Breeding Ecology of the Seaside Sparrow ( Ammodramus Maritimus ) In

BREEDING ECOLOGY OF THE SEASIDE SPARROW ( AMMODRAMUS MARITIMUS ) IN

NORTHERN GULF OF MEXICO TIDAL SALT MARSHES

ANNA JOY JEANETTE LEHMICKE

(Under the Direction of Robert J. Cooper)

ABSTRACT

Tidal salt marshes are critically important habitats that have experienced serious declines

in the United States and still face current and future threats. Marsh birds have been proposed as potential indicators of tidal marsh integrity, yet the basic biology of many species remains poorly

studied along the coast of the Gulf of Mexico. I examined factors affecting nest-site selection,

nest success, density, and extra-pair paternity of a tidal marsh obligate passerine, the Seaside

Sparrow ( Ammodramus maritimus ), in two marsh complexes on the Mississippi coast.

Nest placement and survival both appear to be strongly influenced by predation. Nests

were built in areas with more ground cover than random points and better-concealed nests were

more likely to fledge young. These results contrast with those from Atlantic coast tidal marshes,

where nest placement and nest survival are both driven by tidal flooding. Survival was higher for

nests that had closer active neighbors and successful nests were spatially clustered, while

unsuccessful nests were not. More research is needed to determine if Seaside Sparrows are

clustering in areas of low predator potential or if higher densities of birds can more effectively

drive off or warn one another or predators. Elevation and Seaside Sparrow density were positively related in our study marsh, a result that contrasts with patterns of occupancy at a

broader scale. I suggest that because broad- and fine-scale habitat relationships do not always agree, both must be considered when predicting species-level responses to potential future habitat alterations.

I found that extra-pair paternity in my study populations did not seem to have any adaptive benefit for females and they may be unwilling extra-pair partners. Further research is needed to confirm and clarify these results, but it is possible that extra-pair paternity represents a case of sexual conflict in our populations.

While research across a broader area of the northern Gulf of Mexico is still needed, the results of this study will contribute to a better understanding of the demography and habitat relationships of the Seaside Sparrow and will increase its usefulness as an indicator for tidal marsh integrity.

INDEX WORDS: Seaside Sparrow, Ammodramus maritimus , northern Gulf of Mexico, tidal marsh, nest survival, density, habitat selection, extra-pair paternity

BREEDING ECOLOGY OF THE SEASIDE SPARROW ( AMMODRAMUS MARITIMUS ) IN

NORTHERN GULF OF MEXICO TIDAL SALT MARSHES

ANNA JOY JEANETTE LEHMICKE

B.S., University of Delaware, 2008

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

2014

Anna Joy Jeanette Lehmicke

BREEDING ECOLOGY OF THE SEASIDE SPARROW ( AMMODRAMUS MARITIMUS ) IN

NORTHERN GULF OF MEXICO TIDAL SALT MARSHES

ANNA JOY JEANETTE LEHMICKE

Major Professor: Robert J. Cooper

Committee: Mark S. Woodrey C. Joseph Nairn Michael J. Conroy Sonia M. Hernandez

Electronic Version Approved:

Maureen Grasso Dean of the Graduate School The University of Georgia May 2014

DEDICATION

This dissertation is dedicated to my parents, Albert and Lorraine Lehmicke, for their unending

interest in and support of whatever I choose to do.

ACKNOWLEDGMENTS

Although the final version of this dissertation will bear only my name, it would never have gotten to this point without the help and support of many other people. Going chronologically, I first need to thank my undergraduate ornithology professor, Greg Shriver, for letting me know about this research opportunity and putting in a good word for me. Thanks to my (in all but name) co-advisers Bob Cooper and Mark Woodrey for agreeing to take me on and mentoring me through every step of the process. Thanks also to my other committee members,

Joe Nairn, Sonia Hernandez, and Mike Conroy for their help and suggestions along the way. My dissertation process involved two states with many outstanding people in each. On the

Mississippi side, I am completely indebted to the Grand Bay National Estuarine Research

Reserve for providing me with housing, a boat, supplies, and logistical support. Special thanks to

Tom Stadler for always patiently fixing any mechanical issues with boats and trucks. Much credit is due to my field technicians, Laura Duval, Kelly Morris, Jennifer Jetchev, Amy

Densborn, and Claire Randall, for patiently enduring early mornings, endless no-see-ums, and occasionally intolerable weather. Back in Georgia, my fellow Cooperlabbers were an endless source of support, ideas, and feedback. Special thanks to Ali Leggett for being a partner and source of support across state lines. My foray into genetics would not have been possible without the help and patience of the Nairn Lab. Thanks to Denise Lennon, Bonnie Berry, Brian

Shamblin, Haley Houke, and Billy Kim for their willingness to walk me through every step of the lab work. The Graduate School, the Warnell School of Forestry and Natural Resources, and the Gulf Coast Joint Venture all funded my assistantship along the way, and the Georgia

Ornithological Society provided critical funding for the genetics aspect of this project. On a personal note, thanks to my parents for their interest in and support of my refusal to enter the real world. Last and foremost, thanks to Clark, without whom I would be lost. Without his sense of humor, support, encouragement, and Normal Mondays, I’m not sure how much of my sanity would still be intact today.

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... v

CHAPTER

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

2 NEST-SITE SELECTION AND NEST SUCCESS OF THE SEASIDE SPARROW

IN NORTHERN GULF OF MEXICO TIDAL MARSH ...... 13

3 DENSITY OF SEASIDE SPARROWS IN THE GRAND BAY NATIONAL

ESTUARINE RESEARCH RESERVE ...... 54

4 NEST-LEVEL AND INDIVIDUAL CORRELATES OF EXTRA-PAIR

PATERNITY IN THE SEASIDE SPARROW...... 94

5 SUMMARY AND CONCLUSIONS ...... 133

vii

CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

Globally, tidal salt marsh covers approximately 45,000 km 2 (Greenberg et al. 2006b), an

area slightly larger than the state of Maryland. The United States contains more salt marsh

habitat than any other country, totaling 16,500 km 2, over 95% of which is along the Atlantic and

Gulf coasts. Tidal salt marshes are highly productive ecosystems (Adam 1990) and are important both ecologically and economically. Ecologically, they play an important role in filtering pollutants from water (Gersberg et al. 1986), are an essential part of estuarine nutrient cycles

(Welsh 1980), and help protect coastlines from erosion (Broome et al. 1988). Economically, they act as nurseries for many commercially important species of fish and shellfish (Knieb 1997), and provide numerous opportunities for those seeking outdoor recreation (Niering and Warren 1980).

For centuries, this ecosystem has been threatened in the United States by human activities. Over the 200-year period from 1780 to 1980, the contiguous United States lost 53% of its original wetlands (Dahl 1990). These threats continue even today, with an estimated 1% loss in tidal salt marsh area between 1998 and 2004 (Stedman and Dahl 2008). In the near future, one of the greatest threats to tidal salt marsh is predicted to be sea level rise (McFadden et al. 2007).

Because of its limited extent, high economic and ecological importance, and continued threats, tidal salt marsh health must be closely monitored. Since it is often difficult to monitor the

overall health of an entire ecosystem, managers and researchers may rely on one or a suite of

indicator species as proxies for ecosystem health. For tidal marsh in the United States, the

Seaside Sparrow (SESP; Ammodramus maritimus ) is perhaps the strongest candidate for this

role. It is one of only five species of terrestrial vertebrates worldwide that is completely restricted to coastal marsh (instead of having only a subspecies so completely restricted; Greenberg and

Maldonado 2006). Unlike the Saltmarsh Sparrow (Ammodramus caudacutus) , another such endemic, SESP breeding range includes most of the marsh on the Atlantic and Gulf coasts. They are highly active and visible, making them easy to monitor compared to the other three marsh endemics; Diamondback Terrapins (Malaclemys terrapin), Salt Marsh Harvest Mice

(Reithrodontomys raviventris ), and Salt Marsh Snakes ( Nerodia clarkii ).

SEASIDE SPARROW LIFE HISTORY AND CONSERVATION STATUS

Seaside Sparrows are obligate coastal marsh-dwelling passerines that occur in scattered populations from southern Maine to the northwestern coast of the Gulf of Mexico, in Texas (Post and Greenlaw 2009). They spend their entire life cycle in coastal marshes, staying in the marsh for both the breeding season and the winter. They are socially monogamous breeders, building nests predominately in marsh grass and rushes. They lay between two and five eggs per nest, with the average varying by location, but generally slightly over three (Post and Greenlaw 2009).

The populations on the northern end of the range are migratory, wintering in marshes along the southeastern Atlantic coast. Southern populations are non-migratory. The species is split into seven extant subspecies across its range.

Although many populations of SESP are currently stable, a naturally limited population size (estimated at slightly more than 100,000) and threats to their coastal wetland habitat raise concerns for their future conservation status (National Audubon Society 2007). They have already proven sensitive to habitat alteration, with two subspecies extinct ( A. m. pelonata, A. m. nigrescens ) and one endangered (Cape Sable Seaside Sparrow; A. m. mirabilis ). They are on the

Audubon WatchList of species in need of conservation action due to their highly specialized

habitat needs and continuing habitat loss (National Audubon Society 2007) and are categorized as a species of conservation concern throughout their range by the U.S. Fish and Wildlife Service

(2008).

The life history of SESP has been well-studied in Atlantic coastal marshes (Post 1974,

Marshall and Reinert 1990, Gjerdrum et al. 2005), but similar research is lacking for Gulf coast

SESP populations. The Gulf coast has almost 40% more tidal marsh than the Atlantic coast (2.4 and 1.7 million acres, respectively; Stedman and Dahl 2008) and has important differences in vegetation makeup and tidal regime (Bertness et al. 1992, Odum 1995). Therefore, it is important that the basic life history and breeding ecology of SESP be studied in Gulf coast marshes in addition to those on the Atlantic coast to inform habitat management and better predict the effects of future threats to the tidal marsh ecosystem.

GULF COAST JOINT VENTURE HABITAT MODEL

Due to both its conservation status and year-round reliance on coastal marsh habitat, the

SESP has been chosen as a priority species by the Landbird Monitoring, Evaluation, and

Research Team (MERT) of the Gulf Coast Joint Venture (GCJV; Vermillion et al. 2008), representing salt and brackish marshes. A habitat model has been created to determine population goals, habitat targets, and promote suitable habitat management. However, very little data are available on Gulf Coast SESP populations, so many of the assumptions of the model are based on data from research conducted in Atlantic coast marshes. Distinct differences distinguish

Gulf coast from Atlantic coast marshes, including different tidal regimes and dominant plant species (Stout 1984).

According to the Joint Venture plan, a better understanding of what constitutes suitable and high quality marsh habitat would help improve estimates of SESP area and habitat

requirements. Determining whether a habitat can be considered “high-quality” requires information about both abundance and demographic parameters (Van Horne 1983).

In the Northeast, where most Seaside Sparrow research has been conducted, breeding densities vary widely among marshes (Post and Greenlaw 2009). Several studies have examined the factors driving density differences among marshes. Benoit and Askins (2002) found that larger marshes generally have higher breeding densities, a factor which also has a strong positive effect on marsh occupancy by SESP (Shriver et al. 2004). Past and present marsh management practices can also affect occupancy and/or density of SESP. Marsh plots in Delaware that had experienced extensive open marsh water management (OMWM) had half the territory and nest density of plots that had no or limited OMWM (Pepper and Shriver 2010).

Where marshes are generally small, as in the Northeast, it may be reasonable to treat each marsh as a cohesive unit and consider marsh-level factors, such as size and management history, when attempting to determine what drives marsh bird occupancy or density. However, in large marshes such as those found along the Gulf coast, SESP density can vary widely within a single marsh and marsh-level characteristics will likely not provide any information on what factors are driving patterns of bird density within a marsh.

While optimum SESP nesting habitat has been studied and described in Atlantic Coast marshes, this information may not be transferrable to Gulf Coast marshes. In Atlantic Coast populations, SESP nest predominately in saltmarsh cordgrass ( Spartina alterniflora; Post et al.

1983, Marshall and Reinert 1990, Gjerdrum et al. 2005), which is usually the dominant plant

species. In many Gulf Coast marshes, the dominant plant species is black needlerush ( Juncus

roemerianus ; Battaglia et al. 2012). While S. alterniflora is present, it is in lower density and

may not be as important as a nesting substrate. Seaside Sparrows are year-round residents in Gulf

Coast marshes, while those in Atlantic marshes are migratory (Post and Greenlaw 2009), which could affect territory and breeding dynamics. These important differences highlight the need to revise the GCJV habitat plan by using data collected from Gulf Coast SESP population.

SEASIDE SPARROW GENETICS

Genetics is becoming an increasingly important tool in the conservation of species and biodiversity. Studying genetic patterns in animal populations can provide information that cannot be derived from standard field studies alone. For example, a population that seems sufficiently

large may suffer from low genetic diversity due to natural isolation or a previous bottleneck

event. Low genetic diversity can cause a number of problems including reduced fitness,

increased susceptibility to disease, and a reduced ability to adapt to novel environmental

challenges (Amos and Balmford 2001).

In ornithology, one widely-studied area of genetics is paternity. While it was fairly

recently believed that most bird species were truly monogamous (i.e. a female mated with only

one male during each nest attempt; Lack 1968), studies made possible by recent advancements in

genetics have shown otherwise. While it is true that most bird species are socially monogamous

(one female and one male tend a nest together), social monogamy rarely translates to genetic

monogamy. Over 80% of studied species have shown some level of extra-pair paternity, with

only 14% displaying true monogamy (Griffith et al. 2002). Extra-pair paternity (EPP) is the term

used when a female mates with a male other than her social mate, resulting in a brood where at

least one chick has a different sire than the others. Among bird species that exhibit some level of

EPP, the rates (usually measured as the percentage of young hatched from extra-pair matings)

vary widely, from 0.8% in the Dunnock ( Prunella modularis ; Burke et al. 1989) to 71.6% in the

Superb Fairy-wren ( Malurus cyaneus ; Mulder et al. 1994, Double and Cockburn 2000). Rates

can also vary among closely-related species and among populations of the same species (Griffith et al. 2002).

Multiple explanations have been offered for this variation (Griffith et al. 2002, Westneat and Stewart 2003), though few have been explicitly tested. Perhaps one of the most intriguing hypotheses, and the one most applicable to conservation, is that rates of EPP are influenced by the level of genetic variation among adult birds in a population. If a population has low genetic diversity, then females will be less likely to see a benefit to their offspring by seeking extra-pair matings, as their social mate’s genetic make-up is likely to be similar to that of most other males in the population. If, however, genetic variation is high, then a female increases the potential genetic health of her offspring by seeking extra-pair copulations, since other males are likely to have different genes than her social mate (Westneat and Stewart 2003).

Only one published study has examined rates of EPP in Seaside Sparrows from a single

South Carolina population, and it showed that the rate was lower than expected based on a comparison with the rates of closely related species (Hill and Post 2005). If the genetic diversity hypothesis is supported, this may imply that SESP populations also have lower than expected genetic diversity. Support for this hypothesis has been found previously by comparing island and mainland populations of the same species; island populations had both lower genetic diversity and lower EPP rates (Griffith 2000). Seaside Sparrow habitat is very discontinuous throughout its range, composed of many “islands” of suitable tidal marsh habitat separated by unsuitable habitat that the birds are unlikely to travel across. As a species of conservation concern, it is important to understand more about SESP genetic structure. If the genetic diversity of the species is lower than expected, then it is possible that current target population sizes are too low and habitat conservations plans must be altered accordingly.

DISSERTATION OBJECTIVES AND STRUCTURE

In Chapter Two, I investigate factors influencing the nest site selection and nest success

of SESP in two marsh complexes on the Gulf coast of Mississippi. Both nest-site selection and

nest success are crucial pieces of information to consider when determining what factors define

high-quality habitat for a species. Information on nest-site selection provides an understanding of

what females perceive as components of a high-quality nesting site. However, relying solely on

nest-site selection can be misleading if individuals nest in sub-optimal locations either because

they are young birds that have not yet learned what a good nesting site comprises (Hatchwell et

al. 1999), or if they are excluded from optimal nesting sites by inter- or intraspecific competition.

Therefore, it is also necessary to compare successful and unsuccessful nest sites and see if patterns of nest survival match or contradict those of nest site selection. In tidal marshes, nest- site selection represents a trade-off between risk of flooding and risk of predation (Storey et al.

1988, Greenberg et al. 2006a) and a single nest site might not be able to minimize both risks.

Chapter Three considers habitat selection at a broader scale by examining landscape variables that affect density of SESP in forty 200-m survey plots throughout the Grand Bay

National Estuarine Research Reserve in Jackson Co., MS, USA. While previous research has examined broad-scale landscape factors affecting SESP occupancy (Rush et al. 2009), modeling density can give more information on fine-scale habitat factors that can affect the distribution of

SESP within a single marsh. Combining information on broad-scale site selection with fine-scale nest-site selection can lead to more robust inferences regarding what constitutes high-quality habitat.

Finally, in Chapter Four I combine demographic and genetic data to examine EPP within our study populations. Using data collected on individual nests as well as individual birds, I

attempt to find factors that describe differences in EPP occurrence among nests as well as among individual adult birds. I seek to address predictions made by several hypotheses regarding why rates of extra-pair paternity vary so widely among populations and individuals. While more studies specifically focused on SESP are needed to make broad comparisons among populations, examining individual factors influencing EPP rates can contribute information to existing theories regarding sexual selection and sexual conflict.

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northern and southern populations of the Seaside Sparrow. Pages 123-136 in T. L. Quay,

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of select marsh birds within northern Gulf of Mexico tidal marsh: current estimates and

projected change. Wetlands 29:798-808.

