Habitat Associations of Breeding Marsh Birds Within The

HABITAT ASSOCIATIONS OF BREEDING MARSH BIRDS WITHIN THE

GLACIATED REGION OF OHIO, USA

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Karen Lynn Willard

Graduate Program in Environment and Natural Resources

The Ohio State University

2011

Master's Examination Committee:

Dr. Paul G. Rodewald, Advisor

Dr. Robert J. Gates, Advisor

Copyrighted by

Karen Lynn Willard

2011

Abstract

Many North American marsh birds have experienced significant population declines over the past 40 years, particularly in the northern portion of their ranges.

Wetland habitat loss and degradation are likely factors contributing to population declines. Validation and refinement of marsh bird habitat models will benefit conservation efforts as wetland loss continues in the United States. The purpose of my research was to estimate occupancy probability of marsh birds in the glaciated region of

Ohio to help set reasonable conservation goals and to determine local habitat associations and marsh bird use of restored wetlands to achieve these goals.

I sampled a wide variety of wetland habitat types to conduct marsh bird surveys

Wetlands were randomly selected from National Wetlands Inventory data using a generalized random-tessellation stratified survey design. A second sample was drawn from wetlands restored through the Ohio Wetland Reserve Program. Surveys followed the Standardized North American Marsh Bird Monitoring Protocols, which includes a passive listening and a call-broadcast portion. Observers recorded the presence of nine focal species during May through early July, 2009 and 2010. Habitat data including interspersion, percent areal cover of seven vegetation classes, dominant plant species (≥ 20% of cover type) and water depth were collected within a 100 m radius plot after each survey. The percent areal cover of woodland, cropland, and wetland

within a 500 m radius buffer around each survey wetland were determined using ArcGIS.

I used occupancy modeling in program PRESENCE to estimate occupancy adjusted for

detection probability and to determine habitat associations. I ranked a priori candidate

models using an information theoretic approach and estimated model parameters by

model averaging.

Pied-billed Grebe (Podilymbus podiceps) were common in the Lake Erie marshes

of Ottawa, Lucas, and Sandusky Counties but uncommon in other locations throughout

the glaciated region. Wetland occupancy by Pied-billed Grebe was positively associated

with wetland size and mean water depth. Least Bittern (Ixobrychus exilis) occupancy

was modeled in 2010 only and was positively associated with mean water depth,

persistent emergent vegetation, and negatively associated with percent surrounding

woodland. Virginia Rail (Rallus limicola) were more likely to occupy sites with more

surrounding woodland and persistent emergent vegetation. Sora (Porzana carolina)

occupancy was positively associated with surrounding wetland cover and persistent

emergent vegetation. Common Moorhen (Gallinula chloropus) occupancy was also

positively associated with surrounding wetland cover and persistent emergent vegetation

in addition to mean water depth, interspersion, and aquatic bed vegetation. Occupancy

models for Black Tern (Chlidonias niger), American Bittern (Botaurus lentiginosus),

American Coot (Fulica americana), and King Rail (Rallus elegans) could not be estimated because there were too few detections. My results suggest species-specific associations with cover types surrounding wetlands but a more general positive association with water depth and emergent cover across all species.

iii

Overall, frequency of occurrence of focal species in wetlands restored through

the Ohio Wetland Reserve Program was similar to a random sample of wetlands but lower

than managed wetland complexes. Seven of the nine focal species were detected in

restored wetlands including the state endangered King Rail and state threatened Least

Bittern. Most restored wetlands had water depths suitable for breeding marsh birds

(20-40 cm) but lacked emergent cover and were isolated from other wetlands. Restored wetlands tended to be small (≤ 1 ha) and only the largest wetlands were occupied by area sensitive species such as Pied-billed Grebes. Non-native invasive plants such as common

reed (Phragmites australis) and purple loosestrife ( Lythrum salicaria) were uncommon in

restored wetlands relative to a random sample of natural and managed wetlands. Narrow-

leaved cattail (Typha angustifolia) was the most common non-native plant in wetlands

across glaciated Ohio and these were frequently occupied by focal marsh birds.

Encouraging the use of infrequent drawdowns to encourage emergent growth and a

variable microtopography will likely promote marsh bird occupancy of restored wetlands.

Control of innvasive vegetation should be conducted at a small scale over a large time

frame and planning to establish native vegetation should consider marsh bird habitat

requirements.

Dedicated to:

Michael Perez-Guerra

“…you’re the people that I wanted to know…

…you’re the places that I wanted to go”

~ Isaac Brock

Acknowledgments

I would like to thank my advisors, Paul Rodewald and Robert Gates for giving me the opportunity to work on this project and for their help throughout. I thank Craig Davis for serving as my committee member and for his assistance, particularly in the editorial process. This project was a collaborative effort between two graduate students and I want to thank Ben Kahler for his diligence and contributions to the study. I appreciate all the hard work of our technicians: Bryce Adams, Wilma Bird, Lydia Doerr, Eric

Dougherty, Chad Incorvia, Jay Jordon, John Price, and Katlyn Steinkirchner.

Our field work would not have been possible without access to both public and private wetlands. I am grateful to all the private landowners for granting us permission. I appreciate the support given to us by U.S. Fish and Wildlife Service Biologist Ron

Huffman, state private lands biologists, and state wetland managers. I thank the late

Steve Barry, Ohio Division of Wildlife biologist, who assisted us with the restored wetlands sampling and helped gain access to privately restored wetlands. Funding was provided by the Federal Aid in Wildlife Restoration Program (W-134-P, Wildlife

Management in Ohio) and administered jointly by the U.S. Fish and and Wildlife

Service and the Ohio Division of Wildlife. Thanks to John Simpson, manager of the

Winous Point Shooting Club and Marsh Conservancy for housing and logistical support.

I thank Mark Labarr and Allan Strong whose love and fascination of birds inspired my career path. Lastly, I thank Michael Perez-Guerra for everything else. vi

Vita

March 28,1982 ...... Born- Newport, Vermont

2005...... B.A. Biology and Studio Art, University of

Vermont (UVM)

Summer 2005 ...... Grassland Bird Field Technician, UVM

Summer 2006 ...... Biological Intern, Rachel Carson National

Wildlife Refuge (NWR), U.S. Fish and

Wildlife Service (USFWS)

Summer 2007 & 2008 ...... Waterbird Technician, Lake Umbagog

NWR, USFWS

2009 to 2011 ...... Graduate Teaching Associate, The Ohio

State University

Fields of Study

Major Field: Environment and Natural Resources

Specialization: Fish and Wildlife

Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgments...... vi

Vita ...... vii

List of Tables ...... x

List of Figures ...... xiv

Chapter 1: Introduction ...... 1

Population Trends and Status...... 3

Habitat Selection Theory ...... 6

Marsh Bird Habitat ...... 7

Wetland Restoration ...... 12

Study Area ...... 15

Thesis Outline ...... 18

Chapter 2: Occupancy and Habitat Associations of Breeding Marsh Birds in the Glaciated

Wetlands of Ohio ...... 20

Introduction ...... 20

Methods ...... 22

Results ...... 33

Discussion ...... 67 viii

Management Implications ...... 72

Chapter 3: Comparisons of Marsh Bird Occupancy in Restored and Randomly Sampled

Wetlands in Glaciated Ohio ...... 74

Introduction ...... 74

Methods...... 76

Results ...... 81

Discussion ...... 93

Literature Cited ...... 97

Appendix A: Candidate Model Set ...... 108

Appendix B: Number of Individual Marsh Birds Detected ...... 113

Appendix C: Avian Species Observed at Restored Wetlands ...... 114

Appendix D: 2009 Avian Species Richness and Selected Habitat Variables in Restored

Wetlands ...... 117

Appendix E: 2010 Avian Species Richness and Selected Habitat Variables in Restored

Wetlands ...... 120

List of Tables

Table 1.1. Breeding Bird Survey (BBS) population trends (1966-2007) of focal marsh

bird species in U.S., Canada, and eastern BBBS regions. Trend indicates mean percent

annual change in population for the listed region (Sauer 2008) ...... 4

Table 2.1. List of codes and description of covariates used to model occupancy

probability (ψ) of five species (Pied-billed Grebe, Least Bittern, Virginia Rail, Sora, and

Common Moorhen) in the glaciated subregions of Ohio, USA, May through early July,

2009 and 2010 ...... 30

Table 2.2. List of codes and description of covariates used to model detection probability

(p) of five species (Pied-billed Grebe, Least Bittern, Virginia Rail, Sora, and Common

Moorhen) in the glaciated subregions of Ohio, USA, May through early July, 2009 and

2010...... 32

Table 2.3. Frequency of occurrence, mean number of individuals detected per point and standard error (SE) by year and survey visit for marsh birds surveyed in the glaciated subregions of Ohio, USA May through early July 2009 (n =259) and 2010 (n =281) ...... 34

Table 2.4. Untransformed, model averaged parameter estimates and 95% confidence intervals (CI) for the top model ranked by Akaike’s Information Criterion estimating detection probability of five marsh birds in the glaciated subregions of Ohio, USA during

May through early July 2009 and 2010 ...... 39

Table 2.5. Goodness-of-Fit of global model based on Pearson’s chi-squared statistic of

observed data (χd2), probability of a chi-squared value greater than observed based on

parametric bootstrapping distribution p(χ2> χd2), and estimated overdispersion

parameter (ĉ) for five marsh bird species in the glaciated subregions of Ohio, USA during

May through early July 2009 and 2010 ...... 41

Table 2.6. Top ranked (ΔAIC≤7) candidate models and null model estimating the effect

of habitat covariates on Pied-billed Grebe occupancy (ψ) in the glaciated region of Ohio,

USA, May through early July, 2009 and 2010 ...... 42

Table 2.7. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) estimating Pied-

billed Grebe occupancy in the glaciated region of Ohio, USA, May through early July,

2009 and 2010 ...... 43

Table 2.8. Top ranked (ΔAIC≤7) candidate models and null model estimating the

influence of habitat covariates on Least Bittern occupancy (ψ) in the glaciated region of

Ohio, USA, May through early July 2010. Buffers are percent wetland, cropland, and

woodland within 500 m ...... 46

Table 2.9. Model averaged, untransformed parameter estimates and 95% confidence

intervals (CI) for covariates included in top ranked models (ΔAIC≤2) describing Least

Bittern occupancy in the glaciated region of Ohio, USA, May through early July 2010 ..49

Table 2.10. Top ranked (ΔAIC≤7) candidate models and null model estimating the effect

of covariates on Virginia Rail occupancy (ψ) in the glaciated region of Ohio, USA, May

xi through early July 2009 and 2010. Buffers are percent wetland, cropland, and woodland within 500m ...... 50

Table 2.11. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) estimating Virginia

Rail occupancy in the glaciated region of Ohio, USA, May through early July 2009 and

2010...... 51

Table 2.12. AIC results of top ranked (ΔAIC≤7) candidate models and null model estimating the effect of covariates on Sora occupancy (ψ) in the glaciated region of Ohio,

USA, May through early July 2009 and 2010. Buffers are percent wetland, cropland, and woodland within 500m ...... 55

Table 2.13. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) estimating Sora occupancy in the glaciated region of Ohio, USA, May through early July 2009 and

2010...... 56

Table 2.14. Quasi-AIC results of top ranked (ΔQAIC≤7) candidate models and null model estimating the effect of covariates on Common Moorhen occupancy (ψ) in the glaciated region of Ohio, USA, May through early July, 2009 and 2010. Buffers are percent wetland, cropland, and woodland within 500m ...... 59

Table 2.15. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔQAIC≤2) estimating

Common Moorhen occupancy in the glaciated region of Ohio, USA, May through early

July, 2009 and 2010 ...... 60

xii

Table 3.1. Descriptive Statistics of habitat variables of restored wetland surveyed for secretive marsh birds during May through early July 2009 and 2010 in the glaciated subregions of Ohio, USA ...... 82

Table 3.2. Frequency of occurrence of nine focal marsh birds in restored wetlands, a random sample of impounded, managed and natural wetlands, and state and federally owned wetland complexes surveyed during May through early July, 2009 and 2010 in the glaciated region of Ohio, USA. Includes individuals detected during the 10 minute survey period and within 100 m of survey point only ...... 88

Table 3.3. Frequency of occurrence by physiographic subregion of nine focal marsh birds in restored wetlands and a random sample of impounded, managed, and natural wetlands, surveyed during May through early July, 2009 and 2010 in the glaciated region of Ohio, USA. Includes individuals detected during the 10 minute survey period and within 100 m of survey point only ...... 89

Table 3.4. Frequency of occurrence by wetland type of nine focal marsh birds in restored wetlands and a random sample of impounded, managed and natural wetlands, surveyed during May through early July, 2009 and 2010 in the glaciated region of Ohio, USA. Includes individuals detected during the 10 minute survey period and within 100 m of survey point only ...... 90

xiii

List of Figures

Figure 1.1. Physiographic subregions and wetland complexes managed by state and federal wildlife agencies in Ohio ...... 16

Figure 2.1. Emergent wetland survey points for marsh bird surveys conducted May

through early July, 2009 and 2010 in the glaciated subregions of Ohio, USA ...... 24

Figure 2.2. Mean number of calls per minute of a) Pied-billed Grebe (PBGR), b) Least

Bittern (LEBI), c) American Bittern (AMBI), d) Virginia Rail (VIRA), and e) Sora

(SORA) during passive and call broadcast period, surveyed in the glaciated subregions of

Ohio May through early July, 2009 and 2010. n = total number of individuals detected

within 100 m by vocalizations in all three visits. Survey period includes five minute

passive listening period and one minute of calls for birds indicated by 4 letter AOU code.

American Bittern and second Least Bittern calls used in 2010 only ...... 28

Figure 2.3. Relationship between a) standardized mean water depth b) wetland size and

Pied-billed Grebe occupancy in the glaciated region of Ohio, USA, May through early

July, 2009 and 2010. Mean water depth (Z=0) is 22 cm and one half step in Z-score is

equivalent to approximately 12 cm. Dashed lines indicate 95% confidence interval ..... 36

Figure 2.4. Relationship between a) standardized mean water depth b) percent persistent emergent vegetation c) adjacent woodland cover and Least Bittern occupancy in the glaciated region of Ohio, May through early July 2010. Dashed lines indicate 95%

xiv

confidence interval. Mean water depth (Z=0) is 29 cm and one half step in Z-score is

equivalent to approximately 13 cm ...... 44

Figure 2.5. Relationship between a) percent adjacent woodland b) wetland area c) persistent emergent vegetation and Virginia Rail occupancy in the glaciated region of

Ohio, May through June 2009 and 2010. Dashed lines indicate 95% confidence interval ..

...... 47

Figure 2.6. Relationship between a) percent adjacent wetland b) percent adjacent

cropland c) persistent emergent vegetation and Sora occupancy in the glaciated region of

Ohio, May through early July, 2009 and 2010. Dash lines represent 95% Confidence

Interval ...... 52

Figure 2.7. Relationship between a) percent adjacent wetland b) mean water depth c) persistent emergent vegetation and Common Moorhen occupancy in the glaciated

region of Ohio, May through early July 2009 and 2010. Dash lines represent 95%

confidence interval ...... 57

Figure 2.8. Relationship between a) percent adjacent wetland b) mean water depth

c) persistent emergent vegetation and Common Moorhen occupancy in the glaciated

region of Ohio, May through early July 2009 and 2010. Dash lines represent 95%

confidence interval ...... 61

Figure 3.1. Frequency histogram of estimated percent emergent vegetation in a 100 m radius circular plot at restored wetlands surveyed for secretive marsh birds during May through early July, 2009 and 2010 in the glaciated subregions of Ohio, USA ...... 83

Figure 3.2. Biplot of mean fine-scale habitat variables and 95% Confidence intervals for restored wetland sample and random sample in which marsh birds were detected (Yes) or xv

not detected (No) during May through early July, 2009 and 2010 in the glaciated

subregions of Ohio, USA ...... 86

Figure 3.3. Biplot of mean landscape variables and 95% Confidence intervals for restored wetland sample and random sample in which marsh birds were detected (Yes) or not detected (No) during May through early July, 2009 and 2010 in the glaciated subregions of Ohio, USA ...... 87

xvi

Chapter 1: Introduction

Marsh birds are a taxonomically diverse group of species that depend on emergent wetland habitat for all or most of their life cycle. North American marsh birds range in size from the 11 gram Marsh Wren (Cistothorus palustris) up to the 4,850 gram Greater

Sandhill Crane (Grus canadensis). Species vary in degree of conspicuousness due to

size, behavior, and habitat use. The family Rallidae is representative of the elusive

nature of many marsh bird species in that they are typically heard far more than they are

seen. As a result, relatively little is known about the basic ecology of many species and

reliable estimates of abundance are sometimes lacking (Gibbs et al. 1992, Conway 1995,

Conway 2008).

Breeding Bird Survey (BBS) data suggest that many marsh birds are declining in

the United States (U.S.). In the eastern U.S., significant negative trends are estimated for

twenty-five percent of the wetland breeding birds encountered during surveys (Sauer

2008). However, it is likely that additional species are declining because BBS survey

routes do not sample enough emergent habitat to effectively detect trends (Conway

2008). Standardized marsh bird survey protocols have been developed in North America

to specifically target these populations and will most likely provide more reliable trend

estimates.

A major factor contributing to marsh bird population declines is the loss and

degradation of wetland habitat (Eddleman et al. 1988, Cooper 2007, Rush and Cooper 2010). Dahl (1990) estimated that 53% of total wetland acreage in the U.S was lost between the 1780’s and 1980’s. The state of Ohio is estimated to have lost 90% of pre- settlement wetland cover, mainly to agriculture and development (Dahl 1990). Further, the quality of wetland habitats can decline over time due to hydrological alterations and homogenization of biota. For example, urbanization and development disrupts natural hydrology through increases in impervious surfaces and water diversion (Holland et al.

1995, Mitch and Wilson 1996). Wetlands within developed landscapes may experience a reduction in plant and invertebrate diversity (Lougheed et al. 2008), which can negatively affect food resources and cover for marsh birds (Ward et al. 2010).

Wetland policy in the U.S. has shifted towards protection and conservation in the last half of the 20th century. Legislation, such as the Swampbuster Provision of the Food

Securities Act and Section 404 of the Federal Water Pollution Control Act, has been enacted to conserve wetland habitat or mitigate wetland loss. In addition, a number of programs, such as the federal Wetland Reserve Program administered by the Natural

Resources Conservation Service and the Partners for Wildlife Program administered by the U.S. Fish and Wildlife Service, provide financial incentives to private landowners to restore or create wetlands on their property. These policies have led to a net gain in wetland area, however, the functional capabilities of restored or created wetlands needs further assessment (National Research Council 2001).

The goal of my research was to determine the intrinsic habitat structure associated with patterns of marsh bird occupancy throughout the landscape. Focal species were secretive birds that require freshwater emergent habitat and included: Pied-billed Grebe

(Podilymbus podiceps), American Bittern (Botaurus lentiginosus), Least Bittern

(Ixobrychus exilis), Common Moorhen (Gallinula chloropus), American Coot (Fulica americana), King Rail (Rallus elegans), Virginia Rail (Rallus limicola), Sora (Porzana carolina), and Black Tern (Chlidonias niger). In this chapter I summarize the literature on trends and status of marsh bird populations, habitat selection theory, marsh bird habitat use, wetland restoration and provide a description of the study area where I conducted my research. I use occupancy modeling in chapter 2 to determine marsh bird- habitat associations using within-plot habitat features (100m radius circle) and surrounding cover types (500m buffer around survey wetland). In chapter 3, I compare habitat structure and marsh bird occupancy between wetlands restored through the Ohio

Wetland Reserve Program and a random sample of managed and natural wetlands across glaciated Ohio.

Population Trends and Status

Population changes for many North American birds can be detected with large- scale monitoring programs such as the U.S. Fish and Wildlife Service Breeding Bird

Survey (Robbins et al. 1986). These changes may indicate the need for additional monitoring and research to understand factors that limit population growth or stability.

Marsh birds as a group are exhibiting strong declines, most likely due to loss and alteration of habitat (Eddleman et al. 1988, Gibbs et al. 1992, Conway 1995, Melvin and

Gibbs 1996). Breeding Bird Survey data from 1966 to 2005 indicate significant annual declines in the U. S. and/or Canada for American Bittern, Common Moorhen, American

Coot, King Rail and Black Tern (Table 1.1).

Species Trend (%) P-value Region Pied-billed Grebe 2.69 0.006 U.S. 0.38 0.729 Canada -0.02 0.984 Eastern BBS American Bittern -0.41 0.709 U.S. -2.41 0.003 Canada -1.69 0.006 Eastern BBS Least Bittern 0.58 0.750 U.S. 7.23 0.058 Canada -1.16 0.422 Eastern BBS Common Moorhen -0.45 0.721 U.S. -3.68 0.012 Canada -1.24 0.426 Eastern BBS American Coot 0.89 0.491 U.S. -1.04 0.057 Canada -4.73 0.006 Eastern BBS King Rail -7.47 0.001 U.S. -7.08 0.030 Eastern BBS Virginia Rail 0.93 0.421 U.S. 6.13 0.000 Canada 0.21 0.852 Eastern BBS Sora -0.19 0.770 U.S. 0.13 0.826 Canada -1.17 0.280 Eastern BBS Black Tern 0.38 0.745 U.S. -3.15 0.035 Canada -6.26 0.005 Eastern BBS

Table 1.1. Breeding Bird Survey (BBS) population trends (1966-2005) of focal marsh bird species in U.S., Canada, and eastern BBS regions. Trend indicates mean percent annual change in population for the listed region (Sauer 2008). 4

The North American Marsh Bird Monitoring Program estimated a significant negative annual population trend for Pied-billed Grebe, American Bittern, Common

Moorhen, American Coot, Virginia Rail, and Sora in the Great Lakes basin from 1995 to

2004 (Crewe et al. 2006). Peterjohn and Zimmerman (2001) provided a historical account of breeding birds in Ohio and reported apparent declines for many marsh birds.

