nests found in isolation were excluded because of lower nest search effort in the surrounding area. A log-transformation of distance improved normality and the assumption of homogeneity of variances was met. I pooled data across years; this was justified by a Kruskal-Wallis test

( = 4.16, = 3, = 0.25). A Mann-Whitney-U test was used to determine whether there was a significant effect of dominant land cover category of territory on distance to nearest neighbor.

Results

Rapid surveys were conducted on 33 plots. Approximately half of the plots surveyed (16) were dominated by herb wetland. A large proportion of shrub wetland and water was contained within 14 and two plots respectively. A plot that was dominated by treed wetland was also rapidly surveyed and included in analyses, but because no

Whimbrel were observed, I did not survey any more plots dominated by treed habitat types. Few plots in the study area were dominated by bryoids, a potential breeding

Whimbrel habitat, but six surveyed plots that were dominated by shrub wetland or water also contained > 10% of this land cover category. The range of territorial Whimbrel observed was 2-14 at 18 plots but none were recorded at 15 plots (Figure 4.2). A total of

94 Whimbrel was recorded by rapid surveyors. Of these, 53 birds were in herb wetland- dominated plots, 41 were in shrub wetland, and 9 were in plots containing >10% bryoids.

I observed 42 territorial Whimbrel within the boundaries of 10 intensive survey plots.

The detection ratio ̂ was used to adjust abundance results from the rapid survey

(Bart and Earnst 2005). A detection ratio of 1.31 ± 0.47 SE was obtained. For many shorebirds, detection is < 1 (Brown et al. 2007) but detection ratios > 1 have been calculated (1.27, Bart and Smith 2012). A detection ratio > 1 indicates that rapid surveyors counted more Whimbrel per plot than nests and territorial individuals found by intensive surveyors. This may have occurred because Whimbrel occupy relatively large territories and aggressively defend their nests (Skeel and Mallory 1996); thus, presence of surveyors likely elicited an aggressive response by Whimbrel nesting outside the plots’ boundaries.

Population Size and Density

The negative-binomial regression model used to estimate population size and density included proportion of the plot covered by herb wetland (HERB) and the proportion of the plot covered by shrub wetland and bryoid habitats combined (OTHER); however, neither variable significantly predicted abundance from the rapid survey plots.

For habitat that supports breeding Whimbrel in the entire study area, Whimbrel had an estimated density of 3.2 birds/km2 ± 1.8 SE and the number of individuals was estimated at approximately 410 ± 230 SE. The most recent population estimate for

Whimbrel in the Western Hemisphere is 80,000 individuals, including equal numbers

(40,000) from the eastern and western breeding population (Andres et al. 2012a). The estimate for the eastern breeding population is derived primarily from a maximum estimate of 40,000 birds during spring migration at one stopover site along the Atlantic

Coast (Morrison et al. 2006), assuming that the two populations maintain separation with little overlap (Skeel and Mallory 1996, Morrison et al. 2006). If the mean estimate for the

Churchill population of Whimbrel in the study area is accurate, then it represents approximately 1% of the estimated total size for the eastern breeding population. The large standard error associated with the estimate is because habitat, as defined by the land cover map, poorly predicted the number of breeding Whimbrel present per plot (e.g.,

Figure 4.3).

Measures of habitat not described by the land cover categories of the GIS layer performed better in predicting breeding Whimbrel abundance. The top model retained invertebrate availability during egg-laying and incubation periods (EARLY) and structural complexity (HUMMOCK). The top model ( = 0.44) was favored over the second most supported model by a factor of 1.91. Other supported models (∆AICc < 4) included effects of invertebrate availability during the chick-rearing period, LATE, tall vegetation, VEG, and predators, PREDATORS (Table 4.2); however, model-averaged beta estimates were not significant. Plots with greater invertebrate availability during the egg-laying and incubation periods and enhanced structural complexity corresponded to significantly increased Whimbrel abundance (Table 4.3).

Distance to Nearest Neighbor

Distances between Whimbrel nests varied considerably. The shortest distance between two nests that were known to belong to different pairs of banded birds was only

40 m. The maximum distance between nearest neighbors was almost 2 km (1,971 m).