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CHAPTER 2

NEST-SITE SELECTION AND NEST SUCCESS OF THE SEASIDE SPARROW

(AMMODRAMUS MARITIMUS ) IN NORTHERN GULF OF MEXICO TIDAL MARSH 1

1 Lehmicke, A. J. J., M. S. Woodrey, and R. J. Cooper. To be submitted to The Auk .

ABSTRACT

Understanding the link between landscape variables and demographic parameters is essential when attempting to define high-quality habitat for a species. We monitored Seaside

Sparrow (SESP; Ammodramus maritimus ) nests in two coastal Mississippi marsh complexes nests for two breeding seasons (2011 and 2012). Our objective was to determine the factors, at multiple scales, that differentiated between nest sites and random sites and between failed and successful nests. Female SESP chose nest sites that had more groundcover and taller vegetation, suggesting that they were choosing sites that would be less susceptible to predators. Successful nests had more stems around the nest than failed nests, again highlighting the importance of predator avoidance through increased nest concealment. Successful nests also were closer to concurrently active nests and were more likely than expected to have a successful nearest neighbor. A positive relationship between nest density and nest survival points to a lack of density-dependent predation on our study sites and suggests that dense clusters of birds are better able to drive off predators or birds can recognize areas with lower predation risk. These results suggest that density is a suitable indicator of high quality habitat for SESP and may help explain the semi-colonial nature of the species.

INTRODUCTION

A critical component of any species conservation or management plan is a clear understanding of what defines high-quality habitat for that species (Van Horne 1983, Block and

Brennan 1993, Johnson 2007). Limited resources compel managers to focus attention on areas that are most valuable to the overall conservation or health of a population. This raises the obvious question: how should high-quality habitat be defined? Quality can be considered from

both an individual and a population-level perspective (Van Horne 1983, Pidgeon et al. 2006,

Johnson 2007) – habitat is high-quality for an individual if nesting there leads to successful reproduction, whereas areas that are high-quality at a population level allow for long-term population growth or persistence (Block and Brennan 1993).

Understanding demographic factors is a crucial element when attempting to describe high-quality habitat for a particular species (Pidgeon et al. 2006, Johnson 2007). Adult occupancy or density alone may not reflect the true quality of an area for several reasons (Van

Horne 1983, Maurer 1986). Dominant adults could push young birds into marginal areas, thus inflating abundance in low quality habitats (Oro 2008). Birds may also fall into ecological traps

(Dwernychuk 1972), where individuals preferentially nest in areas where breeding success is too low to maintain population viability (Boal and Mannan 1999, Battin 2004). Population-level productivity of an area is a combination of both individual breeding success and density of breeding adults, and both components must be considered when deciding what constitutes habitat quality for a species.

When determining what environmental components contribute to breeding success for birds, it is necessary to consider both nest-site selection and nest success (Clark and Shutler

1999). Comparing locations used for nests with sites that are available but not used can provide information on what females perceive as components of a high-quality nest site. However, relying solely on nest-site selection can be misleading if individuals nest in sub-optimal locations either because they are young birds that have not yet learned what a good nesting site comprises

(Hatchwell et al. 1999), or if they are excluded from optimal nesting sites by intra- or interspecific competition (Post 1981). Therefore, it is also necessary to compare successful and

unsuccessful nest sites and determine if patterns of nest survival match or contradict those of nest site selection.

For a species that is completely restricted to one habitat, it can be incorrectly assumed that its habitat relationships are already fully understood and no finer-scale studies are necessary.

Even within a seemingly well-defined habitat type, variation in local-level environmental factors can lead to large differences in quality for species that breed there. Thus, fine-scale habitat characteristics may be an important consideration when attempting to reach management goals.

Seaside Sparrows ( Ammodramus maritimus ; SESP) are specialist North American passerines that are completely restricted to tidal salt marsh along the Atlantic and Gulf coasts of the United States (Post and Greenlaw 2009). They spend their entire life cycle in coastal marshes, staying in the marsh for both the breeding season and the winter. Northern Atlantic populations are migratory, while populations that breed on the southern Atlantic and Gulf coasts are presumed year-round residents.

Although there is little information on current population trends for most SESP populations, threats to their coastal wetland habitat raise concerns for the species’ future conservation status (National Audubon Society 2007). They have already proven sensitive to habitat alteration, with two subspecies extinct ( A. m. pelonata and A. m. nigrescens ) and one endangered ( A. m. mirabilis ). They are on the Audubon WatchList of species in need of conservation action due to their highly specialized habitat needs and continuing habitat loss

(National Audubon Society 2007). In addition, SESP are categorized as a species of conservation concern throughout their range by the U.S. Fish and Wildlife Service (2008).

Seaside Sparrows are not evenly distributed within coastal marshes (Benoit and Askins

2002, Shriver et al. 2004, Pepper and Shriver 2010), suggesting that habitat quality varies at a

local scale. In addition to information on factors affecting SESP density, identifying characteristics associated with both individual and population-level productivity would have strong implications for future management decisions.

Several studies have examined SESP nest site selection and nest survival in Atlantic coast marshes, with most study sites north of Maryland. In these marshes, SESP build their nests in vegetation that is either taller than the average height of vegetation at random points (Gjerdrum et al. 2005) or not different from the vegetation surrounding the nest site (Post et al. 1983). Nests that are higher off the ground have a higher probability of survival which suggests that nest site selection is driven by the need to avoid flooding-induced nest failure caused by high spring tides

(Marshall and Reinert 1990, Gjerdrum et al. 2005).

Due to drastic differences in vegetation communities (Wiegert and Freeman 1990), tidal regime (Odum 1995), and SESP genetics (Avise and Nelson 1989) between Atlantic and Gulf coast marshes, it is probably inappropriate to use data collected from Atlantic Coast SESP populations to inform management decisions for Gulf Coast SESP. Unlike Atlantic Coast populations, SESP populations on the Mississippi Gulf Coast do not face regular flood tides, although strong storms or the combination of a high tide and strong winds can push water to higher than normal levels (Odum 1995). These extra-high tide events are infrequent and unpredictable, and the majority of nest failures are caused by predation rather than flooding (Post et al. 1983). Thus, nest location may be optimized to minimize predation risk rather than to avoid flooding.

Only two published papers exist on nest site characteristics of a Gulf Coast SESP population, both based on the same field study (Post 1981, Post et al. 1983). The authors found

that the vegetation at nest sites is shorter than the vegetation at random points and is slightly less

dense. Seaside Sparrow nests were built most often in glasswort ( Salicornia virginica ), yet nests built in this substrate had a very low probability of survival (0.003 – 0.004 over the nesting interval). Nests built in taller, denser Juncus roemerianus had a much higher survival probability

(0.012 – 0.139), but SESP infrequently built nests in this vegetation type possibly due to competitive exclusion by marsh rice rats ( Oryzomys palustris ; Post 1981).

Seaside Sparrows also provide a unique opportunity to examine the relationship between population density and breeding success. Throughout their range, SESP nest at a variety of

densities, ranging from areas with large, clearly-defined territories to semi-colonial areas where

territories are small, ill-defined, and frequently overlapping (Post 1974). This range of variation

can occur within a single marsh and was seen in both of our focal marshes. Previous research on

SESP in New York has found either no relationship between nesting density and nest success

(Post 1974) or a negative relationship, suggesting that nest predation in those marshes may be

density-dependent (Post et al. 1983). If we find a different pattern at our study sites, it could be

due to the difference in primary cause of nest failure (flooding vs. predation) or differences in the predator community.

We sought to determine factors associated with high-quality nesting habitat for Seaside

Sparrows in two northern Gulf Coast tidal marshes. Our first objective was to examine nest-site

selection by female SESP at the within-territory scale. Because production of young directly

affects adult fitness, individuals are expected to choose nest sites that will maximize the probability of reproductive success (Clark and Shutler 1999).

Our second objective was to determine what factors were associated with nest survival. If

a landscape factor appeared to be important both for nest site selection and nest survival, we

would interpret this as evidence that females were correctly assessing the reproductive potential

of nest sites. Because the majority of failed nests in our study area were depredated, we hypothesized that both nest site selection and nest survival would be determined by factors that would minimize predation risk.

METHODS

Study Site

Our demographic work was conducted in two marsh complexes: the Grand Bay National

Wildlife Refuge/National Estuarine Research Reserve (GBNERR) and the Pascagoula River

Marsh Coastal Preserve. Both marshes are located on the Mississippi coast in Jackson County, approximately 20 km apart. Both sites are protected and include large areas of relatively undisturbed tidal marsh. The Grand Bay NERR is composed of about 7,300 hectares of wetlands, pine savannas, and terrestrial habitats. The tidal marsh areas are predominately polyhaline

(salinity 18-35 ppt; Cowardin 1979) and are dominated by Juncus roemerianus , interspersed

with narrow bands of Spartina alterniflora . The Pascagoula River Marsh Coastal Preserve is a

4,700 hectare site of mostly oligohaline and mesohaline (0.5-5 ppt and 5-18 ppt; Cowardin 1979) marsh, dominated by J. roemerianus , S. patens, and S. cynosuroides . Both sites were fully open to public use and did not require special permission for access.

Focal plot selection

At the beginning of our first field season (2010), we conducted marsh bird surveys at established points in each marsh complex as part of a long-running monitoring program, following standard survey protocols (Conway 2011). Prior to plot selection, each marsh was divided into rough quarters to avoid spatial clumping of sites. We then identified the three or four survey points in each quadrat that had the highest number of SESP detections in the early

round of surveys. This ensured that we did not randomly select a point that was not occupied.

From these points, we randomly selected one per quadrat for a total of eight points (four in each marsh complex). These eight points served as the centers of our focal plots. The size of the plots was not strictly controlled, but we limited them to 2-4 ha for practical purposes, as this size was small enough to cover adequately yet large enough to contain a suitable number of nests. One plot at GBNERR was dropped early in the 2010 field season due to low sparrow density, giving a total of 7 sites for analysis.

Field Methods

Nest monitoring

From late March to mid-July 2011 and 2012, we intensively searched for nests on each plot. Nests were found almost exclusively through behavioral observations of adults, though occasionally a nest would be found by accidentally flushing an incubating female. Once a nest was located, it was marked with a single flag at least five meters away. Nests were checked every two to four days and the contents and status were recorded. If a nest failed, we attempted to determine the cause (predation, flooding, or abandonment). If a nest had some shell fragments or was empty and dry we considered it predated and if a nest was empty and wet we considered it flooded. Since tides high enough (or storms strong enough) to flood a nest were rare, we were generally confident about attributing nest failure to flooding. If a nest retained all of its eggs but had no evidence of adult activity we considered it abandoned. After a nest had fledged or failed, we attempted to find renest attempts by the same pair, identified either by the presence of uniquely color-banded adults or by proximity to the previous nest.

Vegetation measurements

After each nest was complete (failed or fledged), we took a series of measurements both at the nest and at a paired random point near the nest, based on vegetation measurements suggested for grassland breeding birds (Wiens 1969). Random points were selected by a random compass bearing and a random distance between 8 and 25 m. If the selected point was in open water we adjusted the compass bearing by 90º. Restricting the maximum distance to 25 m ensured that the point was likely within the territory of the male and represented a potential nest site that the female had not chosen (i.e., the random site was available for use, (Jones 2001). At the nest, we measured nest height (from the ground to the rim of the nest cup) and the height from the rim of the nest cup to the top of the vegetation above the nest. At the random site we measured the height of the vegetation at the center of the point.

At both the nest and random sites, we centered a 1-m2 plot either on the nest or on the

center of the random point. At each corner, we placed a dowel marked in two decimeter

increments and counted the number of stems touching the dowel in each decimeter. We then

estimated the cover of all plant species within the 1-m2 plot and a 5-m2 plot with the same center.

Using a GPS point taken at the nest or random point, we later used ArcGIS (ESRI 2013) to extract the elevation of the point from a digital elevation map (DEM) from the National

Elevation Dataset with a 1/9 arc-second resolution (~3-meter cell size; Gesch et al. 2002). We extracted values for distance to upland and distance to water for each nest from the National

Land Cover Dataset (Fry et al. 2011) to extract values for distance to upland and distance to water of each nest or random point. Finally, we used the GIS coordinates of each nest to calculate both the minimum and average distance of each nest to other concurrently active nests.

Statistical Methods

All statistical analyses were performed in Program R (R Core Team 2013).

Nest site selection

Since nest sites were not independent from their paired random sites, we used paired

logistic regression (1-1 matched case-control; Hosmer and Lemeshow 2000, Compton et al.

2002) to model differences between nest sites and paired random sites. We created a data frame

that contained the difference in covariate measurements between nest and random sites (nest

minus random) and used program R to fit a logistic regression model, using a vector of all 1s as

the response and a no-intercept model. Using this method, coefficients are interpreted as the

effect of a one-unit increase in the difference in habitat measurements between nest sites and

random sites rather than as a one-unit increase in the absolute value of the habitat measurement.

We created four variable sets: vegetation measurements, 1-m2 ground cover, and 5-m2

ground cover (Table 2.1). Elevation was included as a lone variable rather than being included in

a variable set. We then ran models with different combinations of these sets and compared the

models using an information-theoretic approach (Burnham and Anderson 2011) which

determined the support for each candidate model given our data. We ranked models based on

AIC with a small-sample correction (AIC c; Akaike 1973, Hurvich and Tsai 1989). We did not run any models that combined the 1-m2 set and 5-m2 set as each ground cover type was highly

correlated (r > 0.6) between the two scales. In order to incorporate model selection uncertainty

into the parameter estimates, we computed model-averaged coefficients using all models with

ΔAIC c ≤ 2. We scaled these estimates by factors that we determined to be biologically

meaningful (e.g., a 10% change in ground cover) and computed odds ratios for each parameter to

make the results more interpretable.

Daily nest survival

We modeled daily nest survival using a logistic exposure model (Shaffer 2004) in program R using a custom logistic exposure link function (Bolker 2013, Herzog 2013). This method allowed us to model explanatory variables that remained constant over the duration of a nest attempt (e.g., nest height) and those that remained constant within — but varied among — nest check intervals (e.g., nest age). This method also allowed for varying nest check frequencies, as we checked nests at inconsistent intervals throughout the two field seasons.

We created five covariate groups at different scales that we thought may affect nest survival: nest site measurements, 1-m2 ground cover, 5-m2 ground cover, landscape factors, and social factors. (See Table 2.2 for a complete list of covariates in each group). For both 1- and 5- m2 ground cover, we only included vegetation types that were present around at least 20% of nests to prevent uncommon vegetation types from having an inflated influence. Many of our nest site measurements were highly correlated; in these cases, we compared single-variable models and eliminated the variable with the higher AIC c score. Since we had no a priori hypotheses for

which covariates within the first three groups were most important, we represented the group by

finding the best model from each group of covariates (Table 2.3). Temporal factors (date and

nest age) frequently affect nest survival in birds (Grant et al. 2005) so we also created a temporal

covariate group (Table 2.2).

We then created models using all combinations of the best model from each scale and

evaluated the set of models using information-theoretic approaches, using AIC with small-

sample size correction (AIC c) to rank models. We created a confidence set with all models where

ΔAIC c ≤ 2 and used this set to develop model-averaged predictions of daily survival rate. We

generated model-specific predictions across a range of values for covariates of interest and

calculated 95% confidence intervals. We also computed model-averaged estimates and unconditional standard errors (Burnham and Anderson 2011) for a range of covariate values. We then used a logit-link function to transform these estimates into daily survival rates (restricted between zero and one) and plotted the trends and confidence intervals.

After we determined the top model in the set, we created four new mixed-effect models by adding a random effect of plot, year, marsh, or individual female to this top model. We only retained random effects if they affected the parameter estimates of the fixed effects in the model.

RESULTS

General Results

Across the two years, we found a total of 299 nests (146 in 2011 and 153 in 2012). Nests were built in several different grass species (140 in Juncus roemerianus , 102 in Spartina patens ,

36 in Distichlis spicata , 13 in S. alterniflora , and 5 in S. spartinae ). Nests built in S. patens were often in grass supported by small shrubs ( Iva frutescens and Baccharis halimifolia ), but nests were always built exclusively in the grass. Mean nest height (cm) was 37 (SD = 16.83, range 11-

104, n = 290). Mean clutch size was 3.22 eggs (SD = 0.63, range 1-4, n = 258), and was slightly higher in 2011 than 2012 (3.32 vs 3.13; t256 = 2.514, P = 0.013). Average clutch size decreased as the breeding season progressed (β = -0.007; 95% CI -0.01, -0.005). Modal clutch size was three both years.

The mean incubation time (including hatch day) for the 24 nests that were found prior to

clutch completion and survived to hatch was 12.21 days (SD = 0.51, range 11-13). The most

common incubation length was 12 days (17 of the 24) and we used this value to back-calculate

the clutch completion dates for nests that were found during incubation and subsequently

hatched. The mean nestling period (including hatch day until the day first nestling left the nest) was 10.6 (SD = 0.64, range 9-12, n = 77). A total of 239 fledglings was produced from 77

successful nests, 133 (from 40 nests) in 2011 and 106 (from 37 nests) in 2012. The average

number of nestlings fledged per successful nest was 3.10 (SD = 0.82) and was significantly

greater in 2011 (3.33, SD = 0.86) than 2012 (2.86, SD = 0.79; t 75 = 2.44, P = 0.017). Twenty-two eggs failed to hatch across the two years out of the 415 total eggs in nests which hatched (5.3% failure rate).

We were able to assign a known fate (fledged, predated, or flooded) to 262 of the 299 nests (87.6%). Of the failed nests, 164 were predated and 21 were flooded. Of the 37 nests with unknown fates, 19 were abandoned after laying began. Ten nests were never observed with any contents and were excluded from most analyses because it was impossible to determine if they were already failed when found, if they were abandoned prior to laying, or if they were predated while laying. Two nests were found on hatch day with dead nestlings and six were still active at the end of the season.

The average number of young fledged from all nests (failed and successful) was 0.84 (SD

= 1.45) and was not different between the two years (t 281 = 1.35, P = 0.178). The average number

of young fledged per female per year was 1.48 (SD = 1.88, range 0 – 7, n = 161) and was not

different between 2011 and 2012 (t 159 = 1.197, P = 0.233). We found the nests of eight color- banded females in both years. The average number of young produced across both years for

these females was 5.13 (SD = 3.56, range 0 – 12).

