Ecology of Yellow Rail (Coturnicops Noveboracensis) Overwintering in Coastal Pine

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Ecology of Yellow Rail (Coturnicops noveboracensis) overwintering in coastal pine

savannas of the northern Gulf of Mexico

Kelly Marie Morris

A Thesis Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Wildlife, Fisheries and Aquaculture in the Department of Wildlife, Fisheries and Aquaculture

Mississippi State, Mississippi

August 2015

Kelly Marie Morris

2015

Ecology of Yellow Rail (Coturnicops noveboracensis) overwintering in coastal pine

savannas of the northern Gulf of Mexico

Kelly Marie Morris

Approved:

______Scott A. Rush (Major Professor)

______Mark S. Woodrey (Committee Member)

______John C. Rodgers III (Committee Member)

______Eric D. Dibble (Graduate Coordinator)

______George M. Hopper Dean College of Forest Resources

Name: Kelly Marie Morris

Date of Degree: August 14, 2015

Institution: Mississippi State University

Major Field: Wildlife, Fisheries and Aquaculture

Major Professor: Scott A. Rush

Title of Study: Ecology of Yellow Rail (Coturnicops noveboracensis) overwintering in coastal pine savannas of the northern Gulf of Mexico

Pages in Study: 75

Candidate for Degree of Master of Science

The Yellow Rail (Coturnicops noveboracensis) is a migratory nongame bird of

high conservation priority throughout its entire range. The objectives of this study were:

(1) assess Yellow Rail occupancy in the context of prescribed fire regime in pine savanna

habitats of Mississippi and Alabama and (2) assess Yellow Rail habitat use in pine

savanna habitats of coastal Mississippi. Yellow Rail occupancy decreased significantly

with time since fire and increased with grassland patch size. Throughout the study area

Yellow Rail maintained small (mean = 3.37 ha) home ranges aggregated within study

areas, indicating suitable habitat may be limited. Yellow Rail used areas dominated by

Aristida stricta and Carex spp. They used locations with lower woody percent cover,

greater herbaceous height structure and lower frequency of trees than locations outside

their home range. This research highlights the continued need to prioritize conservation

and management of open grasslands and pine savanna habitats.

DEDICATION

I dedicate this thesis to all my close family and friends. To my family, for all their unconditional love and support garnered throughout my journey. To my grandparents,

Dorothy and Peter Luzetsky, who sparked my endless curiosity of the natural world. To

Tracy, whose kindness and endless patience showed me life is a blank canvas. Thank you for the paint.

ACKNOWLEDGEMENTS

I thank Scott Rush and Mark Woodrey for giving me this incredible opportunity

and providing me with support and guidance in every step of this process. I thank my

field crew, countless volunteers and everyone that generously helped with field work

throughout this project, especially Jared Feura, Eamon Harrity, Matt Boone and Jake

Walker. I am grateful to my committee members, Mark Woodrey and John Rodgers, for

their valuable insight and comments. I thank my fellow graduate students who not only

provided friendship and endless support completing this degree. I am grateful to

Mississippi Sandhill Crane National Wildlife Refuge, Grand Bay National Estuarine

Research Reserve / National Wildlife Refuge and Alabama Department of Natural

Resources and Conservation for their support throughout this project especially, Scott

Hereford, Angela Dedrickson, Eric Soehren and John Trent. This thesis was supported by

Mississippi Ornithological Society, U.S. Fish and Wildlife Inventory Monitoring

Program and Mississippi State University.

iii

TABLE OF CONTENTS

DEDICATION ...... ii

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... viii

CHAPTER

I. GENERAL INTRODUCTION ...... 1

Status of pine savanna communities ...... 3 Study objectives ...... 5 Literature cited ...... 6

II. YELLOW RAIL (Coturnicops Noveboracensis) OCCUPANCY IN THE CONTEXT OF FIRE IN MISSISSIPPI AND ALABAMA ...... 10

Abstract ...... 10 Introduction ...... 10 Methods...... 13 Study area...... 13 Survey methods ...... 15 Habitat and site assessment ...... 17 Statistical methods ...... 19 Results ...... 22 Discussion ...... 23 Literature cited ...... 34

III. HABITAT USE OF YELLOW RAIL (Coturnicops noveboracensis) IN PINE SAVANNA HABITATS OF MISSISSIPPI ...... 41

Abstract ...... 41 Introduction ...... 42 Methods...... 45 Study area...... 45 Survey methods ...... 46 iv

Yellow Rail capture surveys ...... 46 Habitat assessment ...... 48 Statistical methods ...... 49 Home range ...... 49 Habitat use ...... 50 Results ...... 52 Home range ...... 52 Habitat use ...... 53 Discussion ...... 54 Home range ...... 54 Habitat use ...... 55 Management implications ...... 56 Literature cited ...... 63

IV. GENERAL SUMMARY AND CONCLUSIONS...... 69

Literature Cited ...... 72

APPENDIX

A. IMPLIMENTATION OF YELLOW RAIL OCCUPANCY MODEL SELECTION ...... 73

LIST OF TABLES

2.1 Metrics used in generating site-occupancy estimates of Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2012– 2013...... 28

2.2 Correlation matrix for habitat characteristics collected in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013...... 29

2.3 Models incorporating landscape and habitat variables used in describing Yellow Rail occupancy (Ψ) in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013, ranked according to deviance information criterion (DIC)...... 30

2.4 Model-averaged mean, standard deviation (SD), and 95% credible intervals (CRI) of landscape and habitat variables included in top-ranking models (ΔDIC ≤ 4) for Yellow Rail occupancy in pine savanna habitats of Mississippi and Alabama, USA, 2012– 2013...... 31

3.1 Mean (± SE) fixed kernel home range (95% kernel) and core area (50% kernel) of Yellow Rail wintering in pine savanna habitats of coastal Mississippi, USA, 2011–2013...... 57

3.2 Mean (± SE) minimum convex polygon (MCP) home range (95% use) and core area (50% use) of Yellow Rail wintering in pine savanna habitats of coastal Mississippi, USA, 2011–2013...... 58

3.3 Summary of dominant plant species noted to occur within areas of Yellow Rail activity by radio-marked individuals in pine savanna habitats of Jackson County, Mississippi, USA, 2011– 2013 ...... 59

3.4 Model-averaged mean, standard deviation (SD), and 95% confidence intervals (CI) of habitat variables used in describing wintering Yellow Rail habitat use in pine savanna habitats of Mississippi, USA, 2011–2013...... 60

A.1 Models incorporating detection variables ranked by deviance information criterion (DIC) for Yellow Rail occupancy in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013...... 74 vi

A.2 Parameter estimates (mean, standard deviation [SD], and 95% credible interval [CRI]) for variables included in the top-ranking detection model for Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013...... 75

vii

LIST OF FIGURES

2.1 Study areas in Mississippi and Alabama, USA, 2012–2013...... 32

2.2 Predictions of the covariates (a) time since fire and (b) patch size as relevant to site-occupancy (with uncertainty) of Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2012– 2013...... 33

3.1 Study areas for wintering Yellow Rail in Jackson County, Mississippi, USA, 2011–2013...... 61

3.2 Mean home range area curve with standard error for 10 Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2011– 2013 (December–March), showing changes in home range size with increasing number of locations by field season...... 62

viii

CHAPTER I

GENERAL INTRODUCTION

The Yellow Rail (Coturicops noveboracensis), the second smallest of North

American rails (Family Rallidae), is among the most enigmatic and least studied birds in

North America (Bookhout 1995). As its name suggests, the Yellow Rail is yellow-brown in color and is distinguished from other rails by the buff and black striped back and white patch on the secondary flight feathers (Pyle 2008). This species is extremely secretive, and is most often identified by call, a ‘tic-tic, tic-tic-tic’ pattern heard typically at night

(Bookhout 1995; COSEWIC 2011). Owing to a wintering range of limited geographic

extent, and indications of local population declines throughout its breeding range this

species is recognized as a Species of Conservations Concern in Canada (COSEWIC

2011), a Migratory Nongame Bird of Special Management Concern by the U.S. Fish and

Wildlife Service (USFWS 2008).

The Yellow Rail’s breeding range, while imperfectly known, extends from the

northern United States and Canada, east of the Rocky Mountains from Saskatchewan,

Canada to the Atlantic coast (Bookhout 1995). Roughly 90% of this species’ breeding

range is limited to Canada (COSEWIC 2011). In the United States, Yellow Rail breed

primarily in Maine, Michigan, Wisconsin, Minnesota, North Dakota and northeastern

Montana, with isolated populations occurring in south-central Oregon and northern

California (Bookhout 1995). Breeding ground habitat is generally fresh and brackish 1

marshes, swampy meadows, and wet cut-over hay fields dominated by sedges

(Cyperaceae spp.), grasses (Poaceae spp.), and rushes (Juncaceae spp.) that remain

saturated or shallowly flooded through summer (Bookhout 1995). Such habitat is

characterized as having a mat of senescent vegetation (Bookhout 1995).

On their breeding grounds, Yellow Rail are less likely found in habitats with

especially dense stands of sedges or other emergent vegetation, or where woody plants

encroach (Stenzel 1982; Conway et al. 2010; Austin and Buhl 2013), a relationship that

suggests disturbance may be fundamental in maintaining habitats suitable for this species.

Specifically, fire regimes that incorporate a short (couple year) rotation have proven

effective in maintaining habitat conditions suitable to this species during the breeding

season (Stenzel 1982; Conway et al. 2010; Austin and Buhl 2013). Management that

incorporates fire may also prove important to Yellow Rail on their wintering grounds as

some of these same habitat features may dictate the quality of habitat used during this

period (Mizell 1998; Grace et al. 2005).

Yellow Rail depart the breeding grounds between mid-August and November,

migrating to wintering grounds along the Atlantic and Gulf Coast Plain regions of the

United States (Stenzel 1982; Bookhout 1995; Popper and Stern 2000; Goldade et al.

2002), and as recently discovered, southeastern Oklahoma (Butler et al. 2010). Despite

the wide geographic area in which Yellow Rail overwinter, few studies have addressed

this species’ ecology and habitat use during winter. The few studies that focus on this

topic are limited geographically to coastal Texas (Mizell 1998; Grace et al. 2005), coastal

South Carolina (Post 2008) and southeastern Oklahoma (Butler et al. 2010). Yellow Rail

are also known to overwinter in pine savannas of the northern Gulf of Mexico with

historical accounts describing Yellow Rail occurrences in Mississippi (eg. Cook 1914;

Hicks 1934; Haberyan 1961; Valentine 1971) and Alabama (eg. Golsan and Holt 1914;

Imhof et al. 1976). These accounts however are largely anecdotal leaving the winter

ecology of Yellow Rail along the northern Gulf Coast poorly understood (Bookhout

1995).

Yellow Rail make use of a variety of habitats in winter, including, but not limited

to, coastal high marsh, coastal prairies, rice fields and open grassland fields (Bookhout

1995). For instance, Wayne’s (1905) observations in South Carolina describe Yellow

Rail winter habitat as “a low wet place of open land with a dense growth of short dead

grass.” In Oklahoma, Butler et al. (2010) and Heck and Arbour (2008) described Yellow

Rail using habitat dominated by grasses, mainly Sporobolus spp., fall Panicum (Panicum dichotomoflorum) and long leafed rushgrass (Sporobolus asper). The most unifying features of Yellow Rail winter habitat are dense vegetative cover with low plant height

(Grace et al. 2005; Post 2008).