Overall, the mean distance to a nearest neighbor was 355 m ± 26 SE. On average, the distance to a nearest neighbor in herb wetland-dominated habitats was significantly less

(292 m ± 26 SE) than in other habitats (519 m ± 57 SE) (Mann-Whitney-U = 1408, <

0.001; Figure 4.4).

Discussion

Evaluation of Land Cover Map and Population Size Estimate

Several surveyed plots that contained a substantial proportion of apparent

Whimbrel breeding habitat were unoccupied. The absence of Whimbrel may indicate avoided habitat features not adequately captured by the GIS layer (Jones 2001). Ground habitat surveys demonstrated breeding Whimbrel was more likely to occupy open habitats associated with increased structural complexity and invertebrate prey availability during nest initiation and incubation periods. Low lying and open habitats with increased structural complexity may attract Whimbrel because of greater nest-site availability

(Skeel 1983, Ballantyne and Nol 2011). Whimbrel may also benefit from increased protection from predators, using hummocks as a vantage point or to obscure their approach to the nest (A. Johnson pers. comm.). Similarly, it was suggested that nests and incubating birds benefited from increased camouflage in habitat with complex or irregular structure and joint effort in predator detection and defense (Skeel 1983).

Whimbrel may also forage in areas of increased invertebrate prey availability and nest nearby so that the off-duty parent remains close to aid in detection and defense against predators. At the same time, although structural complexity and invertebrate prey availability during nest initiation and incubation better predicted Whimbrel abundance than variables gleaned from the GIS layer, the model cannot be used to extrapolate across land cover types. Demographic processes may also contribute to absence from seemingly appropriate habitat. For example, plots that were devoid of breeding Whimbrel may be receptor habitat patches sustained only by emigration from a source population where in some years, local extinction can occur (Hansson 1977).

The land cover categories used to describe Whimbrel breeding habitat may have been either too general or lacked relevance to the species (Bart and Earnst 2005, Andres et al. 2012b). For example, surveyed habitat included shrub wetland, which was defined by the land cover map as areas where the water table was at, near, or above the surface and the majority of vegetation was shrubs of any height, i.e., either tall (≥ 2 m) and/or low (< 2 m) shrubs. Whimbrels likely avoid breeding in areas of tall or dense shrubs, especially if vision of predators is obscured (Götmark et al. 1995, Whittingham et al.

2002).

Whimbrel may have been absent from plots because the EOSD land cover map was not very accurate. When I ground-truthed plots, I found that a number contained a proportion of unsuitable breeding habitat that was larger than anticipated from the land cover map. This may have occurred because the habitat was simply misclassified or the land cover map was based on imagery from a decade ago and some habitat is highly dynamic (e.g., succession following a forest fire). Following ground surveys, the dominant land cover class was correctly assigned to approximately only 61% of plots.

Classification accuracy can be affected by vegetation structure and topography, producing shadows that increase variability in the spectral characteristics of the habitat

(Brook 2001, Pirie 2008). Level landscapes and habitats with little productivity and/or low forms of vegetative growth are more likely to be assigned to correct land cover classes than undulating areas or habitats with taller, more productive vegetation (Brook

2001, Pirie 2008). The dominant habitat was more often correctly assigned to plots containing mostly herb wetland (75%) than other habitat types (47%) but the difference in accuracy was not statistically significant (Chi-squared test =2.70, = 1, = 0.10).

In conclusion, the land cover map did not adequately predict Whimbrel abundance and its low accuracy raises doubt as to whether it or similar products should be used to extrapolate population size and density.

Despite large standard errors, the mean estimates for population size of 410 individuals and population density of 3.2 birds/km2 were reasonable. From Whimbrel nests located in surveyed areas near Churchill, densities of 1.1 birds/km2 and 1.3 birds/km2 were reported for 2007 and 2008 respectively (Ballantyne and Nol 2011).

Higher densities of Whimbrel in Churchill (5.6-21.6 birds/km2, combining years) have also been observed and varied with habitat (Skeel 1983); however, these estimates were based on locating nesting and territorial pairs but were not corrected for detection.

Elsewhere, Whimbrel nesting density was 2.0 birds/km2 in the Yukon Delta National

Wildlife Refuge, Alaska and 6.6 birds/km2 in the Mackenzie delta region, Northwest

Territories; however, estimates were derived from one study site and habitat type respectively (McCaffery 1996, Pirie 2008).