The King Rail, American Bittern, and Least Bittern were considered common in Ohio prior to the 1930’s after which the population began to decline (Peterjohn and

Zimmerman 2001). Declines have been reported even for locations such as the Lake Erie marshes where abundant emergent wetland cover still exists (Christy 1931, Brackney

1979). These trends suggest that additional research efforts are needed to understand habitat needs and limitations to population growth in order to maintain viable marsh bird populations.

State and Federal status of marsh bird populationss across the United States

further illustrates the need for additional consideration of this group. An amendment to the Fish and Wildlife Management Act in 1988 required the United States Fish and

Wildlife Service (USFWS) to identify species likely to become listed under the

Endangered Species Act if no conservation action was taken. Under this mandate,

USFWS has listed the American Bittern, Least Bittern, Pied-billed Grebe and Black Tern as Birds of Conservation Concern in region 3 (USFWS 2008). The American Bittern,

King Rail, and Black Tern are listed as endangered species by the Ohio Division of

Wildlife, whereas the Least Bittern is listed as a Threatened species. Common Moorhen,

Virginia Rail, and Sora are listed as Species of Conservation Concern in Ohio.

Habitat Selection Theory

Organisms are not randomly distributed in their environment. Scientific research

designed to understand distribution patterns of animals has led to theory on habitat

selection. Habitat selection is the process by which specific features of a habitat trigger a

settling response within an organism (Lack 1933). These features, or proximate cues, are

believed to correlate with habitat conditions that ultimately affect fitness and survival

(Hilden 1965). Proximate cues include structural characteristics of vegetation (Lack

1933), presence of competitors or predators (Svardson 1949, Grand 2002), and abiotic

factors such as temperature and precipitation (Karr and Freemark 1983). Greater

pressure from intra-specific competitors should result in use of habitat outside of the optimal range whereas greater inter-specific competition should influence an organism to be more selective of specific habitat features (Svardson 1949). This implies some plasticity or flexibility in an organism’s ability to select a habitat.

Variation in habitat structure and suitability may exist at a range of spatial scales, indicating that habitat selection should be thought of as a hierarchical process (Hutto

1985, Morris 1987, Kolasa 1989, Orians and Wittenberger 1991). Hutto (1985) made the distinction between intrinsic habitat features (within-habitat factors such as food abundance or vegetation structure) and extrinsic habitat features (broad-scale factors such as habitat connectivity). For example, habitat patches that are intrinsically suitable to a migratory bird may not be selected as a result of distance from the migratory route to the patch. The importance of landscape-level features may depend on physiology and mobility of the organism (Bergin 1992, Harvey and Weatherhead 2006). Further, organisms have a patch size limit (grain) below which structural characteristics of the habitat are perceived as homogenous (Kotliar and Wiens 1990). For a marsh bird in an emergent wetland this could be a hummock of vegetation. A focus on habitat variables below the grain of the organism will fail to identify patterns in habitat selection (Kotliar and Wiens 1990).

Marsh Bird Habitat

Wetland Vegetation. Vegetation is an essential component of a bird’s habitat; providing cover, nesting substrate or cavities, and food resources. Many marsh birds are positively associated with the presence or percent cover of emergent vegetation (Faaborg

1976, Johnson and Dinsmore 1986, Post and Seals 2000, Bogner and Baldassarre 2002).

The amount of emergent cover in freshwater marshes depends on annual variability in water depth, muskrat (Ondatra zibethicus)activity and the seed bank (Weller and Spater

1965, van der Valk and Davis 1978). Periods of low precipitation allow for germination of emergent vegetation in shallow standing water or mud-flats. Persistent emergent vegetation then continues to colonize the marsh through vegetative reproduction, dominating the system. Over time, increases in water levels and muskrat activity result in a mostly open water marsh (van der Valk and Davis 1978). Variations in percent emergent cover within a wetland complex may help to promote long-term diversity and abundance of wetland-dependent birds (Murkin et al. 1997).

Habitat Heterogeneity. Avian habitat research has suggested that a more structurally diverse habitat resulting from variable topography or vegetation communities will provide more niches and result in higher species richness (MacArthur and

MacArthur 1961, Rotenberry and Wiens 1980, Swift et al. 1984). In freshwater wetlands, habitat diversity is mainly observed on a horizontal plane with different cover types of vegetation such as obligate wetland shrubs, emergent vegetation and aquatic bed. Gibbs et al. (1991) and Hierl et al. (2007) reported that a high relative richness of cover types was positively associated with waterbird diversity in Maine. The spatial scale of a particular study must be considered before it is applied in a management context. For example, habitat patterns over a large geographic extent are not necessarily applicable within a smaller geographic extent in another region (Block et al. 1994). Therefore, it is important to know the appropriate scale at which to manage for habitat diversity and how this may affect the habitat selection patterns of species of conservation concern.

Hemi-marsh conditions with high interspersion, described as an equal proportion of open water to emergent vegetation, are considered ideal for maximizing bird species abundance and diversity (Weller and Spatcher 1965, Kaminski and Prince 1984, and

Baldassarre 2007a). Interspersion is an attribute of the horizontal structural diversity and can be defined as the degree of mixing between water and emergent cover (Rehm and

Baldassarre 2007a). High interspersion is observed in a convoluted band of vegetation around the perimeter of a basin or in basins with microtopographic heterogeneity resulting in “islands” of vegetation or pools of open water. Aquatic invertebrate abundance, an important component of inherent suitability of marsh bird habitat, is

8 highest in regions where open water is interspersed with emergent vegetation (Voigts

1976, Murkin et al. 1992). Murkin et al. (1982) and Rehm and Baldassarre (2007a) suggested that interspersion can promote high bird densities by reducing inter- and intra- specific competition.

Hydrology. Water level changes can have a strong influence on other abiotic and biotic components of wetland ecosystems. Fluctuations in water levels, for example, can directly affect reproductive success of obligate wetland breeding birds. Nest sites over deep water may reduce terrestrial, mammalian predation (Picman et al. 1993), however, both natural and induced reductions in water levels can also expose nests to predators

(Szell and Woodrey 2003). Nest failure as a result of water inundation is a relatively common occurrence for some marsh birds but is not likely to influence population persistence (Gilbert and Servello 2005).

Despite the direct negative effects of a pulsing hydrology on breeding marsh birds, water level fluctuations influence habitat features associated with wetland- dependent species (Steen et al. 2006). These features include interspersion (Kaminski and Prince 1984), structural and plant species diversity (Wilcox et al. 1992, Wilcox and

Jerrine 2008), and foraging microhabitat (Steen et al. 2006). Further, high abundance of invertebrates may be due to the increase in detritus provided by decomposition of less water tolerant plants in wetlands with fluctuating water levels (Kaminski and Prince

1984). Periodic flooding and drying over a long temporal scale have been positively correlated with occurrence and abundance of some obligate marsh bird species, including

American Coot, Least Bittern, Marsh Wren, and Pied-billed Grebe (Timmermans et al.

2008).

Area and Isolation. The Theory of Island Biogeography is the foundation of a spatially explicit approach to understanding habitat use known as landscape ecology.

MacArthur and Wilson (1967) predicted that the number of species on an island is a dynamic equilibrium resulting from colonization and extinction. The species composition of the island changes over time but once equilibrium is reached the number of species remains relatively constant. Island area and isolation are suggested to be important predictors of species richness. Based on past work on species-area relationships, the number of species occupying a site is positively related to area and is best described by a power function (Arrhenius 1921, Connor and McCoy 1979). Instead of considering the species-area relationship as a result of increased habitat diversity,

MacArthur and Wilson proposed that extinctions occur less frequently on larger islands.

In addition, islands located far from the mainland are colonized less frequently, resulting in lower species richness.

Generalization of the theory to continent-wide wildlife conservation sparked a debate known as the Single Small or Several Large (SLOSS) debate. Diamond (1975) used the Island Biogeography Theory to endorse the creation of large wildlife reserves, making the case for conservation of area-sensitive species and long-term population stability. Simberloff and Abele (1976, 1982) on the other hand, suggest that the theory cannot be used to generalize continental distributions and refuge design should be considered on a regional and focal-species basis. A shortcoming of using island biogeography concepts in habitat patches on a continent is that the surrounding landscape

10 is not comparable to an ocean. It is recognized that spatial patterns and the heterogeneity of the environment surrounding a habitat patch will influence movement and habitat use within a continental landmass.

Empirical evidence from grassland (Benoit and Askins 2002), forest (Ferraz et al.

2007), and wetland (Brown and Dinsmore 1986) study systems has shown that patch size and isolation affects species richness and/or relative density. Caution, however, should be used when applying these generalizations to conservation planning since temporal and spatial landscape variation may confound the effect of patch area (Ribic and Sample

2001, Winter et al. 2006). Wetland specific research suggests that species are variable in their degree of sensitivity to patch area. For example, Black Tern and Pied-billed Grebe abundance exhibits a significant, positive linear relationship with marsh area, whereas

Virginia Rail and Sora are found in some of the smallest wetlands units studied (< 1 ha)

(Brown and Dinsmore 1986, Naugle et al. 1999, Benoit and Askins 2002, but see Riffell et al. 2001). By separating marsh bird species into breeders and those that only use certain wetlands for foraging, Craig and Beal (1992) found differences in how landscape- scale attributes affected species use. Species richness of breeders was positively related to wetland area, whereas forager use was negatively associated with isolation but not associated with wetland area. Effect of area on species richness may interact with the degree of wetland isolation so that only large wetlands are used by area sensitive species in landscapes with very little total wetland cover (Brown and Dinsmore 1986).

Surrounding Land Use. Landscape ecology studies suggest that composition of the surrounding matrix may influence habitat selection and reproductive success of

11 individuals (Naugle et al. 2000, Batary and Baldi 2004). Species richness of waterbirds during the breeding season is positively associated with connectivity, or the distance an individual can move without leaving a mosaic of wetlands (Guadagnin and Maltchik

2007). Within-season movements of species among wetland patches, allowing birds to assess nesting territory and foraging habitat, may be related to reproductive success (Haig et al. 1998). The extent to which these characteristics affect marsh birds may depend on their degree of mobility during the breeding season.

The dominant land cover in Ohio is cropland, accounting for approximately 60% of the state (Reece and Irwin 2002). This cover type has replaced what may have once been a larger wetland complex surrounded by grassland or forested upland. It is not fully understood how these changes affect distribution and productivity of marsh birds.

When other habitat features are controlled, Yellow-headed Blackbirds

(Xanthocephalus xanthocephalus) and Pied-billed Grebes were not influenced by surrounding landscapes dominated by agriculture (Naugle et al. 1999). In contrast, a discriminant function analysis indicated that grassland area was an important component for identifying suitable Black Tern habitat, suggesting that the response to land use practices is species specific (Naugle et al. 2000). Agricultural development leading to eutrophication of adjacent wetlands may limit food resources and reduce productivity of marsh birds, especially in combination with unfavorable environmental conditions such as weather (Beintema 1997).

Wetland Restoration

Dahl (1990) estimated that 53% of total wetland acreage in the contiguous United

States was lost between the 1780’s and 1980’s. The State of Ohio alone is estimated to

have lost 90% of its pre-settlement wetland cover, mainly for agricultural use (Dahl

1990). Wetland policy has shifted in recent decades as the functions and services of these ecosystems are better understood. Under section 404 of the Clean Water Act of 1972, permitted wetland loss must be mitigated by creating or restoring wetlands in another location. In addition, federal and state programs; including the Federal Wetland Reserve

Program, Partners for Fish and Wildlife Program, and the Ohio Wetland Restoration

Program; have provided incentives for wetland restoration on privately owned property.

Restored wetlands can vary greatly in vegetation structure and composition at the local scale. Factors affecting vegetation cover and species composition of restored wetlands include: time since restoration (Confer and Niering 1992, Brown and Smith

1998, Mitch and Wilson 1996), land use prior to restoration (Delphey and Dinsmore

1993), and hydroperiod (Brawley et al. 1998, Snell-Rood and Cristol 2003). Wet- meadow and low-prairie zones typical of natural wetlands in the glaciated Prairie Pothole region (Steward and Kantrud 1971) were absent at most or all restoration sites (Delphey and Dinsmore 1993, Galatowitsch and van der Valk 1996). These variables may have a direct effect on habitat suitability because features of vegetation structure and emergent cover are associated with use by wetland-dependent species (VanRees-Siewert and

Dinsmore 1996, Weller 1999, Fairbairn and Dinsmore 2001).

Studies of bird species richness and/or density in restored wetlands compared to natural wetlands have produced contradictory results. Species richness was lower in recently restored wetlands compared to natural wetlands in the prairie pothole region

(Delphey and Dinsmore 1993). The abundance of wetland dependent birds was observed to be significantly higher in natural wetlands than in created salt marshes (Melvin and

Webb 1998). In contrast, relative abundance and density of birds were not significantly different in restored wetlands compared to natural wetlands in northern New York

(Brown and Smith 1998). However, avian community composition was more similar among restored wetlands than it was between restored and natural wetlands (Brown and

Smith 1998). Avian species richness and abundance were not significantly different between restored and natural wetlands in North and South Dakota (Ratti et al. 2001).

Spring or early summer drawdowns can encourage development of emergent vegetation while water control structures can reduce large fluctuations of water levels during the breeding season. Active management, or movement of water against a hydrological gradient, versus passive management was the best predictor of stopover use by migrant waterbirds, species richness of waterbirds, and density of breeding waterfowl in Illinois wetlands enrolled in the Conservation Reserve Enhancement Program

(O'Neal et al. 2008). The highest levels of all dependent variables were found in actively managed wetlands. In New York, greater abundance and species richness was observed on actively managed wetlands than unmanaged restored wetlands (Kaminski et al. 2006).

Wetland area and percent of wetland cover are predictors of avian species richness and individual species abundance over a large geographic area (Fairbairn and

Dinsmore 2001, Tozer et al. 2010). However, an evaluation of private restoration projects indicates that the majority of wetlands in the prairie pothole region are isolated and do not mimic historic wetland patterns on a regional scale (Galatowitsch and van der

Valk 1996). Isolation of restored wetlands may limit the measured success of the restoration in terms of vegetation and animal diversity (Harris 1988, Fairbairn and

Dinsmore 2001, Seabloom and van der Valk 2003).

Study Area

Glacial movement during the most recent ice age has resulted in unique topography and soil characteristics which have influenced vegetation cover and land use practices in Ohio. The study area includes only portions of the state exposed to ice sheets from the Pleistocene glaciation. The southern extent of the Wisconsinan glacier forms southern and eastern boundaries of the study area while Lake Erie borders the north. The

Indiana –Ohio state line forms the western boundary of the study area. Three physiographic subregions have been identified within this portion of Ohio; Till Plains,

Lake Plain, and Glaciated Appalachian Plateau (Figure 1.1). Exceptions to the study area boundary were made to include all three of the Ohio Department of Natural Resources

(ODNR) Division of Wildlife wetland focus areas. The focus area program sets an ambitious goal of conserving 200 ha of wetlands each year at three separate areas to support viable populations of wetland-dependent wildlife. Wetlands are acquired through restoration, conservation easements and land purchases.

The Appalachian Plateau Province lies in the eastern half of Ohio. The topography is undulating but to a lesser degree in the northern portion, known as the glaciated plateau sub-region, where glacial ice has worn down the hills. The sub-region contains kettle lakes where glacial ice carved out depressions and then melted (Peacefull

1996). The Appalachain Plateau is cut by many rivers and streams and has a relatively

Legend Managed Wetland Complexes Lake Plain Glaciated Appalachian Plateau Till Plains Unglaciated Appalachian Plateau Interior Low Plateau high amount of forest cover compared to the western part of the state. The majority of

scrub-shrub/emergent wetlands and forested/emergent wetlands in this study are located

within the glaciated sub-region. Dominant wetland shrubs in the region include

buttonbush (Cephalanthus occidentalis), dogwood (Cornus spp.), willow (Salix spp.), and

meadowsweet (Spirea alba). The most common emergent vegetation includes swamp

loosestrife (Decodon verticillatus), narrow-leaved (Typha angustifolia) and common cattail (T. latifolia), smartweed (Polygonum spp.), and burreed (Sparganium spp.). This

region includes the Grand River-Mosquito Creek Wetland Focus Area which contains

6,246 ha of state owned hardwood forests, beaver swamps, riparian wetlands and

marshes. The Killbuck Wetland Focus Area is a long glacial outwash valley starting in

the north with the Killbuck Marsh Wildlife Area and following the Killbuck Creek south

into the glaciated plateau subregion. The Killbuck Marsh Wildlife Area is 2,227 ha in

size, 56% of which is marsh and swamp, making it the largest intact wetland complex in

Ohio outside of the Lake Erie region.

The Lake Plain borders the southern edge of Lake Erie and stretches into the

Maumee Valley in northwestern Ohio. This sub-region was once covered by an ancient

lake that has retreated north leaving behind sand ridges and fertile lake sediments

(Peacefull 1996). Northwestern Ohio once included a 193 by 64 km wetland complex

of swamp, marsh, and wet meadow known as the Great Black Swamp. Only about 10%

of the wetland area remains today, having been converted to highly productive farm land.

Common wetland vegetation in the region includes pondweed (Potamogeton spp.),

coontail (Ceratophyllum spp.), lesser duckweed (Lemna spp.), white water-lily

(Nymphaea odorata), smartweed, arrowhead (Sagittaria spp.), common reed (Phragmites

australis), and cattail. This physiographic sub-region is home to the Lake Erie marshes

wetland focus area located along the western basin of Lake Erie. The focus area includes

State wildlife management areas and a federal national wildlife refuge with 7,143 ha of impounded wetland habitat. Additional wetland cover in the area is owned by private hunting clubs supporting threaten and endangered wetland-dependent species as well as waterfowl.

The Till Plains subregion consists of terminal and recessional moraines created from receding ice sheets (Peacefull 1996). The large expanses of low prairie and wet meadow once occurring in this subregion have been drained for agricultural use. The region is now dominated by row crops of corn and soy bean with small woodlots and isolated wetland cover. Wetlands typically are dominated by submerged aquatic vegetation including coontail, lesser duckweed, and pondweed but also contain emergent vegetation such as arrowhead, spike-rush (Eleocharis spp.), and cattail. Wetland complexes in the sub-region include Killdeer Plains Wildlife Area with 323 ha of emergent marsh surrounded by grassland and Big Island Wildlife Area with 485 ha of wetland cover. Privately owned wetland restorations also contribute to the wetland cover in this sub-region as in all regions of Ohio.

Thesis Outline

In chapter 2, I identified habitat features that promote marsh bird occupancy and estimate occupancy probability across glaciated Ohio. I used

fine-scale habitat features (100 m) and percent of cover types surrounding

18 the wetland (500 m) to build a set of a priori candidate models (Appendix A).

Parameters were estimated for each candidate model and ranked using an information theoretic approach to determine occupancy probability for multiple species of marsh birds.

In chapter 3, I examined marsh bird use of privately owned wetlands restored

through the Ohio Department of Natural Resources’ Wetland Reserve Program to

determine management and structural design features that promote occupancy. I

compared habitat features and frequency of occurence between restored wetlands

and a random sample of natural and managed wetlands in glaciated Ohio. Results

include descriptive statistics of habitat features believed to be important for marsh bird

occupancy and recommendations for further research.

Chapter 2: Occupancy and Habitat Associations of Breeding Marsh Birds in the Glaciated Wetlands of Ohio

Marsh birds are a taxonomically diverse group of species that depend on emergent

wetlands for all or most of their life cycle. There are indications that many marsh bird

populations are declining (Gibbs et al. 1992; Conway 1995; Melvin and Gibbs 1996).

North American Breeding Bird Survey data suggests that Black Tern (Chlidonias niger)

and King Rail (Rallus elegans) populations have declined significantly in the United

States over the past 40 years (Sauer et al. 2008). The Marsh Monitoring Program in the

Great Lakes basin estimated significant negative annual trends over a ten year period for

Pied-billed Grebe (Podilymbus podiceps), American Bittern (Botaurus lentiginosus),

Least Bittern (Ixobrychus exilis), Black Tern, Virginia Rail (Rallus limicola), Sora

(Porzana carolina), American Coot (Fulica americana) and Common Moorhen

(Gallinula chloropus) (Crewe et al. 2006).

The main factors contributing to these declines are likely the loss of wetland

habitat and degradation of wetlands (Eddleman et al. 1988; Dahl 2006; Rush and

Cooper 2010). Dahl (1990) estimated that approximately 53% of the total wetland cover within the conterminous United States was lost from the 1790’s to the 1980’s.

Despite more recent net gains in wetland cover between 1998 through 2004; freshwater emergent habitat continues to be lost, being replaced by open water ponds (Dahl 2006).

Understanding habitat use and preferences will aid in conservation and

management of suitable wetland habitat for declining marsh birds. Fine-scale habitat

features such as percent emergent cover and water depth are reliable predictors of habitat

occupancy by many marsh birds (Andrews 1973; Post and Seals 2000; Jobin et al. 2009).

Interspersion, or the amount of emergent-open water edge, is positively associated with

marsh bird abundance and can promote high breeding densities and species diversity

(Murkin et al. 1982; Rehm and Baldassarre 2007a). Patterns of marsh bird response to broad-scale habitat features may be more species-specific. Black Tern occurrence was associated with the percent of grassland within a 25.9 km2 area in the Prairie Pothole

Region while Pied-billed Grebe used sites regardless of adjacent, upland cover type

(Naugle et al. 1999). Occupancy probability of Least Bittern was negatively associated

with woody cover within 400 m of survey points in Arkansas (Budd and Krementz 2010).