Status of pine savanna communities

Southeastern pine savannas once dominated the coastal plain of the southeast

United States, covering approximately 24−36 million hectares and extending from

Virginia to Texas (Wahlenberg 1946; Ware et al. 1993; Noss et al. 1995; Means 1996;

Gilliam and Platt 2006). Pine savanna habitats are characterized by clumps or sparsely

distributed trees that do not form a continuous canopy (Werner 1991; McPherson 1997;

Scholes and Archer 1997). These low-nutrient acidic soil habitats can reflect hundreds of

species of grasses, forbs, shrubs, and various carnivorous plants including pitcher plants

(Sarracenia spp.), butterworts (Pinguicula spp.), bladderworts (Utricularia spp.) and 3

sundews (Drosera spp.) (Folkerts 1982; Christensen 1988; Bridges and Orzell 1989;

Harcombe et al. 1993; Peet and Allard 1993), as well as warm season grasses (Bridges

and Orzell 1989; Peet and Allard 1993). Within pine savannas, ground cover holds the

greatest biological diversity, dominated by warm season grasses and perennial forbs (Peet

and Allard 1993; Kirkman et al. 2001). In fact, pine savanna systems are one of the most

species-rich plant communities in North America (Peet and Allard 1993; Simberloff

1993).

Natural disturbances such as lightning storms, hurricanes and tornados and

herbivory coupled with anthropogenic activities historically maintained pine savanna

habitats (Buckner 1989; Platt et al. 1988; Waldrop et al. 1992). While several of these

factors were likely responsible for the development and structure of longleaf pine

ecosystems, it is also probable that seasonal fires were the primary agent responsible for

shaping these ecosystems (Chapman 1932). In general, fires enhance species diversity of

herbaceous ground cover, suppress encroachment of hardwood trees and prevent the

spread of shrubs (Platt et al. 1988, 1991; Brewer and Platt 1994; Glitzenstein et al. 1995;

Olson and Platt 1995).

Concomitant with European colonization, the distribution of pine savannas

changed considerably, borne out through habitat fragmentation, extensive harvesting of

large timber, and habitat degradation (Vogl 1973; Pyne 1982; Means and Grow 1985;

Noss 1989; Frost 1993; Means 1996). As a result, the frequency and scale of fires

significantly declined in the region, causing once open pine-grasslands communities to

become more densely forested (Pyne 1982). Currently only about 3% of the historical

extent of longleaf pine habitats remains (Kelly and Bechtoldt 1990). Not surprising, the

loss of pine savanna habitat has affected many species, some of which now qualify as federally threatened or endangered including the Mississippi Sandhill Crane (Grus

canadensis pulla), and Dusky Gopher Frog (Lithobates sevosa).

Study objectives

The geographic extent of the Yellow Rail’s winter range is estimated to be no more than 7% the size of their breeding range (COSEWIC 2011). The primary goal of this research is to provide information which would support habitat management activities for wintering Yellow Rails to restore and maintain coastal pine savannas they inhabit. Given their limited winter distribution, the continued loss of pine savannas and the dearth of information on Yellow Rail habitat use during the non-breeding season, the objectives of this study were to evaluate (1) Yellow Rail occupancy in the context of fire in coastal pine savanna habitats of Mississippi and Alabama and (2) habitat use by

Yellow Rail in coastal pine savanna habitats of Mississippi.

Literature cited

Austin, J.E. and D.A. Buhl. 2013. Relating Yellow Rail (Coturnicops noveboracensis) occupancy to habitat and landscape features in the context of fire. Waterbirds 36: 199–213.

Bookhout, T.A. 1995. Yellow Rail (Coturnicops noveboracensis). No. 139 in The Birds of North America (A. Poole and F. Gills, Eds.). The Academy of Natural Sciences, Philadelphia, Pennsylvania; American Ornithologists’ Union, Washington, D.C., USA.

Brewer, J.S. and W.J. Platt. 1994. Effects of fire season and herbivory on reproductive success in a clonal forb, Pityopsis graminifolia (Michx.). Journal of Ecology 82: 665–675.

Bridges, E.L. and S.L. Orzell. 1989. Longleaf pine communities of the West Gulf Coastal Plain. Natural Areas Journal 9: 246–263.

Buckner, E. 1989. Evolution of forest types in the Southeast. General technical report SE- U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station.

Butler, C.J., L.H. Pham, J.N. Stinedurf, C.L. Roy, E.L. Judd, N.J. Burgess and G.M. Caddell. 2010. Yellow Rails wintering in Oklahoma. The Wilson Journal of Ornithology 122: 385–387.

Chapman, H.H. 1932. Is the longleaf type a climax? Ecology 13: 328–334.

Christensen, N.L. 1988. Vegetation of the Southeastern Coastal Plain. Pages 317–363 in North American Terrestrial Vegetation (M.G. Barbour and W.D. Billings, Eds.). Cambridge University Press, Cambridge, England.

Cook, W.W. 1914. Distribution and migration of North American rails and their allies. U.S. Department of Agriculture. Bulletin 128.

Committee on the Status of Endangered Wildlife in Canada (COSEWIC). 2011. COSEWIC assessment and status report on the Yellow Rail Coturnicops noveboracensis in Canada. Committee on the Status of Endangered Wildlife in Canada, Ottawa, Canada.

Conway, C.J., C.P. Nadeau and L. Piest. 2010. Fire helps restore natural disturbance regime to benefit rare and endangered marsh birds endemic to the Colorado River. Ecological Applications 20: 2024–2035.

Folkerts, G.W. 1982. The Gulf Coast pitcher plant bogs (Mississippi, Alabama). American Scientist 70: 260–267.

Frost, C.C. 1993. Four centuries of changing landscape patterns in the longleaf pine ecosystem. Proceedings of the Tall Timbers Fire Ecology Conference 18: 17–43.

Gilliam, F.S. and W.J. Platt. 2006. Conservation and restoration of the Pinus palustris ecosystem. Applied Vegetation Science 9: 7–10.

Glitzenstein, J.S., W.J. Platt and D.R. Streng. 1995. Effects of fire regime and habitat on tree dynamics in north Florida longleaf pine savannas. Ecological Monographs 65: 441–476.

Grace, J.B., L.K. Allain, H.Q. Baldwin, A.G. Billock, W.R. Eddleman, A.M. Given and R. Moss. 2005. Effects of prescribed fire in the coastal prairies of Texas. USGS Open-File Report 2005–1287. Reston, VA: U.S. Department of the Interior, Fish and Wildlife Service, Region 2. U.S. Geological Survey, 54491.

Goldade, C.M., J.A. Dechant, D.H. Johnson, A.L. Zimmerman, B.E. Jamison, J.O. Church and B.R. Euliss. 2002. Effects of management practices on wetland birds: Yellow Rail. Northern Prairie Wildlife Research Center, Jamestown, North Dakota, USA.

Golsan, L.S. and E.G Holt. 1914. Birds of Autauga and Montgomery Counties, Alabama. The Auk 31: 212–235.

Haberyan, H.D. 1961. Gulf Coast notes. Mississippi Ornithological Society Newsletter 6: 4–5.

Harcombe, P.A., J.S. Glitzenstein, R.G. Knox, S.L. Orzell and E.L. Bridges. 1993. Vegetation of the longleaf pine region of the West Gulf Coastal Plain. Pages 83– 104 in The Longleaf Pine Ecosystem: Ecology, Restoration, and Management (S.M. Hermann, Ed.) Proceedings of the 18th Tall Timbers Fire Ecology Conference.

Heck, B.A. and W.D. Arbour. 2008. The Yellow Rail in Oklahoma. Bulletin of the Oklahoma Ornithological Society 41: 13–15.

Hicks, L.E. 1934. Winter birds of the Mississippi Gulf Coast. The Wilson Bulletin 46: 259–260.

Imhof, T.A., R.A. Parks, D.C. Hulse and C.T. Traylor. 1976. Alabama birds. Alabama Department of Natural Resources, Game and Fish Division. University of Alabama Press, Tuscaloosa, Alabama, USA 1976.

Kelly, J.F. and W.A. Bechtoldt. 1990. The longleaf pine resource. Pages 11–22 in Proceedings of the Symposium on the Management of Longleaf Pine (R.M. Farrar, Ed.). USDA Forest Service Southern Forest Experiment Station General Technical Report SO-75. New Orleans, Louisiana, USA.

Kirkman, L.K., R.J. Mitchell, R.C. Helton and M.B. Drew. 2001. Productivity and species richness across an environmental gradient in a fire-dependent ecosystem. American Journal of Botany 88: 2119–2128.

McPherson, G. 1997. Ecology and Management of North American Savannas. University of Arizona Press, Tucson, Arizona, USA.

Means, D.B. 1996. Longleaf pine forest, going, going. Pages 210–219 in Eastern Old- Growth Forests: Prospects for Rediscovery and Recovery (M.B. Davis, Ed.). Island Press, Washington, D.C., USA.

Means, D.B. and G. Grow. 1985. The endangered longleaf pine community. Enfo 85: 1– 12.

Mizell, K.L. 1998. Effects of fire and grazing on Yellow Rail habitat in a Texas coastal marsh. Dissertation. Texas A&M University, College Station, Texas, USA.

Noss, R.F. 1989. Longleaf pine and wiregrass: keystone components of an endangered ecosystem. Natural Areas Journal 9: 211–213.

Noss, R.F., E.T. LaRoe and J.M. Scott. 1995. Endangered ecosystems of the United States: a preliminary assessment of loss and degradation, Vol. 28. Washington, DC, USA: U.S. Department of the Interior, National Biological Service.

Olson, M.S. and W.J. Platt. 1995. Effects of habitat and growing season fires on resprouting of shrubs in longleaf pine savannas. Vegetatio 119: 101–118.

Peet, R.K. and D.J. Allard. 1993. Longleaf pine vegetation of the southern Atlantic and eastern Gulf Coast regions: a preliminary classification. Pages 45–81 in The Longleaf Pine Ecosystem: Ecology, Restoration, and Management (S.M. Hermann, Ed.) Proceedings of the 18th Tall Timbers Fire Ecology Conference.

Platt, W.J., G.W. Evans and S.L. Rathbun. 1988. The population dynamics of a long- lived conifer (Pinus palustris). American Naturalist 131: 491–525.

Platt, W.J., J.S. Glitzenstein and D.R. Streng. 1991. Evaluating pyrogenicity and its effects on vegetation in longleaf pine savannas. Pages 143–161 in Proceedings of the 17th Tall Timbers Fire Ecology Conference.

Popper, K.J. and M.A. Stern. 2000. Nesting ecology of Yellow Rails in southcentral Oregon. Journal of Field Ornithology 71: 460–466.

Post, W. 2008. Winter ecology of Yellow Rails based on South Carolina specimens. The Wilson Journal of Ornithology 120: 606–610.

Pyle, P. 2008. Identification Guide to North American Birds. Part II. Anatidae to Alcidae. Slate Creek Press, Point Reyes, California, USA. 8

Pyne, S.J. 1982. Fire in America. A Cultural History of Wildland and Rural Fire. Princeton University Press, Princeton, New Jersey, USA.

Scholes, R.J. and S.R. Archer. 1997. Tree-grass interactions in savannas. Annual Review of Ecology and Systematics 28: 517–544.

Simberloff, D. 1993. Species-area and fragmentation effects on old-growth forests: prospects for longleaf pine communities. Proceeding of the Tall Timbers Fire Ecology Conference 18: 1–13.

Stenzel, J.R. 1982. Ecology of breeding Yellow Rails at Seney National Wildlife Refuge. Dissertation, Ohio State University, Columbus, Ohio, USA.

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.

Valentine, J.M. 1971. Field observations: Yellow Rails. Mississippi Ornithological Society Newsletter 16: 2–3.

Vogl, R.J. 1973. Effects of fire on the plants and animals of a Florida wetland. American Midland Naturalist 89: 334–347.

Wahlenberg, W.G. 1946. Wahlenberg WG. 1946. Longleaf pine: its use, ecology, regeneration, protection, growth, and management. Charles Lathrop Pack Forestry Foundation and USDA Forest Service Washington, D.C., USA.

