ECOLOGY AND CONSERVATION OF WINTERING

MIGRATORY BIRDS IN EARLY-SUCCESSIONAL HABITATS OF THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

ECOLOGY AND CONSERVATION OF WINTERING

MIGRATORY BIRDS IN EARLY-SUCCESSIONAL HABITATS OF THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

By

ROBERT HUNTER DOSTER, B.A., M.S. Hendrix College, 1989

University of Arkansas, 1991

May 2005 University of Arkansas

This dissertation is approved for recommendation to the Graduate Council Dissertation Co-Directors:

Dissertation Committee:

ACKNOWLEDGEMENTS

I appreciate the assistance given by members of my graduate committee

throughout the development of my degree program. Douglas James, David Krementz,

James Dunn, and Kimberly Smith provided important input and aided the development of

this research project.

Funding for my research was provided by the U.S. Geological Survey and the

U.S. Fish and Wildlife Service. I specifically appreciate the continued support given to

this project by Frank Bowers and Chuck Hunter (U. S. Fish and Wildlife Service, Region

4 - Southeast).

A landscape-scale project such as this could not occur without the aid of field

assistants. Each winter, three different assistants helped me to collect an enormous

amount of data and endure the cold and wet field conditions of the Delta. These

individuals also helped me learn important lessons about supervision and managing

people. For this I acknowledge the help of Michelle Davis, Jim Destaebler, Pam Newton,

Mark Pollock, John Puschock, Laura Quatrini, Annika Samuelsen, Rebecca Schwer, and

Morgan Wilbur.

Housing during portions of the three field seasons was provided by several

sources. The Louisiana Department of Wildlife and Fisheries allowed use of housing on

Wildlife Management Areas each year. The Arkansas Game and Fish Commission (Don

McSwain) provided lodging at Cook’s Lake Lodge in 2001 and 2002. Accommodations

were provided for a portion of the 2001 field season by the Mississippi Department of

Wildlife, Fisheries, and Parks.

iii

The University of Arkansas Museum allowed great flexibility with my graduate

assistantship that enabled me to take time off during winter to conduct fieldwork. I

especially appreciate Nancy McCartney for her understanding throughout the process.

Coordinating land access for this project, which involved many people in multiple

agencies, was one of the most difficult tasks. Cooperation for granting land access and

locating study areas was provided by the U. S. Fish and Wildlife Service at the following

National Wildlife Refuges: Bald Knob, Felsenthal, Overflow, Wapanocca, North

Louisiana Complex, Tensas River, Grand Cote, Handy Brake, North Mississippi

Complex, Coldwater River, Hillside, Morgan Brake, and Yazoo. The Arkansas Natural

Heritage Commission granted access to Roth Prairie and Konecny Prairie natural areas.

The Natural Resources Conservation Service (NRCS) helped identify and provided

access to lands in the Wetland Reserve Program (WRP). I appreciate the help from

NRCS staff in three state offices, regional offices, and multiple county offices. I also

thank all the private landowners who granted access to their lands enrolled in the WRP.

Additional help was given to me by a variety of persons and organizations. For

statistical support I greatly appreciate the aid of James Dunn, Lynette Duncan, and Bret

Collier. Chris Reid and Travis Marsico gave assistance in plant identification. Glenn

Manning provided logistical support in a variety of situations. Diane Moler, Glenn

Piercey, and Barbara Parker with the Arkansas Cooperative Fish and Wildlife Research

Unit offered assistance during all phases of this research project. The Lower Mississippi

Valley Joint Venture provided me with excellent GIS data for the study region. Approval

to mist-net birds and collect feathers was provided by the University of Arkansas’

Institutional Animal Care and Use Committee (IACUC Protocol # 01012).

iv

I am also thankful for the continual encouragement given by my parents, Charles

and Billie. They have always been supportive of my constant pursuit of personal

advancement.

Lastly, I would like to thank my wife, Lisa, for her love and assistance throughout

this undertaking. I’m grateful to her for supporting my goals and helping me to achieve

them.

v

TABLE OF CONTENTS

CHAPTER 1: HABITAT AND LANDSCAPE ASSOCIATIONS OF WINTERING

GRASSLAND AND SHRUBLAND BIRD COMMUNITIES IN THE LOWER

MISSISSIPPI RIVER ALLUVIAL VALLEY. ....................................................... 1

Abstract .................................................................................................................... 2

Introduction.............................................................................................................. 3

Study Area ............................................................................................................... 6

Methods.................................................................................................................... 9

Avian community surveys ................................................................................. 9

Density and species richness estimates.............................................................. 10

Habitat measurements........................................................................................ 11

Landscape measurements .................................................................................. 12

Data analysis...................................................................................................... 13

Results...................................................................................................................... 16

Avian community .............................................................................................. 16

Density and species richness ............................................................................. 18

Habitat and landscape effects ............................................................................ 22

Discussion................................................................................................................ 27

Tables....................................................................................................................... 38

Figures...................................................................................................................... 57

Literature Cited ........................................................................................................ 65

Appendix.................................................................................................................. 70

vi

CHAPTER 2: WINTER HABITAT AFFINITIES OF TWO GRASSLAND BIRD

SPECIES IN THE LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY: SEDGE

WREN AND LE CONTE’S SPARROW................................................................ 73

Abstract .................................................................................................................... 74

Introduction.............................................................................................................. 75

Study Area ............................................................................................................... 77

Methods.................................................................................................................... 78

Results...................................................................................................................... 81

Discussion................................................................................................................ 84

Tables....................................................................................................................... 90

Figure ....................................................................................................................... 98

Literature Cited ........................................................................................................ 99

CHAPTER 3: USING STABLE ISOTOPES IN CONSERVATION PLANNING FOR

MIGRATORY SONGBIRDS: WINTERING SPARROWS IN THE LOWER

MISSISSIPPI RIVER ALLUVIAL VALLEY. ....................................................... 102

Abstract .................................................................................................................... 103

Introduction.............................................................................................................. 104

Methods.................................................................................................................... 107

Feather samples ................................................................................................. 107

Stable isotope analysis....................................................................................... 108

Results...................................................................................................................... 109

Stable isotope analysis....................................................................................... 109

vii

Geographic breeding origins.............................................................................. 109

Correlations with population trends .................................................................. 111

Discussion................................................................................................................ 112

Tables....................................................................................................................... 115

Figures...................................................................................................................... 118

Literature Cited ........................................................................................................ 121

viii

CHAPTER 1

HABITAT AND LANDSCAPE ASSOCIATIONS OF WINTERING

GRASSLAND AND SHRUBLAND BIRD COMMUNITIES IN THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

This chapter written in the format of the journal The Auk.

1

HABITAT AND LANDSCAPE ASSOCIATIONS OF WINTERING

GRASSLAND AND SHRUBLAND BIRD COMMUNITIES IN THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

ABSTRACT.—Grassland and shrubland bird communities have experienced

population declines throughout North America. Much research on these species has

taken place on their breeding grounds but little work has been done to understand habitat

use and community dynamics on the winter grounds. My study addressed some of these

research deficiencies with regard to habitats found in the lower Mississippi River alluvial

valley (LMAV). In winters 1999-2000, 2000-2001, and 2001-2002, I surveyed bird

communities using strip transect surveys at 69 locations throughout the LMAV of

Arkansas, Louisiana, and Mississippi. At each site I measured structural habitat and

landscape characteristics. I produced estimates of bird density and species richness for

all sites. Using regression models, I reviewed the plausibility of a set of candidate

models relating density and species richness to a combination of habitat and landscape

measures. Overall bird populations were highly variable across years. Sixty-four species

of birds were detected across the three winters. Five species were found to be most

numerous: Savannah (Passerculus sandwichensis), Song (Melospiza melodia), and

Swamp (M. georgiana) sparrows, Red-winged Blackbird (Agelaius phoeniceus) and

Eastern Meadowlark (Sturnella magna). Modeling of bird density as the response with

landscape and habitat variables as predictors indicated density decreased with increasing

distance to forest blocks; density decreased with increasing distance from the Mississippi

River; and bird density increased with increasing vegetation height. Models explaining

2

variation in species richness indicated an increase in species richness with increasing

vegetation height which was a function of site age; species richness declined with

increasing distance from the Mississippi River; and smaller sites close to the river were

more likely to hold more species than farther sites. The influence of the Mississippi

River on placement of wintering birds and the effects of vegetation height and proximity

to forest blocks are important in terms of managing these early-successional habitats.

Wintering grassland and shrubland bird species in the LMAV can benefit from land

management that includes maintaining areas of land in early stages of succession and

controlling for the structural conditions named above during the course of forest

restoration efforts.

POPULATIONS OF GRASSLAND and shrubland birds in eastern North America have

been declining since at least the mid 1960s (Sauer and Droege 1992, Sauer et al. 2003).

Several species of grassland and early-successional habitat specialist birds are now listed

as endangered or threatened in some states (Vickery 1992, Askins 1998) as well as being

listed as Birds of Conservation Concern by the U.S. Fish and Wildlife Service (2002). It

is asserted that conservationists have largely ignored early-successional habitat specialist

birds. Many species of grassland and shrubland birds that are still widespread have

suffered continuous, steep population declines during the past few decades (Askins

1998). Most studies that have observed population reductions for early-successional

specialists have focused on species that only breed in North America and their breeding

habitats. Little attention has been given to the numerous short-distance migrant bird

3

species that over-winter in the southeastern United States (Herkert and Knopf 1998).

Many of these birds breed in grasslands and early seral habitats of middle and northern

biomes of North America and migrate short distances to over-winter, compared to true

Nearctic-Neotropical migrants.

An area of the southeastern United States that has seen extensive clearing of

bottomland hardwood forest for agriculture, and thus has vast potential for restoring

early-successional vegetation, is the lower Mississippi River alluvial valley (hereafter

LMAV), the floodplain of the Mississippi River that extends from southern Illinois to

southern Louisiana. The LMAV has undergone the most widespread loss of bottomland

hardwood forest in the United States (Hefner and Brown 1985, Hefner et al. 1994,

Stanturf et al. 2000). Only about 2.8 million ha of an estimated original 10 million ha of

bottomland hardwood forest may remain in this region (King and Keeland 1999). As a

result of this loss of forest, almost entirely due to agricultural conversion, the LMAV has

been considered one of the most endangered ecosystems in the United States (Noss et al.

1995). It is now recognized that deforestation of the LMAV has resulted in loss of

critical wildlife and fish habitat, increased sediment loads in watersheds, and reduced

floodwater retention. Therefore the region has been targeted for the most extensive forest

restoration effort in the United States (Stanturf et al. 2000).

Federal agencies in both the U.S. Departments of Interior and Agriculture have

major directives initiated within the LMAV to reestablish bottomland hardwood forests,

restore wetlands, and improve natural hydrology (Heard et al. 2000, Haynes et al. 1995).

Other federal government programs provide incentives to private landowners for

restoration of forests and wetlands, principally through the Department of Agriculture’s

4

Wetland Reserve Program (WRP). These programs were instigated because most of the

region’s original forested wetlands have been either lost or altered and the rate of forested

wetland loss continues to increase in the region (Stanturf et al. 2000, Hefner et al. 1994).

All of these programs have many goals, the primary one of which is the creation and/or

restoration and management of habitats for the benefit of migratory birds. Since these

projects began in the late 1980s a resulting sizeable amount of land in various stages of

early ecological succession now occur in the LMAV (Schoenholtz et al. 2001).

Birds preferring grassland and shrubland habitats are one assemblage of migratory

birds occurring in the LMAV that are of special interest to wildlife and land managers for

two primary reasons. This group of birds has experienced widespread population

declines and range reductions in recent history (Sauer et al. 2003). Second, the habitats

preferred by these birds require disturbances at regular intervals to set succession back to

earlier stages or maintain the same seral stage, thus necessitating active land management

(Askins 2001).

Additionally, the habitat needs of grassland and scrubland bird species in the non-

breeding season are not well known and poorly researched (Igl and Ballard 1999, Twedt

et al. 1999, Herkert and Knopf 1998). Therefore my study was primarily designed to add

to the limited knowledge of these groups of birds in winter by investigating the

relationship of species richness and density to habitat structure and multiple landscape

variables in early-successional habitats of the LMAV.

The objectives for my research were to determine wintering migrant bird species

richness and density at a variety of forest restoration sites in stages of early-succession in

the LMAV; determine how habitat conditions and landscape features explain observed

5

and estimated species richness and densities in the LMAV; and determine habitat

conditions that are most suitable for the greatest number and the greatest densities of

wintering bird species. I anticipate that this information will be useful in developing a

conservation strategy aimed at providing optimal wintering habitat for the greatest

number and greatest diversity of wintering short-distance migrant bird species occupying

the LMAV.

STUDY AREA

I performed this study in the lower Mississippi River alluvial valley of eastern

Arkansas, northeastern Louisiana, and west-central Mississippi. The LMAV is a distinct

physiographic province of the southeastern United States. Because of this region’s

distinctiveness, it is treated as a unique Bird Conservation Region by the Partners in

Flight bird conservation initiative (Williams and Pashley 2000). This region is

dominated by extensive agriculture with a mosaic of remnant bottomland hardwood

forest tracts.

In winter 1999-2000, I identified 40 sites to be used in this study. In winter 2000-

2001 and 2001-2002 I increased the number of study sites to 69 locations with early-

successional vegetation characteristics (Fig. 1). Figures 2-4 detail the site locations in

Arkansas, Louisiana, and Mississippi. Of the 69 sites, 44 were located on U.S. Fish and

Wildlife Service (U.S. Department of the Interior) National Wildlife Refuges, five at

state-owned management areas, and 20 on private-owned lands enrolled in the Natural

6

Resources Conservation Service’s (U.S. Department of Agriculture) Wetland Reserve

Program (Table 1).

Most of the above study sites were in agricultural tillage before recently being set

aside and planted in bottomland hardwood tree cover. Planted tree species predominately

included Nuttall oak (Quercus nuttallii), water oak (Q. nigra), and willow oak (Q.

phellos). Intermixed with planted trees were herbaceous plants typical of wet old-fields

that included goldenrod (Solidago spp.), rushes (Juncus spp.), smartweed (Polygonum

spp.), and various grasses (Andropogon spp., Tridens spp., Panicum spp., Eragrostis

spp.). In addition to grasses, forbs, saplings, and young hardwood trees, many sites had

considerable woody components that included plants such as trumpet creeper (Campsis

radicans), saltbush (Baccharis halimifolia), and blackberry (Rubus spp.). At a few sites

coffeebean (Sesbania vesicaria) was a dominant plant forming monotypic stands. Four

sites were exceptions to the above: Konecny Prairie Natural Area, Roth Prairie Natural

Area, and two privately-owned prairies, all in Arkansas (Fig. 2; sites AR-18, AR-22, AR-

16, and AR-17, respectively), were relict tallgrass prairies dominated by grasses such as

big bluestem (Andropogon gerardii), little bluestem (A. scoparius), switchgrass (Panicum

virgatum), and Indian grass (Shorghastrum nutans).

Size of the study areas varied because of the diverse histories and ownerships of

the tracts involved. Site sizes ranged from 1,214.1 to 7.3 ha (Table 1) with a mean area

of 188.1 ha (SE = 28.4). Age of the vegetation communities also varied from sites that

had been planted 14 years before the 2001-2002 winter to sites that were planted in early

2002. The four native prairie sites never had been planted and received only periodic

burning or haying as management. Average age since planting was 5.9 years (SE = 0.4).

7

Across these sites, ecological succession was apparent from the range of present

plant communities. Sites in early stages of succession (~ 0 – 4 years) ranged from a mix

of bare ground and scattered grasses and forbs to well-developed grass and herbaceous

plant layers. Young trees typically were not well developed on sites in this early age

class. In middle stages of early succession (~ 4 – 8 years) the grass layer was still present

but was often accompanied by dense compositions of forbs along with small, woody

vegetation such as trumpet creeper, blackberry, and saltbush. Planted trees were often

noticeable on older sites (~ 8 – 14 years) and, if plantings thrived, were a dominant

feature in these areas, often reaching heights > 2m. An understory of grass and forbs,

though thinner than in the earlier age classes, was typically still present.

For purposes of comparison, I also surveyed for birds on a small number (n = 7)

of plowed, unplanted agricultural fields using the same methodology as that of sites with

early-successional vegetation. These fields were representative of much of the landscape

of the LMAV in winter. The information from these surveys is intended to provide

general estimates of bird species richness for open, non-vegetated lands that were also

available to wintering bird species encountered at the other sites.

Selection of my study sites was a non-random convenience sample (Cochran

1977) because these sites were chosen, rather than randomly selected. I did so because of

several logistical factors including accessibility, availability, current hydrologic

conditions, management practices, and regional distribution. Because of these

restrictions, the results of my study may only be directly inferred to those sites that were

examined.

8

METHODS

Avian community surveys.—I used strip transects (Bibby et al. 2000) for surveying

the bird communities because of this method’s ability to cover a large area in a short

time, providing a greater sample size or number of detections. At each site, I placed

transects across habitats at 150 m intervals and no closer than 75 m to the edge of a site.

I systematically placed all transects within each site parallel to each other to avoid

parallel land and/or vegetation features (Thompson et al. 1998). Before collecting field

data, all observers were trained in identifying bird species to help eliminate observer bias

that could affect results (Kepler and Scott 1981). Two persons walking abreast, 10 m

apart (5 m either side of a centerline) counted all birds when flushed from between

observers and to each observer’s side. Discussing observations at the time of detection

reduced the number of double-counts. Observers recorded birds up to 75 m away on

either side of the transect centerline. This method provided intense coverage of the birds

on the centerline, increasing the likelihood that all birds were detected on the transect

centerline. This two-person method also increased the chance that all species within the

entire 150 m-wide strip were detected. Additionally, one person was responsible for

recording transect distance and maintaining a straight line with a compass while the

second person recorded data.

Observers walked parallel to each other at a slow pace (~ 1 km hr-1) and paused

regularly to locate and identify birds visually and aurally, estimate perpendicular

distances, and record data. Data recorded included bird species and their approximate

perpendicular distance from the transect centerline, number of individuals (if more than

9

one bird was flushed as a cluster), general habitat type, and transect length (measured by

pacing). Observers estimated perpendicular distances between birds and the centerline of

each transect. All observers were trained in estimation of distances before field work and

these skills were checked periodically thereafter.