Determining habitat associations of marsh birds is difficult given their secretive

nature and affinity for dense cover. Estimated detection probabilities for many marsh

birds are low (Conway and Gibbs 2005; Darrah and Krementz 2010), creating a high

likelihood of false absences (i.e. failure to detect an individual when it is present at a

site). Modeling through logistic regression can be sensitive to false absences, resulting in

biased parameter estimates, particularly when detection probability is associated with a

particular habitat variable of interest (Gu and Swihart 2003, Tyre et al. 2003). Species

identified as elusive, either because of low abundance and/or low detection probability,

require a more sophisticated analytical approach. Occupancy modeling is a technique in

which the probability of occupancy and detection are modeled simultaneously

(MacKenzie 2006). This approach accounts for differences in detection probability

across space and time. Both parameters can be estimated as a function of covariates to

develop habitat use models.

Many fine-scale marsh bird habitat studies are restricted to a single watershed or

management complex. We surveyed 571 randomly selected points within three

physiographic subregions of Ohio in an effort to create generalized habitat models. We

conducted three surveys per point and used call-broadcasts to increase detectability. I

used an information theoretic approach to rank a priori candidate models that estimated

variation in marsh bird occupancy and detection probability. My objectives were to

identify fine-scale habitat features that promote occupancy of marsh birds and determine

the effect of tilled cropland, wetland, and woodland within 500 m of the survey wetland

on the probability of marsh bird occupancy. The limited wetland habitat available to

maintain and increase marsh bird populations presents wildlife managers with a great

challenge. Managers will require reliable habitat models to maximize wetland species

diversity and abundance. My results will refine and validate marsh bird habitat models to benefit conservation efforts of declining species and provide occupancy probability estimates for the glaciated portion of Ohio.

Methods

Study Area. The study area encompassed approximately two thirds of Ohio,

specifically portions of the state that were most recently glaciated. Three major

physiographic subregions, representing unique topographies and habitat conditions, were

found within the study area: Lake Plain, Till Plains, and Unglaciated Appalachian Plateau

(Figure 2.1) (Peaceful 1996). Surveyed wetlands were owned by local, state, and federal

agencies; individual landowners; land trusts; and private hunting clubs. Management of

selected wetlands ranged from active management, involving manipulation of water

against the hydrologic gradient, removal of invasive plant species, and planting or

seeding of desirable wetland and crop plants; to passive management, with no direct manipulation of hydrology or vegetation. Dominant emergent vegetation included invasive non-native species (≥ 20% of cover type)such as common reed (Phragmites australis) narrow-leaved cattail (Typha angustifolia), narrow-leaved x broad-leaved hybrid (T. x glauca), flowering rush (Butomus umbellatus), and purple loosestrife

(Lythrum salicaria). Land use surrounding the wetlands was largely agricultural but is becoming increasingly urban or forested (Reese and Irwin 2002).

Point Sampling. Marsh bird survey points were randomly selected from National

Wetlands Inventory (NWI) data that were recently updated by Ducks Unlimited and

contributing partners from 2005-2007 aerial photographs (Ducks Unlimited 2008).

Wetlands thought to contain habitat suitable for focal species based on review of the

scientific literature included the lacustrine littoral and palustrine systems in the emergent or aquatic bed class (Cowardin et al. 1979). Wetlands classified as scrub- shrub/emergent, unconsolidated bottom/emergent, or forested/emergent were also included to sample a wider range of potential marsh bird habitat. We excluded wetlands containing no emergent vegetation, such as semi-permanently flooded forest, from the database of over 15,000 wetland habitats in the glaciated region of Ohio.

" " (! " "" " (! " " (!" " !(!"(! " " (! (! (! "(!"(!(!(! "" (!" " (!(!"(! " (! (! "("!("!(!"(!("(!(!"(!"(!(! " " " " "!("!""(!"(!(!" """ (! (! " " " (( ""(!"(!(! (! (! " "(!(!" (! " " (!(! "(! " (! (!(!(!(!(! (! (! " (!" (! "(!(!"(!"(!(!"(!" " !(! (!!("(!" (! "(! " "(!(! " " (!"!"(!""(!" " (!"(!! ( ""(!"" "(!(!(!(!(!(!(! (! " (! (! ("""" (!(" (!""(!""(! (!(! " "(! (! "" (! " (!(!(!(!(! " " " " "(! (! " " " (! " (! " (! (! (!" (! " (! (! (! ( " (! (! " " "" " (! " (!" (! ""(! (! (!" " " " (! " "" (! (! " (! " (! " " " " (! " " (! (! " (!"" "(! " " (! (! " (! (! " (! " " !"" " " "(! ( " " " " " " "" " " " " " " " " " "(!"" " " " (!"(!" " (!(!"(!" " "(!" " (! " " "(!" (! " (!" (! " "" (! (! " " (!(!"!((!(! ! " " " ("(! (! ((! "(!"(!(! "" " " " (! " " " " " (!" " (! (! " (! " (! " " " "(! " "(! (! " (!"" (! " (! " " " " (! " "" (! (!

(! (!(! (!" " " (! (! (! " " " " (! "

Legend " 2009 Survey Points (! 2010 Survey Points Lake Plain Glaciated Appalachian Plateau Till Plains Unglaciated Appalachian Plateau Interior Low Plateau We used a generalized random-tessellation stratified (GRTS) sampling design to

select points within NWI polygons (Kincaid et al. 2008). A stratified random sample of

350 points with a 50% oversample was selected each year. Sample points were stratified

by 1) wetland size: small (0.05-1 ha), medium (1-10 ha) and large (>10 ha) and 2) water regime, categorized as either seasonal (temporarily flooded and seasonally flooded) or semi-permanent (semi-permanently flooded and intermittently exposed) (Cowardin et al.

1979). Wetlands within each stratum were sampled in proportion to their cumulative area across the study area in 2009. Temporarily flooded wetlands were removed from the sampling frame in 2010 because 2009 data indicated that these wetlands typically did not contain suitable emergent vegetation. Equal numbers of seasonally and semi- permanently flooded wetlands were sampled in 2010. A separate GRTS sample was used to select wetlands restored with funds administered by the Ohio Department of Natural

Resources, Division of Wildlife’s (DOW) Wetland Restoration Program. The sample was stratified according to DOW criteria that included 1) wetland size: small (< 0.8 ha), medium (0.8 - < 2 ha), and large (≥ 2 ha) 2) distance from a managed wetland complex:

0-8 km, 8-24 km, 24-48 km and > 48 km. The samples were drawn in R (R Development

Core Team, 2005) using the spsurvey package version 2.0 containing the GRTS function

(Kincaid et al. 2008).

Survey Methods. Marsh bird surveys closely followed methods outlined in the

Standardized North American Marsh Bird Monitoring Protocols (Conway 2009).

Surveys deviated from the protocol in that points were randomly located within a wetland

and not necessarily on dikes, roads, or at the vegetation-water interface. Points were

surveyed three times during the field season with approximately fifteen days between

each survey. We conducted surveys from 8 May through 28 June 2009 and from 8 May

through 22 June 2010. Surveys were conducted 30 minutes before sunrise to 2-3 hours

after sunrise and in 2 hours before sunset to 30 minutes after sunset. Background noise,

wind (Beaufort Scale) and sky conditions (U.S. Weather Bureau code) were recorded

prior to each survey and we did not conduct surveys in windy conditions over 20km/hr or during sustained rain, per protocol guidelines.

Each survey began with a 5-minute period of passive listening followed by a 30

second broadcast of species’ calls, and then 30 seconds of silence. Recorded calls were

obtained from the North American Marsh Bird Monitoring Program survey coordinator.

The order of species in the call broadcast was as follows: Least Bittern, Sora, Virginia

Rail, King Rail, Pied-billed Grebe. We added American Bittern and a second Least

Bittern call to the end of the broadcast series in 2010. A decibel meter was used to

ensure that speakers broadcasted at 80-90 dB from 1 m away. Technicians were trained in marsh bird identification using audio CDs beginning in April and a week of practice surveys in the field. The presence and estimated distance to each individual detected was recorded for nine focal species: Pied-billed Grebe, American Bittern, Least

Bittern, Common Moorhen, American Coot, King Rail, Virginia Rail, Sora, and Black

Tern.

Fine-scale habitat data were collected within a 100 m radius circular plot centered on the survey point. Vegetation cover was mapped in the field after each survey on aerial photographs within the plot and classified according to Cowardin et al. (1979). Observers

26 collected water depth measurements at the survey point and 50 m from the survey point in four cardinal directions. The degree of interspersion at each site was estimated using the cover type index of Stewart and Kantrud (1971) (Figure 2.2). Basal stem density of persistent emergent vegetation including: cattail (Typha spp.), common reed (Phragmites australis), river bulrush (Scirpus fluviatilis), and swamp rose-mallow (Hibiscus moscheutos) was quantified within 0.5 m2 circular frames at three plots per survey point after all three bird surveys were conducted. The first plot was placed at the stand of vegetation closest to the survey point and subsequent plots were spaced 15 m apart.

Stands of invasive macrophytes, common reed, narrow-leaved cattail (Typha angustifolia), and narrow-leaved broad leaved cattail hybrid (T. x glauca) were identified and mapped also after the third survey. Woodland and cropland cover within a 500 m buffer around each survey wetland was ground-truthed and documented on aerial photographs. I selected 500 m because I was interested in the effects of local land use on marsh bird occupancy and marsh bird community integrity has been shown to be influenced by land use at this scale (DeLuca et al. 2004).

Analysis. I used occupancy modeling to estimate the probability of occupancy as a function of habitat covariates while simultaneously accounting for variations in detection probability of species across survey sites and survey periods (MacKenzie et al. 2002). I used the program PRESENCE 3.0 to evaluate a priori candidate models (U.S. Geological

Survey, Patuxent Wildlife Research Center, http://www.mbrpwrc.usgs.gov/software. html). Parameters were estimated using maximum likelihood methods. The fit of each global model was assessed using Pearson’s chi-square test statistic with a distribution

Figure 2.2. Cover types used to classify degree of interspersion in survey wetlands after each of three marsh bird surveys in the glaciated subregions of Ohio, USA May through early July 2009 and 2010. Black represents emergent vegetation and white represents open water or exposed soil. From Steward and Kantrud (1971).

estimated from 1000 parametric bootstraps (MacKenzie and Bailey 2004). I removed

survey points that were missing survey-specific covariate data and surveys in temporarily

flooded wetlands resulting in 259 survey points in 2009 and 281 points in 2010.

I used a two-step approach to estimate detection probability and occupancy

parameters (Kroll et al. 2007). First, factors predicted to influence detection probability were modeled and ranked using Akaike’s Information Criterion (AIC). Detection

probability covariates were then used to model occupancy as a function of multiple

habitat covariates. I created a set of a priori candidate models based on fine-scale habitat

associations reported in the scientific literature and surrounding land cover features

predicted to influence marsh bird occupancy (Appendix A). Only detections recorded

within 100 m from the survey point were used in the analysis. Categorical covariates that

resulted in complete or quasi-complete separation of values were either removed from the

analysis or collapsed into fewer categories. Models were ranked using AIC or quasi-AIC for models showing evidence of overdispersion. I used model averaging based on AIC weights to calculate effect size for each parameter (Burnham and Anderson 2002).

Results included estimates for covariates having a significant effect on occupancy only

(i.e. 95% confidence interval does not include zero). AIC tables include results from models with a Δ AIC ≥ 7, below which there is weak support for the model (Burnham and Anderson 2002).

Model Covariates. I used 12 fine-scale and surrounding land cover features to model marsh bird habitat associations (Table 2.1). The percent aerial cover of seven vegetation classes was estimated using vegetation maps created after the last survey and a

Occupancy Covariate Description EM1 Percent persistent emergent cover within a 100 m radius plot, estimated from ground-thruthed aerial photograph EM2 Percent non-persistent emergent cover within 100 m radius plot RR Relative richness of structural cover types: # cover types at plot/total potential cover types (7) DWD Standardized seasonal variation in water depth XWD Standardized mean water depth taken at survey point and 50m from point in 4 cardinal directions (cm) INT 4 water-emergent interspersion categories (Stewart and Kantrud 1971) PAB Percent palustrine aquatic bed within 100 m radius plot PFO Percent palustrine forest within 100 m radius plot Area Log-transformed size of survey wetland (ha), collected from National Wetland Inventory Data ( NWI) data Wetland (500 m) Percent of 500 m buffer around survey wetland containing wetland cover, from digitized NWI data Cropland (500 m) Percent of 500 m buffer around survey wetland containing tillage agriculture, digitized from ground-truthed photographs Woodland (500 m) Percent of 500 m buffer around survey wetland containing upland woody vegetation, digitized from ground-truthed photographs

Table 2.1. List of codes and description of covariates used to model occupancy probability (ψ) of five species (Pied-billed Grebe, Least Bittern, Virginia Rail, Sora, and Common Moorhen) in the glaciated subregions of Ohio, USA, May through early July, 2009 and 2010.

2.54 cm dot grid overlay with 64 dots per grid cell. Vegetation classes included: open

water, aquatic bed, persistent emergent, non-persistent emergent, temporarily flooded

vegetation, wetland shrub and wetland forest (≥6.1 m). The percent cover of invasive

macrophytes was also estimated using a dot grid but was highly correlated with percent

persistent emergent cover and not included in the models. I ran an analysis of variance

(ANOVA) on water depth for each survey point using visit as a categorical variable. The

square root of the mean square was used to represent seasonal variations in water depth.

Water depth was also averaged across all measurements after all surveys and used as an

additional variable. Percent cover of wetlands within a 500 m radius buffer around the

wetland was quantified from digitized NWI polygons and the calculate geometry feature in ArcGIS 9.3. Woodland and cropland cover were digitized within a 500 m radius buffer around survey wetlands using ground-truthed aerial photographs.

I included both survey-specific and site-specific covariates to explain variation in the probability of detection (Table 2.2). Dummy variables were used to indicate whether or not the survey was conducted within a stand of emergent vegetation. I combined percent cover of open water and submerged aquatic cover types quantified as described above to create a covariate called percent water. Stem density data were collected during

July through August and were assumed to be constant across the three survey occasions.

Given the broad range of our study area, observers were assigned to one physiographic subregion. As a result, the effect of observer on detection probability was confounded with geographic region and was not used in the analysis.

Detection Probability Description Covariate Date Standardized Julian Date Noise Five categories indicating level of background noise from faint to intense/probably can’t hear birds beyond 25 m. ( Conway 2008) Wind Beaufort Scale, categories estimating wind speed (Conway 2008) Sky Five categories indicating cloud cover, fog, or rain (Conway 2008) InEM Categorical variable indicating whether or not survey was conducted within stand of emergent vegetation % Water Percent cover of open water and aquatic bed cover in 100 m radius plot Stem Standardized basal stem counts of persistent emergent vegetation within a 0.5 m2 circular frame averaged over three plots Area Log-transformed size of survey wetland (ha), collected from National NWI data

Table 2.2. List of codes and description of covariates used to model detection probability (p) of five species (Pied-billed Grebe, Least Bittern, Virginia Rail, Sora, and Common Moorhen) in the glaciated subregions of Ohio, USA, May through early July, 2009 and 2010.

Spearman’s correlation coefficient and the variance inflation factor (VIF) were calculated in program R version 2.11.1 (R Development Core Team, 2005) to detect and remove any highly correlated variables before analyses. Covariates with a VIF > 10 or a correlation coeefient ≥ 0.70 were removed from the analysis.

Results

The number of state endangered King Rail and American Bittern detected in 2010 was double the number of individuals in 2009, although in both years numbers were in the single digits (Appendix B). Occupancy model parameters could not be developed for

Black Tern, American Bittern, Least Bittern (2009), and American Coot because of a low number of detections. The majority of rail and bittern detections were estimated to be within 100 m of the survey point whereas approximately two thirds of Pied-billed Grebe detections were beyond 100 m (Appendix B).

Detection probability. The number of marsh bird detections varied across survey visit for all species. The survey visit with the greatest number of detections was consistent across years for some species but varied for others (Table 2.3). Least Bittern detections peaked later in the season from 22 May through 28 June, 2009 and 2010.

There was a steep decline in the total number of Sora detected as the survey season progressed in 2009 and 2010. Maximum number of detections of Pied-billed Grebe,

Virginia Rail, Common Moorhen, and American Coot did not display any patterns across the two breeding seasons. Detections of three Ohio state endangered marsh birds; Black

Tern, American Bittern, and King Rail were sparse in both years. Black Terns were first detected on 31 May 2009 when one nest with four eggs was discovered at Cedar Point

Frequency Survey Mean no. of Species Year of SE visit individuals occurrence Pied-billed Grebe 2009 1 0.124 0.220 0.043 2 0.139 0.247 0.044 3 0.116 0.259 0.054 2010 1 0.128 0.370 0.057 2 0.178 0.416 0.066 3 0.178 0.352 0.070 American Bittern 2009 1 0.012 0.012 0.007 2 0.008 0.008 0.005 3 0.012 0.012 0.007 2010 1 0.014 0.014 0.007 2 0.007 0.007 0.005 3 0.014 0.025 0.013 Least Bittern 2009 1 0.015 0.019 0.011 2 0.031 0.042 0.017 3 0.031 0.042 0.017 2010 1 0.043 0.053 0.017 2 0.068 0.089 0.022 3 0.039 0.057 0.018 Virginia Rail 2009 1 0.039 0.050 0.017 2 0.050 0.077 0.023 3 0.027 0.031 0.012 2010 1 0.053 0.075 0.021 2 0.046 0.075 0.022 3 0.057 0.114 0.035 Sora 2009 1 0.085 0.100 0.020 2 0.012 0.019 0.010 3 0.008 0.015 0.009 2010 1 0.117 0.178 0.034 2 0.050 0.068 0.019 3 0.011 0.011 0.006 Common Moorhen 2009 1 0.061 0.104 0.030 2 0.073 0.174 0.044 3 0.061 0.154 0.046 2010 1 0.139 0.285 0.055 2 0.107 0.249 0.052 3 0.093 0.214 0.056 American Coot 2009 1 0.035 0.054 0.022 2 0.008 0.012 0.009 3 0.023 0.023 0.009 2010 1 0.039 0.053 0.018 2 0.046 0.050 0.014 3 0.036 0.064 0.021

National Wildlife Refuge. A total of five birds were observed in the location. Three juvenile Black Terns were observed in the same area in July 2009. The small colony was observed at the same site in 2010 but breeding was not confirmed. American Bitterns

were detected at five different survey points in 2009 and at ten points in 2010. One King

Rail pair was detected during the third survey period in 2009. King Rails were detected

at three separate sites between 28 May and 9 June 2010.

Marsh bird response to call-broadcasts of conspecifics was variable among species

(Figures 2.3a-e). Pied-billed Grebe, Virginia Rail and Sora calls were detected more often during or directly after conspecific broadcasts than during the passive period or broadcasts of other species. American Bittern calls were broadcasted only in 2010, but

did not appear to increase detection rates. Least Bittern vocalizations were heard

throughout the entire survey period at the majority of survey points where they were

detected and call frequency did not increase during or directly after broadcasts of

conspecifics. Call rates of all species during the passive period were lower or

approximate equal to rates during the call-broadcast portion suggesting that the

broadcasts did not interfere with detectability.

The effect of site-specific and survey-specific covariates on detection probability varied across species (Table 2.4). Mean estimated detection probability (p) for Pied- billed Grebe was 0.47 in 2009 and 0.43 in 2010. The mean probability of detection for

Least Bittern was only estimated in 2010 and was substantially lower (p =0.19) than

Pied-billed Grebe. There was an effect of year on detection probability of Sora resulting in a detection probability of 0.35 in 2010 and 0.11 in 2009. The mean detection

a) Pied-billed Grebe

0.5 0.45 0.4 0.35 0.3 0.25 0.2 2009, n=173 0.15 2010, n=310

Calls/minute/individuals 0.1 0.05 0

Survey sequence

continued

Figure 2.3. Mean number of calls detectedper minute of a) Pied-billed Grebe (PBGR), b) Least Bittern (LEBI), c) American Bittern (AMBI), d) Virginia Rail (VIRA), and e) Sora (SORA) during passive and call broadcast sequence, in the glaciated subregions of Ohio May through early July, 2009 and 2010. n = total number of individuals detected within 100 m by vocalizations in all three visits. Survey period includes five minute passive listening period and one minute of calls for birds indicated by 4 letter AOU code. American Bittern and second Least Bittern calls used in 2010 only. KIRA = King Rail.

Figure 2.3 (continued) b) Least Bittern

0.7 0.6 0.5 0.4 0.3 2009, n=23 0.2 2010, n=53

Calls/minute/individuals 0.1 0

Survey period

c) American Bittern

0.8 0.7 0.6 0.5 0.4

0.3 2009, n=7 0.2 2010, n=10

Calls/minute/individuals 0.1 0

Survey period

continued

Figure 2.3 (continued)

d) Virginia Rail

0.7 0.6 0.5 0.4 0.3 2009, n=41 0.2 2010, n=70

Calls/minute/individuals 0.1 0

Survey period

e) Sora

0.8 0.7 0.6 0.5 0.4

0.3 2009, n=31 0.2 2010, n=67

calls/minute/individuals 0.1 0

Survey Period

95% CI Species Covariate Estimate Upper Lower Pied-billed Grebe Stem 0.85 1.17 0.53 Area -0.58 -0.32 -0.84 Intercept 1.95 3.17 0.73 Least Bitterna InEM 1.65 2.87 0.44 Intercept -1.91 -0.82 -3.00 Sora Date -1.45 -0.98 -1.92 Year 1.66 3.19 0.13 Intercept -2.63 -1.27 -3.99 Common Moorhen Area 0.65 1.15 0.15 Percent Water 1.38 2.84 -0.08 Intercept -3.18 -0.59 -5.77 Virginia Rail Area -0.21 -0.01 -0.41 Percent Water -2.46 -0.09 -4.82 Intercept 0.92 1.87 -0.03 a Least Bittern detection probability estimated from 2010 survey data only

39 probability for Common Moorhen was 0.31 in 2009 and 0.37 in 2010. Mean estimated detection probability for Virginia Rail was 0.46 in 2009 and 0.40 in 2010.