Waldrop, T.A., D.L. White and S.M. Jones. 1992. Fire regimes for pine-grassland communities in the southeastern United States. Forest Ecology and Management 47: 195–210.

Ware, S., C.C. Frost and P.D. Doerr. 1993. Southern mixed hardwood forest: the former longleaf pine forest. Pages 447–493 in Biodiversity of the Southeastern United States: Lowland Terrestrial Communities (W.H. Martin, S.G. Boyce, and A.C. Echternacht, Eds.). John Wiley & Sons, New York, USA.

Wayne, A.T. 1905. Notes on certain birds taken or seen near Charleston, South Carolina. The Auk 22: 395–400.

Werner, P. A. 1991. Savanna Ecology and Management: Australian Perspectives and Intercontinental Comparisons. Blackwell Scientific, Oxford, United Kingdom.

CHAPTER II

YELLOW RAIL (Coturnicops Noveboracensis) OCCUPANCY IN THE CONTEXT OF

FIRE IN MISSISSIPPI AND ALABAMA

Abstract

The Yellow Rail (Coturnicops noveboracensis) is a migratory bird of high

conservation concern throughout the Atlantic and Gulf Coastal Plains region of the

United States. The objective was to estimate detection (ρ) and site occupancy

probabilities (ψ) for Yellow Rail overwintering in coastal pine savannas of Mississippi

and Alabama. Between December–April, 2012–2013, model-averaged ψ for Yellow Rail was 0.55 ± 0.04 (SD) with ρ of 0.99 ± 0.04 (SD). Yellow Rail occupancy related

negatively with time since fire and positively to grassland patch size, with highest

occupancy < 2 years post-burn and in patches > 25 ha, indicating fire and patch size

provide conditions attractive to Yellow Rail overwintering throughout the study area.

Yellow Rail’s use of fire-maintained habitats within coastal Mississippi and Alabama,

coupled with continued loss of open grassland throughout the southeastern United States

highlights the need to prioritize conservation and management of pine savanna habitats.

Introduction

Longleaf pine ecosystems, pine savannas in particular, were once among the most expansive ecosystems in North America (Landers et al. 1995). It is estimated that prior to

European settlement these ecosystems covered over 30 million ha in the southeastern

United States (Frost 1993). These fire-dependent ecosystems frequently experienced low to moderate intensity fires produced by lightning and Native American activities

(Brockway and Lewis 1996). Disruption of historical fire regimes, coupled with anthropogenic activities including habitat fragmentation and degradation, has been deemed responsible for the rapid decline of longleaf ecosystems (Brockway and Lewis

1996). Currently, longleaf pine ecosystems are among the most ecologically imperiled of

all forested ecosystems in North America (Noss 1989; Simberloff 1993), with an area

currently estimated to be less than 1.2 million ha (Outcalt and Sheffield 1996; Brockway

and Lewis 1996; Outcalt 2000; USFWS 2003; Frost 2006).

Disturbance, most notably fire, helped shape both historic and present-day

vegetation structure of longleaf pine ecosystems (Platt 1999). Temporal and spatial

heterogeneity in pyrogenic activity directly influences avian communities that inhabit

fire-dependent ecosystems (Wiens 1974). Studies indicate prescribed fire is a beneficial

management tool for avian species associated with longleaf pine systems (e.g. Tucker et

al. 1998, 2003; Bechtoldt and Stouffer 2005). Subsequently, present day fire management

programs aim to reintroduce fire into the landscape resulting in dynamic habitat mosaics

(Parr and Brockett 1999). However, information is lacking relating the effects of time

since fire on occupancy for many avian species associated with longleaf pine ecosystems.

The Yellow Rail (Coturnicops noveboracensis), the second smallest of the North

American rails, is highly secretive, and consequently little is known about this species’

ecology (Bookhout 1995). Due to a small population size, limited wintering range, and

threats to suitable habitats, the Yellow Rail is listed as a Species of Conservation Concern

in Canada (COSEWIC 2011), and a Migratory Nongame Bird of Conservation Concern in the United States (USFWS 2008). In Mississippi and Alabama this species has a status of “Greatest Conservation Need” (Alabama Department of Conservation and Natural

Resources 2005; Mississippi Museum of Natural Science 2005; East Gulf Coastal Plain

Joint Venture 2008).

Studies addressing Yellow Rail distribution and habitat use on wintering grounds

have been geographically limited to coastal Texas (Mizell 1998; Grace et al. 2005),

coastal South Carolina (Post 2008) and Oklahoma (Butler 2010), an area recently known

to support this species outside migration (Bookhout 1995; Butler 2010). Although

historical accounts describe Yellow Rail overwintering along the Gulf Coast of

Mississippi (e.g. Cook 1914; Hicks 1934; Haberyan 1961; Valentine 1971) and Alabama

(eg. Golsan and Holt 1914; Imhof et al. 1976), no systematic efforts have been made to

survey for this species in this region.

Periodic disturbance across breeding and wintering grounds appears critical in

maintaining Yellow Rail habitat suitability (Burkman 1993; Austin and Buhl 2013). Such

disturbances include seasonal flooding, fire, and herbivory (Platt 1999). These processes

recycle nutrients, increase ground cover biodiversity, remove litter, and top-kill woody

vegetation, limiting encroachment (Johnson and Knapp 1995; Warners 1997; Platt 1999;

Kost and De Steven 2000). In pine savanna habitats suppressed by fire, woody

encroachment increases, limiting herbaceous emergence, altering ground cover structure

and species composition (Lewis and Harshbarger 1976; Masters 1991; Wilson et al.

1995). Burkman (1993) found that on the breeding grounds, Yellow Rail were more

likely to be found in burned habitats than areas not burned. Burkman (1993)

characterized burned habitats having lower percentage of shrubs and higher percentage of sedges (Carex lasiocarpa) than control plots, suggesting suitability of habitat for Yellow

Rail diminishes with the encroachment of woody plant species. Similarly, two previous studies conducted on Yellow Rail in Michigan noted a negative relationship between time since fire and Yellow Rail presence (Burkman 1993; Austin and Buhl 2013). In

Michigan, Austin and Buhl (2013) found fire was the most important factor explaining the presence of Yellow Rail. While Yellow Rail are associated with fire-dependent habitats throughout their breeding and wintering range, limited information is available

on Yellow Rail and their use of wintering habitat.

The goal of this study is to estimate occupancy rates for Yellow Rail as related to

habitat features and management regimesin pine savannas along the northern Gulf Coast

of Mississippi and Alabama. I hypothesized that time since fire influenced Yellow Rail

occupancy through its effects on habitat structure and composition. Given the discussion

from above, I predicted Yellow Rail occupancy should: (1) decrease with time since fire

(2) be negatively related to woody vegetative cover and woody height, and (3) positively

related to herbaceous vegetation density.

Methods

Study area

The study area was comprised of pine savanna habitats in three areas currently

being managed for the restoration or maintenance of wet pine savanna habitat, two in

Jackson County, Mississippi: (1) Mississippi Sandhill Crane National Wildlife Refuge

(MSCNWR), and (2) Jackson County Mitigation Bank (JCMB), and one in Mobile

County, Alabama: Grand Bay Savanna (GBS; Alabama Forever Wild Land Trust) 13

(Figure 2.1). The MSCNWR represent one of the largest intact fire maintained pine savanna habitats remaining in Mississippi and Alabama, which along with the two additional fire-managed sites, collectively encompass over 9,150 ha. Located in the Gulf

Coastal Plain ecoregion, they are characterized by low topography and infertile, acidic,

waterlogged clay soils, with a temperate climate including hot summers (mean = 27˚C;

precipitation = 32 cm) and mild, wet winters (mean = 9˚C; precipitation = 38 cm)

(Norquist 1984; Greller 1989; Martin and Boyce 1993; National Climate Data Center

2015). Primary habitat types throughout these areas include pine savannas, pine

flatwoods, pine scrub, cypress-tupelo swamp, maritime forest and estuarine marsh (Peet

and Allard 1993). Dominant pine savanna herbaceous understory includes wiregrass

(Aristida stricta), muhly grass (Muhlenbergia spp.), toothache grass (Ctenium

aromaticum), bluestem grasses (Andropogon spp.) and panic grasses (Panicum spp.)

(Peet and Allard 1993). Dominant woody vegetation consists of St. John’s-wort

(Hypericum brachyphyllum), gallbery (Ilex glabra), wax myrtle (Morella cerifera),

sweetbay magnolia (Magnolia virginiana), yaupon (Ilex vomitoria) and smilax (Smilax

domingensis) (Peet and Allard 1993).

The MSCNWR encompasses 7,900 ha making it one of the most extensive (and

species-rich habitats) coastal pine savanna-focused management areas in the Southeastern

United States (Frost et al. 1986). In accordance with the MSCNWR fire management

plan prescribed fire is applied at 3−year return intervals where 1/3 of management

compartments (40 total) are burned annually (Wilder 2008). U.S. Fish and Wildlife

implement prescribed fire during both growing and dormant seasons, creating a mosaic of

pyrogenic habitats throughout the refuge (Wilder 2008).

The JCMB is a site owned and managed by Jackson County and is just north of the Grand Bay National Estuarine Research Reserve/National Wildlife Refuge (Figure

2.1). This area extends over 150 ha and is managed periodically by prescribed burning

(3−5 year return interval).

The GBS complex in Mobile County, Alabama comprises four tracts, totaling more than 2,000 ha (Figure 2.1). The specific site where I worked was the GBS which

encompasses 1,100 ha of coastal marsh, maritime forest and piney flatwoods. This savanna is managed with prescribed burns (2−3 year fire return interval) implemented by

Alabama Department of Conservation and Natural Resources.

Survey methods

Within the three conservation areas described above, I conducted surveys for

Yellow Rail December–April, 2012–2013. I define a study site as a singular management

unit within one of the three conservation areas and survey plot as the area surveyed

within each study site, where each study site is associated with one survey plot. I selected

13 study sites based on fire history and feasibility to survey selected habitat, with 11 sites

located within MSCNWR, one site in JCMB and one site in the GBS. The size of each

survey plot varied (1–5 ha) conditional upon logistical constraints, such as tree-line

boundaries.

Surveys began 30 minutes after sunset and stopped after three hours or upon coverage of the entire survey plot (Mizell 1998; Grace et al. 2005; Butler 2010). I

conducted surveys with a four person field crew using a 15 m weighted dragline (Mizell

1998; Grace et al. 2005; Butler 2010). The dragline creates noise and disturbance

throughout the vegetation, causing birds to flush from ground cover. The dragline was 15

equipped with 18, 500 mL plastic bottles evenly spaced (55 cm apart) and attached to the line with 0.5 m sections of rope. To weight the dragline, plastic bottles contained small rocks (¼ full). Each member of the field crew used a K-Light (5-LED 21 V Std Light,

Kelly’s K-Light, Cleveland, Texas) or a Boss Light (24v7, Boss Light, Hastings, Florida), to aid in the identification of flushed birds to the lowest taxonomic level. Two members of the crew pulled the ends of the dragline at a walking speed (one on each end) while the other two members were spread equidistant behind the dragline.

During each survey period, I recorded the beginning and end time of each transect

pass. During each transect pass, the survey crew walked in a straight line navigating to a

fixed position established by lantern hanging from a Shepherd’s hook placed at each

transect origin. At the end of each transect pass, the crew measured the distance (m)

between each end of the bowed dragline. The crew then shifted perpendicular to the

transect of origin, moving one dragline length (15 m) to the right or left. After shifting

over, the crew placed the lantern at the end of the dragline for navigating each pass. I

used a composite of dragline widths and the linear distances covered by each transect to determine the total area covered for each survey. I collected transect lengths using the

Tracks function on a handheld GPS unit (Garmin GPSMap76CSX, WAAS enabled, accuracy ≤ 3 m, Olathe, Kansas). The crew then proceeded parallel to the transect using the lantern to navigate. I standardized starting locations for each survey plot, and the direction of line shift.

