Eastern Bluebird (Sialia Sialis) Survival and Dispersal in Areas With

EASTERN BLUEBIRD (SIALIA SIALIS) SURVIVAL AND DISPERSAL IN AREAS WITH

HIGH AND LOW DENSITIES OF RED IMPORTED FIRE ANTS (SOLENOPSIS INVICTA).

JASON DELBERT LANG

(Under the Direction of Patricia Adair Gowaty)

ABSTRACT

Eastern bluebirds (Sialia sialis) and fire ants (Solenopsis invicta) may compete for food, ground arthropods. I describe eastern bluebird survival and dispersal for the 2001 to 2005 breeding seasons at two locations, Athens, Georgia (higher fire ant density) and Clemson, South

Carolina (lower fire ant density). Using program MARK to model bluebird survival rates and

OpenBUGS to model dispersal distances, I found no evidence that fire ants affected eastern bluebird survival or dispersal for these two populations.

Survival: Adult bluebirds had higher survival rates than juveniles; sexes did not differ

(confidence intervals: AHYAthens 0.4832 - 0.5560; AHYClemson 0.4867 - 0.5566; HYAthens 0.1417 -

0.1820; HYClemson 0.0996 - 0.1280). Mean survival rates at Clemson varied up to 23% for adults and 10% for juveniles, while only ~1.5% for adults and 1% for juveniles at Athens. At Clemson, survival was negatively correlated to growing-degree days + precipitation. No variables correlated to Athens bluebird survival rates.

Breeding dispersal: On average 3.2 ± 1.3 % of breeding adults emigrated each year.

Adults dispersed almost twice as far after nest depredation than after other nest failure causes

( predation = 176 ± 288(SD); other = 91 ± 66(SD)). Individuals that changed mates also moved

farther (95% credible intervals; changed mates: 80-233 m, ~ 1 to 3 territories; same mate: 33-141 m, ~ 0 to 2 territories).

Natal dispersal: About 24% of natal dispersers emigrated. A majority of individuals

(91%) stayed within 1500 m of their natal nest (range 0 to ~ 93 km; = 826 m; 95% Credible

Interval: 666 – 1024 m). Natal males and females dispersed similar distances. Individuals from spring broods moved almost twice as far as those from summer broods ( spring= 1146 ± 208 m

(SD); summer= 620 ± 86 m) and showed a positive correlation to adult survival and an inverse correlation to hatch-year survival. Individuals from summer broods remained closer to their natal nest when they were in better condition (more weight relative to tarsus length). Natal dispersal distance distributions support the resource competition hypothesis and suggest search strategies may differ by sex.

INDEX WORDS: breeding dispersal, eastern bluebirds, fire ants, information theoretic, natal dispersal, OpenBUGS, program MARK, resource competition, Sialia sialis, Solenopsis invicta, survival

EASTERN BLUEBIRD (SIALIA SIALIS) SURVIVAL AND DISPERSAL IN AREAS WITH

HIGH AND LOW DENSITIES OF RED IMPORTED FIRE ANTS (SOLENOPSIS INVICTA).

JASON DELBERT LANG

BS, Iowa State University, 1992

MS, University of Georgia, 1998

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2013

JASON DELBERT LANG

EASTERN BLUEBIRD (SIALIA SIALIS) SURVIVAL AND DISPERSAL IN AREAS WITH

HIGH AND LOW DENSITIES OF RED IMPORTED FIRE ANTS (SOLENOPSIS INVICTA).

JASON DELBERT LANG

Major Professor: Patricia Adair Gowaty

Committee: Robert J. Cooper Sidney A. Gauthreaux, Jr. Stephen P. Hubbell H. Ronald Pulliam

Electronic Version Approved:

Maureen Grasso Dean of the Graduate School The University of Georgia December 2013

DEDICATION

I dedicate this dissertation to my parents, Phil and Sheryl Lang, for encouraging me to pursue education in a field I enjoy.

ACKNOWLEDGEMENTS

Thank you to my advisor, Patty Gowaty, and my committee members, Bob Cooper, Sid

Gauthreaux Jr., Steve Hubbell, and Ron Pulliam; I have learned something unique from each of you to help shape my understanding of research and academics. I would not have been able to complete my degree without teaching opportunities through the University of Georgia Franklin

College Writing Intensive Program, Division of Biological Sciences, and Odum School of

Ecology. I greatly appreciate teaching support and mentorship provided by Michelle Ballif, Kris

Miller, Jim Richardson, Mark Bradford, and Gary Barrett. Thanks also to my wife, Carrie

Straight, for being there to lean on every step of the way.

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS...... v

LIST OF TABLES...... ix

LIST OF FIGURES ...... xi

CHAPTER

1 INTRODUCTION AND LITERATURE REVIEW ...... 1

References...... 6

2 EASTRN BLUEBIRD, SIALIA SIALIS, SURVIVAL RATES DEPEND ON AGE

AND POPULATION-LEVEL VARIABLES ...... 11

Abstract...... 12

Introduction...... 13

Methods...... 17

Results...... 26 Introduction #

Discussion...... 28

References...... 36

3 EASTERN BLUEBIRD BETWEEN-SEASON BREEDING DISPERSAL

PATTERNS: PREDATION AND CHANGING MATES INCREASE DISPERSAL

DISTANCE...... 47

Abstract...... 48

Introduction...... 49

Methods...... 52

Results...... 59 Introduction #

Discussion...... 61

References...... 68

4 EASTERN BLUEBIRD NATAL DISPERSAL DISTANCES CORRESPOND TO

THE NEGATIVE BINOMIAL DISTRIBUTION AND THEORETICAL MODELS

FOR MULTIPLE DISPERSERS...... 82

Abstract...... 83

Introduction...... 84

Methods...... 86

Results...... 93 Introduction #

Discussion...... 97

References...... 108

5 HATCHING IN SPRING OR SUMMER, CONSPECIFIC SURVIVAL, AND

NESTLING CONDITION AFFECT NATAL DISPERSAL DISTANCE OF A

CAVITY NESTING PASSERINE ...... 130

Abstract...... 131

Introduction...... 131

Methods...... 133

Results...... 141 Introduction #

Discussion...... 143

References...... 151

6 CONCLUSIONS ...... 163

vii

References...... 167

APPENDICES

2.1: Latitude and longitude of eight field sites near Athens, Georgia and Clemson, South

Carolina used to study eastern bluebirds, Sialia sialis, during the 2001 to 2005

breeding seasons ...... 46

3.1: Proportion of breeding male and female eastern bluebirds that emigrated from sites

near Athens, Georgia (sites 1-4) and Clemson, South Carolina (sites 5-8) during the

2001 to 2005 breeding seasons ...... 81

4.1: R code for simulating natal dispersal distribution (% settling a given number of

territories away from their natal site)...... 123

4.2: Proportion of male and female natal eastern bluebirds that emigrated from sites near

Athens, Georgia (sites 1-4) and Clemson, South Carolina (sites 5-8) during the 2001

to 2005 breeding seasons ...... 128

4.3: Mean territories (numbers) and distances (meters) dispersed, with their 95%

confidence intervals (CI) for eastern bluebird natal dispersers near Athens, Georgia

and Clemson, South Carolina during the 2001 to 2005 breeding seasons...... 129

5.1: OpenBUGS code for the global eastern bluebird dispersal distance model

representing populations near Clemson, South Carolina and Athens, Georgia during

the 2001 to 2005 breeding seasons ...... 161

viii

LIST OF TABLES

Page

Table 2.1: Model comparison for hypothesized variables (fire ant density, distance to edge,

conspecific density, predator density1, and weather2) affecting eastern bluebird, Sialia

sialis, survival rates at sites near Athens, Georgia and Clemson, South Carolina during

the 2001 to 2005 breeding seasons ...... 40

Table 2.2: Yearly recapture rate estimates for female (F) and male (M) hatch-year (HY) and

after-hatch-year (AHY) eastern bluebirds, Sialia sialis, near Athens, Georgia and

Clemson, South Carolina ...... 41

Table 2.3: Survival probabilities for hatch-year (HY) and after-hatch-year (AHY) female (F) and

male (M) eastern bluebirds, Sialia sialis, near Athens, Georgia and Clemson, South

Carolina during the 2001 to 2005 breeding seasons ...... 42

Table 3.1: Difference in average number of fledglings per nest between year t and t+1 for male

and female eastern bluebirds, Sialia sialis, that nested at the same (philopatric) or

different breeding site (emigrants) the following year; near Athens, Georgia and

Clemson, South Carolina during the 2001 to 2005 breeding seasons...... 74

Table 3.2: Model rankings for four potential correlates to eastern bluebird, Sialia sialis, breeding

dispersal distance; 1) nest success or failure, 2) mate retention or changing mates, 3)

higher or lower conspecific density, and 4) higher or lower fire ant density ...... 75

Table 3.3: Extension of the top two models for eastern bluebird, Sialia sialis, breeding dispersal

distance ...... 76

Table 4.1: Mean territories (numbers) and distances (meters) dispersed, with 95% credible

intervals (CI), for eastern bluebird natal dispersers near Athens, Georgia and Clemson,

South Carolina during the 2001 to 2005 breeding seasons...... 112

Table 5.1: Model comparison for eastern bluebird natal dispersal distance by juveniles near

Clemson, South Carolina and Athens, Georgia during the 2001 to 2005 breeding seasons156

Table 5.2: Mean coefficient estimates and 95% credible intervals for variables in eastern bluebird

natal dispersal distance models...... 157

Table 5.3: Mean coefficient estimates and 95% credible intervals for variables in eastern bluebird

natal dispersal distance models...... 158

Table 5.4: Mean coefficient estimates and 95% credible intervals for variables within the

OpenBUGS models best representing dispersal distances by eastern bluebirds fledging in

the spring when an outlier is included ...... 159

Table 5.5: We tested three hypotheses about natal dispersal distance by eastern bluebirds; H1:

food availability, H2: intraspecific competition, and H3: predation ...... 160

LIST OF FIGURES

Page

Figure 1.1: Study sites near Athens, Georgia (1, 2, 3, 4) and Clemson, South Carolina (5, 6, 7, 8)

where we monitored eastern bluebirds during the 2001 to 2005 breeding seasons...... 10

Figure 2.1: Correlation between survival and weather (growing degree-days + rainfall) for

eastern bluebirds, Sialia sialis, near Clemson, South Carolina during the 2001 to 2005

breeding seasons ...... 44

Figure 2.2: Yearly variation for hypothesized correlates to eastern bluebird, Sialia sialis, survival

rates...... 45

Figure 3.1: Percent of adult male (M) and female (F) eastern bluebirds who remained at, or

emigrated from, one study site (1, 2, 3, 4) to another between consecutive breeding

seasons near Athens, Georgia during 2001 to 2005 (n = 122 observations) ...... 77

Figure 3.2: Percent of adult male (M) and female (F) eastern bluebirds who remained at, or

emigrated from, one study site (5, 6, 7, 8) to another between consecutive breeding

seasons near Clemson, South Carolina during 2001 to 2005 (n = 135 observations) ...... 78

Figure 3.3: The percent of eastern bluebirds, Sialia sialis, estimated to disperse a given distance

(territories represent an average eastern bluebird territory radius, 82 m; Gowaty and

Plissner 1998) compared to 10,000 values drawn randomly from the negative binomial

and gamma distributions...... 79

Figure 3.4: Posterior distribution (95% credible intervals) for eastern bluebird, Sialia sialis,

breeding dispersers whose nests succeeded or failed (A), failed because of predation or

not (B), and for individuals that re-nested with the same mate or changed mates (C)...... 80

Figure 4.1: Representation of rings (circles) of territories (hexagons) surrounding a natal territory

(center) ...... 113

Figure 4.2: Theoretical probability of dispersal away from natal territories by natal dispersers

competing for unoccupied territories (following Tonkyn and Plissner 1991)...... 114

Figure 4.3: Eastern bluebird study sites near Athens, Georgia (1, 2, 3, 4) and Clemson, South

Carolina (5, 6, 7, 8) ...... 115

Figure 4.4: Percent of natal eastern bluebirds moving within and between study sites (1, 2, 3, 4)

near Athens, Georgia during the 2001 to 2005 breeding seasons...... 116

Figure 4.5: Percent of natal eastern bluebirds moving within and between study sites (5, 6, 7, 8)

near Clemson, South Carolina during the 2001 to 2005 breeding seasons...... 117

Figure 4.6: Using rings, the diameter of an average eastern bluebird territory, starting at the

center of a study site and expanding outward, we present the percent of individuals that

fledged from each ring (black bars) and the percentage of fledged individuals that

returned to nest on the study sites (grey bars) based on 2218 fledgings and 199 returns118

Figure 4.7: Distance dispersed by male (black) and female (grey) natal eastern bluebirds during

the 2001 to 2005 breeding seasons near Athens, Georgia and Clemson, South Carolina119

Figure 4.8: Percent of individuals estimated dispersing to each distance category...... 120

Figure 4.9: Percent of natal eastern bluebirds dispersing zero to >21 (represented by "territory"

22) territories from their natal nest ...... 121

xii

Figure 4.10: Percent of natal eastern bluebirds dispersing zero to >21 (represented by "territory"

22) territories from their natal nest ...... 122

xiii

CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

In the dissertation that follows, I examine the influence of red imported fire ants

(Solenopsis invicta) on the survival and dispersal of two eastern bluebird (Sialia sialis) populations breeding in the southeastern United States. Eastern bluebirds are small thrushes that breed in the eastern United States. Males and females are easily distinguishable by their feather coloration; males are brighter blue on their heads, backs, wings, and tails than females (Gowaty and Plissner 1998). Nestling sex can also be determined by feather color when they are thirteen days old (Pinkowski 1974). Bluebirds are secondary cavity nesters and readily use artificial nest boxes. Bluebirds that nest in the Southeast typically raise two broods during a breeding season and remain in the local area year-round (Gowaty and Plissner 1998). They nest in areas near open fields, where they mainly forage on ground arthropods during the breeding season (Gowaty and Plissner 1998). Eastern bluebirds use a perch-to-ground foraging technique ~ 80% of the time (Gowaty and Plissner 1998), making perches an important habitat feature for them

(Pinkowski 1977). These and other eastern bluebird characteristics make them a good species to study. Their use of artificial nest boxes allow researchers to observe a large sample of nests in a short period of time relative to finding and observing natural nests. Since both females and males feed nestlings, we can also use nest boxes to our advantage and capture birds, mark them with individually unique leg band combinations, and assess information about individuals such as weight and size. Bluebirds’ perch-to-ground foraging technique allows researchers to visually observe bluebirds in the open habitats they use for foraging. Their size and behavior also allow

1 us to view them well enough with binoculars and telescopes that we can identify individuals by the bands we’ve placed on their legs. By identifying individuals at different nesting attempts, I was able to estimate the number that survived among years and determine where they moved.

Since the 1970s, Patricia Adair Gowaty (PAG) has studied eastern bluebirds near

Clemson, South Carolina (Gowaty 1980). She began studying a second population near Athens,

Georgia in 1993. Both Athens and Clemson are located in the Piedmont region of the southeastern United States (Fig. 1.1). PAG established eight study sites, four at both locations, by placing artificial nest boxes on fence posts around university pastures managed for grazing cattle, horses, and sheep. Hardwood and pine trees lined the edges of the pastures. During a comparison study of the two populations in 1996, PAG discovered bluebirds near Athens had lower reproductive and foraging success and higher aggression than the Clemson population

(PAG, unpublished data). For my dissertation, I have continued studying the Athens and

Clemson bluebird populations to assess potential reasons for differences in their population biology and behavior.

During the 1996 study, one difference between the two study locations was the presence of red imported fire ants (Solenopsis invicta) at the Athens sites and their absence at the Clemson sites. Solenopsis invicta (hereafter, fire ants) are native to South America and were accidentally introduced to the United States in the 1940s (Tschinkel 2006). Fire ants live in open grassy areas and their main food source is ground arthropods (Tschinkel 2006); researchers have documented fire ants decreasing the richness and abundance of local insect communities (Porter and

Savignano 1990). Since their introduction in the 1940s, fire ants have been successful invaders, expanding their range from coast to coast throughout the southern United States (Tschinkel

2006). Fire ants invaded the Athens area in the late 1970s (Callcott and Collins 1996) and did

2 not reach the Clemson study sites until 1997 (western most Clemson site - number 8, Fig. 1.1; personal observation). Fire ants use of similar habitat and food resources as eastern bluebirds, and their presence in Athens and absence in Clemson during the 1996 study, led us to hypothesize that fire ants caused the lower reproductive and foraging success and higher aggression PAG observed in the Athens bluebird population. To assess the effects of fire ants on eastern bluebird survival and dispersal I observed the Athens and Clemson eastern bluebird populations during the 2001 to 2005 breeding seasons, during which time fire ants had expanded their range throughout the remaining Clemson study sites (sites 5-7, Fig. 1.1).

Fire ant abundance was an obvious difference between the Athens and Clemson study sites, but may not have been the reason for the differences PAG observed between the two bluebird populations. As alternative explanations to the observed bluebird population differences, we considered other potential differences between Athens and Clemson study sites.

Though the Clemson study sites are only ~ 100 km northeast of those in Athens (Fig. 1.1), there could be differences in weather between the two locations that affect bluebirds. Researchers have found extreme winter and spring weather negatively affecting bluebird populations

(Musselman 1941, Pitts 1978, Sauer and Droege 1990). Extreme cold and heat that sometimes occur at the beginning and end of breeding seasons can cause nest failure (Pogue and Carter

1995). Weather can also affect birds food resources (Sillett et al. 2000). Rain and temperature both affect plant growth, upon which many insects forage. To encompass direct and indirect effects of weather, I considered measures of rainfall and temperature as potential correlates to bluebird survival and dispersal. I also considered differences in habitat. By appearance, habitat at the eight study sites seemed similar. Each site differed in shape and size, however, depending upon the distance to forest edge. The farther a nest is from the forest edge, the farther bluebirds

3 may have to travel to use foraging perches. The distance from a nest to the forest edge could affect foraging time and energy expenditure/gain and, therefore, survival (Lemon 1993).

Researchers have also found that predation is more likely when nests are closer to forest edges

(Newton 1993). If there is an edge distance trade-off between foraging and predation, there may be an optimal nest-to-edge distance that maximizes foraging opportunities while minimizing predation risk. Predator density could affect risk of predation and vary among sites also. Since we captured the effects of direct predation within our survival estimates, we used a proxy for predator density to look for non-lethal effects of predators (Lima 1998, 2002, Peacor 2003) on bluebird survival, and on dispersal as well. Another potential difference was the density of bluebirds at Athens and Clemson study sites, which could affect the level of intraspecific competition for resources. Researchers have observed density-dependent effects on survival

(White 2008), and predict greater dispersal distances with increasing numbers of individuals in a population (Howard 1960, Christian 1970, Greenwood 1980, Waser 1985). In chapter two, I describe eastern bluebird survival rates for the Athens and Clemson populations relative to variables representing the potential between location differences described above.

Following chapter two, I focus on the topic of eastern bluebird dispersal. It is common for animals to move from one location to another; adults may move between breeding attempts

(breeding dispersal), and juveniles may move from their birth location to a place where they will attempt to reproduce (natal dispersal; Greenwood 1980). Three stages of dispersal are, 1) individuals choosing to move, 2) transience from one location to another, and 3) selection of a breeding location (Clobert et al. 2001). Measuring the rate of emigration from a location and/or the distance moved to a new place of settlement could provide insight into proximate cues for dispersing (e.g., social or environmental; Ronce et al. 2001, Clobert et al. 2004). Researchers

4 assume that the probability of dispersal decreases as the cost of dispersal increases (Weisser

2001) and that the cost of dispersal increases with distance moved (Gadgil 1971, Ronce et al.

2001). Some common explanations for the evolution of dispersal are 1) competition for resources, 2) intrasexual competition for mates, 3) inbreeding avoidance, and 4) environmental variability (Greenwood 1980, Johnson and Gaines 1990, Hansson 1991, Gandon and Michalakis

2001, Bowler and Benton 2005, Ronce 2007). Dispersal may also just be random movements

(Skellam 1951, Hawkes 2009). If dispersal is evolutionarily adaptive, then individuals that move should improve their chances of survival and/or reproduction (Danchin et al. 2001) and maximize their fitness (Holt 1985).

Researchers have documented different patterns of dispersal depending on the species, sex, and age of study organisms (Wiens 2001). A well documented pattern for birds is that natal dispersers move farther (Greenwood 1980, Clarke et al. 1997) and at greater rates than breeding dispersers (Greenwood and Harvey 1982). Female birds tend to emigrate more often

(Greenwood and Harvey 1982) and disperse farther than males (Greenwood 1980, Clarke et al.

1997). As individuals get older, they become more philopatric. There is also more dispersal between-years than within-years (Greenwood and Harvey 1982). The distribution of individuals staying near or moving farther is affected by both the probability of emigrating and the distance that individuals move (Clarke et al. 1997). Emigration and distance moved may be affected by different factors, however (Bowler and Benton 2005). Potential proximate cues for dispersing include predator density or predation events, conspecific density, and resource fluctuation

(Howard 1960, Bowler and Benton 2005). Moving beyond these perceived constraints should benefit individuals that are able to do so (Ronce et al. 2001, Clobert et al. 2004).

5 In light of previously described dispersal patterns, I consider breeding and natal dispersal separately and estimate dispersal for both females and males. In chapter three, I describe between-season breeding dispersal patterns at Athens and Clemson. I test for correlations between emigration and survival, and, examine four explanations for between-season breeding dispersal distances, 1) nest success or failure at time t, 2) mate retention at time t, 3) conspecific density at time t, and 4) fire ant density at time t. In chapter four, I describe natal dispersal patterns for bluebirds near Athens and Clemson. I also test for effects of competition for breeding territories by comparing the distribution of dispersal from natal nesting territories to random and theoretically predicted distances (Skellam 1951, Murray 1967, Waser 1985, Tonkyn and Plissner 1991). In chapter five, I consider explanations for the natal dispersal patterns I described in chapter four. By developing models that represented natal dispersal distance relative to food availability, intraspecific competition, and predator density, I tested which variables had the strongest correlation to natal dispersal distance.

References

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Callcott, A. M. A. & Collins, H. L. (1996) Invasion and range expansion of imported fire ants (Hymenoptera: Formicidae) in North America from 1918-1995. Florida Entomologist, 79, 240-251.

Christian, J. J. (1970) Social subordination, population density, and mamalian evolution. Science, 168, 84-90.

Clarke, A. L., Saether, B. E. & Roskaft, E. (1997) Sex biases in avian dispersal: a reappraisal. Oikos, 79, 429-438.

Clobert, J., Ims, R. A. & Rousset, F. (2004) Causes, mechanisims and consequences of dispersal. Ecology, Genetics, and Evolution of Metapopulations (eds I. Hanski & O. E. Gaggiotti), pp. 307-335. Elsevier Academic Press, Oxford.

6 Clobert, J., Wolff, J. O., Nichols, J. D., Danchin, E. & Dhondt, A. A. (2001) Introduction. Dispersal (eds J. Clobert, E. Danchin, A. A. Dhondt & J. D. Nichols), pp. xvii-xxi. Oxford University Press, New York.

Danchin, E., Heg, D. & Doligez, B. (2001) Public information and breeding habitat selection. Dispersal (eds J. Clobert, E. Danchin, A. A. Dhondt & J. D. Nichols), pp. 243-260. Oxford University Press, New York.

Gadgil, M. (1971) Dispersal: population consequences and evolution. Ecology, 52, 253-261.

Gandon, S. & Michalakis, Y. (2001) Multiple causes of the evolution of dispersal. Dispersal (eds J. Clobert, E. Danchin, A. A. Dhondt & J. D. Nichols), pp. 155-167. Oxford University Press, New York.

Gowaty, P. A. (1980) The origin of mating system variability and behavioral and demographic correlates of the mating system of Eastern Bluebirds (Sialia sialis). Zoology. Clemson University, Clemson.

Gowaty, P. A. & Plissner, J. H. (1998) Eastern Bluebird, Sialia sialis. The Birds of North America, No. 381 (eds A. Poole & F. Gill), pp. 1-32. The Birds of North America, Inc., Philadelphia, PA.

Greenwood, P. J. (1980) Mating systems, philopatry and dispersal in birds and mammals. Animal Behaviour, 28, 1140-1162.

Greenwood, P. J. & Harvey, P. H. (1982) The natal and breeding dispersal of birds. Annual Review of Ecology and Systematics, 13, 1-21.

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Holt, R. D. (1985) Population dynamics in two-patch dynamics: some anomalous consequences of an optimal habitat distrubution. Theoretical Population Biology, 28, 181-208.

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Lima, S. L. (2002) Putting predators back into behavioral predator-prey interactions. Trends in Ecology & Evolution, 17, 70-75.

Murray, B. G., Jr. (1967) Dispersal in vertebrates. Ecology, 48, 975-978.

Musselman, T. E. (1941) Bird mortality in 1940. Auk, 58, 409-410.

Newton, I. (1993) Predation and limitation of bird numbers. Current Ornithology (ed D. M. Power), pp. 143-198. Plenum Press, New York.

Peacor, S. D. (2003) Phenotypic modifications to conspecific density arising from predation risk assessment. Oikos, 100, 409-415.

Pinkowski, B. C. (1974) Criteria for sexing eastern bluebirds in juvenal plumage. Inland Bird- Banding News, 46, 88-91.

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Ronce, O., Olivieri, I., Clobert, J. & Danchin, E. (2001) Perspectives on the study of dispersal evolution. Dispersal (eds J. Clobert, E. Danchin, A. A. Dhondt & J. D. Nichols), pp. 341- 357. Oxford University Press, New York.

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8 Tonkyn, D. W. & Plissner, J. H. (1991) Models of multiple dispersers from the nest - Predictions and inference. Ecology, 72, 1721-1730.

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White, T. C. R. (2008) The role of food, weather and climate in limiting the abundance of animals. Biological Reviews, 83, 227-248.

Wiens, J. A. (2001) The landscape context of dispersal. Dispersal (eds J. Clobert, E. Danchin, A. A. Dhondt & J. D. Nichols), pp. 96-109. Oxford University Press, New York.

Fig. 1.1. Study sites near Athens, Georgia (1, 2, 3, 4) and Clemson, South Carolina (5, 6, 7, 8) where we monitored eastern bluebirds during the 2001 to 2005 breeding seasons. Both study locations are in the Piedmont region of the southeastern United States (shown in gray).