To improve efficiency of future surveys, plots with ≥ 50% water cover should be excluded (Andres et al. 2012b) because of increased difficulty to navigate terrain. More precise estimates could be obtained if a more accurate or detailed land cover map was used. Specific vegetation classes could then be combined into land cover categories more relevant to breeding Whimbrel. Although there are only four PRISM classes (wetland, vegetated upland, dry, and water), each is derived from more detailed land classifications

(Rausch and Johnston 2012). A detailed land cover map for Wapusk National Park and the Cape Churchill Wildlife Management Area was developed (Brook 2001); however, the boundary did not extend into all surveyed areas of this study. Once a model that

accurately portrays the distribution of breeding Whimbrel is developed, knowledge may contribute to estimates of population size in locations that have not been surveyed (Bart and Earnst 2005).

Distance to Nearest Neighbor and Nest Spacing

Similar to other studies (Skeel 1983, Ballantyne and Nol 2011), Whimbrel pairs were observed nesting in areas with no conspecifics nearby (maximum distance between pairs was 2 km) and in loose aggregations (minimum distance was 40 m). The overall mean distance between nearest neighbors was 355 m ± 26 SE ( 152 nests). Including nests found in isolation, the mean distance between nearest neighbors was 460 m ± 37 SE

( 157 nests). These values are consistent with recent studies on Whimbrel near

Churchill (Ballantyne and Nol 2011) that observed a mean distance to nearest active nest of 481 m ± 63 SE ( 83 nests). Whimbrel occupying herb wetland-dominated territories were significantly closer to conspecifics (292 m ± 25.9 SE) than Whimbrel occupying territories dominated by other land cover categories (519 m ± 57.3 SE).

Previous studies conducted in Churchill also observed that distance between conspecifics varied with habitat but in all instances, the mean of 213-293 m (Skeel 1983) was smaller than recent estimates. Distance to nearest neighbors was similarly small (278 m ± 39 SE) in the Mackenzie delta region, although that value was obtained from nests in optimal wet-sedge habitat only (Pirie 2008). My results may suggest higher population density in herb wetland habitat than in other habitats. This contrasts with the results of the rapid surveys that did not find evidence of significantly higher Whimbrel abundance on plots with a greater proportion of herb wetland.

Densely populated habitats may indicate high-quality if Whimbrel occupying these territories experience higher fitness. Habitats with high population densities may have an abundance of resources or presence of nearby conspecifics may contribute to joint defense against predators (Skeel 1983). Previous work (Skeel 1983) showed that hummock-bog habitat in a site west of the airport and north of the tree line with a higher population density corresponded to increased daily survival rate of nests (Skeel 1983).

Since the 1970s, during which Skeel (1983) conducted fieldwork, fewer individuals have been found nesting in the historically highly used area due to evidence of shrub encroachments, tree line advancement, and decreased water cover (Ballantyne 2009).

Decreasing numbers in this area may also be the result of population declines in the

Churchill region (Jehl and Lin 2001). There is no recent evidence to suggest that current habitat occupied by Whimbrel breeding near Churchill corresponds to differences in nest success (Ballantyne and Nol 2011). However, Whimbrel habitat may still represent a range in quality if selecting among factors other than physical attributes such as predator pressure, competition, and food availability (Jones 2001, Gaillard et al. 2010).

Conversely, population density may be inflated in these habitats (Van Horne

1983, Bock and Jones 2004) and individuals may be subject to increased competition or predation. Relative to other shorebirds, Whimbrel is large and nests are exposed with reduced vegetative cover. Whimbrel actively defends their nests but cryptic coloration of eggs may thwart predation. High nesting densities may decrease the effectiveness of camouflage because predators may concentrate their search efforts around initial discovery (Page et al. 1983, O’Reilly and Hannon 1989). Furthermore, predators may congregate in areas where prey items have been discovered before (Page et al. 1983).

An absence of recent evidence to suggest advantages associated with one habitat type over another may be because the variable habitats Whimbrel occupy in Churchill may represent a compromise. Individuals in habitats with low population density may experience reduced competition and/or detection while bird in habitats that have higher population density may benefit from either abundant resources and/or increased vigilance, but suffer increased detection.