Occupancy. Occupancy models were not tested for King Rail, Black Tern,

American Bittern and American Coot due to low numbers of detections. Least Bittern occupancy models that combined data for 2009 and 2010 failed to converge, possibly due to the low number of detections in 2009. Only results from the 2010 Least

Bittern model are reported here. No variables were removed due to high correlation or potential multi-collinearity as indicated by the estimated variance inflation factor. The categorical variable representing interspersion was collapsed so that categories 4 and 3 were combined due to problems with convergence.

Pearson’s Goodness-of-Fit test indicated that global occupancy models were a good fit at an alpha level of 5% (Table 2.5). Only the global model for Common

Moorhen occupancy had a ĉ value greater than one, suggesting overdispersion. The test suggests that the variation in parameter estimates is greater than predicted from the

Common Moorhen occupancy model.

The best supported models (ΔAIC≤2) estimating Pied-billed Grebe occupancy included fine-scale habitat features, wetland size, and year (Table 2.6). Mean occupancy probability was 0.15 in 2009 and 0.21 in 2010. The probability of Pied-billed Grebe occupancy was positively associated with mean water depth and wetland size (Table 2.7,

Figures 2.4a-b). Pied-billed Grebe were 1.99 (95% CI: 2.72-1.47) times more likely to occupy a site for every 12 cm increase in mean water depth. A two hectare increase in

Wetland area increased the probability of Pied-billed Grebe occupancy by a factor of

2 2 2 Species χd P (χ > χd ) ĉ Pied-billed Grebe 5.391 0.433 0.970 Least Bitterna 3.638 0.715 0.624 Common Moorhen 9.040 0.119 1.727 Virginia Rail 4.836 0.457 0.931 Sora 3.214 0.590 0.693 a Least Bittern model includes 2010 data only

Table 2.5. Goodness-of-Fit of global model based on Pearson’s chi-squared statistic of 2 observed data (χd ), probability of a chi-squared value greater than observed based on 2 2 parametric bootstrapping distribution p(χ > χd ), and estimated overdispersion parameter (ĉ) for five marsh bird species in the glaciated subregions of Ohio, USA during May through early July 2009 and 2010.

−2 Log- Modela Kb AIC ΔAIC w likelihood i ψ(EM1+EM2+PAB+XWD+DWD+Area) 442.53 10 462.53 0.00 0.48 ψ(EM1+EM2+PAB+XWD+DWD+Area+Year) 442.43 11 464.43 1.90 0.19 ψ(EM1+EM2+PAB+XWD+DWD+Area+Year+ 440.49 12 464.49 1.96 0.18 Year*XWD) ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m) 441.38 13 467.38 4.85 0.04 ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area) 441.97 ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO 441.07 13 467.97 5.44 0.03 +Area) ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m+ 439.10 14 469.07 6.54 0.02 Year+Year*XWD) ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m+ 441.18 15 469.10 6.65 0.02 Year) ψ(.) 553.91 4 561.91 99.38 0.00 a Detection probability modeled as a function of stem density and wetland area b K, number of parameters in model; AIC, Akaike’s Information Criterion; ΔAIC, difference in AIC relative to best model, ωi, Akaike weight.

Table 2.6. Top ranked (ΔAIC≤7) candidate models and null model estimating the effect of habitat covariates on Pied-billed Grebe occupancy (ψ) in the glaciated region of Ohio, USA, May through early July, 2009 and 2010. 500 m refers to percent wetland, cropland, and woodland within 500m of wetland.

95% CI Covariate Estimate upper lower EM1 -0.07 1.65 -1.79 EM2 1.14 2.82 -0.54 PAB 0.70 2.34 -0.93 XWD 1.38 2.00 0.77 DWD 0.04 0.34 -0.26 Area 0.89 1.19 0.59 Year -0.01 0.93 -0.96 Year*XWD -0.65 0.28 -1.58 Intercept -5.98 -4.34 -7.61

Table 2.7. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) estimating Pied- billed Grebe occupancy in the glaciated region of Ohio, USA, May through early July, 2009 and 2010.

43 a)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Occpancy Probability Occpancy 0.1 0 -1 -0.5 0 0.5 1 1.5 2 2.5 3 Mean Water Depth (Z-score)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

Occupancy Probability Occupancy 0.1 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 LnArea (ha)

Figure 2.4. Relationship between a) standardized mean water depth b) wetland size and Pied-billed Grebe occupancy in the glaciated region of Ohio, USA, May through early July, 2009 and 2010. Mean water depth (Z=0) is 22 cm and one half step in Z-score is equivalent to approximately 12 cm. Dashed lines indicate 95% confidence interval.

2.44 (95% CI: 3.29-1.80).

The mean occupancy probability of Least Bittern was 0.17 in 2010. Inclusion of

mean water depth and seasonal variation in water depth into the model resulted in a nine

fold increase in support for the top ranked model compared to the second best model

(Table 2.8, Figure 2.5a). Least Bittern were 1.6 (95% CI: 2.62-1.03) times more likely to

occupy a site for every 13 cm increase in water depth. Unlike Pied-billed Grebe, Least

Bittern occupancy was negatively associated with adjacent woodland cover and positively associated with persistent emergent cover (Table 2.9, Figure 4.2b-c). The mean percent woodland cover within a 500 m buffer around wetland sites occupied by

Least Bittern was 8% (range: 0-40%). Mean adjacent woodland cover for unoccupied sites was 25% (range: 0-90%). Least Bittern were 2.03 (95% CI: 3.78-1.10) times more likely to occupy a site for every 20% increase in persistent emergent vegetation.

The model that best described Virginia Rail occupancy included adjacent habitat features and had ten times the support of the same model without adjacent cover types

(Table 2.10). Model estimates indicated a positive association between adjacent woodland cover and occupancy, a relationship opposite to what was estimated for Least

Bittern (Table 2.11, Figure 2.6a). Occupancy estimates were 0.17 in 2009 and 0.25 in

2010. Virginia Rail were 1.72 (95% CI: 2.43-1.22) times more likely to occupy a site for every 15% increase in adjacent woodland cover. Wetland area was also positively associated with Virginia Rail occupancy, but the effect was not as large as for Pied-billed

Grebe (Table 2.11, Figure 2.6b). Percent persistent emergent vegetation was the only statistically significant, fine-scale habitat feature associated with occupancy

−2 Log- Modela Kb AIC ΔAIC w likelihood i ψ(EM1+XWD+DWD+Area+500 m) 199.53 10 219.53 0.00 0.76 ψ(EM1+Area+Buffers) 207.98 8 223.98 4.45 0.08 ψ(EM1+XWD+DWD+RR+Int+Area+ 198.39 13 224.39 4.86 0.07 500 m) ψ(EM1+XWD+DWD+Area) 212.35 7 226.35 6.82 0.03 ψ(Global) 198.38 14 227.30 6.85 0.02 Ψ(.) 241.69 3 247.69 28.16 0.00 a Detection probability (p) modeled as a function of InEM b K, number of parameter is model; AIC, Akaike’s Information Criterion; ΔAIC, difference in AIC score relative to top ranked model, wi =Akaike weight, indicating relative likelihood of model.

Table 2.8. Top ranked (ΔAIC≤7) candidate models and null model estimating the influence of habitat covariates on Least Bittern occupancy (ψ) in the glaciated region of Ohio, USA, May through early July 2010. 500 m refers to percent wetland, cropland, and woodland within 500 m of wetland

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occpancy Probability Occpancy 0.2 0.1 0 -1 -0.5 0 0.5 1 1.50 2 2.5 3 Mean Water Depth (Z-score)

continued

Figure 2.5. Relationship between a) standardized mean water depth b) percent persistent emergent vegetation c) adjacent woodland cover and Least Bittern occupancy in the glaciated region of Ohio, May through early July 2010. Dashed lines indicate 95% confidence interval. Mean water depth (Z=0) is 29 cm and one half step in Z-score is equivalent to approximately 13 cm.

Figure 2.5 (continued)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Occupancy Probability Occupancy 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent Persistent Emergent

0.4 0.35 0.3 0.25 0.2 0.15 0.1 Occupancy Probability Occupancy 0.05 0 0 10 20 30 40 50 60 70 80 90 100

Percent Adjacent Woodland

95% CI Covariate Estimate upper lower EM1 3.55 6.65 0.44 XWD 0.99 1.93 0.06 DWD 0.20 1.24 -0.85 Area 0.05 0.55 -0.44 Wetland 1.92 5.84 -2.01 Cropland -1.87 3.58 -7.32 Woodland -5.73 -0.05 -11.40 Intercept -2.57 1.08 -6.22

Table 2.9. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) describing Least Bittern occupancy in the glaciated region of Ohio, USA, May through early July 2010.

−2 Log- Modela Kb AIC ΔAIC w likelihood i ψ(EM1+EM2+Area+500 m) 418.34 10 438.34 0.00 0.28 ψ(EM1+EM2+Area+500 m+Year) 416.65 11 438.65 0.31 0.24 ψ(EM1+EM2+RR+INT+Area+500 m) 413.91 13 439.91 1.57 0.13 ψ(EM1+EM2+RR+INT+Area+500 m+Year) 413.10 14 441.10 2.76 0.07 ψ(EM1+EM2+RR+INT+XWD+DWD+Area+ 411.19 15 441.19 2.85 0.07 500 m) ψ(EM1+EM2+RR+INT+XWD+DWD+Area+ 409.77 16 441.77 3.43 0.05 500 m+Year) ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+ 410.75 16 442.75 4.41 0.03 Area+500 m) ψ(EM1+EM2+Area) 429.01 7 443.01 4.67 0.03 ψ(EM1+EM2+Area+Year) 427.33 8 443.33 4.99 0.02 ψ(EM1+EM2+RR+INT+XWD+DWD+Area+ 409.69 17 443.69 5.35 0.02 Buffers+Year+Year*XWD) ψ(EM1+EM2+RR+INT+Area) 410.75 10 444.50 6.16 0.01 ψ(EM1+EM2+RR+INT+XWD+DWD+Area) 429.01 12 445.01 6.67 0.01 ψ(Global) 427.33 18 445.15 6.81 0.01 ψ(EM1+EM2+RR+INT+XWD+DWD+Area+ 409.69 13 445.31 6.97 0.01 Year) ψ(.) 475.50 4 483.50 45.16 0.00 a Detection probability modeled as a function of area and percent water b K, number of parameter is model; AIC, Akaike’s Information Criterion; ΔAIC, difference in AIC relative to top ranked model, wi, Akaike weight.

Table 2.10. Top ranked (ΔAIC≤7) candidate models and null model estimating the effect of covariates on Virginia Rail occupancy (ψ) in the glaciated region of Ohio, USA, May through early July 2009 and 2010. 500 m refers to percent wetland, cropland, and woodland within 500m.

95% CI Covariate Estimate upper lower EM1 3.38 4.88 1.88 EM2 0.40 1.95 -1.16 RR 2.02 4.24 -0.20 INT_1a 0.19 1.36 -0.98 INT_2a 0.43 1.62 -0.75 Year 0.47 1.25 -0.31 Area 0.43 0.70 0.16 Wetland 1.83 4.15 -0.50 Cropland 1.61 4.21 -1.00 Woodland 3.61 5.91 1.31 Intercept -6.66 -4.02 -9.29 a Interspersion class 1 and 2 (Stewart and Kantrud 1971)

Table 2.11. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) estimating Virginia Rail occupancy in the glaciated region of Ohio, USA, May through early July 2009 and 2010.

51 a)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occupancy Probability Occupancy 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent Adjacent Woodland

continued

Figure 2.6. Relationship between a) percent adjacent woodland b) wetland area c) persistent emergent vegetation and Virginia Rail occupancy in the glaciated region of Ohio, May through June 2009 and 2010. Dashed lines indicate 95% confidence interval.

Figure 2.6 (continued) b)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occupancy Probability Occupancy 0.2 0.1 0 0 1 2 3 4 5 6 7 LnArea (ac.)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occupancy Probability Occupancy 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent Persistent Emergent

(Figure 2.6c). Virginia Rail were 1.97 (95% CI: 2.65-1.46) times more likely to occupy

a site for every 20% increase in persistent emergent vegetation.

There was substantial support for the Sora occupancy model including only

persistent and non-persistent vegetation, contrary to top-ranked models for other species

presented here (Table 2.12). The two best models predicting occupancy of Sora and the

Virginia Rail were identical. Similarities between the two species diverged as top-ranked

models (ΔAIC≤7) for Sora remained relatively simple while Virginia Rail models

become more complex. Occupancy estimates were 0.36 in 2009 and 0.37 in 2010. Sora

occupancy was positively associated with adjacent wetland cover and was not inhibited

by adjacent cropland cover (Table 2.13, Figure 2.7a-b). Sora were 1.74 (95% CI: 2.73-

1.11) times more likely to occupy a site for every 20% increase in adjacent wetland cover. For every 20% increase in persistent emergent vegetation, Sora occupancy increased by a factor of 1.75 (95% CI: 2.46-1.25) (Figure 2.7c).

Top ranked models predicting Common Moorhen occupancy had a relatively high number of parameters and included both adjacent cover type features and within wetland features (Table 2.14). Removal of the adjacent cover type covariates resulted in ΔQAIC values of 10.31 and lower, indicating a poor fit to the survey data. Common Moorhen occupancy estimates were 0.15 in 2009 and 0.16 in 2010. For every 15% increase in adjacent wetland cover, Common Moorhen were 2.20 (95% CI: 3.35-1.44) times more likely to occupy a site (Table 2.15, Figure 2.8a). Common Moorhen was positively associated with interspersion, which was included in the top three models ranked by

QAIC. Common Moorhen were more likely to occupy sites with greater than average

a b Model −2 Log- K AIC ΔAIC wi likelihood ψ(EM1+EM2+Area+500 m) 420.76 10 440.76 0.00 0.22 ψ(EM1+EM2+Area+500 m+Year) 418.81 11 440.81 0.05 0.22 ψ(EM1+EM2+Year) 427.04 7 441.04 0.28 0.19 ψ(EM1+EM2) 430.24 6 442.24 1.48 0.11 ψ(EM1+EM2+Area+Year) 426.92 8 442.92 2.16 0.08 ψ(EM1+EM2+Area) 430.16 7 444.16 3.40 0.04 ψ(EM1+EM2+RR+INT+Year) 424.86 10 444.86 4.10 0.03 ψ(EM1+EM2+RR+INT+Area+ 500 m+ 417.44 14 445.44 4.68 0.02 Year) ψ(EM1+EM2+RR+INT+Area+500 m 420.07 13 446.07 5.31 0.02 ψ(EM1+EM2+RR+INT+Area+Year) 424.72 11 446.72 5.96 0.01 ψ(EM1+EM2+RR+INT+DWD+XWD+ 423.08 12 447.08 6.32 0.01 Year) ψ(EM1+EM2+RR+INT) 429.20 9 447.20 6.44 0.01 ψ(EM1+EM2+RR+INT+DWD+XWD+ 421.64 13 447.64 6.88 0.01 PFO+Year) ψ(.) 447.11 4 455.11 14.99 0.00 a Detection probability modeled as a function of Date and Year b K, number of parameter is model; AIC, Akaike’s Information Criterion; ΔAIC, difference in AIC relative to top ranked model, wi, Akaike weight.

Table 2.12. AIC results of top ranked (ΔAIC≤7) candidate models and null model estimating the effect of covariates on Sora occupancy (ψ) in the glaciated region of Ohio, USA, May through early July 2009 and 2010. 500 m refers to percent wetland, cropland, and woodland within 500m.

95% CI Covariate Estimate upper lower EM1 2.81 4.50 1.12 EM2 1.45 2.94 -0.04 Year -1.31 0.43 -3.05 Area -0.01 0.15 -0.16 Wetland 2.76 5.02 0.51 Cropland 2.40 4.67 0.13 Woodland 0.72 2.96 -1.52 Intercept -2.66 -0.34 -4.98

56 a)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Occupancy Probability Occupancy 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent Adjacent Wetland

continued

Figure 2.7. Relationship between a) percent adjacent wetland b) percent adjacent cropland c) persistent emergent vegetation and Sora occupancy in the glaciated region of Ohio, May through early July, 2009 and 2010. Dash lines represent 95% Confidence Interval.

Figure 2.7 (continued) b)

1 0.9 0.8 0.7 0.6

Probability 0.5 0.4 0.3

Occupancy Occupancy 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent adjacent cropland

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occupancy Probability Occupancy 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent Persistent Emergent

a Model −2 Log- K QAIC ΔQAIC wi likelihood ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+ 380.25 16 251.80 0.00 0.26 Area+500 m) ψ(EM1+EM2+PAB+XWD+DWD+INT+RR+ 377.72 17 252.34 0.54 0.20 Area+500 m+Year) ψ(Em1+EM2+PAB+XWD+DWD+RR+INT+PFO 378.49 17 252.78 0.98 0.16 +Area+500 m) Ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m) 392.52 13 252.89 1.09 0.15 ψ(EM1+EM2+PAB+XWD+DWD+Area+ 390.02 14 253.45 1.65 0.11 500 m +Year) ψ(EM1+EM2+PAB+XWD+DWD+Area+ 388.76 15 254.72 2.92 0.06 500 m+Year+Year*XWD) ψ(Global Model) 375.47 19 255.03 3.23 0.05 ψ(.) 504.69 4 299.73 47.93 0.00 a Detection probability modeled as a function of wetland area and percent water b K, number of parameter is model; QAIC, Quasi Akaike’s Information Criterion; ΔAIC, difference in AIC relative to top ranked model, wi, Akaike weight.

Table 2.14. Quasi-AIC results of top ranked (ΔQAIC≤7) candidate models and null model estimating the effect of covariates on Common Moorhen occupancy (ψ) in the glaciated region of Ohio, USA, May through early July, 2009 and 2010. 500 m refers to percent wetland, cropland, and woodland within 500 m of wetland.

95% CI Covariate Estimate upper lower EM1 5.13 7.90 2.36 EM2 2.91 5.60 0.23 PAB 3.04 5.54 0.55 XWD 1.30 1.98 0.62 DWD -0.18 0.24 -0.60 RR 1.85 4.60 -0.89 INT_1 1.59 3.20 -0.02 INT_2 1.95 3.26 0.64 PFO -6.07 4.06 -16.20 Area 0.15 0.70 -0.39 Wetland 5.25 8.06 2.45 Cropland 2.42 6.17 -1.34 Woodland -2.01 1.88 -5.90 Intercept -9.44 -4.91 -13.97

Table 2.15. Model averaged, untransformed parameter estimates and 95% confidence intervals (CI) for covariates included in top ranked models (ΔAIC≤2) estimating Common Moorhen occupancy in the glaciated region of Ohio, USA, May through early July, 2009 and 2010.

60 a)

0.9

0.8

0.7

0.6

0.5

0.4

0.3 Occupancy Probability Occupancy 0.2

0.1

0 0 10 20 30 40 50 60 70 80 90 100 Percent Adjacent Wetland

continued

Figure 2.8. Relationship between a) percent adjacent wetland b) mean water depth c) persistent emergent vegetation and Common Moorhen occupancy in the glaciated region of Ohio, May through early July 2009 and 2010. Dash lines represent 95% confidence interval.

Figure 2.8 (continued) b)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occupancy Probability Occupancy 0.2 0.1 0 -1 -0.5 0 0.5 1 1.5 2 2.5 3 Mean Water Depth (Standardized)

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

Occupancy Probability Occupancy 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 Percent Persistent Emergent

62 water depth and a high percentage of persistent emergent vegetation, a pattern similar to that of Least Bittern (Table 2.15, Figure 2.8b-c). Common Moorhen were 1.92 (95% CI:

2.69-1.36) times more likely to occupy a site for every 12 cm increase in mean water depth. For every 15% increase in persistent emergent vegetation, the probability of

Common Moorhen occupancy increased by a factor of 2.16 (95% CI:3.27- 1.42).

Common Moorhen occupancy was also positively associated with non-persistent emergent vegetation and submerged and floating aquatic vegetation.

Occupancy by State Endangered Species. Occupancy models could not be evaluated for three state endangered species; American Bittern, King Rail, and Black

Tern because of a small number of detections. American Bittern were detected at five different survey points in 2009 and ten survey points in 2010. In 2009, American Bittern occupied state and federal property including Big Island Wildlife Area (WA) in Marion

County, Magee Marsh WA in Ottawa County, and Ottawa National Wildlife Refuge in

Ottawa and Lucas counties. In 2010, American Bittern were detected at the former wetland complexes in addition to a private hunting club in Sandusky county, Pickerel

Creek WA in Sandusky County, Metzger Marsh in Lucas County and Willow Creek WA in Erie County. Mean size of occupied wetlands was 86.1 ha (34.2-141.1 IQR) and tended to be part of a wetland complex. American Bitterns occupied sites with a relatively high mean water depth (38 cm). Mean total emergent cover of occupied wetlands was 57% (42-78 IQR). Narrow-leaved cattail and/or hybrid cattail was the dominant persistent emergent species in 80% of occupied wetlands. The non-native flowering rush (Butomus umbellatus) was the dominant non-persistent emergent species

in 53% of occupied sites. Other dominant species found in wetlands occupied by

American Bittern include: smartweed (Polygonum spp.), reed canary grass (Phalaris

arundinacea), common bur-reed (Sparganium eurycarpum), pickerel-weed (Pontederia

cordata), river-bulrush (Schoenoplectus fluviatilis), Broad-leaved cattail (T. latifolia),

swamp rose-mallow (Hibiscus moscheutos), and swamp loosestrife (Decodon

verticillatus).