I conducted strip transects in winter 2000 (14 January – 28 February), 2001 (5

January – 28 February), and 2002 (5 January – 27 February). I surveyed transects only

once each year at each site due to limitations in personnel and the many study sites used.

Transect surveys began at approximately 07:00 and typically continued until 16:00 CST.

Density and species richness estimates.—I used distance sampling (Buckland et

al. 2001, Thomas et al. 2002a) to generate density estimates for birds. This method has

three critical underlying assumptions that must be satisfied for reliable estimation of bird

density: all birds at distance zero on transects were detected; all birds were detected at the

point where they are first encountered; and all distance measurements or estimates were

accurate.

I used program SPECRICH (Hines 1996) for estimates of species richness for

each site, each winter because it is possible that not all bird species were detected during

the course of each field season. Program SPECRICH uses a jackknife estimator

(Burnham and Overton 1979) to estimate species not recorded but likely present in an

area. A possible reason for under-detecting species is that each study site was visited

only once during the course of a winter. Other factors that may have contributed to

incomplete counts of species included weather conditions, intra-seasonal movements of

individuals, and observer variability. To produce estimates of species richness at each

10

site I entered raw counts of observed species richness from individual sites for each year.

Raw counts for overall pooled species richness by year were used for annual study area-

wide richness estimates.

Habitat measurements.—With aid from field assistants, I collected vegetation data

at 100-m intervals on each transect centerline. At each stop an observer stood at the 100-

m interval on the transect line while the other observer was positioned 10 m in each of

the cardinal directions holding vertically a density board (modified from Noon 1980) that

measured 0.5 m in width by 2.0 m in height. This “board” (cotton canvas supported by

two polyvinyl chloride [PVC] pipes) was marked by a two-color 10-cm checkerboard

grid. The grid was colored with alternating black and white for the three rows closest to

ground level (0 – 0.3 m, termed DBL), red and white for the next seven rows (0.3 – 1 m,

DBM), and black and white for the remaining ten rows (1 – 2 m, DBH). The observer at

the stationary point recorded the amount of vegetation visually obstructing each of the

three height classes by noting the number of squares ≥ 50% obscured by vegetation.

Concurrently, the person holding the density board estimated percent ground cover using

a 0.5 x 0.5-m frame made of PVC pipe (modified from Daubenmire 1959) that was

haphazardly tossed to the observer’s side. Ground cover types within this frame were

visually estimated as percentages (totaling 100%) from the following compositional

categories: grass, forbs, woody stems, litter, bare ground, moss, rushes, water, and a

catch-all group termed “other” which accounted for those ground covers not described

above. Both ground cover and vertical structure measures resulted in four replicates at

each 100-m transect interval. These data were collected once at each study site and these

11

single site measurements were used across the three winters with respect to bird

populations. An exception to this existed—two sites (AR-15 and MS-23) were flooded

and inaccessible during winter 2002 when data collection was planned leaving a total of

67 sites where habitat measurements were collected. Single habitat measurements were

obtained because when habitats are sampled more than once only 85% similarity between

measurements will be observed (Cox 1985). Also, the amounts of growth between two

growing seasons for the habitats and plant species involved do not vary significantly

(Hamel 2003).

Landscape measurements.—I quantified several landscape features relative to

each study site to use in modeling landscape effects on winter bird species density and

richness. I measured the following variables for each of the 69 study locations: latitude,

longitude, elevation (m), area (ha), minimum distance (km) to nearest forest patch >1 ha

in area, minimum distance (km) to nearest waterbody >1 ha in area, minimum distance

(km) to nearest perennial flowing water, minimum distance (km) to the Mississippi River,

minimum distance (km) to the West Gulf Coastal Plain/Ozark and Ouachita Highlands

escarpment (west of the Mississippi River), and minimum distance (km) to the East Gulf

Coastal Plain escarpment (east of the Mississippi River). I measured variables on the

ground, when possible, using a handheld Global Positioning System (GPS) unit (Garmin

eTrex Venture, Garmin International Inc., Olathe, Kansas). I also obtained these

landscape measures from a combination of current Geographic Information System (GIS)

data layers, recent aerial photography, and digital USGS topographic maps (1:24,000 and

12

1:100,000 scale). I obtained current, on-the-ground conditions during the course of field

work to correct for changes in land use not visible with remote resources.

Data analysis.—I used distance data collected for all observations to estimate

wintering bird densities (birds ha-1) for each site with program DISTANCE (version 4.0,

release 2; Thomas et al. 2002b). I calculated densities for all birds encountered (pooled

as all-bird density) at each site, each year, and densities of the five most commonly

encountered species were calculated for each site, each year. Program DISTANCE uses

the perpendicular distance between the observed bird(s) and the transect centerline to

calculate a detection function based on probability density functions. I computed

detection functions based on a series of competing models. Models (key function/series

expansion) used were those recommended by Buckland et al. (2001). Models that best fit

the data sets were either half-normal/cosine or hazard-rate/cosine. Data were grouped

into four to six distance intervals to eliminate spikes in the middle and on the tail of the

detection curve. These groupings provided better fitting models than using ungrouped

data.

Program DISTANCE typically requires a minimum of 40-60 detections to

accurately estimate densities. In situations where there were < 40 detections at a site, I

derived a pooled detection function from the entire data set and that detection function

was then applied to data sets from those sites with a below-minimum number of

detections (Buckland et al. 2001).

I evaluated competing models in program DISTANCE for relative suitability

using Akaike’s Information Criterion values (AIC; Buckland et al. 2001, Johnson and

13

Omland 2004). In addition to using AIC values, I used the χ2 goodness-of-fit probability

values and the detection probability plots provided in the program DISTANCE output for

each model to further aid in model selection.

I used both empirical observations as well as richness estimates derived from

program SPECRICH for analysis of species richness at each site in all years. I defined

species richness as the number of bird species observed, or estimated to occur (using

program SPECRICH), within a given study site in a given year.

I analyzed the values resulting from habitat and landscape features using mixed-

model multiple linear regression with bird density or species richness as separate

response variables and habitat and landscape measures as the explanatory variables

(PROC MIXED, SAS Institute 1999). The maximum likelihood parameter estimation

method was used in all model runs. I ran three series of models with the separate

response variables of bird density, observed species richness, and estimated species

richness (using program SPECRICH). I predicted that habitat structure would explain a

portion of the variation in the models and landscape variation would explain additional

variation in the models.

I developed a set of candidate models that considered prior research as well as the

biology of the various bird species involved when trying to explain variation in either

bird densities or species richness. Only habitat and landscape variables that were not

strongly correlated (|r| < 0.70) with each other were included so as to minimize problems

of multicollinearity (Ribic and Sample 2001). The Year variable was not included in any

of the models because of the inability to control for year differences in bird abundance

from a local management perspective. Six variables were ultimately selected to include

14

in the set of candidate models. These variables included: average number of squares

obscured on density board lower tier (DBL), average number of squares obscured on

density board highest tier (DBH), time in years since last treatment (planting) or since

cultivation up to winter 2001-2002 (Age), area (ha) of site (Size), minimum distance

from nearest forest patch >1 ha (Forest), and minimum distance from Mississippi River

(MissR). Interactions between Age and Size (Age|Size = Age, Size, Age × Size) were

also modeled. A combined data set that included only the bird count information from

the years in which vegetation data were collected at a particular site was used for these

analyses. This resulted in a data set with a sample size of 67, two less than the total sites

selected because I was unable to be collect vegetation measurements at these two sites

(AR-15 and MS-23).

Akaike’s Information Criterion adjusted for small sample size (AICc) was used as

a basis of model selection. AICc is calculated from the formula

AICc = - 2loge (L ( )) + 2K + θ̂)1()1(2

−−+

KnKK ,

where K is the number of estimable model parameters, is the maximized log-likelihood

estimation of the K model parameters, and n is the sample size. The model with the

smallest AIC

θ̂

c value was taken into consideration as the best approximation for

explaining variation in the data set. Differences between models (∆AICc) were further

used because the relative differences between AICc values are more important than the

values themselves (Burnham and Anderson 1998). Models that have ∆AICc values of ≤ 2

are considered to be equally plausible and cannot be separated, therefore, more than one

model may be used in explaining variations in the data (Burnham and Anderson 1998).

15

Because frequency distributions of density and species richness data were

strongly Poisson, I used a generalized linear model for analysis of Poisson data to further

assess the influence of habitat and landscape variables on bird density and species

richness (PROC GENMOD, SAS Institute 1999). I used Poisson regression to help

identify significant variables that influenced bird density and species richness that may

not be discernable from the above model selection analysis. In these regression models,

the categorical variable Year was set as a class variable (mean-centered). In the model

where estimated bird density was the response, I set the log of the count area as an offset

variable as a means to re-scale the count data to account for the fact that density values

were generated estimates. I used stepwise backward-elimination selection to remove

predictor variables with the largest P-values based on likelihood-ratio tests (TYPE3

option). Main effect variables included in any interaction term were not removed during

the backward-elimination procedure unless the interaction term had already been

eliminated. The same variables used in model selection above were included in this

analysis along with three interaction terms, all associated with proximity to the

Mississippi River: MissR × DBL, MissR × DBH, and MissR × Size.

RESULTS

Avian community.—In winter 1999-2000 there were 46 species and 6,412

individual birds detected during the course of surveys in early-successional habitats in the

LMAV. Winter 2000-2001 surveys resulted in 50 observed species and 5,443 individual

birds. During winter 2001-2002, 48 species of birds and 8,148 individuals were observed

16

in early-successional habitats. Pooled across three winters, 64 species were recorded in

all study sites. All bird species encountered over the three years of surveys and the

numbers of individuals detected are listed in the Appendix. A number of individuals

were only identified to genus or family (sparrows and blackbirds) and are indicated as

such in the Appendix. These detections, though not specific, were still used in computing

density of all birds for a given site.

Over the three winter field seasons, five bird species were consistently the most

numerous in the LMAV: Savannah, Song, and Swamp sparrows, Red-winged Blackbird,

and Eastern Meadowlark (scientific names of birds in the Appendix). Together these five

species comprised from between 77% and 81% of the total surveyed bird population

(Table 2). Of these five species, no one species was consistently more abundant than

another through the survey period. The rank of each of the five birds fluctuated among

years; though Red-winged Blackbirds were consistently the most abundant species in

2001 and 2002. An example of the fluctuations in detected individuals, Swamp

Sparrows, which were most abundant in 2000, fell to fourth most abundant in 2001 then

rose to second most abundant of the five species in 2002. Eastern Meadowlarks were the

least abundant of the five species in 2000 and 2002, though this species ranked third in

abundance in 2001. No clear patterns of relative abundance for these five species were

observed across all three field seasons.

Other bird species that were detected and comprised a much smaller portion of the

total bird population included a few waterfowl, primarily Mallard, and raptors such as

Northern Harrier and Red-tailed Hawk. Mourning Dove and Wilson’s Snipe were

occasionally but regularly encountered, as were Loggerhead Shrike and Northern

17

Cardinal. Several forest interior or forest edge species, atypical of early-successional

sites, were detected and included Carolina Chickadee and White-throated Sparrow.

Relative bird species composition shifted as sites increased in age and

vegetational complexity. Empirical observations showed that at sites in very early stages

of succession (~ 0 – 4 years) vegetated mainly by grasses and forbs, or both, bird

communities often consisted of grassland species such as Sedge Wren, Savannah

Sparrow, Le Conte’s Sparrow, and Eastern Meadowlark. In mid stages of early

succession (~ 4 – 8 years) where sites consisted mainly of mixed forbs and woody

vegetation, including planted hardwood trees, birds communities included species such as

Sedge Wren, Song and Swamp sparrows, Red-winged Blackbird, and Eastern

Meadowlark. In sites in later stages of early succession (~ 8 – 14 years) that were

dominated by developing reforested trees with a herbaceous understory, the bird

community frequently consisted of Song and Swamp sparrows and Red-winged

Blackbirds.

Density and species richness.—Overall estimates of bird density varied among

years. Mean estimates of density for all wintering birds at all sites throughout the LMAV

in 2000 were 19.7 birds ha-1 (SE = 3.3), 6.1 birds ha-1 (SE = 0.6) in 2001, and 6.9 birds

ha-1 (SE = 0.6) in 2002. Across estimates the CV ranged from 8.5% to 85.4% while the

corresponding CI varied proportionately. Estimates of density varied widely among sites

and within sites among years (Table 3). Large-scale shifts were observed between

consecutive years for many sites surveyed. For example, site LA-20 had an all-bird

density estimate of 122.0 birds ha-1 (CI = 103.1, 144.3; CV = 8.5%) in 2000 while the

18

density estimate dropped to 5.5 (CI = 1.0, 29.7; CV = 69.2%) and 3.0 (CI = 0.6, 15.9; CV

= 68.5%) birds ha-1 in 2001 and 2002, respectively. These ranges in estimated density

corresponded with the variation in observed abundance between years. Some sites

remained fairly consistent in the estimated number of birds found there. In 2000, site

MS-2 held 4.9 birds ha-1 (CI = 2.0, 11.9; CV = 38.7%). In subsequent winters the

estimated density was similar: 4.3 birds ha-1 (CI = 2.0, 9.0; CV = 31.5%) in 2001 and 3.3

birds ha-1 (CI = 2.1, 5.3; CV = 21.6%) in 2002.

Density estimates for the five most abundant bird species listed above were

calculated using program DISTANCE when adequate samples of each (≥ 40) were

encountered at a site (Table 4). Though species such as Red-winged Blackbirds were

among the most numerous species encountered, their tendency to form large flocks is

responsible for them not being detected at many sites. Estimates for densities of

Savannah Sparrows were not wide-ranging (2.6 – 8.3 birds ha-1) in most sites with the

exception of site MS-4 in 2001 that contained an estimated 19.5 Savannah Sparrows ha-1

(CI = 0.04, 9870.38; CV = 51.2%). Song Sparrow densities ranged similarly to those of

Savannah Sparrows: 2.2 – 6.4 birds ha-1. Density estimates from program DISTANCE

for Swamp Sparrows were much greater than the other two sparrow species (Table 4).

Estimates ranged from 83.6 birds ha-1 (CI = 24.1, 290.7; CV = 47.0%) at site LA-20 in

2000 to 2.4 birds ha-1 (CI = 1.1, 5.3; CV = 20.5%) in site MS-8 in 2002. With many

fewer sites where an adequate sample existed to produce estimates with program

DISTANCE, Red-winged Blackbird and Eastern Meadowlark densities were calculated

from two and three sites, respectively. Red-winged Blackbird density estimates ranged

19

from 2.5 – 5.9 birds ha-1 and Eastern Meadowlark density estimates varied from 1.8 – 2.2

birds ha-1 (Table 4).

Across the three winters of this study, observed species richness of the region-

wide early-successional habitat bird community varied little (2000 = 48 species, 2001 =

49 species, 2002 = 48 species). Cumulative observed species richness for the three years

of the study was much higher than any one of the three winter seasons with a total of 64

species detected (Appendix). Program SPECRICH estimates for the regional species

richness varied from the observed species richness and also declined across years (2000 =

68.9, SE = 7.7; 2001 = 58.0, SE = 4.2; 2002 = 57.0, SE = 4.2). Confidence intervals

overlapped among all three estimates indicating that species richness in the LMAV did

not vary significantly across years, suggesting that all sites had the same pool of species

from which a particular site’s species richness was drawn.

In contrast to the relatively constant species richness observed across years area-

wide, species richness between years and between sites was highly variable (Table 5).

Observed richness across sites and years ranged from 22 (site LA-3 in 2000) to as few as

none (no birds observed at AR-11 in 2002). The number of species common within a

site, across years, ranged from as many as 11 (site AR-21) to as little as 0 (site AR-11).

The modal number of species across the entire study area and across all years was 8 ( x =

8.38, SE = 0.28). Some sites experienced high fluctuations in species richness—such as

the range from 4 (2000) to 13 (2002) at site MS-20—while other sites maintained

relatively constant richness across the three winters, such as site LA-6 in which 12, 12,

and 13 species were detected in 2000, 2001, and 2002, respectively.

20

Estimates of site-specific species richness generated by program SPECRICH

varied considerably (Table 5). Estimated number of species present but not detected

ranged from 2 (sites AR-17 in 2002 [SE = 0] and LA-20 in 2002 [SE = 0]) to a high of

44.6 (SE = 12.9) at site LA-6 in 2001. Using these estimates, overall modal number of

species across the study area and across years was 10 ( x = 13.2, SE = 0.7). SPECRICH

estimates of species numbers were positively correlated with the observed numbers of

species (r = 0.72, df = 162, P < 0.0001) across all sites and years. However, some

resulting estimates were much greater than the observed number of species—such as site

AR-8 where the observed species was 8 and the SPECRICH estimate, using the same

count data, was 24.1 (SE 9.0).

Observed bird species richness encountered on the seven bare-soil agricultural

fields had a mean of 1.9 (SE = 0.9, mode = 0) birds. Though the comparison sample size

was quite small, this suggests that habitat patches with early-successional vegetation were

generally more species rich than the adjacent barren fields that were often devoid of

birds. When bird species were found in these sites they were open-country species that

were some of the same encountered in early-successional sites. I encountered a total of

eight species in agricultural fields. These species included Killdeer, Wilson’s Snipe,

Loggerhead Shrike, Horned Lark, American Pipit, Savannah Sparrow, Red-winged

Blackbird, and Eastern Meadowlark. The most numerous of these eight species were

American Pipit, Horned Lark, and Savannah Sparrow. One species, Horned Lark, was

exclusive to agricultural fields.

As a further comparison, the species richness findings from the four Arkansas

native prairie sites (AR-16, AR-17, AR-18, and AR-22) included a total of 16 species and

21

a mean of 5.25 (SE = 0.84, mode = 8) birds detected among all three winters. Of these

species, none were found to be exclusive to native prairie. However, there were several

Birds of Conservation Concern (U. S. Fish and Wildlife Service 2002) regularly

encountered in these grassland habitats: Short-eared Owl, Sedge Wren, and Le Conte’s

Sparrow. Other regularly occurring species included Savannah Sparrow, Red-winged

Blackbird, and Eastern Meadowlark.