Figure 4.1 Study area was arbitrary defined around nine study sites (indicated by stars) within approximately 2 km of road access south and east of Churchill, Manitoba, Canada.

Land cover categories were defined by the EOSD land cover map, created from Landsat

7 images, 1999-2001, with limited analyst intervention.

8 Rapid plot survey

6 Intensive plot survey Number plots Number of 4

0 0 1-3 4-6 7-9 10-12 Number of Whimbrel observed per plot Figure 4.2 Frequency distribution of number of breeding Whimbrels observed per 36 ha plot by rapid surveyors ( 33), 10 of which were also repeatedly searched by intensive surveyors in the Churchill, Manitoba region, 2013.

8 7 6 5 4 3 2

Number of Whimbrel recorded Whimbrelrecorded Number of 1 0 0.00 0.20 0.40 0.60 0.80 1.00 Proportion of herb wetland

700

600

500

400

300

200 Distance to nearest (m) nest nearest to Distance 100

0 herb wetland other Territory habitat

Manitoba, Canada. Territory was delineated as a 250 m radius around the nest ( 152).

Land cover category Plots Study area

Water 18.0 28.2

Rock/rubble 0 0

Exposed land 2.2 5.9

Bryoids 4.4 3.1

Treed wetland 5.4 10.2

Shrub wetland 29.9 30.8

Herb wetland 34.8 14.3

Dense coniferous 0.2 0.7

Open coniferous 1.2 2.3

Sparse coniferous 3.4 4.3

Table 4.2 Candidate negative-binomial models of breeding Whimbrel abundance, near

Churchill, Manitoba, 2013 ( 23). Model predictors included only physical and biological habitat variables measured from ground surveys at random, rapidly surveyed plots. Akaike’s Information Criterion for small sample sizes (AICc), differences in AICc

(∆AICc) from the best fit model AICc, model weight ( ), log likelihood, and number of parameters (K) are provided for supported models (∆AICc < 4).

a Model AICc ∆AICc Log K

likelihood

75.33 0.00 0.44 -32.55 4

76.61 1.28 0.23 -31.54 5

77.53 2.20 0.14 -33.65 4

78.18 2.85 0.10 -32.32 5

78.59 3.26 0.09 -32.53 5 a Global model predictors included the following indexes: = invertebrate biomass during nesting and incubation, = invertebrate biomass during chick rearing, = structural complexity, = density of tall shrubs and trees, and predator = predator pressure

Parametera Estimate SE

-5.228 1.772 0.003

8.752 2.710 0.007

0.1381 2.848 0.004 a Top model predictors retained the following indexes: = invertebrate biomass during nesting and incubation and = structural complexity

Chapter 5 General Conclusion

Estimating adult survival of Whimbrel was of interest because high survival can compensate for poor reproductive potential in long-lived iteroparative species

(Sandercock 2003, Ballantyne 2009). Apparent survival of 0.73 ± 0.06 (95% CI: 0.60-

0.83) was lower than expected based on estimates for other similar-sized shorebirds

(Marks and Redmond 1996, Gill et al. 2001, Roodbergen et al. 2008, Nol et al. 2012) and maximum longevity records (Robinson and Clark 2012, Klima et al. 2013). At the same time, the upper 95% confidence limit of apparent survival obtained from this study is within range of estimates for the western breeding population of Whimbrel obtained on the wintering ground of Chiloé Island, Chile (0.82, B. Andres pers. comm.). A number of non-exclusive hypotheses were suggested for the lower than expected rate of apparent annual survival. Accurate estimates of survival require relatively long-term and intensive study because time-dependent apparent survival and encounter probability cannot be distinguished in the final year. Banding of Whimbrel began in 2010, but efforts to re- sight were opportunistic prior to 2012; thus, the re-sighting based survival rate is primarily derived from only one year. Whimbrel breeding near Churchill have relatively large territories (mean distance between nearest active nests = 355 m ± 26 SE) and can move a substantial distance (at least 4 km) from their nest-site of the previous year, although large distance dispersal may be a rare event (7% of inter-annual dispersal distances). If some banded Whimbrel moved more than a few kilometers and went undetected, then emigration may have been mistaken for mortality, thereby underestimating true survival. Finally, if estimates were representative of the Churchill population then high rates of adult mortality may be a factor contributing to population