Black Terns were detected beginning on 31 May, 2009 at a large (585 ha) impounded wetland in Cedar Point NWR in Lucas County. The wetland is located

adjacent to the western basin of Lake Erie and adult Black Terns were observed flying

out towards the lake on several occasions. At least five individual adults were observed

in a small breeding colony. One nest with four eggs was found on 31 May, 2009 and

it was suspected that other nests were in the area based on the behavior of the adult birds. The nest was build on a large floating mat of smartweed (Polygonum amphibium) and nest substrate was predominately roots that had been removed from the floating mat. One adult of the breeding pair perched on an old sign post approximately

10 m from the nest site, while the other adult incubated the eggs. Multiple Black

Terns joined the pair in trying to distract or remove the intruder if the nest was ever approached, but these birds would usually only remain for a short time (2-5 minutes) leaving the breeding pair to defend the nest. The site was predominately palustrine aquatic bed vegetation (85%) including water milfoil (Myriophyllum spp), hornwort (Ceratophyllum spp.), white waterlily (Nymphaea odorata) and greater duckweed (Spirodela polyrhiza). Emergent vegetation made up the remaining 15% of

the areal cover and species included: purple loosestrife (Lythrum salicaria), swamp

loosestrife, smartweed, and pickerel weed. Mean water depth was 58 cm.

Three juvenile terns were observed at the site in late July, 2009. Leaves of white

water lily had covered most of the open water at the site. The typically floating leaves were

starting to emerge out of the water providing good visual obstruction of the resting

juvenile terns. It is unclear if the terns were the product of one or more breeding pairs

since multiple adults were in the area and defending the young birds.

Black Tern were detected at five sites in 2010, all detections were located in the large impounded wetland at Cedar Point. All detections were of adult birds making foraging flights and none of the birds were found roosting or nesting near the survey points. The colony was observed in the same location as 2009 but breeding was not confirmed. Two survey points were located in Metzger Marsh in 2010, but no Black

Tern were observed there despite sightings of this species at Metzger Marsh in 2009.

A King Rail pair was detected in Pickerel Creek WA on 13 June, 2009. The site

mainly consisted of temporarily flooded vegetation including goldenrod (Solidago spp.),

sedges (Carex spp.), foxtail barley (Hordeum jubatum), spikerush (Eleocharis spp.), reed canary grass, black bulrush (Scirpus atrovirens) and scattered dogwood shrubs

(Cornus spp.). The site also included an inlet of water extending from a large deep pool of open water. Mean water depth of the small inlet was 19 cm. There was a shallow slope from the water to the temporarily flooded vegetation forming a highly diverse plant community at the site. On the edge of the channel dominant species included narrow- leaved cattail, phragmites, smartweed, and soft rush (Juncus effuses).

King Rails were detected at three survey points in 2010. One bird was detected

again at Pickerel Creek WA within a different wetland unit than in 2009. The rail was

detected 600 m from the survey point outside of the survey period. An additional 100 m habitat plot was created for this location and data were collected in mid-July only. The microtopography of the site was very diverse with pools of open water and gradual swells of emergent and temporarily flooded patches. Dominant emergent vegetation included soft rush, hybrid cattail, and sedges. Mean water depth, taken at five points in

July, was 12 cm. Two nest bowls were found at the site in hummocks of soft rush, but eggs were never observed in the nests.

An individual King Rail was detected at a privately restored wetland in Seneca

County on 28 May, but was never detected again. The site was composed of four excavated basins of water connected by narrow channels and forming a square. Inside and surrounding the square was a temporarily flooded plant community containing rushes

(Juncus spp.), sedges, goldenrod, and common vervain (Verbena hastata). Woody encroachment of young cottonwood (Populus deltoides), silver maple (Acer saccharinum), and willow (Salix spp.) was also observed at the restored wetland. The basins were dominated by narrow-leaved cattail and pondweed (Potamogeton spp.).

A King Rail pair was detected at a privately owned restored wetland in Union

County on 28 May, 2010. The site consisted of a 0.3 ha excavated basin with a gradual transition to upland grasses on 1/3 of the perimeter. A second basin (1.5 ha) was located approximately 70 m to the south and also had a gradual transition to upland grasses on

1/3 of the perimeter. The pair had been observed using the edge of both basins as well as

66 the grassland on different survey occasions. Dominant emergent vegetation included: broad leaved and hybrid cattail, spikerush, water-plantain (Alisma plantago-aquatica), and softstem bulrush (Schoenoplectus tabernaemontani). Mean water depth at the occupied wetland was 15 cm. At least two black downy chicks were observed foraging with adults on the edge of the larger wetland in early August. The foraging site was dominated by broad-leaved and hybrid cattail and contained shallow standing water (8 cm).

Although King Rails were detected at a small number of sites, some habitat occupancy patterns emerge. King Rails tended to use shallowly flooded sites (11-15 cm) with a diverse plant community resulting from variation in wetland microtopography.

Occupied wetlands were either managed impoundments where water control structures were used to keep water levels low or privately restored sites containing a gradual transition from wetland to grassland or temporarily flooded vegetation. In addition, large pieces of crayfish carapaces were observed at sites occupied by King Rails. I cannot say with certainty that the crayfish were all consumed by the rails, but it does indicate the presence of this invertebrate at the wetlands. Crayfish have been reported as a common summer food for King Rails (Meanley 1992).

Discussion

My results suggest that broad-scale features, wetland size, and fine-scale habitat features can influence marsh bird occupancy. Wetland size and/or the percent of wetland cover within 500 m were positively associated with four of the five species modeled.

This relationship may give some insight into the larger scale habitat selection of marsh

birds. For example, Virginia Rail are not considered to be area-sensitive (Brown and

Dinsmore 1986, Benoit and Askins 2002, but see Riffell et al. 2001), but our results

suggest that they were more likely to occupy larger wetlands. Considering that the

majority of wetlands surveyed were circular or square rather than linear, large wetlands

may reduce effects of edge such as increased probability of nest predation.

Persistent emergent vegetation is defined as wetland vegetation that remains

standing throughout the winter and into the next growing season (Cowardin et al. 1979).

Four of the five bird-habitat relationships I modeled had a positive association with

persistent emergent vegetation. Stands of persistent emergent vegetation are the main

source of cover in marshes of the northern United States in late April and early May

when marsh birds settle into a breeding site. Non-persistent vegetation such as pickerel

weed (Pontederia cordata) and arrowhead (Saggittaria sp.) emerge in June or early July.

In addition, robust persistent emergent vegetation such as cattail (Typha sp.) is identified as important for Least Bittern because it provides structural support for nests and perching locations for foraging (Sutton 1939, Lor and Malecki 2006).

Pied-billed Grebes were not positively associated with percent of persistent emergent vegetation. This species nests in both dense emergent cover and in open sites anchored to sparse vegetation (Chabreck 1963, Lor and Malecki 2006, personal observation). Unlike other focal species in this study, Pied- billed grebes forage in open water or dive into the water when disturbed on nests (Muller and Storer 1999). While persistent emergent vegetation may provide cover of nests from wind damage, it is possible that the dense stands may also inhibit quick escape from predators during

incubation. Pied-billed Grebes nested in dense stands of flooded reed canary grass

(Phalaris arundinacea) in Ohio but due to the flexible structure of the grass were able to dive and swim through the vegetation (personal observation). Future research on Pied- billed Grebe nest habitat characteristics should consider an interaction between emergent vegetation density and structural integrity of the vegetation.

Least Bittern occupancy was not associated with wetland area in my model. The average wetland size occupied by Least Bittern was 66 ha (range 1.2 - 186.9). Other studies suggest that Least Bittern may prefer larger wetlands, but show that smaller wetlands are occupied if vegetation characteristics and water depth are suitable (Brown and Dinsmore 1986, Lor 2000, Bogner and Baldassarre 2002). Least Bittern nests in

Canada were found only in wetlands ≥ 6.8 ha in size. However, unpaired individuals were found in smaller wetlands, suggesting that wetland size affects the probability of successfully acquiring a mate and reproducing (Tozer et al. 2010). In this case, an ideal free distribution is not achieved since reproductive success is not equal among all occupied sites (Fretwell and Lucas 1970). Most large wetlands (>5 ha) in Ohio are impounded marshes owned by private hunting clubs or state and federal wildlife agencies. The majority of least bittern detections were in these large, impounded wetlands.

Probability of Least Bittern occupancy decreased with increasing woodland cover surrounding the wetland. Similar results were found for Least Bittern occupancy in

Arkansas (Budd and Krementz 2010). Pierluissi (2002) and Darrah and Krementz (2010) suggested that marsh birds may avoid sites with woody vegetation because they are

69 associated with predation risk from avian predators that use trees for perching and from mammalian predators using the woody vegetation for cover. Virginia Rails, on the other hand, were positively associated with woodland cover adjacent to the survey wetland.

Vegetation plots within occupied wetlands had a range of 0 - 100% wetland woody vegetation. Virginia Rail do not appear to be inhibited by woody vegetation and occupy a wider range of habitat types compared to Least Bittern. The Virginia Rail may have greater niche breadth during the breeding season as a result of high intra-specific competition (Svardson 1949). The positive association between adjacent woodland and

Virginia Rail occupancy may also be because of the widespread distribution of Virginia

Rail in Ohio including the highly forested Unglaciated Appalachian Plateau subregion.

In contrast, the majority of Least Bittern detections occurred within the Lake Plain subregion, suggesting a much more limited distribution within Ohio.

There was a difference in occupancy probability estimates between years with higher occupancy for all species in 2010. Seasonal precipitation across glaciated Ohio was higher in 2010, but this pattern was not consistent at a more local scale (Ben Kahler, personal communication). Differences are more likely due to changes in the sampling design between the two years. In 2010, our sample included a higher number of semi- permanently flooded wetlands which tend to have a greater frequency of marsh bird occurrence (Kantrud and Stewart 1984). Additionally, all but two of our observers were different between years which may have had an unaccounted for effect on detection probability and hence an effect on the occupancy estimate.

One assumption of occupancy modeling is that the population is closed to immigration and emigration over the duration of the survey visits (MacKenzie et al.

2006). The timing of our surveys was selected with this in mind; however, there is the

possibility of overlap between migration and breeding of individuals. A steady decline in

Sora detectability was observed in both 2009 and 2010 suggesting a violation of the

closure assumption. Sora have been described as Ohio’s most common migrant rail

(Peterjohn and Zimmerman 2001) and rail migration is suspected to peak in the first week

of May, according to trapping records in the Lake Erie marshes (Tom Kashmer, personal

communication). It is also possible that Sora call frequencies and response to conspecific

calls is greatly reduced after territories have been established. Temporal variation in

marsh bird detections has been attributed to breeding status and variation in the sex ratio

across survey sites (Conway and Gibbs 2001). This study was not design to confirm the

breeding status of marsh birds, so it is possible either or both situations did occur. It is

important to note that if migrants passing through the study site were detected, estimates

of occupancy may be biased.

Another type of violation of the closure assumption most likely occurred as a

result of limiting detections to a 100 m radius circle (3.14 ha) plots. The average home

range for Pied-billed Grebe, for example, is 1.31 ha but could be as large as 35 ha (Muller

and Storer 1999). The 100 m radius plot is only a portion of a grebe’s entire home

range. If movement in and out of the plot is random, the interpretation of the

parameter estimate changes from the probability of sites occupied to the probability of

sites used (Mackenzie et al. 2006). This may make finding a significant association

between detection probability and covariates difficult because there is an added element

of uncertainty related to the probability that the individual bird is using the site during a

given survey visit.

Pied-billed Grebe occupancy in the glaciated portion of Ohio was lower than

estimates in the Illinois and Upper Mississippi River Valleys which averaged 0.21 and

0.31 in 2005 and 2006, respectively (Darrah and Krementz 2010). The majority (86%) of

wetlands surved in the river valleys were large impounded marshes and may have been

why a higher estimate was obtained compared to the broad sampling design used in my

study. Pied-billed Grebe occupancy was localized in Ohio and occupancy probability

was highest in the Lake Plain subregion where a large complex of impounded marshes is

found. Occupancy estimates of Pied-billed Grebe in eastern Arkansas were 0.13 in 2005 and 0.20 in 2006 (Budd 2007). My Least Bittern occupancy estimates were similar to those in the Illinois and Upper Mississippi River Valleys which was 0.17 in 2005 and

0.14 in 2006.

Management Implications

Managers should consider a breeding marsh bird occupancy goal of 0.20 for more localized species such as the Pied-billed Grebe, Least Bittern and Common Moorhen and

0.30 for generalist species such as the Virginia Rail and Sora, based on my results and occupancy models developed for other states. My estimates predict that an occupancy probability of 0.20 for Pied-billed Grebe is achieved with a wetland size of 33.1 ha and an approximate mean water depth of 48 cm, when all other variables in the model are at their average. A 0.20 occupancy probability for Least Bittern is achieved with 60%

72 persistent emergent cover or a mean water depth of 59 cm, when other model variables are at their average. The occupancy probability of Common Moorhen is 0.2 in wetlands with approximately 80% surrounding wetland cover, 85% persistent emergent cover, or a mean water depth of 70 cm. Virginia Rail occupancy probability of 0.30 is achieved in wetlands with 83% persistent emergent cover or 85% surrounding woodland habitat when all other variables are at their average. Sora occupancy probability is 0.30 in wetlands with at least 15% surrounding wetland cover, or 17% persistent emergent cover.

Chapter 3: Comparisons of Marsh Bird Occupancy in Restored and Randomly

Sampled Wetlands in Glaciated Ohio

Functional wetlands provide many ecological services including habitat for recreationally and commercially important wildlife species. Despite their recognized value, loss of vegetated freshwater wetlands in the lower 48 states continues at a rate of

33,000 hectares per year, based on 1998-2004 estimates (Dahl 2006). Naturally occurring wetlands are being replaced by created or restored wetlands resulting in a net gain of total wetland area. Permitted wetland filling or dredging under section 404 of the

Clean Water Act of 1972 requires compensatory mitigation through restoration, enhancement, or preservation. In addition, restoration programs have been initiated to mitigate wetland loss including the Wetland Reserve Program administered by the

Natural Resources Conservation Service, the U.S. Fish and Wildlife Service Partners for

Wildlife, joint venture projects under the North American Waterfowl Management Plan, and state wetland restoration programs.

Wetlands are dynamic ecosystems, receiving water and nutrient inputs from ground, surface, and/or rain water (Mitch and Gosslink 2007). However, hydrological conditions within many watersheds have been altered by development, making restoration or creation of functioning wetlands difficult (Mitch and Wilson 1996, NRC

2001). Successful mitigation of wetland loss requires the restoration of ecological function, practical and effective methods for which have proved elusive. 74

Quantifying wetland use by avian species is a useful method to assess ecosystem

function because it is relatively simple yet informative. Estimates of avian species

composition and abundance provide information on food chain support and can be an

indicator of cumulative effects on the system (U.S. EPA 2002). Use by wetland-

dependent birds is often documented soon (1-3 years) after restoration or creation before

ecological functions and services are fully established (Delehanty and Svedarsky 1993,

Hemesath and Dinsmore 1998, Locky et al. 2005).

Comparisons of avian use between restored and natural wetlands have produced

conflicting results. Avian species richness and abundance did not differ significantly

between restored and natural wetlands in the Lake Ontario-St. Lawrence River Plains in

New York (Brown and Smith 1998) or in North and South Dakota (Ratti et al. 2001). In

contrast, an earlier study in the Prairie Pothole Region found that species richness of

breeding birds and abundance of four wetland-dependent passerines was lower in

restored wetlands (Delphey and Dinsmore 1993). Wetland-dependent birds were more abundant in natural salt marshes than in created marshes on the Atlantic coast (Melvin and Webb 1998). However, restoring tidal flow to coastal wetlands can result in a higher abundance of birds than natural wetlands over time (Brawley et al. 1998).

Comparisons of marsh bird occupancy among restored wetlands can also provide valuable information for future projects. Presence of levees, water control structures, excavated basins and active management was a better predictor of species richness in restored wetlands than vegetation cover or wetland isolation in Illinois (O’Neal et al.

2008). Restored wetlands under moist soil management to promote growth of emergent

75 vegetation had higher species diversity and abundance of wetland dependent birds than unmanaged wetlands in New York (Kaminski et al. 2006). Complete drawdowns during the spring or early summer can have an immediate negative impact on breeding marsh bird abundance but can recover the year following water-level management (McWilliams

2010). The ideal timing and frequency of drawdowns required to establish desired vegetation structure without limiting marsh bird occupancy and reproduction needs further study.

It is important to understand the contribution of restoration projects as breeding habitat given the negative population trends estimated for wetland-dependent birds in the

Great Lakes Region (Crewe et al. 2006). In addition, very little is known about avian use of restored and/or created wetlands in Ohio (but see Porej 2004). My objectives were to

1) determine differences in habitat characteristics and marsh bird occupancy between wetlands restored through the Ohio Wetland Reserve program and a random sample of natural and managed wetlands across glaciated Ohio 2) identify habitat features that promote marsh bird occupancy.

Methods

Study Area. The study area encompassed approximately two thirds of Ohio, specifically portions of the state that were most recently glaciated. Three major physiographic subregions, representing unique topographies and habitat conditions, were found within the study area: Lake Plain, Till Plains, and Unglaciated Appalachian Plateau

(Peaceful 1996). Four of the five state wildlife districts within Ohio were included in the

study area. Land use surrounding the wetlands was largely agricultural but is becoming

increasingly urban or, in the northeast, increasingly forested (Reese and Irwin 2002).

The intensity of the restoration process varied among sites. Some consisted of

removal of the drainage tile with no further excavation or water control structures. Many

of the sites were excavated and the soil was used to create small levees around all or a

portion of the wetland basin. Water control structures were present at 35% of the

restored wetlands. Restoration sites were passively managed with no direct manipulation

of hydrology or emergent vegetation, although 11% were actively managed at wetlands in the Lake Erie region. Water control structures used to control water levels within the basin included stoplogs, overflow pipes, and flap gates. Stoplogs were the most frequently installed structure. Stoplogs consisted of boards set at incremental heights that allow water to flow out of the system when boards are over-topped. The overflow pipe operates on a similar principle but the water depth cannot be changed and the structure is more likely to get plugged by debris. Flap gates were less prevalent in privately restored wetlands. These structures require manual opening and closing of gates to control water levels.

I used a generalized random-tessellation stratified (GRTS) sampling design to select from a database of 248 wetlands restored through the Ohio Wetland Restoration

Program (Kincaid et al. 2008). A sample of 50 points with a 15% oversample was selected in both 2009 and 2010. The sample was stratified by 1) wetland size: small

(<0.8 ha), medium (0.8-1.9 ha), and large (≥2 ha) and 2) distance from a managed wetland complex: 0-8 km, 8-24 km, 24-48 km and >48 km. A second sample of 350

points was selected for all wetlands identified by National Wetlands Inventory data (see

Chapter 2). Sample points were stratified by 1) wetland size: small (0.05-1 ha), medium

(1-10 ha) and large (>10 ha) and 2) water regime, categorized as: seasonal (temporarily

flooded and seasonally flooded) and semi-permanent (semi-permanently flooded and intermittently exposed) (Cowardin et al. 1979). The larger sample included 15 restored wetland survey points in 2009 and sixteen in 2010. These points were removed from the random sample and added to the restored wetland sample prior to the comparative analyses. GRTS sampling was performed using the spsurvey package version2.0 (Kincaid et al. 2008). Implemented in the R statistical program (R

Development Core Team, 2005).

Surveys. Marsh bird surveys closely followed methods outlined in the

Standardized North American Marsh Bird Monitoring Protocols (Conway 2009).

Surveys deviated from the protocol in that points were randomly located within a wetland and not necessarily on dikes, roads, or at the vegetation-water interface. Points were surveyed three times during the field season with approximately 15 days between each survey. We conducted surveys from 8 May through 28 June 2009 and from 8 May through 22 June 2010. Surveys were conducted 30 minutes before sunrise to 2-3 hours after sunrise and in 2 hours before sunset to 30 minutes after sunset. Background noise, wind (Beaufort Scale) and sky conditions (U.S. Weather Bureau code) were recorded prior to each survey and we did not conduct surveys in windy conditions over 20 km/hr or duringsustained rain, per protocol guidelines (Conway 2008).

Each survey began with a 5-minute period of passive listening followed by a 30 second broadcast of species’ calls, and then 30 seconds of silence. Recorded calls were obtained through the North American Marsh Bird Monitoring Program. The order of species in the call broadcast was as follows: Least Bittern (Ixobrychus exilis), Sora

(Porzana carolina), Virginia Rail (Rallus limicola), King Rail (Rallus elegans),

Pied-billed Grebe (Podilymbus podiceps). We added American Bittern (Botaurus

lentiginosus) and a second Least Bittern call to the end of the broadcast series in 2010 in

an attempt to enhance the detectability of these species. A decibel meter was used to

ensure that speakers broadcasted at 80-90 dB from 1 meter away. Technicians were

trained in marsh bird identification using audio CDs beginning in April and a week of

practice surveys in the field. Observers recorded and estimated distance to nine focal

species: Pied-billed Grebe, American Bittern, Least Bittern, Common Moorhen

(Gallinula chloropus), American Coot (Fulica americana), King Rail, Virginia Rail, Sora,

and Black Tern (Chlidonias niger). The presence of non-focal avian species was recorded

opportunistically at restored wetlands. Breeding evidence for all species was collected

following the guidelines of the Ohio Breeding Bird Atlas II Volunteer Handbook.