Nest Site Selection

The top model for nest site selection included vegetation measurements and 1-m2 ground cover (Table 2.4). This model was 2.85 times more likely than the second-ranked model, which

added elevation. No models were within 2 AIC c units of the top model, so we did not perform

model averaging. The amount of bare ground within 1-m2 was negatively correlated with nest presence. When bare ground increased by 10%, the probability of being a nest site decreased by

2.33 times (Table 2.5). The percentages of S. patens and D. spicata in a 1-m2 around the point were positively associated with nest presence. A 10% increase in each corresponded to an increase in nest site probability of 1.19 and 1.23 times, respectively.

As vegetation height in the center of the point increased, the probability of nest presence also increased. A 10-cm increase in height made a nest 1.54 times more likely. A more positive difference between vegetation height in the center and edge of the 1-m2 plot (i.e., when the

vegetation at the center of the plot was taller than at the edge of the plot) was also positively

correlated with nest presence. A 10-cm increase in the difference made a nest 1.20 times more

likely. The odds-ratio confidence intervals for woody vegetation cover, J. roemerianus cover, and total stems broadly overlapped zero and so the direction of the relationship was not clear

(Table 2.5).

Daily Nest Survival

Under the constant-survival model, the daily survival rate (DSR) for nests was 0.922

(95% CI: 0.909 – 0.933) for 2011 and 2012 combined and did not differ between the two years

(2011: 0.920, 95% CI 0.901 – 0.936; 2012: 0.923, 95% CI 0.906 – 0.938). Assuming a modal 24- day nesting period (from first egg to fledge), this leads to an overall success rate of 14.26% (95%

CI: 10.24 – 18.95).

The top model of daily nest survival included nest measurements, 1-m2 measurements, landscape factors, and nearest-neighbor distance (Table 2.6). Three other models were within 1

AIC c unit and received almost as much support as the top model. The confidence set contained

eight models. We averaged the parameter estimates of these models and used the model- averaged estimates for further predictions (Table 2.7). None of the random effects we included had any influence on model parameters when added to the top model. This is supported by the extremely small variance estimates associated with each random factor (female = 5.04 x 10 -13 ; year = 7.82 x 10 -12 ; marsh = 2.80 x 10 -14 ; plot = 1.30 x 10 -11 ).

Daily survival rate increased with the age of a nest (β = 0.043, SE = 0.014), from 0.895 on day one (first egg) to 0.961 on day twenty-four (generally fledge day; lower 95% CI 0.861 –

0.940, upper 95% CI 0.922 – 0.974, Fig. 2.1). The number of stems around the nest in the same two-dm height class as the nest was positively related to survival (β = 0.011, SE = 0.005).

Model-averaged predicted DSR across the natural range of stem densities we observed (5 – 120) ranged from 0.913 to 0.977 (lower CI 0.886 - 0.941; upper CI 0.934 – 0.991, Fig. 2.2). Nearest- neighbor distance was negatively correlated with DSR (β = -0.009, SE = 0.003); that is, as a nest was farther from another active nest, its DSR declined. Predicted across our observed range of nearest-neighbor distances (excluding several high outliers; 8 – 100), DSR ranged from 0.946 –

0.889 (lower 95% CI 0.932 – 0.844; upper 95% CI 0.958 – 0.922, Fig. 2.3).

The percentage of bare ground within a 1-m2 around the nest and the laying date of a nest’s first egg appeared to have a negative effect on nest success, though both confidence intervals narrowly included zero (ground: β = -0.026, SE = 0.013; first egg date: β = -0.007, SE =

0.004; Figs. 2.4 and 2.5). For all other covariates in the confidence set, the 95% confidence interval of the beta estimate broadly overlapped zero (Table 2.7).

Because nest survival increased with decreasing nearest-neighbor distance, we performed a chi-square analysis to test if nearest neighbors shared the same fate more often than would be expected, based on the overall proportion of successful and unsuccessful nests. We found that

successful nests were much more likely to have a successful nearest neighbor than expected — approximately 50/50 successful/unsuccessful instead of the overall ratio of about 25/75 (χ 2 =

18.646, df = 1, P > 0.0001) while failed nests were not more likely to have a failed nearest

neighbor (χ 2 = 0.065, df = 1, P = 0.798, Table 2.8).

DISCUSSION

General findings

Our nest results showed some differences among years. While three-egg clutches were most common in both years, both the average number of eggs and the average number of fledged young per successful nest were higher in 2011. The daily survival rate did not differ between the two years, and even though more young fledged in 2011, the per-nest and per-female reproductive rates did not differ. Since number of eggs differed but not DSR, the discrepancy in clutch may have been caused by differing resource availability for adult birds. Birds weighed in

2012 were lighter on average than those in 2011 (19.9 g vs 20.5 g; t 130 = 2.156, P = 0.033) which also suggests a difference in resource availability. When looking at females alone, the difference in mass was suggestive but not significant (2012: 19.1 g, 2011: 19.5 g, t 66 = 1.598, P = 0.115).

The 2011-2012 winter was warmer than 2010 – 2011 (from December 1 st to February 14 th : 2010

– 2011, mean minimum temperature 1.1º C; 2011 – 2012, mean minimum temperature 6.7º C)

leading to an explosion in mosquito populations at our field sites. As one of few terrestrial

vertebrates inhabiting the marsh in the summer, SESP likely served as a major food source for

female mosquitoes, and vector abundance has been linked to prevalence of pathogen infection

(Martinez-De La Puente et al. 2013). Infection by mosquito-borne pathogens could tax a

female’s immune system, which can in turn lead to depressed clutch sizes (Hanssen et al. 2005).

This phenomenon deserves more study.

Nest Site Selection

On our study sites, nest sites had more vegetation cover at the 1-m2 scale than random points, demonstrated by nest sites having both less open ground and more cover of two common grass species. Increased vegetation cover around nest sites is common in grassland-nesting bird species (e.g., Dieni and Jones 2003, Lusk et al. 2003, Aguilar et al. 2008, Schill and Yahner

2009) and likely contributes to nest concealment. This supports our hypothesis that female SESP choose nest sites that will decrease predation risk by increasing nest concealment.

Seaside Sparrows built nests in vegetation that was taller than at random points. This relationship remained even when we excluded random points (and their associated nests) where the vegetation was clearly too low to support a nest. This result agrees with studies of SESP and

Sharp-tailed Sparrow ( Ammodramus caudacutus , a closely related congener that breeds in the same habitat on the Atlantic Coast) nest placement from the Atlantic coast (Marshall and Reinert

1990, DiQuinzio et al. 2002, Gjerdrum et al. 2005) but disagrees with the one study conducted on the Gulf coast (Post et al. 1983). On the Atlantic coast, where flooding is the most common cause of nest failure, it is clear why SESP should nest in vegetation that is taller than average. In our system, where flooding is much less common, it is not as apparent why SESP should nest in taller vegetation. One possibility is that SESP are still responding to the threat of flooding, even though it is rarer and less predictable than in Atlantic marshes. Females could also be attempting to raise their nests above the reach of ground predators such as marsh rice rats and saltmarsh snakes ( Nerodia clarkia ), which are more common at our study sites than aerial predators.

However, successful nests were not higher than unsuccessful nests, which would be expected if predation risk was affected by nest height.

The vegetation in which nests were built was also taller than the vegetation immediately surrounding the nest, while the vegetation at the center of random points was the same height as the surrounding vegetation. This finding reflects the search image we developed for finding nests

— females tended to nest in discrete clumps of taller vegetation. This is most likely an artifact of how taller vegetation is distributed throughout the landscape — as compact clumps of grass rather than as uniform areas of tall vegetation.

Nest Survival

Temporally-varying daily nest survival is found in many bird species with survival rates varying with nest initiation date and/or with nest age. Daily survival rates may vary across a season due to changing predator activity (Schaub et al. 1992) or weather (Lockwood et al. 2001,

Dreitz et al. 2012). Trends may be linear, both positive (Rush et al. 2010) or negative (Carter et al. 2011), or quadratic, with a peak or trough of nest survival in the middle of the season

(Bakermans et al. 2012). Our results suggested a trend of declining survival throughout the summer. These results could be explained by increased predator activity or density or by decreased ability of adults to defend their nests due to worsening body condition caused by repeated nesting attempts. In our system, females continually renest after both failed and successful attempts and likely have less energy to put into later reproductive efforts. This is supported at our study sites by the trend of declining clutch size throughout the season. However, female mass did not decline throughout the season, which does not support the hypothesis that female body condition was deteriorating. Later nests are also predated sooner after egg-laying (β

= -0.078; 95% CI -0.109 – -0.046), which would be expected if predator activity was increasing.

Therefore, we believe that it is more likely that increasing nest failure throughout the season was due primarily to an increase in predator activity and/or density.

Our results also indicated a significant positive linear relationship between nest age and

DSR. This pattern is commonly seen in passerines (e.g. (Peak et al. 2004, Segura and Reboreda

2012) and could be caused by early predation of poorly-placed nests (Martin et al. 2000) or by increased adult defense of older nests due to the increased reproductive value of the nest

(Fernandez and Llambias 2013). Because the amount of concealment, measured by the number of stems at the height of the nest, did differ between successful and unsuccessful nests, it is possible that the pattern we observed was simply due to predation of poorly-concealed nests early in the nesting attempt.

At the habitat level, there was a clear positive relationship between nest stems and nest

survival, suggesting that nests placed in denser vegetation had higher daily survival rates. Nest

concealment is generally assumed to be related to nest survival in birds (Lima 2009), an assertion

that is supported by some studies (e.g. (Martin and Roper 1988) but not supported by many

others (e.g., Holway 1991, Howlett and Stutchbury 1996, Burhans and Thompson 1998). In

systems with a varied suite of predators with diverse search methods, there may not be optimal

nest sites that protect nests from all predation (Filliater et al. 1994, Liebezeit and George 2002).

Tidal salt marsh systems have a low diversity of potential nest predators, so increasing nest

concealment may be a viable strategy for minimizing all predation.

Our results that nest success increased with vegetation density directly contradict results

from Atlantic coast marshes which found that successful nests were associated with less dense

vegetation than unsuccessful nests (Gjerdrum et al. 2005). This is not surprising given that dense

vegetation does not offer any protection against nest flooding, which is the major cause of nest

failure in North Atlantic marshes. While Post (1981) did not explicitly measure vegetation density or nest concealment, he did find that nests built in grass species that are generally more dense ( Juncus roemerianus and Distichlis spicata ) were far more successful than nests built in sparse vegetation ( Salicornia virginica ).

Nest success increased with decreasing bare ground cover, a result that is frequently seen in studies of ground and near ground-nesting birds (Lusk et al. 2003). Since bare ground is necessarily directly inverse to ground cover, this result could also be interpreted as increasing nest success with increasing vegetation cover. This gives more support to our conclusion that nests built in areas of generally higher vegetation density were more successful. Bare ground was also negatively associated with nest presence, suggesting that female SESP are choosing sites with characteristics that are associated with nest success.

Our findings on the relationship between breeding density and nest success directly contradict previous research on both SESP and many other bird species. When attempting to define high-quality habitat, density is generally easier to measure than breeding success, but may be a misleading indicator if density and breeding success are not positively related (Van Horne

1983). Research examining the connection between nest density and nest survival has had varying results. Early hypotheses (Tinbergen et al. 1967) predicted that predation pressure on camouflaged nests should be greater at higher nesting densities because predators will develop a search image as a density-dependent response. While many studies have supported this prediction (Larivière and Messier 1998, Barber et al. 2001) others have found no relationship

(Vickery et al. 1992, Clotfelter and Yasukawa 1999) or the opposite — that nests in higher- density areas were more successful (Cairns 1980, Ackerman et al. 2004).

Seaside Sparrows offer a unique opportunity to examine the connection between density and demographic parameters because they naturally breed at a wide range of densities that varies both within and among marshes (Post and Greenlaw 2009). Post (1974) monitored SESP nests in

New York marshes with both dense and dispersed breeding birds and found no difference in predation rates between the nesting patterns. He also tested for an interaction between nearest-

neighbor distance and predation (i.e., is a nest more likely to share its fate with its nearest

neighbor than with a random nest?) and did not find an effect for either predated or successful

nests. He attributed the lack of an interaction to the paucity of ground predators and generally

low predation rates in the study marshes. However, later research in the same marshes found that

nests with closer neighbors were more likely to be predated (Post et al. 1983), hypothetically due

to the tendency of crows ( Corvus sp. ), a major predator in the system, to increase hunting efforts

in areas where they have previously found prey (Sugden and Beyersbergen 1986).

Contrary to previous research on SESP, our results support a positive relationship between nest density and nest survival in two ways. First, decreasing nearest-neighbor distance

was positively related to daily nest survival. The contrary — that having a closer nearest

neighbor increases predation risk — is more commonly seen in studies of nest predation

(Cresswell 1997, Aguilar et al. 2008), though some studies have found no effect of nearest-

neighbor distance (Rendell and Robertson 1989, Ackerman et al. 2004). Much less common are

results such as we found, where nest survival increases as nearest-neighbor distance decreases

(Ringelman et al. 2012, Ringelman et al. 2014).

Second, successful nests were much more likely to have a successful nearest neighbor

than would be expected based on the overall ratio of successful to unsuccessful nests.

Conversely, unsuccessful nests were not more likely to have an unsuccessful nearest neighbor —

their nearest neighbors instead matched the expected success/failure ratio almost exactly. So while successful nests tended to be clustered together, unsuccessful nests were not. This suggests that predation events were not spatially related, which does not support density-dependent predation in our system. This is likely due to a lack of avian predators in our study marshes (AJL

and MSW, pers. obs.), as birds are generally more visual hunters and have been shown to form

search images for specific prey items (Tinbergen 1960, Sugden and Beyersbergen 1986, Reid

and Shettleworth 1992).

Spatial clustering of successful nests in a system where predation is the dominant cause

of nest failure suggests two possibilities: higher densities of birds are better able to drive off,

distract, or warn one another of nest predators, or birds are preferentially settling in areas with

lower predator density. Birds living in colonies or clustered territories may have a greater ability

to warn one another of potential predators (Beletsky et al. 1986, Brown and Brown 1987, Perry

and Andersen 2003), decreasing the time it takes for an individual bird to detect a predator, and

nests near the interior of such clusters may have higher survival (Brown and Brown 1987, Perry

et al. 2008). Some birds have been shown to preferentially nest in areas of low predator density

(Forstmeier et al. 2001, Morosinotto et al. 2010, Trnka et al. 2011), suggesting an ability to

assess predation potential before choosing a territory. These hypotheses could be distinguished by observing and comparing adult anti-predator behavior at low- and high-nest density sites and by estimating predator density at each site. In either case, whether density is a cause or an effect of low predation rates, adult density is likely a good indicator of high-quality breeding habitat and landscape factors that predict high SESP density could also serve as indicators of highly productive habitat.

LITERATURE CITED

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dependent?– Tests at three spatial scales. Oikos 107:128-140.

Aguilar, T. M., R. I. Dias, A. C. Oliveira, and R. H. Macedo. 2008. Nest-site selection by Blue-

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Table 2.1 . Explanatory variables used to model nest success of Seaside Sparrows ( Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012. The top models for each group were combined to find the top overall model. Group Variable Name Variable Description center height height of vegetation containing nest or in center of random point Vegetation max1m maximum height of vegetation on edge of 1-m2 around nest or random point measurements totstems total stems counted at corners of 1-m2 centered on nest or random point ground percent bare ground in a 1-m2 around the nest or random point juncus percent cover of Juncus roemerianus in a 1-m2 around nest or random point 1-m2 patens percent cover of Spartina patens in a 1-m2 around nest or random point dist percent cover of Distichlis spicata in a 1-m2 around nest or random point woody percent cover of woody vegetation in a 1-m2 around nest or random point ground percent bare ground in a 5-m2 around the nest or random point juncus percent cover of Juncus roemerianus in a 5-m2 around nest or random point 5-m2 patens percent cover of Spartina patens in a 5-m2 around nest or random point dist percent cover of Distichlis spicata in a 5-m2 around nest or random point woody percent cover of woody vegetation in a 5-m2 around nest or random point Elevation elev elevation of nest site or random point

Table 2.2 . Explanatory variables used to model nest success of Seaside Sparrows ( Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012. The top models for each scale were combined to find the top overall model. Scale Variable Name Description nestht distance from ground to nest rim nesttop distance from nest rim to top of vegetation above nest nestht distance from ground to top of vegetation above nest nestprop relative height of nest in vegetation (0-1) Nest-level neststems stems around nest in same dm as nest max1m maximum height of vegetation within 1-m2 around nest htdiff difference between max1m and totht totstems all stems around nest ground % bare ground in a 1-m2 and 5-m2 around the nest juncus % cover of Juncus roemerianus in a 1-m2 and 5-m2 around nest 1-m2 and 5-m2 patens % cover of Spartina patens in a 1-m2 and 5-m2 around nest dist % cover of Distichlis spicata in a 1-m2 and 5-m2 around nest woody % cover of woody vegetation in a 1-m2 and 5-m2 around nest disth2o distance from nest to water Landscape-level DistUp distance from nest to upland elev elevation of nest site MinDist minimum distance to another active nest Social AvgDist average distance to another active nest AvDayRd average Julian date of interval*, rounded to nearest whole number Temporal FirstEgg Julian date of nest's first egg AvgAge average age of nest during interval* * interval = time between two consecutive nest checks

Table 2.3. Top models from each scale used in subsequent multi-scale models of nest success for Seaside Sparrow ( Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012.

Scale Best Model Nest-Level survival ~ NestStems + NestProp 1-m2 survival ~ ground1 5-m2 survival ~ juncus5 Landscape survival ~ DistH2O + DistUp Social survival ~ MinDist Temporal survival ~ FirstEgg + AvgAge

Table 2.4 . Nest-site selection models for Seaside Sparrow (Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012. Models did not include an intercept. Minimum AIC c was 214.69.