decline observed for the eastern breeding population in particular (Bart et al. 2007, Watts and Truitt 2011, Ross et al. 2012). Quantifying what seems to be a straightforward demographic parameter is much more challenging than it first seems, because survival can be affected by factors at any stage in their annual life-cycle. In the meantime, intensive efforts to re-sight Whimbrel near Churchill should continue while there is a population of 90 marked adults, in order to reduce the large standard error and confidence intervals associated with the current survival estimates. Increased efforts to band and re- sight birds could occur in other locations of the species’ breeding range or on wintering grounds of the eastern breeding population to address whether apparent survival varies spatially or temporally. Finally, although progress has been made following media attention to hunting in countries where it is not prohibited (e.g., bag limits of 20 birds per hunter instituted in Guadeloupe, McClain 2013, Sorenson 2013), efforts to quantify hunting pressure and its potential impact on shorebird populations should be further investigated. Compounded with relatively low nest success, the current estimate of

Whimbrel adult survival near Churchill may suggest an unsustainable population for the species in the study area. The results of this study are consistent with evidence of population decline and/or shifting distribution of Whimbrel in the study area over the last

50 years (Skeel 1983, Jehl and Lin 2001, Ballantyne 2009).

Recent estimates of Whimbrel nest success (Pirie 2008, Ballantyne 2009,

Ballantyne and Nol 2011) have been lower than previous reports (Jehl 1971, Skeel 1983,

Grant 1991). In spite of this, the range in annual nest success exhibited by Whimbrel breeding near Churchill in only four years was substantial (2.5-70.8%); this degree of variation was not fully realized prior to this study. Year-to-year differences in

productivity have been reported for other arctic and subarctic breeding birds (Smith et al.

2007, Iles et al. 2013). As long as there is shorebird research effort in the Churchill region study area, Whimbrel nest success should continue to be monitored to determine whether there is an overall downward trend or higher estimates typical of earlier studies were by chance, with data collected during a productive breeding season, as I observed during the summer of 2013.

Annual variation in nest success and breeding phenology have been shown, in a number of studies to be driven by changes in predator-prey dynamics and local climate

(Bêty et al. 2001, Smith et al. 2007, 2010b, Iles et al. 2013, McKinnon et al. 2013).

Although I found no significant evidence of this hypothesized relationship, Whimbrel productivity is likely affected by predator abundance, as nest loss to predation for this species can be particularly high, owing to its conspicuous behaviour and relatively exposed nests. Use of cameras to monitor nest activity has been beneficial in other studies (Liebezeit and Zack 2008, McKinnon and Bêty 2009) and may be used to reveal differences in parental behaviour and composition of nest predators for Whimbrel near

Churchill. If mammalian nest predators are more important than avian predators, it would be worth further investigating parallel annual fluctuations in nest success and abundance of small mammals and predators. Simply developing the habit of recording observer effort (hours in the field) and the number of predators observed could produce a useful index of predator pressure (Smith et al. 2010b). If avian predators also contribute significantly to nest failure, then between individual differences in experience, incubation, or anti-predator behaviour would also be interesting to investigate. In summary, further study is needed to gain an understanding of the effect of predators and

food availability on Whimbrel nest success both between individuals and years and would assist in evaluating the effect of climate change on ecological dynamics.

Nest success of Whimbrel and other shorebirds can be low but survival of chicks from hatching to fledging also impacts reproductive potential of shorebirds. There is a lack of studies on hatchling and juvenile survival. Fledgling success could be estimated by tracking broods using parental behaviour (Ruthrauff and McCaffery 2005) or tracking technology. The number of chicks opportunistically banded in the study area was 58 and can be used to quantify natal philopatry and hatchling survival, with continued population monitoring and long-term study (Nol et al. 2010). Rates of natal philopatry are typically low for shorebirds (Nol et al. 2010, Lishman et al. 2010); thus either directly following chicks until they fledge on their breeding ground and/or estimating juvenile survival from wintering grounds where they usually remain until their third year would be more appropriate to obtain age-specific rates of survival (Marks and Redmond 1996).