(www.ohiobirds.org/obba2).

Fine-scale habitat data were collected within a 100 m radius circular plot centered

on each survey point. Vegetation cover was mapped for seven cover classes after each

survey on aerial photographs and classified according to Cowardin et al. (1979).

collected water depth at the survey point and 50 m away in four cardinal directions.

Degree of interspersion was estimated using the cover type index of Stewart and Kantrud

(1971) (Chapter 2). Stands of invasive macrophytes, common reed (Phragmites australis), narrow-leaved cattail (Typha angustifolia), and hybrid narrow-leaved x broad leaved cattail hybrid (T. x glauca) were identified and mapped also after the third survey. Observers also identified the dominant (≥ 20% of cover class) plant species for each cover class.

Habitat Variables. Percent areal cover of seven vegetation classes was estimated

using vegetation maps created after the last survey and a dot grid overlay with 64 dots in

each 2.5 cm2 cell. Vegetation classes included: open water, aquatic bed, persistent

emergent, non-persistent emergent, temporarily flooded vegetation, wetland shrub and

wetland forest (≥ 6.1 m). Percent areal cover of invasive macrophytes was also estimated

using a dot grid but the distributions were highly skewed and not included in the models.

Percent cover of wetlands within a 500 m radius buffer around the wetland was quantified

from digitized National Wetland Inventory polygons and the calculate geometry feature

in ArcGIS 9.3. The presence and type of water control structure was recorded at each

survey wetland in 2010. We estimated percent of the wetland-upland interface with a

gradual transition from wetland to upland vegetation using an aerial photograph and by

walking along the perimeter. A gradual transition was defined as vegetation zones

transitioning from emergent to wet meadow to grassland or from wetland shrub to upland

shrub or forest with a slope grade < 15:1. A gradual transition included > 2 m of upland

cover identified by the presence of upland and facultative upland vegetation. This

transition was in sharp contrast to edges with a steep slope abruptly transitioning from an

80 emergent zone or aquatic bed zone to upland or where upland vegetation was mowed up to the wetland edge.

Data Analyses. The results of avian use of restored wetlands include mainly descriptive statistics and correlations because the number of survey points with focal species detections was too low for occupancy modeling. I reported the interquartile range (IQR) of habitat variables rather than the standard deviation because distributions were non-normal. I used Mann-Whitney rank sum tests to compare habitat variables between 1) restored wetlands and randomly sampled wetlands in the glaciated region of

Ohio and 2) occupied and unoccupied wetlands. I calculated the frequency of occurrence of five common non-native invasive plants in restored and random sample wetlands.

Selected species included: common reed, narrow-leaved cattail and narrow-leaved x broad-leaved hybrid, flowering rush (Butomus umbellatus), and purple loosestrife

(Lythrum salicaria). Restored wetlands included in the random sample were removed for the comparison and added to the restored wetland group. All statistics were calculated in program R (R Development Core Team, 2005).

Results

Habitat Features. The majority of privately restored wetlands sampled could be described as small (~1 ha), deep water ponds with little emergent cover (Table 3.1).

Percent emergent vegetation was highly skewed and 15% of the wetlands surveyed contained no emergent vegetation (Figure 3.1). Wetland age ranged from 1-19 years and was not highly correlated with percent persistent emergent vegetation (2009 Rs= 0.17,

2010 Rs= 0.05) or percent total emergent vegetation (persistent and non-persistent

3rd Year Variable 1st Quartile Mean Median Quartile 2009 Wetland Area (ha) 0.5 4.5 0.9 2.1 Non-persistent 0.0 11.4 0.0 15.3 Emergent (%) Persistent Emergent 0.0 19.2 11.0 32.5 Temporarily Flooded (%) 0.0 25.8 17.0 37.3 Aquatic Bed (%) 0.0 27.6 17.0 53.3 Mean Water Depth (cm) 3 19 17 29 Transitional Boundary (%) 0.0 23.5 0.0 42.5 Surrounding Wetland (%) 0.0 7.6 0.9 3.2 2010 Wetland Area (ha) 0.4 5.2 1.3 3.2 Non-persistent 0.0 13.3 8.0 19.0 Emergent (%) Persistent Emergent (%) 0.0 10.1 9.0 20.0 Temporarily Flooded (%) 0.0 66.4 0.0 0.3 Aquatic Bed (%) 0.0 26.3 16.0 50.0 Mean Water Depth (cm) 12.6 41.6 33.5 71.3 Transitional Boundary (%) 0.0 33.6 25.0 55.0 Surrounding Wetland (%) 0.0 8.5 1.8 7.0

Table 3.1. Descriptive Statistics of habitat variables of restored wetland surveyed for secretive marsh birds during May through early July 2009 and 2010 in the glaciated subregions of Ohio, USA.

Figure 3.1. Frequency histogram of estimated percent emergent vegetation in a 100 m radius circular plot at restored wetlands surveyed for secretive marsh birds during May through early July 2009 and 2010 in the glaciated subregions of Ohio, USA.

emergent vegetation combined) (2009 Rs=0.15, 2010 Rs=0.03). The total percent

emergent vegetation within restored wetlands with water control structures and without

was not different (two sample t-test, t=0.671, P=0.51). Mean wetland cover within a 500 m radius was low (Table 1.3) however, in Ottawa County ranged from 38 to 96%.

Percent emergent vegetation did not differ between the restored wetlands and a random sample of wetlands within Ohio (U=15,574, P=0.39). Median percent emergent vegetation was 17% (0 - 51%, IQR) in the random sample and 23 % (9 - 46%, IQR) in restored wetlands. Water depth of restored wetlands was greater than the random sample

(U=20,045, P<0.001). Median water depth in the random sample was 6 cm (0 - 20 cm,

IQR) and 21 cm (9 - 47 cm, IQR) in restored wetlands. Wetland size (U=9,087,

P<0.001) and the percent cover of wetland within a 500 m buffer (U=5,524, P<0.001) were lower in restored wetland sites. Median size of random wetlands was 8.2 ha (2.8 -

30.5 ha, IQR) and 1.2 ha (0.5 - 3.0 ha, IQR) for restored wetlands. Median wetland cover of random wetlands was 8% (1 - 26%, IQR) and 2% (0 - 6%, IQR) for restored wetlands.

Narrow-leaved and hybrid cattail were the most frequently occurring non-native, invasive plants in both restored wetlands (33% of sites) and the random sample (25% of sites). Common reed was a dominant species mainly in the Lake Plains subregion. Only

7% of restored wetlands surveyed contained common reed while it was dominant in 14% of the random wetlands. Flowering Rush was identified as a dominant species in 18% of the random sample and in 2% of restored wetlands. Purple loosestrife was a dominant species at 3% of random sample wetlands and was not observed in any restored wetlands.

Avian Occurrence. We detected 104 bird species at or adjacent to the restored wetlands including 31 species identified as wetland-dependent (Appendix C). Seven of the nine focal marsh birds were detected at restored wetlands over the two years

(Appendix D & E). Forty percent of restored wetlands with water control structures were

occupied by at least one of the focal marsh birds and thirty percent of sites without water

control structures were occupied by one or more focal species. Wetlands occupied by

one or more focal species tended to have a higher percent of emergent vegetation and mean water depth than unoccupied sites in restored and randomly sampled wetlands

(Figure 3.2). Occupied wetlands from the random sample tended to be larger and have more surrounding wetland cover than occupied, restored wetlands (Figure 3.3). Wetland occupied by Virginia Rail, King Rail and Sora tended to have a higher relative richness of cover types than unoccupied sites.

Occupancy frequencies between restored and randomly sampled wetlands were

similar, but were typically lower than occupancy in wetlands managed by state and

federal wildlife agencies (Table 3.2). One exception to this pattern was Sora occupancy

in 2009, which was highest in restored wetlands. Seven of the nine focal species were

locally distributed with the highest frequency of occurrence in the Lake Plain subregion

(Table 3.3) In contrast, Virginia Rail and Sora occupancy was distributed fairly evenly

across the three physiographic subregions. These species also tended to use a greater

variety of wetland types including forested/emergent and scrub-shrub/emergent wetlands

(Table 3.4). Soras, however, occupied more semi-permanently flooded wetlands while

Virginia Rails occupied both seasonal and semi-permanent wetlands. Forested/emergent

wetlands were sampled less intensively than other wetlands (n=15 in 2009, n=8 in 2010)

resulting in a relatively high frequency of occurrence for this wetland type in some cases.

Figure 3.2. Biplot of mean fine-scale habitat variables and 95% Confidence intervals for restored wetland sample and random sample in which marsh birds were detected (Yes) or not detected (No) during May through early July, 2009 and 2010 in the glaciated subregions of Ohio, USA.

Frequency of Occurrence Species Year State and Random Restored Wetlands Federal Wetlands Wetlands Pied-billed Grebe 2009 0.10 0.07 0.37 2010 0.03 0.13 0.28 Least Bittern 2009 0.02 0.03 0.09 2010 0.03 0.09 0.14 American Bittern 2009 0.00 0.02 0.07 2010 0.00 0.04 0.05 Common Moorhen 2009 0.07 0.07 0.18 2010 0.03 0.16 0.25 American Coot 2009 0.05 0.03 0.07 2010 0.00 0.08 0.22 Virginia Rail 2009 0.05 0.07 0.14 2010 0.03 0.11 0.15 Sora 2009 0.13 0.07 0.12 2010 0.13 0.10 0.16 King Rail 2009 0.00 0.01 0.02 2010 0.03 0.00 0.01 Black Tern 2009 0.00 0.01 0.04 2010 0.00 0.02 0.04

Table 3.2. Frequency of cccurrence of nine focal marsh birds in restored wetlands, a random sample of impounded and natural wetlands, and state and federally owned wetland complexes surveyed during May through early July, 2009 and 2010 in the glaciated region of Ohio, USA. Includes individuals detected during the 10 minute survey period and within 100 m of survey point only.

Glaciated Species Year Lake Plain Tilled Plain Appalachian Plateau

Pied-billed Grebe 2009 0.40 0.08 0.01 2010 0.50 0.15 0.03 Black Tern 2009 0.02 0.00 0.00 2010 0.04 0.00 0.00 Least Bittern 2009 0.14 0.02 0.00 2010 0.34 0.05 0.02 American Bittern 2009 0.04 0.02 0.00 2010 0.07 0.03 0.00 King Rail 2009 0.01 0.00 0.00 2010 0.02 0.02 0.00 Virginia Rail 2009 0.13 0.03 0.12 2010 0.11 0.08 0.14 Sora 2009 0.10 0.15 0.09 2010 0.17 0.16 0.14 Common Moorhen 2009 0.24 0.06 0.01 2010 0.34 0.05 0.09 American Coot 2009 0.13 0.05 0.02 2010 0.17 0.03 0.02

Table 3.3. Frequency of occurrence by physiographic subregion, of nine focal marsh birds in restored wetlands and a random sample of impounded and natural wetlands, surveyed during May through early July, 2009 and 2010 in the glaciated region of Ohio, USA. Includes individuals detected during the 10 minute survey period and within 100 m of survey point only.

Species Year Aquatic Bed Semi-permanent, Seasonal, Semi- Seasonal, Semi- Seasonal, Emergent Emergent permanent, Shrub- permanent, Forested Shrub-Scrub Scrub Forested Pied-billed Grebe 2009 0.12 0.30 0.04 0.00 0.00 0.00 0.00 2010 0.00 0.21 0.05 0.00 0.00 0.00 0.00 Black Tern 2009 0.00 0.04 0.00 0.00 0.00 0.00 0.00 2010 0.00 0.05 0.00 0.00 0.00 0.00 0.00 Least Bittern 2009 0.06 0.18 0.02 0.00 0.00 0.00 0.00 2010 0.04 0.15 0.03 0.00 0.00 0.00 0.33 American Bittern 2009 0.00 0.07 0.01 0.00 0.00 0.00 0.00 2010 0.00 0.06 0.03 0.00 0.00 0.00 0.00 Virginia Rail 2009 0.00 0.13 0.04 0.00 0.08 0.00 0.00 2010 0.00 0.11 0.08 0.07 0.10 0.60 0.33 King Rail 2009 0.00 0.00 0.01 0.00 0.00 0.00 0.00 2010 0.00 0.01 0.05 0.00 0.00 0.00 0.00 Sora 2009 0.06 0.16 0.07 0.00 0.05 0.00 0.00 2010 0.04 0.17 0.05 0.13 0.00 0.40 0.00 Common Moorhen 2009 0.00 0.24 0.02 0.00 0.00 0.00 0.00 2010 0.00 0.26 0.00 0.06 0.00 0.40 0.00 American Coot 2009 0.00 0.08 0.04 0.00 0.00 0.00 0.00 2010 0.00 0.11 0.03 0.00 0.00 0.00 0.00

Figure 3.4. Frequency of occurrence by wetland type (Cowardin et al. 1979) of nine focal marsh birds in restored wetlands, surveyed during May through early July, 2009 and 2010 in the glaciated region of Ohio, USA. Includes individuals detected during the 10 minute survey period and within 100 m of survey point only.

Pied-billed Grebe were detected at eight restored wetlands points in 2009 and five in 2010. Mean wetland area of sites occupied by Pied-billed Grebe was 41.1 ha. (20.7-

73.8 ha IQR). Occupied wetlands were permanently and semi-permanently flooded with large areas of open water or aquatic bed and a mean water depth of 49 cm (46-61cm IQR).

Five of the occupied sites were within an impounded wetlands complex owned by a private hunting club in Ottawa County. Mean percent emergent vegetation on sites occupied by Pied-billed Grebe was 37% (29-50% IQR). The edges of seventy-five percent of occupied sites were completely impounded or had steep slopes. Mean wetland cover surrounding occupied wetlands 27% (14-36% IQR).

The state threatened Least Bittern was detected at three restored wetlands in 2009 and two in 2010. Least bittern occupied relatively large wetlands with a mean area of

43.5 ha (2.0-101.8 ha IQR). Occupied sites had relatively deep water with a mean of 49 cm (39-67 cm IQR). Mean percent emergent cover was 39% (32-50% IQR). Three occupied sites were completely impounded. American Bittern was not detected on any of the restored wetlands surveyed.

American Coots were detected at four restored wetlands in 2009. All but one of these sites were > 10 ha. The smallest occupied wetland was 1.17 ha and 30% of the surrounding area within a 500 m buffer contained wetland cover. Mean wetland cover within a 500 m buffer at occupied sites was 34% (37-50% range). Occupied sites had a slightly greater mean water depth than unoccupied restored wetlands (52 cm vs. 46 cm). Mean emergent vegetation of occupied sites was 31%.

Common Moorhens were detected at five restored wetlands in 2009 and three in 2010. Mean wetland area of occupied sites was 64.7 ha (57.7-101.9 ha IQR).

Vegetation cover types of sites occupied by Common Moorhen ranged from 100% floating-leaf and submerged aquatic vegetation to a mix of 80% emergent and 20% submerged aquatic vegetation. Occupied wetlands were permanently to semi- permanently flooded with a mean water depth of 39 cm (36-43 cm IQR). Mean percent wetland cover adjacent to the survey wetland was 43% (51- 60% IQR).

Virginia rail was detected at two sites in 2009 and four sites in 2010. Mean water depth of occupied sites was substantially lower than sites occupied by other focal species:

12 cm (9-18 cm IQR). Virginia Rail occupied both large and small wetlands with a mean of 20.7 ha (0.2-36.6 ha IQR). Total emergent vegetation ranged from 8% to 47%, but sites with low emergent vegetation contained a high percent of saturated to temporarily flooded vegetation cover. Dominant species of persistent emergent vegetation in occupied wetlands included broad-leaved cattail (Typha latifolia) and smooth rose- mallow (Hibiscus laevis). Mean percent wetland cover adjacent to the survey wetland was 15% (3-17% IQR). The majority of occupied wetlands included a gradual transition to undisturbed upland cover around 100 to 50% of the perimeter. One occupied site was completely impounded.

Sora were detected at six restored wetlands in 2009 and seven in 2010. Mean wetland area of occupied sites was 12.7 ha (0.5-18.0 ha IQR). Occupied sites had greater mean percent emergent vegetation than unoccupied sites (42% vs. 30%). Mean water depth was 36 cm (10-59 cm IQR). Sora were detected in sites with a wide range of

92 adjacent wetland cover from 0% to 96%. The wetland upland interface ranged from 0% to 100% gradual transition around the perimeter.

The state endangered King Rail was detected at two restored wetlands sites in

2010 only. One site contained a 0.30 ha wetland basin surrounded by grassland and a second larger basin approximately 20 m away. Emergent vegetation covered 47% of the wetland area and mean water depth was 15.47 cm. In early august the pair was discovered with a small brood of chicks still covered in black down. A single adult was detected at a second restored wetland on 28 May 2010. The bird was not detected during the survey prior to or after the initial detection. The occupied site was predominately a mix of temporarily flooded vegetation and small wetland shrubs. Persistent emergent vegetation covered approximately 17% of the site.

Discussion

Specific fine-scale and landscape-level features influence marsh bird occupancy of restored wetlands. Emergent vegetation is an important component of secretive marsh bird habitat (Meanley 1953, Brackney 1979, Johnson and Dinsmore

1986, Moore et al. 2009). However, approximately 24% of the restored wetlands surveyed in 2009 and 2010 contained < 10% emergent vegetation. A preponderance of small, isolated wetlands within the sample may also contribute to the low occupancy

rates observed. The number of species observed tends to increase with increasing area (Preston 1960). Larger areas can have a greater diversity of habitat types allowing for more specialized habitat utilization (Rafe et al. 1985) and are more likely to be colonized by area-sensitive species or species that occur at low densities (Brown and

Dinsmore 1986, Craig and Beal 1992). Additional research should investigate the actual area of emergent cover instead of the percent because there may be an interaction between wetland size and percent emergent cover.

It is not clear from my study if water control structures were used to encourage growth of emergent vegetation. I did not collect information on any specific water level management performed by private landowners. The total percent of emergent vegetation within restored wetlands with and without water control structures did not differ, suggesting that moist soil management used to promote emergent growth does not occur on all wetlands with water control structures. Actively managed, impounded wetlands along the coast of Lake Erie were occupied by a number of focal marsh birds; however, it is difficult to say whether this can be explained by active restoration and management alone. In Illinois, the degree of hydrologic management was the best predictor of waterbird species richness, but the candidate model set did not include wetland size or a combination of both local and landscape habitat variables (O’Neal et al. 2008). Actively managed sites in Ohio tended to be larger, have greater water depths, and were located within a large wetland complex.

Forty-eight percent of the restored wetlands sampled lacked a gradual transition from wetland to an undisturbed buffer. Studies on amphibian habitat use indicate that the slope grade of excavated wetlands and the land cover immediately surrounding a restored wetland is important for population persistence (Semlitsch et al. 2003, Gibbons 2003). A shallow littoral zone was found to be an important design feature for pond-breeding amphibians in Ohio (Porej 2004) which may provide a source of protein for rails and

94 other waterbirds during the breeding season. A shallow zone transitioning to grassland may be an important habitat feature for the state endangered King Rail during the breeding season. King Rails nest and forage in relatively shallow wetlands (0-25cm) (Eddleman et al. 1988, Meanly 1992). A shallow transition will ensure that at least some portion of the wetland is inundated at the ideal depth for foraging King Rails as the season progresses and wetlands dry up. This feature may also provide important brood rearing habitat for the King Rail. A brood of King Rails was found in a restored wetland in Ohio during the

2010 field season foraging in a stand of cattails with a water depth of 5-10 cm.

Overall, use of restored wetlands in Ohio by secretive marsh birds was uncommon, suggesting poor habitat quality. Many sites lacked emergent vegetation needed for nest building and cover. The majority of sites were > 1 ha, which may be unattractive to area-sensitive species like Pied-billed Grebe. Our surveys indicate that rails will use small wetlands < 0.5 ha in size. Projects planned for smaller restoration projects may want to focus on habitat features that promote use by these birds such as shallow water (~20 cm) and emergent cover mixed with pools of open water.

More research is needed to determine specific design features that promote use by wetland-dependent birds. I recommend studies that explore the effect of infrequent, partial drawdowns, watershed or landscape-level attributes and the presence of a shallow littoral zone on marsh bird use.

Presence of an individual is dependent on the species being at the site and the observer successfully detecting the organism. I was not able to account for variations in detection probability so the results may underestimate marsh bird occupancy. Estimates

95 of detection probability for many secretive marsh birds suggest that observers fail to detect a bird over 50% of the time (Darrah and Krementz 2010, Chapter 2). Other factors such as date of survey, time of day, and background noise can affect the probabilityof detecting a species at a wetland (Conway 2004,Rehm and Baldassarre

2007b). However, low detection probability is probably only partially responsible for the low occupancy number I obtained in restored wetlands.

From observations of restored wetlands in the field and review of the scientific literature on habitat use by marsh birds a number of recommendations can be made.

1. Emergent vegetation is positively associated with marsh bird occupancy. Practices such as mowing or use of herbicides should be avoided while infrequent drawdowns to promote emergent growth should be encouraged.

2. Wetlands with a high relative richness of vegetation cover types tend to be occupied by rails. This habitat feature can be established with an uneven basin topography resulting in pools of open water interspersed with emergent or temporarily flooded vegetation.

3. A regional prospective is likely to promote marsh bird occupancy in restored wetlands. For example, large wetlands with deep water should be encouraged in the Lake Plains region where localized populations of Pied-billed Grebe and Least Bittern occur.

4. Stocking wetlands with predatory fish is a common practice in private wetlands in Ohio. Additional research is needed to understand the effect of predatory fish on food web interactions and marsh bird occupancy in restored wetlands.