Model K Δ AIC c wi Log likelihood 1-m2 + veg 8 0 0.74 -99.09 1-m2 + veg + elev 9 2.06 0.26 -99.06 5-m2 + veg 8 11.98 0 -105.08 1-m2 9 14.10 0 -105.07 5-m2 + elev + veg 5 43.96 0 -124.22 1-m2 + elev 6 45.94 0 -124.17 veg 3 47.82 0 -128.21 veg + elev 4 49.22 0 -127.89 5-m2 5 129.86 0 -167.17 5-m2 + elev 6 131.66 0 -167.03 elev 1 184.93 0 -198.81

Table 2.5 . Parameter estimates, scaled odds-ratios (OR), and lower and upper 95% confidence limits (LCL, UCL) for nest site selection by Seaside Sparrows (Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012 based on the top model. An asterisk (*) denotes an odds-ratio confidence interval that does not include 1.

Parameter Estimate (SE) Scalar Scaled OR OR 95% LCL OR 95% UCL Ground1 -0.083 (0.021) 10 0.43* 0.29 0.66 Juncus1 0.005 (0.007) 10 0.95 0.82 1.10 Patens1 0.017 (0.008) 10 1.19* 1.02 1.38 Distich1 0.021 (0.008) 10 1.23* 1.05 1.45 Woody1 0.023 (0.014) 10 1.25 0.95 1.65 totht 0.043 (0.007) 10 1.54* 1.33 1.78 totstems 0.006 (0.004) 20 1.08 0.90 1.28 totminmax 0.018 (0.008) 10 1.20* 1.03 1.40

Table 2.6 . Daily nest survival models for Seaside Sparrow ( Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012. We ran a total of 33 models with a minimum AIC c of 763.37. Only models with wi ≥ 0.05 are shown.

Model K Δ AIC c wi Log likelihood 1-m2 + nest site + landscape + social 9 0 0.15 -372.58 1-m2 + landscape + social 7 0.30 0.13 -374.77 1-m2 + nest site + social 7 0.40 0.12 -374.82 1-m2 + 5-m2 + landscape + social 8 0.93 0.09 -374.07 nest site + landscape + social 8 1.11 0.09 -374.15 1-m2 + 5-m2 + nest site + social 8 1.40 0.07 -374.30 1-m2 + 5-m2 + nest site + landscape + social 10 1.42 0.07 -372.27 nest site + social 6 1.79 0.06 -376.53 landscape + social 6 2.37 0.05 -376.82

Table 2.7. Model-averaged coefficient estimates used to predict daily survival of Seaside Sparrows (Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012 across a range of covariate values. Asterisks indicate confidence intervals that do not include zero.

Parameter β Estimate (SE) 95% CI (Intercept) 3.19 (0.670) (1.875, 4.521)* Ground1 -0.026 (0.013) (-0.052, 0.0006) Juncus5 -0.004 (0.004) (-0.012, 0.004) NestStems 0.011 (0.005) (0.001, 0.022)* NstProp -0.417 (0.772) (-1.931, 1.097) DistH2O 0.004 (0.003) (-0.0006, 0.009) DistUp -0.0004 (0.0003) (-0.001, 0.0002) MinDist -0.009 (0.003) (-0.014, -0.003)* FirstEgg -0.007 (0.004) (-0.015, 0.0002) AvgAge 0.043 (0.014) (0.015, 0.072)*

Table 2.8 . Nearest-active neighbor fate for all Seaside Sparrow (Ammodramus maritimus ) nests in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012, with known fate. Successful nests were more likely than expected to have a successful nearest neighbor, while the fates of unsuccessful nests' nearest-neighbors almost exactly fit the expected ratio (28.7% successful and 71.3% failed). Nests with no concurrently active nest at the same site were excluded from the analysis.

Successful Nearest Unsuccessful Nearest Neighbor Neighbor Successful Nests ( n = 77) 38 (49%) 39 (51%)

Unsuccessful Nests ( n = 203) 54 (26%) 149 (74%)

Figure 2.1. Predicted daily survival rate of Seaside Sparrow (Ammodramus maritimus ) nests in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012, with increasing nest age. Gray lines represent 95% confidence intervals.

Figure 2.2. Predicted daily survival rate of Seaside Sparrow (Ammodramus maritimus ) nests in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012, across the observed range of number of stems in the same dm as the nest. Gray lines represent 95% confidence intervals.

Figure 2.3. Predicted daily survival rate of Seaside Sparrow (Ammodramus maritimus ) nests in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012, across the observed range of nearest-neighbor distances. Gray lines represent 95% confidence intervals.

Figure 2.4. Predicted daily survival rate of Seaside Sparrow (Ammodramus maritimus ) nests in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012, across the observed range of bare ground in a 1-m2 centered on the nest. Gray lines represent 95% confidence intervals.

Figure 2.5. Predicted daily survival rate of Seaside Sparrow (Ammodramus maritimus ) nests in the Grand Bay National Estuarine Research Reserve and the Pascagoula River Marsh Coastal Preserve, 2011 – 2012, across the range of first egg dates observed during our study. Gray lines represent 95% confidence intervals.

CHAPTER 3

DENSITY OF SEASIDE SPARROWS IN THE GRAND BAY NATIONAL ESTUARINE

RESEARCH RESERVE 2

2 Lehmicke, A. J. J., A. H. Leggett, M. S. Woodrey, and R. J. Cooper. To be submitted to… 54

ABSTRACT

A clear understanding of fine-scale habitat preferences increases the utility of indicator

species and helps predict potential changes in a species’ distribution in response to changing

landscapes. We conducted general marsh bird surveys from 2010-2012 at 40 points within the

Grand Bay National Estuarine Research Reserve in Jackson Co., Mississippi, USA. We used package ‘unmarked’ in program R to estimate the relationships between various land-cover and

landscape metrics and the density of Seaside Sparrows (SESP; Ammodramus maritimus ). Seaside

Sparrow density was negatively related to the percent of tree cover within 200 m of a point and

to the distance of a point to any upland habitat, and was nearly significantly negatively related to

the proportion of upland habitat within 1 km of a point. Density was positively correlated with

elevation, possibly because of a positive relationship between elevation and the preferred nesting

sites of SESP, suggesting that SESP may be a valuable indicator of healthy intermediate,

irregularly flooded marsh along the northern Gulf coast. The relationship between SESP density

and elevation may appear to differ depending on the spatial scale of study, and is important to

consider in light of future sea-level rise.

INTRODUCTION

Globally, tidal salt marsh covers about 45,000 km 2 (Greenberg et al. 2006b), an area

slightly larger than the state of Maryland, USA. Of this total, about 22% (9,880 km 2) is found along the Gulf of Mexico (hereafter: the Gulf) in the United States (Greenberg and Maldonado

2006). Tidal salt marshes are highly productive ecosystems (Adam 1990) and are important both ecologically and economically. Ecologically, they play an important role in filtering pollutants from water (Gersberg et al. 1986), are an essential part of estuarine nutrient cycles (Welsh 1980), and help protect coastlines from erosion (Broome et al. 1988). Economically, they serve as

nurseries for many commercially important species of fish and shellfish (Knieb 1997), and provide numerous opportunities for those seeking outdoor recreation (Niering and Warren 1980).

For centuries, this ecosystem has been threatened in the United States by human activities. Farming, dredging, mosquito ditching, and coastal development have all contributed to a 30-40% loss in total wetland area (Horwitz 1978). These threats continue even today, with an estimated 1% loss in tidal salt marsh area between 1998 and 2004 (Stedman and Dahl 2008).

In the near future, one of the greatest threats to tidal salt marsh is predicted to be sea level rise

(SLR; McFadden et al. 2007). While marshes have historically been able to adapt to changing sea levels through vertical accretion and inland migration, predicted future rates of sea level rise and coastal development may hinder the ability of coastal wetlands to respond to current rates of

SLR (Nicholls et al. 1999).

Because of its limited extent, high economic and ecological importance, and continued threats, the integrity of tidal salt marsh must be closely monitored. Because it is challenging to monitor the condition of an entire ecosystem, managers and researchers may rely on one or more indicator species as proxies for ecosystem health. Marsh birds have been proposed as indicators of tidal marsh integrity because they connect food webs through multiple tropic levels and are easily monitored through non-invasive survey techniques (Novak et al. 2006, Woodrey et al.

2012).

For marshes along the northern Gulf coast, the Seaside Sparrow ( Ammodramus maritimus ; SESP), is a strong indicator species candidate. Seaside Sparrows are endemic to

North America and are obligate residents of salt marshes. Seaside Sparrows occur in scattered populations from southern Maine to southern Florida on the Atlantic coast, and from central

Florida to southern Texas on the Gulf coast (Post and Greenlaw 2009). With the exception of a

subspecies found in the Florida Everglades ( A. m. mirabilis ), the species is completely restricted to salt and brackish coastal tidal marsh and is the only bird species breeding along the Gulf coast that is so restricted (Greenberg et al. 2006b). Species that are completely specialized for one habitat type may be the strongest indicators for that habitat (Hutto 1998). Seaside Sparrows remain in tidal marsh for their entire life cycle, making them completely reliant on the ecosystem for foraging, breeding, and wintering habitat. Two subspecies are now extinct ( A. m. pelonata and A. m. nigrescens ), showing that the species is sensitive to salt marsh disturbance, another

important quality of a potential indicator species (Carignan and Villard 2002).

The IUCN Red List of Threatened Species classifies SESP as “least concern” but does

assign the species a decreasing population trend (Birdlife International 2012). Within the United

States, the U. S. Fish and Wildlife Service categorizes SESP as a species of national conservation

concern (U.S. Fish and Wildlife Service 2008) and the National Audubon Society has given

SESP “red” status on its WatchList of species in need of conservation action, meaning that the

species has a limited range, faces major conservation threats, and may be of global conservation

concern (National Audubon Society 2007).

Seaside Sparrows have been the focus of several demographic studies along the Atlantic

coast (Post 1974, Post et al. 1983, Marshall and Reinert 1990, Gjerdrum et al. 2005, Pepper and

Shriver 2010, Kern et al. 2012), but are poorly studied along the Gulf. One study has focused on

nesting biology (Post 1981, Post et al. 1983), one considered factors affecting SESP occupancy

(Rush et al. 2009) and another examined the effect of prescribed fire on SESP abundance

(Gabrey and Afton 2000). Inferences drawn from Atlantic Coast SESP populations may not be

useful for management of Gulf coast populations because of important differences between

Atlantic and Gulf coast marshes including different tidal regimes and dominant plant species

(Stout 1984). The birds themselves also differ: northern Atlantic populations are migratory, while all populations along the Gulf are year-round residents (Post and Greenlaw 2009). The

Atlantic group ( A. m. maritima and A. m. macgillivraii ) and the Gulf group ( A. m. peninsulae, A.

m. juncicola, A. m. fisheri, and A. m. sennetti ) are genetically distinct and likely have not

interbred for at least 250,000 years (Avise and Nelson 1989).

Examining landscape factors that correlate with SESP density on the Gulf coast will help

refine our understanding of how SESP interact with, and respond to change in, their coastal

marsh habitat. Increased understanding of species-habitat relationships of potential indicator

species makes it easier to interpret how changes in population dynamics may reflect changes in

the underlying ecosystem. While the general habitat requirements of SESP are well understood,

there is much less understanding of what drives patterns of density and abundance within

marshes, information that is essential when trying to predict how small-scale changes to tidal

marshes will affect SESP populations.

Information on the relationship between density and landscape variables can also be used

to inform decisions on how to manage existing marsh to increase habitat quality for SESP. While

density of individuals may not always correlate with habitat quality (Van Horne 1983), SESP do

not possess any of the suggested attributes of bird species that are most likely to experience this

disconnect, such as high territoriality and a habitat with a high degree of anthropogenic

disturbance (Bock and Jones 2004). The results from our nest success analyses (Chapter 2) also

suggest a positive relationship between density and habitat quality. Nearest-neighbor distance

was negatively associated with daily nest survival, suggesting a positive relationship between

SESP density and breeding success. Therefore, it is likely that density of birds is a true indicator

of habitat quality within our study area.

In the Northeast, where most SESP research has been conducted, breeding densities vary widely among marshes (Post and Greenlaw 2009). Benoit and Askins (2002) found that larger marshes generally have higher breeding densities, a factor which also has a strong positive effect on marsh occupancy by SESP (Shriver et al. 2004). Past and present marsh management practices can also affect occupancy and/or density of SESP. Marsh plots in Delaware that had

experienced extensive open marsh water management (OMWM; a management technique used

to control mosquitoes) had half the territory and nest density of plots that had no or limited

OMWM (Pepper and Shriver 2010). In Louisiana, plots that were burned during the winter had

lower SESP abundance throughout most of the following breeding season but similar abundance

to unburned plots one year post-burn (Gabrey and Afton 2000). In contrast, plots along the

Chesapeake Bay experienced their highest nest and territory densities zero years post-burn (Kern

et al. 2012).

Where marshes are generally small, as in the Northeast, it may make the most sense to

treat marshes as cohesive units and examine marsh-level factors, such as size and management

history, when attempting to determine what drives marsh bird occupancy or density. However, in

large marshes such as those found along the Gulf coast, SESP density can vary widely within a

single marsh and focusing solely on marsh-level characteristics is not sufficient to determine

what is driving patterns of density within a marsh.

The only published study concerning spatial patterns of SESP along the Gulf coast

examined factors affecting SESP occupancy at 227 survey points spread among six marshes in

Mississippi and Alabama (Rush et al. 2009). Seaside Sparrow occupancy at a point was positively related to salinity and the amount of emergent marsh within 200 m of a point, and was

negatively related to the percent of developed land within 500 m of a point. These results

generally agree with the marsh-level studies done in the Northeast — more marsh and less human disturbance may increase SESP occupancy and/or density. While studies of local occupancy can provide useful information over broad scales such as those covered by Rush et al.

(2009), they fail to detect relationships between landscape factors and abundance, especially in marsh complexes where occupancy of surveyed points approaches 100%.

We developed a series of biological hypotheses that might explain the relationship between landscape variables and SESP density. We believed that SESP density was related to the ability of individual birds to carry out necessary activities related to survival and reproduction and thus structured the following hypotheses to reflect these underlying processes:

1. SESP density is influenced by the probability of nest success.

Risk of nest predation, which is directly related to reproductive success, can influence

choice of habitat by adult birds (Eggers et al. 2006, Fontaine and Martin 2006, Peluc

et al. 2008). In our study population, a concurrent demography study (Chapter 2)

showed that most nest failures were caused by predation, which led us to hypothesize

that landscape factors related to predator presence might influence SESP density. A

small proportion of nest failures are flood-induced, so we also included elevation as a

potential factor influencing habitat selection.

2. SESP density increases with availability of foraging sites.

Resource availability can also be a major factor influencing species distributions

(Orians and Wittenberger 1991, McCollin 1998, Johnson and Sherry 2001), so

availability of foraging areas such as beach and salt panne may directly affect SESP

density.

3. SESP density is positively influenced by the availability of nest sites.

For many bird species, nest site availability is a limiting factor (Brawn and Balda

1988, Burke and Nol 1998). We therefore hypothesized that SESP density would be

positively related to the two marsh types (intermediate and high) used almost

exclusively for nest sites in our study.

4. SESP density is positively related to the diversity of cover types.

Because SESP often use disparate areas of the marsh for foraging and nesting (Post

1974), we hypothesized that SESP may be choosing areas with a high number or

diversity of available cover types.

5. SESP density is influenced by the same factors that predict occupancy at a

larger scale.

Finally, we hypothesized that factors previously found to influence SESP occupancy

in Gulf coast marshes (Rush et al. 2009) would similarly influence SESP density.

We used data from ongoing marsh bird surveys to test the above hypotheses and determine which habitat variables may be affecting Seaside Sparrow density in a large tidal salt marsh in coastal Mississippi. Understanding the potential effects of local-level variables on

Seaside Sparrow densities will make SESP a more useful indicator species and will help better inform future management and restoration practices and land acquisition decisions.

METHODS

Study Area

We conducted our research in the Grand Bay National Estuarine Research Reserve and

National Wildlife Refuge (hereafter; GBNERR) in Jackson County, Mississippi. The GBNERR is comprised of approximately 7,500 hectares of pine savannah, maritime forest, bays, bayous, and tidal salt marsh. The tidal marsh areas are predominately polyhaline (salinity 18-35 ppt,

(Cowardin 1979) and are dominated by black needlerush (Juncus roemerianus ) interspersed with narrow bands of smooth cordgrass ( Spartina alterniflora ). While the marshes of the GBNERR were originally formed by deposits from the Escatawpa River (Otvos 2007), there is currently no riverine input to the GBNERR.

Bird Surveys

Seaside Sparrows were surveyed as part of a larger marsh bird monitoring program from

2010-2012. Following the survey methods outlined in the North American Marsh Bird

Monitoring protocol (Conway 2011), we surveyed 40 sampling points throughout the GBNERR, each separated by at least 400m to reduce the chance of double-counting an individual bird (Fig.

3.1). The same points have been surveyed annually since 2005, with the initial placement of the first point was random. Each point was visited three times per year between late March and mid-

June. Points were grouped into four routes of ten points each, and points within routes were always surveyed in the same order. Surveys began with a 5-minute silent period, followed by eight minutes of alternating 30-second secretive marsh bird calls and 30 seconds of silence. We used call sequence #35 developed by Courtney Conway which featured the following bird calls, always in the same order: Black Rail (Laterallus jamaicensis ), Least Bittern (Ixobrychus exilis ),

King Rail (Rallus elegans ), Clapper Rail (Rallus longirostris ), Common Gallinule ( Gallinula

galeata ), Purple Gallinule (Porphyrio martinica ), American Coot (Fulica americana ), and Pied- billed Grebe (Podilymbus podiceps ) . During the 13-minute survey we estimated the distance to each individual SESP that was seen or heard. Each bird was tracked throughout the survey, but only its initial distance was recorded. One observer was exclusively focused on primary species

(the eight species for which calls were broadcast) while a second concentrated on secondary marsh birds – Marsh Wren ( Cistothorus palustris ), Common Yellowthroat ( Geothlypis trichas ),

Seaside Sparrow, Red-winged Blackbird ( Agelaius phoeniceus ), Common Grackle ( Quiscalus quiscula ), and Boat-tailed Grackle ( Quiscalus major ). This separation made it easier for each observer to detect and keep track of individual birds. At each point we also recorded wind speed, temperature, sky condition, salinity, and background noise to use as detection and availability covariates in our modeling efforts.