CHAPTER 2

EASTRN BLUEBIRD, SIALIA SIALIS, SURVIVAL RATES DEPEND ON AGE AND

POPULATION-LEVEL VARIABLES1

1 Lang, J. D. and P. A. Gowaty. To be submitted to Ecology.

11 Abstract

The long-term presence of fire ants, Solenopsis invicta, around Athens, Georgia, and their absence around Clemson, South Carolina, in conjunction with differences in comparative observations of behavior and productivity of bluebirds in these near-by locations during the

1990s, led us to hypothesize that fire ants, which eat the same ground arthropods as bluebirds, reduce bluebird survival. To test this hypothesis and four alternatives, weather differences, conspecific density, predator density, and the forest edge hypothesis, we monitored banded bluebirds near Athens and Clemson during five successive (2001 to 2005) breeding seasons as fire ants established populations in the Clemson area. Variation in bluebird survival at Clemson was greater between years than at Athens (up to 23% for mean adult and 10% for mean juvenile survival at Clemson vs. ~1.5% for adults and 1% for juveniles at Athens). We rejected the interspecific food competition hypothesis for the Clemson bluebird population, where the weather differences hypothesis best explained variance in survival rates. At Athens, the no- covariate model was best, but not distinguishable from the five hypothesis models (likelihood rations < 4:1 from the no-covariate model), including the interspecific food competition hypothesis. Survival rates were higher for after-hatch-year (AHY) than hatch-year (HY) birds but not different between sexes (confidence intervals: AHYAthens 0.4832 - 0.5560; AHYClemson

0.4867 - 0.5566; HYAthens 0.1417 - 0.1820; HYClemson 0.0996 - 0.1280). Lack of differences in correlative variables between breeding sites suggests population-level variables, such as weather, are affecting eastern bluebird survival most for these two southeastern Piedmont populations.

Key words: eastern bluebirds, environmental variation, fire ants, scale, Sialia sialis,

Solenopsis invicta, survival, weather

12 Introduction

In the mid 1990s, we discovered differences in productivity and behavior between eastern bluebird, Sialia sialis, populations at Clemson, South Carolina and Athens, Georgia, locations that are geographically close (~ 100 km apart) and climatically similar. At that time, invasive red imported fire ants, Solenopsis invicta, were established, common, and locally abundant at

Athens study sites but had not yet expanded their range to the Clemson study sites in northwest

South Carolina (Callcott and Collins 1996). Solenopsis invicta (hereafter fire ants) thrive in habitats similar to those eastern bluebirds use for foraging, open with short-grass (Gowaty and

Plissner 1998). Fire ants also preferentially forage on ground arthropods (Tschinkel 2006), potentially competing with eastern bluebirds which are primarily insectivorous (Gowaty and

Plissner 1998). Thus, lower productivity, less foraging success, and higher rates of conspecific aggression by bluebirds in Athens (PAG unpublished data), where fire ants were abundant, led to the hypothesis that fire ants reduce bluebird survival and productivity. Several alternative hypotheses could also account for the observed differences; in this paper, we examine potential differential effects of weather, conspecific density, predation, and habitat on eastern bluebird survival at the Athens and Clemson study sites.

HYPOTHESES AND PREDICTIONS

Interspecific food competition

Exotic invasive species, such as Solenopsis invicta, can change the density and structure of arthropod communities (Porter and Savignano 1990) and potentially constrain food resources of native insectivorous species, such as eastern bluebirds. To test the effects of fire ants on eastern bluebirds, we compared survival rates of eastern bluebirds near Athens, GA, where fire

13 ants established themselves in the late 1970s (Callcott and Collins 1996), to those near Clemson,

SC where fire ants were expanding their range and beginning to establish at the time of this study

(personal observation). We assume that bluebirds in Athens and Clemson evolved under similar conditions and that individuals in both populations would respond similarly to environmental challenges such as fire ant invasion. Predictions from the interspecific food competition hypothesis include (1) an inverse relationship between fire ant density and eastern bluebird survival, and (2) survival rates in Clemson and Athens converging as fire ant densities in

Clemson approached those in Athens.

Predator density

Eastern bluebirds expend energy defending nests from predators (Gowaty and Plissner

1998). Modifying behavior in response to predators can have indirect effects on prey (Lima

2002, Peacor 2003), such as bluebirds, and decreases prey (e.g., bluebird) survival (Lima 1998).

We assumed that bluebirds would increase predator vigilance and nest defense as predation increased at a breeding site. Observing a negative correlation between bluebird survival and predator density would support the predator density hypothesis.

Conspecific density

Intraspecific competition can lead to density-dependent constraints on individuals within a population (White 2008). Bluebird territory size fluctuates throughout the breeding season and can shrink in size when neighbors set up territories (Allen 1988). Assuming neighbors can affect territory size, and thereby the availability of resources, we hypothesized conspecific density

14 could affect bluebird survival at the breeding-site scale. An inverse relationship between breeding-site specific density and survival would support the conspecific density hypothesis.

Forest edge

Increased search time for food decreases energy gain and avian survival (Lemon 1993).

As perch-to-ground foragers, bluebird energy expenditure may depend on the distance to foraging perches. Forest edges provide a natural source of perches for bluebirds. Two studies of radio-tagged eastern bluebirds in South Carolina found eastern bluebirds used edges in greater proportion than its availability for foraging (Allen 1988, Savereno 1991). We assumed bluebirds nesting farther from edges would expend more energy accessing foraging areas. Nesting closer to edges, however, may increase predation rates (Newton 1993). Thus, there may be a trade-off between foraging energy expenditure and potential predation for bluebird nest-to-edge distance, leading us to hypothesize that distance to forest edge could affect bluebird survival. Assuming a distance to edge trade-off between foraging benefits and predation susceptibility, the forest edge hypothesis would be supported if survival rates are highest at breeding sites where cavities are intermediate distances from edges.

Weather differences between Athens and Clemson

Differences in weather between Athens and Clemson could affect bluebird survival via direct challenges of extreme temperatures and/or precipitation. Both growing degree-days and rainfall can affect growing conditions (Miller et al. 2001), so weather differences could also affect plant growth (insect food) and the insect populations bluebirds typically exploit. For example, a study on western bluebirds (Sialia mexicana) found low arthropod abundance and

15 low bluebird productivity in a drought year (Brawn 1991). We assumed that more growing degree-days would mean fewer potentially harmful extreme temperature days and that the chance of drought decreased with an increase in precipitation. We also assumed more growing degree- days and precipitation would improve vegetative growing conditions and, thereby, food for insects that bluebirds prey upon. Predictions that follow from the weather differences hypothesis are (1) a positive correlation between bluebird survival rates and growing-degree-days and rainfall, and (2) Athens and Clemson survival rate differences corresponding to growing degree- day and rainfall differences between the two locations.

Global model

Another possibility is that the independent variables associated with each hypothesis had stronger additive than independent effects on eastern bluebird survival. A global model, one including the variables from all of the other models, best estimating eastern bluebird survival rates would support the importance of additive effects.

No-covariate model

A basic model including age, sex, and time, but no covariates, will have the most support if none of the variables from the hypotheses we tested affected bluebird survival. Support for the no-covariate model would indicate there is some other hypothesis that we did not consider that would better explain eastern bluebird survival rates.

To test the hypotheses listed above, we estimated survival rates and compared the alternative predictions for bluebird survival at breeding sites near Athens and Clemson from

2001 to 2005.

16 Methods

STUDY SITES

We used eight study sites; four located in Clarke and Oglethorpe counties, near Athens,

GA, and four in Anderson County, near Clemson, SC (see Appendix 2.1 for geographic coordinates). Clemson is approximately 100 km NE of Athens and both locations are within the southern Piedmont. PAG established study sites on university farms at both locations by placing artificial nest boxes on fence posts around fields the universities managed for livestock (Clemson sites in mid 1970s; Athens sites in 1993, except one site established by JDL in February 2001).

Nest boxes were all the same dimensions and material (cedar). Because this was part of a larger study, we equipped each box with a Noel predator guard (Noel 1991) to decrease predation and maximize the opportunity for behavioral observations. To decrease direct predation by fire ants, we placed Tree Tanglefoot Insect Barrier, or equivalent, on the nails connecting the box to the post. Within a pasture matrix, bluebirds used mixed pine/hardwood edges, fences, and utility wires as foraging perches.

FIRE ANTS

Solenopsis invicta are native to South America. After their accidental introduction to

Mobile, AL, U.S.A., around 1940, fire ants spread throughout the southeastern U.S.; from North

Carolina to California, and northward into Tennessee, with a few populations further north along the Atlantic coastline (Tschinkel 2006). Fire ants are omnivorous foragers and consume mostly insects (Tschinkel 2006). Their mounds have above and belowground structure, the former being useful for estimating mound numbers and size. The volume of a fire ant mound is positively correlated to its colony size (Tschinkel et al. 1995).

17 During a 1996 comparative study of bluebirds at Athens and Clemson, we observed the presence of fire ants at Athens, but not at Clemson. We first observed fire ants at Clemson in

1997, when we documented fire ant presence in the most western Clemson study site. Over the next three years, fire ants expanded their range to three additional study sites that were ~ 7-8 km east of the first site invaded.

From 2001 to 2005, during peak spring and summer bluebird incubation periods, we censused fire ant mounds within a 60 m2 area in front of every nest box (2 m in front of and 15 m on either side of a box; N boxesAthens = 147, N boxesClemson = 120). Using a mound’s widest point, we recorded the number of mounds within three size categories, 1) < 30 cm, 2) 30-60 cm, or 3) > 60 cm. To estimate fire ant mound surface area, we multiplied the number of mounds we observed within each category times the median mound diameter for that category. We calculated a year and site-specific mound area average using the spring and summer estimates from in front of every nest box on a study site. We assumed mound area and volume had a positive correlation and used the average mound area for each breeding site as a proxy for fire ant density (Dr. Ken Ross concurs, personal communication).

EASTERN BLUEBIRDS

Eastern bluebirds are small (~ 30 g) sexually dimorphic thrushes; sex can be determined by feather coloration when nestlings are 13 d old (Pinkowski 1974). Their breeding range extends from southern Arizona eastward to New York. Bluebirds in the southeastern U.S., where we conducted this study, tend to remain in the vicinity of their breeding sites year round, while northern breeders are short distance migrants and travel south during the winter (Gowaty and Plissner 1998). Bluebirds are secondary cavity nesters, readily recruit to artificial nest boxes

18 in open areas, and defend territories around their nesting cavities. In the southern portion of their breeding range, eastern bluebirds are able to raise two to three broods between March and

August (Gowaty and Plissner 1998). Eastern bluebirds typically use a perch-to-ground foraging technique (Gowaty and Plissner 1998) and forage, on average, 72 m from their nest box

(unpublished data). While only females incubate, both males and females feed nestlings and attend fledglings.

During the 2001 to 2005 breeding seasons (March through August), we monitored all nest boxes (N = 267) two times a week and recorded contents within the box and color band combinations (described below) of bluebirds near each nest box. We observed 1403 nesting attempts, which we checked daily near times of laying, hatching, and fledging, to determine exact dates for those events. We considered a nest depredated if eggs disappeared, if we found broken egg shells, if nestlings disappeared prior to age 13 d, or if we found body parts of nestlings or adults in the nest. Nestlings that disappeared when 13 or 14 days old could have prematurely fledged (typical fledging range is day 15-19; Gowaty and Plissner 1998), therefore, we did not include those nests in the percent nest predation estimates; we did include those individuals in the survival analyses, however, because some individuals that disappeared when

13-14 d old returned during subsequent breeding seasons.

During a breeding season, we attempted to capture each adult (90% success). We placed trap doors within nest boxes to capture adults when they entered to feed nestlings. Capturing adults during the nestling period reduces the probability of nest abandonment (Gowaty and

Bridges 1991). We banded nestlings when they were ten days old, one week before their average age of fledging (Gowaty and Plissner 1998). We placed an individually unique combination of four bands, one aluminum U.S. Fish and Wildlife Service band and three colored bands on each

19 unbanded bluebird we captured (N = 2692); we recorded the band number of recaptured individuals. For every bluebird we captured or recaptured, we measured and recorded wing, tail, and tarsus length (mm), and weight (g). We also collected a 75-150 µl blood sample from the distal portion of the tibio-tarsus of each individual bluebird, which should not affect their return rates (Perkins et al. 2004). For each nesting attempt, we used Questar® telescopes to read bands of birds near nest boxes. This method allowed us to “recapture” individuals that survived from year (i) to year (i+1), even if their nesting attempt(s) failed in year (i+1). We considered resightings as recapture events for our survival analyses. We observed 392 individuals returning among years.

Survival estimation

We used mark-recapture data and program MARK (White and Burnham 1999, Cooch and White 2006) to estimate yearly survival. MARK uses maximum likelihood methods (Cooch and White 2006) for estimating apparent survival probabilities, which are based upon capture rate and number of returns observed (observed survival) in a study population (Lebreton et al.

1992). We attempted to run live and dead/multi-state (site-specific) models but did not have enough recoveries or returns per site to estimate the beta parameters for those models. While using site-specific data to parameterize the models (described below), we report location-specific

(Athens and Clemson) apparent survival and recapture rates. Survival rates for birds commonly differ for different age classes and between sexes (Lebreton et al. 1992) and we expected bluebird survival rates would differ by sex and age also. Age and sex specific survival violates one of MARK’s assumptions, that all individuals have the same probability of surviving from time (i) to time (i+1) (Cooch and White 2006). To avoid violating this assumption, we included

20 age and sex within the models and estimated survival probabilities for juveniles and adults, by sex. In this paper, we consider juveniles as individuals who were between fledging and their first breeding season; commonly referred to as hatch-year (HY) birds. After the first breeding season, we refer to individuals as after-hatch-year (AHY) birds or adults. Our sample size was: Athens:

N = 423 female (F) HY, 379 male (M) HY, 65 HY (sex undetermined), 98 F-AHY, 70 M-AHY;

Clemson: N = 739 F-HY, 629 M-HY, 84 HY (sex undetermined), 109 F-AHY, 96 M-AHY. In order to estimate sex specific survival rates for HY birds, we did not include within our analyses the 149 HY birds for which we did not determine sex and never observed as adults. Any bird that we observed within a subsequent breeding season (March to August) we considered having survived from year (i) to year (i+1). Because we recaptured/resighted individuals at their nest boxes, we assumed that we would have an equal probability of identifying birds of either sex or age (all adults) in year (i+1) when they returned to a site for breeding. We observed one natal disperser that moved between Athens and Clemson during the 2003 and 2004 breeding seasons

(Lang and Gowaty in prep_C). While this indicates that there is a possibility of genetic exchange between populations, because of our limited observations of dispersal between Athens and Clemson and potential differences between the two locations (e.g., the known difference in fire ant density), we modeled Athens and Clemson data separately.

To test the hypotheses we introduced above, we developed seven linear models within program MARK for estimating and comparing apparent survival rates (our dependent variable).

(1) To test the interspecific food competition hypothesis, we modeled bluebird survival in

relation to fire ant density at each breeding site using the fire and density measure

described above.

21 (2) To test the predator density hypothesis, we modeled bluebird survival relative to a proxy for

predator density, the percent of nests depredated within each breeding site. Because nest

boxes were in open habitat, nest defense behavior was ‘public information’ (Valone

1989, Danchin et al. 2001). We assumed bluebirds would increase the time they spent

guarding their nests against predators as nest predation increased, but did not measure

defense behavior directly. We also assumed that there was a direct correlation between

predator density and the number of nests depredated within a breeding site. Not having a

direct measure of predator density (i.e., predator surveys), we thought percent nest

depredation provided an estimate of bluebird predators that did not bias the model; direct

adult and juvenile mortalities would have biased the predator density model because they

are directly related to survival. We did not know if bluebirds’ behavioral response to

house sparrows, Passer domesticus, as nest site competitors, or as predators. Bluebirds

compete with house sparrows for cavities, and respond to house sparrows differently than

to predators (Gowaty 1981). However, though not eaten by house sparrows, house

sparrows sometimes kill bluebirds that are defending their nests (Gowaty 1984). We ran

the predation density models both with and without house sparrows included in the

predation percentage. For both Athens and Clemson the models with house sparrows had

higher Akaike information criterion values, therefore, we only present the models that

include house sparrow “predation” within the percent predation per site estimates.

(3) To test the conspecific density hypothesis, we used the number of females per number of nest

boxes per site per average distance to the nearest two boxes as a proxy for conspecific

density. This measure of conspecific density accounts for nest site availability and the

number of nesting bluebirds for sites of different sizes and nest box geography.

22 (4) To test the forest edge hypothesis, we used the average distance to edge (forest-field) from a

nest box (for each site) as the correlate. This variable was constant for each site among

years.

(5) To test for effects of weather between Athens and Clemson (weather differences hypothesis),

we modeled the additive effect of growing degree-days and rainfall (year-specific totals

for March-August), as the correlate to survival. We used growing degree-days (gdd), a

measure for when temperature is within an “optimal” range for growth by a given species

(Miller et al. 2001). For many insect species, gdd between 11˚C and 27˚C is optimal for

growth (Weston and Diaz 2005). In the southeastern United States, photosynthesis

occurs between 10˚C and 45˚C (Loehle 2000). Photosynthetic rate decreases at

temperatures above 30˚C, however (Blackman 1905). Therefore, we used 10˚C as the

minimum and 30˚C the maximum to estimate gdd because they represent an “optimal”

range for insect and plant growth. We also included rainfall as a weather variable

because rain may have a direct effect on bluebird survival, especially when temperatures

are near freezing (personal observation). Additionally, the interaction of soil moisture

and gdd affects growing conditions (Miller et al. 2001). We assume there is a correlation

between soil moisture and rainfall. We also ran gdd and precipitation models separately

to see if one of those two variables was driving the weather differences hypothesis. We

will reject the weather differences hypothesis if, 1) survival rate estimates do not

correlate to weather and, 2) differences in Athens and Clemson survival rates do not

correspond to differences in weather between Athens and Clemson.

(6) To test the global model, we used all of the variables from the other five models as covariates

to eastern bluebird survival.

23 (7) We used the no-covariate model to test goodness-of-fit because it is not possible to test

goodness-of-fit for models with individual covariates in MARK (Cooch and White 2006).

The following linear equations represent the structure we used for modeling apparent survival (psi) and recapture (p) rates: logit(psi) = β0(intercept) + β1(group; age and sex) + β2(individual covariate; hypothesis driven) logit(p) = β3(intercept) + β4(time)

Because the individual covariates were year-specific, we did not include time as a separate variable within the apparent survival linear model. To account for potential variation in recaptures among years, we included time in the recapture linear model; we did not include group (age and sex) because we assumed an equal chance of seeing both sexes and ages at nest boxes. Prior to modeling, we used a z-transformation ( (xi " x )/SD ) to standardize, for comparison, variables with different measures. The transformed variables had a mean of zero and standard deviation of one. To link parameters! to a linear formula and to keep survival and recapture estimates between zero and one we used the logit-link function (Lebreton et al. 1992,

Cooch and White 2006).

As the number of parameters increase, estimates become less biased but sampling variance increases, creating a trade-off (Lebreton et al. 1992). We ranked all models using

Akaike’s information criterion (AIC) comparisons, which rank models according to the best likelihood and the lowest number of parameters, to account for the estimate bias/variance trade- off (Burnham and Anderson 2002). Program MARK reports AICc, which adjusts AIC to account for differences in effective sample size (Cooch and White 2006). The model with the greatest

AICc weight is best (Burnham and Anderson 2002). The ratio of two models’ AICc weights show how much support one model has over another (Cooch and White 2006). For example, models

24 with weights of 0.75 and 0.25 would indicate the better model has three times the support of the

second model. The ratio of model likelihoods provides strength of evidence for one hypothesis

over another; a likelihood ratio of 8:1 is strong evidence that one model is better than another,

and a ratio of 32:1 is considered quite strong evidence (Royall 1997). Because models with

likelihood ratios of < 8:1 from the top model are not conclusively different, we include them

within our best model set. We used the best model sets to determine survival rate estimates via

model averaging (Burnham and Anderson 2002, Cooch and White 2006).

MARK does not have the ability to test goodness-of-fit (GOF) for models with individual

covariates (Cooch and White 2006), so we tested for GOF using the same model structure (psi by

group and p by time) as our hypothesis models, but without the individual covariates. We used

MARK’s bootstrap goodness-of-fit procedure to determine whether we needed to adjust the

variance inflation factor, cˆ , for overdispersion. Cooch and White (2006) suggest looking at

estimates of cˆ two ways, (1) a ratio of the model and bootstrap estimated cˆ , and (2) a ratio of

the model and bootstrap! deviances, and then using the larger of the two estimates to adjust cˆ

among! models. Our no-covariate model did not fit well (P < 0.01).! However, when comparing

the two cˆ estimates for our data, the cˆ ratio method indicated underdispersion and! the deviance

ratio method indicated overdispersion for both the Athens and Clemson data. Because these

!methods indicated both over!- and underdispersion, we did not adjust cˆ .

We used 95% confidence intervals to compare survival between sexes, ages, and years.

When reporting estimate averages between sexes, ages, and/or! among years, we used the delta

method to estimate variance (Powell 2007). We ran a regression to test for a correlation between

yearly survival rates and covariates from the best model(s). We were unable to conduct a

repeated measures analysis (SAS 2000) because we did not have enough data to estimate site-

25 specific survival (i.e., only one estimate per year for each age-sex group at Athens and Clemson; multiple estimates per year needed for estimating variance within the repeated measures analysis). To compare yearly change in covariate values between Athens and Clemson we used paired t-tests. We used an analysis-of-variance (ANOVA) to test for differences in weather between Athens and Clemson. For each Athens and Clemson we used an ANOVA and Tukey’s test to assess differences between years and between sites for conspecific density, fire ant density and predator density (SAS 2000).

Results

BEST MODELS: NO-COVARIATE AND WEATHER DIFFERENCES HYPOTHESIS

In Athens, the simplest model, no-covariate, was best (Table 2.1). Five additional models had likelihood ratio values that were less than 4:1 from the no-covariate model (Table 2.1), therefore we included them in our best model set. In Clemson, weather was 3.2 times more likely than the global model, which was the only other viable model for Clemson (model likelihood ratio > 32:1 for remaining models; Table 2.1). Running each weather component separately did not improve model rank over the growing degree-day and rainfall additive model.

There was a negative correlation between survival and gdd plus rainfall at Clemson (Fig. 2.1).

Between 2001 and 2002 the change in weather was 4.7 times greater at Clemson than at

Athens (Fig. 2.2). There was no statistical difference for year-to-year variation in weather, however, or for conspecific density or percent predation (P > 0.05; Fig. 2.2). Average fire ant density at Athens was approximately twice the density at Clemson, as was between year variation in density (P = 0.002; Fig. 2.2). Correlates increased and decreased similarly between years, with the exception of predation which increased at Athens and fluctuated at Clemson (Fig.

26 2.2). For Athens and Clemson, correlate values did not differ among years (P > 0.05). We found differences in conspecific density between sites at Athens and Clemson (P < 0.05) and differences in fire ant density between sites at Clemson (P < 0.05).

SEX AND AGE

We recaptured/resighted 18.7% of the birds we banded (Athens: 9.5% F-HY, 15.8% M-

HY, 49% F-AHY, 51.4% M-AHY; Clemson: 8.8% F-HY, 9.5% M-HY, 49.5% F-AHY, 43.8%

M-AHY). The recapture rate was ~ 72% among years. The lowest recapture rates were for

Athens and Clemson bluebirds returning after 2004 (Table 2.2). We recovered 21 individuals; their deaths were caused by mammals (N = 7), house sparrows (N = 4), humans (N = 4; 3 hit by cars), snake (N = 1), and undetermined (N = 5). Adult survival rates were greater than juvenile survival rates (CI-AHYAthens: 0.4832 - 0.5560; CI-AHYClemson: 0.4867 - 0.5566; CI-HYAthens:

0.1417 - 0.1820; CI-HYClemson: 0.0996 - 0.1280). Within-year survival rates were similar for after-hatch-year birds at Athens and Clemson (Table 2.3). Hatch-year males at Athens had greater survival rates than male and female HY birds at Clemson during 2002 and 2003 (Table

2.3). Clemson had more variation in survival rates among years; 2001 rates were greater than in

2002 and slightly overlapped with 2003 estimates (Table 2.3). Variation in mean survival rates between years was as great as 23% for AHY and 10% for HY birds at Clemson, while only

~1.5% for AHY and 1% for HY birds at Athens.

27 Discussion

LACK OF FIT

There are four assumptions for mark-recapture data (Cooch and White 2006).

1) animals present at time (i) have the same probability of being recaptured

2) each animal marked at time (i) has the same probability of surviving to time (i+1)

3) marks remain viable and are not missed

4) sampling is instantaneous relative to time (i) and time (i+1)

Poor fit by the most parameterized model suggests we did not meet one or more of the model assumptions. To meet assumptions one and two we grouped data by age and sex; apparent survival rates were different between ages, but there was overlap in survival rates between sexes

(Table 2.3), which may have affected model fit. Bluebirds’ use of artificial nest boxes decreased the likelihood of missing marks, helping us meet assumption three. While sampling was not instantaneous, assumption four, time (i) and time (i+1) were distinctly different. It is likely that our lack of fit problem is because we had to use the least parameterized model instead of the most parameterized model to test for GOF; the global model is recommended for testing GOF

(Cooch and White 2006). Lack of fit by the global model indicates lack of support for any of the hypotheses (Burnham and Anderson 2002) and that an alternative hypothesis we did not consider better explains eastern bluebird survival for these two populations. We consider this possibility below.

Additionally, four to five capture/recapture occasions is near the minimum for model selection (Lebreton et al. 1992); we had four recapture occasions (2002 to 2005 breeding seasons). We do not think this was a problem, however. During our study, we observed a number of long-distance dispersal events and non-consecutive breeding season returns that

28 should have helped decrease the variance in our estimates. In the 2002-2005 breeding seasons, we observed 10.4% of 2170 bluebird fledglings returning to breed. This is lower than the 14.6% of HY bluebirds returning at Clemson from 1985 to 1990 (Plissner and Gowaty 1996) but much higher than the 3.9% of 1209 wood thrush fledglings returned to breed during a 22 yr study

(Brown and Roth 2004). Additionally, we observed 33 emigrants that we did not see for at least one breeding season and then observed nesting in subsequent years (Lang and Gowaty in prep_C, in prep_D); compare to Siefferman and Hill (2008) who only observed one eastern bluebird emigrant return in an eight year study, and Brown and Roth (2002) who had only three wood thrush emigrants return in 22 yrs. Almost 50% of the birds we banded as adults returned, and approximately 6% of adults moved among sites between breeding seasons (Lang and

Gowaty in prep_D). Observing dispersal events improves apparent survival estimates

(Cilimburg et al. 2002), and returned emigrant observations reduce some of the bias caused by permanent emigration, making us confident in our estimates.