Literature Cited

Andrews, D. A. 1973. Habitat utilization by Sora, Virginia Rails, and King Rails near southwestern Lake Erie. Thesis, Ohio State University, Columbus, USA.

Arrhenius, O. 1921. Species and area. Journal of Ecology. 9:95-99.

Batary, P., and A. Baldi. 2004. Evidence of an edge effect on avian nesting success. Conservation Biology. 18:389-400

Beintema, A. J. 1997. European Black Terns (Chlidonias niger) in trouble: Examples of dietary problems. Colonial Waterbirds. 20:558-565.

Benoit, L. K, and R. A. Askins. 2002. Relationships between habitat area and the distribution of tidal marsh birds. Wilson Bulletin. 114:314-323.

Bergin, T. M. 1992. Habitat selection by the Western Kingbird in Western Nebraska: A hierarchical analysis. The Condor. 94:903-911.

Block, W. M., M. L. Morrison, J. Verner, and P. N. Manley. 1994. Wildlife-habitat- relationships models: A case study with California oak woodlands. Wildlife Society Bulletin. 22:549-561.

Bogner, H. E. and G. A. Baldassarre. 2002. Home range, movement, and nesting of Least Bittern in western New York. Wilson Bulletin. 114:297-308.

Brackney, A.W. 1979. Population ecology of common gallinules in southwestern Lake Erie marshes. Thesis, Ohio State University, Columbus,USA.

Brawley, A. H., R. S. Warren, and R. A. Askins. 1998. Bird use of restoration and reference marshes within the Barn Island Wildlife Management Area, Stonington, Connecticut, USA. Environmental Management. 22:625-633.

Brown, M., and J. J. Dinsmore. 1986. Implications of marsh size and isolation for marsh bird management. Journal of Wildlife Management. 50:392-397.

Brown, S. C., and C. R. Smith. 1998. Breeding season bird use of recently restored versus natural wetlands in New York. Journal of Wildlife Management. 62:1480-1491.

Budd, M. J., and D. G. Krementz. 2010. Habitat use by Least Bitterns in the Arkansas Delta. Waterbirds. 33:140-147.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: A practical information-theoretic approach. Springer-Verlag, New York, New York, USA.

Chabreck. R. H. 1963. Breeding habits of the Pied-billed Grebe in an impounded coastal marsh in Louisiana. The Auk. 80:447-452.

Confer, S. R., and W. A. Niering. 1992. Comparison of created and natural freshwater emergent wetlands in Connecticut (USA). Wetlands Ecology and Management. 2:143-156.

Connor, E. F., and E. D. McCoy. 1979. The statistics and biology of the species-area relationship. American Naturalist. 113:791-833.

Conway, C. J. 1995. Virginia Rail (Rallus limicola). Number 173 in A. Poole and F. Gills, editors. The birds of North America. Academy of Natural Sciences, Philadelphia, Pennsylvania and American Ornithologists’ Union. Washington, D.C, USA.

Conway, C. J. 2008. Standardized North American marsh bird monitoring protocols. Wildlife Research Report #2008-01. U.S. Geological Survey, Arizona Cooperative Fish and Wildlife Research Unit, Tucson, AZ, USA.

Conway, C. J., and J. P. Gibbs. 2001. Factors influencing detection probabilities and the benefits of call broadcast surveys for monitoring marsh birds. Final Report, USGS Patuxent Wildlife Research Center, Laurel, Maryland, USA.

Conway, C. J., and J. P. Gibbs. 2005. Effectiveness of Call-broadcast surveys for monitoring marsh birds. The Auk. 122:26-35.

Conway, C.J., C. Sulzman, B. E. Raulston. 2004. Factors affecting detection probability of California Black Rails. Journal of Wildlife Management. 68:360-370.

Cooper, T. R (Plan Coordinator). 2007. King Rail conservation action plan and status assessment, Version 1.0. U.S. Fish and Wildlife Service, Fort Snelling, Minnesota, USA.

Cowardin, L. M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of wetlands and deepwater habitats of the United States. U.S. Fish and Wildlife Service Report No. FWS/OBS/-79/31. Washington, D.C.. USA. 98

Craig, R. J., and K. G. Beal. 1992. The influence of habitat variables on marsh bird communities of the Connecticut River estuary. Wilson Bulletin. 104:295-311.

Crewe, T. L., S. T. A. Timmermans, and K. E. Jones. 2006. The marsh monitoring program 1995 to 2004: A decade of marsh monitoring in the Great Lakes region. Bird Studies Canada in cooperation with Environment Canada. 28pp.

Dahl, T.E. 1990. Wetland losses in the United States 1780’s to 1980’s. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C., USA.

Dahl, T. E. 2006. Status and trends of wetlands in the conterminous United States 1998 to 2004. U.S. Department of the Interior; Fish and Wildlife Service, Washington, D.C., USA.

Darrah, A. J., and D. G. Krementz. 2010. Occupancy and habitat use of the Least Bittern and Pied-billed Grebe in the Illinois and Upper Mississippi River Valleys. Waterbirds. 33:367-375.

Delphey, P. J. and J. J. Dinsmore. 1993. Breeding bird communities of recently restored and natural Prairie Potholes. Wetlands. 13:200-206.

DeLuca, W. V., C. E. Studds, L. L. Rockwood, and P. P. Marra. 2004. Influence of land use on the integrity of marsh bird communities of Chesapeake Bay, USA. Wetlands. 24:837-847.

Diamond, J. M. 1975. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biological Conservation. 7:373-382.

Ducks Unlimited. 2008. Updating the National Wetland Inventory (NWI) for the Great Lakes project workplan. accessed 19 April 2011.

Eddleman, W. R., F. L. Knopf, B. Meanley, F. A. Reid, and R. Zembal. 1988. Conservation of North American rallids. Wilson Bulletin. 100:458-475.

Faaborg, J. 1976. Habitat selection and territorial behavior of the small grebes of North Dakota. Wilson Bulletin. 88:390-399.

Fairbairn, S. E., and J. J. Dinsmore. 2001. Local and landscape-level influences on wetland bird communities of the Prairie Pothole Region of Iowa, USA. Wetlands. 21:41-47.

Ferraz, G., J. D. Nichols, J. E. Hines, P. C. Stouffer, R. O. Bierregaard, and T. E. Lovejoy. 2007. A large-scale deforestation experiment: Effects of patch area and isolation on Amazon birds. Science. 315:238-421.

Galatowitsch, S. M., and A. G van der Valk. 1996. Characteristics of recently restored wetlands in the Prairie Pothole Region. Wetlands. 16:75-83.

Gibbons, J.W. 2003. Terrestrial habitat: A vital component for hertofauna of isolated wetlands. Wetlands. 23:630-635.

Gibbs, J. P., J. R. Longcore, D. G. McAuley, and J. K. Ringelman. 1991. Use of wetland habitats by selected nongame water birds in Maine. U.S. Fish and Wildlife Service, Fish and Wildlife Research 9. Washington D.C., USA.

Gibbs, J. P., F. A. Reid, and S. M. Melvin. 1992. Least Bittern (Ixobrychus exilis). Number 17 in A. Poole and F. Gills, editors. The birds of North America. Academy of Natural Sciences, Philadelphia, Pennsylvania and American Ornithologists’ Union. Washington, D.C, USA.

Gilbert, A. T., and F. A. Servello. 2005. Water level dynamics in wetlands and nesting success of Black Terns in Maine. Waterbirds. 28:181-187.

Grand, R. C. 2002. Alternative forms of competition and predation dramatically affect habitat selection under foraging-predation-risk trade-offs. Behavioral Ecology. 13:280-290.

Gu, W., and R. K. Swihart. 2003. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation. 116:195- 203.

Guadagnin, D. L., and L. Maltchik. 2007. Habitat and landscape factors associated with neotropical waterbird occurrence and richness in wetland fragments. Biodiversity Conservation. 16:1231-1244.

Haig, S. M., D. W. Mehlman, and L. W. Oring. 1998. Avian movements and wetland connectivity in landscape conservation. Conservation Biology. 12:749-758.

Harris, L. D. 1988. The nature of cumulative impacts on biotic diversity of wetland vertebrates. Environmental Management. 12:675-693.

Harvey, D. S., and P. J. Weatherhead. 2006. A test of the hierarchical model of habitat selection using eastern massasauga rattlesnakes (Sistrurus c. catenatus). Biological Conservation. 130:206-216.

100

Heimlich, R.E., K.D. Wiebe, R. Claassen, D. Gadsby, and R.M. House. 1988. Wetlands and Agriculture: Private Interests and Public Benefits. USDA. Agricultural Economics Report No. (AER765) 104pp.

Hierl, L. A., C. S. Loftin, J. R. Longcore, D. G. McAuley, and D. L. Urban. 2007. A multivariate assessment of changes in wetland habitat for waterbirds at Moosehorn National Wildlife Refuge, Maine, USA. Wetlands. 27:141-152.

Hildén, O. 1965. Habitat selection in birds: A review. Annales Zoologici Fennici 2:53-75.

Holland, C. C., J. Honea, S. E. Gwin, and M. E. Kentula. 1995. Wetland degradation and loss in the rapidly urbanizing area of Portland, Oregon. Wetlands. 15:336-345.

Hutto, R. L. 1985. Habitat selection by nonbreeding, migratory land birds. Pages 455-473 in . M.L. Cody. Habitat selection in birds. Academic Press Inc., New York, USA.

Jobin, B., L. Robillard, and C. Latendresse. 2009. Response of a Least Bittern (Ixobrychus exilis) population to interannual water level fluctuations. Waterbirds. 32:73-80.

Johnson, R. R., and J. J. Dinsmore. 1986. Habitat use by breeding Virginia Rail and Soras. Journal of Wildlife Management. 50:387-392.

Kaminski, M. R., G. A. Baldassarre, and A. T. Pearse. 2006. Waterbird responses to hydrological management of wetlands reserve program habitats in New York. Wildlife Society Bulletin. 34:921-926.

Kaminski, R. M., and H. H. Prince. 1984. Dabbling duck-habitat associations during spring in Delta Marsh, Manitoba. Journal of Wildlife Management. 10:37-50.

Kantrud, H. A. and R. E. Steward. 1984. Ecological distribution and crude density of breeding birds on prairie wetlands. Journal of Wildlife Management. 48:426-437.

Karr, J. R., and K. E. Freemark. 1983. Habitat selection and environmental gradients: Dynamics in the “stable” tropics. Ecology. 64:1481-1494.

Kincaid, T., and T. Olson with contributions from D. Stevens, C. Platt, D. White, and R. Reminton . 2008. Spsurvey: Spatial survey design and analysis. R package version 2.0.

Kolasa, J. 1989. Ecological systems in hierarchical perspecitive: Breaks in community structure and other consequences. Ecology. 70:36-47.

101

Kotliar, N. B., and J. A. Wiens. 1990. Multiple scales of patchiness and patch structure: A hierarchical framework for the study of heterogeneity. Oikos. 59:253-260.

Kroll, A. J., S. D. Duke, D. E. Runde, E. B. Arnett, and K. A. Austin. 2007. Modeling habitat occupancy of Orange-crowned Warblers in managed forests of Oregon and Washington, USA. Journal of Wildlife Management. 71:1089-1097.

Lack, D. 1933. Habitat selection in birds. With special reference to the effects of afforestation on the Breckland avifauna. Journal of Animal Ecology. 2:239-292.

Lor, S. K. 2000. Population status and breeding ecology of marsh birds in western New York. Thesis, Cornell University, Ithaca, New York, USA.

Lor, S. K., and R. A. Malecki. 2006. Breeding ecology and nesting habitat associations of five marsh bird species in western New York. Waterbirds. 29:427-436.

Loughseed, V. L., M. D. McIntoch, C. A. Parker, and R. J. Stevenson. 2008. Wetland degradation leads to homogenization of the biota at local and landscape scales. Freshwater Biology. 53:2402-2413.

MacArthur, R. H., and J. W. MacArthur. 1961. On bird species diversity. Ecology. 42:594-598.

MacArthur, R. H., and Wilson, E. O. 1967. The theory of island biogeography. Princeton University Press. Princeton, New Jersey, USA.

MacKenzie, D. I. 2006. Modeling the probability of resource use: The effect of, and dealing with, detecting a species imperfectly. Journal of Wildlife Management. 70:367-374.

MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. Occupancy Estimation and Modeling: Inferring patterns and dynamics of species occurrence. Elsevier Inc. Burlington, Massachusetts, USA.

MacKenzie, D. I., and L. L. Bailey. 2004. Assessing the fit of site-occupancy models. Journal of Agricultural, Biological, and Environmental Statistics. 9:300-318.

MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 83:2248-2255. Meanley, B. 1953. Nesting of the King Rail in the Arkansas rice fields. The Auk. 70:261- 269.

102

Meanley, B. 1992. King Rail (Rallus elegans). In The Birds of North America no. 3 (A. Poole and F. Gill, Eds.). Academy of Natural Sciences, Philadelphia, and American Ornithologists’ Union. Washington, D.C.

Melvin, S. L., and J. W. Webb Jr. 1998. Differences in the avian communities of natural and created Spartina alterniflora salt marshes. Wetlands. 18:59-69.

Melvin, S. M., and J. P. Gibbs. 1996. Sora (Porzana Carolina). Number 250 in A. Poole and F. Gills, editors. The birds of North America. Academy of Natural Sciences, Philadelphia, Pennsylvania and American Ornithologists’ Union. Washington, D.C, USA.

Mitch, W. J., and R. F. Wilson. 1996. Improving the success of wetland creation and restoration with know-how, time, and self-design. Ecological Applications. 6:77- 83.

Moore, S. , J.R. Nawrot, and J.P. Severson. 2009. Wetland-scale habitat determinants influencing Least Bittern use of Created Wetlands. Waterbirds. 32: 16-24.

Morris, D. W. 1987. Ecological scale and habitat use. Ecology. 68:362-369.

Muller, M. J., and R. W. Storer. 1999. Pied-billed Grebe (Podilymbus podiceps). In The Birds of North America no. 410 (A. Poole and F. Gill, Eds.). Academy of Natural Sciences, Philadelphia, and American Ornithologists’ Union. Washington, D.C.

Murkin, E. J., H. R. Murkin, and R. D. Titman. 1992. Nektonic invertebrate abundance and distribution at the emergent vegetation-open water interface in the Delta Marsh, Manitoba, Canada. Wetlands 12:45-52.

Murkin, H. R., E. J. Murkin, and J. P. Ball. 1997. Avian habitat selection and prairie wetland dynamics: A 10-year experiment. Ecological Applications. 7:1144-1159.

Murkin, H. R., R. M. Kaminski, and R. D. Titman. 1982. Responses by dabbling ducks and aquatic invertebrates to an experimentally manipulated cattail marsh. Canadian Journal of Zoology. 60:2324-2332.

National Research Council (NRC). 1992. Restoration of Aquatic Ecosystems. National Academy Press, Washington, DC. 552 pp. Ch 6: Wetlands P262-340.

Naugle, D. E., K. F. Higgins, S. M. Nusser, and W. C. Johnson. 1999. Scale-dependent habitat use in three species of prairie wetland birds. Landscape Ecology. 14:267- 276. 103

Naugle, D. E., K. F. Higgins, M. E. Estey, R.R. Johnson, and S.M. Nusser. 2000. Local and landscape-level factors influencing Black Tern habitat suitability. Journal of Wildlife Mangement. 64:253-260.

National Research Council. 2001. Compensating for wetland losses under the Clean Water Act. National Academies Press. Washington, D.C., USA.

Novitzki, R.P., R.D. Smith, and J.D. Fretwell. 1996. Wetland functions, values, and assessment. U.S. Geological Survey Water-Supply Paper 2425. National water summary on water resources.

O’Neal, B. J., E. J. Heske, and J. D. Stafford. 2008. Waterbird response to wetland restored through the Conservation Reserve Enhancement Program. Journal of Wildlife Mangement. 72:654-664.

Orians, G. H. and J. F. Wittenberger. 1991. Spatial and temporal scales in habitat selection. American Naturalist. 137:S29-S49.

Peaceful, L., editor. 1996. A geography of Ohio. The Kent State University Press. Kent, Ohio, USA.

Peterjohn, B. G., and D. A. Zimmerman. 2001. The birds of Ohio. Indiana University Press, Bloomington, Indiana,USA.

Picman, J., M. L. Milks, and M. Leptich. 1993. Patterns of predation on passerine nests in marshes: Effects of water depth and distance on edge. Auk. 110:89-94.

Pierluissi, S. 2002. Breeding waterbird use of rice fields in southwestern Louisiana. Thesis, Louisiana State University, Baton Rouge, USA.

Post, W., and C. A. Seals. 2000. Breeding biology of the Common Moorhen in an impounded cattail marsh. Journal of Field Ornithology. 71:437-442.

Porej, D. 2004. Faunal aspects of wetland creation and restoration. Ph.D. Dissertation, The Ohio State University, Columbus, OH.

Ratti, J. T., A. M. Rocklage, J. H. Giudice, E. O. Garton, and D. P. Golner. 2001. Comparison of avian communities on restored and natural wetlands in North and South Dakota. Journal of Wildlife Management. 65:676-684.

Reese, J. W., and E. G. Irwin. 2002. Land cover in Ohio’s townships: An analysis of township land cover and population change. The Exurban Change Project Report

104

# EX-3. Ohio Agriculture Research and Development Center. Columbus, Ohio, USA.

Rehm, E. M., and G. A. Baldassarre. 2007a. The influence of interspersion on marsh bird abundance in New York. Wilson Journal of Ornithology. 119:648-654.

Rehm, E.M. and G.A. Baldassarre. 2007b. Temporal variation in detection of marsh birds during broadcast of conspecific calls. Journal of Field Ornithology. 78:56-63.

Ribic, C. A., and D. W. Sample. 2001. Associations of grassland birds with landscape factors in southern Wisconsin. American Midland Naturalist. 146:105-121.

Riffell, S. K., B. E. Keas, and T. M. Burton. 2001. Area and habitat relationships of birds in Great Lakes coastal wet meadows. Wetlands. 21:492-507.

Robbins, C. S., D. Bystrak, and P. H. Geissler. 1986. The Breeding Bird Survey, its first fifteen years, 1965-1979. U.S. Fish & Wildlife Service Resource Publication 157. Washington, D.C., USA.

Rotenberry, J. T., and J. A. Wiens. 1980. Habitat structure, patchiness, and avian communities in North American steppe vegetation: A multivariate analysis. Ecology. 61:1228-1250.

Rush, S. A. and R. J. Cooper. 2010. The 2008 marsh bird ecology symposium: An introduction. Waterbirds. 33:364-366.

Sauer, J. R., J. E. Hines, and J. Fallon. 2008. The North American breeding bird survey, results and analysis 1966 - 2007. Version 5.15.2010. USGS Patuxent Wildlife Research Center, Laurel, Maryland, USA.

Seabloom, E. C., and A. G. van der Valk. 2003. Plant diversity, composition and invasion of restored and natural prairie pothole wetlands: Implications for restoration. Wetlands. 23:1-12.

Semlitsch, R.D. and J.Russell Bodie. 2003. Biological criteria for buffer zones around wetlands and riparian habitats for amphibians and reptiles. Conservation Biology. 17:1219-1228

Simberloff, D. S., and L. G. Abele. 1976. Island biogeography theory and conservation practice. Science. 191: 285-286.

Simberloff, D. S. and L. G. Abele. 1982. Refuge design and island biogeographic theory- effects of fragmentation. American Naturalist. 120:41-56.

105

Snell-Rood, E. C., and D. A. Cristol. 2003. Avian communities of created and natural wetlands: Bottomland forests in Virginia. Condor. 105:303-315.

Steen, D. A., J. P. Gibbs, and S. T. A. Timmermans. 2006. Assessing the sensitivity of wetland bird communities to hydrologic change in the Eastern Great Lakes Region. Wetlands. 26:605-611.

Stewart, R.E. 2007. Wetlands as Bird Habitat in National Water Summary on Wetland Resources Supply Paper 2425. US Geological Survey.

Stewart, R. E., and H. A. Kantrud. 1971. Classification of natural ponds and lakes in the glaciated prairie region. Resource Publication 92. Bureau of Sport Fisheries and Wildlife. Washington D.C., USA.

Sutton, G. M. 1936. Food capturing tactics of the Least Bittern. The Auk. 53:74-75.

Svӓrdson, G. 1949. Competition and habitat selection in birds. Oikos. 1:157-174.

Swift, B. L., J. S. Larson, and R. M. DeGraaf. 1984. Relationship of breeding bird density and diversity to habitat variables in forested wetlands. Wilson Bulletin. 96:48-59.

Szell, C. C., and M. S. Woodrey. 2003. Reproductive ecology of the Least Tern along the lower Mississippi River. Waterbirds. 26:35-43.

Timmermans, S. T. A., S. S. Badzinski, and J. W. Ingram. 2008. Associations between breeding marsh bird abundances and great lakes hydrology. Journal of Great Lakes Research. 34:351-364.

Tozer, D. C., E. Nol, and K. F. Abraham. 2010. Effects of local and landscape-scale habitat variables on abundance and reproductive success of wetland birds. Wetlands Ecology Management. 18:679-693.

Tyre, A. J., B. Tenhumberg, S. A. Field, D. Niejalke, K. Parris, and H. P. Possingham. 2003. Improving precision and reducing bias in biological surveys: Estimating false-negative error rates. Ecological Applications. 13:1790-1801.

U.S. Fish and Wildlife Service. 2008. Birds of Conservation Concern 2008. United States Department of Interior, Fish and Wildlife Service, Division of Migratory Bird Management, Arlington, Virginia, USA.