Developing Covariates

We used ArcGIS (ESRI 2013) to extract landcover variables from a highly detailed (2.8- m cell size) land-cover map of the GBNERR (Fig. 3.1). While vegetation cover estimated on the ground can add explanatory power to models, covariates extracted from satellite imagery can be reliable predictors of abundance for birds (Stralberg et al. 2010). The land-cover map included seven different cover types within the surveyed area. Inundated/low marsh included areas that flooded almost on a daily basis, intermediate marsh only flooded during especially high tides or storm, and high marsh almost never flooded. The map also distinguished salt panne, shrub, tree, and beach/sand within the marsh. We overlaid the marsh bird survey points on the map and created a 200-m buffer around each point to correspond to our observation truncation value. To determine the amount of each cover type around each point, we calculated the percentage of cells within the buffer representing each land-cover category. We then considered the number and

proportion of cover types within each 200-m buffer and calculated a reciprocal Simpson’s

diversity index for cover type at each point. We predicted that a measure of the diversity of cover

types would be important because SESP use diverse areas for nesting and foraging (Post 1974).

We then increased the buffer size to 1000 m and calculated the percentage of cells within the buffer representing upland cover types (industrial, roads, trees, and upland grasses). We used the

same land cover map to calculate the distance to upland and distance to development for each point by creating Euclidean distance rasters and extracting the values to each individual survey point.

For elevation we used a digital elevation map (DEM) from the National Elevation

Dataset with a 1/9 arc-second resolution (~3-meter cell size; Gesch et al. 2002). We clipped the

DEM to the non-water portions of our land-cover map and calculated the average elevation of

the land within the same 200 m buffer. This left us with 13 predictor variables (Table 3.1). Based

on the hypotheses outlined at the end of the introduction, we used these variables to create a set

of ten a priori models regarding what specific landscape factors we thought might affect SESP

density (Table 3.2).

To avoid potential problems due to multicollinearity, we calculated Pearson correlation

coefficients for all pairs of density predictor variables. If two variables were strongly correlated

(r > 0.6), we did not include both variables in the same model. All covariates were mean-

centered and scaled (divided by standard deviation) prior to analysis to improve model performance.

Statistical Analysis

We used the “gdistsamp” function (Chandler et al. 2011) in package unmarked (Fiske and

Chandler 2011) in program R (R Core Team, 2013) to simultaneously model detection,

availability, and density of SESP throughout the GBNERR. This function allows multiple visits to each point (as opposed to the related “distsamp” which is based on a single visit to each point) and allows the modeling of availability based on survey-specific covariates. While the stated purpose of the availability component of the gdistsamp model is to account for temporary emigration (Chandler et al. 2011), it also allows the possibility that a bird is present at a point but is not “available” to be detected during a specific survey because it is not visible or does not vocalize during the survey period.

Prior to any analysis, we truncated observations at 200 m in an attempt to avoid double- counting individual birds at neighboring points. While this may seem like a high truncation value, the marsh habitat is flat and open and many SESP observations were by sight. Therefore, we believe that we could accurately identify birds up to 200 m away. Because unmarked does not yet have a function to allow for multiple visits per year over multiple years, we analyzed all nine visits together. We felt that this was appropriate because SESP at our sites had very high site fidelity, so patterns of density remained consistent over the three years of our surveys. We were also not interested in annual variations in density, but rather in spatial patterns.

Our first step was to determine which detection key function and latent abundance distribution best described the shape and spread of our data. We did this by running null models with each of three key functions (half-normal, hazard-rate, and uniform), which use different parameters to describe how detection probability changes with distance from the observer. We

chose not to include the exponential key function because Buckland et al. suggest that it should

only be used with “…poorly collected data where there is strong reason to believe that the

distance data are truly spiked” (2001; p. 24). We did not believe that this described our data so

we excluded the exponential key function. We modeled each key function with both Poisson and

negative binomial latent abundance distributions. While the Poisson distribution constrains the mean and standard deviation of the detection data to be equal, negative binomial distributions allow for data that are overdispersed; that is, data for which the standard deviation is greater than the mean. This gave us a total of six null models – one for each key function/latent distribution combination. We evaluated all models using an information-theoretic approach (Burnham and

Anderson 2011) which determined the support for each candidate model given our data. We ranked models based on AIC score with a small-sample correction (AIC c; Akaike 1973, Hurvich and Tsai 1989). Once we determined the key function/latent distribution combination with the most support, we used the same combination in all further models.

After we selected the best-supported key function model, we introduced detection covariates while still keeping the availability and abundance components null. We used wind speed, noise, and observer as detection covariates. Wind speed was a continuous variable and noise and observer were treated as factors. We fit all possible detection models excluding higher- order terms and interactions (seven total) and ranked the models using AIC c scores. The top detection model was then held constant through the rest of the modeling process.

We then fit availability models, a capability unique to the gdistsamp function. We included temperature, Julian date, and minutes past sunrise as covariates that we felt may affect availability by influencing movement or male singing rates. As with our detection models, we fit all possible models but also included a quadratic term for temperature, since it is reasonable that both extreme low and extreme high temperatures could negatively affect singing or movement rate.

Once we had determined the best key function, latent abundance distribution, detection model, and availability model we began building abundance models based on our prior

hypotheses, using the same key function, detection model, and availability model for all. All abundance models included an offset for the proportion of the count area that was marsh, since

SESP do not utilize open water and the proportion of marsh differed widely among our survey points (from 0.23 to 0.83). Including an offset also greatly improved model fit. The models in the set, including a null model, were ranked based on AIC c. We then took the final top model and re-

ran it with any other detection probability or availability models that were within two AIC c units of the top model in its respective set.

We created a confidence set of models that included all models where ΔAIC c ≤ 2

(including those with modified detection and/or availability components), and, if necessary, created a composite model using averaged parameter estimates from all models in the confidence set. All inferences were based on model-averaged parameter estimates. We evaluated the fit of the top model with a Freeman–Tukey test based on a parametric bootstrap (Fiske and Chandler

2011). We created 100 simulated data sets from our top model and calculated a fit statistic for each simulated data set. We then compared a fit statistic for our observed data to those computed from the simulated data. The model was considered to fit if the observed data were not too extreme when compared to the simulated data. Finally, using the top model or composite model, we plotted the estimated densities and 95% confidence intervals over the observed range of each of the covariates in the top or composite model.

RESULTS

Eight observers detected a total of 550 Seaside Sparrows during all point visits from 2010

– 2012, with a maximum of eight birds detected at a single point visit. Across all surveys, the mean temperature was 22.2° C (range: 5.5° C to 33.8° C) and the mean wind speed was 1.7 m/s

(range: 0.0 m/s to 5.3 m/s). The average date for first surveys was 28 March (range: 23 March –

1 April), for second surveys was 4 May (range: 27 April – 9 May), and for third surveys was 3

June (range: 31 May – 8 June). The average survey began 84 minutes past sunrise (range: -26 to

226).

The best-supported key function/mixture combination was a half-normal key function

with a negative binomial mixture, which received essentially all of the support (second-best

model ΔAIC c = 12.85; Table 3.3). Our count data did show evidence of overdispersion (mean =

0.382, SD = 0.753). The dispersion parameter of the null negative binomial model was small

(3.63) and significantly non-zero (P = 0.0002), again confirming that our data were likely

overdispersed.

The best detection model included observer, with no other models within two ΔAIC c

units (Table 3.4). For availability, the null model had the most support, though Julian date was

within one AIC c unit (Table 3.5). However, when we reran our top a priori density model with

Julian date as the availability model, it was not within two AIC c units of the top density model

with a null availability model. Our “nest success” a priori model was the top model for density,

with no other models within two AIC c units (Table 3.6). The predicted per-point densities based

on the top model ranged from 0.68 to 13.58 birds/ha, averaging 4.58 birds/ha with a mean

standard error of 1.33.

The top a priori model, “nest success”, included elevation, distance to upland, percent

trees, and percent upland within 1 km. Average elevation within 200 m of a point had a positive

relationship with density (β = 0.472, 95% CI 0.285 – 0.659; Fig. 3.2). Distance to upland had a

negative relationship with density – that is, sites farther from upland had a lower density of SESP

(β = -0.255, 95% CI -0.471 – -0.129; Fig. 3.3). Percentage of tree cover within 200 m had a

negative relationship with density (β = -0.155, 95% CI -0.302 – -0.008; Fig. 3.4), and percentage of upland within one km had a mostly negative relationship with density, but the confidence interval slightly overlapped zero (β = -0.121, 95% CI -0.271 – 0.029, Fig. 3.5). Based on the results of a Freeman-Tukey test on our bootstrap simulations, there was no evidence that our top model did not sufficiently fit our data ( P = 0.257).

DISCUSSION

The strongest relationship to emerge from our modeling was between SESP density and elevation. The average elevation within 200 meters of a point (excluding water) was positively related to density. This supports our hypothesis that birds would preferentially nest in areas of higher elevation to lessen the risk of nest failure through flooding. Saltmarsh and Nelson’s

Sparrows ( Ammodramus caudacutus and A. nelsoni ), also marsh-obligate passerines and congeneric with SESP, chose nest sites whose elevation was higher than random points (Shriver et al. 2007), presumably to avoid flooding by high tides. Even though the majority of nest failures that we observed were caused by predation, 11% of failures were a result of flooding

(Chapter 2). This is not an insignificant proportion of nests, so it is possible that SESP are still responding to the threat of flooding even though it is not the primary cause of nest failure on our study sites.

In salt marshes, where both predation and flooding threaten nests, nest-site selection is constrained by the need to minimize both of these risks (Storey et al. 1988, Greenberg et al.

2006a, Reinert 2006). Rather than females balancing these two competing threats at the individual nest-site level (Reinert 2006, Ricketts 2011), SESP may be initially choosing broader

areas of higher elevation as a strategy to avoid nest loss by flooding. This would allow females to then choose specific nest sites that minimize risk of predation.

The apparent importance of elevation could also be due to correlations with other landscape variables. Elevation was significantly correlated with intermediate marsh ( r = 0.476, P

= 0.002), which was used almost exclusively for nesting in our study areas. When modeled alone, the percent of intermediate marsh has a clear positive effect on density, so it is possible that the positive effect of intermediate marsh is having some influence on the estimated effect of elevation. At most of our points the intermediate marsh is dominated by saltmeadow cordgrass

(Spartina patens ) and black needlerush, which Rush et al. (2009) found was positively correlated with SESP occupancy. Over 60% of the SESP nests we found at the GBNERR were built in one of those two grass species, showing the importance of intermediate marsh for SESP reproduction.

In salt marshes, the plant community is determined by a combination of elevation and salinity (Chapman 1960, Pennings et al. 2005) so rather than responding to elevation directly,

SESP may instead be choosing the plant community that is associated with higher elevation marsh within the GBNERR because it represents preferred nesting habitat. The range of average elevation of our survey points was fairly small (0.092 – 0.680 meters) and certainly does not represent the full range of elevations at which SESP are found along the northern Gulf coast. It is likely that there is an optimal elevation within a marsh that is associated with preferred vegetation types, and SESP may exhibit a quadratic relationship with elevation in marshes with a wider elevational range.

Our finding that SESP density increased with elevation is very important from a management perspective. Marsh elevation is one factor that is likely to be affected by future SLR

(Rybczyk and Cahoon 2002, Erwin et al. 2006, Kirwan et al. 2010, Glick et al. 2013), particularly in a marsh such as the GBNERR that does not have regular sediment input from an associated riverine system (Morris et al. 2002, FitzGerald et al. 2008). Decreasing marsh elevation can lead to increased tidal flooding (Erwin et al. 2006), which would likely convert black needlerush-dominated intermediate marsh to smooth cordgrass-dominated tidally inundated marsh, as smooth cordgrass is generally confined to areas of low elevation and high salinity (Pennings et al. 2005). Seaside Sparrows along the Atlantic Coast preferentially use smooth cordgrass as nesting substrate (Marshall and Reinert 1990, Gjerdrum et al. 2005), which led Rush et al. (2009) to hypothesize that SLR could lead to an increase in SESP occupancy if coverage of smooth cordgrass increased. However, the birds on our study sites very rarely nested in smooth cordgrass, preferring intermediate marsh plants such as black needlerush and saltmeadow cordgrass. Areas of smooth cordgrass on the GBNERR were flooded almost daily and so likely would not be suitable nesting habitat for SESP. The plants also grew sparsely and likely would not provide suitable protection from nest predators. If areas of black needlerush and saltmeadow cordgrass were converted to smooth cordgrass, it is possible that SESP density and/or occupancy would actually decrease because of a decrease in suitable nesting sites.

The relationship between elevation and salinity in the GBNERR also has implications for future SESP density. Previous research on the northern Gulf coast (Rush et al. 2009) found that

SESP occupancy was positively associated with cover of black needlerush. In the study, black needlerush was used as a proxy for salinity, with increasing cover associated with increasing salinity on a landscape scale. This led the authors to conclude that SLR-induced salinity increases may have a positive effect on SESP occupancy in northern Gulf coast marshes. Our finding that density increases with elevation could lead to the exact opposite conclusion, as

elevation and salinity were strongly negatively correlated in our system ( r = -0.793, P > 0.001).

When a study area includes large portions of fresh or nearly-fresh marsh which are already

known to be unsuitable for SESP (as did that surveyed by Rush et al. 2009), then it will appear

that salinity positively influences SESP presence. However, our study was restricted to areas

where SESP occupancy is nearly 100%. Within this narrower habitat band, SESP displayed a

trend that was the opposite of that seen at a broader scale.

We further examined the relationships between density, elevation and salinity by

analyzing another set of data collected from points in the Pascagoula River Marsh Coastal

Preserve, also in Jackson County, MS. We did not include these points in the full analysis because we did not have a detailed land-cover map like we did for the GBNERR. We did have

salinity data for each point and we were able to calculate average elevation from the same DEM

used for the GBNERR analysis. The marshes along the Pascagoula extend far upriver and

transition from brackish to fresh marsh about 10 km inland from the mouth of the river. Interstate

10 is a fairly clear dividing line between where SESP occur and where they do not (AJL and

MSW, pers. obs.). North of Interstate 10, the marsh becomes essentially fresh and is not suitable

for SESP. When we ran single-factor density models for elevation and salinity that included all

of the Pascagoula survey points, elevation had a significant negative effect on SESP density (β =

-1.43, 95% CI -2.16 – -0.70) and salinity had a significant positive effect (β = 3.29, 95% CI 1.77

– 4.81). However, when we excluded the points north of I-10, both relationships became non-

significant (elevation: β = -0.63, 95% CI -1.72 – 0.46; salinity: β = -0.13, 95% CI -0.99 – 0.73).

This result shows that relationships inferred from an examination of landscape-scale data may

not hold at a finer scale, especially when the landscape analysis includes areas of habitat that are

already known to be unsuitable. It also demonstrates that relationships may vary among marshes,

and managers should be cautious in applying results garnered from a large-scale study to a single smaller system.

With different relationships between density, elevation, and salinity at different scales, it is difficult to predict how SESP may respond to changes brought about by future SLR. The broader positive relationship between salinity and SESP presence does suggest that SESP may be

able to shift their range in large marsh systems that extend far inland and contain a wide salinity

gradient. However, in marshes such as the GBNERR that are not contiguous with fresh riverine

marsh, it is possible that decreases in elevation and/or increases in salinity will make all habitat

unsuitable for SESP. Even in marsh systems where SESP are able to migrate inland, generally

narrowing marshes will be in closer proximity to both trees and upland habitat, which may make

the new habitat less suitable due to increasing predator populations.

Contrary to our hypotheses, distance to upland was negatively related to SESP density.

Upland habitat likely serves as a source of predators of both nests and adults such as raccoons

and corvids. Marsh rice rats ( Oryzomys palustris ), another potentially important nest predator

(Post 1981), nest in the marsh but use upland habitat during the winter and spring months and

during high tides (Kruchek 2004). Therefore, we believed that SESP would avoid areas close to

upland habitat because of potentially higher concentrations of predators. Previous research on

SESP occupancy (Rush et al. 2009) found that occupancy was negatively associated with the

amount of development within 500 m of a point. While development and upland habitat are not

equivalent, we thought that SESP density may exhibit a similar negative relationship with non-

marsh habitat. However, two studies on nest survival of Red-winged Blackbirds ( Agelaius phoeniceus ) in freshwater marshes did not find a relationship between nest survival and

proximity to the marsh/upland interface (Picman et al. 1993, Grandmaison and Niemi 2007) so

our initial assumption of higher predator density nearer to the edge may be incorrect.

We do not believe that correlations with other variables were driving the relationship between density and distance to upland. While it may seem that elevation would increase with proximity to upland habitat, the mean elevation within 200 m of a point was not correlated with distance to upland ( r = -0.011, P = 0.945), nor was the maximum elevation within the same radius ( r = -0.205, P = 0.204). Therefore, we do not believe that the strong positive effect of elevation was confounding the true effect of distance to upland. Salt panne was the only cover type that was significantly correlated with distance to upland, with more salt panne occurring closer to upland ( r = -0.526, P = 0.0005). Salt panne by itself does have a slight positive effect on density (β = 0.294, 95% CI: 0.043 – 0.545), so the effect of distance to upland may be slightly confounded with the effect of salt panne. It also may benefit SESP to be closer to upland habitat that could be used as a refuge during storms or extreme high tides.

Another possible explanation for the observed relationship is that SESP are using upland habitat as a visual proxy for the minute elevation changes (often less than one meter) found in tidal marshes, even though there may not be a true relationship between the two. Sites that are closer to upland are also generally farther from open water and may be better protected from storm surges and wind-generated high tides. The relationship could also be an artifact of our point locations. There are several areas of the GBNERR that have high densities of SESP and are far from any upland habitat (> 2 km), but could not be sampled effectively because of regular rough water and strong winds. It is possible that if these high-density sites were included, the negative relationship between density and distance to upland would become non-significant.