The survey crew directly noted Yellow Rail detections by visual observation, using field identification characteristics (i.e., white secondary wing patches) and flight behavior (i.e., rapid, shallow wing-beats in a low arcing path) particular to this species. I

marked Yellow Rail detection locations as the point from which the bird flushed.

Detection locations were recorded using a Garmin GPS unit (GPSMap76CSX, WAAS

enabled, accuracy ≤ 3 m, Olathe, Kansas), with a 1 m PVC pole stuck in the ground to

facilitate collection of vegetation metrics during daylight hours the following day. I

surveyed each survey plot 3 times throughout each field season (December−April), with

surveys at the same site all ≥ 14 days apart.

Habitat and site assessment

I measured plant community structure the day following each survey, at two

locations per rail: (1) at each location where I detected a Yellow Rail and (2) at a paired

control point. I determined control point locations using a random numbers table,

selecting a number between 0−360 for compass bearing and a number between 20–100,

as a distance (in m) away from the detection point. I measured herbaceous and woody

height, point-intercept density, and percent woody and herbaceous cover (by dominant

plant species) within a circular plot, 20 m in diameter, centered on each Yellow Rail

flush, control and non-detection location. Using a 2 m pole, I measured maximum height

of woody and herbaceous vegetation contacting a 25 cm radius around the pole (Robel et

al. 1970). I measured vegetation height in 0.1 m intervals, collecting 20 height

measurements per plot. Five vegetation height measurement were taken at 2 m intervals

along transect lines in each of the four cardinal directions, radiating out from the center of

the plot. I recorded a point-intercept density reading at the end of each 10 m transect,

from the edge to the center of the plot at a height of 1 m. To measure point-intercept

density I placed the 2 m pole in the center of the plot and recorded at the lowest

decimeter where the pole was more than 50% obscured. I recorded diameter at breast 17

height (DBH) of all trees ≥ 7.5 cm (DBH) within the plot. Across the entire plot I visually estimated percent coverage of major woody (tree and shrub species) and herbaceous vegetation to the nearest 1%, totaling 100% across the plot. I also measured habitat characteristics at study plots where we detected no Yellow Rail (hereafter “non-detection location”) throughout the field season. For non-detection locations, I selected five

locations within each study plot using a random number table, first selecting one transect

from among those traversed during the final survey, and then selecting a distance along that transect.

For analytical purposes, I summarized vegetation characteristics by averaging all

vegetation metrics collected at all random points to describe the vegetation at the plot

level associated with a given site. At sites where we detected no Yellow Rail, I

summarized site characteristics by averaging all vegetation metrics collected at all

random points associated with a given site. I included site-averaged metrics in subsequent

occupancy models.

For each survey site, I calculated patch size (ha) for each individual plot and

survey area (ha) for each individual site using ArcGIS 10 (ESRI, Inc., Redlands, CA). I

utilized 2013 multi-band Landsat satellite imagery (U.S. Geological Survey's Earth

Resources Observation and Science Center) with 1 m resolution in calculating the area of

open grassland in each study site using the area measure tool in ArcGIS 10 (ESRI, Inc.,

Redlands, CA). I calculated survey area using a composite of dragline widths and the linear distance covered by each transect for each survey event. I eliminated any areas of transect overlap from the calculations. I averaged survey areas among each site visits for use in modeling site-occupancy by Yellow Rail.

Statistical methods

I employed a Bayesian approach to model Yellow Rail site occupancy. This method extends logistic regression models with Bernoulli distributions for state (Ψ) and detection (ρ), accounting for error in detection (MacKenzie et al. 2002). Site-occupancy

models use replicated surveys within a survey plot to resolve the uncertainty of a

recorded absence, which can occur when a species is absent, or present but not detected

(MacKenzie et al. 2006). Occupancy models provide estimates for Ψ, the estimated

proportion of study sites occupied by the species, and ρ, the probability of detecting the

species given it is present during a survey event. Assumptions of occupancy models

include a closed population, absence of false presence, and that selection and scale of

sampling areas are ecologically significant to the study species (MacKenzie et al. 2006).

Prior to running models, I standardized mean and variance of all continuous site

covariates using the scale function in R (R Core Team 2013). Site covariates included

herbaceous (HHT) and woody height (WHT), herbaceous (HERB) and woody percent

cover (WOOD), point-intercept density (DENSITY), patch size (PATCH), survey area

(SA), stand DBH (DBH) and time since fire (TIME) (Table 2.1). To address

multicollinearity I examined correlations between all predictor variables using r > 0.5, α

= 0.05 as a suitable criterion for omitting a variable (eg. Elbroch et al. 2015; Gehring et

al. 2015). I report the correlation matrix in Table 2.2.

Due to the inverse correlation of HERB and WOOD among sites, I selected

WOOD to remain in the global model as previous studies indicate a negative relationship

between Yellow Rail occurrence and woody cover (Burkman 1993; Martin 2012; Austin

and Buhl 2013). Subsequently, I designated WOOD as a proxy for collected woody

features and selected DENSITY to remain in the model to represent habitat structure. By

including WOOD and DENSITY as habitat features in modeling occupancy, I was able to

assess Yellow Rail occupancy as it relates to both vegetative structure and composition

(Ribic et al. 2009). As PATCH was highly correlated with SA, I selected PATCH to

remain in occupancy modeling to evaluate the strength towards area sensitivity for

Yellow Rail in pine savanna habitats (Robbins et al. 1989). Area sensitivity occurs when

the size of a fragmented area influences a species’ occupancy or abundance (Robbins et

al.1989). I developed subsequent models using the covariates DENSITY, DBH, WOOD,

TIME and PATCH.

I constructed occupancy models using a two-step selection process. First, I

modeled ρ while Ψ was held constant (MacKenzie et al. 2002), only including covariates

that could influence ρ. I then selected the best supported ρ model and incorporated this

model into Ψ models. In all models study site is the experimental unit. I considered those

factors potentially influencing the detection probability of Yellow Rail using a dragline

method to be DENSITY and WOOD. These covariates were therefore included as

detection-model covariates. I specified four detection models: WOOD and DENSITY,

DENSITY only, WOOD only, and a null model with no covariates. I compared detection models using deviance information criterion (DIC; Link and Barker 2009; a hierarchical

modeling generalization of Akaike’s information criterion and Bayesian information

criterion), used when posterior distribution of models are acquired using Markov Chain

Monte Carlo (MCMC) procedures (Spiegelhalter et al. 2002). Smaller DIC values

indicate a better fit among models. I selected the model with the lowest DIC value and

incorporated this model into all subsequent Ψ models. The top-ranked detection model

included the covariates DENSITY and WOOD (See Table A.1 for complete results of detection model ranking, and Table A.2 for parameter estimates of the top-ranking

detection model).

Next, I constructed models in which Yellow Rail Ψ varied as a function of the

following variables: PATCH, TIME, DBH, WOOD, and DENSITY, reflected in 31

candidate models, including all possible combinations of these variables. Models were

constructed in program JAGS 3.4.0 (Plummer 2013) using the MCMC procedure run

using the package R2jags in R (Su and Yajima 2012; R Core Team 2013). Bayesian

models followed the same maximum likelihood approach as described by MacKenzie et

al. (2002). I assigned all model parameters uninformative priors (0, 0.01) with uniform

distributions. For each candidate model, I ran three MCMC chains with 50,000 iterations

per Markov chain. I discarded the first 5,000 iterations (burn in) with a thinning factor of

10 to minimize autocorrelation among iterations, producing 13,500 estimates for each

model parameter. I estimated the convergence of random Markov Chains using the

Gelman-Rubin convergence statistic, R-hat (Gelman and Hill 2006), where R-hat values

< 1.1 indicate adequate convergence to the principal distribution. For each occupancy

model, I calculated DIC, ΔDIC (differential between a given model and the model with

the lowest DIC), and DIC weight (w). I calculated model-averaged parameter estimates

and associated standard errors based on posterior means and standard deviation for all

models ≤ 4 DIC of the top-ranked model. I considered these models as having adequate

support as being the “best” model given the data (Burnham and Anderson 2002). I

calculated model-averaged variance estimates and 95% credible intervals (CRI)

following (Burnham and Anderson 2002). I considered parameters in which the 95% CRI did not overlap zero to have a significant effect on Yellow Rail detection and occupancy.

Results

We detected Yellow Rail at least once in 76.9% of study sites (naïve estimate), 10

of the 13 sites surveyed in 2012–2013. I report all top-models with ΔDIC ≤ 4 of the top-

ranked model in Table 2.3. Mean occupancy probability among all sites, as derived from

model-averaged estimates was 0.55 ± 0.04 (± SD) with a detection probability of 0.99 ±

0.04 (± SD). Top-ranking models included the covariates TIME, PATCH, DBH,

DENSITY and WOOD on Yellow Rail occupancy, and WOOD and DENSITY as covariates for detection probability (Table 2.1; Table 2.3). I summarize model-averaged

posterior summaries and derived parameters from top-ranking site-occupancy models that

indicate statistically significant effects for the covariates PATCH and TIME on Yellow

Rail occupancy (Table 2.4). R-hat values for parameters in all reported models were ≤

1.01 indicating adequate convergence to the principal distribution (Gelman and Hill

2006). Results show the probability of Yellow Rail occupancy significantly decreased > 2 years post burn (Figure 2.3). Yellow Rail occupancy also significantly increased with

PATCH, with greatest occupancy in patches > 25 ha (Figure 2.3).

Tree DBH was negatively associated with Yellow Rail occupancy and a negative effect of DENSITY on detection probability was also evident (Table 2.3). However, CRI for both DBH and DENSITY overlapped zero indicating that associated effects were not

statistically significant.

Discussion

This research reinforces the importance of implementing prescribed fire in managing pine savanna habitats for Yellow Rail and also suggests that this wintering species is area sensitive in pine savanna habitats of Mississippi and Alabama. My model results indicate time since fire and habitat features are important factors influencing habitat use of Yellow Rail in pine savanna habitats of Mississippi and Alabama. Similar to Burkman (1993), I found Yellow Rail occurrence was greatest < 2 years post burn, while we detected individuals in few sites > 2 years post burn. Austin and Buhl (2013) report similar results where, at a larger scale, time since fire was the most important factor explaining Yellow Rail presence during the breeding season.

Fragmentation of grassland habitat has exacerbated habitat loss for grassland birds

(Herkert 1994; Vickery et al. 1999; Brennan and Kuvlesky 2005). Information on the

effects of habitat fragmentation and patch size on wintering bird species is imperative for

the management of grassland habitats (Herkert et al. 1996). While no studies have

evaluated patch size as a factor influencing Yellow Rail occupancy, my results indicate

Yellow Rail occupancy significantly increased with patch size (ha), with occupancy

highest within patches > 25 ha. Several factors may account for this area sensitivity.

Historically pine savanna habitats spanned expansive geographic regions, whereas

currently, smaller more fragmented patches of these grassland habitats remain. Certain

remnants of habitat may be too small to support wintering populations. Heterogeneity

over space and time created by variation in fire and hydrology constantly shifts the

suitability of habitat for species (Noss 2013). Under the habitat-diversity hypothesis,

large continuous grasslands include a greater variety of vegetation structures than smaller

patches, therefore larger patches may be more likely to meet species’ habitat structural requirements (Lack 1976; Ribic et al. 2009). Another reason for the spatial requirements of Yellow Rail may relate to the quality, quantity and distribution of resources in pine savanna systems. Herein, Yellow Rail may need larger home ranges to acquire sufficient resources in pine savanna habitats.