Comparisons were made between age of sites, relating to general seral stage

development, and bird density and species richness measures. I divided sites into three

age classes (0 – 4 [n = 25], 5 – 8 [n = 23], and 9 – 14 [n = 19] years) and compared these

three classes using one-way analysis of variance. I found that there were no statistically

significant differences between age classes and bird density, observed species richness,

and estimated species richness. I further used Student’s t-tests to examine the differences

between the means of each of the three age categories and the measures of density and

species richness and also found no statistically significant differences.

Habitat and landscape effects.—Model selection results for bird density indicated

that much uncertainty existed between models (Table 6). Sixteen of the 17 models tested

had ∆AICc values < 8 suggesting that some support for those 16 models was indicated

(Burnham and Anderson 1998). Models with the most support included the top three,

with Size, Forest, and Age being variables of greatest importance. Forest was the single

most important variable as it was found in all three top models selected. The relationship

between Forest and density was negative, i.e., density declined as the distance to the

nearest forest block increased (Fig. 5). The next most important variables were Size

22

followed by Age and MissR. When examining the parameter estimates for both Size and

Age, I noticed that in both cases the estimates were close to zero and the CIs overlapped

with zero. When I developed the original set of candidate models I did not include all

possible models because I wanted to only test those models that made the most biological

sense based on previous research. Based on the small parameter estimates for Size and

Age, I opted to run those two models a posteriori. In both cases the ∆AICc values were

small (3.7 and 3.5, respectively) but those scores suggested that neither Size nor Age

were equally plausible with Forest as an explanatory variable. For this reason I suggest

that of the variables that I tested only Forest explained a significant amount of the

variation in bird density among sites (Fig. 5). Models with some support (2 < x < 8)

included three additional variables, MissR, DBH, and DBL, that could be important. Of

these later variables, the two habitat variables (DBL and DBH) were positively related to

density while distance to the Mississippi River (MissR) was negatively associated with

bird density.

The same candidate model sets were used in comparing the two species richness

measures—observed richness and richness estimated by program SPECRICH. Of the 17

models exploring the relationship between observed species richness and habitat and

landscape features, all but one had ∆AICc values < 7 suggesting that some support

existed for almost all models (Table 7). Models with the greatest support (∆AICc ≤ 2)

were the top three that included the variables DBL and DBH. Both variables DBL and

DBH were nearly equivalent in their importance as they appeared either alone or together

in these top three ranking models. The relationships between DBL and DBH and the

observed species richness were both positive indicating that as vegetation height in these

23

two categories (0 – 0.3 m and 1 – 2 m, respectively) increased, observed species richness

increased (Figs. 6 and 7). The variables MissR, Forest, Age, and Size were the next most

important variables. I examined the parameter estimates and corresponding CIs for each

of these four variables and found that the estimates for Age and Size were small with CIs

overlapping zero while the parameter estimate for MissR and Forest were larger, but the

CIs did still include zero for the variable Forest. To further test the ability of these four

variables to explain variations in observed species richness, I ran models Age, MissR,

and Forest by themselves, a posteriori, and observed that the ∆AICc values for two of

these three models were small (1.8, 0, and 6.4, respectively). The size of the ∆AICc value

for the models Age and MissR suggests that the site Age and distance to Mississippi

River (MissR) variables may be equally plausible as explanatory variables as are DBL

and DBH. Age was positively related with both of the habitat variables DBL and DBH

indicating that increased height in both of these vertical vegetation measures is related to

increasing maturation of vegetation as a site ages. The variable MissR was negatively

associated with observed species richness, indicating that as sites are further from the

Mississippi River they hold fewer species.

Models using estimates of species richness from program SPECRICH as the

response variable were somewhat different in ranking (Table 8) to those models with

observed species richness as the response (Table 7). Ten models had ∆AICc values < 8

indicating that some level of support existed for this group of models. Models with

variables DBH, DBL, Size, MissR, and Forest had the greatest support. The variable

MissR was the most important as it occurred in the top three models. Variables DBH and

DBL were the next most important variables as they were found in five of the top ten

24

models. The relationship between DBH and DBL was positive indicating that estimated

species richness increased as vegetation height in the two size classes increased. The

variable MissR was negatively associated with estimated species richness indicating that

as a site was further from the Mississippi River, its estimated species richness declined

(Fig. 8). MissR, when run as a model by itself (a posteriori), resulted in an AICc of

478.9, less than the top-ranking model listed in Table 8. The parameter estimate for

MissR was negative with a small corresponding SE and a narrow CI signifying that this

variable may be a good predictor of estimated species richness. The variables Size and

Forest were the next most important variables as they were included in the first-, third-,

fourth-, and fifth-ranking models. Parameter estimates for Size was near zero and the CI

overlapped zero. Because of this small parameter estimate, I again elected to run the

model Size a posteriori. The ∆AICc from this model run was relatively small (5.2)

suggesting that Size also is plausible, along with DBL, DBH, and MissR in explaining

variation in estimated species richness. The relationship between Size and estimated

species richness showed a positive association between the two (as the area of a site

increased, estimated species richness increased). The model Forest had a large, negative

parameter estimate with a correspondingly large SE and an even greater CI that

overlapped zero indicating that the variable Forest by itself may not be a good predictor

of estimated species richness. Therefore, MissR, DBL, DBH, and Size seem to be the

most plausible variables for explaining variations in species richness as estimated by

program SPECRICH.

Poisson regression models (Table 9) provided additional support for the above

results. With respect to estimated bird density, the main effects of DBH, Age, Size,

25

Forest, and MissR were significant in explaining variations in the data. Density of birds

increased with increasing vegetation height in the 1 – 2 m class (DBH), with increasing

site age, and with increasing distance to the Mississippi River. The later relationship is in

contrast to the above modeling where decreasing distance to the river resulted in

increased bird density. However, the CIs for the parameter estimate from the preceding

models overlapped zero and therefore make MissR less plausible while the Poisson

models take the Year variable into account as a class variable thereby removing year-

bias. Site size and distance to nearest forest block variables were negatively correlated

with bird density. Interactions between MissR and both DBH and Size were significant

and negative. The interactions between MissR and DBH suggest that sites with taller

vegetation were more likely to hold a higher density of birds as those sites were closer to

the Mississippi River. The influence of Size was such that sites that were smaller in size

and closer to the Mississippi River were more likely to host a greater density of wintering

birds.

Poisson regression models for both observed and estimated species richness

(Table 9) provided similar results. Species richness was significantly related to two

landscape and two habitat variables. Both models retained the variables DBH and MissR

as significant predictors for explaining variation in the data. For each model DBH was

positively associated with increasing species richness, indicating an increase in species

richness as vegetation height increased in the highest category. The distance of a site to

the Mississippi River variable was negatively associated with each response variable. As

a site was farther from the Mississippi River, the species richness dropped. The variables

Size and DBL were significant within the estimated species richness model but not in the

26

observed species richness model. Size and DBL were both inversely related to estimated

species richness. Interactions between MissR and DBL, DBH, and Size were significant

for both of the species richness response variables. These interactions indicate that

species richness was influenced by the proximity of the Mississippi River and vegetation

in lower (0 – 0.3 m) and upper (1 – 2 m) height categories as well as the size of a site.

Further stated, species richness increased as plots with higher vegetation (DBH) were

closer to the Mississippi River. Species richness also increased with decreased distance

to the Mississippi River and an increase in measured height in the lower vegetation

category (DBL). Small sites within close proximity of the Mississippi River were more

likely to harbor greater numbers of bird species than those sites further away.

DISCUSSION

An obvious pattern observed from the total of each year’s resulting avian surveys

was that the total population numbers are highly variable from year to year (Table 3,

Appendix). Reasons for these wide-ranging abundances among years were probably

many but I believe that the primary reasons included weather and climate, previous year’s

breeding success, or cyclical population fluctuations.

Weather and climatic variability may be more of a source for bird population

fluctuations than is discernable in this study. Recent research has documented shifts in

populations of plants and animals, proportional to their physiological constraints, as a

result of climate change (Parmesan and Yohe 2003, Root et al. 2003). These shifts were

more gradual, noted over the course of 100 years. Still, localized weather patterns or

27

specific events may be responsible for some regional movements or mortality in these

wintering bird populations. James (1963, 1962, 1961, 1960, 1959) studied the

association between cold weather and winter bird populations from Christmas Bird

Counts for four species: Eastern Phoebe, House Wren, Eastern Bluebird, and Hermit

Thrush (Catharus guttatus). James found statistically significant negative correlations

between temperature and population levels in all species but the House Wren (except in

extreme southern latitudes of the United States) indicating that whenever a winter had a

particularly large number of cold days the population levels of the species concerned

were relatively lower the ensuing winter, and vice versa.

Precipitation for winter 2001-2002 (December – February) in the LMAV was

varied with Arkansas recording above normal precipitation, below normal precipitation in

Louisiana, and normal precipitation levels in Mississippi. Nationwide this period ranked

as the 14th driest winter on record. Temperatures were much warmer than in the

previous winter but similar to those in 2000. Based upon preliminary data, winter 2001-

2002 was the fifth warmest winter season on record for the contiguous United States.

However, in the LMAV, only Arkansas recorded above normal temperatures for the

period while Louisiana and Mississippi experienced near normal temperatures (National

Climatic Data Center 2002). With milder weather in winter 2001-2002 it is conceivable

that the striking declines observed in overall winter bird abundance of 2000-2001 were

directly related to more harsh weather conditions. Sub-freezing temperatures in

November and December 2000, combined with two ice storm events in December over

the northern portion of the LMAV, may have impacted wintering birds that were

physiologically unprepared for harsh weather resulting in mortality and overall

28

population reduction. Alternatively, unfavorable weather conditions may have caused

emigration from the LMAV to regions experiencing warmer weather, especially early in

the winter season when migratory movements have not ceased.

Because bird reproduction is, by definition, confined to the breeding range,

numbers there may be limited if breeding success was so poor that subsequent breeding

numbers could not reach levels necessary to fill either the available breeding habitat or

the available non-breeding habitat. This situation could arise even with relatively good

year-round survival (Newton 2004). A bird species might easily be limited on the

breeding grounds in one year or area and limited on its winter quarters in another year or

area (Newton 1998). These conditions are likely reflected in the observed patterns of

abundance by the birds that were surveyed on the winter grounds.

Breeding success or failure as a contributing factor to observed winter population

fluctuation for the grassland and shrubland birds in the LMAV may be another factor

influencing the population number I observed in this study. The North American

Breeding Bird Survey (BBS) (Sauer et al. 2003) is the primary source for detection of

population change and relative abundance of many bird species. Of the five species cited

above as having comprised the majority of the sampled winter bird population in the

LMAV (Table 2), three migrate from the region to breed (Savannah, Song, and Swamp

sparrows) while two (Red-winged Blackbird and Eastern Meadowlark) are year-round

residents. Knowing the origins of over-wintering migratory birds is important to

understanding how breeding success relates to winter populations and abundance.

Additional methods are needed to further establish linkages between wintering and

breeding sites to further assess the effects of breeding success on winter populations.

29

Of the two resident species described above, Red-winged Blackbirds are

experiencing breeding population decreases throughout the LMAV states of Arkansas,

Louisiana, and Mississippi, according to BBS trend information (Sauer et al. 2003).

Fluctuations noted in this study are likely not indicative of annual population levels

because of Red-winged Blackbird’s habit for flocking in winter (Yasukawa and Searcy

1995). Red-winged Blackbirds form large, nomadic feeding flocks in winter that use a

wide variety of microhabitats for foraging (Jaramillo and Burke 1999). Because of this

species’ behavior and habitat preferences, it is not unexpected that varying densities and

population levels were detected in this study.

North American Breeding Bird Survey data show steep declines in breeding

populations for Eastern Meadowlarks across the three states in this study (Sauer et al.

2003). In winter, some northern populations of Eastern Meadowlarks migrate into

southern states and flock with resident birds (Lanyon 1995). If populations of resident

and wintering Eastern Meadowlarks are pooled in winter, the great fluctuations in

populations seen in the LMAV may indicate that the species’ populations are in decline

throughout a much greater portion of their range.

Waterfowl and other waterbirds (bitterns, herons, shorebirds, etc.), though not

specifically targeted by my study, were an important group of birds utilizing these early-

successional habitats, particularly when sites had some amount of standing water.

However, because of the wariness of these birds, particularly waterfowl, line transect

surveys were not appropriate for surveying these species as they would flush well in

advance of an observer’s approach. As such, my study was not effective at estimating

population densities of waterfowl and waterbirds and investigating the relationship

30

between habitat, landscape features, and their abundance. A few detections of waterfowl

and other waterbirds were recorded (Appendix) but, in general, these bird groups were

under-detected.

Observed species richness across all study sites and all three winters (Appendix)

was higher than in individual years. Several of the species detected involved small

numbers of individuals not typical of early-successional habitats but more associated with

forest or edge environments and wetland habitats which often surrounded or were in

close proximity to study sites. Examples of these rarely-detected species include

American Bittern, Red-bellied Woodpecker, Ruby-crowned Kinglet, and Yellow-rumped

Warbler. Overall species richness in my study (64 species) was similar to that found by

Hamel (2003) (62 species) who surveyed winter bird communities at a forest

experimental plot in the LMAV of Mississippi that included a canopy component

responsible for the presence of forest-interior bird species. Study-area wide estimates of

species richness generated by program SPECRICH for each year (68.9, 58, and 57,

respectively) were similar to the three-year total of all observed species combined (64

species) suggesting that this richness estimation method may produce realistic estimates

of species richness using empirical abundance data. A possible reason for under-

detecting species, and thus necessitating the use of SPECRICH, is that each study site

was surveyed only once during the course of each winter, likely under-counting the total

suite of bird species present. Other influences that may have contributed to incomplete

counts of species include habitat differences across sites, weather conditions, intra-

seasonal movements of individuals, and observer variability.

31

From the small sample of barren, unplanted agricultural fields in the LMAV it is

obvious that species richness was much reduced from early-successional sites ( x = 1.9).

Species composition was shifted to more open-country bird species that are not

dependent on vegetation found in early-successional sites. In contrast, native prairie sites

in Arkansas that were dominated by grasses and included some herbaceous plant

components had a much higher species richness ( x = 5.25) and were consistent with that

found in other grass-dominated sites not of prairie origin. Because prairie sites were host

to Birds of Conservation Concern (Short-eared Owl, Sedge Wren, and Le Conte’s

Sparrow), management of reforestation sites to maintain very early-stage succession

which includes grasses as dominant cover is recommended for providing overwinter sites

for these species.

The effects of habitat structure and composition along with landscape-level

features on density of all wintering birds were explained, in part, a site’s distance to the

nearest forest patch > 1 ha, the size of a site, and by the age of a site’s vegetation (time

since reforestation), which can be further equated to the increased structural complexity

of vegetation at a site. Results from my study indicated that as an early-successional site

was more distant from a forest patch, density of birds decreased. This relationship has

similarities to island ecosystems in that isolated sites, those disjunct from forest patches

and surrounded by barren fields, receive less influx of bird species that are adapted to

both forest edge and early-successional habitats. This suggests that forest blocks serve as

a source for individual birds when early-successional habitat patches are near those

blocks while isolated patches of early-successional habitat may hold fewer individuals,

thus acting as sinks for wintering birds. Some bird species likely derived from forest or

32

forest edge habitats that were encountered in early-successional habitats include Northern

Flicker, Carolina Chickadee, Yellow-rumped Warbler, and Northern Cardinal.

Results also indicated that as site size increased, bird density decreased slightly.

A possible explanation for this is that within larger area sites there are more food and

cover resources available than there are birds to occupy them completely. I frequently

observed this situation when surveying large sites; fewer birds were seen in a given

portion of a large site while smaller sites tended to have a more concentrated bird

community.

Modeling further showed that bird density is partially explained by a site’s age; as

age increased, bird density also increased. This association with age is related to the

maturation of vegetation as plantings develop over time on a site. With this vegetational

maturation likely comes a more dense vegetation structure that is more conducive to

harboring greater numbers and more species of birds, i.e., ecological niche diversity.

With greater density of vegetation that is developed over time, more birds are able to

utilize the added amounts of food and cover and thus their densities increase. This is

similar to the hypothesis of spatial heterogeneity and corresponding increase in biotic

diversity proposed by Ricklefs (1973) and demonstrated in the field by James (1998).

James quantified the relationship between increase in faunal diversity and corresponding

increase in habitat complexity using shrubland bird communities in western Africa.

James found that spatial heterogeneity is a factor promoting high biotic diversity. James

(pers. comm.) also found this same relationship in shrubland bird communities in

northwestern Arkansas.

33

Another factor that I found to significantly influence variations in overall bird

density was the proximity of sites to the Mississippi River, as well as the interactions

with this variable and the size of a site and upper-level vegetation height (DBH). I found

that sites located closer to the Mississippi River had higher bird densities than those

further from the river. I also learned that sites smaller in area and with taller vegetation

(1 – 2 m class) located closer to the Mississippi River were more likely to host greater

densities of birds. The association between bird densities and proximity to the

Mississippi River is significant in that it demonstrates probable use of the Mississippi

River as a migratory corridor and the subsequent dispersal and settlement of wintering

birds. With greater concentrations of birds being located closer to the river this suggests

that these birds may be using the river as a corridor for travel and then disperse into the

LMAV at appropriate latitudes and settle in the first available habitat patches. Such use

of landscape features as leading-line navigation aids has been noted in previous migration

studies (Able 1995) as a secondary source of migratory assistance. Although, since most

of the species in my study typically migrate at night, the use of the Mississippi River

corridor may not be as much of a leading-line navigation aid as the adjacent lands along

the river are attractive to migrating birds. Areas adjacent to and near the Mississippi

River corridor do retain some forested woodlands, especially between the levee system

and the river itself. Because these areas are more prone to flooding and have somewhat

fewer agricultural fields than areas farther from the river (which are also slightly higher

in elevation) the associated retention of natural vegetation in these areas may serve as an

added attractant to migrating birds. This set of conditions could also contribute to

wintering bird dispersal and settlement closer to the Mississippi River corridor.