There is little evidence that type of land cover near Churchill conveys a fitness advantage since survival and nest success did not vary significantly between habitats defined by the EOSD land cover map. Although distance between conspecifics was significantly closer in herb wetland (292 m ± 26 SE) than the combination of other occupied habitats (519 m ± 57 SE), increasing proportion of particular land cover classes did not correspond to significantly higher abundance. Confidently describing distribution and abundance of Whimbrel and other animals will depend on the development of an updated and detailed land cover map from which to construct habitat classes relevant to the species or group of species under investigation. A detailed land cover map is currently lacking for the immediate Churchill area. In conclusion, coupled with

observations from other studies (Ballantyne 2009, Pirie et al. 2009, Ballantyne and Nol

2011), habitats that Whimbrel occupy may vary in density, as measured in this study by distances between conspecifics. These habitats may represent a trade-off between food availability, predation, and competition as there is little evidence to suggest that habitats influence individual performance (Ballantyne and Nol 2011). In addition to high densities of Whimbrel, herb wetland habitat in the Churchill region supported other shorebird populations and the loss of this habitat to effects of climate change or development could have detrimental effects on shorebird numbers in the area.

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Appendix 1. NAD83 decimal degree coordinates of Whimbrel nest-sites located in the

Churchill area, Manitoba, Canada, 2010-2013.

Nest ID Year Latitude (°N) Longitude (°W) wh01c 2010 58.74154 -93.87703 wh01u 2010 58.68105 -93.83875 wh02c 2010 58.74047 -93.96871 wh02u 2010 58.67179 -93.85012 wh03c 2010 58.67048 -93.83334 wh03u 2010 58.67037 -93.82178 wh04c 2010 58.65607 -93.81184 wh04u 2010 58.66745 -93.82732 wh05c 2010 58.67425 -93.82906 wh05u 2010 58.73439 -93.79714 wh06u 2010 58.73150 -93.80631 wh07c 2010 58.74478 -93.97173 wh07u 2010 58.74060 -93.82410 wh08u 2010 58.72974 -93.78759 wh09c 2010 58.67757 -93.83364 wh09u 2010 58.73987 -94.05946 wh10c 2010 58.74427 -93.96782 wh10u 2010 58.73399 -93.80250 wh11c 2010 58.74168 -94.08275 wh11u 2010 58.74671 -94.05221 wh12c 2010 58.68670 -93.84119 wh12u 2010 58.74619 -94.05269 wh13c 2010 58.68463 -93.83241 wh13u 2010 58.74490 -93.87836 wh14u 2010 58.66742 -93.82500 wh15c 2010 58.67234 -93.82721 wh15u 2010 58.67537 -93.83182 wh16c 2010 58.67908 -93.83677 wh16u 2010 58.74058 -93.87782 wh17c 2010 58.74389 -93.87881 wh17u 2010 58.65611 -93.80555 11WHIM01 2011 58.65582 -93.80679 11WHIM02 2011 58.67884 -93.83403 11WHIM03 2011 58.74198 -93.96554 11WHIM04 2011 58.66744 -93.82594 11WHIM05 2011 58.68247 -93.82373 11WHIM06 2011 58.68052 -93.83297 11WHIM08 2011 58.67712 -93.82582