Of particular interest to this study is Yellow Rail occupancy within a site diminished with encroachment of woody plants, a result supported by studies conducted on the Yellow Rail’s breeding grounds (Burkman 1993). While I found no significant effect of woody percent cover on Yellow Rail occupancy, I believe several factors may have contributed to this result. First, I selected study sites based on logistics and methodology, precluding sampling the complete range of woody encroachment. Fire season, the season in which study sites last burned, could have also influenced woody and herbaceous composition throughout study sites. According to Platt et al. (2015), fire

season is best delineated into a dry season (October−April), wet season

(June−November) and fire season (April−June). I was unable to use seasonality as a

predictor variable as 12 of the 13 sites were burned during the fire season (Platt et al.

2015). Fires occurring throughout the fire season, in contrast with fires into the late wet

season or early dry season (September—December), typically produce slow, hot fires,

that top-kill and limit resprouting of shrubs at a time when they are actively growing, limiting woody encroachment (Linde 1969; Drewa et al. 2002; Platt et al. 2015).

Seasonality of fire regimes are an essential component to understanding ecological and

evolutionary interplay between fire and vegetation structure and composition (Platt et al.

2015). Future studies on Yellow Rail in pine savanna systems should investigate fire season as a covariate potentially influencing Yellow Rail occupancy.

Another factor influencing the distribution of habitat structure and composition is fire return interval. Historically, pre-settlement fire frequencies occurred in intervals of

1–3 years in pine savanna habitats of the Southeastern United States (Frost 1995; 1998).

Gonzalez-Benecke et al. (2015) found throughout the southeastern United States,

increased fire frequency significantly reduced woody ground cover, but had little effect

on herbaceous ground cover. They also found reduced fire frequency significantly

increased basal area of trees throughout the study area (Gonzalez-Benecke et al. 2015).

Thus, a more thorough exploration of the relationship between fire return intervals and

Yellow Rail occupancy is also warranted.

Survey protocols for Yellow Rail on their breeding grounds incorporate the

vocalization of the species for detection (eg. Bazin and Baldwin 2007; Austin and Buhl

2013). However, anecdotal observations of wintering Yellow Rails suggest they rarely

vocalize during the winter in pine savanna habitats (Woodrey unpublished data),

observations consistent with a general pattern that temperate birds almost never vocalize

during the nonbreeding season (Fletcher et al. 2000). Thus, surveys targeting winter grassland bird species, such as the yellow rail, will require a specific method not involving the use of call-broadcast techniques.

Literature pertaining to survey protocols of overwintering Yellow Rail includes two major survey methods; dragline (Mizell 1998; Grace et al. 2005; Butler et al. 2010)

and all-terrain vehicles (Grace et al. 2005). To minimize extensive damage and

disturbance to threatened wet pine savanna ecosystems, we chose not to use all-terrain

vehicles in our study sites. Thus, we adopted dragline survey methods, which greatly reduce the impact to sensitive habitats. Further, because many management areas prohibit the use of ATVs off of designated trails or outside designated-use areas, and many pine savanna areas across the Southeast contain scattered pine trees which would impede the use of ATVs, we suggest the use of dragline surveys, standardized, across sensitive habitats Yellow Rail occupy during the winter months. I utilized a dragline method throughout this study with an associated Yellow Rail detection probability of 0.99 ± 0.04

(± SD) (model-averaged among site) in pine savanna habitats of Mississippi and

Alabama. Thus, I conclude that the implementation of dragline surveys is a particularly efficient and practical survey method for Yellow Rails because the focal species detection probability strongly influences the effectiveness of secretive marsh bird surveys (Martin et al. 2014). While the parameters woody percent cover and vegetation density had no

significant effect on Yellow Rail detection probability, I did note a moderate relationship

between vegetation density and detection probability. I found dragline surveys especially

effective for Yellow Rail open grassland habitats with overall low vegetation density and

limited woody encroachment. However, in areas where understory is overgrown, dense

and encroached with woody vegetation they become difficult and inefficient to survey

using draglines, reducing detectability of the focal species.

In pine savanna habitats, fire and hydrology are two major factors controlling the

distribution of vegetation and habitat structure (Noss 2013). Although I observed most

study sites inundated with water at the soil level, rarely was there measureable surface

water. I attempted to assess soil moisture levels with several instruments, but ultimately

these instruments proved ineffective at providing objective and independent measures of

soil moisture. On their wintering grounds in coastal Texas Mizell (1998) found standing water depth was a strong determinant of Yellow Rail habitat use (mean = 1.3 cm).

However, working in similar coastal prairie areas in eastern Texas, Grace et al. (2005) did not find a strong relationship between water depth and Yellow Rail habitat use.

Yellow Rail are most often associated with a variety of emergent wetland habitats

(Bookhout 1995), including pine savanna systems. To assess hydrological conditions associated with Yellow Rail habitat use in pine savanna habitats I recommend delineating plant species according to their wetland indicator status or assessing soil moisture directly by collecting soil samples.

Fire is a key ecological process central to the restoration and management of longleaf pine systems (Noss 2013; Platt 1999). Based on evidence provided here coupled with historical fire intervals, I recommend managers burn on a 2-year interval to maintain habitat features attractive to wintering Yellow Rail in pine savanna habitats. Although this 2-year fire interval could provide habitats directly supporting Yellow Rail, a mosaic of habitat succession should be established to support broader longleaf pine management objectives. I also recommend targeting management patches > 25 ha, as it would provide beneficial to area sensitive species such as the Yellow Rail. At the local level, managers can also reduce habitat fragmentation by combining management units and, where possible, removing unnecessary fire breaks. While fire breaks allow for the compartmentalization and control of prescribed fire, they also promote the fragmentation of habitat, disrupt hydrology and enable the spread of invasive species (Noss 2013).

Given the interplay between fire and hydrology in structuring pine savanna habitats, I

advocate further investigation into how these forces create habitat conditions attractive to

Yellow Rail overwintering in pine savanna habitats of Mississippi and Alabama.

Table 2.1 Metrics used in generating site-occupancy estimates of Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013.

Variable Description DBH Average diameter at breast height (cm) of trees ≥ 7.5 cm among sampled vegetation plots DENSITY Average height of point-intercept density (m) among vegetation plots HERB Average herbaceous vegetation percent cover among vegetation plots HHT Average height (m) of herbaceous vegetation among vegetation plots PATCH Patch size (ha) of open grassland habitat associated with each study plot SA Average area (ha) surveyed among each survey visit TIME Average time since fire (days) among each survey visit WHT Average height (m) of woody vegetation among sampled vegetation plots WOOD Average woody vegetation percent cover among sampled vegetation plots

Table 2.2 Correlation matrix for habitat characteristics collected in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013.

Parametera,b DBH DENSITY HERB HHT PATCH SA TIME WHT WOOD DBH 1.00

DENSITY -0.39 1.00

HERB 0.33 -0.11 1.00

HHT -0.26 0.82 0.00 1.00

PATCH -0.13 -0.31 0.22 -0.45 1.00

SA -0.25 -0.22 0.59 -0.11 0.59 1.00

TIME 0.11 -0.12 -0.06 -0.37 0.34 -0.31 1.00

WHT 0.06 -0.32 -0.66 -0.31 -0.07 -0.34 -0.07 1.00

WOOD -0.33 0.11 -1.00 0.00 -0.22 -0.59 0.06 0.66 1.00 Bold values indicate a significant correlation at r > 0.5, α = 0.05. a DBH = Average diameter at breast height (cm) of trees ≥ 7.5 cm among sampled vegetation plots; DENSITY = Average height of point-intercept density (m) among sampled vegetation plots; HERB = Average herbaceous vegetation percent cover among sampled vegetation plots; HHT = Average height (m) of herbaceous vegetation among sampled vegetation plots; PATCH = Patch size (ha) of open grassland habitat associated with each study plot; SA = Average area (ha) surveyed among each survey visit; TIME = Average time since fire (days) among each survey visit; WHT = Average height (m) of woody vegetation among sampled vegetation plots; WOOD = Average woody vegetation percent cover among sampled vegetation plots. b Mean and variance of all predictor variables were standardized.

Table 2.3 Models incorporating landscape and habitat variables used in describing Yellow Rail occupancy (Ψ) in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013, ranked according to deviance information criterion (DIC).

Modela,b pD DIC ΔDIC w Ψ(TIME + PATCH + DBH ) 2.87 5.32 0.00 0.27 Ψ(TIME + PATCH + DENSITY + DBH) 3.02 5.53 0.21 0.25 Ψ(TIME + PATCH + WOOD + DBH) 3.60 6.51 1.19 0.15 Ψ(TIME + PATCH + WOOD) 4.02 7.10 1.78 0.11 Ψ(TIME + PATCH + WOOD + DENSITY + DBH) 4.01 7.12 1.80 0.11 Ψ(TIME + PATCH + WOOD + DENSITY) 4.25 7.28 1.96 0.10 Reported here are effective number of parameters (pD), DIC, difference in DIC from the top-ranking model (ΔDIC), and DIC weight (w) for all models considered to have adequate support of the top-ranked model (ΔDIC ≤ 4). a Ψ indicates occupancy submodel; TIME = average time since fire (days) among each survey visit; PATCH = patch size (ha) of open grassland habitat associated with each study plot; DBH = average diameter at breast height (cm) of trees ≥ 7.5 cm among sampled vegetation plots; DENSITY = average height of point-intercept density (m) among sampled vegetation plots; WOOD = average woody vegetation percent cover among sampled vegetation plots. b Mean and variance of all predictor variables were standardized to improve model fit.

Table 2.4 Model-averaged mean, standard deviation (SD), and 95% credible intervals (CRI) of landscape and habitat variables included in top-ranking models (ΔDIC ≤ 4) for Yellow Rail occupancy in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013.

Parametera,b Mean SD 95% CRI Ψ(.) 4.19 3.21 (-2.07, 10.44) Ψ(DBH)* -6.83 3.96 (-14.56, 0.90) Ψ(DENSITY) 0.59 4.85 (-8.86, 10.04) Ψ(PATCH)** 11.85 4.91 (2.28, 21.43) Ψ(TIME)** -13.33 5.13 (-23.33, -3.33) Ψ(WOOD) 4.47 4.57 (-4.44, 13.38) ρ(.) 9.92 4.55 (1.04, 18.80) ρ(DENSITY)* 8.57 5.88 (-2.90, 20.04) ρ(WOOD) -6.11 4.36 (-14.61, 2.40) Two asterisks (**) indicates a significant parameter where the 95% CRI does not overlap zero. Single asterisk (*) indicates support of a moderate effect on occupancy where the 90% CRI does not overlap zero. R̂ values for all parameter estimates were ≤ 1.01. a Ψ indicates occupancy parameter; ρ indicates detection parameter; DBH = average diameter at breast height (cm) of trees ≥ 7.5 cm among sampled vegetation plots; DENSITY = average height of point-intercept density (m) among sampled vegetation plots; PATCH = patch size (ha) of open grassland habitat associated with each study plot; TIME = Average time since fire (days) among each survey visit; WOOD = average woody vegetation percent cover among sampled vegetation plots. b Mean and variance of all predictor variables were standardized to improve model fit.

Figure 2.1 Study areas in Mississippi and Alabama, USA, 2012–2013.

From west to east: 1) Mississippi Sandhill Crane National Wildlife Refuge 2) Jackson County Mitigation Bank 3) Grand Bay Savanna (Alabama Forever Wild Land Trust).

Figure 2.2 Predictions of the covariates (a) time since fire and (b) patch size as relevant to site-occupancy (with uncertainty) of Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013.

Black line indicates the posterior mean and gray lines depict the relationship based on a random posterior sample of size 200 to illustrate estimate uncertainty.