HYPOTHESES AND MODELS

We found no support for the interspecific food competition hypothesis for the Clemson bluebird population; it had weak support for the Athens population. Data from this study suggest larger scale variables, such as weather, were most influential on survival for these two

Southeastern U.S. bluebird populations. In Clemson, the weather differences hypothesis had the most support; it was 3.2 times more likely than the global model, which was the only other viable model for Clemson bluebirds, and carried 76% of the AICc weight (Table 2.1). The correlation to weather was opposite of what we predicted, survival decreased with an increase in growing degree-days and rainfall (Fig. 2.1), suggesting our assumptions about the effects of

29 weather were incorrect. If increased gdd and precipitation improved growing conditions, it may have extended the bluebirds’ breeding season and increased their energy expenditure. Increased effort toward reproduction is thought to decrease survival due to a life-history trade-off (Ricklefs

2000). Using this logic, weather would have decreased survival for breeding birds but benefited juveniles who were not expending energy for breeding. This was not the case, however, because juvenile survival increased and decreased in parallel with adult rates (Table 2.3). We may also have chosen the wrong weather measures. Perhaps the effects of extreme weather, hot or cold, or rain storms/intensity, may have had a greater influence on energy expenditure and/or foraging behavior than growing degree-days and total rainfall.

In Athens, the no-covariate model had the most support; it was 2.5 times more likely than the next best models, but did not carry enough weight to stand alone (Table 2.1). This suggests the hypothesized covariates had some effect on survival. Interestingly, the global model had no support (Table 2.1), indicating the covariates were not additive in their effects. Since each covariate had about equal weight (Table 2.1), perhaps the environment as a whole, and not one component, was affecting survival.

The data support environmental differences between Athens and Clemson as an explanation for survival rates at Clemson having higher variation among years and also tending to be lower than at Athens (Table 2.3). Mean survival rates at Clemson varied approximately

23% for adults and 10% for juveniles over four years but were only ~ 1.5% for adults and 1% for juveniles at Athens (Table 2.3), a huge difference. One explanation for these patterns is Clemson having a more variable environment than Athens. Between 2001 and 2002, when Clemson’s survival rates significantly decreased (Table 2.3), gdd plus rainfall made its largest increase, a

4.7 times greater change than at Athens that year (Fig. 2.2). While the variation in weather

30 between 2001 and 2002 was quite different between Athens and Clemson, overall, between-year variation was not different for weather, conspecific density, or predator density between the two locations. Fire ant density differed between sites at Clemson, but change in fire ant density between-years was greater at Athens sites. These data do not support Clemson having a more variable environment.

Differences in between-year variability and which variables correlated to survival rates for Athens and Clemson bluebirds may have been an attribute of distance between study sites. In

Athens, study sites were up to 21 km apart; in Clemson, 8 km was the furthest distance between study sites. Site-specific variables (conspecific density, distance to edge, percent predation, fire ant density) each impacted bluebird survival rates at Athens (Table 2.1) where study sites were farther apart. Conspecific density, which varied among sites, was also the second best model for

Athens (Table 2.1). Perhaps weather, which we measured at a location-specific (Athens and

Clemson) but not site-specific scale had the least support in Athens (Table 2.1) because there were differences between-sites due to their distance apart, and the most support at Clemson where sites were closer together. Weather did not differ statistically between Athens and

Clemson, nor did its variation from year-to-year, however, decreasing the likelihood that site- specific weather would improve the weather model. Weather increased and decreased similarly at Athens and Clemson, as did conspecific density and fire ant density. Perhaps there is a correlation between weather and conspecific or fire ant density. For example, fire ant density and growing degree-days increased and decreased in the same years; conspecific density appeared to lag or be intermediate between gdd + rainfall (Fig. 2.2). If weather was influencing other variables we tested, their correlation might have confounded our results.

31 Distance between useable habitat patches is another landscape level variable that might be affecting bluebird survival, especially if the amount of useable habitat changes from year-to- year. The configuration of useable habitat, or other variables at the landscape level, could be especially important to survival for animals moving across the landscape (i.e., dispersers).

Differential dispersal across a variable landscape could be a plausible explanation for survival rate differences such as those we observed between age classes and sexes (Table 2.3). However, there were no differences in dispersal distances between male and female HY birds (Lang and

Gowaty in prep_C) that would explain the tendency toward higher survival by male HY birds in

Athens (Table 2.3). Additionally, although juvenile birds dispersed farther than adults (Lang and

Gowaty in prep_C, in prep_D), juvenile and adult survival rates increased and decreased similarly among years (Table 2.3) suggesting survival constraints affected both age classes similarly.

We also considered the effect that adding new boxes may have had on bluebird movement and the likelihood of resighting individuals (we placed new boxes at one Athens site and replaced missing boxes at Clemson where we had not monitored for two years prior to this study). We were able to discount new boxes as a source of variation; the percent of boxes used at each site did not differ between years (unpublished data). There may be variables that we did not consider that might be important to bluebird survival, such as diseases and parasites. For example, more nests at Clemson had blowfly larvae in them (JDL personal observation) and there was variation in nest and feather mites between Athens and Clemson (unpublished data). If the variation in disease or parasites at Clemson is greater than at Athens, it could help explain the variance in survival for that bluebird population. Some researchers have found declines in bluebird populations due to harsh winters (Musselman 1941, Pitts 1978, Sauer and Droege

32 1990). We only looked for correlations to weather during the breeding season, but winter weather around Clemson and Athens may have been a factor also, since these populations are resident. Another possibility is that extreme weather, such as high or freezing temperatures or cold-rainy days, has more effect on avian survival during the breeding season than gdd and total rainfall. Perhaps these, or other factors, influenced bluebird survival and the variation we observed.

AGE AND SEX

Survival rates for adults (95% CI: 0.4954 - 0.5458) were higher than for juveniles (95%

CI; 0.1255 - 0.1501). Juvenile survival was 26.5% of adult rates, which is near the expected value (25%) for resident birds (Gardali et al. 2003). We observed higher survival than Plissner and Gowaty (1996) reported at Clemson (38% for females and 41% for males), and Pinkowski

(1971) reported in Wisconsin (47%), perhaps because we used Noel predator guards. Noel guards inhibited some mammal predation, but did not prevent predators such as snakes from getting into the nest boxes (JDL personal observation). Even with predator guards, mammals caused about one-third of the mortalities we observed. Additionally, our analyses did not account for unbanded individuals that we found deceased when checking nest boxes, often killed by house sparrows (JDL personal observation). Our adult survival rate estimates might have been more similar to those reported by Plissner and Gowaty (1996) and Pinkowski (1971) had we not used predator guards, or had we been able to included unbanded bluebird mortality information in our analyses. As estimated, adult bluebird survival rates from this study were comparable to an eastern bluebird study in Alabama (50%; Siefferman and Hil 2008) and to another thrush species, wood thrush (Hylocichla mustelina; x adult ~ 58%, x juvenile ~ 28%),

33 ! ! nesting in the Piedmont (Powell et al. 2000). Powell et al.’s (2000) juvenile rates may have been higher because they based their estimates upon weekly survival prior to migration, whereas we estimated survival using second-year birds returning to their breeding sites. A radio-telemetry study on eastern bluebird fledglings in Virginia also found higher survival (65.4%) in the first 40 days after fledging (Jackson et al. 2011). If Jackson et al.’s (2011) daily survival rate estimate

(0.989) remained consistent until the next breeding season (~ 240 to 300 days), about 4-8% of juveniles in that population would survive to breed; that is about half the rate of survival we estimated for juvenile bluebirds around Clemson and Athens (Table 2.3). A study on western bluebirds, Sialia mexicana, found survival rates for adults that compared to what we found (male

64%, female 52%), but higher rates for juveniles (26%; Keyser et al. 2004). “Resident”

Swainson’s thrushes, Catharus ustulatus, also had similar survival rates to what we observed for eastern bluebirds (adults: 56%; juveniles: 25%; Gardali et al. 2003). In some thrush studies there were no differences in adult and juvenile survival; for example, survival of both age classes was

~ 57% for wood thrushes nesting in northeastern woodlots (Brown and Roth 2002), and ~ 60% for song thrushes (Robinson et al. 2004). Additionally, Hermit thrushes, Catharus guttatus, using southeastern pine plantations during the winter had survival rates that were similar (63%) to thrush species studied during the breeding season (Brown et al. 2000). A study on olive thrushes, Turdus olivaceus olivaceus, estimated higher survival rates than found in other thrush populations (~ 79%, adult and juvenile combined; Bonnevie 2007). We found no differences in adult survival rates between sexes; similar to observations on wood thrush adults (Brown and

Roth 2009), but not for white-starred robins (Pogonocichla stellata; males (83%), females

(43%); Githiru and Lens 2006).

34 In conclusion, survival rates for two eastern bluebird populations in the southeastern

Piedmont region of the United States were similar to those for other eastern bluebird populations and thrush species. Adult survival was higher than juvenile survival and we did not find differences in survival rates between sexes for adults and most years for juveniles (Table 2.3).

Different environmental factors at Clemson and Athens appear to have influenced survival rates at each location, weather in Clemson and a number of covariates in Athens (Table 2.1). Holmes

(1995) suggests interactions of mechanisms affect survival rates. The best model sets for bluebirds at Athens and Clemson support multiple variables affecting survival simultaneously

(Table 2.1); using experimental manipulations may help to tease apart the relationship among survival correlates. We also observed high between-year variation in mean survival rates at

Clemson and low variation at Athens (Table 2.3). We were able to discount a number of potential reasons for this difference, but did not find an explanation for our observation.

Considering alternative population-level correlates, such as connectivity of useable habitat between study sites or effects of parasites, may help explain the survival rate variance difference we observed between the Athens and Clemson bluebird populations.

ACKNOWLEDGEMENTS

We thank M. J. Conroy, L. A. Powell, and Rachel Katz for advice on survival analyses.

Carrie Straight provided helpful comments. We could not have accomplished fieldwork without the assistance of LeAnne Bonner, Lena Chamblis, Kristin Connell, Shannon Fitzgerald, Lynn

Hayes, Mindi Hertzog, Beth Tyler Lebow, Jessica Melgey, Cathy Rickets, Brian Snyder, and

Gayle Weber. Thanks to Mr. Jewett Tucker for use of his property. National Science

Foundation grants IOS 0076100 and an NIH R01 to PAG supported the research. We operated

35 under a University of Georgia animal care and use permit (# A2005-10013-0). PAG designed the study and protocols for collecting demographic data, PAG and JDL designed protocols for measuring fire ant density; PAG and JDL supervised technicians who collected, recorded, and computerized data; JDL analyzed the data and wrote the paper, which PAG edited.

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TABLE 2.1. Model comparison for hypothesized variables (fire ant density, distance to edge, conspecific density, predator density1, and weather2) affecting eastern bluebird, Sialia sialis, survival rates at sites near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons. For each location, Athens and Clemson, we estimated apparent survival rates using a recapture model in program MARK3. Delta AICc Model No. of Location Model AICc AICc weights likelihood parameters Deviance 4no cov 1219.90 0 0.3536 1 8 1203.77

con den 1221.73 1.83 0.1415 0.4003 9 1203.57

edge 1221.76 1.86 0.1393 0.3940 9 1203.61

Athens predation 1221.83 1.93 0.1349 0.3814 9 1203.67

fire ant 1221.86 1.96 0.1328 0.3756 9 1203.70

weather 1222.54 2.64 0.0945 0.2673 10 1202.35

5global 1229.20 9.30 0.0034 0.0095 14 1200.84

weather 1570.12 0 0.7598 1 10 1550.00

5global 1572.46 2.34 0.2364 0.3111 14 1544.23

predation 1581.52 11.39 0.0026 0.0034 9 1563.42

Clemson 4no cov 1585.04 14.92 0.0004 0.0006 8 1568.96

fire ant 1585.65 15.52 0.0003 0.0004 9 1567.55

con den 1585.80 15.67 0.0003 0.0004 9 1567.70 edge 1586.25 16.13 0.0002 0.0003 9 1568.15 Notes: 1 we used percent nest predation as a proxy for predator density; 2 weather (growing degree-days and rainfall additive model); 3 (White and Burnham 1999, Cooch and White 2006); 4 no covariates; 5 global model included all covariates

TABLE 2.2. Yearly recapture rate estimates for female (F) and male (M) hatch- year (HY) and after-hatch-year (AHY) eastern bluebirds, Sialia sialis, near Athens, Georgia and Clemson, South Carolina. We used recapture data from the 2001 to 2005 breeding seasons and program MARK1 to estimate recapture rates. We present model average estimates from the best model set (likelihood ratios < 8:1 from the top model2) for Athens and Clemson. Athens Clemson Year Estimate Lower CI Upper CI Estimate Lower CI Upper CI

2001 0.7158 0.5793 0.8216 0.7021 0.5715 0.8064

2002 0.7845 0.6504 0.8769 0.7614 0.6113 0.8663

2003 0.8534 0.6319 0.9518 0.8945 0.7257 0.9645

2004 0.4800a 0.3158 0.6486 0.5454b 0.3859 0.6962 Notes: 1 (White and Burnham 1999, Cooch and White 2006); 2 (Royall 1997); a lower than Athens 2002 and Clemson 2003; b lower than Clemson 2003

TABLE 2.3. Survival probabilities for hatch-year (HY) and after-hatch-year (AHY) female (F) and male (M) eastern bluebirds, Sialia sialis, near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons. We estimated apparent survival rates using recapture data within program MARK1 and present results from weighted averages of the best model set (likelihood ratios < 8:1 from the top model2). Lower and Upper CI represent 95% confidence intervals. Athens Clemson Age3- Estimate Difference Sex Year Estimate Lower CI Upper CI Estimate Lower CI Upper CI (Athens – Clemson)

HY-F 2001 0.1235 0.0890 0.1688 0.1691a 0.1218 0.2300 -0.0456

2002 0.1233 0.0883 0.1695 0.0739 0.0505 0.1071 0.0494

2003 0.1215 0.0861 0.1688 0.0861 0.0587 0.1245 0.0354

2004 0.1290 0.0755 0.2117 0.1050 0.0756 0.1440 0.0240

HY-M 2001 0.1982 0.1524 0.2537 0.1846a 0.1334 0.2497 0.0136

2002 0.1979b 0.1511 0.2549 0.0816 0.0551 0.1191 0.1163

2003 0.1952b 0.1483 0.2525 0.0948 0.0652 0.1360 0.1004

2004 0.2061 0.1283 0.3140 0.1153 0.0829 0.1583 0.0908

AHY-F 2001 0.4827 0.3933 0.5733 0.6330a 0.5309 0.7244 -0.1503

2002 0.4823 0.3944 0.5713 0.4036 0.3114 0.5031 0.0787

2003 0.4779 0.3896 0.5675 0.4439 0.3517 0.5403 0.0340

2004 0.4930 0.3608 0.6262 0.4980 0.4012 0.5950 -0.0050

AHY-M 2001 0.5541 0.4619 0.6428 0.6832a 0.5801 0.7709 -0.1291

2002 0.5537 0.4633 0.6407 0.4583 0.3586 0.5614 0.0954

2003 0.5493 0.4588 0.6366 0.4995 0.4014 0.5977 0.0498

2004 0.5638 0.4299 0.6890 0.5535 0.4498 0.6528 0.0103 Notes: 1 (White and Burnham 1999, Cooch and White 2006); 2 (Royall 1997); 3After-hatch-year survival was greater than hatch-year survival; a Clemson 2001 survival rates were greater than Clemson 2002 rates and just overlapped with 2003 estimates; bAthens’ within year survival rates were greater than for male and female HYs in Clemson

0.8

0.7

0.6 M-AHY 0.5 F-AHY 0.4 M-HY Survivalrate F-HY 0.3

0.2

0.1

0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 Growing degree-days + precipitation (z-transformed)

FIG. 2.1. Correlation between survival and weather (growing degree-days + rainfall) for eastern bluebirds, Sialia sialis, near Clemson, South Carolina during the 2001 to 2005 breeding seasons. We estimated apparent survival rates for female (F) and male (M) hatch-year (HY) and after- hatch-year (AHY) birds using program MARK (White and Burnham 1999, Cooch and White 2006). Survival rates were negatively correlated to weather: F-HY: P = 0.027, R2 = 0.95; M-HY: P = 0.027, R2 = 0.95; F-AHY: P = 0.031, R2 = 0.94; M-AHY: P = 0.033, R2 = 0.94.

Athens Clemson 0.9 0.9

0.7 0.7

0.5 0.5

0.3 0.3

0.1 0.1

-0.1 2001 2002 2003 2004 -0.1 2001 2002 2003 2004 z-transformedvalue -0.3 -0.3

-0.5 -0.5 gdd+precip con den -0.7 -0.7 fa den

-0.9 pred den -0.9

FIG. 2.2. Yearly variation for hypothesized correlates to eastern bluebird, Sialia sialis, survival rates. We present the z-transformed

( (xi " x )/SD ) values for weather (growing degree-days (gdd) + precipitation (precip)), conspecific density (con den), fire ant density (fa den), and percent nest predation as a proxy for predator density (pred den) between 2001 and 2004 at breeding sites near Athens, Georgia and Clemson, South Carolina. The transformed variables have a mean of zero; their values represent deviation above (positive) and below (negative) the mean. !

Appendix

Appendix 2.1: Latitude and longitude of eight field sites near Athens, Georgia and Clemson, South Carolina used to study eastern bluebirds, Sialia sialis, during the 2001 to 2005 breeding seasons. Location Site Latitude Longitude 1 – Dairy 33.90652 -83.24501 2 – Smithonia 34.0025 -83.17681 Athens 3 – Whitehall 33.901 -83.37181 4 – Whitehall Ext. 33.89729 -83.35634 5 – 5 Acres Pasture 34.62575 -82.73126 6 – Plant Pathology 34.66422 -82.73152 Clemson 7 – Peach 34.64867 -82.72568 8 – South Forest 32.6383 -82.81435

CHAPTER 3

EASTERN BLUEBIRD BETWEEN-SEASON BREEDING DISPERSAL PATTERNS:

PREDATION AND CHANGING MATES INCREASE DISPERSAL DISTANCE2

2 Lang, J. D. and P. A. Gowaty. To be submitted to Journal of Animal Ecology.

Abstract

1. At two locations in the southeastern United States, we describe patterns of eastern bluebird,

Sialia sialis, breeding disperser emigration.

2. For sites 1 to 21 km apart, the average percent emigration was 3.2 ± 1.3 per year.

3. We also considered four variables that may affect between-season breeding dispersal

distances, 1) previous nest success or failure, 2) mate retention, 3) conspecific density, and 4)

fire ant density.

4. Predictions: 1) Bluebirds whose nests fail at time t will disperse farther to nest at time t+1 than

those whose nests succeed. 2) Bluebirds who change social mates between time t and time t+1

will disperse farther to nest in time t+1 than those who do not change social mates. 3) On

breeding sites where conspecific density is higher at time t, bluebirds will disperse farther to nest

at time t+1 than individuals on breeding sites with lower conspecific density at time t. 4) On

breeding sites where fire ant density is higher at time t, bluebirds will disperse farther to nest at

time t+1 than individuals on breeding sites with lower fire ant density at time t.

5. We used a Bayesian information theoretic approach to compare models representing the four

predictions. Nest success best explained breeding dispersal distance. Bluebirds dispersed almost

twice as far after a predation event than after other types of nest failures ( x predation = 176 ±

288(SD); x other = 91 ± 66(SD)).

6. The second best model, mate retention, showed individuals that! changed mates tended to

!move about one territory distance farther than individuals that retained their mate (95% credible

intervals; changed mates: 80-233 m, ~ 1 to 3 territories; same mate: 33-141 m, ~ 0 to 2

territories).

7. The top model correlates, nest success and mate retention, suggest environmental variability

(predation) and competition for mates are constraints affecting the evolution of breeding dispersal for these two southeastern United States bluebird populations.

Key-words: breeding dispersal, competition, dispersal costs, dispersal distribution, eastern bluebird, emigration, environmental variability, fire ants, mate retention, nest success, Sialia sialis, Solenopsis invicta

Introduction

Between reproductive attempts, individual animals may remain at their current location

(breeding philopatry) or move to a new location (breeding dispersal; Greenwood 1980). There could be multiple causes of dispersal (Dobson and Jones 1985); those given most commonly for the evolution of dispersal are: competition for resources, environmental variability, and inbreeding avoidance (Hansson 1991, Gandon and Michalakis 2001, Ronce 2007). Dispersal could also be a random process (Skellam 1951, Hawkes 2009). To be evolutionarily adaptive, individuals should disperse only if it maximizes their fitness (Holt 1985), making environmental predictability (Switzer 1993) a factor in individuals choosing whether or not to disperse. The decision to disperse, or not, could affect survival and/or reproduction (Danchin et al. 2001). One assumption researchers have is that the probability of dispersal will decrease as costs increase

(Weisser 2001). Another assumption is that dispersal cost increases with distance moved

(Gadgil 1971, Ronce et al. 2001). Individuals can overcome costs, however, if the new breeding location increases the probability of survival and reproduction. A large number of variables

could affect dispersal costs (see reviews by; Johnson and Gaines 1990, Bowler and Benton

2005), and can also vary by species, sex, and age (Wiens 2001).

Predation events, increasing conspecific density, and resource fluctuation can serve as proximate cues for breeding dispersal (Howard 1960, Bowler and Benton 2005); individuals will benefit from dispersal if they can move beyond the constraint (Ronce et al. 2001, Clobert et al.

2004). Previous experience may factor into individuals’ dispersal decisions. For example, longer-lived individuals tend to move less as they get older (Greenwood and Harvey 1982), perhaps due to accumulated knowledge. Depending upon which social or environmental factors are affecting them (Ronce et al. 2001, Clobert et al. 2004), males and females may disperse different distances or at different rates.

Researchers have noted age and sex specific dispersal patterns such as natal dispersers traveling farther than breeding dispersers and female birds dispersing farther than males

(Greenwood 1980, Clarke et al. 1997). Additional observations include, females changing sites more often than males, philopatry increasing with age, and less dispersal within- compared to between-years (Greenwood and Harvey 1982). For species that attempt to raise more than one brood per year, time is an additional constraint that could affect within-season dispersal decisions. Because dispersal cues are likely to differ within- and between-seasons, it is necessary to assess those time periods separately.

Both emigration probability and distance moved affect the distribution (number near to far) of dispersers (Clarke et al. 1997) and likely have different selection pressures (Bowler and

Benton 2005), making it important to consider both dispersal measures. Whether studying rates or distance, there are three stages of dispersal to consider, the decision to leave, transience, and

selection of breeding location (Clobert et al. 2001). We focus on the first step of the process, the decision to leave, and the costs and benefits of that decision.

In this paper, we describe the pattern of between-season breeding dispersal for two populations of eastern bluebirds, Sialia sialis, in the Piedmont region of the southeastern United

States, one near Athens, Georgia and another near Clemson, South Carolina. Athens and

Clemson are ~ 100 km apart. At each location, there are four field sites; they range 1 to 21 km

(Athens) and 2 to 8 km (Clemson) apart. The habitat at both locations is similar, a combination of fields with mixed hardwood/pine edges that is on university farms used for livestock. In a previous comparison study, PAG found differences between the two bluebird populations, namely lower reproductive and foraging success, and more aggression toward conspecifics by bluebirds near Athens (1996, unpublished data). At that time, exotic invasive fire ants,

Solenopsis invicta, had established populations at the field sites near Athens (in the late 1970s;

Callcott and Collins 1996); fire ants did not reach the Clemson area until 1997 (personal observation). Ground arthropods are the primary food for red imported fire ants (Tschinkel

2006) and bluebirds (Gowaty and Plissner 1998). Because fire ants are known to decrease arthropod species richness and abundance (Porter and Savignano 1990), and fire ant and bluebird diets overlap, fire ants may be competing with bluebirds for food resources. This competition may have been responsible for the differences in reproduction and behavior PAG observed in

1996. In this paper, we assess the effects of fire ant density on eastern bluebird breeding dispersal distances during the 2001 to 2005 breeding seasons at both Athens, where fire ants were established, and Clemson, where fire ants were establishing their populations. Additionally we address three alternative explanations for dispersal distance 1) competition for food resources among conspecifics, 2) competition for mates, and 3) response to previous nest success or

failure. If competition (Howard 1960, Christian 1970, Greenwood 1980, Waser 1985) is driving dispersal, we expect to see longer between-season breeding dispersal distances when interspecific (fire ant density) or intraspecific (conspecific density) competition increases, or when competing for a new mate (change mates). Specifically, to escape competition for food with fire ants, we predicted bluebirds would disperse farther from breeding sites where fire ant density was higher at time at time t than individuals on breeding sites with lower fire ant density at time t. If competition with conspecifics was driving dispersal distance, bluebirds on breeding sites where conspecific density was higher at time t, should disperse farther than individuals on breeding sites with lower conspecific density at time t. If competition for mates influenced dispersal distance the most, bluebirds who changed social mates between time t and time t+1 should disperse farther to nest in time t+1 than those who did not change social mates. Previous research suggests individuals whose nests fail disperse farther (Harvey et al. 1979, Greenwood

1980, Greenwood and Harvey 1982) to escape the perceived poor environment (Howard 1960,

McPeek and Holt 1992, Ronce et al. 2001). If bluebirds use nest success as a proximate cue, bluebirds whose nests failed at time t should disperse farther to nest at time t+1 than those whose nests succeed at time t. We used a Bayesian modeling approach to test these predictions.

Methods

FIRE ANTS

Fire ants are an exotic invasive species that has spread to both coasts since its accidental introduction at Mobile, Alabama in the 1940s (Tschinkel 2006). As noted above, by foraging on other arthropods they may be competing with eastern bluebirds for food. Fire ants build mounds that are visible above ground. We estimated the density of fire ants twice each breeding season,

corresponding to bluebird brooding periods, by counting the number of fire ant mounds within a

60 m2 (2 x 30 m) area in front of each artificial nest box. We estimated three mound size categories: 1) < 30 cm, 2) 30 to 60 cm, and 3) > 60 cm at their widest point. Because mound volume is correlated to colony size (Tschinkel et al. 1995), we assumed that mound area would also be correlated to colony size (concurrence by Dr. Ken Ross, personal communication). We estimated mound area for each site by multiplying the number of mounds within each category by the median mound diameter for that category and averaging all estimates for a site. Our assumption was that there was an inverse correlation between fire ant density and the density of other ground arthropods (food for bluebirds).

EASTERN BLUEBIRDS

Eastern bluebirds are small (~ 30 g) thrushes (Family: Turdidae) that breed across the eastern part of North America, from the Atlantic coast to the Midwest and from southern Canada south to central Florida (Gowaty and Plissner 1998). Eastern bluebirds are short distance migrants; individuals nesting in the northern latitudes move south during the winter, whereas those nesting in the southern parts of their range may stay remain year-round (Gowaty and

Plissner 1998). During the breeding season, eastern bluebirds are mostly insectivorous and attack prey from perches such as trees, fences, and utility wires. Bluebirds are secondary cavity nesters; they build nests in natural and previously excavated cavities and readily use artificial nest boxes. In the southeastern United States, eastern bluebirds typically raise two broods, but can raise up to three (Gowaty and Plissner 1998). Males and females are sexually dimorphic; males have brighter blue feathers on their backs and wings, and more chestnut on their breasts than females.