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11WHIM09 2011 58.74230 -93.87705 11WHIM10 2011 58.74192 -93.88229 11WHIM12 2011 58.66908 -93.83398 11WHIM13 2011 58.67835 -93.83266 11WHIM14 2011 58.68063 -93.82753 11WHIM15 2011 58.74421 -93.97151 11WHIM16 2011 58.74363 -93.96476 11WHIM17 2011 58.74390 -93.87945 11WHIM18 2011 58.73814 -93.79166 11WHIM19 2011 58.73383 -93.77954 12WHIM01 2012 58.68122 -93.83839 12WHIM02 2012 58.68871 -93.84135 12WHIM03 2012 58.68528 -93.83198 12WHIM04 2012 58.74331 -93.88131 12WHIM05 2012 58.73214 -93.78886 12WHIM06 2012 58.73137 -93.78299 12WHIM07 2012 58.67415 -93.83268 12WHIM08 2012 58.67655 -93.83021 12WHIM09 2012 58.67759 -93.82763 12WHIM10 2012 58.72960 -93.76613 12WHIM11 2012 58.67298 -93.82743 12WHIM12 2012 58.67999 -93.83627 12WHIM13 2012 58.66895 -93.82416 12WHIM14 2012 58.74135 -93.80505 12WHIM15 2012 58.65863 -93.78293 12WHIM16 2012 58.73368 -93.79562 12WHIM17 2012 58.66750 -93.82828 12WHIM18 2012 58.66763 -93.83261 12WHIM19 2012 58.72347 -93.79930 12WHIM20 2012 58.66600 -93.85146 12WHIM21 2012 58.68347 -93.83404 12WHIM22 2012 58.75302 -93.92422 12WHIM23 2012 58.68139 -93.83266 12WHIM24 2012 58.74270 -93.96596 12WHIM25 2012 58.67112 -93.81925 12WHIM26 2012 58.67151 -93.81022 12WHIM27 2012 58.65623 -93.80432 12WHIM28 2012 58.73279 -93.80662 12WHIM29 2012 58.69060 -93.83527 12WHIM30 2012 58.67863 -93.83062 12WHIM31 2012 58.74514 -93.96037 12WHIM32 2012 58.72714 -93.77466 12WHIM33 2012 58.67162 -93.81545

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12WHIM34 2012 58.69269 -93.84423 12WHIM35 2012 58.74419 -93.87750 12WHIM36 2012 58.74232 -93.96229 12WHIM37 2012 58.65743 -93.80822 12WHIM38 2012 58.73376 -93.78773 12WHIM39 2012 58.75527 -93.83816 12WHIM40 2012 58.73836 -94.08052 12WHIM41 2012 58.74665 -93.88197 12WHIM42 2012 58.74321 -93.88184 12WHIM43 2012 58.67872 -93.83128 12WHIM44 2012 58.65709 -93.79739 12WHIM45 2012 58.65924 -93.78239 12WHIM46 2012 58.65629 -93.78598 12WHIM47 2012 58.68468 -93.84239 12WHIM48 2012 58.74169 -93.88089 12WHIM49 2012 58.74258 -93.87696 12WHIM50 2012 58.66908 -93.80406 12WHIM51 2012 58.72760 -93.77012 12WHIM52 2012 58.72868 -93.76813 13WHIM01 2013 58.66963 -93.82301 13WHIM02 2013 58.65864 -93.81145 13WHIM03 2013 58.65932 -93.80951 13WHIM04 2013 58.66314 -93.85360 13WHIM05 2013 58.66179 -93.85237 13WHIM06 2013 58.72603 -93.77465 13WHIM07 2013 58.67693 -93.83254 13WHIM08 2013 58.68014 -93.82543 13WHIM09 2013 58.68101 -93.83661 13WHIM10 2013 58.69230 -93.84445 13WHIM11 2013 58.68007 -93.82985 13WHIM12 2013 58.67997 -93.83051 13WHIM13 2013 58.68258 -93.82549 13WHIM14 2013 58.68330 -93.82573 13WHIM15 2013 58.75651 -93.92085 13WHIM16 2013 58.74485 -93.95925 13WHIM17 2013 58.74298 -93.96491 13WHIM18 2013 58.66868 -93.82753 13WHIM19 2013 58.74399 -93.80781 13WHIM20 2013 58.65736 -93.80678 13WHIM21 2013 58.68801 -93.83275 13WHIM22 2013 58.69071 -93.83025 13WHIM23 2013 58.68252 -93.83502 13WHIM24 2013 58.67657 -93.82786