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CHAPTER III

HABITAT USE OF YELLOW RAIL (Coturnicops noveboracensis) IN PINE

SAVANNA HABITATS OF MISSISSIPPI

Abstract

Limited information is available regarding the winter ecology of Yellow Rail

(Coturnicops noveboracensis) in fire-maintained pine savanna habitats of the

Southeastern United States. Between 2011–2013 (December–April), 49 Yellow Rail were captured, 31 of which were radio-tagged to evaluate habitat use. Mean fixed kernel home

range was 3.37 ± 1.62 ha and mean core area was 0.75 ± 0.35 ha (n = 21). Home range

and core areas overlapped among individuals, suggesting Yellow Rail are social or the

availability of preferred habitat may be limited. Yellow Rail use locations (home range,

core area) had significantly lower woody percent cover, greater maximum herbaceous

heights and lower frequency of trees than non-use locations. Yellow Rail were most often

associated Aristida stricta and Carex spp. Understanding the spatial ecology and habitat

use of Yellow Rail in pine savanna habitats of Mississippi is critical for the management

of this species and the ecosystems they inhabit.

Introduction

The Yellow Rail (Coturnicops noveboracensis) is a small, secretive marsh bird that breeds east of the Rocky Mountains from the northern United States into Canada, wintering along the northern Gulf Coastal Plain from North Carolina to Texas (Bookhout

1995). Given an estimated small population size, uncertain population trends, limited wintering range, and loss of supporting habitat, the Yellow Rail is listed as a Species of

Conservation Concern in Canada (COSEWIC 2011), and a Migratory Nongame Bird of

Conservation Concern in the United States (USFWS 2008). In Mississippi this species has a status of high conservation concern (Mississippi Museum of Natural Science 2005;

East Gulf Coastal Plain Joint Venture 2008). Studies addressing Yellow Rail distribution and habitat use on their wintering grounds have been limited to coastal Texas (Mizell

1998; Grace et al. 2005), coastal South Carolina (Post 2008) and Oklahoma (Butler

2010). Yellow Rail have been described during winter in Mississippi (e.g. Cook 1914;

Hicks 1934; Haberyan 1961; Valentine 1971), yet information on the spatial ecology, and key habitat characteristics associated with this species remain limited for pine savanna ecosystems (Bookhout 1995).

Coastal pine savannas are characterized by clumps of, or sparsely distributed trees with open ground cover dominated by warm season grasses (Peet and Allard 1993;

Kirkman et al. 2001). Historically, natural disturbance such as warm season fires, seasonal flooding and herbivory maintained pine savanna habitats (Buckner 1989; Platt et al. 1988; Waldrop et al. 1992). Longleaf pine ecosystems once dominated the coastal plain of the southeast United States covering approximately 24−36 million hectares, an area extending from Virginia to Texas (Wahlenberg 1946; Ware et al. 1993; Noss et al.

1995; Means 1996; Gilliam and Platt 2006). Today, however, longleaf pine systems are among the most ecologically degraded of all forested ecosystems (Noss 1989; Simberloff

1993), with an area currently estimated less than 1.2 million hectares (Brockway and

Lewis 1996). Several factors have contributed to the decreased area of longleaf pine.

Dominant mechanisms behind this reduction include agriculture and farming, habitat fragmentation deforestation, alteration of hydrology and habitat degradation due to a lack

of periodic disturbance including fire (Vogl 1973; Pyne 1982; Means and Grow 1985;

Noss 1989; Frost 1993; Brockway and Lewis 1996; Means 1996).

Fire maintains pine savanna habitats by enhancing species diversity of herbaceous

ground cover and suppressing the encroachment of hardwood trees and woody shrub

species (Platt et al. 1988, 1991; Brewer and Platt 1994; Glitzenstein et al. 1995; Olson

and Platt 1995). Variation in intensity and frequency of disturbance, particularly fire,

helped shape both historic and present-day communities of longleaf pine ecosystems

(Platt 1999). The resulting heterogeneity of habitat across space and time has a direct

influence on avian communities that inhabit these fire dependent areas (Wiens 1974). In

particular, studies have shown that fire may be one of the most important factors

explaining the presence of Yellow Rail on their breeding and wintering grounds

(Burkman 1993; Austin and Buhl 2013). From observations of Yellow Rail on both

breeding and wintering grounds, it is known that Yellow Rail are associated with fire

maintained habitats with similar vegetation structure, including dense vegetative cover,

low plant height, and limited woody encroachment (eg. Bookhout 1995; Grace et al.

2005; Post 2008). These findings indicate fire likely plays an important role in providing

conditions attractive to Yellow Rail (Bookhout and Stenzel 1987; Hanowski and Niemi

1988; Burkman 1993; Popper and Stern 2000; Grace et al. 2005; Austin and Buhl 2013).

On their wintering grounds in Texas, agencies implement prescribed fire to

manage coastal habitats used by Yellow Rail (Mizell 1998; Grace et al. 2005; although

details as to fire regime are not reported). While no studies have estimated home range

size for Yellow Rail in pine savanna habitats, estimates obtained from coastal Texas

indicate home ranges of radio-telemetered Yellow Rail were small (< 2.0 ha on average)

and overlapping (Mizell 1998; Grace et al. 2005), suggesting this species is gregarious in

winter .(Bart et al 1984; Bookhout and Stenzel 1987; Mizell 1998; Grace et al. 2005).

Given the limited geographical extent of fire-maintained pine savanna habitats, here we

address Yellow Rail home range to estimate the size of habitat to manage for this species.

I hypothesize Yellow Rail home range in pine savanna habitats of Mississippi will be

small (< 2.0 ha) and overlapping due to the limited availability of suitable habitat.

Utilizing telemetry data from radio-tracked Yellow Rail, my objectives were to

assess Yellow Rail habitat use by evaluating differences in vegetative composition and

structure between locations within home range (95% distribution), core area (50%

distribution) and at locations outside of Yellow Rail home range. I predict habitat

variables associated with Yellow Rail use locations (home range and core area) will have:

1) lower woody percent cover, 2) lower herbaceous plant height and 3) lower woody

plant height relative to locations outside Yellow Rail home range. I also predict no

significant differences in habitat metrics between Yellow Rail use locations associated

with home range and core area.

Methods

Study area

I conducted surveys for Yellow Rail in pine savanna habitats of two conservation areas (defined as my study area) in Jackson County, Mississippi: (1) Mississippi Sandhill

Crane National Wildlife Refuge (MSCNWR), (2) Jackson County Mitigation Bank

(JCMB) (Figure 3.1). Located in the Gulf Coastal Plain, primary habitat types include pine savannas, pine flatwoods, pine scrub, cypress-tupelo swamp, maritime forest and estuarine marsh habitats. Understory of pine savanna habitats of Mississippi is comprised of wire grass (Aristida spp.), bluestem (Andropogon spp., Schizachyrium spp.), toothache grass (Ctenium aromaticum), Muley grass (Muhlenbergia capillaries), and beak rush

(Rhyncospora spp.), along with several forb species (e.g. Aster spp., Drosera spp.,

Eriocaulon spp., Sarracenia spp.). Understory shrubs include titi (Cyrilla spp. and

Cliftonia spp.), gallbery (Ilex glabra), and greenbriar (Smilax spp.). Dominant tree species associated with pine savanna habitats include slash pine (Pinus elliotti) and longleaf pine (P. palustris) (Norquist 1984, Clewell and Raymond 1995).

MSCNWR, located approximately 5 km north of Gautier, Mississippi, spans over

7,900 ha and contains 40 management units burned at different intervals and in different seasons, allowing for a mosaic of fire managed habitats throughout the refuge (Wilder

2008). Also managed with prescribed fire, located in Jackson County, Mississippi, JCMB is a privately owned and managed area located approximately 12 km east of Moss Point,

Mississippi. These management areas protect one of the most extensive and species-rich coastal pine savanna habitats remaining in the southeast (Frost et al. 1986).

Survey methods

Yellow Rail capture surveys

I conducted Yellow Rail surveys during two winter field seasons spanning 2011‒

2013 (December–April) among seven study sites. I define a study site as a singular

management unit within one of the two study areas and a survey plot as the area surveyed

within each study site, where each study site is associated with one survey plot

(MSCNWR and JCMB). In 2012, we surveyed for Yellow Rail at two study sites at

MSCNWR and one study site at JCMB, and in 2013 we surveyed six study sites at

MSCNWR and one study site at JCMB. A four member field crew began surveys approximately 30 minutes after sunset utilizing a weighted drag line (Mizell 1998; Grace

et al. 2005; Butler 2010). Each drag line measured 15 m with 1-L weighted plastic

bottles, evenly spaced and attached to the line with sections of rope (Mizell 1998; Grace

et al. 2005; Butler 2010). Bottles were filled ¼ full and weighted with small rocks, to

create noise and disturb vegetation. Two crew members pulled each end of the drag line

(one at each end) while the other 2 members spread out evenly behind the rope. Crew

members were equipped with high powered headlamps (K-Light: 5-LED 21 V Std Light,

Kelly’s K-Light, Cleveland, Texas; Boss Light: 24v7, Boss Light, Hastings, Florida) to

aid in the identification of flushed avian species.

Survey crew proceeded through a survey plot, in straight line transects, using

fixed positions established to aid with nocturnal navigation. We marked fixed positions

by hanging a lantern from a Shepard’s hook at each transect origin. At the end of each

transect the crew shifted perpendicularly to the transect of origin, rotating one drag line

length (15 m) from the fixed point. The crew then placed the lantern at the end of the

drag line following the line shift. The crew then proceeded parallel to the transect of origin using the lantern to navigate. I standardized starting locations for each survey plot as well as direction of line shift by plot.

We determined Yellow Rail detections through observation, identifying flushed birds to species on the basis of morphology. Specifically, we identified Yellow Rail in flight observing the white coloration on secondary feathers of each wing (Pyle 2008).

Once the crew detected a Yellow Rail, the bird was spotlighted and a hand net (Frabill, model 3439, St. Plano, IL) was held over the area where the Yellow Rail came to rest.

The crew removed the bird from the net by hand. I used a global positioning system

(GPS; Garmin PSMap76CSX, WAAS enabled, accuracy ≤ 3 m, Garmin Ltd., Olathe,

Kansas) to record each bird location. Each plot was surveyed ≥ 3 times throughout the winter season with ≥ 14 days between survey events.

We banded each captured Yellow Rail with a U.S.G.S. aluminum leg band, on the

left tarsus. We equipped up to 4 Yellow Rail per study plot with a radio transmitter (BD-

2: Holohil Systems Ltd., Carp, Ontario) or (SOPB-2028 HWSC, Wildlife Materials, Inc.,

Murphysboro, Illinois). I selected these transmitters to be ≤ 3% of an individual’s mass

with a lifespan ranging 43–74 days. We attached transmitters with light elastic string

using a modified synsacrum harness (Rappole and Tipton 1991; Haramis and Kearns

2000). Once an individual was equipped with a radio-transmitter, we released each bird at

the flush location and we monitored the individual for up to 5 minutes post-release, to

ensure the transmitter did not interfere with animal’s movements.

After a 12 hour adjustment period, we located each Yellow Rail at least once

daily, between 0700 and 1800 hours, using a Biotracker radio-telemetry receiver (Lotek

Wireless) equipped with an H antennae (RA-14K VHF Antenna, Telonics, Inc.). We separated telemetry fix locations by ≥ 4 hrs to reduce potential autocorrelation among

locations. Individuals were located using a homing method described by Mech (1983)

where we tracked each Yellow Rail by following the strongest directional signal until the

gain on the telemetry receiver was adjusted to the lowest position, indicating proximity,

within 3–5 m of a radio-marked individual. At this point we recorded the geographic

location using a GPS unit (Garmin PSMap76CSX, WAAS enabled, accuracy ≤ 3 m,

Garmin Ltd., Olathe, Kansas). In 2012, we collected telemetry locations until transmitters

failed or fell off the bird. In 2013 we collected up to a total of 30 telemetry locations per

bird, or the end of the transmitter’s battery life or fell off the bird.