Monitoring and banding

During the 2001 to 2005 breeding seasons, we monitored 120 nest boxes at Clemson and

147 at Athens twice a week and daily during laying, hatching, and fledging periods. Monitoring included checking box contents and identifying any banded bluebirds near the box. We attempted to capture all adults once each year just after the eggs hatched (abandonment is less likely after eggs hatch; Gowaty and Bridges 1991). When capturing individuals we banded them with one United States Fish and Wildlife Service aluminum band and three color bands, in unique combinations, allowing identification from a distance using binoculars or Questar® telescopes (for details see Lang and Gowaty in prep_A). We banded 373 adults during the 2001 to 2004 breeding seasons (Athens: n = 98 females (F), 70 males (M); Clemson: n = 109 F, 96

M). Additionally, we observed breeding dispersal by 199 individuals that we had banded as nestlings (Athens: n = 31 F, 49 M; Clemson: n = 60 F, 59 M; Lang and Gowaty in prep_C).

BREEDING DISPERSAL RATES AND DISTANCES

We assessed breeding dispersal in two ways, 1) looking at percent emigration among breeding sites to determine dispersal effects on survival and reproduction, and 2) testing four predictions for breeding dispersal distances.

Between-site (1-21 km) breeding dispersal emigration

The data we used to create our observation histories included any confirmed observation of individuals between year t and t+1. For the between-site emigration analyses, we defined

“emigration” as an observed movement between sites that occurred between time t and t+1.

Individuals that remained at the same site we considered “philopatric” for these analyses. We

estimated site-specific emigration by determining the percent of individuals that moved to a different breeding site between year t and year t +1.

Using the average emigration from a site, by sex, we conducted an analysis of variance

(SAS 2000) to look for differences by sex, location (Athens and Clemson), and the interaction of sex and location. We considered P < 0.05 significant. To test for a correlation between emigration and survival, we regressed percent emigration on yearly survival, by sex, using yearly survival rates for Athens and Clemson after-hatch-year birds (Lang and Gowaty in prep_A).

Additionally, we used program MARK (White and Burnham 1999, Cooch and White 2006) to estimate survival rates of emigrant and philopatric eastern bluebirds. We modeled Athens and

Clemson separately because each location was best represented by different models (Lang and

Gowaty in prep_A). For these analyses, we included group (philopatric or emigrant, by sex) within the resight model. We considered rates of emigrant and philopatric birds different when their 95% confidence intervals did not overlap.

We assessed the effects of dispersal on reproductive success by comparing individuals’ nesting success between consecutive breeding seasons. We used the average number of nestlings fledged per nest as our measure of nesting success, and calculated the differences between breeding season t and breeding season t +1. For each group, philopatric or emigrant, by sex and location, we determined an effect using the 95% confidence intervals surrounding the differences. Confidence intervals that did not include zero indicated higher or lower fledging success between consecutive breeding seasons.

Breeding dispersal distance: patterns and correlates

We used only individuals whom we observed nesting in the summer (June and later) during year t and also in the spring (March to May) of year t+1 to determine breeding dispersal distances, the movement between consecutive breeding locations (Greenwood 1980). Of 322 females and 297 males, 108 females (69 second-year, SY; 31 after-second-year, ASY; and 8, age undetermined) and 93 males (43 SY, 40 ASY, and 10 age undetermined) met our criteria for the dispersal distance analyses. We only used the first between-season nesting attempt we recorded for each individual to avoid bias caused by repeated observations of the same individuals among years. To test for a cost of dispersal on reproduction, we regressed the number of fledglings per number of eggs on dispersal distance.

Determining the breeding dispersal distribution shape

To use an appropriate probability distribution for model testing, we wanted to determine which probability distribution eastern bluebird breeding dispersal distances matched most closely. Using 201 eastern bluebird breeding dispersal distance observations to drive a categorical distribution model in OpenBUGS (10,000 iterations; McCarthy 2007), we estimated the mean percent of dispersers, and credible intervals, for distance categories that corresponded to the average radius of an eastern bluebird territory, 82 m (Gowaty and Plissner 1998). For comparison, we estimated the percent of individuals expected to be in each distance category for different probability distributions. To do this we used a two-step process. First, we used

OpenBUGS (Spiegelhalter et al. 2007) to estimate a mean dispersal distance and variance for the

201 bluebird observations using different probability distributions (normal, log-normal, gamma,

Poisson, negative binomial). Then, we randomly selected 10,000 numbers around each mean,

using R (Urbanek and Iacus 2010) and the variance around the mean, and determined what percentage fell into each of the 82 m distance categories. We also simulated random movement using a random number generator in Excel® and generating expected values for the range of breeding dispersal distances we observed during our study. When comparing the eastern bluebird categorical distribution to the probability distribution estimates, we considered the probability distribution with the least difference from the categorical eastern bluebird estimates as the best match to eastern bluebird dispersal and used that probability distribution for model testing.

Prediction testing

We took a Bayesian modeling approach and used OpenBUGS to test the four breeding dispersal predictions for 1) fire ant density, 2) conspecific density, 3) mate retention, and 4) previous nest success or failure. We created a model for each prediction; dispersal distance was the dependent variable for linear models that included the following categorical data (by prediction): 1) above or below average fire ant density, 2) above or below average conspecific density, 3) retained or changed mate, and 4) nest success or failure. Because age (Greenwood and Harvey 1982) and sex (Greenwood 1980) may affect dispersal, we looked for their effects to see if we needed to include either as covariates within our models. To do this, we ran two additional models, one with second-year and after-second-year categories and another with male and female categories. We thinned by five (used every fifth iteration) to reduce autocorrelation, used a burn-in of 10,000 and ran 100,000 iterations for three chains, which provided posterior density distributions from 300,000 iterations.

We determined the best model by comparing their deviance information criterion (DIC) values. The model with the lowest DIC value is the best representation of the data (Spiegelhalter et al. 2007). Models whose DIC values are different by two or less have only weak support for being different from one another. Models with differences in DIC values of three to seven have much less support than the top model (Spiegelhalter et al. 2002). To help determine the likelihood of each model we also calculated model weights. We determined model weight by using Buckland et al.’s (1997) method for calculating AIC and BIC weights.

(1)

Delta DICi is the difference between model “i” and the model with the lowest DIC value, for all

(K) models being compared. The likelihood is determined by looking at the ratio of one model weight to another (Cooch and White 2006). For example, if one model has a weight of 0.6 and the next best model a weight of 0.2, the first model is three times more likely than the second.

After determining the best model(s), we explored further the effects of the independent variable on breeding dispersal distance by developing additional models with more specific information

(e.g., the number fledged per number of eggs instead of nest succeeded or failed). To assess independent variable effects we compared dispersal distance 95% credible intervals (95% credible intervals represent 95% of the posterior distribution; whereas 95% confidence intervals represent values you would expect your data to fall within during 95 out of 100 sampling events). The amount of credible interval overlap represents how similar the estimates are to one another.

Results

BETWEEN-SITE (1-21 KM) EMIGRATION

We did not observe any emigration between the Athens and Clemson locations; the data we present is for emigration events made within locations (sites 1-21 km apart). The probability of between-site movement was relatively low; average per-site emigration was 3.2% per year

(95% confidence interval: 0.6 to 5.7%). There were fewer movements between Athens sites

(Fig. 3.1) than between Clemson sites (Fig. 3.2). At Clemson, bluebirds emigrated among most sites (Fig. 3.2). We found no differences in emigration between sexes, locations, or their interaction (P > 0.05).

There were five males and four females that we did not detect breeding on our sites during consecutive breeding seasons, but returned to breed one or two (one female) breeding seasons later. Seven of these individuals returned to the same site; two females emigrated, one from site 4 to 3 (~ 1 km; Fig. 3.1) and the other from site 5 to 8 (~7 km; Fig. 3.2).

Emigration and survival

Philopatric bluebirds in Athens and Clemson had lower survival rates than emigrants

(Athens: 95% CIemigrants: 0.9999 – 1.0, n = 6; CIphilopatric: 0.6668 - 0.7804, n = 150; Clemson:

CIemigrants: 0.9999 – 1.0, n = 10; CIphilopatric: 0.6280 - 0.8531, n = 116). We did not observe any correlation between percent emigration and survival rates (P > 0.05).

Effects of emigration on reproduction

In Clemson, philopatric and emigrant bluebirds fledged similar numbers of offspring in year t+1 (Emigrantsfemale = 3.1 ± 0.8(SE), n = 5; Emigrantsmale = 3.0 ± 1.2, n = 3; Philopatricfemale

= 2.6 ± 0.2, n = 62; Philopatricmale = 2.5 ± 0.2, n = 52). Philopatric females at Clemson fledged more young during breeding season t+1 than t (Table 3.1). Philopatric individuals in Athens had similar reproductive success in year t and t+1 (Table 3.1). We did not observe any nesting attempts by Athens emigrants in year t+1.

BREEDING DISPERSAL DISTANCE: PATTERNS AND CORRELATES

Between-season breeding dispersal distances ranged from zero to 1888 m (one sample for each of 108 females and 93 males). Fifty-three percent of males and 46% of females did not change nest boxes between seasons. A majority of bluebirds (70% of males and 65% of females) remained within 82 m (the average radius of an eastern bluebird territory; Gowaty and Plissner

1998) of their previous nest box. We did not find a correlation between dispersal distance and nest success (number fledged per number of eggs; P = 0.58). Breeding dispersal distances closely matched the shape of negative binomial and gamma distributions, with negative binomial having slightly less difference from the eastern bluebird distribution (1.9 x 1017; Fig. 3.3).

Therefore, we used the negative binomial distribution to model the breeding dispersal predictions.

Neither age nor sex had much influence on dispersal distance (Table 3.2), so we did not include those covariates within our hypothesis testing models. Of the four prediction models, the nest success model was best for describing breeding dispersal distance. Mate retention was second best (DIC difference of only 2), and together these two models encompassed 83% of the model weights (Table 3.2). For individuals whose nests were successful or failed, there was an

86% overlap in their 95% credible intervals (Table 3.2; Fig. 3.4A). However, individuals whose nests failed because of predation tended to move almost twice as far as those that failed for other

reasons (Table 3.3; Fig. 3.4B). Results for the mate retention model showed individuals that changed mates moved greater distances (Fig. 3.4C); almost twice as far, on average (Table 3.2), as those that kept the same mate in the following breeding season. To examine the combined effects of nest success and mate retention we ran two additional models, 1) combinations of mate retention and nest success categories, and 2) mate retention with the additive effect of percent of eggs that fledged. These combined nest success and mate retention models did not show any clearer patterns in dispersal distance (Table 3.3) than the individual mate retention or failed nests models (Fig. 3.4).

Discussion

PATTERNS OF EASTERN BLUEBIRD BREEDING DISPERSERS IN THE SOUTHEASTERN UNITED STATES

Emigration

Overall, 3.5% of breeding eastern bluebirds emigrated among years. Site-specific emigration (Figs. 3.1 and 3.2) was similar to percentages of two other cavity nesting species, house wrens, Troglodytes aedon, (< 5%; Drilling and Thompson 1988) and tree swallows,

Tachycineta bicolor (4% males, 14% females; Winkler et al. 2004). At Clemson, where sites were closer together, we observed more breeding dispersal events (compare Fig. 3.2 to 3.1). The linear arrangement of Clemson’s sites 5, 6, and 7, all within 5 km of each other (Fig. 3.2), may have made it easier for breeding dispersers to detect other breeding sites; three of four Athens sites were farther apart and sites 3 and 4, while only 1 km apart, had forest in between (Fig. 3.1).

For eastern bluebirds, nesting sites that are connected with corridors are more likely to be occupied than when forested in-between (Stokstad 2005). At both locations, artificial nest boxes spanned a distance of 1450 m, on average, within breeding sites. Some individuals moving

between the most distant boxes within a breeding site may have moved farther than individuals that emigrated between sites that were 1-2 km apart. For this reason, using emigration status to estimate dispersal effects may not be as useful as distance.

We observed nine of 212 (4.2%) adults “skipping” breeding seasons during this study

(i.e., returned to nest, but not in consecutive breeding seasons); similar for house wrens (Drilling and Thompson 1988) and mountain bluebirds (Sialia currucoides; Citta and Lindberg 2007), but only one of 302 eastern bluebirds during an 8 yr study in Alabama (Siefferman and Hill 2008).

Of the nine adults we observed in non-consecutive years, only two bred at a different site when returning (~ 1 km and 7 km distances; Figs. 3.1 and 3.2), so “skipping breeding seasons” did not appear to be for emigrating long distances. Additionally, 31 bluebirds we banded as nestlings returned to nest for the first time in their third year, or later (Lang and Gowaty in prep_C). After returning, eleven of those 31 continued to nest at the same site for multiple years. We do not know if individuals that “skipped” breeding seasons nested elsewhere or were floaters that remained at the breeding site waiting for a nesting opportunity. We made a number of between- year resightings of individuals that we did not observe nesting (Lang and Gowaty in prep_C).

This suggests that at least some individuals may have used the floater strategy. Additional observations of males re-nesting within a few days of female mortalities (JDL personal observation) also suggest non-breeding bluebirds (females in this case) were present.

Sex did not appear to affect emigration for these populations. We observed less long distance emigration by females in Athens (Fig. 3.1) than one would expect given dispersal patterns described in review studies (Greenwood 1980, Clarke et al. 1997). Emigration at

Clemson did not appear female biased either; males and females moved between sites that were both closer (2 km) and farther (7 km) away from one another (Fig. 3.2).

Emigration costs

The risk of predation can be greater for transitioning (Small et al. 1993, Yoder et al.

2004) and settling individuals (Rousset and Gandon 2002, Ronce 2007). We did not find a negative effect of emigration on survival, through either correlation or when comparing survival rates of emigrant and philopatric bluebirds. Opposite the cost of emigration assumption, we found philopatric bluebirds had lower survival rates than emigrants. However, small sample sizes of emigrants (Clemson, n = 10; Athens, n = 6) provided poor estimates for making comparisons. Larger sample sizes of natal bluebirds at these same sites showed no difference in survival rates between emigrant and philopatric birds (Lang and Gowaty in prep_C).

In our small sample of reproduction comparisons before and after emigration, reproduction did not decrease after emigration (Table 3.1). These observations echo results for other cavity nesters (Drilling and Thompson 1988, Part and Gustafsson 1989, Hakkarainen et al.

2001, Fisher and Wiebe 2006, Citta and Lindberg 2007) and species using early successional habitat (Payne and Payne 1993, Arlt and Part 2008). Fisher and Wiebe (2006) noted a trend in breeding dispersal literature that distance did not affect subsequent nesting success. Site philopatric individuals at Clemson improved their nesting success between year t and t+1, whereas philopatric birds in Athens did not (Table 3.1). A higher percentage of young (second- year) bluebirds nesting during year t at Clemson, relative to Athens, could provide one explanation for improved nesting success during year t+1 at Clemson; this was not the case, however (Athens: n = 30-SY and 47-ASY; Clemson: n = 26-SY and 45-ASY). Changing mates could also affect reproductive success (Greenwood and Harvey 1982). There were similar numbers of individuals that kept or changed mates, however, at both Athens and Clemson

(Athens: n = 35-same mate and 42-changed mate; Clemson: n = 34-same mate and 37-changed

mate). Neither age nor mate retention seem like probable explanations for improved nest success by Clemson’s philopatric birds. Perhaps territory quality varied more at Clemson sites than at

Athens sites, benefitting those that obtained and held onto the higher quality territories.

Theoretically, spatial variation decreases dispersal, temporal variation increases dispersal, and when spatial and temporal variation occurs simultaneously the strength of each influences dispersal rates (Johnson and Gaines 1990). There was temporal and spatial variation at the site level for the variables we tested (unpublished data). Since a majority of breeding birds did not emigrate, it may be more appropriate to use measures at the territory scale, instead of at the site scale, to gain insight about the costs and benefits of philopatry or dispersal. If microhabitat heterogeneity at the territory scale is affecting reproduction and survival, then dispersal may follow predictions drawn from the site-dependent regulation hypothesis (Rodenhouse et al.

1997).

Distance and distribution

Most breeders (~70%) remained within one territory’s distance from where they nested the previous year (Fig. 3.3). Remaining within a territory seems common for passerine breeders

(Drilling and Thompson 1988, Murphy 1996, Gowaty and Plissner 1997, Beheler et al. 2003,

Andreu and Barba 2006, Arlt and Part 2008). Females in this study moved about 40 m farther than males, on average (Table 3.2). Forty meters does not seem biologically significant for bluebirds in regards to improving their environment or interacting with different conspecifics; e.g., dispersal distance did not affect reproductive success. Additionally, birds often travel multiple territory distances (0.5-1 km) during breeding season exploratory movements (Beheler et al. 2003; JDL personal observation, Lehnen and Rodewald 2009). Breeding dispersal

distances of under 2 km are also common for birds (Harvey et al. 1979, Drilling and Thompson

1988, Murphy 1996, Haas 1998, Shutler and Clark 2003, Andreu and Barba 2006, Fisher and

Wiebe 2006, Citta and Lindberg 2007, Siefferman and Hill 2008), and moving 2-5 km is not uncommon (Part and Gustafsson 1989, Payne and Payne 1993, Arlt and Part 2008). We observed adult birds that dispersed ~ 10 km between breeding seasons (Fig. 3.1), and researchers for two other studies found breeding birds as far away as 150-350 km between seasons (Paradis et al. 1998, Winkler et al. 2004). Given the range of breeding dispersal observations, a negative binomial distance distribution for birds, such as the one in Figure 3.3, with low but consistent percentages of breeding dispersers moving longer distances (20 or more territories) may be typical for many bird species. In this study, there were a number of 7-10 km breeding dispersal events. For researchers interested in breeding dispersal and/or dispersal effects on population dynamics, an estimate of some individuals moving 7-10 km between breeding seasons seems reasonable for passerines.

CORRELATES TO EASTERN BLUEBIRD BREEDING DISPERSAL IN THE SOUTHEASTERN UNITED STATES

Of the four explanations for between-season breeding dispersal that we addressed, 1) previous nest success or failure, 2) mate retention, 3) conspecific density, and 4) fire ant density, the original explanation, fire ant density, had the least support (Table 3.2). Nest success best explained breeding dispersal distance and was 2.8 times more likely than the second best model, mate retention (Table 3.2). Because the top two models only differed by two in DIC value, and made up 83% of the model weight (Table 3.2), we focus our discussion on the top two models.

Data from the nest success model (Fig. 3.4A) did not support trends observed by other researchers that individuals with successful nests were more likely to return between years

(Harvey et al. 1979, Haas 1998), return to the same site (Howlett and Stutchbury 2003, Winkler et al. 2004), return to the same territory (Payne and Payne 1993, Hoover 2003), return to the same nest cavity (Gowaty and Plissner 1997), or move shorter distances (Citta and Lindberg

2007). For bluebirds nesting around Athens and Clemson, the cause of nest failure seemed to play a role in dispersal distance. Eastern bluebirds whose nests failed due to predation moved almost twice as far as those whose nests failed for other reasons (Table 3.3; Fig. 3.4B). Other researchers have made similar post-predation observations; e.g., females being less likely to re- nest within a season (Beheler et al. 2003), and individuals moving farther for their next nesting attempt (Harvey et al. 1979, Fisher and Wiebe 2006). Moving farther after predation events could be adaptive if dispersers move beyond a predator’s range (Powell and Frasch 2000, Fisher and Wiebe 2006). For between-season dispersers to benefit from a longer distance post- predation dispersal, predation pressure would need to be predictable between years. If predation pressure was more predictable at Clemson than Athens breeding sites, it would provide an explanation for philopatric bluebirds in Clemson having greater reproductive success while philopatric birds in Athens did not (Table 3.1).

The second best model for eastern bluebird breeding dispersal distance was mate retention (Table 3.2). When individuals changed mates, they moved twice as far, on average, as those that retained their previous mates (Table 3.2, Fig. 3.4C). In this study, whether retaining or changing mates, males and/or females sometimes re-nested in the same nest-box they used the previous season. However, dispersal estimates from the mate retention model suggests individuals that changed mates also changed nest cavities and/or territories more often than individuals that stayed with the same mate (i.e., their mean dispersal distance was greater than one territory radius, 82 m; Tables 3.2 and 3.3; Fig. 3.4C). Previous studies have also observed

that individuals who changed mates moved farther (Harvey et al. 1979, Payne and Payne 1993,

Murphy 1996, Blondel et al. 2000, Pampus et al. 2005, Andreu and Barba 2006). It seems logical that individuals would need to move around to find and/or compete for a new mate. To obtain a new mate while only moving one to three territories distance (Fig. 3.4C) suggests birds in these populations were exploring large areas before settling or that a high percentage of individuals (nearest two neighbors) changed mates.

Given the weight of the top two models, we expected that the combined models

(variables from both) would provide clearer pattern for dispersal distances, especially if there was an interaction between nest success and mate retention. However, the combined models did not show any stronger patterns than those of the failed nests (Table 3.3) or mate retention models

(Table 3.3) on their own, suggesting nest success and mate retention are separate constraints for breeding dispersal in these populations.

CONCLUSION

Understanding dispersal is challenging because multiple factors affect individuals at the same time (Clobert et al. 2004). The dispersal distance distribution for eastern bluebirds in the southeastern United States was not random - it matched a negative binomial distribution and ~

70% of individuals remained within one territory’s distance of the previous year’s nest. Two factors, nest success and mate retention, affected breeding dispersal distance. We present some support for environmental constraints causing longer dispersal movements than social constraints

(Ronce et al. 2001, Clobert et al. 2004); the upper credible interval for movement following predation (520 m; Table 3.3 and Fig. 3.4B) was more than twice that of individuals changing mates (233 m; Table 3.2 and Fig. 3.4C). For this study, the best model (nest success) supports

environmental variability, and the second best model (mate retention) supports competition for mates as the most likely evolutionary explanations for breeding dispersal in these two eastern bluebird populations.

ACKNOWLEDGEMENTS

National Science Foundation grants IOS 0076100 and an NIH R01 to PAG supported the research. We operated under a University of Georgia animal care and use permit (# A2005-

10013-0).

We thank LeAnne Bonner, Lena Chamblis, Kristin Connell, Shannon Fitzgerald, Lynn

Hayes, Mindi Hertzog, Beth Tyler Lebow, Jessica Melgey, Cathy Rickets, Brian Snyder, and

Gayle Weber for their assistance collecting field data and Mr. Jewett Tucker for access to site 2.

Thanks to Carrie Straight for assistance with ArcGIS. PAG designed the study and protocols for collecting demographic data, PAG and JDL designed protocols for measuring fire ant density;

PAG and JDL supervised technicians who collected, recorded, and computerized data; JDL analyzed the data and wrote the paper, which PAG edited.

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Table 3.1. Difference in average number of fledglings per nest between year t and t+1 for male and female eastern bluebirds, Sialia sialis, that nested at the same (philopatric) or different breeding site (emigrants) the following year; near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons. Differences that include zero indicate no difference in nesting success between years. Location Sex Philopatric n Emigrants n Female 0.1 – 1.1a 62 -1.8 – 3.6 5 Clemson Male -0.2 – 0.8 52 -3.3 – 3.0 3 Female -0.6 – 0.6 47 b Athens Male -0.1 – 1.1 49 b a – positive interval indicates greater nest success in year t+1 b – no data for nest success in year t+1

Table 3.2. Model rankings for four potential correlates to eastern bluebird, Sialia sialis, breeding dispersal distance; 1) nest success or failure, 2) mate retention or changing mates, 3) higher or lower conspecific density, and 4) higher or lower fire ant density. We used a Bayesian approach and OpenBUGS to rank the models. Lower deviance information criterion (DIC) values represent better models. ΔDIC is the difference in DIC value from the best model. Model weights are relative to the other models (i.e., Nest Success is 2.8 times more likely than Mate Retention and 7.6 times more likely than Conspecific Density). pD is the estimated number of model parameters. Additionally, we looked for age and sex effects but did not include either as covariates in the hypothesis models due to their low model weight. Mean 95% Credible distance Interval Model Models Variable (m) Lower Upper ΔDIC DIC pD weight

Successful 112 63 197 Nest Success 0 1281 5.126 0.61 Failed 85 45 158

Same 68 33 141 Mate Retention 2 1283 5.377 0.22 Changed 137 80 233

Above 123 48 303 Conspecific Density 4 1285 5.462 0.08 Below 100 61 161

Fire Ant Density Above 87 48 158 5 1286 4.99 0.05 Below 119 63 224

SY1 108 63 182 Age 7 1288 5.174 0.02 ASY2 103 46 228

Sex Male 83 44 156 7 1288 5.402 0.02 Female 126 68 231 1 - second-year 2 - after-second-year

Table 3.3. Extension of the top two models for eastern bluebird, Sialia sialis, breeding dispersal distance. For the Mate+Nest Success linear model, we used Mate as the intercept and Nest Success (number fledged per number of eggs) as the covariate. In the Mate:Nest Success model, we estimated dispersal distance for each of the four categories shown - combinations of same or changed mate and nest succeeded (fledged at least 1) or failed. The dispersal distance estimates presented for the Failed Nests model are for individuals whose nests failed because of predation or non-predation causes. pD is the estimated number of model parameters. Model results are from 148 breeding bluebirds nesting near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005. Mean 95% Credible Interval Models distance (m) Lower Upper DIC pD Same 92 35 220 Mate+Nest Success 1283 5.699 Changed 179 84 365

Same:Successful 72 27 183 Same:Failed 112 27 377 Mate:Nest Success 1283 9.072 Changed:Successful 169 81 349 Changed:Failed 97 46 206

No Predation 91 33 244 Failed Nests 472a 4.483 Predation 176 54 520 a – this deviance information criterion (DIC) value is not comparable to the other model DIC values because a subset (n = 32 no predation; n = 20 predation) of the data (n = 148) is used in the Failed Nests model

Fig. 3.1. Percent of adult male (M) and female (F) eastern bluebirds who remained at, or emigrated from, one study site (1, 2, 3, 4) to another between consecutive breeding seasons near Athens, Georgia during 2001 to 2005 (n = 122 observations). Markers indicate the direction of movement. Heavier line weight represents greater percentage. Dashed line represents movement not observed between consecutive breeding seasons; we observed a female nesting at site 4 in 2002 re-nesting again at site 3 in 2004. We provide yearly site-specific percentages and number of individuals observed in Appendix 3.1. This map is not to scale; representative between-site distances given.