34

Variations in species richness, both observed and estimated using program

SPECRICH, throughout early-successional sites in the LMAV were explained by the

density of vegetation at lower levels (0 – 0.3 m; DBL) and at upper levels (1 – 2 m;

DBH) of the young reforested canopy. These measures are suggestive of the influence of

dense vegetation at ground level (DBL) and the influence that young trees have on

wintering birds. In both cases, greater vegetation density in theses area was positively

correlated with greater numbers of bird species. It is possible that more dense vegetation

could provide added elements of both food and cover that could account a greater

attraction to a larger number of species. The differences between rankings in candidate

models for observed and estimated species richness is likely a result of several estimates

from program SPECRICH being much higher than the observed number of species at

those sites.

In examining winter birds and habitat density, Pulliam and Mills (1977) studied

use and foraging in wintering sparrows in southeastern Arizona and presented

conclusions about the sparrow’s preference for dense vegetation as a function of

enhancing predator avoidance. Another study of wintering sparrows in northeast Georgia

concluded that density and species richness were shown to have a positive influence on

the diversity and abundance of sparrows with more “weedy” fields having both greater

numbers and species than more open fields (Watts 1996). Watts suggested that this

preference was driven by the sparrow’s use of more dense vegetation as a “visual refuge”

where the birds are more concealed from predator detection. In this study I observed

that the most common raptor in early-successional sites, Northern Harrier (Appendix),

35

tended to hunt more in open, less dense sites whereas they were less often found in older,

taller sites with greater vegetation density.

In the forested Piedmont of northeast Georgia, Pearson (1993) found that

variation in winter bird species richness and diversity was explained solely by landscape

matrix variables. Pearson found that the landscape influence extended beyond habitat

immediately adjacent to the study plots. Pearson’s study was, however, conducted in a

forest environment with only small amounts of early-successional habitat.

O’Leary and Nyberg (2000) found that birds breeding in grass-dominated fields

of Illinois were sensitive to area and experienced reduced density in small (> 15 ha) fields

partitioned by treelines. This is in contrast to my findings which show density is reduced

with increased plot size. This difference may likely be attributed to the divergence in

seasons between the two studies: breeding vs. wintering.

Prior research on the relationship of habitat structure and landscape variation

(Maurer 1986, Herkert 1994, Pearson 1993, Fletcher and Koford 2002) in explaining

observed variations of bird density and species richness provide useful comparisons for

my study. These earlier studies looked at many of the same species as my research,

though these previous works on grassland and shrubland birds were conducted during the

breeding season when these birds are more territorial and may have different habitat

dependencies.

The results of my research help to clarify some of the interactions between winter

bird community density and species richness with habitat and landscape features. The

influence that the Mississippi River has on the placement of wintering birds in the LMAV

and the effects of vegetation height and proximity to forest blocks are important in terms

36

of managing early-successional habitats in the region. These findings provide further

support of the value of early-successional habitats to wintering birds in the LMAV. In

order to manage lands for biological diversity in this region, as well as provide critical

over-winter habitats for species in need of conservation attention, it is important to

maintain habitats in early stages of re-growth in concert with on-going forest restoration

efforts to provide for the wintering needs of the greatest number of species.

37

TABLE 1. Size, age, and ownership of early-successional vegetation tracts (reforestation and wetland restoration) in the lower Mississippi River alluvial valley of Arkansas, Louisiana, and Mississippi studied during winter 2000, 2001, and 2002.

Sitea Size (ha)b Agec Ownership AR-1 62.6 4 USF&WS (Overflow NWR) AR-2 98.8 6 Private (WRP) AR-3 63.4 3 USF&WS (Bald Knob NWR) AR-4 27.1 2 USF&WS (Bald Knob NWR) AR-5 13.7 6 USF&WS (Bald Knob NWR) AR-6 208.8 6 USF&WS (Bald Knob NWR) AR-7 21.8 2 USF&WS (Bald Knob NWR) AR-8 221.8 2 USF&WS (Bald Knob NWR) AR-9 83.0 6 Private (WPR) AR-10 57.5 6 Private (WPR) AR-11 30.6 6 Private (WRP) AR-12 31.6 6 Private (WPR) AR-13 31.2 6 Private (WPR) AR-14 23.9 6 Private (WRP) AR-15 121.4 7 USF&WS (Overflow NWR) AR-16 9.7 0d Private AR-17 7.3 0d Private AR-18 20.1 0d ANHC/Private (Konecny Prairie Natural Area) AR-19 838.1 5 Private (WRP) AR-20 36.0 4 Private (WRP) AR-21 830.8 7 USF&WS (Felsenthal NWR) AR-22 16.6 0d ANHC (Roth Prairie Natural Area) AR-23 156.2 11 USF&WS (Wapanocca NWR) AR-24 60.7 6 USF&WS (Overflow NWR) AR-25 49.0 9 USF&WS (Wapanocca NWR) AR-26 233.1 6 Private (WRP) LA-1 37.2 9 USF&WS (North Louisiana Refuge Complex) LA-2 242.4 9 USF&WS (North Louisiana Refuge Complex) LA-3 161.9 8 LDW&F (Bayou Macon WMA) LA-4 36.4 11 LDW&F (Bayou Macon WMA) LA-5 143.0 0e LDW&F (Big Colewa Bayou WMA) LA-6 270.3 11 USF&WS (North Louisiana Refuge Complex) LA-7 651.1 12 USF&WS (Tensas River NWR) LA-8 792.8 5 USF&WS (Tensas River NWR) LA-9 599.0 7 USF&WS (North Louisiana Refuge Complex) LA-10 118.6 10 USF&WS (Tensas River NWR) LA-11 104.0 12 USF&WS (Grand Cote NWR) LA-12 189.4 14 USF&WS (Handy Brake NWR) LA-13 278.0 10 USF&WS (North Louisiana Refuge Complex) LA-14 186.2 4 Private (WRP)

38

TABLE 1. Continued.

Sitea Size (ha)b Agec Ownership LA-15 134.4 6 USF&WS (North Louisiana Refuge Complex) LA-16 284.9 9 Private (WRP) LA-17 246.5 7 USF&WS (North Louisiana Refuge Complex) LA-18 339.5 9 Private (WRP) LA-19 64.8 7 USF&WS (Tensas River NWR) LA-20 104.4 5 USF&WS (Tensas River NWR) MS-1 82.6 3 USF&WS (North Mississippi Refuge Complex) MS-2 112.1 3 USF&WS (North Mississippi Refuge Complex) MS-3 68.8 2 USF&WS (North Mississippi Refuge Complex) MS-4 79.0 3 USF&WS (Coldwater River NWR) MS-5 84.1 3 USF&WS (Coldwater River NWR) MS-6 219.8 2 USF&WS (North Mississippi Refuge Complex) MS-7 607.1 6 Private (WRP) MS-8 138.8 11 USF&WS (North Mississippi Refuge Complex) MS-9 80.9 4 USF&WS (Hillside NWR) MS-10 168.4 3 USF&WS (North Mississippi Refuge Complex) MS-11 202.4 10 USF&WS (Morgan Brake NWR) MS-12 86.6 4 Private (WRP) MS-13 112.1 10 Private (WRP) MS-14 91.5 3 USF&WS (North Mississippi Refuge Complex) MS-15 32.4 9 USF&WS (North Mississippi Refuge Complex) MS-16 36.4 9 USF&WS (North Mississippi Refuge Complex) MS-17 97.1 7 USF&WS (Yazoo NWR) MS-18 640.2 3 USF&WS (Morgan Brake NWR) MS-19 88.6 9 USF&WS (North Mississippi Refuge Complex) MS-20 92.3 2 USF&WS (North Mississippi Refuge Complex) MS-21 86.6 2 USF&WS (North Mississippi Refuge Complex) MS-22 1214.1 6 Private (WRP) MS-23 220.6 8 Private (WRP) a Site names correspond to locations in Figs. 2-4. b Size of unit surveyed. Some sites have multiple units; not all units were included. c Approximate time (years) between planting or conversion from agriculture and winter

2002. d Native tallgrass prairie site. e Site planted winter 2002. Abbreviations: AR = Arkansas, LA = Louisiana, MS = Mississippi, USF&WS = U.S.

Fish and Wildlife Service, ANHC = Arkansas Natural Heritage Commission, LDW&F = Louisiana Department of Wildlife and Fisheries, NWR = National Wildlife Refuge, WMA = Wildlife Management Area, WRP = Wetland Reserve Program.

39

TABLE 2. Total survey numbers and proportion of the winter bird populations for the five

most numerous bird species detected in transect surveys of early-successional habitats in the lower Mississippi River alluvial valley in winter 2000, 2001, and 2002.

2000 2001 2002 Species n % of total n % of total n % of total

Savannah Sparrow 548 8.5 1137 20.9 859 10.5 Song Sparrow 1042 16.3 497 9.1 936 11.5 Swamp Sparrow 1965 30.6 715 13.1 1478 18.1 Red-winged Blackbird 888 13.8 1261 23.2 2477 30.4 Eastern Meadowlark 480 7.5 782 14.4 785 9.6% of total detected population 76.7 80.7 80.1

40

TABLE 3. Estimated densities ( D̂ ) of all birds (birds ha-1) detected in strip transect surveys of early-successional habitats in the lower Mississippi River alluvial valley in winter 2000, 2001, and 2002. Density estimates are derived from program DISTANCE (Thomas et al. 2002b). Confidence intervals (95% CI) and analytic coefficient of variation (CV) are provided for each density estimate.

Site Year D̂ 95% CI CV % AR-1 2000 25.8 14.5 - 45.8 18.4 2001 5.8 3.0 - 11.0 29.9 AR-2 2001 12.7 4.9 - 33.1 46.2 2002 1.8 0.9 - 3.8 33.5 AR-3 2001 9.3 5.1 - 17.0 21.6 2002 11.0 1.6 - 73.9 47.9 AR-4 2001 4.4 3.1 - 6.3 17.5 2002 20.1 4.2 - 97.2 27.9 AR-5 2001 6.7 2.4 - 18.7 40.6 2002 6.0 2.6 - 13.6 32.6 AR-6 2001 6.8 4.6 - 9.9 18.3 2002 9.4 6.6 - 13.5 16.3 AR-7 2001 6.6 4.4 - 10.0 18.7 2002 7.6 4.3 - 13.3 24.2 AR-8 2001 2.1 1.1 - 3.9 28.8 2002 8.4 5.8 - 12.0 16.4 AR-9 2001 9.3 6.6 - 13.2 17.5 2002 13.3 10.6 - 16.7 11.7 AR-10 2001 18.0 10.7 - 30.4 26.0 2002 4.8 1.4 - 17.2 62.7 AR-11 2001 5.5 2.0 - 15.3 46.0 2002 0a - - AR-12 2001 9.4 5.9 - 15.0 22.4 2002 14.9 11.0 - 20.1 15.0 AR-13 2001 16.1 0.7 - 385.5 56.2 2002 0.6 0.2 - 2.3 56.7 AR-14 2001 10.0 5.0 - 20.0 32.8 2002 11.4 0.8 - 153.3 57.3 AR-15 2000 5.6 3.5 - 9.2 21.5 2001 2.7 1.8 - 4.2 21.4 2002 4.5 3.1 - 6.4 16.6 AR-16 2001 2.2 0.4 - 11.6 62.9 2002 10.0 6.1 - 16.4 24.6 AR-17 2002 2.2 0.6 - 8.1 65.0 AR-18 2001 14.2 8.3 - 24.5 25.0 2002 6.0 2.0 - 18.3 43.4

41

TABLE 3.

Continued.

Site Year D̂ 95% CI CV % AR-19 2001 1.5 0.7 - 3.3 37.3 2002 7.1 3.4 - 14.5 33.8 AR-20 2001 16.3 7.9 - 33.7 36.5 2002 9.3 1.0 - 88.0 69.6 AR-21 2000 7.9 5.0 - 12.3 21.7 2001 4.3 3.0 - 6.1 17.2 2002 6.6 5.1 - 8.5 12.5 AR-22 2000 8.5 4.5 - 16.1 26.3 2001 15.9 2.4 - 105.7 85.4 2002 6.2 3.8 - 10.3 22.6 AR-23 2000 11.6 2.9 - 45.8 48.6 2001 1.9 0.9 - 3.9 34.5 2002 3.2 1.9 - 5.2 23.7 AR-24 2000 20.8 7.9 - 54.6 40.6 2001 4.4 2.5 - 7.5 24.9 2002 17.4 6.3 - 47.7 37.3 AR-25 2000 26.8 15.3 - 46.9 26.0 2001 3.2 1.8 - 5.6 27.6 2002 7.0 3.5 - 13.9 32.5 AR-26 2001 9.0 0.5 - 177.4 44.7 2002 3.3 0.2 - 45.8 69.9 LA-1 2000 28.9 7.8 - 107.0 33.1 2001 2.4 0.3 - 18.0 37.5 2002 20.0 8.7 - 46.0 38.0 LA-2 2000 24.6 12.5 - 48.5 28.7 2001 6.3 0.3 - 144.2 81.7 2002 7.2 4.5 - 11.6 22.1 LA-3 2000 36.7 17.3 - 78.0 21.9 2001 6.9 3.3 - 14.1 19.8 2002 9.4 4.8 - 18.6 22.9 LA-4 2000 29.7 19.8 - 44.6 14.3 2001 9.7 7.5 - 12.7 13.0 2002 1.6 1.0 - 2.5 21.2 LA-5 2000 8.5 4.5 - 15.9 27.0 2001 1.3 0.6 - 2.7 37.9 2002 3.5 2.4 - 5.1 18.5 LA-6 2000 22.2 15.4 - 32.2 17.8 2001 4.5 2.8 - 7.0 22.7 2002 12.1 6.2 - 23.8 25.0 LA-7 2000 33.7 22.6 - 50.4 18.1 2001 9.0 5.8 - 14.2 21.8 2002 5.3 2.3 - 11.7 35.9

42

TABLE 3.

Continued.

Site Year D̂ 95% CI CV % LA-8 2000 8.2 4.9 - 13.5 23.0 2001 6.7 4.1 - 11.0 23.2 2002 4.5 2.3 - 8.8 32.3 LA-9 2000 63.2 46.5 - 85.7 13.5 2001 4.4 3.4 - 5.7 13.3 2002 2.3 1.2 - 4.4 27.1 LA-10 2000 2.0 1.1 - 3.5 27.3 2001 5.0 2.2 - 11.3 29.1 2002 3.5 2.2 - 5.7 22.3 LA-11 2000 26.6 18.7 - 37.9 15.9 2001 4.8 2.7 - 8.7 27.5 2002 8.5 5.8 - 12.6 17.5 LA-12 2000 15.9 7.2 - 34.8 34.6 2001 2.9 1.0 - 8.0 48.5 2002 78.0 2.3 - 25.1 53.2 LA-13 2000 13.8 4.2 - 45.7 46.1 2001 3.0 1.1 - 8.4 44.8 2002 12.8 5.2 - 31.6 37.3 LA-14 2001 5.9 3.7 - 9.3 22.4 2002 5.4 2.6 - 11.4 33.2 LA-15 2000 14.9 10.4 - 21.4 15.6 2001 1.7 0.6 - 4.7 45.5 2002 4.0 1.0 - 16.5 45.7 LA-16 2001 11.1 7.4 - 16.7 19.9 2002 2.9 1.7 - 4.9 25.6 LA-17 2000 45.7 29.3 - 71.4 19.7 2001 4.6 0.8 - 24.6 36.1 2002 1.3 0.5 - 3.6 42.8 LA-18 2001 4.5 2.9 - 7.1 21.1 2002 2.6 1.5 - 4.6 26.1 LA-19 2000 8.5 6.0 - 11.9 15.3 2001 6.2 4.6 - 8.3 15.0 2002 7.1 3.1 - 16.2 30.9 LA-20 2000 122.0 103.1 - 144.3 8.5 2001 5.5 1.0 - 29.7 69.2 2002 3.0 0.6 - 15.9 83.9 MS-1 2000 1.0 0.3 - 4.2 68.5 2002 6.0 2.5 - 14.6 41.7 MS-2 2000 5.0 2.0 - 11.9 38.7 2001 4.3 2.0 - 9.0 31.5 2002 3.3 2.1 - 5.3 21.6

43

TABLE 3.

Continued.

Site Year D̂ 95% CI CV % MS-3 2000 15.9 5.8 - 43.3 32.9 2002 1.1 0.5 - 2.5 36.8 MS-4 2000 12.1 6.0 - 24.7 25.5 2001 3.5 1.9 - 6.5 30.4 MS-5 2000 15.8 6.5 - 38.4 32.0 2001 18.2 0.2 - 1945.7 56.2 MS-6 2000 15.2 10.6 - 21.8 16.8 2001 3.7 2.2 - 6.2 26.1 2002 7.7 5.1 - 11.7 19.9 MS-7 2001 3.3 1.7 - 6.4 32.1 2002 3.7 2.2 - 6.2 25.4 MS-8 2000 12.7 9.3 - 17.3 15.2 2001 1.6 0.4 - 6.5 65.8 2002 5.8 3.5 - 9.6 24.8 MS-9 2000 12.0 5.2 - 27.6 30.0 2001 3.1 1.5 - 6.6 34.3 2002 11.7 7.9 - 17.4 17.5 MS-10 2000 7.0 4.7 - 10.4 19.0 2001 2.2 1.5 - 3.5 21.8 2002 7.4 4.8 - 11.4 21.0 MS-11 2000 9.6 5.3 - 17.6 22.3 2001 1.7 1.1 - 2.6 21.4 2002 13.7 10.2 - 18.4 14.2 MS-12 2001 2.5 1.5 - 4.3 25.7 2002 6.6 3.2 - 13.6 30.8 MS-13 2001 1.5 0.6 - 3.6 42.2 2002 1.9 0.9 - 4.0 37.3 MS-14 2000 16.4 11.2 - 24.1 16.6 2001 4.3 1.8 - 10.1 37.1 2002 4.6 2.8 - 7.5 22.9 MS-15 2000 16.4 5.2 - 52.0 22.9 2002 8.6 5.7 - 12.9 19.7 MS-16 2000 11.8 5.8 - 24.4 30.3 2001 5.0 2.7 - 9.3 29.6 MS-17 2002 5.6 3.9 - 7.9 17.6 MS-18 2000 4.9 3.6 - 6.6 14.9 MS-19 2000 17.6 6.0 - 52.1 43.3 2001 1.7 0.8 - 3.7 38.3 2002 6.7 2.7 - 16.5 41.7 MS-20 2000 6.0 0.1 - 376.7 44.1 2001 2.1 1.1 - 3.9 30.6 2002 5.8 3.0 - 11.2 30.4

44

TABLE 3.