Percent cover of trees within 200 m of a point had the expected negative relationship with density, but the confidence interval slightly overlapped zero. However, the bulk of the interval was below zero, suggesting that the true relationship is negative. While we did not collect information on the identity of nest predators, raccoons ( Procyon lotor ) are common nest predators in wetlands (Post 1981, Jobin and Picman 1997, Ricketts 2011) and it is likely that they were at least occasional nest predators on our study sites. Even when raccoons forage in wetlands, they will often still use trees as den sites (Ivey 1948) so the percentage of trees within

200 meters of a point could be influencing the density or likelihood of raccoons. Other upland predator species, such as the long-tailed weasel ( Mustela frenata ), have also been observed foraging in marshes (Picman et al. 1993).

Our results highlight the importance of using locally-collected data to inform habitat or species management plans. While SESP along the Atlantic coast preferentially nest in smooth cordgrass, in our study area the low-elevation areas occupied by this grass species had lower

SESP density than higher elevation areas with different dominant plant species. If only information from Atlantic coast SESP were considered, managers may incorrectly target areas dominated by smooth cordgrass as preferential SESP habitat. Likewise, the positive relationship inferred between SESP and salinity from surveys at a regional scale may not hold at a more local level and may in fact be contradictory.

The conclusions drawn from this research suggest that SESP may be strong indicators for the presence of healthy intermediate (irregularly flooded) marsh in northern Gulf coast marshes.

These areas are generally safe from flooding-induced nest failure and also provide suitable nesting substrate. Higher detected densities of SESP may indicate an elevation threshold or the

presence of a specific plant community, and bird surveys are likely easier and cheaper to carry

out than intensive elevation or vegetation surveys.

Continued monitoring of SESP populations in a variety of marsh types could provide

more clarity on landscape-level habitat associations. A specific focus on predator identity and

density could help increase understanding of the relationship between SESP and upland habitat

and how these relationships could be affected by SLR. Seaside Sparrows may exhibit different

habitat relationships among marshes and individual marshes may have differing management

needs. It is critical that managers consider the individual system in which they are working rather

than assuming that broad-scale habitat relations apply to all marshes equally.

LITERATURE CITED

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Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle.

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Table 3.1. Landscape variables, with mean and range, used to model the density of Seaside Sparrows ( Ammodramus maritimus ) in the Grand Bay National Estuarine Research Reserve, Jackson Co., MS, USA, 2010 – 2012. All percentages are cover within 200m of a survey point.

Variable Mean (range) % Low marsh 63.50 (0 - 97.75) % Intermediate marsh 25.99 (0 - 94.01) % High marsh 1.39 (0 - 15.76) % Shrub 0.29 (0 - 5.82) % Beach / sand 1.82 (0 - 40.59) % Salt pan 4.59 (0 - 22.50) % Tree 2.07 (0 - 22.52) # Classes 4.2 (2 - 8) Simpson's Reciprocal Diversity Index 0.41 (0.11 - 0.75) Upland within 1km 5.20 (0 - 47.23) Average elevation (m) 0.42 (0.09 - 0.68) Distance to upland (m) 629 (35 - 1542) Distance to development (m) 1494 (109 - 3398)

Table 3.2. Set of a priori candidate models to assess relationships between habitat and density of Seaside Sparrows in GBNERR, Jackson Co, MS, USA, 2010 – 2012.

Model Covariates Nest Success : trees and upland serve %tree + distance to upland + % upland within as nest predator sources, while 1 km + elevation elevation affects nest flooding risk

Nest Site : SESP nest preferentially in % intermediate marsh + % high marsh intermediate and high marsh

Predation : sparrows are choosing % tree + distance to upland + % upland sites based on nest predation risk, within 1 km but not flooding risk

Diversity: SESP require a diversity diversity + # of cover classes of habitats for nesting and foraging

Foraging: SESP require open % beach + % saltpan + % low marsh ground and mud for foraging

Previous Research: SESP density is % intermediate marsh + % upland within 1 affected by the same variables found km + distance to development previously to affect occupancy.

% tree + distance to upland + % upland Nest Success + Nest Site within 1 km + elevation + % intermediate marsh + % high marsh

% tree + distance to upland + % upland Nest Success + Foraging within 1 km + elevation + % beach + % saltpan + % low marsh

% intermediate marsh + % high marsh + % Nest Site + Predation tree + distance to upland + % upland within 1 km

diversity + # of classes + % beach + % Diversity + Foraging saltpan + % low marsh

Table 3.3 . Null models for Seaside Sparrow in GBNERR, Jackson Co, MS, 2010 – 2012, with varying key functions and distributions. P = Poisson, NB = negative binomial. All models included an offset for proportion of marsh. Minimum AIC c was 1867.38

Model K ΔAIC c wi Log Likelihood half-normal NB 4 0 1 -929.12 hazard-rate P 4 12.85 0 -935.54 half-normal P 3 21.26 0 -940.99 uniform NB 3 303.06 0 -1081.88 hazard-rate NB 5 308.16 0 -1081.88 uniform P 2 324.45 0 -1093.75

Table 3.4 . Detection models for Seaside Sparrow in GBNERR, Jackson Co, MS, 2010 – 2012. All models included an offset for proportion of marsh. Minimum AIC c was 1851.9.

Model K ΔAIC c wi Log Likelihood obs 11 0 0.69 -910.24 obs + wind 12 2.64 0.18 -909.49 noise + obs 12 3.81 0.1 -910.08 global 13 6.98 0.02 -909.44 noise 5 14.85 0 -927.49 null 4 15.48 0 -929.12 wind + noise 6 17.02 0 -927.19 wind 5 17.03 0 -928.58

Table 3.5 . Availability models for Seaside Sparrow in GBNERR, Jackson Co, MS, USA, 2010 – 2012. Jul = Julian date, min = minutes past sunrise, temp = temperature. All models included an offset for proportion of marsh. Minimum AIC c was 1851.9.

Model K ΔAIC c wi Log Likelihood null 11 0 0.36 -910.24 jul 12 0.35 0.3 -908.35 min 12 3.07 0.08 -909.7 temp 12 3.22 0.07 -909.78 temp 2 13 3.24 0.07 -907.57 jul + min 13 3.46 0.06 -907.68 jul + temp 13 4.38 0.04 -908.14 temp + min 13 7.21 0.01 -909.56 jul + temp + min 14 8.21 0.01 -907.65

Table 3.6. A priori models for Seaside Sparrow ( Ammodramus maritimus ) density in GBNERR, Jackson Co, MS, USA, 2010 – 2012. All models include an offset for proportion of marsh. Minimum AIC c was 1841.92.

Model K ΔAIC c wi Log Likelihood nest success 15 0 0.98 -895.96 null 11 9.98 0.01 -910.24 nest site 13 10.84 0 -906.38 nest site+success 16 11.27 0 -898.77 forage+nestsuccess 17 16.29 0 -898.2 diversity 13 17.12 0 -909.52 foraging 14 18.15 0 -907.63 predators 14 19.2 0 -908.16 prev research 14 21.6 0 -909.36 preds+site 17 23.88 0 -901.99 div+forage 16 25.93 0 -906.1

Figure 3.1 . Land-cover map used to extract modeling variables, including distribution of marsh bird survey points at the Grand Bay National Estuarine Research Reserve, Jackson Co., MS, USA. Points were established in 2005.

Figure 3.2 . Predicted relationship (with 95% confidence intervals) between elevation and density of Seaside Sparrows ( Ammodramus maritimus ) in the GBNERR, Jackson CO, MS, USA, 2010 – 2012 across the range of elevations of survey points. Prediction is based on the top model (λ ~ elev + DTU + up1k + tree, Φ ~ 1, p ~ observer).

Figure 3.3 . Predicted relationship (with 95% confidence intervals) between distance to upland habitat and density of Seaside Sparrows ( Ammodramus maritimus ) in the GBNERR, Jackson CO, MS, USA, 2010 – 2012 across the range of values of survey points. Prediction is based on the top model (λ ~ elev + DTU + up1k + tree, Φ ~ 1, p ~ observer).

Figure 3.4 . Predicted relationship (with 95% confidence intervals) between percent tree coverage within 200 m of a survey point and density of Seaside Sparrows ( Ammodramus maritimus ) in the GBNERR, Jackson CO, MS, USA, 2010 – 2012 across the range of values of survey points. Prediction is based on the top model (λ ~ elev + DTU + up1k + tree, Φ ~ 1, p ~ observer).

Figure 3.5 . Predicted relationship (with 95% confidence intervals) between percent of upland habitat within 1000 m of a survey point and density of Seaside Sparrows ( Ammodramus maritimus ) in the GBNERR, Jackson CO, MS, USA, 2010 – 2012 across the range of values of survey points. Prediction is based on the top model (λ ~ elev + DTU + up1k + tree, Φ ~ 1, p ~ observer).

CHAPTER 4

NEST-LEVEL AND INDIVIDUAL CORRELATES OF EXTRA-PAIR PATERNITY IN THE

SEASIDE SPARROW ( AMMODRAMUS MARITIMUS )3

3 Lehmicke, A. J. J., C. J. Nairn, M. S. Woodrey, and R. J. Cooper. To be submitted to Behavioral Ecology . 94

ABSTRACT

Extra-pair paternity (EPP) is common among socially monogamous passerines, with rates varying widely among species, populations, and individuals. Much research has been focused on hypotheses attempting to explain this variation both among and within species. We collected

EPP data for two breeding seasons from two populations of Seaside Sparrows (SESP) breeding in tidal marshes on the northern coast of the Gulf of Mexico in Mississippi and attempted to explain differences in EPP rates using characteristics of nests and individual adults. Thirty of 146 nestlings from 20 of 63 nests were classified as extra-pair; rates that fall at the low end of published numbers for family Emberizidae. Extra-pair young were not more genetically diverse or heavier than within-pair siblings, suggesting no indirect genetic benefits for females. No male traits predicted whether a male lost paternity at his nest or differentiated between males who were cuckolded and the specific males who cuckolded them. Female mass was the only significant correlate of extra-pair paternity rates; heavier females had fewer extra-pair young.

This result is difficult to explain under the assumption that female SESP are willing participants in extra-pair copulations (EPCs) when combined with the apparent lack of benefits. We suggest that smaller females may be coerced into accepting EPCs through the use or the threat of force, adding more evidence to recent suggestions that EPP may be an example of sexual conflict in some species or populations.

INTRODUCTION

While it was once believed that the majority of bird species were truly monogamous (i.e., a female mated with only one male during each nest attempt; Lack 1968), recent studies fueled by advancements in genetics have shown otherwise. Despite most bird species being socially

monogamous (one female and one male tend a nest together), over 80% of studied species show some level of extra-pair paternity, with only 14% displaying true monogamy (Griffith et al.

2002). Extra-pair paternity (EPP) is the term used when a female mates with a male other than her social mate, resulting in a brood where at least one chick has a different sire than the others.

Two main questions surrounding extra-pair paternity: why does it happen and why do rates vary so widely among and within species?

The benefits to males who engage in extra-pair partnerships are clear. They are able to father more offspring with no obligation for increased parental care, therefore increasing their total reproductive success. While there are some potential costs to males such as aggression from the female’s social male and sexually-transmitted diseases (Sheldon 1993), the genetic benefits of producing additional offspring likely far outweigh the costs. Extra-pair mating does contribute to an increase in variation in male reproductive success in some species (Whittingham and Dunn

2005, Albrecht et al. 2007), suggesting that EPP can directly increase male fitness.

For females, the benefits are less clear. A female who seeks extra-pair copulations

(EPCs) faces the risk of aggression from her social mate (Valera et al. 2003) and the social mate

of her extra-pair partner (Mays and Hopper 2004), without the clear benefit of producing

additional offspring. In addition, extra-pair young may receive less care from the social male,

putting an additional burden on the female (Houston and McNamara 2002, Perlut et al. 2012,

García-Navas et al. 2013). One possibility is that there may not be any benefits for females and

extra-pair copulations are simply forced upon unwilling females (Sorenson 1994, Low 2005).

This may be the case for some species, especially waterfowl (McKinney and Evarts 1998), but in

numerous species it is the female who actively seeks EPCs from extra-pair males (e.g., Sheldon

1994, Gray 1996, Double and Cockburn 2000, Dunn and Whittingham 2007). Because there are

rarely clear direct benefits to the female, females are generally assumed to engage in EPCs because it provides some potential advantage (likely genetic) to her offspring. Two hypotheses related to a genetic benefit have been proposed: the ‘good genes’ hypothesis and the ‘compatible genes’ hypothesis (Mays and Hill 2004).

The good genes hypothesis states that females seek or accept EPCs with high-quality males in the hopes that her offspring will inherit the same fitness-conferring traits. Research based on this hypothesis has centered on comparing physical traits of males to their rates of paternity loss or gain, with the prediction that larger males or males with more conspicuous secondary sexual characteristics will retain more paternity in the nest of their social mate and/or gain more paternity through EPCs. This prediction has been supported in multiple studies (e.g.,

Yezerinac and Weatherhead 1997, Albrecht et al. 2009, Canal et al. 2011) but not supported in many others (e.g. Cordero et al. 1999, Kempenaers et al. 1999, Stewart et al. 2006).

Genetic compatibility depends on the interaction between male and female genotypes and so is a relative rather than an absolute measurement. If a female is more closely related to her social mate than to a random male in the population, she increases the potential heterozygosity of her offspring by mating with an extra-pair male. Heterozygosity is frequently correlated with fitness (Marshall et al. 2003, Reed and Frankham 2003, Seddon et al. 2004), so a female could increase the potential fitness of her offspring by seeking or accepting EPCs. Extra-pair young may also be more immuno-competent than their within-pair half-siblings (Johnsen et al. 2000,

Garvin et al. 2006), suggesting a role for the highly diverse major histocompatibility complex

(Freeman-Gallant 1996, Gohli et al. 2013).

Among bird species that exhibit some level of EPP, the rates (usually measured as either the percentage of young hatched from extra-pair matings or the percentage of nests that contain

extra-pair young) vary widely, ranging from 0.8% in the Dunnock ( Prunella modularis ; Burke et al. 1989) to over 50% in the Superb Fairy-wren ( Malurus cyaneus ; Mulder et al. 1994, Double and Cockburn 2000, Varian Ramos and Webster 2012). Rates can also vary among closely- related species and among populations of the same species (Griffith et al. 2002). Multiple explanations have been offered for this variation in EPP rate, primarily focused on the differences among species (Griffith et al. 2002, Westneat and Stewart 2003), though few have been rigorously tested. Three primary hypotheses center on breeding density, breeding synchrony, and adult genetic diversity.

Breeding density has frequently been suggested as a correlate of EPP frequency among and within bird species, as individuals nesting at higher density have greater access to potential extra-pair mates. Early observations that colonially- and semi-colonially breeding birds had higher rates of EPCs and extra-pair offspring than birds that did not nest in colonies (Møller

1985, Møller and Birkhead 1993) likely led to the development of the breeding density hypothesis. Based on a review of existing studies, Griffith et al. (2002) suggested that breeding density may be better at explaining variation in EPP within and among populations of the same species rather than among higher taxonomic levels. While interspecific variation in EPP rates is not well-explained by breeding density (Westneat and Sherman 1997, Wink and Dyrcz 1999), differences in EPP rates among populations of the same species or individuals in the same population are often affected by local breeding density (Gowaty 1991, Møller and Ninni 1998,

Charmantier and Perret 2004). However, there are also examples where EPP rates were not correlated with breeding density within a species (Rätti et al. 2001, Stewart et al. 2006, Lee et al.

2009).

Variation in breeding synchrony has also been offered as an explanation for inter- and intra-specific differences in EPP rates through two opposing hypotheses. Stutchbury and Morton

(1995) proposed that variation in breeding synchrony could explain some of the variation in EPP rates among species, hypothesizing that females in populations where breeding is highly synchronized have a better opportunity to compare and choose among males and males must expend less energy to obtain extra-pair copulations, thus leading to higher EPP rates. Where high breeding density creates a spatial concentration of fertile females, high breeding synchrony creates a temporal concentration. Much of the empirical support for this idea comes from comparisons among species (Stutchbury and Morton 1995, Carvalho et al. 2006), though studies have found that increased spatial (Chuang et al. 1999) and temporal (Stutchbury et al. 1997b) breeding synchrony within a single population can increase local rates of EPP.

While the breeding synchrony hypothesis proposed by Stutchbury and Morton (1995) has been the subject of much attention and debate (Weatherhead 1997, Stutchbury 1998a, Stutchbury

1998b, Weatherhead and Yezerinac 1998) the reverse hypothesis — that breeding synchrony and

EPP rates are inversely related — was proposed years earlier (Birkhead and Biggins 1987). If all females in an area are fertile at the same time, males must spend time guarding their own mates to the exclusion of seeking out or engaging in extra-pair copulations. Multiple later studies (e.g. van Dongen and Mulder 2009, Cramer et al. 2011, Canal et al. 2012) examining the effect of breeding synchrony have offered support to this opposing hypothesis. Yet other studies have found no relationship between breeding synchrony and EPP rates (e.g., Westneat and Mays

2005, Lee et al. 2009, LaBarbera et al. 2010).

A third hypothesis predicts that rates of EPP are influenced by the level of genetic variation among adult birds in a population (Petrie and Lipsitch 1994). If a population has low

genetic diversity, then a female will be less likely to see a benefit to her offspring by seeking extra-pair matings, as her social mate’s genetic make-up is likely to be similar to that of most other males in the population. If, however, genetic variation is high, then a female increases the potential genetic health of her offspring by seeking extra-pair copulations, since other males are likely to have different genes than her social mate (Petrie and Lipsitch 1994, Westneat and

Stewart 2003). Therefore, the hypothesis predicts that species or populations with higher additive genetic diversity should also have higher rates of EPP (Petrie and Kempenaers 1998).