Habitat assessment

Once we established a location for an individual, we assessed plant community

structure at two locations; the fix location (hereafter, “use location”) and a control point

associated with each fix location (hereafter, “control point”). I selected control points

using a random numbers table, generating a number between 0−360 for compass bearing

and a number 20–100, as a distance (in m) away from the point of detection. We measured herbaceous and woody maximum height compositions, percent woody and

herbaceous cover (by dominant plant species), and diameter at breast height (DBH) of

trees within a 10 m radius circular plot, centered on each use location and control point.

Using a 2 m pole, we measured the maximum height of woody and herbaceous

vegetation contacting a 25 cm radius around the pole (Robel et al. 1970). We measured

vegetation height composition (woody and herbaceous) five times at 2 m intervals

transecting each cardinal direction from the center of the plot. I averaged herbaceous and 48

woody height compositions among each vegetation plot to obtain a single value for inclusion in further analyses. We also measured the DBH of all trees ≥ 7.5 cm within each vegetation plot. I converted tree DBH measurements to count data, representing the total number of trees per plot. We visually estimated percent cover of major woody and herbaceous vegetation within the plot to the nearest 1%, totaling 100% across the plot. In

2012, we assessed half of the total use locations and paired control points for habitat features, and in 2013 we assessed all use and control points for habitat features.

Statistical methods

Home range

I define the minimum sample for unbiased home range estimation as the point at which home range estimates do not change with increasing sample size (Odum and

Kuenzler 1955; Bond et al. 2001; Gosselink et al. 2003). I determined the minimum

number of telemetry locations required for robust home range estimates by calculating a

home range area curve, showing changes in home range size with an increasing number

of locations. I developed home range curves using five Yellow Rail with ≥ 30 locations

each, for each field season (2011–2012 and 2012–2013). I calculated home ranges with

an increasing number of randomly selected location data and plotted home range size by

number of locations (Figure 3.2). I determined 20 observations was the minimum sample

size, beneath which fewer locations affected home range estimates. Therefore, home

range analyses included only individuals with ≥ 20 telemetry locations.

I define Yellow Rail home range as the area most commonly frequented, given

the suite of telemetry locations collected, by each bird (Kenward 2001). I calculated the

utilization distribution for each Yellow Rail using the fixed kernel method, reflecting the 49

probability of use throughout the animal’s home range (Worton 1989). In estimating the utilization distribution I focused on two probability contours; 0.95 reflecting each Yellow

Rail’s home range (95% of the distribution of use locations) and 0.50 denoting the core use area within each home range (50% of the distribution of use locations). I calculated home range and core area of radio-marked birds using the packages adehabitatHR and ks

in the statistical software R (Calenge 2006; R Core Team 2014). The bivariate plug-in

bandwidths (hplug-in) were averaged to determine the most suitable smoothing parameter

for use in estimating home range and core area for each individual bird. After I calculated

home range and core area for each bird, I created a GIS polygon layer for the

corresponding use areas using the function writePolyShape in the package maptools

(Bivand and Lewin-Koh 2014).

I also calculated home ranges (95% locations) minimum convex polygons, and

core areas (50% locations) minimum convex polygon using adehabitatHR in the statistical software R (Calenge 2006; R Core Team 2014) and report mean home range and core area size to compare my results to those of other studies of the Yellow Rail

(Mizell 1998).

Habitat use

To assess Yellow Rail habitat use in pine savanna habitats of coastal Mississippi I compared microhabitat metrics between home range, core area and control points

(hereafter, treatments). Habitat variables considered for use in the models include woody

and herbaceous cover, woody and herbaceous height compositions and number of trees

per plot measuring ≥ 7.5 cm DBH. To visually assess which locations fell within each

treatment group, I graphically mapped all individual home range and core areas using 50

ArcGIS 10 (ESRI, Inc., Redlands, CA). To account for GPS error I created a 3 m buffer around all use and control point locations, where I excluded any points of overlap from the models.

Prior to running my models, I applied a log(x + 1) transformation to herbaceous

cover, herbaceous and woody height, and a square root transformation to woody percent

cover to correct for non-normality and heterogeneity of variance. To assess

multicollinearity I used correlation (function cor in the package stats; R Core Team

2014) to identify pairs of highly correlated variables using the statistical software R,

assessed at r ≥ 0.5, α = 0.05, where this value was considered suitable for omitting a

variable (eg. Elbroch et al. 2015; Gehring et al. 2015). Woody and herbaceous percent

cover were inversely correlated, therefore I selected woody percent cover to remain in the

model as previous studies show a significant relationship between Yellow Rail

occurrence and woody cover (Burkman 1993; Martin 2012; Austin and Buhl 2013). I also found a strong correlation between woody height and woody percent cover. I selected woody percent cover to include in further analysis as a surrogate for collected woody

features.

To assess differences in habitat metrics among treatments, I constructed linear

mixed-effects models (LMMs) using the statistical software R (R Core Team 2014).

LMMs were fit using restricted maximum likelihood using the function lmer in the

package lme4 (Bates and Maechler 2010). To determine the error structure of the LMMs,

I fitted all combinations of the nested random effects (site, bird) to the full model

including all explanatory variables to account for non-independence of replicates,

including a null model. I ranked models by Akaike’s information criterion (AICc),

corrected for small sample size, with a lower AICc value indicating the best suitable model among all possible models. The top ranking random effect model included the nested effect of bird within site.

After determining the error structure, I ran LMMs assessing each habitat variable univariately by treatment. For these models, habitat metrics were the response variable

(woody percent cover and herbaceous height composition), fixed effect was treatment and random effect was bird within site. I performed Tukey's HSD test (function glht from

the multcomp package; Hothorn et al. 2008) as a post-hoc test to determine significant

pairwise differences between treatment levels for all variables. I utilized a generalized

linear mixed model (GLMM) fitted with a zero-inflated Poisson distribution to determine

potential differences of tree count data by treatment (function glmmadmb in package

glmmADMB; Skaug et al. 2014). I used this function to account for the overdispersion in

count data of trees as well as the nested structure of the random effects.

Results

Home range

During two winter field seasons, 2011–2013 (December–April), I radio-tracked

29 Yellow Rail. Of these individuals, 21 reached the minimum of 20 telemetry locations,

the sample size deemed adequate to assess with home range and habitat use. The number

of locations collected for individual birds ranged from 20–54 points (x̄ = 32.5 ± 7.4).

Mean fixed kernel home range of Yellow Rail, combined among years, was 3.37 ± 1.62 ha and mean core area was 0.75 ± 0.35 ha (n = 21) (Table 3.1). On average, fixed kernel core area accounted for 22% of their home range area. Fixed kernel home range and core

area did not differ by year (F1, 19= 1.66, P = 0.92). Mean MCP home range among all 52

sites was 1.43 ± 0.89 ha and mean core area was 0.42 ± 0.26 ha (Table 3.2). On average,

MCP core area accounted for 29% of their home range area. Home ranges calculated using the MCP method did not differ by year (F1, 19= 0.01, P = 0.93).

Habitat use

I report all results from LMMs in Table 3.4. Results of the LMM and subsequent

Tukey’s HSD indicate woody percent cover did not differ between Yellow Rail use

locations (home range and core area) (P = 0.192; Tukey’s HSD), however woody percent cover was significantly greater at non-use locations that use locations (home range: P <

0.001; core area: P < 0.001; Tukey’s HSD). While I found no significant difference in

herbaceous height compositions between Yellow Rail use locations (home range and core

area) (P = 0.999; Tukey’s HSD), herbaceous height composition was significantly lower

at non-use locations than use locations (home range: P < 0.001; core area: P < 0.001;

Tukey’s HSD). I found no difference between tree counts and Yellow Rail use locations

(home range and core area) (P = 0.56; Tukey’s HSD), however results indicate there were

significantly more trees at non-use locations that use locations (home range: P < 0.001;

core area: P < 0.001; Tukey’s HSD).

Common vegetation associated with Yellow Rail use locations included wire grass (Aristida stricta), Muhly grass (Muhlenbergia capillaries), needle leaf witch grass

(Dicanthelium aciculare), big bluestem (Andropogon gerardii), Carex spp. and gallberry

(Ilex glabra) (Table 3.3). Other common herbaceous species occurring at lower frequencies include Rhynchospora spp., switchgrass (Panicum virgatum), Dicanthelium spp., toothache grass (Ctenium aromaticum), hat pin (Eriocaulon decangulare) and pitcher plants (Sarracenia spp.). Common woody understory species occurring at lower 53

frequencies include yaupon holly (Ilex vomitoria), wax myrtle (Morella Caroliniensis),

St. John’s wort (Hypericum brachyphyllum), sweet bay magnolia (Magnolia virginiana)

and swamp titi (Cyrilla racemiflora). Trees ≥ 7.5 cm DBH typically occurred at lower

frequencies and included longleaf pine (Pinus palustris), slash pine (P. elliottii), bald and

pond cypress (Taxodium distichum, T. ascendens) and swamp tupelo (Nyssa biflora).

Discussion

Home range

In Jackson County, Mississippi, Yellow Rail maintained small home ranges between December–April, 2011–2013. Site fidelity was high within each season, with

few movements between adjacent savannas recorded. Similar to studies conducted on

both breeding (Terrill 1943; Bart et al. 1984) and wintering grounds (Mizell 1998; Grace

et al. 2005). I found that home ranges of individual Yellow Rail frequently overlapped.

Frequent overlap in home range and core areas indicate Yellow Rail may be sociable in

winter or the availability of preferred habitat is limited.

On their wintering grounds in Texas, Yellow Rail home range size averaged 1.16

ha (fixed kernel method; Grace et al. 2005) and 1.8 ha (MCP method; Mizell 1998). Our

results indicate the average fixed kernel home range of Yellow Rail in Mississippi (3.37

ha) was approximately 2-3 times larger than estimates from coastal Texas (Grace et al.

2005). However estimates produced using the MCP method were relatively similar with

estimates calculated by Mizell (1998). According to Barg et al. (2005), kernel estimates

of home range are far more accurate and informative than estimates produced using

MCP, particularly in studies that compare “used” versus “available” habitat. In

comparing fixed kerned estimates of Yellow Rail one plausible explanation effecting 54

Yellow Rail home range size is the quality, quantity and distribution of resources in pine savanna habitats. Resources may be sparse, further distributed, or contain lower nutritional value than that of their wintering habitat of in coastal Texas. Such differences in habitat and resource availability could lead to larger home ranges in Mississippi as birds move over broader areas in order to fulfill their needs.

Habitat use

The Yellow Rail is most commonly associated with shallow emergent wetlands dominated by sedges, grasses and rushes (Bookhout 1995). Particularly on their wintering grounds, in coastal Texas and South Carolina, Yellow Rail are frequently associated with

Gulf cordgrass (Spartina spartinae), smut grass (Sporobolus indicus), marshhay

cordgrass (Spartina patens), saltgrass (Distichlis spicata), Panic grass (Panicum

vaginatum), and Scirpus spp. (Bookhout 1995; Mizell 1998; Grace et al. 2005; Post

2008). In Oklahoma Yellow Rail were most commonly associated with dropseed grasses

(Sporobolis spp.) (Butler et al. 2010). At MSCNWR, Yellow Rail used areas dominated

by wiregrass (Aristida stricta), while at JCMB they used areas dominated by Carex spp.

This suggests these wetland plant species may provide key habitat features necessary to

support overwintering Yellow Rail in Mississippi, although further research is warranted.