Fig. 3.2. Percent of adult male (M) and female (F) eastern bluebirds who remained at, or emigrated from, one study site (5, 6, 7, 8) to another between consecutive breeding seasons near Clemson, South Carolina during 2001 to 2005 (n = 135 observations). Markers indicate the direction of movement. Heavier line weight represents greater percentage. Dashed line represents movement not observed between consecutive breeding seasons; we observed a female nesting at site 5 in 2001 re-nesting again at site 8 in 2003. We provide yearly site-specific percentages and number of individuals observed in Appendix 3.1. This map is not to scale; representative between-site distances given.

Fig. 3.3. The percent of eastern bluebirds, Sialia sialis, estimated to disperse a given distance (territories represent an average eastern bluebird territory radius, 82 m; Gowaty and Plissner 1998) compared to 10,000 values drawn randomly from the negative binomial and gamma distributions. We used 201 between-season breeding dispersal events to drive the categorical distribution in OpenBUGS, 10,000 iterations, and estimate the percent of bluebirds expected to disperse to each distance category. The error bars represent 95% credible intervals.

0.18 A

0.16

0.14

0.12 succeeded

0.1 failed

Percent 0.08

0.06

0.04

0.02

0.14 B

0.12

0.1 failed:no predation

0.08 predation Percent 0.06

0.04

0.02

0.2 C 0.18

0.16

0.14 same mate

0.12 changed mate

0.1 Percent 0.08

0.06

0.04

0.02

10 m distance categories Fig. 3.4. Posterior distribution (95% credible intervals) for eastern bluebird, Sialia sialis, breeding dispersers whose nests succeeded or failed (A), failed because of predation or not (B), and for individuals that re-nested with the same mate or changed mates (C). Nest predation and mate retention were the two best models for eastern bluebird breeding dispersal distances near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons.

Appendix

Appendix 3.1. Proportion of breeding male and female eastern bluebirds that emigrated from sites near Athens, Georgia (sites 1-4) and Clemson, South Carolina (sites 5-8) during the 2001 to 2005 breeding seasons. n represents the number of individuals observed (some individuals seen multiple years). Emigration Average Philopatric Emigrated 2001 2002 2003 2004 emigration n n Site F M F M F M F M F M F M F M 1 0 0 0 0.14 0 0 0 0 0 0.036 17 20 0 1 2 0 0 0 0 0 0 0 0 0 0 11 16 0 0 3 0 0 0 0 0 0 0 0 0 0 11 16 0 0 4 0 0 0 0 0 0 0 0 0 0 18 12 0 0 5 0 0 0 0.17 0 0 0 0 0 0.042 19 19 0 1 6 0 0 0.40 0 0.14 0 0 0 0.136 0 14 15 3 0 7 0.33 0 0 0 0 0.25 0 0 0.083 0.063 14 17 2 2 8 0 0 0 0 0 0 0 0 0 0 17 12 0 0 All 0.027 0.018 121 127 5 4

CHAPTER 4

EASTERN BLUEBIRD NATAL DISPERSAL DISTANCES CORRESPOND TO THE

NEGATIVE BINOMIAL DISTRIBUTION AND THEORETICAL MODELS FOR MULTIPLE

DISPERSERS3

3 Lang, J. D. and P. A. Gowaty. To be submitted to Journal of Animal Ecology.

Abstract

1. Fluctuating environments and intraspecific competition pose challenges to natal dispersers and

affect the way they search for their first breeding site.

2. We describe natal dispersal patterns for eastern bluebirds (Sialia sialis) nesting at eight study

sites, located 1 to 104 km apart in the southeastern United States, and test the resource

competition hypothesis by comparing observed dispersal distances to theoretical distributions

derived from models representing straight-line and spiral movement away from natal territories.

3. The eastern bluebird natal dispersal distances we observed matched a negative binomial

distribution. Most natal dispersal events (91%) were within 1500 m of the natal nest. The

average natal disperser moved 826 m (95% Credible Interval: 666 – 1024 m) and the longest

dispersal was ~ 93 km. Mean dispersal for spring broods was almost twice that of summer

broods ( x spring= 1146 ± 208 m (SD); x summer= 620 ± 86 m).

4. Dispersal distances did not differ by sex. However, ten percent of males, and zero females,

!remained at their natal nest box.! Females from summer broods dispersed more territories away

from their natal nest than did summer brood males ( x female= 4.0 ± 0.46 (SD); x male= 2.7 ± 0.28).

5. The percent of natal dispersers that nested within fifteen territories of their natal nest was in- ! ! between predictions by theoretical models for multiple dispersers that search for nesting

territories by moving in a straight-line or by spiraling away from their natal nest. These results

are consistent with the resource competition hypothesis.

6. Natal dispersal distributions compare to a combination of individuals using straight-line and/or

spiral search strategies. Females more closely matched a modified straight-line strategy (search

2 territories per ring of territories) whereas males more closely matched a modified spiral search

strategy (search 20% of territories per ring of territories).

Key-words: competition, distribution, eastern bluebird, multiple dispersers, natal dispersal, negative binomial, Sialia sialis, spiral search strategy, straight-line search strategy, theoretical models

Introduction

Dispersal can have a large effect on whether individual organisms find and obtain the resources necessary for survival and reproduction. Studying dispersal may give us insight about how organisms respond to challenges that affect survival and reproduction, such as intraspecific competition and environmental conditions. Previously observed patterns of dispersal were specific to the age and sex of an organism. For example, natal dispersers typically move farther and at greater rates than breeding dispersers (Greenwood and Harvey 1982). Additionally, female birds are often the dispersing sex (Greenwood 1980, Clarke et al. 1997). Several hypotheses for the evolution of dispersal could explain these patterns. Three commonly tested hypotheses are, 1) resource competition, 2) intrasexual competition for mates, and 3) inbreeding avoidance (Greenwood 1980, Johnson and Gaines 1990, Bowler and Benton 2005). Theoretical models representing these hypotheses provide predictions of dispersal rates and spatial patterns that are testable with field data (Johnson and Gaines 1990). In this paper, we test the resource competition hypothesis by comparing eastern bluebird, Sialia sialis, natal dispersal patterns to predictions from models of intraspecific competition for breeding territories (Skellam 1951,

Murray 1967, Waser 1985, Tonkyn and Plissner 1991). These models predict the percentage of individuals that will disperse zero or more territories away from their natal territory based upon competition with conspecifics. Unoccupied territories (based upon adult mortality) and natal individuals searching for unoccupied territories by moving in a straight-line, spiral, or something

in-between determines dispersers’ spatial distribution (Figs. 4.1 and 4.2; Waser 1985, Tonkyn and Plissner 1991).

Eastern bluebirds are secondary cavity nesters that use open habitat during the breeding season. Their use of open habitat and artificial nest boxes make bluebirds a useful species for observing dispersal patterns. Researchers can also manipulate breeding sites by varying distance among groupings of artificial nest boxes (Plissner and Gowaty 1996). We observed eastern bluebird natal dispersal (movement from where an organism is born to its location of first breeding; Greenwood 1980) within two populations of eastern bluebirds in the southeastern

United States, one near Clemson, South Carolina and another near Athens, Georgia (Fig. 4.3). In this paper, we describe natal dispersal patterns for these two eastern bluebird populations. The geographical arrangement of our study sites (Fig. 4.3) allows us to describe larger scale dispersal patterns, among-sites, as well as within breeding-site dispersal patterns. Secondly, we test the resource competition hypothesis by comparing the distribution of natal dispersers to distributions predicted by theoretical resource competition models (Waser 1985, Tonkyn and Plissner 1991).

The premise of these models is a correlation between adult survival rates and natal dispersal: when adult survival in a local population is higher, fewer territories near the natal site will be available and will increase the distance natal birds will have to move to find an unoccupied territory. If resource competition for territories was driving natal eastern bluebird dispersal, we expected to observe 1) a positive correlation between adult survival and percent natal emigration, and 2) the percent of natal dispersers moving a given number of territories corresponding to values predicted by theoretical resource competition models for dispersal (Waser 1985, Tonkyn and Plissner 1991).

Methods

STUDY ORGANISM AND SITE DESCRIPTION

Eastern bluebirds

Eastern bluebirds are sexually dimorphic thrushes. They breed in the eastern half of the

United States and use open fields and edges for foraging and nesting (Gowaty and Plissner

1998). Eastern bluebirds are perch-to-ground foragers, eating mostly insects during the breeding season. A longer breeding season in the South, March through August, allows most pairs to have two and sometimes three broods (Gowaty and Plissner 1998). When re-nesting, adults chase first brood juveniles away from the breeding territory (Plissner 1994). Individuals breeding in the northern part of their range typically migrate south during the winter, whereas eastern bluebirds inhabiting the Southeast tend to remain year-round; we have observed banded individuals on our sites throughout the fall and winter.

Site description

We conducted our study on university farms near Athens, Georgia and Clemson, South

Carolina in the Piedmont region of the southeastern United States (sites 2 and 8 are ~ 79 km apart; Fig. 4.3). To establish study sites for breeding bluebirds we placed artificial nest boxes on fences around the university pastures. PAG established field sites in Clemson in the late 1970s and in Athens in 1993 (except site four, established by JDL in 2001). Pastures, within the matrix of secondary forest (pines and hardwoods), provided open foraging areas and perches for foraging. Bluebirds also took advantage of fences and utility wires for perching.

Monitoring

During the 2001 to 2005 breeding seasons, we monitored all nest boxes (147 in Athens and 120 in Clemson) twice weekly and recorded contents and adults present. During egg laying, hatching, and fledging, we checked boxes daily to record exact dates for those events. We observed 632 nests in Athens and 622 in Clemson during the annual breeding seasons from 2001 to 2004. These nests produced 829 (in Athens) and 1389 (in Clemson) fledglings, providing

2218 potential returning natal dispersers among the 2002 to 2005 breeding seasons.

Banding

We banded nestlings when they were 10 d old, using three colored bands and one U.S.

Fish and Wildlife Service aluminum band in unique combinations so we could identify individuals from a distance (Athens: n = 423 female (F) hatch-year (HY); 379 male (M) HY;

Clemson: n = 739 F-HY, 629 M-HY). We used Questar® telescopes to identify individuals from a distance.

ANALYSES

Emigration

Of 2218 fledglings, we observed 199 return to breed; 168 as second-year birds. For this paper, we consider individuals “emigrants” if they moved from one study site to another and

“philopatric” if they remained on their natal study site. Because of low emigrant sample sizes

(Athens: Nmales = 7 emigrants and 32 philopatric, Nfemales = 5 emigrants and 21 philopatric;

Clemson: Nmales = 9 emigrants and 40 philopatric, Nfemales = 12 emigrants and 42 philopatric) we pooled across years preventing us from conducting repeated measures analyses. Using sex, year,

and location (Athens and Clemson) specific data, we regressed percent emigration on survival to test for a correlation. We used the average emigration per year (averaged among sites) to assess differences in emigration between sexes, location, and their interaction using an analysis of variance (SAS 2000). We considered P < 0.05 significant.

To estimate survival rates of emigrant and philopatric eastern bluebirds, we used program

MARK (White and Burnham 1999, Cooch and White 2006). Athens and Clemson were best represented by different models (Lang and Gowaty in prep_A), so we modeled each separately for these analyses following methods described in Lang and Gowaty (in prep_A). One modification we made from the Lang and Gowaty (in prep_A) analyses was including age and sex within the resight model. We assessed differences between emigrant and philopatric survival by comparing 95% confidence intervals for overlap.

Territory and distance measures

When assessing distance and numbers of territories dispersed, we restricted our dataset to the 144 individuals that we observed nesting prior to May of the year after they fledged (first nesting attempts of the year). We determined the number of territories natal bluebirds traveled by drawing a straight line from their natal nest-box to the first box they nested in and then counting the number of active territories they crossed. We considered a territory “active” if the box was used at any time during the 2001 to 2005 breeding seasons. Using GPS coordinates for each nest box, and ArcGIS, we determined the straight-line distance from each disperser’s natal box to the box they used for their first nesting attempt. Because fledging earlier or later could affect competition for territories and, therefore, the number of territories or distance dispersed, we categorized fledglings into two groups, “spring” and “summer” for analyses. We consider

nestlings that we banded during April and May as spring brood dispersers and those banded during June and later as summer brood dispersers. To assess differences in distance, and the number of territories dispersed, we compared credible intervals for overlap. We conducted an analysis of variance to test for differences in dispersal distance between siblings (fledglings from the same brood) and non-siblings (SAS 2000).

To determine which probability distribution best represented the eastern bluebirds’ natal dispersal distances, we compared data driven values to expected values from gamma, log- normal, negative binomial, normal, and Poisson probability distributions. We estimated a data driven probability distribution following methods by McCarthy (2007). Using the categorical distribution in OpenBUGs (Spiegelhalter et al. 2007) and sampling the 144 natal dispersal distances 10,000 times, we estimated a percentage of individuals within each distance category, and, a corresponding 95% credible interval (95% of the estimates surrounding the mean; in contrast, a 95% confidence interval represents a distribution of values that 95 of 100 samples should fall within). We did not try to find nesting locations of individuals outside of our study sites; this limited our observations of dispersal distance to the size of the sites (1450 m diameter on average) and the distances between study sites. Because we had few observations at longer distances, we pooled the longer dispersers into broader categories. The distance categories we used were: 100 m distances from zero to 3 km, 1000 m from 3 to 15 km, and 15,000 m distance categories from 15 to 90 km. We estimated the expected values for each probability distribution

(gamma, log-normal, negative binomial, normal, and Poisson) using two steps. First, using each of the probability distributions to drive OpenBUGS models, we estimated the mean dispersal distance and its variance. Then, we used R (Urbanek and Iacus 2010) and randomly drew 10,000 samples based upon each probability distribution mean and variance. We calculated the

percentage of draws that were within each distance category and used those as expected values to compare to the natal disperser categorical distribution. We considered the probability distribution with the smallest mean difference from the categorical estimates to be most representative of the natal dispersal distance distribution we observed.

Dispersal direction

Using ArcGIS we obtained an azimuth bearing between an individual’s natal and first nesting box. We then categorized the direction individuals moved into nine categories: N, NE,

E, SE, S, SW, W, NW, and None (remained at the natal nest box) and looked for differences from random using a chi-square analysis. We defined east (E), S, W, and N as 22 degrees either side of 90, 180, 270, and 360 degrees, respectively, and northeast (NE), SE, SW, and NW as 22 degrees either side of 45, 135, 225, and 315 degrees, respectively.

Nest location within a breeding site

Natal nest location, near the center or outskirts of a study site, can bias the probability of observing dispersal events because there is a greater chance that individuals from nests near the edge could move off of the study site (Barrowclough 1978). We tested for effects of nest location on the probability of observing returning dispersers by comparing the percent of dispersers returning from areas closer and farther from the center of a study site. To do this, we used ArcGIS and overlaid each site with rings the size of an average eastern bluebird territory,

82 m radius (Gowaty and Plissner 1998), outward from the centermost nest box (e.g., drawing of center and two rings in Fig. 4.1). We then compared the percent of dispersers returning to a ring to the percent that dispersed from that ring, using a Pearson’s chi-square test. We pooled Athens

and Clemson since the percent fledged per ring was not different between the two locations

(paired t-test; P = 0.23).

Theoretical distributions

Theoretically, competition among same-sex conspecifics determines the number of territories or distances natal dispersers traverse. One assumption for these models is that adults outcompete juveniles for territories. Dispersal distances should match, or range between, probabilities given by straight-line (Waser 1985; eq. 1) and spiral (Waser 1985; eq. 2) distributions (Fig. 4.2A).

P(n) = (1" t)n • t (1)

! &$ t,n = 0 P(n) = % 6n 1+3n(n "1) (2) '& [1" (1" t) ] • (1" t) ,n #1

P(n) is the probability! of moving n categories (territories) away from the natal nest. Working under the assumption that an adult will out-compete a juvenile, the probability of remaining at the natal site is t, adult mortality. If moving in a straight line, the probability of an individual settling depends upon a territory, n, being vacant when found (eq. 1). If searching for territories in a spiral pattern, Waser (1985) estimated juveniles would have six opportunities (assuming an hexagonal array of nest territories) to nest in the nth “ring” adjacent to the natal territory and 6n opportunities in successive rings (Fig. 4.1). The probability of settling in the nth ring then includes the probability that all previous rings are occupied [(1 - t)1+3n(n-1)], and the probability of an open territory in the ring being searched (1 - (1 - t)6n). Although the placement of artificial

boxes on fences did not create a hexagonal array of boxes, we felt the theoretical model was a

good starting point for comparison.

Tonkyn and Plissner (1991) noted that Waser’s (1985) probability estimates are for single

individuals dispersing (Fig. 4.2A) and do not account for multiple juveniles searching for

territories at the same time. Tonkyn and Plissner (1991) modified eqs. 1 and 2 to estimate the

probability of settlement for multiple straight-line (eq. 3) and spiral (eq. 4) dispersers (Fig. 4.2B

and C; note, eq. 4 below includes notational corrections to Tonkyn and Plissner’s (1991)

equation).

* 0, n = 0,1,...,i " 2 , Pi (n) = +# n & n "i+1 i (3) ,% (( 1" t) t , n ) i "1 -$ i "1'

+ 0, i >1+ 3n(n +1) - ! P (n) = , b $ $ 6n '' (4) i & & )) - #& B[ j,1+ 3n(n "1)] • & #B(k,6n))) , i *1+ 3n(n +1) . j =a% % k=i" j ((

!In eq. 4, B is the binomial probability of x successes out of y trials: B(x,y) = [y!/x!(y-x)!](1 - t)y-

xtx, a is the max of (0, i-6n) and b is the minimum of [i-1, 1+3n(n-1)]. For this paper, we used

apparent survival rate estimates from this study (average annual adult mortality rate = 0.48; Lang

and Gowaty in prep_A) to estimate theoretical dispersal distributions. To determine which

theoretical model best described the resulting pattern of eastern bluebird natal dispersal, we made

a visual comparison of the data’s distribution to the theoretical models’ shapes.

To speculate on how individuals might search for territories, we developed a model that

simulated modified straight-line and spiral search strategies. Following the premises of the

theoretical models we tested (Waser 1985, Tonkyn and Plissner 1991), we created a landscape of

rings of territories with the natal territory at the center (Fig. 4.1). Upon this landscape we simulated four search strategies: (a) searching a random ring and territory, (b) searching a random ring and territory when the ring choice is weighted; we weighted the rings using a negative binomial distribution with mean and variance of natal dispersers observed in this study,

(c) searching a specific number of territories within each ring encountered and (d) searching a percentage of territories within each ring encountered as an individual moved away from its natal site. For the specific number of territories strategy, we simulated individuals searching 1, 2, 3, 4,

5, and 6 territories within a ring. For the percent of a ring strategy, we simulated individuals searching 1/6, 1/5, 1/4, 1/3, 1/2, and 2/3 of the territories in each successive ring. For each simulation, we ran 1000 replicates of five dispersers. Prior to each replicate, we created a new landscape by randomizing which territories were available or occupied. We used adult survival rates to randomly determine whether a territory was occupied or unoccupied. When a disperser occupied an available territory, that territory was no longer available for the next disperser (i.e., a multiple disperser model; simulation code in Appendix 4.1). As with the theoretical distributions, we made a visual comparison of the eastern bluebird natal dispersers’ distribution to the simulated model distribution shapes.

Results

BETWEEN-SITE MOVEMENT: PERCENT EMIGRATION

We observed 199 of 2218 fledglings (9%) return to breed (31 females and 49 males in

Athens; 60 females and 59 males in Clemson). We observed one male disperse 93.3 km from site

1 in Athens to site 5 in Clemson (Fig. 4.3), all other movements were within or among sites at a

given location (21 km or less apart at Athens or Clemson). Of the 168 individuals we first observed nesting as second year birds, 33 (19.6%) moved between study sites.

The mean annual percentage of natal emigration from a breeding site was 24 ± 4.5(SE).

Emigration varied among sites, ranging from zero to fifty percent of observed natal dispersers

(Figs. 4.4 and 4.5; Appendix 4.2). The largest number of movements occurred between sites that were within 1-2 km of one another (Fig. 4.4, sites 3 and 4 (10 of 33 dispersers); Fig. 4.5, sites 6 and 7 (13 of 34 dispersers)). Additionally, there were 31 individuals that we first observed breeding during their second or third breeding season (Athens: 5 females, 10 males; Clemson: 6 females, 10 males). Of these 31 individuals, 28 nested at their natal site when they returned

(three emigrants represented by dashed lines in Fig. 4.5). At Athens, we resighted four additional males who emigrated, but for whom we did not observe nests; our first observations of these four individuals were also during their second or third breeding season (see dashed lines in

Fig. 4.4). Survival rates did not differ between emigrant and philopatric natal bluebirds (Athens:

95% confidence intervals - CIemigrants: 0.4131 – 0.9950, n = 20; CIphilopatric: 0.6097 - 0.7819, n =

67; Clemson: CIemigrants: 0.5703 – 0.7619, n = 23; CIphilopatric: 0.7448 - 0.8479, n = 87).

Sex

Similar percentages of natal males and females emigrated (Athens emigrants: 16% of females and 14% of males; Clemson emigrants: 20% of both males and females). In Athens, however, we observed natal females moving between the two closest sites only (Fig. 4.4). In

Clemson, females emigrated from all of the sites. Males emigrated from all of the sites in both

Athens and Clemson (Figs. 4.4 and 4.5). Though we only observed females emigrating from 6 of the 8 sites, there were no statistical differences in emigration percentages between sexes

( x females 26 ± 6%; x males 22 ± 6%; t = 0.31, P = 0.72), nor locations ( x Athens 21 ± 6%; x Clemson

27 ± 6%; t = -0.72, P = 0.49) or their interaction (t = 0.04, P = 0.97). Male emigration did not

! correlate !to adult or hatch-year survival (P > 0.05). Females !dispersed farther !when female

hatch-year survival was higher (F = 22.44, df = 3, P = 0.042) but not when adult female survival

was higher (F = 17.82, df = 3, P = 0.052).

NUMBER OF TERRITORIES AND DISTANCE DISPERSED

One hundred forty-four individuals met our criteria for natal dispersal (observed nesting

in the spring of their second-year). These individuals’ natal nest location, center or outer portion

of a breeding site, did not affect the percentage we observed returning (χ2 = 0.13, df = 11, P >

0.99; Fig. 4.6). Most observed dispersal events (91%) were within 1500 m of the natal nest; we

observed the other 9% dispersing intermittent distances from 1.8 km to 93.3 km (Fig. 4.7). This

distribution of natal dispersal distances most closely matches a negative binomial distribution

(Fig. 4.8).

Mean dispersal distance for observed individuals fledging from spring broods was almost

twice that of those from summer broods ( x spring = 1146 m, x summer = 620 m; Table 4.1).

Additionally, spring dispersers also had more variation (wider credible intervals) for both

territories moved and distance dispersed! (Table 4.1).! Notably, a higher percentage of both males

and females fledging from spring broods settled more than nine territories away from their natal

nest than did summer brood dispersers (Fig. 4.9). Whether from spring or summer broods, no

female natal dispersers nested in their natal nest box. Ten percent of male natal dispersers nested

in their natal nest boxes, however (Fig. 4.7). Females from summer broods moved

approximately one territory farther than males from summer broods (Table 4.1). These were the only differences in dispersal between males and females.

2 2 We did not see an effect of brood (χ spring = 7.35, df = 8, P = 0.50; χ summer = 7.72, df = 8,

2 2 P = 0.46) or sex (χ female = 11.86, df = 8, P = 0.16; χ male = 4.05, df = 8, P = 0.85) on dispersal direction. In fact, the cardinal direction natal bluebirds’ dispersed appeared random (χ2 = 8.19, df = 8, P = 0.42). Siblings tended to disperse in the same general direction, however; within 56 degrees of one another, on average. Siblings did not differ from non-siblings in dispersal distance (F = 0.19, df = 18, P = 0.99) or number of territories moved (F = 0.59, df = 18, P =

0.90).

Theoretical distributions

The distribution of natal eastern bluebird dispersers was similar to predictions by the multi-disperser theoretical models (Fig. 4.2B and C). A majority of natal dispersers remained within four territories (as predicted by the spiral search model); a lower percentage dispersed 5-

21 territories, similar to expected values for straight-line searchers (Fig. 4.9).

Simulated distributions

Dispersal was not random; observed distributions were not uniform in shape, as predicted by the completely random model, and had two to three times higher percentages of individuals within the 0 to 3 territory categories than predicted by the weighted random model (Fig. 4.10; compare A-E to F). Two strategies produced distributions similar to those we observed by natal eastern bluebird dispersers, the two-territory sampling strategy and the 1/5 of a ring (20%) sampling strategy (Fig. 4.10). Males more closely fit the 20% strategy (Fig. 4.10A and D) and

females more closely fit the two-territory sampling strategy (Fig. 4.10B and E). Simulating use of the 20% and two-territory sampling strategies simultaneously closely matched observed male and female dispersal distributions (Fig. 4.10C).

Discussion

PATTERNS OF NATAL EASTERN BLUEBIRD DISPERSERS IN THE SOUTHEASTERN UNITED STATES

Nine percent of eastern bluebirds fledging from Athens and Clemson study sites returned to breed. Whether fledging from the interior or outskirts of a study site the return rate was fairly consistent (Fig. 4.6), suggesting our observations were not biased by a nest’s proximity within a breeding site. Dispersal distances ranged from zero (remaining at their natal nest box) to 93 km, the majority being within 1.5 km of the natal nest (Fig. 4.7). This distribution shape and distance seems to be common for passerines. Eastern bluebirds near Clemson had a similar pattern of dispersal distances ranging zero to 9 km; one male was recovered a year later 800 km from its natal site (Plissner and Gowaty 1996). A study on western bluebirds, Sialia mexicana, found longer average natal dispersal distances, 2.3 km (male) and 7.8 km (female; Keyser et al. 2004).

Average natal dispersal distances for mountain bluebirds, Sialia currucoides, were more similar to observations from the western bluebird study, 5.9 km (female) and 7 km (male; Citta and

Lindberg 2007). However, similar to what we observed for eastern bluebirds, the probability of mountain bluebirds choosing a nest site closer to a natal territory was higher than choosing one farther away and individuals from both sexes moved longer distances (> 38 km; Citta and

Lindberg 2007). Seventy percent of great tits, Parus major, another cavity nester, dispersed within 1 km of their natal nest and ranged out to 3.5 km (Greenwood et al. 1979). A high percentage of marsh tits, Parus palustris, nested within two territories of their natal cavity while

others dispersed 4 to 7 km (Nilsson 1989). Emigrant song sparrows, Melospiza melodia, moved to islands 1.3 km and 25 km from their natal sites (Arcese 1989). Payne (1991) observed ninety percent of natal indigo bunting, Passerina cyanea, dispersers within 2 km of their natal territories, and two males that moved 52 km and 350 km. These natal dispersal distance observations suggest the categorical estimates in Fig. 4.8 may be a good representation of the percent of individuals settling at distances and locations that are difficult to observe. We suggest other researchers consider using the negative binomial distribution (Fig. 4.8) for calculating dispersal distance means and variances (for a comparison of negative binomial and normal distribution estimates see Table 4.1 and Appendix 4.3).