Studies focused on species that are comprised of both island and mainland populations have given support to the genetic diversity hypothesis. Isolated island populations often have lower rates of EPP than mainland populations (Griffith et al. 1999, Griffith 2000) and often also have lower genetic diversity than their mainland counterparts (Frankham 1997). However, there is no evidence that the specific island populations examined in the studies used by Griffith et al. are actually less genetically diverse than those on the mainland, so alternate explanations for low

EPP in these island populations are possible. At a species level, Petrie et al. (1998) found that species with a higher proportion of polymorphic loci also tended to have higher EPP rates, as would be expected under the genetic diversity hypothesis. However, it is possible that high EPP rates in a species or population can lead to increased genetic diversity (Gohli et al. 2013), thus confounding cause and effect.

The genetic diversity hypothesis can also be taken to the level of individual birds. If a pair is more closely related than would be expected by chance, and if the female can somehow discern this, then it will benefit her offspring if she seeks EPCs, as the offspring of closely related individuals are generally less fit than the offspring of genetically dissimilar parents

(Amos et al. 2001, Hoglund et al. 2002, Hoffman et al. 2004). This aspect of the genetic diversity

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hypothesis is frequently referred to as genetic compatibility, because the expectation is that females will seek EPCs with males whose genes are compatible with hers rather than with males who have unequivocally “good” genes (Neff and Pitcher 2005). If this is the case, females with extra-pair young would be expected to be more genetically similar to her social mate, compared to social pairs without extra-pair young (Bensch et al. 1994). This prediction has been supported by multiple studies (Freeman-Gallant 1996, Blomqvist et al. 2002, Eimes et al. 2005, F οssøy et al. 2008, Brouwer et al. 2011), while others have not found any difference in parental relatedness between pairs with and without extra-pair young (Schmoll et al. 2005, Akçay and Roughgarden

2007, Augustin et al. 2007, Huyvaert and Parker 2010) and some have even found the opposite

— that extra-pair partners are more closely related to one another than are social mates — suggesting a role for kin selection in some species (Kleven et al. 2005, Wang and Lu 2011).

Seaside Sparrows (SESP; Ammodramus maritimus ) present a unique opportunity to examine the breeding density and breeding synchrony hypotheses at both a within-species and within-population level. Seaside Sparrows are socially monogamous and breeding densities vary widely both among and within marshes, ranging from 0.3 to 20.0 males/ha among Atlantic coast marshes (Post and Greenlaw 2009) and from 0.7 to 13.6 birds/ha within a single Gulf Coast marsh (Chapter 3). This range of densities within a single marsh complex provides an opportunity to test the hypothesis that rates of EPP increase with increasing density among pockets of varying breeding density within the same population.

Across a wider geographic range, SESP also vary in their degree of breeding synchrony.

Among many Atlantic Coast SESP populations, high spring tides frequently flood most of the nests in a population at the same time, forcing breeding to become highly synchronous as there is just enough time to incubate and fledge a brood between successive spring flood tides (Marshall

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and Reinert 1990). The northern Gulf coast, conversely, does not experience such dramatic and predictable high tides. The majority of nest losses there are caused by predation (Chapter 2, Post et al. 1983), leading to much more asynchronous breeding within a population. Therefore, if Gulf and Atlantic coast populations are found to have significantly different rates of EPP, some of the variation may be attributable to differences in breeding synchrony.

The one published study examining extra-pair paternity in Seaside Sparrows (Hill and

Post 2005), conducted in North Carolina, found that EPP was less frequent than expected when considering rates found in other sparrow species. Five of 47 chicks (10.6%) from three of 18 broods (16.7%) had fathers other than the female’s social mate. This is considerably lower than published rates for other sparrow species including 23% for Savannah Sparrows ( Passerculus sandwichensis ; Freeman-Gallant 1996) and 34-38% for White-crowned Sparrows ( Zonotrichia leucophrys ; Sherman and Morton 1988). Hill and Post (2005) suggested that high inter-annual variability in resource availability in salt marshes may encourage females to reject extra-pair advances in order to retain help in rearing young from her social mate. However, female SESP can successfully rear some young to independence without male assistance (Greenlaw 1985) so while male assistance is helpful, it is not necessary. Hill and Post collected samples from a population that nested in irregularly-flooded high marsh, so it likely did not experience the level of breeding synchrony seen in more regularly flooded Atlantic Coast marshes which could have depressed EPP rates as well.

While males in our study populations on the Gulf coast did not aggressively mate-guard or have many aggressive male-male interactions in general, they did often closely follow their female while she was building her nest. This may serve to deter other males from attempting

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extra-pair copulations during the female’s fertile phase or prevent the female from sneaking onto a neighboring male’s territory to solicit EPCs.

Our study sites split well into high- and low-density sites, allowing us to examine whether local population density affects rates of EPP. Females in dense breeding areas often foraged and collected nesting material on common activity spaces far from their nest sites, while those in less dense areas tended to stay within one all-purpose territory (Post 1974). We hypothesized that the high-density sites would have a higher rate of EPP due to the greater number of potential extra-pair mates and increased chances for interaction away from the core territory area. Breeding synchrony may also vary among our study sites. Nest failure rates varied among our sites, potentially leading to different levels of breeding synchrony. Because male

Seaside Sparrows follow their mates during the nest-building period, we hypothesized that increased synchrony would lead to decreased levels of EPP. We also estimated density and synchrony for each individual nest, and hypothesized the same relationships — that EPP would increase with increasing density and decrease with increasing synchrony. Finally, we hypothesized that extra-pair young would be more diverse than their within-pair half-siblings, supporting a role for indirect genetic benefits in explaining why females engage in EPCs.

METHODS

Study sites

We collected Seaside Sparrow blood samples on seven study plots spanning two marsh complexes on the southeastern Mississippi Gulf Coast: the Grand Bay National Estuarine

Research Reserve and Wildlife Refuge (hereafter, GBNERR; 30º19'N, 88º27'W) and the

Pasagoula River Marsh Coastal Preserve (hereafter, Pascagoula; 30º23'N, 88º34'W). The

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GBNERR is composed of approximately 18,400 acres of wetlands, pine savannas, and terrestrial habitats. We worked on three study plots within the GBNERR, two with a high density of breeding SESP and one with a low density. The Pascagoula is an 11,500 acre site of lower salinity marsh, grading into freshwater riverine marsh in the northern reaches. We had four plots at the Pascagoula; one with high breeding density and three with low breeding density.

Field methods

During the breeding seasons of 2011 and 2012 we found and monitored SESP nests across our study plots. Once a nest hatched, we attempted to capture both the female and her social mate if they had not been previously banded. After capturing the adults, we fitted each bird with a unique combination of three color-bands and an aluminum USGS band with a unique

9-digit ID number. We took standard weight and morphometric measurements and drew a small

(< 40 μl) blood sample from the brachial vein. Blood samples were immediately put on ice (in

2011) or transferred to a tube containing a buffer solution (in 2012).

We determined the identity of the social male by observing which male was carrying food to the nest once it hatched. Once the male was banded, we would re-visit the nest at some point prior to fledging to confirm that we had captured and sampled the correct male. During

three years of nest observations, we never observed more than one male feeding the same

nestlings so we felt comfortable designating the male feeding the nest as the female’s sole social

mate. We collected blood samples from nestlings once they reached five or six days post-hatch.

At this age the nestlings weigh between 8 and 16 grams, heavier than the adults of many

warblers and other small passerines. Nestlings were also weighed and fitted with USGS

aluminum bands.

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Genetic analysis

We isolated genomic DNA from the blood samples using Qiagen® DNEasy Blood &

Tissue Kits (Qiagen, Valencia, CA) and diluted each extraction to 10 ng/μl DNA. From each

sample, we amplified alleles from nine SESP-specific polymorphic microsatellite loci previously

developed for this project (Am02, Am03, Am08, Am12, Am15, Am18, Am20, Am27, and

Am32; Lehmicke et al. 2012). Assuming full genotypes for each nestling, mother, and social

father, program CERVUS calculated the combined non-exclusion probability of paternity

assignment of these nine loci to be 2.40 × 10 -5. All loci were in Hardy-Weinberg equilibrium.

PCR was carried out in 10 μL reactions containing 10 mM Tris pH 8.4, 50 mM KCl, 0.5

μM of a GTTT ‘pig-tailed’ locus-specific primer, 0.05 μM of a CAG-

(CAGTCGGGCGTCATCA) or M13-(GGAAACAGCTATGACCAT) tagged locus-specific primer, 0.45 μM fluorescently labeled CAG or M13 tag (Boutin-Ganache 2001), 1.5 mM MgCl 2,

0.125 mM dNTPs, 0.5 U Taq polymerase, and 20 ng template DNA. Fluorescently labeled tags included FAM (Integrated DNA Technologies), VIC, PET, or NED (Applied Biosystems). PCR cycling conditions followed a ‘touchdown’ protocol (Don 1991): 95 °C for 5 min, 20 cycles at 95

°C for 30 s, 65 °C - 0.5 °C per cycle for 30 s and 72 °C for 1 min, then 30 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 1 min, and a final, 10-min-extension at 72 °C. GeneScanTM 500

LIZ® size standard (Applied Biosystems) was added and amplicon sizes were determined using an Applied Biosystems 3730xl DNA Analyzer. Allele sizes were scored using program

GeneMarker, with visual inspection and confirmation of automatically generated allele calls.

Statistical analysis

We used program CERVUS 3.0.3 (Kalinowski et al. 2007) to assess parentage of social males. CERVUS allows testing of both males and nestlings with incomplete genotypes and

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calculates the relative likelihood that a certain male is the genetic father of a nestling, given information from the population as a whole. Before running a paternity analysis, CERVUS requires a parentage simulation, based on observed allele frequencies, that calculates a 95% and

80% critical LOD score for paternity assignment. The LOD score for a potential genetic male is calculated as the natural logarithm of the likelihood that the proposed father is the true genetic sire of the given offspring divided by the likelihood that proposed father is not related to the offspring. Thus, a positive LOD score indicates that the candidate male is more likely to be the father than a random male, while a negative LOD score suggests that the candidate male is less likely to be the father than a random male from the same population.

In order to address hypotheses regarding genetic diversity of both parents and offspring, we calculated three separate measures of individual diversity. Homozygosity by loci (HL;

Aparicio et al. 2006) is a measure of individual heterozygosity that takes into account the population-level expected heterozygosity of each locus, thus accounting for both the allelic richness and allelic distribution of each individual locus. Standardized heterozygosity (SH;

Coltman et al. 1999) calculates the proportion of loci for which an individual is heterozygous and divides it by the mean heterozygosity of all of the loci which were typed in that individual. This standardizes the heterozygosity of each individual by accounting for the fact that not all individuals are typed at the same number of loci. Finally, we calculated internal relatedness (IR;

Amos et al. 2001), which estimates an individual’s level of inbreeding by comparing the extent of allele sharing between an individual’s two haploid genomes to the population-level allele frequency. Negative IR values suggest outbreeding, values around zero suggest a ‘normal’ level of parental relatedness, and positive values suggest inbreeding.

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We calculated a breeding synchrony index for each nest included in the genetic analyses, modifying the formula developed by Björklund and Westman (1986) (and corrected by

Kempenaers 1993) to represent a nest-level measurement rather than a population-level metric.

We also calculated a synchrony index for each site for each year by averaging the nest-level synchrony of all of the nests within each site during each breeding season.

To test the hypothesis that extra-pair young are more heterozygous than their within-pair half-siblings, we created several linear mixed-effect models using each of the three measures of individual genetic diversity as response variables, with the parentage status of the nestling

(within-pair from an all within-pair nest, within-pair from a mixed nest, or extra-pair) as the explanatory variable of interest. We included random effects of nest, female, and male in each model because nestlings from the same nest or with the same mother or social father were not genetically independent from one another.

For each nest included in the genetic analysis, we calculated a number of covariates that might affect whether or not that nest contained extra-pair young, based on our literature review

(Table 4.1). Following Stewart et al. (2006), we treated each individual nestling as the unit of analysis in a general linear model with binomial error and a logit link, because being extra-pair is a binomial event that occurs at the level of the nestling. Because nestlings are grouped within a nest, a mother, and a social male, we initially included nest, female identity, and social male identity as random effects in all models.

To test variables associated with individual males and females (body size and individual

genetic diversity variables; Table 4.2), we combined all nesting attempts for each individual and

used EP young / total young as the response variable of a binomial logistic model. We chose to

use mass instead of a scaled condition index because for both sexes the two measures were

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extremely correlated (females: r = 0.999, males: r = 0.995) and we felt that mass coefficients

would be more easily interpretable. Further, total mass has been found to be as good an indicator

of fat mass (and thus body condition) as other morphometric characters and condition indices

(Labocha and Hayes 2012). We used either the ‘lmer’ or ‘glmer’ function in package lme4

(Bates et al. 2013) in Program R (R Core Team 2013) for all mixed-effect models.

We evaluated all models using an information-theoretic approach (Burnham and

Anderson 2011) which determined the support for each candidate model given our data. We

ranked models based on AIC score with a small-sample correction (AIC c; Akaike 1973, Hurvich and Tsai 1989). If necessary, within each set we averaged the parameter estimates of all models within two AIC c units of the top model and used these average estimates for all further

interpretation. For the nestling-based models, we calculated odds ratios (OR) for each parameter

included in the confidence set.

RESULTS

General Results

We extracted DNA from and genotyped 307 individuals, including 201 nestlings from 66 different nests. We obtained genotypes for both parents (mother and social mate) of 59 of those nests, and several adults that were not associated with a known nest, for a total of 57 females and

49 males. Some adults were associated with more than one nest over the course of the study. Of the 66 nests from which we obtained blood samples, 43 were found at GBNERR and 23 were from the Pascagoula. Fifty-five were from densely populated sites and eleven were from sparsely populated sites. This disparity represents a true difference in the number of nests from which we

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were able to collect blood samples; we genotyped every available sample from sparsely populated sites. We only included nests where we had DNA from both adults in all analyses.

Parentage Assignment

Based on the parentage simulation in program CERVUS, the critical LOD value for 95%

confidence in male parentage assignment, given a known mother, was -2.50, which categorized

all offspring with more than one loci mismatch from their social fathers as extra-pair. We were

tolerant of a single locus mismatch because about 6% of offspring had a single-locus mismatch

with their known mother, suggesting that either small genotyping errors or single-repeat shift

mutations were somewhat common in our data.

Using this critical LOD value, 30/194 offspring were classified as extra-pair (15.46%,

95% CI: 11.1 – 21.2). Of the 63 nests from which nestlings were genotyped, 20 contained at least

one extra-pair young (31.75%, 95% CI: 21.6 – 44.0). Eleven of the mixed-paternity nests had

only a single extra-pair offspring and one was completely extra-pair. Thirteen of the extra-pair

nestlings were assigned to other males in the population, all from adjacent territories. Proportion

of extra-pair young did not differ among years (2011: 15/86, 2012: 15/109; P = 0.612) so both years were lumped for all analyses.

Statistical Analyses

The average HL across all genotyped individuals (n = 307) was 0.200 (SD = 0.128, range: 0 – 0.548) and the average SH was 1.00 (SD = 0.168, range: 0.573 – 1.289). The average

IR was 0.039 (SD: 0.154, range: -0.251 – 0.461). For each measure of diversity, extra-pair young did not differ from either their within-pair half-siblings or young from fully within-pair nests.

Before comparing nestling-based models, we examined which of our three random effects (nest, female, and male) influenced variation in EPP rates. We found that female identity

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had the strongest influence, with male identity having a lesser influence and nest identity having no influence (respective variances: 1.549, 0.015, 0). We therefore included random effects of female and male, but not nest, in all nestling models. The top model for predicting the extra-pair status of a nestling included first egg date, but the null model received almost identical support

(Table 4.3). The model with parental relatedness was also within two AIC c units. The probability

of being extra-pair tended to increase with the date of clutch initiation (a seven-day increase in

first egg date made a nestling 1.14 times more likely to be extra-pair), but the 95% confidence

interval of the odds ratio included one (0.96 – 1.34). The odds-ratio confidence interval for parental relatedness broadly overlapped one so the direction of the relationship was not clear

(95% CI: 0.77 – 2.36).

For the female-only models, female mass plus HL was the top model for predicting the proportion of her young that were extra-pair (Table 4.4). The model with mass by itself was within two AIC c units and was included in model-averaged predictions. The proportion of a female’s young that were extra-pair decreased as her mass increased (β = -0.462, 95% CI -0.869

– -0.055). Homozygosity by locus seemed to be positively related to proportion of extra-pair young, but the confidence interval around the estimate slightly overlapped zero (Table 4.5). For male-only models, the null model received the most support (Table 4.6) and no covariates had a clear relationship with the proportion of extra-pair young in the nests of a male’s social mate.

DISCUSSION

We found that 15.5% of nestlings resulted from EPCs and 31.8% of nests contained at least one extra-pair nestling. This rate is slightly higher than the average level found in all temperate passerines (11.3% of young and 17.6% of broods; Griffith et al. 2002, Macedo et al.

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2008) and slightly higher than the rate found in the one published study on SESP (10.6% of nestlings and 16.7% of broods; Hill and Post 2005). However, EPP rates in our population were still lower than most published rates in other members of family Emberizidae (Old World buntings and New World sparrows) — in a review by Bonier et al. (2014), only 5 out of 24 studies on EPP in emberizids found a lower proportion of broods with extra-pair young. The same review found that emberizids breeding at lower latitudes (< 45° N) had lower EPP rates. At

30° N, our study populations certainly qualify as “low latitude” and so it may not be surprising that our EPP rate was at the low end for the family. However, the explanation for decreased EPP at lower latitudes is linked to breeding synchrony, which we did not find to be a significant correlate of EPP rates in our study populations.

Female Seaside Sparrows in our populations did not appear to gain any indirect genetic benefit from EPCs. Extra-pair young were not more diverse than either their within-pair half siblings or nestlings from completely within-pair nests, nor did mass differ between extra- and within-pair young. Nests with unhatched eggs were not more likely to contain extra-pair young, so it also does not appear that females used EPCs as a form of insurance against social mate infertility. Of the 10 cases where we had genotyped both the social male and the extra-pair male from the same nest, there were no differences in size or genetic diversity between cuckolded males and the extra-pair partner, so it does not appear that females were choosing to seek or accept EPCs from males who were superior to their social mates.