Continued.

Site Year D̂ 95% CI CV % MS-21 2000 3.9 2.7 - 5.6 17.9 2001 2.5 1.7 - 3.7 17.9 2002 4.3 2.0 - 9.1 26.4 MS-22 2001 5.7 3.9 - 8.2 17.6 2002 9.8 5.1 - 18.9 30.4 MS-23 2001 1.8 0.8 - 4.2 39.9

a No detections occurred at this site this year.

45

TABLE 4. Estimated densities ( D̂ ) of five bird species (birds ha-1) most frequently detected on transect surveys of early-successional habitats in the lower Mississippi River alluvial valley in winter 2000, 2001, and 2002. Density estimates are derived from program DISTANCE (Thomas et al. 2002b) and are based on ≥40 individual detections. Confidence intervals (95% CI) and analytic coefficient of variation (CV) are provided for each density estimate.

Speciesa Site Year D̂ 95% CI CV % SAVS AR-6 2002 2.6 0.8 - 8.2 23.9 AR-8 2002 8.3 5.1 - 13.7 28.1 AR-21 2000 2.7 1.7 - 4.3 28.1 AR-21 2002 2.6 1.5 - 4.6 30.5 LA-5 2000 5.7 2.4 - 13.7 51.2 MS-4 2001 19.5 0.03 - 9870.4 20.3 MS-22 2002 3.7 1.8 - 7.6 23.7 SOSP AR-6 2002 3.4 2.3 - 5.2 18.3 LA-1 2000 6.4 2.8 - 14.6 30.9 LA-1 2002 2.8 0.7 - 10.8 39.2 LA-2 2000 5.2 2.3 - 11.9 19.6 LA-6 2000 4.6 3.2 - 6.7 26.9 LA-6 2002 3.5 2.1 - 5.6 20.4 LA-9 2000 16.7 11.6 - 24.0 31.6 LA-17 2000 13.0 6.9 - 24.7 20.2 MS-6 2000 3.7 2.7 - 5.1 78.7 MS-6 2002 2.2 1.3 - 3.6 16.7 SWSP AR-6 2002 2.6 1.5 - 4.7 30.4 AR-24 2002 10.3 3.1 - 34.3 37.6 AR-25 2000 14.4 5.7 - 36.6 34.4 LA-1 2000 19.8 3.2 - 123.7 25.3 LA-1 2002 10.8 5.2 - 22.1 27.8 LA-2 2000 16.6 7.4 - 37.4 16.4 LA-3 2000 20.9 9.6 - 45.5 18.7 LA-3 2001 5.4 2.7 - 11.0 38.5 LA-4 2000 10.2 1.8 - 58.6 26.7 LA-6 2000 11.8 6.8 - 20.6 21.5 LA-7 2000 7.9 5.2 - 12.1 29.6 LA-7 2002 3.5 1.8 - 6.9 27.9 LA-9 2000 22.1 14.2 - 34.5 45.5 LA-11 2002 6.6 4.4 - 10.0 28.4 LA-15 2000 7.5 4.0 - 13.9 16.0 LA-16 2001 7.1 4.6 - 11.1 46.4 LA-17 2000 26.6 14.2 - 49.9 34.7 LA-18 2001 3.7 2.1 - 6.4 27.0 LA-19 2002 3.7 1.0 - 13.4 22.8

46

TABLE 4. Continued. Species Site Year D̂ 95% CI CV % SWSP LA-20 2000 83.6 24.1 - 290.7 47.0 MS-6 2000 3.7 2.1 - 6.7 25.2 MS-6 2002 3.8 2.0 - 7.2 26.0 MS-8 2002 2.4 1.1 - 5.3 20.5 RWBL AR-21 2000 5.9 2.4 - 14.8 34.9 AR-21 2002 2.5 1.3 - 4.9 47.5 EAME AR-21 2000 2.1 1.2 - 3.7 35.5 AR-21 2001 2.2 1.4 - 3.3 35.2 AR-21 2002 1.8 1.3 - 2.4 26.2

a Abbreviations: SAVS = Savannah Sparrow, SOSP = Song Sparrow, SWSP = Swamp Sparrow, RWBL = Red-winged Blackbird, EAME = Eastern Meadowlark.

47

TABLE 5. Bird species richness observed and estimated using program SPECRICH (Hines 1996) resulting from transect surveys of early-successional habitats in the lower Mississippi River alluvial valley during winter 2000, 2001, and 2002. Standard error (SE) is provided for calculated estimates using SPECRICH.

Site Year No. of observed

species SPECRICH estimates

(no. species) SE AR-1 2000 7 9 2 2001 5 6 1.4 AR-2 2001 8 10 2 2002 4 5 1.4 AR-3 2001 9 14 3.2 2002 6 6 0 AR-4 2001 8 11 2.5 2002 7 7 0 AR-5 2001 5 6 1.4 2002 5 5 0 AR-6 2001 8 9 1.4 2002 16 20 2.8 AR-7 2001 7 8 1.4 2002 8 10 2 AR-8 2001 8 24.1 9.0 2002 8 17.2 5.5 AR-9 2001 6 18.2 7.8 2002 7 7 0 AR-10 2001 8 12 2.8 2002 4 4 0 AR-11 2001 6 7 1.4 2002 0 0 0 AR-12 2001 6 6 0 2002 6 7 1.4 AR-13 2001 7 10 2.5 2002 3 4 1.4 AR-14 2001 5 6 1.4 2002 6 10 2.8 AR-15 2000 9 13 2.8 2001 6 12.3 4.4 2002 7 9 2 AR-16 2001 3 4 1.4 2002 3 3 0 AR-17 2002 2 2 0 AR-18 2001 8 12 2.8 2002 7 27.3 10.1

48

TABLE 5. Continued.

Site Year No. of observed

species SPECRICH estimates

(no. species) SE AR-19 2001 6 24.9 9.5 2002 8 11 2.5 AR-20 2001 5 5 0 2002 3 4 1.4 AR-21 2000 18 23 3.2 2001 16 21 3.2 2002 17 38.4 9.3 AR-22 2000 8 10 2 2001 6 7 1.4 2002 5 5 0 AR-23 2000 8 10 2 2001 12 18 3.5 2002 12 16 2.8 AR-24 2000 5 5 0 2001 7 9 2 2002 8 10 2 AR-25 2000 13 22.4 5.2 2001 11 15 2.8 2002 10 38.4 12.0 AR-26 2001 8 9 1.4 2002 4 6 2 LA-1 2000 5 6 1.4 2001 8 10 2 2002 12 17 3.2 LA-2 2000 11 13 2 2001 6 8 2 2002 8 9 1.4 LA-3 2000 22 41.8 8.4 2001 11 15 2.8 2002 7 10 2.8 LA-4 2000 17 30.3 6.2 2001 10 34.5 11.1 2002 5 7 2 LA-5 2000 10 21.0 5.9 2001 7 10 2.5 2002 7 9 2 LA-6 2000 12 16 2.8 2001 12 44.6 12.9 2002 13 18 3.2

49

TABLE 5. Continued.

Site Year No. of observed

species SPECRICH estimates

(no. species) SE LA-7 2000 10 19.7 5.0 2001 11 15 2.8 2002 7 10 2.5 LA-8 2000 16 21 3.2 2001 10 12 2 2002 10 11 1.4 LA-9 2000 7 8 1.4 2001 9 12 2.5 2002 10 27.8 8.7 LA-10 2000 13 18 3.2 2001 15 20 3.2 2002 16 35.6 8.8 LA-11 2000 3 4 1.4 2001 8 11 2.5 2002 9 10 1.4 LA-12 2000 5 17.2 7.8 2001 7 12 3.2 2002 4 5 1.4 LA-13 2000 8 11 2.5 2001 5 5 0 2002 9 12 2.5 LA-14 2001 6 7 1.4 2002 7 24.9 9.1 LA-15 2000 10 12 2 2001 5 14.5 5.7 2002 7 16.3 5.5 LA-16 2001 9 11 2 2002 14 17 2.5 LA-17 2000 12 16 2.8 2001 12 19 3.7 2002 7 10 2.5 LA-18 2001 9 13 2.8 2002 9 13 2.8 LA-19 2000 12 16 2.8 2001 9 14 3.2 2002 8 11 2.5 LA-20 2000 7 13.2 4.3 2001 2 2 0 2002 3 4 1.4 MS-1 2000 4 18.1 8 2002 8 10 2

50

TABLE 5. Continued.

Site Year No. of observed

species SPECRICH estimates

(no. species) SE MS-2 2000 7 11 2.8 2001 10 14 3.2 2002 10 13 2.5 MS-3 2000 7 10 2.5 2002 6 9 2.5 MS-4 2000 11 19.9 5.4 2001 6 6 0 MS-5 2000 8 9 1.4 2001 7 8 1.4 MS-6 2000 9 9 0 2001 11 39.5 12 2002 9 11 2 MS-7 2001 5 5 0 2002 8 11 2.5 MS-8 2000 11 15 2.8 2001 16 37.5 8.3 2002 18 24.6 3.8 MS-9 2000 4 4 0 2001 6 8 2 2002 8 11 2.5 MS-10 2000 10 16.3 4.4 2001 10 12 2 2002 9 33.3 11.1 MS-11 2000 9 11 2 2001 12 34.7 10.4 2002 12 16 2.8 MS-12 2001 6 6 0 2002 8 11 2.5 MS-13 2001 7 16.3 5.6 2002 9 25.7 8.6 MS-14 2000 8 12 2.8 2001 12 14 2 2002 8 10 2 MS-15 2000 7 16.3 5.5 2002 8 9 1.4 MS-16 2000 7 9 2 2001 4 5 1.4 MS-17 2002 19 22 2.5 MS-18 2000 12 16 2.8

51

TABLE 5. Continued.

Site Year No. of observed

species SPECRICH estimates

(no. species) SE MS-19 2000 11 16 3.2 2001 6 10 2.8 2002 5 7 2 MS-20 2000 4 5 1.4 2001 8 12 2.8 2002 13 15 2 MS-21 2000 6 7 1.4 2001 7 18.2 6.7 2002 8 9 1.4 MS-22 2001 11 13 2 2002 8 10 2 MS-23 2001 5 7 2

52

TABLE 6. Linear regression models and their corresponding AICc scores for estimated density of birds observed in transect surveys of early-successional habitats in the lower Mississippi River alluvial valley in winter 2000, 2001, and 2002. Models are listed in rank order of AICc scores with ∆AICc indicating the difference between each model and the model with the lowest AICc value. Number of parameters for each model is represented by k.

Rank Model k AICc ∆AICc

1 Size, Forest 4 461.1 0.0 2 Forest 3 461.4 0.3 3 Age, Forest 4 462.3 1.2 4 MissR, Forest 4 463.3 2.2 5 MissR × DBL, Forest 4 463.4 2.3 6 MissR × DBH, Forest 4 463.4 2.3 7 Age|Size, Forest 6 463.8 2.9 8 MissR × Size 3 463.9 2.8 9 Size 3 464.8 3.7 10 DBH 3 465.0 3.2 11 DBL 3 466.0 4.9 12 MissR × DBL 3 466.2 5.1 13 MissR × DBH 3 466.2 5.1 14 Age|Size 5 466.9 5.8 15 DBL, DBH 4 467.2 6.1 16 DBL, DBH, Age|Size, Forest 8 468.2 7.1 17 DBL, DBH, Age|Size, MissR, Forest 9 470.2 9.1

Explanatory variables: Age = time (years) since last treatment (planting) or since cultivation up to winter 2002, DBL = average number of squares obscured on density board lower tier (0 – 0.3 m), DBH = average number of squares obscured on density board highest tier (1 – 2 m), MissR = minimum distance from Mississippi River, Size = area of site (ha), and Forest = minimum distance from nearest forest patch >1 ha.

53

TABLE 7. Linear regression models and their corresponding AICc scores for observed species richness of birds observed in transect surveys of early-successional habitats in the lower Mississippi River alluvial valley in winter 2000, 2001, and 2002. Models are listed in rank order of AICc scores with ∆AICc indicating the difference between each model and the model with the lowest AICc value. Number of parameters for each model is represented by k.

Rank Model k AICc ∆AICc

1 DBL, DBH 4 380.8 0.0 2 DBL 3 381.0 0.2 3 DBH 3 381.7 0.9 4 MissR, Forest 4 383.0 2.2 5 Size 3 383.4 2.6 6 Age|Size 5 384.1 3.3 7 Age, Forest 4 384.4 3.6 8 MissR × Size 3 384.6 3.8 9 MissR × DBL 3 385.3 4.5 10 Size, Forest 4 385.3 4.5 11 Age|Size, Forest 6 385.7 4.9 12 DBL, DBH, Age|Size, Forest 8 386.3 5.5 13 MissR × DBH 3 386.8 6.0 14 Forest 3 387.0 6.2 15 DBL, DBH, Age|Size, MissR, Forest 9 387.3 6.5 16 MissR × DBL, Forest 4 387.4 6.6 17 MissR × DBH, Forest 4 388.9 8.1

Explanatory variables: Age = time (years) since last treatment (planting) or since cultivation up to winter 2002, DBL = average number of squares obscured on density board lower tier (0 – 0.3 m), DBH = average number of squares obscured on density board highest tier (1 – 2 m), MissR = minimum distance from Mississippi River, Size = area of site (ha), and Forest = minimum distance from nearest forest patch >1 ha.

54

TABLE 8. Linear regression models and their corresponding AICc scores for estimates of species richness using program SPECRICH (Hines 1996) based on birds observed in strip transect surveys of early-successional habitats in the lower Mississippi River alluvial valley in winter 2000, 2001, and 2002. Models are listed in rank order of AICc scores with ∆AICc indicating the difference between each model and the model with the lowest AICc value. Number of parameters for each model is represented by k.

Rank Model k AICc ∆AICc

1 MissR, Forest 4 480.2 0.0 2 MissR × DBL 3 483.8 3.6 3 MissR × DBL, Forest 4 485.3 5.1 4 Size 3 485.4 5.2 5 DBH 3 485.8 5.6 6 DBL 3 486.3 6.1 7 DBL, DBH 4 487.2 7.0 8 MissR × Size 3 487.2 7.0 9 Size, Forest 4 487.3 7.1 10 Forest 3 487.8 7.6 11 Age|Size 5 488.2 8.0 12 MissR × DBH 3 488.4 8.2 13 Age, Forest 4 488.9 8.7 14 MissR × DBH, Forest 4 490.0 9.8 15 DBL, DBH, Age|Size, MissR, Forest 9 490.4 10.2 16 Age|Size, Forest 6 490.5 10.3 17 DBL, DBH, Age|Size, Forest 8 493.4 13.2

Explanatory variables: Age = time (years) since last treatment (planting) or since cultivation up to winter 2002, DBL = average number of squares obscured on density board lower tier (0 – 0.3 m), DBH = average number of squares obscured on density board highest tier (1 – 2 m), MissR = minimum distance from Mississippi River, Size = area of site (ha), and Forest = minimum distance from nearest forest patch >1 ha.

55

TABLE 9. Results of Poisson regression using habitat and landscape variables as predictors of density and species richness in winter bird communities of early-successional habitats in the lower Mississippi River alluvial valley during winter 2000, 2001, and 2002.

Model Variable

Parameter estimate SE χ2 P

Estimated Density a DBH 0.088 0.016 30.84 < 0.0001Age 0.046 0.014 10.43 0.001 Size -0.0006 0.0003 5.21 0.02 Forest -0.277 0.076 13.26 0.0003 MissR 0.0116 0.002 34.58 < 0.0001MissR × DBH -0.002 0.0004 22.57 < 0.0001MissR × Size < -0.00001 0 11.99 0.0005

Observed Species Richness DBL -0.1001 0.06 2.82 0.09 DBH 0.069 0.019 13.27 0.0003 MissR -0.034 0.013 7.14 0.008 Size -0.0003 0.0003 1.03 0.31 MissR × DBL 0.002 0.001 5.9 0.02 MissR × DBH -0.001 0.0004 10.0 0.002 MissR × Size < 0.00001 0 7.66 0.006

Estimated Species Richness b DBL -0.263 0.04 35.33 < 0.0001DBH 0.07 0.01 22.54 < 0.0001MissR -0.07 0.01 50.15 < 0.0001Size -0.0005 0.0002 5.41 0.02 MissR × DBL 0.005 0.0008 39.76 < 0.0001MissR × DBH -0.001 0.0003 16.35 < 0.0001MissR × Size < 0.00001 0 19.94 < 0.0001

a Estimated using program DISTANCE. b Estimated using program SPECRICH. Explanatory variables: Age = time (years) since last treatment (planting) or since cultivation up to winter 2002, DBL = average number of squares obscured on density board lower tier (0 – 0.3 m), DBH = average number of squares obscured on density board highest tier (1 – 2 m), MissR = minimum distance from Mississippi River, Size = area (ha) of site, and Forest = minimum distance from nearest forest patch >1 ha.

56

FIG. 1. Study sites of early-successional habitats in the lower Mississippi River alluvial valley (shaded region) of Arkansas, Louisiana, and Mississippi. Bird communities in 69 study areas, represented by triangles, were surveyed across winter 2000, 2001, and 2002.

57

FIG. 2. Location of study areas in the northern portion of the lower Mississippi River alluvial valley (shaded area). Bird communities in these early-successional habitats were surveyed across winter 2000, 2001, and 2002. Site names correspond with descriptions in Table 1.

58

FIG. 3. Location of study areas in the central section of the lower Mississippi River alluvial valley (shaded region). Bird communities in these early-successional habitats were surveyed across winter 2000, 2001, and 2002. Site names correspond with descriptions in Table 1.

59

FIG. 4. Location of study areas in the southern portion of the lower Mississippi River alluvial valley (shaded area). Bird communities in these early-successional habitats were surveyed across winter 2000, 2001, and 2002. Site names correspond with descriptions in Table 1.