Between-site movements and percent emigration

There was a lot of variability in emigration among sites (Figs. 4.4 and 4.5); some sites had more emigration (sites 3, 4, 6, and 7) and others less (sites 1, 2, 5, and 8). Most between-site movement, for both sexes of bluebirds, occurred between sites that were within 1-2 km of one another (Fig. 4.4, sites 3 and 4; Fig. 4.5, sites 6 and 7). A number of individuals dispersed greater distances; 5 to 8 km movements were common (Fig. 4.5) and we observed one male dispersing 12 km and another male 93 km (Fig. 4.4). Between site distance may have affected our ability to detect dispersers. For example, we observed fewer numbers of emigrants from sites 1, 2, 5, and 8 (Figs. 4.4 and 4.5; Appendix 4.2). The habitat matrix between sites may have also affected emigration patterns. At sites 5, 6, and 7 (Clemson), at least one other site was visible to the human eye. In Athens, however, even the closest sites (3 and 4; only 1 km apart) were not visibly detectable at ground level, though possibly visible to birds when flying above the treetops. For bluebirds, it is more likely individuals will find other suitable habitat when it is

connected with a corridor than when forested in-between (Stokstad 2005). Having a greater distance between dispersal locations increases the probability that the habitat types between sites will vary. The configuration of different habitat patches could make it harder for dispersers to find suitable nesting sites. Breeding site size may affect detectability as well. Larger sites (e.g.,

3 and 5) had more cavities and may have been easier for dispersers to detect and/or increased the probability of conspecific attraction (Stamps 2001). An interaction of site isolation, between-site habitat types, and breeding site size, therefore, may play a role in shaping natal dispersal distributions.

Overall, movement among sites was relatively balanced (Holt and Barfield 2001, Clobert et al. 2004) and not directional as might occur from a source to a sink (Pulliam 1988).

Emigration was greater for natal (95% Confidence Interval: 15 – 33%) than breeding dispersers

(95% CI: 0.6-5.7%; Lang and Gowaty in prep_D), analogous to a documented pattern

(Greenwood and Harvey 1982). The natal bluebird estimate for emigration also falls within the range predicted by evolutionary stable strategy models for dispersal (10-20%; Holt and McPeek

1996).

Within-site movements

Over eighty percent of the natal dispersers we observed remained at, or returned to, their natal site. Dispersal within a study site was likely, given the average dispersal distance was 826 m and nest boxes typically spanned a distance of 1450 m at each breeding site. Movements between some sites (3 and 4, Fig. 4.4; 6 and 7, Fig. 4.5) were no farther than dispersal within larger study sites, providing an explanation for close sites having the most between-site movements. However, it was not unusual for individuals to disperse greater than 1 km (Table

4.1); 25% of dispersers moved > 1 km (Fig. 4.7) and 5% moved > 8 km (Figs. 4.4 and 4.5).

Plissner and Gowaty (1996) observed eastern bluebird natal dispersers moved 1-1.4 km, on average, at sites near Clemson during the late 1980s, farther than the range we observed (666 -

1024 m: 95% credible interval). The close proximity (< 0.5 km) of their sites could have made additional nest sites easier for the dispersers to detect, and, may have increased the likelihood

Plissner and Gowaty would observe slightly longer dispersal events. Seven of eight sites for this study were greater than 1 km apart. The size and distance among sites in this study may have prevented us from detecting as many 1 to 2 km dispersal events as Plissner and Gowaty (1996) were able to do with their study site arrangement. As seen in these two studies, the geographical arrangement and distance between sites can bias observations of dispersal events and the resulting dispersal distance estimates. This may have been why researchers observed longer average dispersal distances for western and mountain bluebirds too (2-8 km; Keyser et al. 2004,

Citta and Lindberg 2007). Though we did not sample all potential bluebird nesting habitat occurring among the study sites, the range of distances between our sites (Figs. 4.3-4.5) helped us capture variability in natal dispersal distances.

Sex effects

Similar numbers of natal males and females returned to breed at Athens and Clemson study sites. Both sexes moved similar distances (Fig. 4.7, Table 4.1) and only females from summer broods moved more territories (Table 4.1, Fig. 4.9D), similar to observations by Plissner and Gowaty (1996). We observed females emigrating among more sites in Clemson than in

Athens (Figs. 4.4 and 4.5). The reason for females moving between only two sites in Athens is unclear since we observed similar percentages of males and females emigrating in Athens, and

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males emigrated from all four sites. Because study sites were farther apart and female birds typically disperse greater distances (Greenwood and Harvey 1982, Clarke et al. 1997), we had expected to observe more female natal dispersal in Athens. Plissner and Gowaty (1996) observed females in their study moving significantly farther than males within a “sub- population” (sites within 0.5 km) but did not when dispersing “greater distances” (sites 6 km apart). Perhaps there are different constraints on male and female dispersers at within and between breeding-site scales. For example, in this study, ten percent of males remained at their natal box while no females did (Figs. 4.7 and 4.9). Since adult male and female survival rates did not differ (Lang and Gowaty in prep_A), why did males remain at their natal nest boxes while females did not? Perhaps male natal dispersers defended natal boxes more aggressively.

Plissner (1994) observed that male brood mates were dominant to females when juvenile eastern bluebirds showed aggression. Another possibility for juvenile males obtaining their natal territory, and females not, is that there is a sex-biased advantage for males remaining at their natal territory and females dispersing. Male western bluebirds nested closer to their natal nest than females (Kraaijeveld and Dickinson 2001) and received preferential care from their mothers during the winter (Dickinson and McGowan 2005). About one-third of adult western bluebird males that remained at a breeding site also served as helpers when both parents were alive

(Kraaijeveld and Dickinson 2001). Because helping is rare for eastern bluebirds (Gowaty and

Plissner 1998), helping is an unlikely reason for males to receive preferential treatment and remain at their natal nest box. It is more likely that there is a different benefit, such as lower aggression with neighbors. When nesting next to kin, male western bluebirds were less aggressive (Duckworth 2008). Because eastern bluebird males are territorial, it could benefit

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male eastern bluebirds more than females to remain at their natal box if it increases the probability that their neighbors will be relatives and decreases aggressive interactions.

Time of fledging

Fledging from a spring or summer brood had more effect on dispersal distance than did sex. Individuals fledging in the spring moved almost twice as far as those from summer broods, and had larger variation in their dispersal distances (Table 4.1). Plissner (1994) also observed eastern bluebirds from early broods dispersing farther, including siblings and non-siblings that fledged from the same nest box. House wrens, Troglodytes aedon, fledging from earlier nests also dispersed farther (Drilling and Thompson 1988). A review of 75 species of birds banded in

Europe found earlier banded individuals tended to disperse farther than those banded later in a breeding season (Paradis et al. 1998). For this eastern bluebird study, brood period affecting dispersal distance more than sex suggests resource competition with parents (Hamilton and May

1977, Greenwood and Harvey 1982) affected dispersal more than competition with other conspecifics. If competition with non-parental conspecifics was influencing dispersal, later born individuals should have dispersed farther (Murray 1967, Greenwood and Harvey 1982).

RESOURCE COMPETITION HYPOTHESIS

Emigration

Under the assumption that adults out compete juveniles for territories (Gauthreaux 1978), one prediction from the resource competition hypothesis is that natal emigration rates will increase as survival rates increase because there will be fewer territories available for natal individuals. We did not find a correlation between adult survival and percent emigration,

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however, perhaps because adult survival rates were 52% during this study (Lang and Gowaty in prep_A) making it likely that breeding sites were not at carrying capacity. Female natal dispersers did emigrate at a higher rate when hatch-year female survival was higher, suggesting competition with other hatch-year birds was strong for natal females (Ronce et al. 2001). While only finding a correlation between natal female emigration and survival, dispersal distributions for both sexes support the resource competition hypothesis. The few differences we observed between males and females (emigration/survival correlation; males returning to natal territories; summer females moving one territory farther) may be attributable to different search strategies when dispersing.

Theoretical distributions

Natal eastern bluebird dispersal distances were not random (Fig. 4.10F). The percent of individuals dispersing shorter (1-2 km) and longer (> 2 - 100 km) distances (Fig. 4.7) was in- between the predicted values for the straight-line and spiral dispersal models for multiple dispersers (Figs. 4.2 and 4.9). Dispersal distances that are in-between straight-line and spiral predictions support the resource competition hypothesis (Tonkyn and Plissner 1991).

To speculate on how individuals might search for territories, and why we observed dispersal distributions in-between expected values for straight-line and spiral search strategies, we modeled two approaches to searching, (a) searching a specific number of territories within each ring and (b) searching a percentage of territories in each ring as an individual moved away from its natal site. Two strategies produced distributions whose shapes matched our data better than the random (Fig. 4.10F) or Tonkyn and Plissner (1991) models, (1) searching just two territories in each ring and (2) searching 20% of a ring (compare Fig. 4.10 to 4.9). Perhaps some

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individuals use a modified straight-line strategy (search two-territories) and others use a modified spiral search method (search 20% of the territories in a ring). A combination of these two strategies produced a distribution that closely matches natal dispersal observed in this study

(Fig. 4.10C). Why might an individual choose one dispersal strategy or the other? When dispersing, siblings moved in similar directions, suggesting individuals might inherit a strategy.

Environmental or social conditions may also influence direction and distance moved, and give rise to use of both strategies within a population. Duckworth (2009) found male offspring phenotype varied relative to resource availability. Sex, therefore, may also affect strategy use.

In this study, male distributions matched the 20% search strategy well (Fig. 4.10A and D), and female distributions matched the two-territory search strategy (Fig. 4.10B and E). If adult males and females have equivalent survival rates, but juveniles search differently, spiral searchers will find nest sites closer than straight-line searchers (Fig. 4.2B and C). Females’ using a straight- line search method provides an explanation for why we observed females from summer broods moving farther than summer brood males (Table 4.1). Detecting a correlation between emigration and survival for females but not males could also be attributed to females using a straight-line search method. Straight-line searches by females and spiral searches by males would also provide a proximate explanation for observations of avian sex biased dispersal

(Greenwood 1980, Clarke et al. 1997), while allowing for similar dispersal distances based upon territory availability. Since over six months time passes between the time eastern bluebirds fledge and when they nest the following spring, it is not realistic to think that individuals travel in a straight line or in a spiral pattern throughout that entire time period. They could use a specific search method during a short period of time when looking for a territory, however. Use of radio-telemetry during the non-breeding season may provide insight into questions of

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searching time and behavior. Without better information about territory selection behavior, we are essentially describing patterns that match a given distribution but are purely speculating on the behavior that leads to that pattern.

Another interesting dispersal pattern is individuals “disappearing” for one or more breeding seasons before returning to breed. On our study sites, approximately sixteen percent of returning birds “disappeared” for at least one breeding season before returning. A small percentage of these individuals emigrated. Two of four males at Athens that we observed emigrating during non-consecutive breeding seasons moved farther distances than we observed for natal dispersers (Fig. 4.4). At Clemson, the non-consecutive breeding season emigrants paralleled natal dispersers (Fig. 4.5). We do not know if individuals that “disappeared” remained on our study sites and did not nest (floaters), or if they nested off-site before returning. Nest box availability varied among sites and years, so natal dispersers may have taken advantage of locations nearby that had open territories and then moved back to their natal site if cavity availability or environmental conditions were better another year. Ninety percent of the fledglings that “disappeared” for a year or two nested at their natal site when returning, suggesting some advantage of returning to their natal site. Other researchers have also observed natal birds “disappearing” and later returning to their natal site. Sixteen percent of natal mountain bluebirds returned during non-consecutive breeding seasons (Citta and Lindberg

2007), the same percentage we observed for the eastern bluebird populations we studied. One- third of male great tits banded as nestlings were not seen until after their second-year, but did not nest farther from their natal nest sites than individuals observed nesting as second-year birds

(Greenwood et al. 1979). A small percentage of indigo buntings (0.5%) first returned to their natal site after their second-year also (Payne 1991). Lack of food resources can create floaters

105

(Arcese 1989), and may be an explanation for observations of individuals moving off of and back onto breeding sites. Tracking birds throughout the non-breeding season would be one way to assess search strategies and correlate environmental conditions to nest site selection. In a variable environment, spiral searchers would have a higher probability of finding open territories closer to their natal site than would straight-line dispersers. Sixty-five percent of individuals that we observed “disappearing” and returning in later years were males, and males more closely matched the spiral search model (Fig. 4.10A and D). Because spiral searchers move shorter distances (compare Fig. 4.2C to 4.2B), it increases the probability that spiral searchers might assess their natal site in future breeding seasons and return if territories are available or conditions are more favorable. Environmental variation, therefore, may be a proximate cue for choosing a search strategy.

CONCLUSION

We observed eastern bluebirds at sites ranging 1 km to 104 km apart and tested the resource competition hypothesis (Waser 1985, Tonkyn and Plissner 1991) by comparing observed natal dispersal distances to theoretical distributions. Natal dispersal distances for eastern bluebirds in the southeastern United States ranged from zero to greater than 93 km and were comparable to a negative binomial distribution (Fig. 4.8). The distribution of distances natal dispersers moved fell in-between predicted values for multiple dispersers using straight-line or spiral search methods (Fig. 4.9), providing support for the resource competition hypothesis.

Individuals born in the spring dispersed farther than those born during the summer (Table 4.1), which also supports competition for resources (nesting territories in this case) as a factor in natal

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dispersal movements. Males and females dispersed similar distances (Table 4.1), but may have used different search strategies to find available territories (Fig. 4.10).

Multiple factors operate simultaneously to affect disperser behavior (Clobert et al. 2004).

Ronce et al. (2001) suggest the strongest selective pressure will influence dispersal distance; parent-offspring conflict causes individuals to move the shortest distances, kin competition/inbreeding medium distances, and habitat quality the longest dispersal events.

Evidence from the two eastern bluebird populations we studied suggests parent-offspring competition is strongest for natal males and kin competition is strongest for natal females.

Perhaps individuals use different search strategies, modified straight-line or spiral, based upon the type of competition, or other constraint they are facing. Individuals choosing a dispersal strategy that matches a perceived constraint (parent, kin, habitat) provides another explanation for observing dispersal distances whose pattern matched a negative binomial distribution (Figs.

4.8 and 4.10).

ACKNOWLEDGEMENTS

National Science Foundation grants IOS 0076100 and an NIH R01 to PAG supported the research. We operated under a University of Georgia animal care and use permit (# A2005-

10013-0).

Thanks to Mr. Jewett Tucker for access to site 2. We greatly appreciate assistance in the field from LeAnne Bonner, Lena Chamblis, Kristin Connell, Shannon Fitzgerald, Lynn Hayes,

Mindi Hertzog, Beth Tyler Lebow, Jessica Melgey, Cathy Rickets, Brian Snyder, and Gayle

Weber. J. H. Plissner, Ron Pulliam, M. C. Freeman, and the UGA statistics department provided technical assistance with the probability distributions. Ron Pulliam also developed R code for

107

the simulation model presented in Appendix 4.1; Drew Kramer assisted with the random movement code. Thanks to Carrie Straight for assistance with ArcGIS and providing comments on this manuscript. PAG designed the study and protocols for collecting demographic data, PAG and JDL designed protocols for measuring fire ant density; PAG and JDL supervised technicians who collected, recorded, and computerized data; JDL analyzed the data and wrote the paper, which PAG edited

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McCarthy, M. A. (2007) Bayesian methods for ecology, Cambridge University Press, Cambridge.

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Table 4.1. Mean territories (numbers) and distances (meters) dispersed, with 95% credible intervals (CI), for eastern bluebird natal dispersers near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons. We estimated means and credible intervals using OpenBUGS (Spiegelhalter et al. 2007) and the negative binomial distribution (most appropriate for these data; see Fig. 4.8). Spring and Spring Summer Summer Male and Female Male Male and Female Male Male and female female (n=30) (n=29)a female (n=37) (n=47)b (n = 143)c x CI x CI x CI x CI x CI x CI x CI Territories 3.7 3.0-4.7 4.2 3.0-5.7 3.4 2.3-4.8 3.2 2.8-3.8 4.0 3.2-5.0 2.7 2.2-3.3 3.4 3.0-3.9 Distance 1146 809-1620 1110 764-1608 1274 678-2377 620 472-814 648 493-852 619 393-972 826 666-1024 aIncluding an outlier male, which dispersed 93 km, increases the spring male mean dispersal distance ( x = 4961 m, credible interval 2236 to 10,830) b n =! 46 for territories ! ! ! ! ! ! c n = 139 for territories

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Fig. 4.1. Representation of rings (circles) of territories (hexagons) surrounding a natal territory (center). There are six territories in the first ring and 12 territories in the second ring away from the natal territory. Dispersers may use a straight-line search strategy (solid arrow) or a spiral search strategy (dashed line) when they look for a new territory.

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0.5

0.4 Straight-line 0.3 Spiral

0.2

0.1

0 0 1 2 3 4 5 6 7 8 9 >9

B 0.5 1st disperser 0.45 2nd disperser 0.4 3rd disperser 0.35 4th disperser 0.3 0.25 5th disperser 0.2 avgerage 0.15 Probability of Dispersal of Probability 0.1 0.05 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

C 1 1st disperser 0.9 2nd disperser 0.8 3rd disperser 0.7 0.6 4th disperser 0.5 5th disperser 0.4 average 0.3 0.2 0.1 0 0 1 2 3 4 Number of Territories Away from Natal Territory Fig. 4.2. Theoretical probability of dispersal away from natal territories by natal dispersers competing for unoccupied territories (following Tonkyn and Plissner 1991). Probability of one individual searching in a straight-line or in a spiral pattern (A). Probability of multiple individuals simultaneously dispersing, and searching in a straight-line (B), or in a spiral pattern (C). Individuals occupy the first unoccupied territory, making it unavailable for another individual. For the above distributions we used the average mortality rate (0.48) for adult eastern bluebirds nesting near Athens, GA and Clemson, SC during the 2001 to 2005 breeding seasons (Lang and Gowaty in prep_A) to estimate territory availability.

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Fig. 4.3. Eastern bluebird study sites near Athens, Georgia (1, 2, 3, 4) and Clemson, South Carolina (5, 6, 7, 8). Athens and Clemson are in the southern Piedmont region of the southeastern United States (shown in gray).

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Fig. 4.4. Percent of natal eastern bluebirds moving within and between study sites (1, 2, 3, 4) near Athens, Georgia during the 2001 to 2005 breeding seasons. Markers indicate the direction of movement for males (M) and females (F); heavier line weight represents greater movement percentage. We provide yearly site-specific percentages and number of individuals observed in Appendix 4.2. Dashed lines represent emigration by natal birds first observed after their second year; we observed four males – one, banded at site 2 in 2001, was at site 3 in 2003; the second, banded at site 1 in 2001, was at site 2 in 2003; the third, banded at site 3 in 2001, was at site 4 in 2004; the fourth, banded at site 3 in 2002, was at site 1 in 2004. This drawing is not to scale; representative between-site distances given.

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Fig. 4.5. Percent of natal eastern bluebirds moving within and between study sites (5, 6, 7, 8) near Clemson, South Carolina during the 2001 to 2005 breeding seasons. Markers indicate the direction of movement for males (M) and females (F). Heavier line weight represents greater movement percentage. We provide yearly site-specific percentages and number of individuals observed in Appendix 4.2. Dashed lines represent emigration by natal birds first observed after their second year; we observed three males returning to nest in non-consecutive years – one, banded at site 6 in 2001, nested at site 7 in 2004; the second, banded at site 6 in 2002 nested at site 5 in 2004; the third, banded at site 5 in 2001, nested at site 6 in 2004. This drawing is not to scale; representative between-site distances given.

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0.18 fledged returned 0.16

0.14

0.12

0.1

0.08 PercentDispersers of

0.06

0.04

0.02 0 1 2 3 4 5 6 7 8 9 10 >10 Number of Rings from Center of Breeding Site

Fig. 4.6. Using rings, the diameter of an average eastern bluebird territory, starting at the center of a study site and expanding outward, we present the percent of individuals that fledged from each ring (black bars) and the percentage of fledged individuals that returned to nest on the study sites (grey bars) based on 2218 fledgings and 199 returns. The percent returned is not different from the percent fledged (χ2 = 0.13, df =11, P > 0.99). We conducted our study on eight study sites, four near Athens, GA and four near Clemson, SC during the 2001 to 2005 breeding seasons.

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0.15

0.12

0.09

0.06 PercentNatal Dispersersof 0.03

0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90

Distance Category (100 m intervals)

Fig. 4.7. Distance dispersed by male (black) and female (grey) natal eastern bluebirds during the 2001 to 2005 breeding seasons near Athens, Georgia and Clemson, South Carolina. Distance category zero represents no dispersal. The final distance category represents > 9000 m. All other categories are 100 m intervals (i.e., category 2 represents 100 to 199 m).

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Fig. 4.8. Percent of individuals estimated dispersing to each distance category. We obtained the eastern bluebird estimates and 95% credible intervals using observed natal dispersal distances to drive a categorical distribution model (10,000 iterations) in OpenBUGS (McCarthy 2007, Spiegelhalter et al. 2007). To obtain estimates for negative binomial, gamma, log-normal, normal, and Poisson distributions we drew 10,000 random samples around the mean eastern bluebird dispersal distance, for each distribution. We present the best fitting distribution, negative binomial, and second best, gamma, above.

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0.6 A 0.6 B

0.5 0.5

0.4 Spring Males 0.4 Spring Females

Straight-line Model Straight-line Model 0.3 0.3 Spiral Model Spiral Model

0.2 0.2 PercentNatal Dispersersof 0.1 0.1

0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

0.6 C 0.6 D

0.5 0.5

0.4 Summer Males 0.4 Summer Females

Straight-line Model Straight-line Model 0.3 0.3 Spiral Model Spiral Model

0.2 0.2 PercentNatal Dispersersof 0.1 0.1

0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Number of Straight Line Territories Number of Straight Line Territories

Fig. 4.9. Percent of natal eastern bluebirds dispersing zero to >21 (represented by "territory" 22) territories from their natal nest. We present data for males (A and C) and females (B and D) fledging from spring (March to May; A and B) and summer (June to August; C and D) broods during the 2001 to 2005 breeding seasons near Athens, GA and Clemson, SC. Solid line and markers represent eastern bluebird percentages (A: N = 30; B: N = 30; C: N = 47; D: N = 37). Broken lines represent expected percentages based upon theoretical distributions for multiple-dispersers searching for an open territory by moving in a straight line or spiraling away from their natal nest. Theoretical percentages are averages for five natal dispersers, the number of both males and females per study site expected to survive and breed each year, when adult yearly survival is 0.52 (based upon estimates from Lang and Gowaty in prep_A).

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0.35 A 0.35 B 0.35 C

0.3 0.3 0.3 Spring Males Spring Females All Males

0.25 samples 2 territories in each ring 0.25 samples 2 territories in each ring 0.25 All Females

samples 20% of territories in each ring samples 20% of territories in each ring combination of 2 territory and 20% 0.2 0.2 0.2 territory search stragegies

0.15 0.15 0.15

0.1 0.1 0.1 PercentNatal Dispersersof

0.05 0.05 0.05

0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

0.35 D 0.35 E 0.35 F

0.3 0.3 0.3 Summer Males Summer Females Random

0.25 samples 2 territories in each ring 0.25 samples 2 territories in each ring 0.25 Random - weighted negative binomial

samples 20% of territories in each ring samples 20% of territories in each ring 0.2 0.2 0.2

0.15 0.15 0.15

0.1 0.1 0.1 PercentNatal Dispersersof

0.05 0.05 0.05

0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Number of Straight Line Territories Number of Straight Line Territories Number of Straight Line Territories

Fig. 4.10. Percent of natal eastern bluebirds dispersing zero to >21 (represented by "territory" 22) territories from their natal nest. We present data for males (A and D) and females (B and E) fledging from spring (March to May; A and B) and summer (June to August; D and E) broods during the 2001 to 2005 breeding seasons near Athens, GA and Clemson, SC. Solid line and markers represent eastern bluebird percentages (A: N = 30; B: N = 30; D: N = 47; E: N = 37). Broken lines represent expected percentages based upon simulated distributions for multiple-dispersers sampling two territories (open diamonds) in each successive ring of territories away from their natal site, searching twenty percent of territories (star) in each ring, or moving randomly among rings and territories (F; open triangle: random movements weighted using a negative binomial distribution, as shown in Fig. 4.8). The simulated distributions are averages for five natal dispersers, the number of both males and females per study site expected to survive and breed each year, when adult yearly survival is 0.52 (based upon estimates from Lang and Gowaty in prep_A).