U.S. EPA. 2002. Methods for evaluating wetland condition: Biological assessment methods for birds. Office of Water, U.S. Environmental Protection Agency, Washington, DC. EPA-822-R-02-023.

106

Van der Valk, A. G., and C. B. Davis. 1978. The role of seed bands in the vegetation dynamics of prairie glacial marshes. Ecology. 59:322-335.

VanRees-Siewert, K. L., and J. J. Dinsmore. 1996. Influence of wetland age on bird use of restored wetlands in Iowa. Wetlands. 16:577-582.

Voigts, D. K. 1976. Aquatic invertebrate abundance in relation to changing marsh vegetation. American Midland Naturalist. 95:313-322.

Walkinshaw, L.H. 1937. The Virginia Rail in Michigan. Auk. 54:464-475.

Ward, M. P., B. Semel, and J. R. Herkert. 2010. Identifying the ecological causes of long- term declines of wetland-dependent birds in an urbanizing landscape. Biodiversity Conservation. 19:3287-3300.

Ward, M. P., T. J. Benson, B. Semel, and J. R. Herkert. 2010. The use of social cues in habitat selection by wetland birds. Condor. 112:245-251.

Weller, M. W. 1999. Wetland birds: Habitat resources and conservation implications. Cambridge University Press. Cambridge, United Kingdom.

Weller, M. W., and C. S. Spatcher. 1965. Role of habitat in the distribution and abundance of marsh birds. Agriculture and Home Economics Experiment Station Special Report 43, Iowa State University, Ames, Iowa, USA.

Wilcox, D. A., and N. S. Jerrine. 2008. The effects of water-level fluctuations on vegetation in a Lake Huron wetland. Wetlands. 28:487-501.

Wilcox, D. A., J. E. Meeker, and J. Elias. 1992. Impacts of water-level regulation on wetlands of the Great Lakes. Phase 2 Report to Working Committee 2. International Joint Commission Water-Level Reference Study.

Winter, M., D. H. Johnson, J. A. Shaffer, T. M. Donovan, and W. D. Svedrsky. 2006. Patch size and landscape effects on density and nesting success of grassland birds. Journal of Wildlife Management. 70:158-172.

107

Appendix A: Candidate Model Set by Species

Species Candidate Occupancy Models1 Parameters Pied- Ψ(.) 4 billed Grebe Ψ(Area) 5 Ψ(Area+Year) 6 Ψ(Area+500 m) 8 Ψ(Area+500 m+Year) 9 Ψ(EM1+EM2+PAB) 7 Ψ(EM1+EM2+PAB+Year) 8 Ψ(EM1+EM2+PAB+Area) 8 Ψ(EM1+EM2+PAB+Area+Year) 9 Ψ(EM+EM2+PAB+Area+500 m) 11 Ψ(EM1+EM2+PAB+Area+500 m+Year) 10 Ψ(EM1+EM2+PAB+XWD+DWD) 9 Ψ(EM1+EM2+PAB+XWD+DWD+Year) 10 Ψ(EM1+EM2+PAB+XWD+DWD+Area) 10 Ψ(EM1+EM2+PAB+XWD+DWD+Area+Year) 11 Ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m) 13 Ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m+Year) 14 Ψ(EM1+EM2+PAB+XWD+DWD+Year*XWD) 10 Ψ(EM1+EM2+PAB+XWD+DWD+Year*XWD+Area) 11 Ψ(EM1+EM2+PAB+XWD+DWD+Year*XWD+Area+500 m) 14 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT) 12 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Year) 13 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area) 13 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area+Year) 14 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area+500 m) 16 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area+500 m+Year) 17 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO) 12 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Year) 13 ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Area) 13 ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Area+Year) 14 ψ (EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Area+500 m 17 Ψ(EM1+EM2+PAB+XWD+DWD+INT+RR+PFO+Area+500 18 m+Year)2 1. Detection probability was modeled as a function of Stem and Area in all occupancy models 2. Global model

continued 108

Appendix A (continued)

Species Candidate Occupancy Models1 Parameters Least Ψ(.) 3 Bittern Ψ(Area) 4 Ψ(Area+500 m) 7 Ψ(EM1) 4 Ψ(EM1+Area) 5 Ψ(EM1+XWD+DWD) 6 Ψ(EM1+XWD+DWD+Area) 7 Ψ(EM1+XWD+DWD+Area+500 m) 10 Ψ(EM1+XWD+DWD+RR+INT) 9 Ψ(EM1+XWD+DWD+RR+INT+Area) 10 Ψ(EM1+XWD+DWD+RR+INT+Area+500 m) 13 Ψ(EM1+XWD+DWD+RR+INT+PFO) 10 Ψ(EM1+XWD+DWD+RR+INT+PFO+Area) 11 Ψ(EM1+XWD+DWD+Phrag+INT+RR+Shrub+PFO+Area+500 m)2 14 1. Detection probability was modeled as a function of InEM in all occupancy models 2. Indicates global model

continued

109

Appendix A (continued)

Species Candidate Occupancy Models1 Parameters Virginia Ψ(.) 4 Rail Ψ(Area) 5 Ψ(Area+Year) 6 Ψ(Area+500 m) 8 Ψ(Area+500 m+Year) 9 Ψ(EM1+EM2) 6 Ψ(EM1+EM2+Year) 7 Ψ(EM1+EM2+Area) 7 Ψ(EM1+EM2+Area+Year) 8 Ψ(EM+EM2+Area+500 m) 10 Ψ(EM1+EM2+Area+500 m+Year) 11 Ψ(EM1+EM2+RR+INT) 8 Ψ(EM1+EM2+RR+INT+Year) 9 Ψ(EM1+EM2+RR+INT+Area) 9 Ψ(EM1+EM2+RR+INT+Area+Year) 10 Ψ(EM1+EM2+RR+INT+Area+500 m) 12 Ψ(EM1+EM2+RR+INT+Area+500 m+Year) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD) 11 Ψ(EM1+EM2+RR+INT+XWD+DWD+Year) 12 Ψ(EM1+EM2+RR+INT+XWD+DWD+Year*XWD) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area) 12 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+Year) 13 Ψ(EM+EM2+RR+INT+XWD+DWD+Area+Year*XWD) 14 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+500 m) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+500 m+Year) 14 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+500 m+Year*XWD) 15 Ψ(EM1+EM2+RR+INT+XWD+DWD+PFO) 12 Ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+ Year) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+Area) 13 Ψ(EM+EM2+RR+INT+XWD+DWD+PFO+Area+Year) 14 ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+Area+500 m) 17 Ψ(EM1+EM2+PAB+XWD+DWD+INT+RR+PFO+Area+ 18 500 m+Year)2 1. Detection probability was modeled as a function of Area and Percent Water in all occupancy models 2. Global model

continued

110

Appendix A (continued)

Species Candidate Occupancy Models1 Sora Ψ(.) 4 Ψ(Area) 5 Ψ(Area+Year) 6 Ψ(Area+500 m) 8 Ψ(Area+500 m+Year) 9 Ψ(EM1+EM2) 6 Ψ(EM1+EM2+Year) 7 Ψ(EM1+EM2+Area) 7 Ψ(EM1+EM2+Area+Year) 8 Ψ(EM+EM2+Area+500 m) 10 Ψ(EM1+EM2+Area+500 m+Year) 11 Ψ(EM1+EM2+RR+INT) 8 Ψ(EM1+EM2+RR+INT+Year) 9 Ψ(EM1+EM2+RR+INT+Area) 9 Ψ(EM1+EM2+RR+INT+Area+Year) 10 Ψ(EM1+EM2+RR+INT+Area+500 m) 12 Ψ(EM1+EM2+RR+INT+Area+500 m+Year) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD) 11 Ψ(EM1+EM2+RR+INT+XWD+DWD+Year) 12 Ψ(EM1+EM2+RR+INT+XWD+DWD+Year*XWD) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area) 12 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+Year) 13 Ψ(EM+EM2+RR+INT+XWD+DWD+Area+Year*XWD) 14 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+500 m) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+500 m+Year) 14 Ψ(EM1+EM2+RR+INT+XWD+DWD+Area+500 m+Year*XWD) 15 Ψ(EM1+EM2+RR+INT+XWD+DWD+PFO) 12 Ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+ Year) 13 Ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+Area) 13 Ψ(EM+EM2+RR+INT+XWD+DWD+PFO+Area+Year) 14 ψ(EM1+EM2+RR+INT+XWD+DWD+PFO+Area+500 m) 17 Ψ(EM1+EM2+PAB+XWD+DWD+INT+RR+PFO+Area+ 18 500 m+Year)2 1. Detection probability was modeled as a function of Date and Year in all occupancy models 2. Global model

continued 111

Appendix A (continued)

Species Candidate Occupancy Models1 Parameters Common Ψ(.) 4 Moorhen Ψ(Area) 5 Ψ(Area+Year) 6 Ψ(Area+500 m) 8 Ψ(Area+500 m+Year) 9 Ψ(EM1+EM2+PAB) 7 Ψ(EM1+EM2+PAB+Year) 8 Ψ(EM1+EM2+PAB+Area) 8 Ψ(EM1+EM2+PAB+Area+Year) 9 Ψ(EM+EM2+PAB+Area+500 m) 11 Ψ(EM1+EM2+PAB+Area+500 m+Year) 10 Ψ(EM1+EM2+PAB+XWD+DWD) 9 Ψ(EM1+EM2+PAB+XWD+DWD+Year) 10 Ψ(EM1+EM2+PAB+XWD+DWD+Area) 10 Ψ(EM1+EM2+PAB+XWD+DWD+Area+Year) 11 Ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m) 13 Ψ(EM1+EM2+PAB+XWD+DWD+Area+500 m+Year) 14 Ψ(EM1+EM2+PAB+XWD+DWD+Year*XWD) 10 Ψ(EM1+EM2+PAB+XWD+DWD+Year*XWD+Area) 11 Ψ(EM1+EM2+PAB+XWD+DWD+Year*XWD+Area+500 m) 14 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT) 12 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Year) 13 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area) 13 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area+Year) 14 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area+500 m) 16 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+Area+500 m+Year) 17 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO) 12 Ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Year) 13 ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Area) 13 ψ(EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Area+Year) 14 ψ (EM1+EM2+PAB+XWD+DWD+RR+INT+PFO+Area+500 m 17 Ψ(EM1+EM2+PAB+XWD+DWD+INT+RR+PFO+Area+ 18 500 m+Year)2 1. Detection probability was modeled as a function of Area and Percent Water in all occupancy models 2. Global model

112

Appendix B: Maximum number of Individuals Detected and Percent within 100 m

Species Year Max. no. detected % within 100 m Pied-billed Grebe 2009 75 24 2010 132 38 Black Tern 2009 5 0 2010 6 0 Least Bittern 2009 13 54 2010 30 77 American Bittern 2009 4 50 2010 8 50 King Rail 2009 2 100 2010 4 100 Virginia Rail 2009 28 89 2010 36 92 Sora 2009 32 66 2010 64 66 Common Moorhen 2009 46 43 2010 88 70 American Coot 2009 17 65 2010 23 70

113

Appendix C: Avian Species Observed at Restored Wetland

Wetland Use1 Common Name Scientific Name Wetland Dependent Canada Goose Branta canadensis Trumpeter Swan Cygnus buccinators Wood Duck Aix sponsa Mallard Anas platyrhynchos Blue-winged Teal Anas discors Hooded Merganser Lophodytes cucullatus Pied-billed Grebe Podilymbus podiceps Double-crested Cormorant Phalacrocorax auritus American Bittern Botaurus lentiginosus Least Bittern Ixobrychus exilis Great Blue Heron Ardea herodias Great Egret Ardea alba Green Heron Butorides virescens Bald Eagle Haliaeetus leucocephalus King Rail Rallus elegans Virginia Rail Rallus limicola Sora Porzana carolina Common Moorhen Gallinula chloropus American Coot Fulica americana Sandhill Crane Grus canadensis Killdeer Charadrius vociferous Spotted Sandpiper Tringa solitaria American Woodcock Scolopax minor Ring-billed Gull Larus delawarensis Belted Kingfisher Ceryle alcyon Alder Flycatcher Empidonax alnorum Willow Flycatcher Empidonax trailii Marsh Wren Cistothorus palustris Northern Waterthrush Seiurus noveboracensis Common Yellowthroat Geothlypis trichas Savannah Sparrow Passerculus sandwichensis Swamp Sparrow Melospiza georgiana Red-winged Blackbird Agelaius phoeniceus 1.Categorized based on list from Steward (2007).

continued

114

Appendix C (continued)

Wetland Use Common Name Scientific Name Wetland Associated or Blue-gray Gnatcatcher Polioptila caerulea Upland Eastern Bluebird Sialia sialis Hermit Thrush Catharus guttatus Wood Thrush Hylocichla mustelina American Robin Turdus migratorius Gray Catbird Dumetella carolinensis Northern Mockingbird Mimus polyglottos Brown Thrasher Toxostoma rufum European Starling Sturnus vulgaris Cedar Waxwing Bombycilla cedrorum Nashville Warbler Vermivora ruficapilla Northern Parula Parula americana Yellow Warbler Dendroica petechia Chestnut-sided Warbler Dendroica pensylvanica Prairie Warbler Dendroica discolor Black-and-white Warbler Mniotilta varia Hooded Warbler Wilsonia citrine Yellow-breasted Chat Icteria virens Scarlet Tanager Piranga olivacea Eastern Towhee Pipilo erythrophthalmus Chipping Sparrow Spizella passerine Field Sparrow Spizella pusilla Vesper Sparrow Poecetes gramineus Grasshopper Sparrow Ammodramus savannarum Song Sparrow Melospiza melodia House Sparrow Passer domesticus Northern Cardinal Cardinalis cardinalis Rose-breasted Grosbeak Pheucticus ludovicianus Indigo Bunting Passerina cyanea Dickcissel Spiza americana Bobolink Dolichonyx oryzivorus Eastern Meadowlark Sturnella magna Common Grackle Quiscalus quiscula Brown-headed Cowbird Molothrus ater Orchard Oriole Icterus spurious Baltimore Oriole Icterus galbula House Finch Carpodacus mexicanus American Goldfinch Carduelis tristis

116

Appendix D. 2009 Avian Species Richness and Selected Habitat Variables Summary for Restored Wetlands

Wetland Species Richnessa Focal Species Water Control Estimated Total Distance to Managed ID (Wetland - (Max # Detected) Structure Transitional Emergent Complex (mi) Dependent) Boundary (%) (%) Site-001 16(4) None Unknown 50 27 24-48 Site-002 22(8) None None 0 9 >48 Site-003 - None None 0 0 >48 Site-004 3(1) None Unknown 100 100 8-24 Site-005 12(4) None None 100 60 8-24 Site-006 7(2) None None 0 14 >48 Site-008 10(4) None Unknown - 8 8-24 Site-009 12(3) None None 0 23 0-8 Site-010 4(2) None Overflow Pipe 0 19 >48 Site-011 6(3) None Unknown - 37 24-48 Site-012 7(2) None Overflow Pipe 0 4 8-24 Site-013 24(5) None None 0 37 24-48 Site-014 2(1) None None 0 20 >48 Site-016 - None Overflow Pipe 0 34 8-24 Site-018 27(8) None None 10 16 >48 Site-019 6(5) Sora(1) Unknown 0 52 >48 Site-020 - None None 60 100 24-48 Site-021 12(6) None Unknown 100 73 8-24 Site-022 23(3) None Stop Log 0 33 >48 Site-047 - None Unknown - 63 24-48 Site-049 11(5) Virginia Rail(3), None 60 22 8-24 Sora(1) Site-111 - None None 0 92 24-48 Site-112 14(5) None None 30 3 24-48 Site-113 - None Stop Log 0 10 >48 Site-114 27(7) None None 80 26 8-24 continued Appendix D (continued)

Wetland Species Richness Focal Species (Max Water Control Estimated Total Distance to Managed ID (Wetland - # Detected) Structure Transitional Emergent Complex (mi) Dependent) Boundary (%) (%) Site-115 1(1) Pied-billed Grebe Stop Log 0 19 >48 (1) Site-116 8(2) None Stop Log 0 48 8-24 Site-117 25(3) None None 100 53 24-48 Site-119 1(1) Sora(1) Stop Log 55 74 8-24 Site-121 4(4) None None 0 21 15-30 Site-122 11(2) None Overflow Pipe 0 26 0-5 Site-123 7(1) None None 0 73 >30 Site-124 6(2) Sora(1) None 0 53 >30 Site-125 14(5) None None 0 43 0-5 Site-126 11(6) Sora(1) None 10 32 5-15 Site-127 16(9) Sora(1), Least Stop Log 0 28 15-30 Bittern(1), Pied- billed Grebe(1), Common Moorhen(2)b Site-146 14(4) None Unknown 0 19 0-5 Site-200 - None Unknown - 0 >30 Site-202 13(5) None Active Pump 0 16 0-5 Site-203 - None Stop Log 0 91 15-30 Site-204 29(4) None Overflow Pipe 0 10 15-30 Site-206 - None None 0 0 Site-208 25(6) Pied-billed Stop Log 35 17 Grebe(2) Site-222 - None Stop Log 19 42

continued Appendix D (continued)

Wetland Species Richness Focal Species (Max Water Control Estimated Total Distance to Managed ID (Wetland - # Detected) Structure Transitional Emergent Complex (mi) Dependent) Boundary (%) (%) 150 - Virginia Rail(1) None 100 8 - 296 - Pied-billed Grebe None 100 42 - (5), American Coot(2), Common Moorhen(2) a. Time invested in collecting non-focal species counts was not standardized so species richness should not be considered a measure of habitat quality between sites. b. Site-127, Common Moorhen and Least Bittern detected outside of vegetation plot but within same wetland basin as survey point

Appendix E. 2010 Avian Species Richness and Selected Habitat Variables Summary for Restored Wetlands

Wetland Species Richness Focal Species Water Control Estimated Total Emergent Distance to Managed ID (Wetland - (Max. # Detected) Structure Transitional (%) Complex (mi) Dependent)1 Boundary (%) Site-023 28(5) None None 50 37 5-15 Site-026 17(3) None Overflow pipe 0 0 >30 Site-027 21(4) None Stop log 0 57 15-30 Site-028 No Data None None 50 29 15-30 Site-029 19(2) None Stop log 30 19 15-30 Site-030 20(4) None Stop log 95 24 >30 Site-031 15(5) King Rail (1) None 80 17 15-30 Site-032 No Data None None 40 20 5-15 Site-034 19(5) Pied-billed Grebe Stop log 0 18 15-30 (1)a Site-035 22(4) None None 40 23 5-15 Site-036 16(5) Sora(2) None 60 65 5-15

Site-037 21(10) Virginia Rail (2) Stop log 50 47 15-30 Site-038 16(2) None None 0 24 >30 Site-039 24(6) None None 15 8 5-15 Site-041 15(4) None Unknown 0 6 0-5 Site-042 19(3) None Overflow pipe 85 0 >30 Site-043 25(5) Virginia Rail(1)b None 100 40 15-30 Site-044 19(3) None None 0 27 15-30 Site-045 18(5) Sora(2) None 100 66 15-30 Site-048 20(5) None Overflow pipe 0 1 5-15 Site-128 19(3) None None 75 4 15-30 Site-130 20(4) None Unknown 0 0 15-30 Site-131 23(6) None None 90 0 15-30

continued Appendix E (continued)

Wetland Species Richness Focal Species (Max Water Control Estimated Total Emergent Distance to Managed ID (Wetland - # Detected) Structure Transitional (%) Complex (mi) Dependent) Boundary (%) Site-136 23(3) None Overflow pipe 5 6 15-30 Site-138 18(9) American Coot(1), None 50 32 0-5 Least Bittern(1) Site-139 14(4) Sora(1) None 0 93 >30 Site-140 30(10) None Stop log 95 53 >30 Site-141 17(4) None None 90 42 >30 Site-142 20(8) None None 35 17 15-30 Site-143 20(8) Least Bittern(1) Unknown 30 50 15-30 Pied-billed Grebe(2) Site-144 27(6) None None 0 6 5-15 Site-149 1(1) Virginia Rail(1) Stop log 40 18 15-30 Site-151 21(6) None None 0 34 15-30 Site-153 11(7) None Unknown 100 28 0-5 Site-154 15(7) None Stop log 0 13 0-5 Site-209 14(6) Pied-billed Overflow pipe 10 20 15-30 Grebe(3) Site-210 20(10) Pied-billed Flap gate 0 72 0-5 Grebe(5) Site-212 19(5) None None 30 23 >30 Site-214 18(10) Sora(1) Stop log 30 5 5-15 Site-216 11(4) None Overflow pipe 25 34 15-30 Site-217 9(4) None Stop log 35 25 15-30 Site-218 11(5) None Unknown 0 46 0-5

continued Appendix E (continued)

Wetland Species Richness Focal Species (Max Water Control Estimated Total Emergent Distance to Managed ID (Wetland - # Detected) Structure Transitional (%) Complex (mi) Dependent) Boundary (%) Site-225 19(8) None Flap gate 0 81 0-5 Site-226 13(6) None None 0 27 0-5 671 - Virginia Rail(1) None 100 13 759 - Pied-billed None 0 0 Grebe(1), American Coot(1) 779 - Pied-billed Screwgate 0 21 0-5 Grebe(1), Common Moorhen(1), Sora(1), Virginia Rail(1) 781 - Sora(1) Unknown 100 11 - 795 - Common Screwgate 0 0 - Moorhen(1) 839 - None Unknown 10 30 - 878 - None Screwgate 0 33 - 896 - Common Screwgate 0 54 - Moorhen(2), Sora(1) a. Site-034, Pied-billed Grebe detected 300m away, not at restoration site b. Site-043, Virginia Rail detected 150m away, in wetland basin outside of vegetation plot