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13WHIM25 2013 58.72679 -93.77175 13WHIM26 2013 58.67821 -93.83500 13WHIM27 2013 58.66843 -93.82078 13WHIM28 2013 58.66980 -93.81661 13WHIM29 2013 58.67289 -93.82797 13WHIM30 2013 58.74268 -93.87752 13WHIM31 2013 58.74196 -93.87806 13WHIM32 2013 58.74276 -93.88230 13WHIM33 2013 58.68620 -93.83062 13WHIM34 2013 58.68428 -93.84258 13WHIM35 2013 58.65673 -93.79002 13WHIM36 2013 58.66335 -93.84949 13WHIM37 2013 58.66722 -93.84956 13WHIM38 2013 58.69291 -93.84515 13WHIM39 2013 58.65896 -93.81299 13WHIM40 2013 58.66333 -93.81094 13WHIM41 2013 58.65261 -93.79066 13WHIM42 2013 58.67909 -93.83589 13WHIM43 2013 58.74575 -94.04753 13WHIM44 2013 58.74397 -93.96985 13WHIM45 2013 58.72849 -93.76778 13WHIM46 2013 58.68052 -93.82253 13WHIM47 2013 58.67183 -93.81191 13WHIM48 2013 58.67525 -93.82287 13WHIM49 2013 58.68178 -93.81889 13WHIM50 2013 58.67397 -93.82590 13WHIM51 2013 58.73736 -93.88436 13WHIM52 2013 58.74461 -93.95634 13WHIM53 2013 58.67916 -93.82799 13WHIM54 2013 58.74488 -93.79278 13WHIM55 2013 58.73458 -93.78281 13WHIM56 2013 58.73368 -93.73524 13WHIM57 2013 58.72784 -93.79974

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Appendix 2a. Cormack-Jolly-Seber candidate models for estimates of apparent survival

( ) and recapture probability ( ) from data modified to demonstrate the effects of moderate nonbreeding season mortality on a population of Whimbrel breeding near

Churchill, Manitoba, Canada, 2010-2013 ( 72). Quasi-Akaike’s Information Criterion for small sample sizes (QAICc), differences in QAICc (∆QAICc) from the best fit model

QAICc, model weight ( ), and number of parameters (K) are provided.

a b Model QAICc ∆QAICc K

94.826 0.000 0.505 4

96.657 1.831 0.202 2

97.038 2.212 0.167 5

97.596 2.770 0.126 4 a Model factors included: t = annual variation, c = constant b Corrected Quasi-Akaike’s Information Criterion, where estimated ĉ = 1.567

118

Appendix 2b. Cormack-Jolly-Seber candidate models for estimates of apparent survival

( ) and recapture probability ( ) from data modified to demonstrate the effects of high nonbreeding season mortality on a population of Whimbrel breeding near Churchill,

Manitoba, Canada, 2010-2013 ( 72). Quasi-Akaike’s Information Criterion for small sample sizes (QAICc), differences in QAICc (∆QAICc) from the best fit model QAICc, model weight ( ), and number of parameters (K) are provided.

a b Model QAICc ∆QAICc K

122.874 0.000 0.641 4

125.055 2.181 0.215 5

127.024 4.150 0.080 4

127.512 4.638 0.063 2 a Model factors included: t = annual variation, c = constant b Corrected Quasi-Akaike’s Information Criterion, where estimated ĉ = 2.054

119

Appendix 3. EOSD land cover map class legend

Land cover class Description

Rock/rubble Bedrock, rubble, talus, blockfield, rubbley mine spoils, or lava bed

Exposed land River sediments, exposed soils, pond or lake sediments, reservoir

margins, beaches, landings, burned areas, road surfaces, mudflat

sediments, cutbanks, moraines, gravel pits, tailings, railway

surfaces, buildings and parking, or other non-vegetated surfaces

Water Lakes, reservoirs, rivers, streams, or salt water

Bryoids Bryophytes (mosses, liverworts, and hornworts) and lichen;

minimum of 20% ground cover or one-third of total vegetation

must be a bryophyte or lichen

Treed wetland Land with a water near/at/above soil surface for enough time to

promote wetland or aquatic processes; the majority of vegetation is

coniferous, broadleaf, or mixed wood

Shrub wetland Land with a water near/at/above soil surface for enough time to

promote wetland or aquatic processes; the majority of vegetation is

tall (≥ 2m), low (< 2m), or a mixture of tall and low shrub

Herb wetland Land with a water near/at/above soil surface for enough time to

promote wetland or aquatic processes; the majority of vegetation is

herb (vascular plant without woody stem - grasses, crops, forbs,

gramminoids)

Dense coniferous Greater than 60% crown closure; coniferous trees are 75% or more

of total basal area

120

Open coniferous 26-60% crown closure; coniferous trees are 75% or more of total

basal area

Sparse coniferous 10-26% crown closure; coniferous trees are 75% or more of total

basal area

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