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Appendices

Appendix 4.1. R code for simulating natal dispersal distribution (% settling a given number of territories away from their natal site). The code below represents dispersal by eastern bluebirds. To set up the landscape we use concentric rings of territories surrounding the natal nest and randomly select the number of available territories per ring based upon adult survival rates (Lang and Gowaty in prep_A). We simulated individuals searching randomly, a percentage of a ring, or a specific number of territories within a ring as they moved away from the natal territory. Individuals settled within the first unoccupied territory. That territory then became occupied, and unavailable for the next disperser.

nr = 1000 # number of replicate trials R = 11 # total number of rings (natal territory plus 10 rings) nt <- c(1,6,12,18,24,30,36,42,48,54,60) # number of territories available in each successive ring maxcell = max(nt) # maximum number of cells in any single ring occupy = matrix(NA,R,maxcell) # sets up the landscape to have: # NA occupancy not available yet (the data) # R number of rings of territories (the rows) # maxcell number of territories within a ring (the columns) N = 5 # N = number of dispersers ns = matrix(0,R) # number of dispersers settling in each ring ncell = (1/3)*nt; ncell[1]=1 # number of cells (territories) each individual samples; # two searching strategies: # 1) individual samples a fraction of the territories (e.g., 1/3; 1/2; 2/3) # 2) individual samples a specific number of territories (e.g., 2; 4; 6) result = array(0,c(1000,R)) # array to set up the landscape with all cells (start at zero, 1000 replicates, for R rings) # for R number of rings po = 0.52 # probability that a ring is occupied; for this study, adult eastern bluebird survival rate

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# this next step sets up the initial unoccupied landscape for (trial in 1:nr) { # for trial 1 to number of replications (nr) success = matrix (0,N) # sets up a matrix with success all zero for an individual prior to dispersal nring = matrix(0,R) # number that settle in ring R

# this next step sets up the initial occupied landscape

for (ring in 1:R) { # for ring 1 to number of rings (R) for (cell in 1:nt[ring]) { # for cell 1 to the number of cells within the given ring occupy[ring,cell]=0 # occupy initially equal to zero if (runif(1, min=0, max=1) > po) { # draw a random number between zero and 1; if it is less than the # probability of being occupied (adult survival (po)), occupy[ring,cell] = 1 } # the cell is occupied; and equal to 1 } }

# non-random movements

for (i in 1:N) { # for each disperser (i), from 1 to number of dispersers (N) for (ring in 1:R) { # the probability of staying at the natal ring (ring one for this # simulation) is always 1 - adult survival (po, above) for (cell in 1:ncell[ring]) { # for cell one to the number of cells and individual samples if (success[i]==0 & occupy [ring, cell] == 0) { # if cell unoccupied then give that cell the value of 1, # i.e., the individual stops occupy [ring, cell] = 1 # that cell is now occupied success [i] = 1 # the individual is successful and success = 1 nring [ring] = nring [ring] + 1 # when unsuccessful, move out one ring } } } }

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# records the number of individuals settling in each ring for (ring in 1:R) {result[trial,ring]=nring[ring]} }

# presenting the results f=matrix(0,R) # dispersal function for (ring in 1:R) {f[ring]=mean(result[,ring])/N} # calculate percent of successes per ring plot(c(1:R),f,xlab="Distance",ylab="Fraction of Dispersers"); lines(c(1:R),f,col=4) # plots results f # prints percent per ring

###### # random movement; in place of the non-random movements for (i in 1:N) { # for each disperser (i), 1 to number of dispersers (N)

while (success[i]==0) { ring<-sample(1:11,size=1) # randomly choosing a ring cell<-sample(nt[ring],size=1) # randomly choosing a cell

{if (success[i]==0 & occupy [ring, cell] == 0) # if cell is found is not occupied then

{occupy [ring, cell] = 1 # give that cell the value of 1, i.e., the individual stops; # that cell is now occupied success [i] = 1 # the individual is successful and success = 1

nring [ring] = nring [ring] + 1 # data; how many in ring } } } }

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###### # random movement - each ring is weighted with a negative binomial probability for observed average eastern bluebird dispersal; # in place of the non-random movements for (i in 1:N) { # for each disperser (i), from 1 to number of dispersers (N)

while (success[i]==0) { p<- c(0.001297718,0.122093652,0.106304747,0.098518438,0.083378393,0.074726938,0.071266357,0.062074186,0.053855304,0.05071 9152,0.039472261,0.038282686,0.031685952,0.028009084,0.023683357,0.024007786,0.019898345,0.017411052,0.016005191,0.014 491186,0.012328323,0.010489889) # weighted probability of landing nearer relative to farther

ring<-sample(1:22,size=1,prob=p) # randomly choosing weighted a ring

cell<-sample(nt[ring],size=1) # randomly choosing a cell

{if (success[i]==0 & occupy [ring, cell] == 0) # if unoccupied cell is found then

{occupy [ring, cell] = 1 # give that cell the value of 1, i.e., individual stops, # that cell and it is now occupied success [i] = 1 # the individual is successful and success = 1

nring [ring] = nring [ring] + 1 # data; how many in ring } } } }

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####### modifications of “ncell” to have individuals sample a specific number of territories per ring; examples for sampling 1-6 territories, and, sampling 2 and 20% of territories as dispersing ncell = c(1,1,1,1,1,1,1,1,1,1,1) ncell = c(1,2,2,2,2,2,2,2,2,2,2) ncell = c(1,3,3,3,3,3,3,3,3,3,3) ncell = c(1,4,4,4,4,4,4,4,4,4,4) ncell = c(1,5,5,5,5,5,5,5,5,5,5) ncell = c(1,6,6,6,6,6,6,6,6,6,6) ncell = c(1,2,3,2,5,2,7,2,10,2,12) # combination of 2 and 20%; 2 territories first and then 20% of a ring ncell = c(1,1,2,4,2,6,2,8,2,11,2) # combination of 2 and 20%; 20% of a ring first and then 2 territories

##########

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Appendix 4.2. Proportion of male and female natal eastern bluebirds that emigrated from sites near Athens, Georgia (sites 1-4) and Clemson, South Carolina (sites 5-8) during the 2001 to 2005 breeding seasons. N represents the number of individuals observed. Emigration Average Philopatric Emigrated 2001 2002 2003 2004 emigration N N Site F M F M F M F M F M F M F M 1 0 0 0 0 na 0.25 0 0 0 0.06 9 12 0 1 2 na 1 0 0 0 0 0 0 0 0.25 5 4 0 1 3 0.25 0 0.5 0.25 na 0.5 0.5 na 0.42 0.25 6 7 4 2 4 0 0.29 na 1 1 0 na 0 0.50 0.32 1 9 1 3 5 0.09 0.10 0.11 0.00 0.00 0.00 0.25 0.22 0.11 0.08 27 26 3 3 6 1.00 0.67 0.33 na na na 0.00 0.33 0.44 0.50 6 3 4 3 7 0.33 0.17 0.00 0.00 0.67 1.00 0.50 0.00 0.38 0.29 5 7 4 2 8 0.50 na 0.00 1.00 0.00 0.00 0.00 0.00 0.13 0.33 4 4 1 1 All 0.25 0.26 63 72 17 16 na – none available; i.e., no emigration from that site

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Appendix 4.3. Mean territories (numbers) and distances (meters) dispersed, with their 95% confidence intervals (CI) for eastern bluebird natal dispersers near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons. We estimated the means and confidence intervals using a normal distribution for comparison with estimates calculated using a negative binomial distribution (see Table 4.1), which is more appropriate to use with these data (Figure 4.8). Spring and Spring Summer Summer Female Male Male and Female Male Both sexes Male and female (n=30) (n=29)a female (n=37) (n=47)b (n = 143)c x CI x CI x CI x CI x CI x CI x CI Territories 3.7 2.7-4.7 4.1 2.5-5.7 3.3 2.0-4.5 3.2 2.7-3.7 3.9 3.0-4.9 2.7 2.1-3.2 3.4 2.9-3.9 Distances 1111 722-1501 1072 654-1490 1152 486-1818 608 284-932 636 259-1012 587 64-1110 816 562-1069 aIncluding an outlier male, which dispersed 93 km, increases the spring male mean dispersal distance (normal distribution: x = 4224 m, confidence interval! -174 to 8622) ! ! ! ! ! ! b n = 46 for territories c n = 139 for territories !

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CHAPTER 5

HATCHING IN SPRING OR SUMMER, CONSPECIFIC SURVIVAL, AND NESTLING

CONDITION AFFECT NATAL DISPERSAL DISTANCE OF A CAVITY NESTING

PASSERINE4

4 Lang, J. D. and P. A. Gowaty. To be submitted to Behavioral Ecology.

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Abstract

Numerous hypotheses for observed patterns of natal dispersal exist. We tested intraspecific competition, food availability, and predation hypotheses as explanations for natal dispersal distance by eastern bluebirds, Sialia sialis, near Athens, GA and Clemson, SC, USA.

Using a Bayesian information theoretic approach to compare models representing these hypotheses, the intraspecific competition hypothesis had the most support. Natal dispersers from spring broods were positively correlated to adult survival and an inversely correlated to hatch- year survival. Summer brood nestlings in better condition remained closer to their natal nest.

For both males and females, individuals from spring broods dispersed farther. Overall, hatching in spring or summer played the largest role in eastern bluebird natal dispersal, affecting both dispersal cues and distance.

Introduction

In 1996, PAG conducted a comparison study of two eastern bluebird, Sialia sialis, populations within the southeastern Piedmont region of the United States. Eastern bluebirds near

Athens, Georgia had lower reproductive and foraging success, and higher intraspecific aggression than those near Clemson, South Carolina (unpublished data). One notable difference between the two study locations, in 1996, was the abundance of red imported fire ants,

Solenopsis invicta, in Athens and their absence in Clemson. Red imported fire ants are opportunistic omnivores that eat mostly insects (Tschinkel 2006) and have the ability to decimate local arthropod communities (Porter and Savignano 1990). Eastern bluebirds are insectivorous, eating mostly ground dwelling arthropods during the breeding season (Gowaty and Plissner

1998). Therefore, one potential explanation for the observed reproductive and behavioral

131

differences between bluebirds at Athens and Clemson is that bluebirds were competing for food with fire ants. During the 2001 to 2005 breeding seasons, we conducted a second comparison study of eastern bluebird populations breeding near Athens and Clemson, as red imported fire ants were in the process of invading the area around Clemson. We used this invasion front as a natural experiment to compare population parameters for eastern bluebirds using habitats with established fire ant populations (Athens) and where fire ants were establishing (Clemson).

In this paper, we consider the effects of fire ants on eastern bluebird natal dispersal

(moving from place of birth to a first breeding location, Greenwood 1980). Because fire ants reduce ground arthropod richness and abundance (Porter and Savignano 1990), we assumed more food would be available when there were fewer fire ants. We also assumed food would be easier to access when breeding sites were closer to forest edges (because eastern bluebirds are perch-to-ground foragers). Therefore, our “food availability” hypothesis was natal dispersal distance is inversely correlated to food availability measures. Two predictions from the food availability hypothesis, are 1) a positive relationship between dispersal distance and fire ant density and 2) a positive relationship between dispersal distance and the distance to forest edge.

Intraspecific competition could also affect natal dispersal distances (Howard 1960,

Murray 1967, Christian 1970, Waser 1985). Dispersing juvenile bluebirds could compete for resources, such as breeding territories, with other juveniles or with adult bluebirds. Age and size affect dominance (Christian 1970, Gauthreaux 1978), therefore, we assumed adults would outcompete juveniles and larger individuals would dominate smaller conspecifics. Additionally, juveniles from earlier broods are typically dominant (Murray 1967, Christian 1970, Greenwood and Harvey 1982). Juveniles that fledged earlier would also be more familiar with the natal site.

Our second hypothesis, therefore, was natal dispersal distance is positively correlated to

132

intraspecific competition. The intraspecific competition hypothesis, and our assumptions, led to the following predictions, 1) a positive relationship between dispersal distance and survival (both juvenile and adult survival), 2) a positive relationship between dispersal distance and conspecific density, and 3) an inverse relationship between dispersal distance and nestling condition (greater weight relative to body size).

Bowler and Benton (2005) suggest emigration increases as predation increases.

Predation was a contributing variable to bluebird survival at Athens and Clemson (Lang and

Gowaty in prep_A) and may affect natal dispersal distances if bluebirds disperse to avoid areas with higher predator density. These ideas led to the predation hypothesis, that predators affect eastern bluebird dispersal, and the prediction that bluebirds would disperse farther when predation was greater.

Methods

STUDY SITES

We used four field sites at each Athens, GA, in Clarke and Oglethorpe counties, and

Clemson, SC, in Anderson county. Athens and Clemson are in the southeastern Piedmont and are ~100 km apart. PAG established study sites on Clemson University farms in the mid 1970s and on University of Georgia farms in 1993 (except one site established by JDL in February

2001). We placed artificial nest boxes of the same size and material on fence posts to provide easily monitored nest sites. The farm fields, used mostly for livestock, provided bluebirds with fences, mixed pine/hardwood edges, and utility wires for perching and foraging.

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EASTERN BLUEBIRDS

Background

Eastern bluebirds are secondary cavity nesters and breed throughout the eastern United

States (Gowaty and Plissner 1998). These smaller (~ 30 g) sexually dimorphic thrushes breed from March through August in the southern part of their range. Most pairs are able to raise two, and sometimes three, broods during a breeding season (Gowaty and Plissner 1998). Adults are territorial when nesting, even against fledglings from their earlier brood(s) (Plissner 1994). As short distance migrants, individuals that breed in the north typically migrate south in winter. In the Southeast, bluebirds tend to remain year-round. During fall and winter we have observed banded individuals remaining on site. Eating mostly insects during the breeding season, bluebirds use open fields and edges for both foraging (perch-to-ground) and nesting (Gowaty and

Plissner 1998).

Banding

During the 2001 to 2005 breeding seasons we banded nestlings around Athens and

Clemson; part of a long-term study. We banded nestlings when they were ten days old, approximately one week before fledging (Gowaty and Plissner 1998). On each individual we placed four bands, three color bands and one U. S. Fish and Wildlife Service band. Each unique combination allowed us to identify individuals from a distance using Questar® telescopes or binoculars. When banding, we also measured mass to the nearest 0.5 g using a Pescola scale, and measured tarsus length (Pyle 1997) to the nearest 0.1 mm using digital calipers. We avoided temporal bias in measurement of growth (Kunz and Ekman 2000) by measuring nestlings when they were of the same age, ten days old.

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Monitoring

We monitored all nest boxes (147 in Athens and 120 in Clemson) twice weekly during the 2001 to 2005 breeding seasons. We recorded nest box contents and the color bands of bluebirds present. To obtain exact dates for egg laying, hatching, and fledging we checked the nests approaching those stages daily. Bluebirds at Athens produced 829 fledglings and at

Clemson 1389 during the 2001 to 2004 breeding seasons. From these 2218 fledgings we documented 144 natal dispersal events during the 2002 to 2005 breeding seasons. Additionally, we observed 22 second-year birds that we banded as nestlings breeding on our sites. We did not include these 22 individuals in our analyses because our first observations of them nesting were in the summer, after most pairs had completed their first nesting attempts (i.e., may not have been their first breeding location – a criteria for natal dispersal). We determined natal dispersal distances by using GPS locations and ArcGIS to calculate the straight-line distance between a disperser’s natal and first nesting box.

RED IMPORTED FIRE ANTS

Background

After being accidentally introduced into the U.S.A. near Mobile, Alabama around 1940, fire ants, native to South America, have expanded their range to both coasts and north as far as

Tennessee (Tschinkel 2006). Fire ants are omnivorous and eat mostly insects (Tschinkel 2006).

Invading fire ants decrease both species richness and the total numbers of insects in the invasion front (Porter and Savignano 1990); these changes could affect food availability for other insectivores.

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Fire ants build and live within mounds. The mounds have above- and below-ground structure. The above ground structure allowed us to monitor the number and size of active fire ant mounds. Fire ant mound volume is positively correlated to the size of its colony (Tschinkel et al. 1995).

Monitoring

Fire ants were in the Athens area around 1977 (Callcott and Collins 1996) and only reached Clemson in the late 1990s. We observed fire ants for the first time at our western most

Clemson field site in 1997. By the start of this study, in 2001, fire ants had expanded their range to three additional Clemson study sites, 8 km east of the first study site they invaded.

We surveyed fire ant density twice each breeding season, once during the spring and once in the summer, to correspond with the two times when a majority of bluebirds was brooding. To determine fire ant density, we censused 2 m in front of and 15 m on either side of every nest box whether or not bluebirds occupied the box. We recorded the number of mounds and their relative size (< 30 cm, 30-60 cm, or > 60 cm in diameter at its widest point), within the 60 m2 survey areas. We assumed mound area and volume would have a positive correlation and used mound area as a proxy for fire ant density (Dr. Ken Ross corroborates this assumption; personal communication). To calculate mound area we multiplied the number of ant mounds in a category times the average area for a mound in its respective size category. We used all surveys from a site to estimate the average mound area per site for each year.

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DISPERSAL MODELS

We used a Bayesian approach and program OpenBUGS (Spiegelhalter et al. 2007) to test the food availability, intraspecific competition, and predation hypotheses. Using methodology that allowed us to rank models based upon their fit to the data offered a number of advantages.

First, although the hypotheses were not mutually exclusive we were able to use deviance information criterion to determine a best model and its probability relative to the other models.

Additionally, we were able to test alternative models that included variables from all three hypotheses. The Bayesian approach also allowed us to specify distributions for model parameters. Details of our models are below.

Among model information

We had a number of assumptions when building our models. First, we assumed a linear model would describe the relationship between dispersal distance and the predictive variables.

Second, we assumed a priori that sex would likely affect natal dispersal distance. Sex biased dispersal is common for birds, with females typically dispersing farther (Greenwood 1980,

Clarke et al. 1997). To account for potential sex biased dispersal we made sex the intercept within the models. Third, individuals from spring or summer broods may experience different cues about food availability, intraspecific competition, or predation during the breeding season.

To account for temporal variation among dispersal correlates, we modeled spring and summer broods separately (we categorized nestlings we banded during April and May as “spring brood” and those banded during June and later as “summer”).

Sixty dispersal events from spring (30 male, 30 female) and 81 from summer (46 male,

35 female) formed the datasets we used to drive Monte Carlo Markov Chain simulations and

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estimate the coefficients for each variable within the models. We allowed both the intercept and

the variable coefficients to vary within the models. Because data ranged in size (i.e., survival

rate < 1 and fire ant mound densities around 300 m2 per ha) we used a z-transformation

[ (xi " x )/SD ] to help independent variables conform to a normal distribution with a mean equal

to zero and standard deviation of one. The z-transformation also aided with model convergence.

! To prevent our models from being constrained to positive numbers, and to help linearize the

regression equation, we used a log link function. We used a negative binomial distribution for

our dependent variable (dispersal distance) prior. The negative binomial model (simulating an

overdispersed Poisson distribution) had the best fit to dispersal distances (Lang and Gowaty in

prep_C). For our beta variable coefficients, we drew from a normal distribution and used small

priors to let the data drive the models (code for our global model is in Appendix 5.1). We ran

300,000 simulations; 100,000 samples for each of three chains after a burn-in of 25,000. To

avoid autocorrelation, we thinned by three (used non-sequential data points; every third

simulated value) for all models. When thinning, the total number of simulations needed to

estimate parameters increased by a factor of the thin (e.g., a thin of three ran 900,000

simulations, of which, 300,000 were used to estimate model parameters).

Food availability hypothesis

To test the food availability hypothesis, we developed a model with three variables: 1)

fire ant density (average per site - as described above), 2) the average distance to edge habitat

from a nest box, and 3) the interaction of the distance to forest edge with fire ant density.

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Intraspecific competition hypothesis

To test the intraspecific competition hypothesis, we developed a model that included: 1) nestling body condition (weight divided by average tarsus length for an individual), 2) after- hatch-year (AHY) survival rates, 3) hatch-year (HY) survival rates, 4) conspecific density

(number of females divided by the number of nest boxes per site divided by the average distance to the nearest two nest boxes; this measure accounted for nest site availability and density for sites of different sizes and nest box geography), and 5) the interaction of AHY survival, HY survival, and conspecific density.

Weight divided by tarsus length is used to assess the condition of birds (Johnson et al.

1985). Higher ratios of weight to tarsus suggest a bird has more body mass for its skeletal size, and, therefore, should be in better condition relative to individuals with lower ratios. For example, mass and tarsus length was greater for individuals with an unlimited food supply than for food limited individuals (Searcy et al. 2004). Because condition may have a large effect on dispersal distance, we ran an additional competition model that included just condition and the intercept.

Predation hypothesis

We used the percent of nests predated at each breeding site as our determinant variable in the predation model. We assumed percent nest predation would correlate to predator density.

Because nest boxes were in open areas, defensive behavior by adults was visible to neighbors, making nest predation ‘public information’ (Valone 1989, Danchin et al. 2001). We included nests destroyed by house sparrows, Passer domesticus, within the percent predation estimates

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because house sparrows kill bluebirds (Gowaty 1984), and, their inclusion as “nest predators” improved survival estimates (Lang and Gowaty in prep_A).

Additional models tested

We included two additional models in our analyses, 1) condition+AHY survival+fire ant density, and 2) a global model. Because condition (Bowler and Benton 2005) and AHY survival

(Murray 1967, Waser 1985) are considered important dispersal correlates, and fire ant density differed between sites at Athens and Clemson, we thought a simple model with these three variables may account for a majority of dispersal distance variation. Finally, the global model included all nine variables from the food availability, intraspecific competition, and predation hypotheses. Within all of our models we used site and year-specific (hatching year) estimates, except for survival, for which we used sex, year, and location (Athens and Clemson) specific estimates (Lang and Gowaty in prep_A); distance to edge, was constant within sites and among years.

Model ranking

To rank the models we used their deviance information criterion (DIC). The model with the lowest DIC value has the most support (Spiegelhalter et al. 2003). When DIC values differ by only 1 or 2, support for those models are similar, whereas, models that have DIC values differing by 3-7 have less support than the better model (Spiegelhalter et al. 2002). Following

Buckland et al.’s (1997) method of determining model weights (for AIC and BIC) relative to the other models tested, we report model weight (wi) as:

(1) wi = exp("#DICi /2)/$exp("#DICi /2) i=1

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where ∆ DICi is the DIC of an individual model “i” minus the lowest model DIC value for all (K) models being compared. We report 95% credible intervals (i.e., 95% of the estimates fell into that range) for model variable posterior distributions. We assessed differences in dispersal distances between sexes, and brood periods, by comparing their 95% credible intervals for overlap; we interpreted non-overlapping intervals as different from one another. We used beta coefficient credible intervals to determine the strength of correlation between dispersal distance and a variable. When credible intervals include zero there is no correlation. Positive credible intervals represent a positive correlation and negative intervals represent a negative correlation to dispersal distance. Coefficient size represents the effect of one model variable relative to another.

Variables with overlapping credible intervals do not differ in effect.

REPRODUCTION

We ran a regression to determine the effects of dispersal distance on first nest fledging success (number fledged and number fledged per egg). To compare fledging success of individuals remaining on their natal breeding site to those that emigrated to a different site, we used a student’s t-test. For both analyses, we considered alpha < 0.05 significant.

Results

Of the six natal dispersal distance models we tested, the global model was best for eastern bluebirds from spring broods; it had 4 and 4.5 times the weight of the next best models, predation and condition+AHY survival+fire ant density (Table 5.1). Together, the top three spring brood models accounted for over 80% of model weight. We found a positive correlation between after-hatch-year survival and natal dispersal distance, and an inverse correlation

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between natal dispersal distance and hatch-year survival (Table 5.2). Though predation was the second best model for dispersers from spring broods, we did not find a correlation between predation and dispersal distance (Table 5.2). Condition was the top model for individuals from summer broods and carried four times to weight of competition, the second best model (Table

5.1). These two models accounted for over 89% of model weight and both showed individuals from summer broods dispersed shorter distances when they were in better condition (Table 5.3).

No other variables within the top models correlated to dispersal distance (Tables 5.2 and 5.3).

EFFECTS OF BROOD AND SEX ON DISPERSAL DISTANCE

Fledglings from spring broods had much more variation in their dispersal distances and dispersed farther than fledglings from summer broods (best model credible interval for spring males: 116 to 9996 m; spring females: 71 to 8374 m; summer males: 199 to 544 m; summer females: 255 to 540 m). Male and female dispersal distances did not differ (credible intervals overlap within brood periods).

INCLUDING AN OUTLIER CHANGES THE TOP MODEL AND DISPERSAL DISTANCE ESTIMATES

For Clemson and Athens bluebird populations, natal dispersal distances typically ranged from 0 to 11,407 m (mean = 826 m; detailed natal dispersal patterns are described in Lang and

Gowaty in prep_C). We documented one spring brood male dispersing 93 km (from Athens to

Clemson). Because this male’s dispersal was 100 times greater than an average individual, including this outlier in the dataset would bias the results; both the model ranking and correlative variables change when this individual is included (Table 5.4; compare to Table 5.2). When including this longer distance disperser, food availability becomes the top model and natal

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dispersal distance is positively correlated to the interaction of fire ants and the distance to forest edge, while being negatively correlated to distance to edge by itself (Table 5.4). None of the variables in the global model correlate to dispersal distance when the outlier is included (Table

5.4). Including the long distance male disperser increases the average dispersal distance estimate for spring brood males approximately six-fold ( x with_outlier = 11,790 m compared to x without_outlier

= 2006 m), and greatly increases the 95% credible interval (220 to 70,610 m). ! !

EFFECTS OF DISPERSAL DISTANCE ON REPRODUCTION

2 Dispersing farther had no effect on number fledged (R = 0.002, F1,111 = 0.19, P = 0.67)

2 or number fledged per egg (R = 0.004, F1,101 = 0.43, P = 0.51) for individuals’ initial nesting attempts. Twenty-seven individuals dispersed to new breeding sites, for which we determined

18 nest fates. Individuals dispersing to new breeding sites did not differ in the number fledged than those that remained on their natal site (t = -0.22, df = 111, P = 0.83).

Discussion

We addressed three hypotheses for natal dispersal distances by eastern bluebirds, food availability, intraspecific competition, and predation. Our original hypothesis, food availability, had little to no support (Tables 5.1 and 5.5). Intraspecific competition best explained natal dispersal in the southeastern Piedmont. We found support for two of the five intraspecific competition hypothesis predictions, and no support for predictions coming from the food availability and predation hypotheses (Table 5.5). In the top three models for spring dispersers, only competition variables (AHY and HY survival) correlated to dispersal and we observed a positive relationship between adult survival and natal dispersal distance (Table 5.2). In the

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summer, nestlings in better condition remained closer to their nests (Table 5.3). Opposite of one of the intraspecific competition hypothesis predictions (Table 5.5), we found an inverse relationship between hatch-year survival and dispersal distance (Table 5.2). While not what we predicted from the intraspecific competition hypothesis, an inverse relationship between juvenile survival and dispersal distance for summer dispersers is consistent with resource competition for the evolution of dispersal (Howard 1960, Murray 1967, Christian 1970, Clark 1978, Gauthreaux

1978, Greenwood 1980) when considering adults are more aggressive toward juveniles earlier in the breeding season (Plissner 1994).

RESOURCE COMPETITION

Condition and survival

Because age and size affect dominance (Christian 1970, Gauthreaux 1978) we assumed spring brood individuals would have a competitive advantage over summer fledglings. We observed the opposite, however, potentially because eastern bluebird parents push spring fledglings off the breeding territories when fledglings become independent; summer offspring sometimes remain with their parents through the start of the next breeding season (Plissner

1994). Nestling condition, therefore, may be important for individuals from summer broods who are competing for breeding territories near their natal nesting cavity, matching our observations

(Tables 5.3 and 5.5). After becoming independent of adults, juvenile eastern bluebirds form foraging flocks with fledglings from different parents (Plissner 1994). Juvenile survival within these flocks could be a reliable cue for breeding-site quality (high hatch-year survival - indicating higher quality sites; low hatch-year survival - lower quality sites). Using hatch-year survival as a cue for habitat quality is a potential explanation for the inverse relationship we

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observed between hatch-year survival and dispersal distance (Table 5.2). Coefficient size for after-hatch-year and hatch-year survival was similar (Table 5.2), suggesting territory availability

(via AHY survival) and site quality (via HY survival) weigh equally for natal dispersal decisions.

If juveniles cue in on site quality via hatch-year survival, perhaps adult survival affects dispersal most when breeding sites become saturated.

Hansson (1991) suggested that in unsaturated habitats healthy individuals disperse because they are in good enough condition to breed when reaching their new site; in saturated habitats subordinate individuals are forced to leave and should have lower reproductive success.