In this study, Yellow Rail used areas with less woody encroachment and greater

herbaceous height structure than locations outside of their home range, providing further

support that vegetation structure is an important factor explaining Yellow Rail habitat use

in pine savanna habitats of Mississippi. These results are consistent with other studies

conducted on grassland birds, suggesting birds cue into specific structural features of

vegetation when selecting habitat (e.g. Cody 1985; Wilson and Belcher 1989; Davis and 55

Duncan 1999; McCoy et al. 2001). Yellow Rail may key in on areas with greater

herbaceous height compositions because they provide greater cover and protection from

predators or provide other assets such as microclimate. Similarly, areas with greater

woody encroachment may provide less herbaceous cover necessary for overwintering

Yellow Rail in pine savanna habitats.

Throughout the Coastal Plain, the interaction of fire and hydrology are two key

factors controlling the distribution of vegetation (Noss 2013). Minor fluctuation in fire

and hydrology can greatly influence plant community structure and composition. Yellow

Rail habitats are often associated with standing water (eg. Bookhout and Stenzel 1987;

Gibbs et al. 1991; Mizell 1998; Robert et al. 2000). Throughout the study area, I

observed soils inundated with water, however it was rare that any standing water was

measureable at the surface level. Although I attempted to measure soil moisture with

several field instruments, these measurements proved inconsistent and were not included

in this analysis. Variations in fire regimes coupled with hydrological processes, create

heterogeneity throughout habitat. Fire intensity and frequency as well as hydrology are

likely key factors that explain Yellow Rail habitat use in pine savanna habitats. While the

results of this study implicate relations between Yellow Rail habitat use, vegetative

structure and fire, we remain limited on our understanding of the interplay between

Yellow Rail space use and pine savanna hydrology. Towards evaluating these

relationships, I advocate further research.

Management implications

Yellow Rail require specific habitat features in fire-dependent pine savanna

habitats of coastal Mississippi. While this study did not directly address Yellow Rail 56

habitat use in the context of fire, research has shown prescribed fire is essential in maintaining habitat structure and composition suitable for overwintering Yellow Rail populations (Burkman 1993; Austin and Buhl 2013; Morris, unpublished). Fire maintains these pine savanna ecosystems by reducing woody encroachment, and promoting new herbaceous growth, both key indicators to Yellow Rail habitat use. In this study Yellow

Rail used habitat with less woody encroachment and greater herbaceous height compositions than locations outside of their home range, indicating the implementation of fire may be critical to maintaining habitat features attractive to this species in pine savanna habitats of Mississippi. Understanding the ecological needs of overwintering

Yellow Rail is essential to providing adequate management and conservation of these threatened ecosystems.

Table 3.1 Mean (± SE) fixed kernel home range (95% kernel) and core area (50% kernel) of Yellow Rail wintering in pine savanna habitats of coastal Mississippi, USA, 2011–2013.

Jackson County Mitigation Mississippi Sandhill Crane Bank NWR 2011 –2012 n (individuals) — 7 n (locations) — 263 Home range (ha) — 3.31 ± 1.72 Core area (ha) — 0.74 ± 0.34 2012–2013 n (individuals) 2 12 n (locations) 60 360 Home range (ha) 3.19 ± 0.20 3.43 ± 1.77 Core area (ha) 0.74 ± 0.10 0.75 ± 0.39 — indicates no data.

Table 3.2 Mean (± SE) minimum convex polygon (MCP) home range (95% use) and core area (50% use) of Yellow Rail wintering in pine savanna habitats of coastal Mississippi, USA, 2011–2013.

Jackson County Mitigation Mississippi Sandhill Crane Bank NWR 2011–2012 n (individuals) — 7 n (locations) — 263 Home range (ha) — 1.41 ± 0.43 Core area (ha) — 0.93 ± 0.17 2012–2013 n (individuals) 2 12 n (locations) 60 360 Home range (ha) 0.69 ± 0.30 1.52 ± 0.96 Core area (ha) 0.50 ± 0.04 0.44 ± 0.32 — indicates no data.

Table 3.3 Summary of dominant plant species noted to occur within areas of Yellow Rail activity by radio-marked individuals in pine savanna habitats of Jackson County, Mississippi, USA, 2011–2013

Mississippi Sandhill Jackson County Crane NWR Mitigation Bank n (individuals) 19 2 n (locations) 473 60 Percent cover (%) Aristida stricta 48.9 ± 19.8 (0.0, 0.0) Muhlenbergia 23.5 ± 17.7 (0.0, 0.0) capillaries Dicanthelium aciculare 6.3 ± 3.2 (1.4, 1.5) Ilex glabra 5.3 ± 2.4 (0.8, 1.4) Andropogon gerardii 3.4 ± 1.8 (5.0, 5.1) Carex spp. 0.0 ± 0.0 (40.0, 41.0) Erianthus gigantius 0.0 ± 0.0 (14.6, 16.1) Other herbaceous cover 4.3 ± 4.6 (11.4, 12.9) Other woody cover 8.3 ± 6.4 (22.7, 23.7) For the Mississippi Sandhill Crane NWR I report mean ± standard deviation, and for the Jackson County Mitigation Bank I report maximum and minimum percent cover for dominant plant species associated with Yellow Rail activity by radio-marked individuals.

Table 3.4 Model-averaged mean, standard deviation (SD), and 95% confidence intervals (CI) of habitat variables used in describing wintering Yellow Rail habitat use in pine savanna habitats of Mississippi, USA, 2011–2013.

Parametera,b Estimate SD 95% CI Woody percent cover (Intercept) 0.421 0.020 (0.381, 0.464) Core area* -0.108 0.013 (-0.133, -0.083) Home range* -0.088 0.014 (-0.116, -0.060) Maximum herbaceous height (Intercept) 0.629 0.042 (0.540, 0.717) Core area* 0.029 0.007 (0.016, 0.042) Home range* 0.029 0.008 (0.014, 0.044) Tree count (Intercept) 0.668 0.329 (-0.046, 1.323) Core area* -1.457 0.081 (-1.618, -1.298) Home range* -1.788 0.110 (-2.009, -1.575) Fixed effects include home range, core area and random locations outside of Yellow Rail home range. Asterisk (*) indicate a significant effect where the 95% CI does not overlap zero. a Reference condition = “random”, non-use locations outside home range. b Transformations used in deriving estimates are as follows: woody percent cover = square root; maximum herbaceous height = log( x + 1).

Figure 3.1 Study areas for wintering Yellow Rail in Jackson County, Mississippi, USA, 2011–2013.

From west to east: 1) Mississippi Sandhill Crane National Wildlife Refuge and 2) Jackson County Mitigation Bank.

Figure 3.2 Mean home range area curve with standard error for 10 Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2011–2013 (December–March), showing changes in home range size with increasing number of locations by field season.

Home ranges were calculated using 95% fixed kernel area.

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CHAPTER IV

GENERAL SUMMARY AND CONCLUSIONS

The Southeastern United States ecologically, a historically fire-dependent region

of the country, is recognized as one of the most ecologically degraded of all forested

ecosystems in North America (Noss 2013). Given current threats to this imperiled

ecosystem it is imperative to gather information to promote the conservation of species

that utilize these threatened habitats (Frost et al. 1986). The objectives of this study were twofold (1) assess Yellow Rail occupancy in the context of fire in Mississippi and

Alabama and (2) assess Yellow Rail habitat use in coastal pine savanna habitats of

Mississippi. In modeling Yellow Rail occupancy, time since fire and patch size were the most important factors explaining occupancy, a relationship that suggests periodic disturbance is fundamental in maintaining habitats suitable for this species (Chapter 2).

Yellow Rail occupied small home ranges that frequently overlapped, indicating this

species is sociable or suitable habitat may be limited in coastal Mississippi (Chapter 3).

Habitat structure and composition, specifically herbaceous height and woody percent

cover, were statistically strong determinants of Yellow Rail habitat use throughout the

study area. Specifically, Yellow Rail used habitats with greater herbaceous height

composition and less woody percent cover than locations outside of their home range

(Chapter 3).

Managers seeking to restore and maintain pine savanna habitats must consider the natural history of the ecosystem, including the processes that structured that ecosystem, to promote its conservation (eg. Noss 2013; Platt et al. 2015). Historically, fire-dependent

pine savanna habitats spanned vast areas (eg. Platt 1999; Noss 2013; Platt et al. 2015).

While present day management programs implement prescribed burning, there are

inherent ecological impacts that must be taken into consideration, particularly pertaining

to management with fire and habitat fragmentation. Conservation areas can reduce habitat

fragmentation by removing superfluous fire breaks. Fire breaks are essential components

to compartmentalize and control prescribed fires, but these artificial breaks in vegetation

promote fragmentation of habitat and populations, disrupt hydrology and facilitate the

spread of invasive species (Noss 2013). As an area sensitive species, the Yellow Rail

requires tracts of land over seven times their home range size (Chapter 2, 3). I suggest

managers target conservation areas > 25 ha and burn on a 2-year interval as it is

beneficial to Yellow Rail populations overwintering in pine savanna habitats of

Mississippi and Alabama (Chapter 2).

Managing habitats for Yellow Rail can also benefit other to grassland bird species that occupy pine savanna ecosystems (Plentovich et al. 1998; Thatcher et al. 2006).

Several studies demonstrate species that occupy pine savanna grasslands including the

Sedge Wren (Cistothorus platensis), Savannah Sparrow (Passerculus sandwichensis),

Grasshopper Sparrow (Ammodramus savannarum) and Henslow’s Sparrow (A.

henslowii), prefer large patches of habitat (Johnson and Temple 1990; Herkert 1994;

Bollinger 1995; Johnson and Igl 2001). Specifically, Herkert (1994) estimated individual

area requirements to be 30 ha for Grasshopper Sparrow, 40 ha for Savannah Sparrow and

55 ha for Henslow’s Sparrow. In theory, larger patches of habitat can provide greater variety of vegetation structure and are therefore more likely to meet species’ requirements. Fire, another major driver of habitat heterogeneity, is also beneficial to avian species that occupy threatened grassland ecosystems (e.g. Walsh et al. 1995;

Tucker and Robinson 2003; Thatcher et al. 2006). To effectively manage and conserve

grassland species, we must improve our understanding of the ecological needs of

grassland bird species, particularly as it pertains to area requirements and the implementation of prescribed fire.

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APPENDIX A

IMPLIMENTATION OF YELLOW RAIL OCCUPANCY MODEL SELECTION

Table A.1 Models incorporating detection variables ranked by deviance information criterion (DIC) for Yellow Rail occupancy in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013.

Modela.b pD DIC ΔDIC w Ψ(.), ρ(WOOD + DENSITY) 9.17 11.52 0.00 0.99 Ψ(.), ρ(WOOD) 16.81 20.03 8.51 0.01 Ψ(.), ρ(DENSITY) 23.74 29.11 17.59 0.00 Ψ(.), ρ(.) 38.66 48.70 37.18 0.00 I report effective number of parameters (pD), DIC, difference in DIC from the top- ranking model (ΔDIC), and DIC weight (w) for all models. I incorporated the top-ranking detection model for Yellow Rail into all subsequent occupancy models. a Ψ indicates occupancy submodel; ρ indicates detection submodel; WOOD = average woody vegetation percent cover among sampled vegetation plots; DENSITY = average height of point-intercept density (m) among sampled vegetation plots. b All predictor variables were standardized to improve model fit.

Table A.2 Parameter estimates (mean, standard deviation [SD], and 95% credible interval [CRI]) for variables included in the top-ranking detection model for Yellow Rail in pine savanna habitats of Mississippi and Alabama, USA, 2012–2013.

Parametera,b Mean SD 95% CRI INTERCEPT 11.83 6.31 (0.43, 25.92) WOOD 3.17 6.40 (-7.46, 17.64) DENSITY -4.05 6.10 (-16.68, 7.44) a WOOD = average woody vegetation percent cover among sampled vegetation plots; DENSITY = average height of point-intercept density (m) among sampled vegetation plots. b All predictor variables were standardized to improve model fit.