60

FIG. 5. Relationship between estimated bird density (birds ha-1) and distance (km) to nearest forest block > 1 ha in area for 67 sites of early-successional habitat in the lower Mississippi River alluvial valley among three winters (1999-2002). Solid line represents least-squares regression between estimated bird density and distance to forest blocks (r = 0.26, r2 = 0.07, F1, 65 = 5.04, P = 0.03, y = 9.85 – 2.86 x).

0

5

10

15

20

25

30

35

40

Bird

den

sity

(bird

s ha

-1)

0 0.5 1 1.5 2 2.5

Distance to forest (km)

61

FIG. 6. Relationship between vegetation height in the range 0 – 0.3 m (DBL) and observed bird species richness for early-successional habitat sites in the lower Mississippi River alluvial valley among three winters (1999-2002). Units for variable DBL correspond to a 15-square grid on a density board. Solid line represents least-squares regression between increasing vegetation height and observed species richness (r = 0.30, r2 = 0.09, F1, 65 = 6.27, P = 0.01, y = 2.60 + 0.45 x).

0

5

10

15

20

Num

ber o

f spe

cies

0 5 10 15

DBL

62

FIG. 7. Relationship between vegetation height in the range 1 – 2 m (DBH) and observed bird species richness for early-successional habitat sites in the lower Mississippi River alluvial valley among three winters (1999-2002). Units for DBH correspond to a 25-square grid on a density board. Solid line represents least-squares regression between increasing vegetation height and observed species richness (r = 0.28, r2 = 0.08, F1, 65 = 5.51, P = 0.02, y = 7.39 + 0.23 x).

0

5

10

15

20

Num

ber o

f spe

cies

0 5 10 15 20 25

DBH

63

FIG. 8. Relationship between distance (km) to the Mississippi River and estimated number of bird species using program SPECRICH (Hines 1996) for early-successional habitat sites in the lower Mississippi River alluvial valley among three winters (1999-2002). Solid line represents least-squares regression between increasing distance from the Mississippi River and estimated species richness (r = 0.36, r2 = 0.13, F1, 65 = 9.87, P = 0.003, y = 18.68 – 0.10 x).

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100 120 140

Distance to Mississippi River (km)

Num

ber o

f spe

cies

(SP

EC

RIC

H)

64

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69

APPENDIX. Species and number of individual birds detected in transect surveys of early-successional habitats throughout the lower Mississippi River alluvial valley of Arkansas, Louisiana, and Mississippi in winter 2000, 2001, and 2002.

Species 2000 2001 2002 Gadwall (Anas strepera) 11 0 0

Mallard (Anas platyrhynchos) 92 35 95

Northern Shoveler (Anas clypeata) 0 0 7

Northern Bobwhite (Colinus virginianus) 0 9 28

American Bittern (Botaurus lentiginosus) 0 1 2

Great Blue Heron (Ardea herodias) 0 1 1

Turkey Vulture (Cathartes aura) 0 1 3

Northern Harrier (Circus cyaneus) 49 73 81

Sharp-shinned Hawk (Accipiter striatus) 1 0 2

Cooper’s Hawk (Accipeter cooperii) 1 0 2

Red-tailed Hawk (Buteo jamaicensis) 17 17 28

American Kestrel (Falco sparverius) 0 3 1

Killdeer (Charadrius vociferus) 7 2 12

Wilson’s Snipe (Gallinago delicata) 30 66 37

American Woodcock (Scolopax minor) 1 0 3

Mourning Dove (Zenaida macroura) 22 28 91

Barn Owl (Tyto alba) 1 0 0

Short-eared Owl (Asio flammeus) 30 71 37

Red-headed Woodpecker (Melanerpes erythrocephalus) 0 2 0

Red-bellied Woodpecker (Melanerpes carolinus) 1 3 1

Downy Woodpecker (Picoides pubescens) 1 6 5

Northern Flicker (Colaptes auratus) 4 40 18

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APPENDIX. Continued.

Species 2000 2001 2002 Eastern Phoebe (Sayornis phoebe) 4 4 7

Loggerhead Shrike (Lanius ludovicianus) 16 32 23

Blue Jay (Cyanocitta cristata) 0 6 0

American Crow (Corvus brachyrhynchos) 0 1 0

Carolina Chickadee (Poecile carolinensis) 9 20 26

Tufted Titmouse (Baeolophus bicolor) 1 3 0

Carolina Wren (Thryothorus ludovicianus) 3 2 31

Bewick’s Wren (Thryothorus bewickii) 1 0 0

House Wren (Troglodytes aedon) 0 1 0

Winter Wren (Troglodytes troglodytes) 0 1 0

Sedge Wren (Cistothorus platensis) 167 164 171

Marsh Wren (Cistothorus palustris) 4 1 7

Ruby-crowned Kinglet (Regulus calendula) 1 4 4

Eastern Bluebird (Sialis sialis) 0 0 5

American Robin (Turdus migratorius) 0 0 1

Brown Thrasher (Toxostoma rufum) 0 0 1

Northern Mockingbird (Mimus polyglottos) 6 10 18

American Pipit (Anthus rubescens) 1 1 1

Orange-crowned Warbler (Vermivora celata) 0 2 0

Yellow-rumped Warbler (Dendroica coronata) 2 16 4

Palm Warbler (Dendroica palmarum) 1 0 1

Common Yellowthroat (Geothlypis trichas) 2 1 1

71

APPENDIX. Continued.

Species 2000 2001 2002 Eastern Towhee (Pipilo erythrophthalmus) 0 4 13

American Tree Sparrow (Spizella arborea) 0 12 0

Chipping Sparrow (Spizella passerina) 1 0 0

Field Sparrow (Spizella pusilla) 17 16 54

Vesper Sparrow (Pooecetes gramineus) 0 0 1

Savannah Sparrow (Passerculus sandwichensis) 548 1137 859

Le Conte’s Sparrow (Ammodramus leconteii) 79 83 41

Fox Sparrow (Passerella iliaca) 21 2 14

Song Sparrow (Melospiza melodia) 1042 497 936

Lincoln’s Sparrow (Melospiza lincolnii) 1 0 0

Swamp Sparrow (Melospiza georgiana) 1965 715 1478

White-throated Sparrow (Zonotrichia albicollis) 30 4 10

White-crowned Sparrow (Zonotrichia leucophrys) 4 4 40

sparrow sp. 808 129 582 Northern Cardinal (Cardinalis cardinalis) 5 38 52

Red-winged Blackbird (Agelaius phoeniceus) 888 1261 2477

Eastern Meadowlark (Sturnella magna) 480 782 785

Rusty Blackbird (Euphagus carolinus) 22 16 7

blackbird sp. 5 47 1 Common Grackle (Quiscalus quiscula) 1 0 0

Brown-headed Cowbird (Molothrus ater) 0 2 0

American Goldfinch (Carduelis tristis) 3 58 0

72

CHAPTER 2

WINTER HABITAT AFFINITIES OF TWO GRASSLAND BIRD SPECIES

IN THE LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY:

SEDGE WREN AND LE CONTE’S SPARROW

This chapter written in the format of the journal The Wilson Bulletin.

73

WINTER HABITAT AFFINITIES OF TWO GRASSLAND BIRD SPECIES

IN THE LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY:

SEDGE WREN AND LE CONTE’S SPARROW

ABSTRACT.—Grassland bird populations have been declining in recent years.

Research directed at the reasons for declines have chiefly focused on habitat loss and

elements of the bird’s breeding biology; consequently little research has been done to

understand the winter habitat requirements of these species. A recent surge of forest

restoration activities in the lower Mississippi River alluvial valley (LMAV) have resulted

in increasing amounts of early-successional habitats available to wintering birds. Two

species using these habitats in winter that are of conservation concern, Sedge Wren

(Cistothorus platensis) and Le Conte’s Sparrow (Ammodramus leconteii), were the

subject of winter habitat quantification work in 2002 at 20 locations in the LMAV of

Arkansas, Louisiana, and Mississippi. Measures of habitat included: vertical and

horizontal structure (density and diversity), litter depth, vegetation height, and

groundcover classes. I collected an equal number of randomly selected samples (Sedge

Wren, n = 24; Le Conte’s Sparrow, n = 22) for comparison as reference to available

habitat. I used partitions of Mahalanobis D2 to discern those habitat features that varied

the least across study sites and therefore were selected by the birds. Sedge Wrens over-

wintering in the LMAV preferred uniformity in vegetation height; selected for a balance

between plant litter depth and vertical vegetation diversity; and favored a corresponding

increase in composition of plant litter and coverage of forbs as ground covers. Analysis

of data also indicated that Sedge Wrens were likely to occur in areas with grass cover as a

74

significant proportion of the available substrate. Le Conte’s Sparrows favored a

corresponding increase in grass and plant litter as ground covers; occurred when a

parallel increase between % forbs and % grass were present; and were present when

mean vegetation height increased along with an increase in % plant litter and % forbs

coverage. Le Conte’s Sparrows also were predicted to occur in sites where vegetation

was shorter than the available habitat along with low amounts of bare ground. My

research should help to provide a better understanding of the specific habitats preferred

by wintering Sedge Wrens and Le Conte’s Sparrows, thus being of use to land managers

in targeting winter habitat needs of these two bird species of conservation concern.

Populations of grassland birds in eastern North America have experienced

declines since at least the mid 1960s (Sauer and Droege 1992, Sauer et al. 2003). Many

species of shrubland birds that are still widespread have suffered continuous, steep

population declines during the past few decades as well (Askins 1998). Most of the

studies that have observed population reductions for early-successional specialists have

focused on species that only breed in North America and their breeding habitats. Little

attention has been given to the numerous short-distance migrant bird species that over-

winter in the southeastern United States (Herkert and Knopf 1998). Many of these

species breed in grasslands and early-successional habitats of middle and northern

biomes of North America and, compared to true Nearctic-Neotropical migrants, only

migrate short distances to over-winter.

75

In the southeastern United States is the expansive floodplain of the lower

Mississippi River alluvial valley (LMAV), an area that has seen extensive clearing of

bottomland hardwood forest for agriculture, and subsequently has vast potential for

restoring early-successional vegetation. The LMAV has experienced the most

widespread loss of bottomland hardwood forests in the United States (Hefner and Brown

1985, Hefner et al. 1994, Stanturf et al. 2000). Only about 2.8 million ha of an estimated

original 10 million ha of bottomland hardwood forest may remain in this region (King

and Keeland 1999). As a result, the region has been targeted for the most extensive forest

restoration effort in the United States (Stanturf et al. 2000).

Two species, which are comparatively uncommon in occurrence throughout the

LMAV and merit conservation attention, were the focus in the present study of wintering

habitat. Sedge Wren (Cistothorus platensis) and Le Conte’s Sparrow (Ammodramus

leconteii), which co-occur in similar habitats throughout the LMAV, were chosen based

on preliminary avian surveys conducted in winter 2000 (Chapter 1) that showed their

relatively low abundance in the region. These two species have also been identified as

species of conservation concern by the U. S. Fish and Wildlife Service (2002). Because

of the concern in the rate of decline, or the potential for decline, more information is

needed regarding their habitat requirements, particularly in winter.

I designed this study to quantitatively assess the winter habitat preferences of

Sedge Wren and Le Conte’s Sparrow and thus to provide needed information to land

managers in the position of providing appropriate habitat. This work describes structural

complexity and ground cover composition that were selected by these two species.

Further, this work adds to the limited amount of knowledge on winter habitat of Le

76

Conte’s Sparrow (Lowther 1996) and is the first to describe winter habitat preferences for

Sedge Wrens (Herkert et al. 2001).

STUDY AREA

I conducted fieldwork at 20 locations throughout the lower Mississippi River

alluvial valley (Table 1, Fig. 1) in January and February 2002. Of the 20 sites visited, ten

were in Arkansas, six in Louisiana, and four in Mississippi. These sites were located on

national wildlife refuges, state wildlife management Areas, state natural areas, and

private lands enrolled in the Wetland Reserve Program (WRP). All sites were all early-

successional in nature with most being in a grassland or scrub-shrub stage of

development. Most sites were previously in agricultural tillage prior to recently being set

aside and planted in bottomland hardwood tree species such as Nuttall oak (Quercus

nuttallii), water oak (Q. nigra), and willow oak (Q. phellos). Growing among planted

trees were assorted herbaceous vegetation species characteristic of wet old fields. These

areas were often dominated by plants such as goldenrod (Solidago spp.), rushes (Juncus

spp.), smartweed (Polygonum spp.), and various grasses (Andropogon spp., Tridens spp.,

Panicum spp., Eragrostis spp.). Two sites, Konecny Prairie Natural Area and Roth

Prairie Natural Area in Arkansas (Fig. 1, open squares in east-central Arkansas) are relict

tallgrass prairies dominated by grasses such as big bluestem (Andropogon gerardii), little

bluestem (A. scoparius), switchgrass (Panicum virgatum), and Indian grass

(Shorghastrum nutans).

77

METHODS

I obtained habitat measurements from winter territories (Herkert et al. 2001,

Lowther 1996) of 24 Sedge Wrens and 22 Le Conte’s Sparrows throughout the study

region. I determined the site for habitat measurements by locating the individual birds

during the course of area surveys (see Chapter 1). When individuals of either of the two

species were first encountered, I marked their locations with flagging and this area was

presumed to be a winter territory and consequently the center for measurement of

vegetation characteristics.

At the point where a bird’s winter territory was marked, I took five vegetational

samples. Beginning at the initial point where a bird was located, I obtained vegetational

data using methods modified from Whitmore (1981) that include measuring vertical

density, vertical diversity, horizontal density, horizontal diversity, total vegetation

density, litter depth, percent ground cover, mean height, and maximum height. After I

made the initial measurements centered on a bird’s location, four replicate measurements

were then taken 10 m away in each of the cardinal directions from the central point. I

assumed that the replicate samples were also located within the bird’s winter territory.

I determined vertical density and diversity by dropping a 2-m long polyvinyl

chloride (PVC) pipe (19 mm diameter) marked in 10-cm intervals vertically into the

vegetation at the point where a bird was first located. I recorded the number of

vegetative pieces (stems, leaves, stalks, etc.) that touched the pipe in each of the 20

height intervals. The total number of hits for the 20 intervals was the vertical density

from which I later calculated the vertical diversity using the Shannon-Weaver diversity

index (Shannon and Weaver 1963) for the totals in each of the height classes.

78

I measured horizontal density by holding a 1-m long PVC plastic pipe (19 mm

diameter) parallel to the ground and against the vertical pipe at each of the 20 intervals

described above. I placed the horizontal pipe in an east – west orientation at each

location for consistency. I recorded the number of vegetative pieces that came in contact

with the horizontal pipe for each of the 20 vertical height intervals. The total of the hits

recorded for the 20 height intervals was the horizontal density and from this I later

calculated the horizontal diversity again using the Shannon-Weaver diversity index for

the totals in all height classes. I then derived a measure of total vegetation density by

summing all of the hits in each of the 20 horizontal classes in all five samples.

I measured litter depth as the vertical depth (cm) of dead plant matter

accumulated on the ground at the point where the vertical pipe was placed. I defined

dead plant matter as that which was loose from any of the surrounding plants and which

formed a mat on the ground surface.

I determined percent ground cover by placing a 0.5 m x 0.5 m PVC pipe quadrat

frame centered on the ground at the point where the vertical pipe was dropped through

the vegetation. I then qualitatively estimated the percent coverage in that quadrat for the

following six categories: forbs, grass, bare ground, woody stems, litter, and other. The

ground cover category “other” typically included three primary types: rushes, moss, and

patches of standing water.

I recorded maximum height for the plot as the highest point of vegetation above

each of the five 0.5 m x 0.5 m quadrat frames used for ground cover measurements. I

determined mean height as the mean of the maximum height measurements for the five

samples.

79

I made an equal number of random vegetation samples 100 m from the bird-

centered plots. The locations of these samples were determined by using a table of

random numbers ranging from 0 to 360 that represented compass bearings. The random

numbers used were the compass direction in which the companion samples were located.

If a random sample landed at a location outside the study field, this sample point was

rejected and a new random sample was taken. In selecting random samples, the

assumption was made that the bird species did not inhabit that randomly selected location

at any time during the winter.

Prior to statistical analysis, I transformed data from the six ground cover

measurements into log ratios using geometric mean as the divisor (Aitchison 1986). I

conducted analytical analyses of the resulting data using partitioning of Mahalanobis D2

(Dunn and Duncan 2000, Duncan and Dunn 2001, Rotenberry et al. 2002). This

application of partitioning of Mahalanobis D2 involves partitioning D2 into orthogonal

components and selecting an informative (k) subset of principal components that

correspond to the species’ requirements (Rotenberry et al. 2002). This procedure is

applied to the set of habitat variables measured at locations where a specific bird species

was found. The last two or three principal components with eigenvalues of the

correlation matrix greater than zero (k) define habitat requirements based on those

variables that were chosen. The last two or three principal components were chosen

because they contain information about which characteristics a given organism wants to

vary the least in their environment (Jennelle 2000, Rotenberry et al. 2002). The most

heavily weighted variable loadings within the given eigenvectors were reviewed for their

role in defining the major restrictions implied by a component.

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Because multivariate normality is difficult to assess (Browning et al. in press), I

evaluated the P-value assumption by plotting the empirical cumulative distribution

function for both bird-selected sites and randomly selected sites in conjunction with the

approximating χ2 (k) distribution. The χ2 (k) distribution approximated the empirical

cumulative distribution function of both bird-selected and randomly selected sites with

some separation in the upper distribution for randomly selected sites. From these

distributions I concluded that it was feasible to transform D2 (k) values to P-values.

Additionally I conducted logistic regression with a stepwise procedure on the

mean-centered vegetation coefficients for both bird-selected and randomly selected

habitat variables from the above data to determine which set of explanatory variables best

predicted habitat usage by wintering Sedge Wrens and Le Conte’s Sparrows. The

probability of a bird’s occurrence within a site (probability of occupancy) was modeled

as the response. All 12 variables used in the above analyses were initially included as

model predictors. This modeling was set up as a block effect where corresponding bird

and random samples were paired. I conducted the above statistical analyses using SAS

version 8.2 (procedures PRINCOMP, LOGISTIC; SAS Institute 1999) and JMP version

5.0.1 (SAS Institute 2002).