Juveniles bypassed unoccupied boxes before settling and about one-third of the nest boxes in

Athens and Clemson remained unused during a breeding season, suggesting the populations were not saturated. Individuals that moved to new sites did not have lower reproductive success, suggesting dispersing individuals were in good condition and supports Hansson’s (1991) hypothesis. However, our model results show bluebirds in better condition remained nearer their natal nest box (Table 5.2), which is opposite of Hansson’s (1991) prediction for unsaturated habitats. Heavier individuals remaining closer to the nest supports the dominance hypothesis

(Gauthreaux 1978) instead.

Effect of brood

Similar to this study, previous studies of eastern bluebirds near Clemson (Plissner 1994) found early broods dispersed farther and were less philopatric (~ 4 % spring vs. ~ 9 % summer).

Males and females from spring broods dispersed similar distances too (Plissner 1994), lending support to brood period having a stronger influence on dispersal distance than does sex for southeastern Piedmont bluebird populations. Time of fledging appears to influence dispersal

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distance for other species as well. In a review of multiple species, juveniles banded early in the breeding season dispersed farther than those banded later (Paradis et al. 1998). Individuals from early broods of house wrens, Troglodytes aedon, (Drilling and Thompson 1988) and indigo buntings, Passerina cyanea, (Payne 1991) also dispersed farther from their natal sites.

If the scale of movement (between breeding-sites vs. within breeding-sites) differs for spring and summer dispersers, as we observed (27% of spring fledglings moving to different breeding sites vs. 13 % from summer broods), search patterns and settlement cues for individuals dispersing at different times during the breeding season may differ also. Different correlative variables for the spring and summer models (described above; Tables 5.2 and 5.3) also supports the idea that dispersers use different cues at different times of the year.

ALTERNATIVE EVOLUTION OF DISPERSAL HYPOTHESES

Relatedness

Both sexes dispersing, and small to no differences in dispersal distances between sexes

(Plissner and Gowaty 1996, Lang and Gowaty in prep_C) is evidence against the inbreeding avoidance hypothesis (Greenwood 1980, Perrin and Goudet 2001). Additionally, we observed sibling eastern bluebirds dispersing similar directions (56 degrees apart on average, compared to the random direction that non-siblings went when dispersing; Lang and Gowaty in prep_C).

Plissner (1994) found sibling eastern bluebirds moved the same direction (median 5o of one another) and nested closer together than control pairs. The distance male and female siblings dispersed were not different from one another either (Plissner 1994). Hatch-year male western bluebirds dispersed farther when both parents were dead (Kraaijeveld and Dickinson 2001).

Nesting closer when parents are alive suggests there may be advantages of nesting near kin.

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Western bluebirds whose territories were next to kin were also less aggressive (Duckworth

2008). Eastern bluebirds sometimes provision young into the next breeding season (Plissner

1994), suggesting eastern bluebirds benefit from relationships with kin over the course of more than one breeding season as well.

Parents’ natal dispersal distance could be a good predictor of the distance their offspring will move (Greenwood et al. 1979, Doligez and Part 2008). If dispersal is not density- dependent, individuals crossing over suitable habitat supports the innate dispersal hypothesis also

(Howard 1960). Eastern bluebirds crossed over open territories (this study; Plissner 1994), which supports heredity potentially affecting bluebird dispersal. Studying genetic and environmental influences simultaneously would provide insight into their effects on natal dispersal.

Environmental stochasticity

Spatiotemporal stochasticity, whether due to variation in local habitat (Weisser 2001) or to demographic variance (Clobert et al. 2004), can affect dispersal decisions. Knowing more about the transition and immigration periods of dispersal might give us better insight into spatial stochasticity effects. For example, we do not know what habitats the juveniles “sampled” and bypassed when searching for a breeding site. Using radio-telemetry would help with understanding where natal individuals go and what types of landscape they sample between the time they leave their natal territory and when they end up at their site of first reproduction.

Knowing more about winter movements would also give insight into the effects of spatial questions at the landscape level. Understanding correlations to immigration would be beneficial also. Two things prevented us from looking at immigration factors. First, the average distance

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moved (826 m) kept most birds on their natal breeding sites (Lang and Gowaty in prep_C), providing a small sample size for modeling natal dispersers moving between study sites. This small sample size kept models from converging when attempting to model immigration factors.

Second, we did not observe biased movement among sites (i.e., always from one site to another;

Lang and Gowaty in prep_C), making before and after movement comparisons difficult because there was essentially a “balanced exchange” (Holt and Barfield 2001, Clobert et al. 2004) of emigrants and immigrants between sites. Data from all three dispersal components (emigration, transition, immigration) simultaneously would aid in understanding effects of resource competition, inbreeding avoidance, and environmental stochasticity.

COSTS OF DISPERSAL: REPRODUCTION AND SURVIVAL

One potential cost for dispersing is lower reproductive success. Distance moved by natal bluebirds did not affect reproductive success in this study, nor did emigrating to a different breeding site. Perhaps there was little cost, energetically, for moving less than 12 km (143 of

144 observed natal dispersal events). For birds the size of eastern bluebirds, 12 km is a relatively short distance to disperse, given the hundreds or thousands of kilometers migratory birds of similar size travel and still successfully breed. If there were a cost to dispersal for the resident bluebirds we studied, perhaps it would only be detectable by assessing lifetime reproductive success of birds moving short and long distances. Another possibility is that dispersers moved to an equivalent or higher quality territory, making dispersal advantageous.

Other studies have also found no effect of dispersal on reproduction. Plissner and

Gowaty (1996) did not find dispersal distance (or territories moved) affected eastern bluebird reproductive success. Great tits, Parus major, another cavity nester, showed no correlation of

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dispersal distance to reproductive success, nest predation or nest desertion (Greenwood et al.

1979). Neither did blue grouse, Dendragapus obscurus, (Hines 1986) nor male marsh tit, Parus palustris, (Nilsson 1989) reproductive success correlate to distance dispersed. Opposite the prediction of dispersal cost, female marsh tits that dispersed farther had more recruits (Nilsson

1989). Alternatively, aggressive male western bluebirds dispersed farther (Duckworth 2006a) and had lower reproductive success (Duckworth 2006b). Duckworth (2006b) attributed lower reproductive success to spending less time provisioning incubating females, which suggests the cost to reproduction was due to defense behavior and not energy spent on dispersal.

There also seems to be a growing amount of data showing a lack of correlation between dispersal distance and cost to avian survival. Natal dispersal distance did not affect the mortality rate of great tits (Greenwood et al. 1979). A study of radio-tagged blue grouse, showed that dispersal distance did not affect survival, and, that mortalities did not occur at higher rates during dispersal (Hines 1986). In another study of radio-tagged grouse, (ruffed grouse, Bonasa umbellus) the mortality rate was not different for transient grouse relative to those establishing a new site. However, grouse that were transient longer had a higher mortality probability (Small et al. 1993). House wrens that dispersed farther had a higher return rate (4 % vs. 1 %) than those nesting closer to their natal nest box (Drilling and Thompson 1988). Female marsh tits that dispersed farther had higher survival, opposite the expected correlation; there was no correlation of survival to male marsh tit dispersal distance (Nilsson 1989).

Eastern bluebird natal survival was less than adult survival (Lang and Gowaty in prep_A) and natal dispersers moved ten times farther than most breeding birds (Lang and Gowaty in prep_C, in prep_D). At first glance, it would appear that dispersing farther is costly to survival.

However, survival differences between juveniles and adults may be because of experience or

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emigration to unfamiliar areas and not just dispersal distance differences. Knowing site-specific survival rates would have allowed us to look for patterns of emigration from sites with lower survival rates and immigration to sites with higher survival rates, as predicted by source-sink theory (Pulliam 1988). Unfortunately, we were unable to estimate site-specific survival rates

(Lang and Gowaty in prep_A).

CONCLUSION

While examining the interaction of hypotheses, and variables within, is helpful for understanding dispersal (Gandon and Michalakis 2001, Perrin and Goudet 2001, Ronce et al.

2001), current dispersal models do not do a good job of using multiple determinants to predict responses (Clobert et al. 2004). The Bayesian modeling approach we used gave us the advantage of simultaneously assessing multiple factors within competing hypotheses. Among the top models, only variables representing competition correlated to natal dispersal distance for the two eastern bluebird populations we studied in the southeastern United States. Time of hatching, conspecific survival, and nestling condition all factored into natal dispersal distances. The use of conspecific survival rates in theoretical competition models helps predict the shape of dispersal distance distribution extremes (Murray 1967, Waser 1985, Tonkyn and Plissner 1991) and intermediate shapes (Lang and Gowaty in prep_C), but does not predict distances well.

Including information that helps explain variation in the dispersal range, such as time of birth and body condition, could improve theoretical models. Adding relatedness and stochasticity variables to competition models such as the multiple-disperser model by Tonkyn and Plissner

(1991) may also improve prediction accuracy because they would help account for similar dispersal direction and distance by siblings (this study; Plissner 1994) and environmental

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variance such as site quality. A new theoretical model including competition, a coefficient of relatedness, and a measure of spatiotemporal stochasticity would incorporate ideas from the three most studied evolution of dispersal hypotheses and may provide a relatively simple yet accurate prediction of dispersal distance distributions for any species of interest.

ACKNOWLEDGMENTS

National Science Foundation grants IOS 0076100 and an NIH R01 to PAG supported the research. We operated under a University of Georgia animal care and use permit (# A2005-

10013-0).

We thank Ron Pulliam for BUGs modeling advice. We greatly appreciate assistance in the field from LeAnne Bonner, Lena Chamblis, Kristin Connell, Shannon Fitzgerald, Lynn

Hayes, Mindi Hertzog, Beth Tyler Lebow, Jessica Melgey, Cathy Rickets, Brian Snyder, and

Gayle Weber. Thanks to Mr. Jewett Tucker for field site usage. PAG designed the study and protocols for collecting demographic data, PAG and JDL designed protocols for measuring fire ant density; PAG and JDL supervised technicians who collected, recorded, and computerized data; JDL analyzed the data and wrote the paper, which PAG edited.

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Table 5.1 Model comparison for eastern bluebird natal dispersal distance by juveniles near Clemson, South Carolina and Athens, Georgia during the 2001 to 2005 breeding seasons. Variables we included in our OpenBUGs models were sex, nestling condition (weight/tarsus length), yearly after-hatch-year (AHY) and hatch-year (HY) survival rates, and year-site specific: conspecific density, distance to forest edge, fire ant density, and percent nest predation. The food availability model included fire ant density, distance to edge, and their interaction. The intraspecific competition model included nestling condition, AHY survival, HY survival, conspecific density, and the interaction of survival and density variables. Condition, AHY survival, and fire ant density made up the CondSurvFA model. Sex was the intercept for all of the models. We ran, separately, the same models for birds fledging from spring broods and summer broods due to potential temporal variation in cues for dispersal. Models are in rank order by the deviance information criterion (DIC) value. We also present the difference (∆) in DIC values from the top model and each model’s DIC weight (wi) relative to the other models. pD represents the number of variables estimated for the model

Spring Broods Summer Broods Model DIC ∆ DIC wi pD Model DIC ∆ DIC wi pD Global model 928.0 0 0.549 12.79 Condition 1164.0 0 0.731 5.03 Predation 930.8 2.8 0.135 5.06 Competition 1167.0 3 0.163 8.99 CondSurvFA 931.0 3.0 0.122 6.98 CondSurvFA 1168.0 4 0.099 7.02 Food availability 932.0 4.0 0.074 7.01 Global model 1174.0 10 0.005 12.84 Competition 932.0 4.0 0.074 8.89 Predation 1176.0 12 0.002 5.04 Condition 933.0 5.0 0.045 5.02 Food availability 1178.0 14 0.001 7.02

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Table 5.2 Mean coefficient estimates and 95% credible intervals for variables in eastern bluebird natal dispersal distance models. We used 144 natal dispersal events from the 2001 to 2005 breeding seasons near Athens, Georgia and Clemson, South Carolina to drive OpenBUGS models representing intraspecific competition, predation, and food availability hypotheses. The three top models, global, predation, and a three variable model (condition, After-Hatch-Year survival, Fire Ant density), accounted for over 80% of model weight for individuals from spring broods. Credible intervals that overlap zero are not correlative. The bolded values represent variables positively or negatively correlated to dispersal distance

Top spring brood models (model weight) Global (54.9%) Predation (13.5%) Condition/AHY/FA (12.2%) 95% Credible 95% Credible 95% Credible Interval Interval Interval Model variables1 Mean Lower Higher Mean Lower Higher Mean Lower Higher Weight/tarsus 0.01 -0.37 0.39 0.02 -0.34 0.40 AHY survival 1.23 0.46 1.96 0.29 0.03 0.54 HY survival -1.43 -2.51 -0.32 Conspecific density -0.16 -0.73 0.46 AHYsurv*HYsurv*ConDen 0.28 -0.14 0.69 Distance to forest edge -0.60 -1.36 0.20 Fire ant density -0.02 -0.57 0.53 0.34 -0.05 0.72 EdgeDist*FAdens 0.25 -0.34 0.84 Predation -0.04 -0.44 0.36 -0.24 -0.56 0.06 1AHY (After-Hatch-Year); HY (Hatch-Year); ConDen (conspecific density); EdgeDist*FAdens (interaction of distance to edge and fire ant density); Predation (percent nest predation)

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Table 5.3 Mean coefficient estimates and 95% credible intervals for variables in eastern bluebird natal dispersal distance models. We used 144 natal dispersal events from the 2001 to 2005 breeding seasons near Athens, Georgia and Clemson, South Carolina to drive OpenBUGS models representing intraspecific competition, predation, and food availability hypotheses. Two models, condition and competition, accounted for over 89% of model weight for individuals from summer broods. Credible intervals that overlap zero are not correlative. The bolded values represent variables negatively correlated to dispersal distance

Top summer brood models (model weight) Condition (73.1%) Competition (16.3%) 95% Credible 95% Credible Interval Interval Model variables1 Mean Lower Higher Mean Lower Higher Weight/tarsus -0.42 -0.64 -0.19 -0.36 -0.62 -0.10 AHY survival -0.27 -0.69 0.17 HY survival 0.60 -0.01 1.19 Conspecific density 0.37 -0.03 0.79 AHYsurv*HYsurv*ConDen -0.15 -0.43 0.14 1AHY (After-Hatch-Year); HY (Hatch-Year); ConDen (conspecific density)

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Table 5.4 Mean coefficient estimates and 95% credible intervals for variables within the OpenBUGS models best representing dispersal distances by eastern bluebirds fledging in the spring when an outlier is included. During the 2001 to 2005 breeding seasons, one male eastern bluebird dispersed 93 km (100 times farther than average) from Athens, Georgia to Clemson, South Carolina. Including this outlier in the dataset changed the top model and correlative variables (see Table 5.2). Credible intervals that overlap zero are not correlative. The bolded values represent variables positively or negatively correlated to dispersal distance

Top spring disperser models (model weight) Food availability (81%) Global (14%) 95% Credible 95% Credible Interval Interval Model variables1 Mean Lower Higher Mean Lower Higher Weight/tarsus -0.02 -0.51 0.45 AHY survival 0.22 -0.57 1.02 HY survival 0.04 -1.19 1.26 Conspecific density -1.16 -0.85 0.59 AHYsurv*HYsurv*ConDen 0.26 -0.24 0.76 Distance to forest edge -0.43 -0.75 -0.09 -0.56 -1.43 0.35 Fire ant density -0.26 -0.69 0.17 -0.27 -0.95 0.41 EdgeDist*FAdens 0.39 0.01 0.76 0.38 -0.27 1.05 Predation -0.17 -0.65 0.30 1AHY (After-Hatch-Year); HY (Hatch-Year); ConDen (conspecific density); EdgeDist*FAdens (interaction of distance to edge and fire ant density); Predation (percent nest predation)

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Table 5.5 We tested three hypotheses about natal dispersal distance by eastern bluebirds; H1: food availability, H2: intraspecific competition, and H3: predation. We present the predicted and observed correlations (positive, “+”, negative, “-“, or no correlation, “=”) of dispersal distance to variables within each hypothesis. Results are from the best models for dispersers from spring and summer broods (Table 5.1) who ranged zero meters to 11.5 km from their breeding sites near Athens, Georgia and Clemson, South Carolina during the 2001 to 2005 breeding seasons Correlation to Dispersal Distance Spring Summer Hypotheses (H) and model variables Prediction Broods Broods H1: FOOD AVAILABILITY Fire Ant Density + = Distance to Edge + = 1Edge Distance*FA Density + =

H2: INTRASPECIFIC COMPETITION Condition (weight/tarsus) - = - Adult Survival + + = Juvenile Survival + - = Conspecific Density + = = 1Survival*Density + = =

H3: PREDATION + = 1Interactions

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Appendix

Appendix 5.1. OpenBUGS code for the global eastern bluebird dispersal distance model representing populations near Clemson, South Carolina and Athens, Georgia during the 2001 to 2005 breeding seasons. Beta values are coefficients for the intercept (sex specific), nestling condition (weight/tarsus), after-hatch-year (AHY) survival and hatch-year (HY) survival (sex, year, and location specific), and site-year-specific: conspecific density, fire ant density, distance to forest edge, and percent nest predation. We transformed our independent variables using a z-transformation [ (xi " x )/SD ]. To account for effects of fledging dates we modeled spring and summer broods separately.

# Global Model ! model { for (i in 1:n) { sex[i]<-y[i,3] y[i,1]~dnegbin(p.ind[i], r.ind[i]) p.ind[i]<-r.ind[i]/(r.ind[i]+lambda.ind[i]) log(lambda.ind[i])<-beta[sex[i]]+beta[3]*y[i,5] + beta[4]*y[i,6] + beta[5]*y[i,7]+ beta[6]*y[i,10] + beta[7]*y[i,6]*y[i,7]*y[i,10] + beta[8]*y[i,9] + beta[9]*y[i,11] + beta[10]*y[i,9]*y[i,11] + beta[11]*y[i,8] r.ind[i]<-r[sex[i]] } lambda[1]<-exp(beta[1]+beta[3]+beta[4]+beta[5]+beta[6]+beta[7]+beta[8]+beta[9]+beta[10]+beta[11]) lambda[2]<-exp(beta[2]+beta[3]+beta[4]+beta[5]+beta[6]+beta[7]+beta[8]+beta[9]+beta[10]+beta[11]) for (j in 1:2) { r[j]~dgamma(0.001,0.001) di[j] <-(1+lambda[j]/r[j]) var[j] <- lambda[j]*di[j] p[j] <- r[j]/(r[j]+lambda[j]) } for (k in 1:11) {beta[k]~dnorm(0.0, 0.001)} }

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# Inits list( beta=c(0,0,0,0,0,0,0,0,0,0,0), r=c(1,1) ) list( beta=c(1,1,1,1,1,1,1,1,1,1,1), r=c(2,2) ) list( beta=c(3,3,3,3,3,3,3,3,3,3,3), r=c(3,3) )

#Data

# Data columns: 1=Distance, 2=location (Athens, Clemson), 3=sex, 4=brood(Spring, Summer), 5=weight/tarsus, 6=AHY surv, 7=HY surv, 8=predation, 9=Distance to forest edge, 10=Conspesific Density, 11=Fire Ant Density

# Summer Broods list(n=81,y=structure(.Data=c( 514,1,1,2,-0.369330163,0.204471706,1.002832026,0.87940859,-0.648283197,-0.593930887,-0.142001483, ...... 288,2,2,2,1.63905879,1.789104787,-0.904651264,-0.447399784,-0.433974702,0.549490229,0.822880391), .Dim=c(81,11)))

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CHAPTER 6

CONCLUSIONS

I studied two eastern bluebird populations for which a previous study had found demographic and behavioral differences in 1996 (PAG unpublished data). Red imported fire ants, a potential competitor for ground arthropod food resources, had an established population near Athens, GA and had recently invaded study sites near Clemson, SC when we conducted this study during the 2001 to 2005 breeding seasons. We hypothesized that fire ants may have caused the previously observed differences between the two bluebird populations because bluebirds and fire ants both eat ground arthropods. While finding some demographic and behavioral differences between Athens and Clemson bluebirds, this study yielded no support for fire ants affecting eastern bluebird survival or dispersal for these two populations.

SURVIVAL

Survival rates for eastern bluebirds near Athens and Clemson were similar to those in other eastern bluebird and thrush studies (confidence intervals for after-hatch-year (AHY) and hatch-year (HY): AHYAthens 0.4832 - 0.5560; AHYClemson 0.4867 - 0.5566; HYAthens 0.1417 -

0.1820; HYClemson 0.0996 - 0.1280). Also comparable to previous studies, adults at Athens and

Clemson had higher survival rates than juveniles. Survival rates of males and females did not differ for adults, and during most years for juveniles (Table 2.3). At Clemson, we found a negative correlation between survival rates and growing degree-days + rainfall (Fig. 2.1). No covariates best represented survival rates for the Athens population (Table 2.1). Survival rates at

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Clemson were highly variable between years, while remaining more constant for the Athens population (Table 2.3). This difference in variability may be because of correlates at the population-level that we did not measure, such as effects of parasites or differences in connectivity of suitable habitat between study sites.

DISPERSAL

A majority of breeding and natal dispersers remained at the same study site between year t and year t+1. Annually, approximately 3.5% of breeding dispersers and 24% of juveniles emigrated. Breeding and natal disperser distances followed a negative binomial distribution

(Figs. 3.3 and 4.8). Breeding dispersers tended to range between zero and 1888 m, with a few individuals moving 7-10 km (Figs. 3.1 and 3.2). Natal dispersers ranged from zero to 93 km with 25% moving greater than 1 km (Figs. 4.4, 4.5, and 4.8). Approximately 70% of adults remained within one territory’s distance of their nest from the previous year, compared to about

10% of natal dispersers (Figs. 3.3 and 4.8). In contrast to the assumption of a cost to dispersal

(Gadgil 1971, Ronce et al. 2001), we found no effect of dispersal distance on survival or nesting success for either natal or breeding dispersers. Some breeding (4.2%) and natal dispersers

(15.6%) “disappeared” for a year or more and then returned to our study sites to nest.

For breeding dispersers, we found nest success and mate retention had the strongest correlation to dispersal for these two eastern bluebird populations (Table 3.2). Although the average distance moved was similar for individuals whose nests were depredated and those who changed mates, after a predation event individuals moved farther than individuals changing mates (upper credible interval for movement following predation = 520 m, Table 3.3 and Fig.

3.4B; upper credible interval after changing mates = 233 m, Table 3.2 and Fig. 3.4C).

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Natal dispersers from spring broods dispersed approximately twice as far as those from summer broods (Table 4.1). More individuals from spring broods dispersed to different study sites also (27% from spring vs. 13% from summer broods). Both sexes dispersed similar distances (Table 4.1), but males and females may have searched for territories using different strategies (Fig. 4.10). Males’ dispersal distances match a simulated pattern for searching 20% of a ring of territories as they moved away from their natal nest (Fig. 4.10A and D). Females’ dispersal distance distribution matched a two-territory per ring search simulation more closely

(Fig. 4.10B and E). When considering males and females together, the percent of natal dispersers first nesting within fifteen territories of their birth location fell within theoretically predicted values for multiple dispersers (Fig. 4.9). These data support the resource competition hypothesis (Waser 1985, Tonkyn and Plissner 1991), which predicts natal dispersal distances based upon competition for nesting territories that are unoccupied by surviving adults.

We used a Bayesian modeling approach to test the effects of intraspecific competition, food availability, and predation on natal dispersal distance. For individuals from spring broods, a global model best represented dispersal distances. We found a positive correlation between spring dispersal distance and two correlates, adult survival and condition (weight divided by tarsus; Table 5.2). These correlates suggest parent-offspring competition is important for spring dispersers. We also observed a negative correlation between spring dispersers and hatch-year survival (Table 5.2), causing us to speculate that hatch-year survival may indicate environmental quality. The condition model best represented dispersers from summer broods (Table 5.1). The inverse correlation between condition and dispersal distance by individuals from summer broods suggests larger individuals are better able to compete with adults for nesting territories,

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supporting the dominance hypothesis (Gauthreaux 1978). We did not find any evidence that fire ants (in the food availability model) affected natal dispersal (Tables 5.1 and 5.5).

Dispersers face multiple challenges and opportunities simultaneously (Clobert et al.

2004). The strength of those challenges and opportunities may be what influences dispersal distance the most. Ronce et al. (2001) suggest parent-offspring conflict causes individuals to move the shortest distances, kin competition/inbreeding medium distances, and habitat quality the farthest. Our observations of eastern bluebird natal dispersal patterns (Fig. 4.10) suggest parent-offspring competition affects male natal dispersal most and that competition with kin for territories affects female natal dispersal distances most. For breeding dispersers, habitat quality cues (predation) had the strongest correlation to dispersal distance, and competition for mates the second strongest (Table 3.2, Fig. 3.4).

In summary, mean adult survival rates were similar between the Athens and Clemson eastern bluebird populations. However, mean juvenile survival tended to be higher at Athens.

Survival rates for Clemson bluebirds varied more among years than at Athens (Table 2.3), potentially because of variation between the two locations at the landscape scale; e.g., weather or variables we did not measure. Breeding dispersers did not change study sites often (Figs. 3.1 and

3.2), typically remaining within one territory’s distance of their nest from the previous year (Fig.

3.3). Natal dispersers moved farther than breeding dispersers (Fig. 4.8) and emigrated more often (Figs. 4.4 and 4.5). Between-site movements occurred more frequently at Clemson (Figs.

3.2 and 4.5) than at Athens (Figs. 3.1 and 4.4), likely because Clemson sites were in closer proximity to one another. While variation in dispersal may be attributable to between-site distances, variation in survival may be because of population-level differences between the

Athens and Clemson study areas that we did not detect in this study. Nest depredation and

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changing mates affected breeding dispersal distance (Fig. 3.4). Natal dispersal distance distributions matched theoretical predictions from the resource competition hypothesis, which assumes natal birds disperse to the nearest open territory (Fig. 4.9); territory availability is based upon adult survival rates. Male and female natal dispersers appeared to use different search strategies. Female distributions more closely matched a modified straight-line search strategy and males’ were more similar to modified spiral search distributions (Fig. 4.10). We found no support for red imported fire ants affecting eastern bluebird survival or dispersal for these two

Southeastern bluebird populations.

References

Gadgil, M. (1971) Dispersal: population consequences and evolution. Ecology, 52, 253-261.

Gauthreaux, S. A., Jr. (1978) The ecological significance of behavioral dominance. Perspectives in ethology (eds P. P. G. Bateson & P. H. Klopfer), pp. 17-54. Plenum, London.

Tonkyn, D. W. & Plissner, J. H. (1991) Models of multiple dispersers from the nest - Predictions and inference. Ecology, 72, 1721-1730.

Waser, P. M. (1985) Does competition drive dispersal? Ecology, 66, 1170-1175.

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