We found no relationship between EPP and any of our nest-level covariates. Extra-pair paternity was not related to breeding density or nest synchrony at either the site level or the

individual nest level. This conclusion adds to many previous studies that have found no

relationship between rates of EPP and either density (e.g. Dunn et al. 1994, Rätti et al. 2001,

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Stewart et al. 2006, Lee et al. 2009) or synchrony (e.g. Weatherhead 1997, Westneat and Mays

2005, Lee et al. 2009, LaBarbera et al. 2010). On most of our study plots, both males and females would make distant foraging trips several hundred meters from the immediate nest area

(AJL, pers. obs.). If EPCs occur during these trips, then both the density of breeding birds and the synchrony of other females close to the nest may not be the only relevant measurements. If females could be tracked to feeding areas, perhaps the density of other birds foraging there may be a more realistic indicator of the number of potential extra-pair mates that a female comes into contact with. If most EPCs are obtained away from the nest area, then local breeding synchrony may have little influence. While we did not sample every male at any of our sites, we were only able to assign the extra-pair father to 13 of the 30 extra-pair young, suggesting that the other

EPCs may have occurred far from the nests. Similarly, Hill and Post (2005), who were more confident that they had sampled every male within their study area, could not assign any of their extra-pair young to a true genetic father, suggesting that extra-pair males were either floaters or from distant territories, neither of which would be represented by measures of local breeding density or synchrony.

Female mass was the only covariate that explained variation in EPP in our populations.

When looking at the effect of individual covariates on the proportion of that individual’s young that were extra-pair, lighter females had a greater proportion of extra-pair young. Heavier males did not lose more paternity than lighter males, suggesting that female mass alone was the important covariate rather than female size relative to her social mate. Female phenotypic covariates are rarely considered on their own or found to be meaningful when modeling EPP (but see Stewart et al. 2006, Rosivall et al. 2009, Brekke et al. 2013), while male body size and condition are often included as modeling covariates. This discrepancy is likely because of wide

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acceptance that, in passerines, EPP is driven by female choice of extra-pair mates (Petrie and

Kempenaers 1998, Westneat and Stewart 2003), which has led to a large body of research focused on female selection of male extra-pair partners based on physical traits (Table 3 in

Griffith et al. 2002 lists 23 such studies). Conversely, few hypotheses have been expressed regarding the relationship between female body size or condition and EPP rates. Female age has been more frequently considered (Stutchbury et al. 1997a, Dietrich et al. 2004, Bouwman and

Komdeur 2005), but generally as a measure of breeding experience rather than as a correlate of any physical characteristics.

The question then becomes why lighter Seaside Sparrow females have a higher proportion of extra-pair young. One of the hypothesized detriments to females of engaging in

EPCs is reduced offspring care by her social mate (Whittingham et al. 1992, Houston and

McNamara 2002, Sheldon 2002). Following this logic, the ‘constrained female’ hypothesis predicts that females needing more help from their social mates to raise young should be less likely to engage in EPCs in order to avoid the risk of lost paternal care (Mulder et al. 1994,

Gowaty 1996). If female mass is assumed to correlate with quality or ability to raise offspring

(Blomqvist et al. 1997), lighter females should be less likely to engage in EPCs, as they are more likely to require male help to successfully raise nestlings. However, female SESP can fledge offspring after removal of the social male (Greenlaw 1985, AJL pers. obs.) so the constrained female hypothesis may not be relevant for this species.

Among passerines, it is widely accepted that females may actively seek and/or choose to accept EPCs, rather than being passive or unwilling partners (Bouwman and Komdeur 2005,

Olsen et al. 2008), and therefore they must gain some sort of benefit, either direct (e.g., greater access to food resources) or indirect (e.g., increased offspring heterozygosity) from engaging in

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EPCs (Griffith et al. 2002). If this is true in our population, lighter females must be gaining some greater benefit than heavier females from having extra-pair offspring. In some species, females gain material benefits as a result of EPCs, usually access to the extra-pair male’s territory for foraging (e.g. Gray 1997). However, Seaside Sparrow females often forage on distant communal feeding grounds (Post 1974) so access to a neighboring male’s territory would not be likely to increase access to food resources. A female’s mass was not related to her own genetic diversity or to her offspring’s genetic diversity, nor were extra-pair nestlings heavier or more genetically diverse than within-pair young, so it is not clear what benefits might be gained by low mass females from engaging in EPCs.

An alternative explanation for the negative relationship between female size and proportion of extra-pair offspring is that lighter females are less able to resist advances from extra-pair males or they accept EPCs in order to avoid the potential costs of resistance (Clutton-

Brock and Parker 1995), which may be higher for smaller females. While the lack of an intromittent organ in passerines (Briskie and Montgomerie 1997) may make forced extra-pair copulations (FEPC) difficult, there are examples among passerines of clear female resistance to

(or passive acceptance of) male extra-pair copulation attempts (Westneat 1992, Venier et al.

1993, Castro et al. 1996, Low 2005). If male Seaside Sparrows attempt FEPC, lighter females may be less able or less likely to resist such attempts. In a similar vein, among Stitchbirds

(Notiomystis cincta ), a New Zealand endemic passerine known for its high rate of FEPCs,

Brekke et al. (2013) found that lower-quality females had higher rates of extra-pair offspring than high-quality females. The authors interpreted this as an inability of low-quality females to resist unwanted male advances.

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Our results add to a growing body of evidence that, in some species, EPP may actually be the result of a male reproductive strategy that results in sexual conflict between males and females and may not provide any benefit, material or genetic, to females (Westneat and Stewart

2003, Arnqvist and Kirkpatrick 2005, Akçay and Roughgarden 2007). More research on Seaside

Sparrows, focused on field observations of mating behavior, is needed to determine whether

EPCs are in fact forced upon unwilling females by force or the threat of force. Collecting behavioral data along with genetic data may be necessary if we truly want to understand the correlates of extra-pair paternity within a species or population.

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Table 4.1. Predictor variables for each nest used in binomial logistic regression models of per-nest presence and proportion of extra-pair young (n = 55)

Covariate Description Mean (Range)

Eggs Max number of eggs in the nest 3.42 (1 - 4)

Unhatched Number of unhatched eggs 0.18 (0 - 2)

FirstEgg Julian date of nest's first egg 113.1 (77 - 165)

SyncInd Nest-specific synchrony index 0.05 (0 – 0.25)

Average per-nest synchrony index for each SiteYrSync 0.06 (0.04 – 0.12) nest's site/year combination

Density High- or low-density site 0/1

NearNestSim Distance to nearest simultaneously active nest 36.41 (9 – 130)

Nests50Sim Simultaneously active nests within 50 m 2.46 (0 – 8)

Nests100Sim Simultaneously active nests within 100 m 6.22 (0 – 13)

Distance to nearest nest at any point during the NearNestAll 21.47 (3.00 – 97.92) breeding season

Number of females who had a nest within 50 m Nests50All 4.36 (0 – 10) at any point during the breeding season

Number of females who had a nest within 100 Nests100All 9.982 (2 – 19) m at any point during the breeding season

P.Relate Relatedness of social parents 0.23 (0.06 – 0.61)

Difference between social male and female MassDiff 2.09 (-2 – 5) mass

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Table 4.2. Predictor variables used in binomial logistic regression models of per-adult proportion of extra-pair young (female n = 48, male n = 39)

Covariate Description Female Mean (Range) Male Mean (Range)

Mass Adult mass 19.10 (16 - 22) 21.23 (18-24)

Tarsus Tarsus length 22.58 (21.3 - 23.9) 23.41 (21.3 - 25.0)

Wing Wing length 57.50 (54 - 63) 60.64 (56 - 65)

Bill volume, assuming a conical shape Bill Volume 82.90 (69.67 - 110.90) 88.04 (63.99 - 107.30) (Olsen et al. 2013) Internal relatedness, a measure of IR 0.015 (-0.24 - 0.46) -0.015 (-0.24 - 0.31) inbreeding Standardized heterozygosity, a SH 1.03 (0.57 - 1.29) 1.06 (0.72 - 1.29) measure of individual genetic diversity Homozygosity by loci, a measure of HL 0.18 (0 - 0.55) 0.16 (0 - 0.46) individual genetic diversity Difference between individual mass MassDiff and the average mass of males at the n/a -0.30 (-2.90 - 2.67) same site Difference between individual tarsus TarsDiff length and the average tarsus length of n/a 0.06 (-1.95 - 1.20) males at the same site Difference between individual wing WingDiff length and the average wing length of n/a 0.07 (-3.90 - 4.24) males at the same site

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Table 4.3. Models predicting whether or not a nestling is extra-pair (n = 171). Minimum AIC c was 162.88.

Model K ΔAIC c wi Log likelihood Time 4 0 0.35 -77.32 Null 3 0.22 0.32 -78.48 Genetics 4 1.28 0.19 -77.96 Eggs 5 3.60 0.06 -78.06 Synchrony 5 4.08 0.05 -78.30 Density 6 4.17 0.04 -77.27 Global 11 12.21 0 -75.72

Table 4. 4. Models predicting the proportion of extra-pair young for each female (n = 48). Minimum AIC c was 105.79. HL = homozygosity by locus, IR = internal relatedness, SH = standardized heterozygosity.

Model K ΔAIC c wi Log likelihood Mass + HL 3 0 0.35 -49.62 Mass 2 0.46 0.28 -50.99 Null 1 2.91 0.08 -53.3 Tarsus 2 3.83 0.05 -52.67 Bill Volume 2 3.95 0.05 -52.73 Global 5 4.04 0.05 -49.2 HL 2 4.25 0.04 -52.88 IR 2 4.64 0.03 -53.08 SH 2 4.80 0.03 -53.16 Wing 2 4.93 0.03 -53.22

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Table 4. 5. Model-averaged parameter estimates and 95% lower (LCL) and upper (UCL) confidence limits for models predicting the proportion of extra-pair young for each female. HL = homozygosity by locus.

Parameter Estimate SE LCL UCL Intercept 6.763 3.791 Female Mass -0.462 0.208 -0.869 -0.055 HL 2.906 1.760 -0.545 6.356

Table 4.6. Models predicting the proportion of extra-pair young for each male (n = 39). A total of 17 models were run with a minimum AIC c of 97.09. Only models with wi ≥ 0.05 are shown. HL = homozygosity by locus, IR = internal relatedness, SH = standardized heterozygosity.

Model K ΔAIC c wi Log likelihood Null 1 0 0.18 -47.49 HL 2 0.96 0.11 -46.86 IR 2 1.31 0.09 -47.03 SH 2 1.61 0.08 -47.19 Wing 2 1.83 0.07 -47.29 Tarsus 2 1.83 0.07 -47.29 WingDiff 2 1.92 0.07 -47.34 Bill Volume 2 2.20 0.06 -47.48 TarsDiff 2 2.21 0.06 -47.48 MassDiff 2 2.21 0.06 -47.48 Mass 2 2.22 0.06 -47.49

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CHAPTER 5

SUMMARY AND CONCLUSIONS

This dissertation focused on the demography of the Seaside Sparrow (SESP;

Ammodramus maritimus ) in two northern Gulf of Mexico tidal salt marshes. Seaside Sparrows

are North American tidal marsh endemics and may serve as indicators of tidal marsh health.

Coastal tidal marsh is globally limited in extent and faces current and future threats, especially

from climate change-induced sea level rise (SLR). Increasing our basic knowledge of SESP and

the relationships of the species with fine-scale habitat characteristics makes the species more

valuable as an indicator and refines our ability to make predictions about future distributions and population trends.

Demographic information, particularly on survival and reproduction, must be considered

when defining high-quality habitat for a species (Jones 2001). Using density alone to reflect

habitat quality can be uninformative or even misleading in some situations (Van Horne 1983,

Bock and Jones 2004); for example, if young animals are forced into areas of marginal habitat by

dominant adults, density could actually be higher in poor-quality habitats. In Chapter 2 we

examined factors affecting both nest site selection and nest survival of SESP in two marsh

complexes on the Gulf coast of Mississippi. If nest-sites are different from areas that are

available but not chosen, this suggests that females are perceiving those areas as better nest sites.

Nest survival must also be considered, as females may not actually choose nest sites that improve

nest survival or different factors may influence nest-site choice and nest survival.

133

While the same variables were not the most important for nest-site selection and nest survival, together the results point to the importance of predation both in nest-site selection and nest survival. Nest sites had more surrounding ground cover than random sites, suggesting that females are choosing sites with denser nearby vegetation. Similarly, successful nests had more stems around the nest than failed nests. Higher survival with increasing nest concealment suggests that predator avoidance is a major reproductive strategy in our study populations. This conclusion contrasts with studies on SESP in Atlantic coast marshes which often find that tidal flooding drives nest placement and survival (Post et al. 1983, Marshall and Reinert 1990) and highlights the importance of using demographic data collected from local populations to inform local management plans.

We also found a significant relationship between nest survival and nesting density. Nest survival increased as distance to the closest simultaneously active nest decreased, suggesting that nests in more densely populated areas had higher survival. This contradicts the results of many other avian studies that have suggested a role for density-dependent predation (Barber et al.

2001, Aguilar et al. 2008). Density-dependent predation is generally associated with predators that hunt visually (Tinbergen et al. 1967), so the lack of avian predators near our study sites may explain our unusual results. More importantly, the positive relationship we observed between density and nest survival suggests that using density as an indicator of high-quality SESP habitat may be an appropriate strategy in northern Gulf coast tidal marsh.

In Chapter 3, we provided the first information on the effect of local land-cover variables on SESP density in a Gulf coast tidal marsh. While SESP occupancy has been considered in the same area (Rush et al. 2009), occupancy and abundance provide different information about a species and should not be treated as interchangeable. Occupancy estimates reflect broad-scale

134

habitat selection and, if repeated over time, can be used to examine patch-level extinctions and colonizations. Abundance and density estimates provide information on finer-scale habitat and resource selection and can be used to monitor population trends. Abundance may be the only meaningful metric in areas where the species of interest nears 100% occupancy, as do SESP in the Grand Bay National Estuarine Research Reserve (GBNERR). The results of this chapter suggest that SESP density is greater in areas of higher elevation and lower salinity within the

GBNERR, possibly due to a preference for the associated plant community.

The negative association between salinity and SESP density within the GBNERR seems to contradict Rush et al. (2009), who found that SESP occupancy was positively associated with salinity across a broader range of Gulf of Mexico coastal marsh. We believe that this reflects the differing scales of the two studies. When areas of nearly fresh marsh are included in survey efforts, SESP presence will be associated with salinity on a broader scale. However, if surveys only focus on areas occupied by SESP, there is more likely to be an intermediate optimal salinity. These results have important implications for SESP management in the face of future

SLR. Rising sea levels will cause marsh surface elevation to decline (Kirwan et al. 2010, Glick et al. 2013) and will increase the salinity of coastal tidal marsh, leading to changes in the plant community (Spalding and Hester 2007). Inferences drawn from broad-scale surveys may lead to the conclusion that an increase in salinity will be beneficial for SESP, but our finer-scale study produces the opposite prediction, at least in areas where brackish marsh will be unable to migrate

inland or upriver.

In Chapter 4, I examined nest-level and individual correlates of extra-pair paternity (EPP)

in my study populations. Offspring fathered outside of the pair-bond are common among socially

monogamous bird species (Griffith et al. 2002). For males the benefits seem clear — extra-pair

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copulations (EPCs) potentially increase the number of offspring a male leaves without increasing his obligation for parental care. Females do not produce extra offspring by engaging in EPCs so the benefits are not as obvious. However, because forced copulation is assumed to be nearly impossible in birds (such as passerines) that lack an intromittent organ (Gowaty and Buschhaus

1998), females are assumed to be active participants in EPCs and so must gain some advantage.

The primary hypotheses focus on genetic benefits that a female may gain for her offspring, making the offspring more fit and more likely to pass on the genes of their mother (Griffith et al.

2002, Westneat and Stewart 2003).

Recently, several papers have questioned whether female birds actually gain genetic benefits from EPCs that are great enough to counteract the potential costs (primarily a loss of paternal care from the social male; Westneat and Stewart 2003, Akçay and Roughgarden 2007,

Eliassen and Kokko 2008). A lack of apparent benefits for females suggests that, in some instances, EPCs may actually be a case of sexual conflict where the interests of males and females are not aligned. My results support a possible role for sexual conflict in EPCs within our populations. Females did not seem to gain any genetic benefits from EPCs, as extra-pair young were neither more genetically diverse nor heavier than their within-pair half-siblings. In addition, female mass was the only factor that explained any nest- or individual-level variation in EPP rates — lighter females had higher rates of extra-pair young in their nest. This suggests that low- quality females are more likely to engage in EPCs, which is difficult to explain if the females are active and willing participants.

Female phenotypic characteristics are rarely included in hypotheses explaining aspects of

EPP so it is difficult to compare this result to previous research. When female mass or quality have been explicitly considered, most studies have found either no effect (e.g., (Kempenaers et

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al. 1999, Stewart et al. 2006) or that older, presumably higher-quality females have a higher proportion of extra-pair offspring (Dietrich et al. 2004, Bouwman and Komdeur 2005). However, among Stitchbirds ( Notiomystis cincta ) a passerine species where forced EPCs are well- documented and common, high-quality females have fewer extra-pair offspring, hypothetically because of an increased ability to resist unwanted male EPC attempts (Brekke et al. 2013). The similar results of my EPP models suggest that, in SESP, males may use force or the threat of force to coerce unwilling females to accept EPCs.

Tidal marshes along the Gulf of Mexico are almost certain to undergo significant changes in the future (Forbes and Dunton 2006, Day et al. 2008), which will in turn affect the species that depend on the habitat. A more thorough understanding of factors affecting the distribution and life history of tidal marsh obligate species will help us to better predict the potential effects of a changing climate. This dissertation is a first step in obtaining such information for SESP, but robust inferences will require more research across a wider range of northern Gulf of Mexico coastal marshes. It is possible that habitat relationships, and therefore species responses, will vary from marsh to marsh and these small-scale variations must be considered when planning for the future of Gulf of Mexico tidal marshes.

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