RESULTS

Table 2 reports a listing of the mean values for 12 structural habitat variables for

territories and respective random samples (nonterritories) measured for both Sedge

Wrens and Le Conte’s Sparrows throughout the LMAV. These values were based on a

sample size of 24 for Sedge Wren and 22 for Le Conte’s Sparrow (Table 1, Fig. 1).

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Eigenvalues of the first four principle components accounted for approximately

76% of the total variation in the habitat dataset for Sedge Wrens and approximately 80%

for Le Conte’s Sparrows. The correlation matrix was full rank with the last eigenvalue

being zero.

Given the criterion that the smallest non-zero eigenvalues are believed to

represent species requirements (Dunn and Duncan 2000), D2 (k) should be based on PCs

9 through 11 (Table 3). I calculated D2 (k = 3) solutions using PCs 9 – 11 for the bird-

selected samples and randomly collected samples (Tables 4 – 7). Comparisons

correspond by sample number between tables. The calculated D2 (k = 3) solution for sites

where birds were located resulted in P-values > 0.05, the generally-accepted level for

statistical significance (α = 0.05), for Sedge Wren sites (Table 4) and Le Conte’s Sparrow

sites (Table 6) in > 90% of the samples. The calculated D2 (k = 3) solution for random

samples resulted in P-values > 0.05 in approximately 30% of the samples for Sedge

Wrens (Table 5) and approximately 45% for Le Conte’s Sparrows (Table 7). Based on

the later solution it could be interpreted that several of the random samples were

incorrectly classified as being occupied by each species. However, no further field work

was done to verify that these species were never present in the randomly selected

locations. Selection of the random sites followed the assumption that if the species was

not detected at the time the sample was made, it was not present the entire winter. Based

on the D2 (k = 3) solution for the random sites, one could interpret this as a violation of

the above assumption of non-occupancy for both the Sedge Wren and Le Conte’s

Sparrow.

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The highest loaded variables (> |0.40|) associated with the last three principal

components (k = 3) indicated the most restrictive structural vegetation factors for Sedge

Wren and Le Conte’s Sparrow. The eigenvectors for PCs 9, 10, and 11 are shown in

Table 3. Variables that define the distance of any particular site from the idealized

species requirement for Sedge Wren include mean vegetation height and maximum

vegetation height. For these variables to keep PC11 = 0 (the idealized requirement) then

as the mean height increases in a Sedge Wren’s winter territory, so must the maximum

height suggesting that Sedge Wrens prefer uniformity in vegetation height within winter

territories. Loadings from PC10 suggest that as plant litter increases in depth, wintering

Sedge Wrens prefer that vertical vegetation diversity decrease, and the converse. The

PC9 loadings indicated that if % plant litter as ground cover increases so also must the %

forbs as ground cover for preferred Sedge Wren habitat. For Le Conte’s Sparrow the

important variables in this species’ idealized habitat requirements include % grass cover

and % plant litter. For PC11 = 0 then as % grass cover increases so must the % plant

litter as ground cover. For PC10, as % forbs cover increases, % grass cover must also

increase to satisfy the idealized habitat requirements for Le Conte’s Sparrow. The

loadings for PC9 suggest that as mean vegetation height increases in conjunction with %

plant litter, so must the % forbs cover increase.

From the 12 variables introduced into logistic regression modeling (Table 8) for

Sedge Wrens, two were significant predictors of occupancy within a site: % grass cover

(χ2 = 4.9, df = 1, P = 0.03) and % other ground cover types (χ2 = 4.1, df = 1, P = 0.04).

The probability that Sedge Wrens occupied a site increased with an increasing proportion

83

of grass cover and other cover types (primarily rushes, moss, and standing water) that

differed from the five primary types measured.

Logistic regression modeling (Table 8) for Le Conte’s Sparrows resulted in four

significant predictors of occupancy within a site: mean height (χ2 = 9.6, df = 1, P =

0.002), horizontal diversity (χ2 = 7.7, df = 1, P = 0.005), % grass cover (χ2 = 9.8, df = 1,

P = 0.002), and % bare ground (χ2 = 6.8, df = 1, P = 0.009). Modeling indicated that the

probability that Le Conte’s Sparrows occupied a site increased with increasing

proportions of grass cover and a consistent horizontal diversity of vegetation structure.

The probability of site occupancy also increased with a decrease in mean vegetation

height and a decreasing proportion of bare ground.

DISCUSSION

Analysis of the partitions of Mahalanobis D2 (k) identified specific vegetation

features that influenced habitat suitability for Sedge Wren and Le Conte’s Sparrow. I

found that several distinct structural and compositional vegetation characteristics were

identified as elements of habitat suitability and preferences for these two bird species.

Of the variables I measured in Sedge Wren winter territories, those identified as

important to the species’ idealized requirements were mean height and maximum height.

Based on the sign of the eigenvector for these variables, a dependent relationship of the

two was such that as the mean vegetation height increased so must the maximum

vegetation height. This relationship suggests that Sedge Wrens are likely to occupy

suitable habitat that has uniformity in height. Vertical vegetation diversity and depth of

plant litter were also found to be of importance in Sedge Wren winter territories. As

84

plant litter increased in depth, Sedge Wrens preferred that vertical vegetation diversity

decrease. The converse of this relationship is also applicable. Such an association could

be an indication that Sedge Wrens have preferences for both a dense mat of plant litter at

ground surface and also a dense vegetative overstory. Each of these habitat features

could relate to a desire by the species for dense cover in which to forage or escape

predation, or both. This analysis further identified a relationship between % plant litter

and % forbs cover: for idealized Sedge Wren winter habitat, as % plant litter increase so

also must the % forbs cover. This relationship is consistent with processes of plant

succession in that as areas increase in coverage by forbs, the amount of plant litter that

falls from that stand of forbs will also increase.

Partitions of Mahalanobis D2 (k) for habitat variables measured for Le Conte’s

Sparrow suggest that the important habitat features in this species’ idealized winter

habitat include % grass cover and % plant litter. The proportion of grass within a Le

Conte’s Sparrow’s winter territory must increase as the proportion of plant litter

increased. Combinations of these two variables suggest that Le Conte’s Sparrows

selected winter habitat patches that had high amounts of grass, consistent with other

observations (Lowther 1996). Le Conte’s Sparrows were also likely to prefer an increase

in cover of forbs in addition to increasing grass cover in idealized habitats. My analysis

further noted that with an increase in mean vegetation height and an increase in the

proportion of plant litter in a territory, the amount of forbs within a territory must also

increase in order to satisfy the idealized habitat requirement.

Logistic regression models gave some support to the findings from partitioning of

Mahalanobis D2 (k) and provided additional information on the winter habitat utilization

85

of Sedge Wrens and Le Conte’s Sparrows. I found that the regression model for Sedge

Wrens differed from the D2 (k) approach in that it identified grass cover and the

combined category “other” cover types as significant predictors of occupancy at a site.

This finding is consistent with the observations of others that Sedge Wrens can be found

in grass-dominated areas in winter (Herkert et al. 2001). The retention of the “other”

ground cover type category in the model suggests that there likely may be additional

types of vegetative ground cover important to wintering Sedge Wrens that were not

measured directly in my study. For example, Herkert et al. (2001) refer to the winter-

time occurrence of Sedge Wrens in sedge meadows, though only in coastal areas. James

and Neal (1986) note that Sedge Wrens have occurred in rice fields in Arkansas. Further

refinement of the habitat categories used in my study to include other ground cover types,

particularly rushes and sedges, may clarify this usage.

For Le Conte’s Sparrow winter habitat, the logistic regression model showed that

mean vegetation height and proportion of grass cover were significant in predicting the

occurrence of the species in winter sites. The D2 (k) analysis indicated these two

variables as important to wintering Le Conte’s Sparrows. The logistic regression model

further retained the horizontal vegetation diversity and proportion of bare ground

variables. The horizontal vegetation diversity variable, which had a very high odds ratio

of predicting the occupancy of a site by Le Conte’s Sparrow, is suggestive of dense

habitat with many stems being a significant element of this species habitat. However, the

difference between mean horizontal vegetation diversity measured in territories and

random sites is negligible which brings this variable in to question as to its usefulness in

describing Le Conte’s Sparrow habitat. The % bare ground variable had a low odds ratio

86

indicating that Le Conte’s Sparrows were more likely to occur in areas where vegetation

or plant litter concealed any bare ground surface. The significance of low amounts of

bare ground within territories is suggestive of the species’ tendency to occupy more

densely vegetated areas where the added vegetation may provide optimal amounts of

food resources and sufficient vegetative structure to aid in predator avoidance.

The above findings are important because of the lack of published information on

winter habitat use by Sedge Wrens and Le Conte’s Sparrows. For Sedge Wren, little

information has been available on the specific requirements in winter habitat, according

to Herkert et al. (2001). In their review, Herkert et al. describe Sedge Wrens in winter

from only a few locations in the southeastern area of North America. In these

localities—Florida, Alabama, and Louisiana—the species’ winter locations are described

as primarily in coastal sedge meadows and grassy marshes. Imhof (1976) characterizes

Sedge Wrens as having a preference for broomsedge (A. virginicus) and dry, grassy fields

where cover is at least 60 – 90 cm tall. The vegetation height described by Imhof is

consistent with the results of my study. Lowery (1974) describes Sedge Wrens in

Louisiana as frequently occurring in grassy marshes in coastal areas but in dry grass

fields in inland areas, especially with broomsedge.

In the LMAV, I found Sedge Wrens in early-successional habitats that were

composed of dense, tall grasses or forbs, or combinations of both. In many reforestation

areas with recently planted trees, I located Sedge Wrens where annual and perennial forbs

were growing densely around young hardwood saplings. It is unlikely that the hardwood

saplings contributed significantly to the suitability of those areas to Sedge Wrens as

woody stems were not identified as critical components of winter habitat in my study.

87

However, it was likely that the density of vegetation was attractive to Sedge Wrens since

the birds that were sampled showed a preference for sites with significantly greater

vertical diversity than was found in available habitat. In general, Sedge Wrens were

found in sites that were composed of dense vegetation without hardwood trees larger in

size than saplings.

Little work has quantified the specific habitat needs of wintering Le Conte’s

Sparrows. Lowther (1996) indicates that the species prefers old fields and prairies

providing dense cover of grass or sedge. In Arkansas, James and Neal (1986) note that

Le Conte’s Sparrow is found in fields with matted Panicum sp., in fields of broomsedge,

in unmowed grasslands and pastures, in wet flats with rank vegetation, and in fields of

rice stubble. Le Conte’s Sparrow is noted to prefer moist fields of broomsedge in

Louisiana (Lowery 1974). Grzybowski (1983) found Le Conte’s Sparrows in prairie

dominated by dense stands of big bluestem and Indian grass or little bluestem at heights

averaging 63.2 cm (SD = 21.6).

Le Conte’s Sparrows in the LMAV typically occupied grass-dominated habitat

types that are in early searl stages of development. In a small number of locals, these

sites were climax communities of tallgrass prairie. I observed that most sites hosting Le

Conte’s Sparrows were predominately covered by either broomsedge or low-growing

Panicum grasses as the major ground cover. Most of these areas often had homogenous

stands of grass. The results of my habitat quantification confirm this species dependence

on grass-dominated areas in winter and provide some description of the physical structure

of these areas.

88

The quantification and description of the winter habitat preferences of Sedge

Wrens and Le Conte’s Sparrows in the LMAV presented in my study should provide land

managers with a base of information on the type and structure of winter habitat that these

two species require. Land and resource managers with the ability to manage for the

physical habitat features and floristic components described above likely will be able to

aid in increasing over-winter survival of these two species of conservation concern.

89

TABLE 1. Locations for winter habitat data samples for two bird species in the lower Mississippi River alluvial valley in winter 2001-2002.

Sample size

Species Arkansas Louisiana Mississippi

Sedge Wren (24 total samples) 10 11 3

LeConte’s Sparrow (22 total samples) 13 4 5

90

TABLE 2. Mean values of structural habitat features measured for Sedge Wren (n = 24) and Le Conte’s Sparrow (n = 22) winter territories and nonterritories (random controls) in the lower Mississippi River alluvial valley during winter 2001-2002.

Sedge Wren Le Conte’s Sparrow

Territory Nonterritory Territory Nonterritory

Variable x SE x SE x SE x SE

Total vegetation density 734.3 59.6 543.9 51.9 494.3 45.5 408.6 36.8

Litter depth (cm) 3.9 0.6 3.8 0.5 2.9 0.5 2.1 0.4 Mean vegetation

height (cm) 104.0 24.9 107.1 10.1 79.7 4.7 99.8 14.0

Maximum vegetation height (cm) 140.3 8.1 170.3 23.2 111.1 7.3 117.1 8.4

Vertical vegetation diversity 1.9 0.1 1.4 0.1 1.2 0.1 1.2 0.1

Horizontal diversity 2.2 0.1 2.2 0.1 1.8 0.1 1.9 0.1

% Forbs cover 18.9 3.3 18.6 3.3 18.6 2.8 23.7 3.9

% Grass cover 43.2 5.0 24.9 4.0 52.3 4.2 37.6 5.3

% Bare ground 1.1 0.4 3.2 1.0 2.1 0.9 2.7 1.2

% Woody stems 2.0 0.8 6.6 2.0 1.2 0.5 1.9 0.6

% Litter 22.1 2.7 33.9 4.1 21.1 2.6 17.8 2.7

% Other 12.7 4.2 9.0 3.0 4.9 1.7 13.5 5.2

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TABLE 3. Eigenvectors of the correlation matrix for the final three principal components associated with 12 habitat features measured for Sedge Wren and Le Conte’s Sparrow in winter 2001-2002 in the lower Mississippi River alluvial valley.

Sedge Wren (n = 24)

Le Conte’s Sparrow (n = 22)

Variable PC9 PC10 PC11 PC9 PC10 PC11

Total vegetation density -0.096 0.171 -0.167 -0.121 0.343 0.041

Litter depth -0.033 0.640 0.107 0.283 0.232 0.350

Mean vegetation height 0.294 0.236 -0.645 0.517 0.382 0.197 Maximum vegetation

height 0.366 -0.241 0.589 -0.387 -0.297 -0.207 Vertical vegetation

diversity -0.174 0.423 0.324 -0.110 0.153 -0.008 Horizontal vegetation

diversity -0.114 -0.219 0.057 0.212 -0.263 0.025

% Forbs cover -0.584 a -0.287 -0.153 -0.467 0.427 0.057

% Grass cover 0.158 0.108 0.126 0.085 -0.537 0.548

% Bare ground 0.306 0.181 0.036 0.096 0.041 0.001

% Woody stems -0.188 -0.028 -0.048 -0.030 0.041 -0.106

% Litter 0.456 -0.304 -0.209 0.435 -0.100 -0.685

% Other -0.152 0.009 0.064 -0.080 0.130 0.089

a Italicized and bolded values represent retained variables (loadings >|0.40|) associated with idealized habitat suitability for each species.

92

TABLE 4. Values of D2 and associated P-values from each sample (bird-selected) for structural habitat associations of Sedge Wrens in the lower Mississippi River alluvial valley during winter 2001-2002.

Sample D2 (k = 11)

estimate P-valueaD2 (k = 2) estimate P-value

D2 (k = 3) estimate P-value

1 0.041 0.841 0.154 0.926 0.184 0.980 2 0.282 0.595 2.785 0.249 3.206 0.361 3 0.000 0.999 2.313 0.315 2.354 0.502 4 0.031 0.861 0.521 0.771 1.465 0.690 5 0.019 0.890 0.101 0.951 2.243 0.524 6 0.012 0.911 0.240 0.887 0.553 0.907 7 0.039 0.843 1.931 0.381 1.972 0.578 8 6.600 0.010 6.621 0.037 7.141 0.068 9 0.005 0.942 0.906 0.636 1.245 0.742 10 2.932 0.087 0.146 0.207 6.259 0.100 11 0.061 0.804 0.132 0.936 0.142 0.986 12 0.883 0.347 1.483 0.476 1.667 0.644 13 0.007 0.935 0.403 0.817 1.320 0.724 14 0.099 0.752 0.176 0.916 7.513 0.057 15 2.196 0.138 2.777 0.249 3.201 0.362 16 0.018 0.893 0.345 0.842 0.361 0.948 17 0.010 0.919 0.314 0.855 2.304 0.512 18 2.969 0.085 4.653 0.098 5.787 0.122 19 1.236 0.266 1.482 0.477 3.333 0.343 20 0.025 0.874 2.941 0.230 3.136 0.371 21 0.104 0.747 3.437 0.179 3.443 0.328 22 2.153 0.142 5.057 0.080 5.116 0.164 23 3.244 0.072 3.901 0.142 4.865 0.182 24 0.032 0.858 0.184 0.912 0.190 0.979

a P-value based an approximate χ2 distribution with k = 11 degrees of freedom.

93

TABLE 5. Values of D2 and associated P-values from corresponding randomly

selected samples for structural habitat associations of Sedge Wrens in the lower Mississippi River alluvial valley during winter 2001-2002.

Sample D2 (k = 11)

estimate P-valueaD2 (k = 2) estimate P-value

D2 (k = 3) estimate P-value

1 0.085 0.771 0.368 0.832 3.496 0.321 2 23.034 < 0.001 25.979 < 0.001 40.903 < 0.001 3 60.080 < 0.001 229.481 < 0.001 325.608 < 0.001 4 0.123 0.726 7.195 0.027 12.474 0.006 5 61.918 < 0.001 74.053 < 0.001 97.959 < 0.001 6 0.101 0.751 0.107 0.948 37.730 < 0.001 7 0.013 0.910 13.312 0.001 36.183 < 0.001 8 16.880 < 0.001 21.742 <0.001 22.250 < 0.001 9 0.822 0.364 1.608 0.448 4.845 0.183 10 4.308 0.038 4.666 0.097 7.812 0.050 11 1.860 0.173 7.345 0.025 11.387 0.010 12 1.961 0.161 10.476 0.005 11.464 0.009 13 3.493 0.062 3.710 0.156 4.360 0.225 14 5.675 0.017 5.800 0.055 5.818 0.121 15 0.363 0.547 7.281 0.026 7.483 0.058 16 5.219 0.022 11.534 0.003 11.536 0.009 17 3.544 0.060 10.818 0.004 11.637 0.009 18 0.029 0.865 4.504 0.105 4.542 0.209 19 0.081 0.776 4.159 0.125 4.171 0.244 20 9.817 0.002 12.988 0.002 13.164 0.004 21 0.238 0.625 7.566 0.023 11.022 0.012 22 4.800 0.028 22.527 < 0.001 24.670 < 0.001 23 0.037 0.848 0.156 0.925 0.539 0.910 24 8.278 0.004 41.489 < 0.001 41.552 < 0.001

a P-value based an approximate χ2 distribution with k = 11 degrees of freedom.

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TABLE 6. Values of D2 and associated P-values from each sample (bird-selected) for structural habitat associations of Le Conte’s Sparrows in the lower Mississippi River alluvial valley during winter 2001-2002.

Sample D2 (k = 11)

estimate P-valueaD2 (k = 2) estimate P-value

D2 (k = 3) estimate P-value

1 3.836 0.050 4.269 0.118 8.203 0.042 2 0.005 0.945 0.019 0.991 0.519 0.915 3 0.903 0.342 1.180 0.554 1.338 0.720 4 0.039 0.843 0.044 0.978 3.471 0.325 5 0.021 0.885 1.552 0.460 1.850 0.604 6 0.050 0.823 0.209 0.901 1.428 0.699 7 0.005 0.945 0.759 0.684 0.764 0.858 8 5.024 0.025 5.488 0.064 6.183 0.103 9 2.800 0.094 6.275 0.043 7.284 0.063 10 0.646 0.421 0.662 0.718 2.462 0.482 11 0.700 0.403 0.925 0.630 1.363 0.714 12 0.060 0.807 0.116 0.944 1.628 0.653 13 0.827 0.363 2.891 0.236 3.027 0.387 14 0.536 0.464 2.867 0.238 6.274 0.099 15 0.486 0.486 2.117 0.347 2.118 0.548 16 0.188 0.665 2.968 0.227 3.165 0.367 17 1.732 0.188 2.281 0.320 2.599 0.458 18 0.017 0.897 1.011 0.603 1.637 0.651 19 0.614 0.433 3.703 0.157 3.708 0.295 20 2.505 0.113 2.568 0.277 3.832 0.280 21 0.003 0.957 0.071 0.965 0.087 0.994 22 0.004 0.952 0.024 0.988 0.061 0.996

a P-value based an approximate χ2 distribution with k = 11 degrees of freedom.

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TABLE 7. Values of D2 and associated P-values from corresponding randomly selected sample for structural habitat associations of Le Conte’s Sparrows in the lower Mississippi River alluvial valley during winter 2001-2002.

Sample D2 (k = 11)

estimate P-valueaD2 (k = 2) estimate P-value

D2 (k = 3) estimate P-value

1 62.823 < 0.001 62.897 < 0.001 63.088 < 0.001 2 13.254 < 0.001 19.417 < 0.001 21.035 < 0.001 3 6.176 0.013 6.530 0.038 7.426 0.060 4 2.012 0.156 2.034 0.362 3.462 0.326 5 7.091 0.008 7.173 0.028 7.680 0.053 6 1.797 0.180 1.871 0.392 1.998 0.573 7 4.035 0.045 16.461 < 0.001 17.824 < 0.001 8 3.056 0.080 3.296 0.192 3.900 0.272 9 0.151 0.698 3.632 0.163 5.693 0.128 10 0.127 0.721 0.328 0.849 2.466 0.481 11 79.537 < 0.001 346.158 < 0.001 607.281 < 0.001 12 0.001 0.978 0.003 0.999 0.008 0.999 13 0.062 0.804 14.611 0.001 15.084 0.002 14 0.232 0.630 0.572 0.751 0.630 0.890 15 0.945 0.331 1.637 0.441 1.724 0.632 16 0.101 0.750 1.222 0.543 1.236 0.744 17 3.988 0.046 4.387 0.112 9.107 0.028 18 0.015 0.901 3.112 0.211 14.135 0.003 19 2.548 0.110 11.688 0.003 15.334 0.002 20 94.421 < 0.001 132.681 < 0.001 132.681 < 0.001 21 177.653 < 0.001 198.263 < 0.001 233.623 < 0.001 22 0.449 0.503 0.523 0.770 0.575 0.902

a P-value based an approximate χ2 distribution with k = 11 degrees of freedom.

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TABLE 8. Probability of site occupancy from logistic regression based on structural habitat variables for Sedge Wren and Le Conte’s Sparrow in winter 2001-2002 in the lower Mississippi River alluvial valley.

Model Parameter estimate SE Wald χ2 P Odds

ratio

Sedge Wren a

Intercept - 21.56 125.9 0.03 0.86 — % Grass 0.54 0.24 4.94 0.03 1.72 % Other 0.29 0.14 4.13 0.04 1.34

Le Conte’s Sparrow b Intercept - 12.31 6781.7 0.00 0.99 — Mean height - 0.34 0.11 9.55 0.002 0.71 Horizontal diversity 12.54 4.51 7.73 0.005 > 999.99 % Grass 0.51 0.16 9.76 0.002 1.66 % Bare ground - 1.90 0.72 6.85 0.009 0.15

a – 2 log likelihood for intercept and covariates = 24.52, χ2 = 42.02, P = 0.02 b – 2 log likelihood for intercept and covariates = 21.27, χ2 = 39.73, P = 0.03

97

FIG. 1. Locations in the lower Mississippi River alluvial valley (shaded area) where

habitat samples were measured for Sedge Wren and Le Conte’s Sparrow during winter 2001-2002. Locations of samples are represented as follows: diamonds = Sedge Wren, circle = Le Conte’s Sparrow, square = both species.

98

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ASKINS, R. A. 1998. Restoring forest disturbances to sustain populations of shrubland birds. Restoration and Management Notes 16:166-173.

ATCHISON, J. 1986. The statistical analysis of compositional data. Chapman and Hall,

New York. BROWNING, D. M., S. J. BEAUPRÉ, AND L. K. DUNCAN. In press. Using partitioned

Mahalanobis D2 (k) to formulate a GIS-based model of timber rattlesnake hibernacula. Journal of Wildlife Management.

DUNCAN, L. AND J. E. DUNN. 2001. Partitioning Mahalanobis D2 to Improve GIS

classification. 26th SAS User’s Group International Conference Proceedings. <http://www.uark.edu/misc/statlab/reports.html> (22 February 2004).

DUNN, J. E. AND L. DUNCAN. 2000. Partitioning Mahalanobis D2 to sharpen GIS

classification. University of Arkansas Statistics Laboratory Technical Report no. 29. <http://www.uark.edu/misc/statlab/reports.html> (22 February 2004).

GRZYBOWSKI, J. A. 1983. Sociality of grassland birds during winter. Behavioral Ecology

and Sociobiology 13:211-219. HEFNER, J. M. AND J. D. BROWN. 1985. Wetland trends in the southeastern United States.

Wetlands 4:1-11. HEFNER, J. M, B. O. WILEN, T. E. DAHL, AND W. E. FRAYER. 1994. Southeast wetlands;

status and trends, mid-1970s to mid-1980s. U.S. Department of the Interior, Fish and Wildlife Service, Atlanta, Georgia.

HERKERT, J. R. AND F. L. KNOPF. 1998. Research needs for grassland bird conservation.

Pages 273-282 in Avian conservation: research and management (J. M. Marzluff and R. Sallabanks, Eds.). Island Press, Washington, D.C.

HERKERT, J. R., D. E. KROODSMA, AND J. P. GIBBS. 2001. Sedge Wren (Cistothorus

platensis). in The birds of North America, no. 582 (A. Poole and F. Gill, Eds.). The Birds of North America, Inc., Philadelphia, Pennsylvania.

IMHOF, T. A. 1976. Alabama birds. 2nd ed. University of Alabama Press, Tuscaloosa. JAMES, D. A. AND J. C. NEAL. 1986. Arkansas birds: their distribution and abundance.

University of Arkansas Press, Fayetteville.

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JENNELLE, C. 2000. Avian communities, habitat associations, and reproductive success in even-age managed areas of Ouachita National Forest, Arkansas. M.S. Thesis, University of Arkansas, Fayetteville.

KING, S. L. AND B. D. KEELAND. 1999. Evaluation of reforestation in the lower

Mississippi River alluvial valley. Restoration Ecology 7:348-359. LOWERY, G. H., JR. 1974. Louisiana birds. 3rd ed. Louisiana State University Press,

Baton Rouge. LOWTHER, P. E. 1996. Le Conte’s Sparrow (Ammodramus leconteii). in The birds of

North America, no. 224 (A. Poole and F. Gill, Eds.). The Academy of Natural Sciences, Philadelphia, Pennsylvania, and The American Ornithologist’s Union, Washington, D.C.

ROTENBERY, J. T., S. T. KNICK, AND J. E. DUNN. 2002. A minimalist approach to mapping

species’ habitat: Pearson’s planes of closest fit. in Predicting species occurrences: issues of scale and accuracy (J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall, and F. B. Samson, Eds.). Island Press, Washington, D.C.

SAS INSTITUTE. 2002. JMP User’s guide, version 5. SAS Institute Inc., Cary, North

Carolina. SAS INSTITUTE. 1999. SAS User’s guide, version 8. SAS Institute Inc., Cary, North

Carolina. SAUER, J. R. AND S. DROEGE. 1992. Geographic patterns in population trends of

Neotropical migrants in North America. Pages 26-42 in Ecology and conservation of Neotropical migrant landbirds (Hagan, J. M. and D. W. Johnston, Eds.). Smithsonian Institution Press, Washington, D.C.

SAUER, J. A., J. E. HINES, AND J. FALLON. 2003. The North American breeding bird

survey, results and analysis 1966-2002. Version 2003.1. USGS Patuxent Wildlife Research Center, Laurel, Maryland. <http://www.mbr-pwrc.usgs.gov/bbs/bbs.

html> (8 February 2004). SHANNON, C. E. AND W. WEAVER. 1963. A mathematical theory of communication.

University of Illinois Press, Urbana. STANTURF, J. A., E. S. GARDINER, P. B. HAMEL, M. S. DEVALL, T. D. LEININGER, AND M.

E. WARREN JR. 2000. Restoring bottomland hardwood ecosystems in the lower Mississippi alluvial valley. Journal of Forestry 98:10-16.

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U.S. FISH AND WILDLIFE SERVICE. 2002. Birds of conservation concern 2002. Division of Migratory Bird Management, Arlington, Virginia. <http://www.migratorybirds. fws.gov/reports/bcc2002.pdf> (8 February 2004).

WHITMORE, R. C. 1981. Structural characteristics of Grasshopper Sparrow habitat.

Journal of Wildlife Management 45:811-814.

101

CHAPTER 3

USING STABLE ISOTOPES IN CONSERVATION PLANNING FOR

MIGRATORY SONGBIRDS: WINTERING SPARROWS IN THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

This chapter written in the format of the journal The Condor.

102

USING STABLE ISOTOPES IN CONSERVATION PLANNING FOR

MIGRATORY SONGBIRDS: WINTERING SPARROWS IN THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

Abstract. To understand which factors on the breeding grounds may affect

wintering grassland bird communities, a means to determine the origins of these

wintering birds is required. Recent advances in stable isotope ecology have demonstrated

links between migratory birds’ wintering locations and their breeding sites through

analysis of stable isotopes incorporated in feathers. At seven locations in the lower

Mississippi River alluvial valley (LMAV) of Arkansas, Louisiana, and Mississippi, I

captured and removed the central rectrices from 90 birds of four species, Savannah

(Passerculus sandwichensis), Le Conte’s (Ammodramus leconteii), Song (Melospiza

melodia), and Swamp (M. georgiana) sparrows. I analyzed these feathers for stable

hydrogen isotope ratios and compared the resulting values to existing North American

hydrogen isotope maps. I determined the breeding origins of these birds by comparing

the known breeding range of each species with the resulting isobars on the isotope map. I

compared the estimated breeding ranges for the four sparrow species wintering in the

LMAV with trend data from the North American Breeding Bird Survey (BBS). These

comparisons indicated that Savannah Sparrows wintering in the LMAV may be derived

from declining populations. Le Conte’s Sparrows in the LMAV appear to have

originated from areas of their breeding range where this species is experiencing

increasing populations. Wintering Song Sparrows had the potential to originate from

portions of their breeding range that show mixed population trends. Swamp Sparrows

103

sampled in the LMAV were derived from the most northerly breeding populations,

specifically northern Alberta and Saskatchewan and southern areas of the Northwest

Territories. BBS data indicate that populations of Swamp Sparrow in Alberta and

Saskatchewan are in decline and no data exist for the Northwest Territories. The use of

stable isotopes to delineate breeding season origins of wintering birds in the LMAV is a

useful tool in establishing links with bird populations that may be in need of conservation

attention as indicated by trends from continental-scale monitoring data. My work further

demonstrates the need to maintain early-successional habitats throughout the LMAV as

winter refugia for these species.

INTRODUCTION

A primary concern related to the conservation of migratory songbirds is the ability to link

a species’ breeding site to its wintering grounds. Banding of birds is an often-used

method to attempt to locate migrating birds’ origins. However, traditional banding and

observational efforts have produced minimal results with respect to small birds, are

geographically biased to marking locations, do not correspond proportionally to bird

densities, and only show fragmentary information on the breeding location of migrants

(Wassenaar and Hobson 2001).

The use of stable isotopes in tracing the origins and migration of birds has been

recognized as an important and powerful tool in establishing migratory connectivity in

birds (Hobson 2002, Webster et al. 2002). Recently, the use of stable isotopes,

particularly hydrogen (Deuterium; δD), have been used to link organisms to broad areas

104

of geographic origin in North America (Hobson 1999). This method depends on a

continental-scale gradient of isotopic contours of growing season average δD values in

precipitation. For birds with appropriate molt schedules, the method is based on the

principle that certain feathers are grown on the breeding grounds just prior to fall

migration and those feathers have fixed in their keratin the growing-season hydrogen

stable isotope ratio of their diet from that local area (Hobson and Wassenaar 1997). Once

a feather is grown, the non-exchangeable portion of δD is preserved throughout the life of

the feather (Chamberlain et al. 1997, Hobson and Wassenaar 1997). This ratio of

hydrogen to δD from the feather keratin can be compared with a predictable, mapped

gradient of hydrogen to meteorological δD ratios resulting from growing-season

precipitation (Hobson and Wassenaar 1997) to determine the approximate latitude of

feather growth. This map uses a correction factor of -25‰ to adjust for mean isotopic

fractionation between rainfall, diet, and feather keratin (Wassenaar and Hobson 2001).

Continental monitoring of bird populations in North America has been

accomplished through the U.S. Geological Survey’s North American Breeding Bird

Survey (BBS). The BBS has been the principal source of information on population

change and relative abundance for many North American bird species. The BBS has also

been effective in identifying regional or local population trends of readily detectable bird

species (Sauer and Droege 1992, Sauer et al. 2003a). Understanding the breeding season

population trends of bird species is of importance to resource managers that are charged

with managing appropriate habitat. For managers of winter habitat, knowing if a

wintering population of birds originated from a declining breeding population is of

considerable importance in conservation.

105

The lower Mississippi River alluvial valley (LMAV), the broad floodplain of the

Mississippi River that spans between southern Illinois and southern Louisiana, is a

distinct physiographic region of North America that has undergone extensive clearing of

its native bottomland forest for agricultural use. Some estimate that over 72% of the

original 10 million ha of forest has been felled (King and Keeland 1999). Recent efforts

to replant and restore the function and values of bottomland systems have resulted in a

large area of early-successional habitats that provide optimal winter refugia for several

grassland-dependent bird species. With an increasing amount of this habitat available,

the region could play a considerable role in the conservation of declining bird species by

providing needed winter-ground habitat.

In this study I evaluate the use of δD in linking breeding and wintering sites for

four grassland bird species: Savannah Sparrow (Passerculus sandwichensis), Le Conte’s

Sparrow (Ammodramus leconteii), Song Sparrow (Melospiza melodia), and Swamp

Sparrow (M. georgiana). Three of these species, Savannah, Song, and Swamp sparrows,

were selected because of their abundance in grassland and scrub-shrub habitats in winter

within the LMAV. Le Conte’s Sparrow was chosen because of its relative rarity within

the study region, because of its fairly specific habitat requirements (Lowther 1996), and

because it is considered a Species of Conservation Concern by the U.S. Fish and Wildlife

Service (U.S. Fish and Wildlife Service 2002).

By using analysis of stable isotopes incorporated in feathers for each species, I

was able to approximate the breeding origins of the above species of birds. I then

compared these extrapolated breeding ranges, where possible, with each species’ current

population trend as determined by the BBS. My overall goal with this work was to

106

utilize stable isotopes analysis methods to establish connectivity between bird species’

breeding and wintering grounds to aid in identifying wintering sites for birds that

originate from declining breeding populations for the purpose of conservation planning.

METHODS

FEATHER SAMPLES

I made feather collections at seven locations in the LMAV of Arkansas, Louisiana, and

Mississippi in January and February of 2001 and 2002 (Table 1, Fig. 1). I captured

sparrows in mist nets placed at the edge of appropriate early-successional habitats. Inner

rectrices (R1 and R2) were collected from a total of 90 individual birds in the following

proportions: Savannah Sparrow (n = 21), Le Conte’s Sparrow (n = 17), Song Sparrow (n

= 24), and Swamp Sparrow (n = 28). No attempt was made to age, sex, or identify birds

as to subspecies. All collected feathers were stored in zippered plastic bags until they

were used in stable isotope analysis.

I prepared feathers for stable isotope analysis by cleaning them of surface

contaminants using a rinse of 2:1 chloroform to methanol solution. After rinsing,

feathers were allowed to air-dry under a fume hood for 30 min and were then placed in a

drying oven at 45˚C for approximately one hr. Of the two rectrices collected (R1 and R2)

from each bird, only the central rectrices (R1) were selected for analysis. Because

rectrices are grown centrifugally, the central retrecies had the greatest likelihood of

having been grown on the breeding grounds, according to the molt phenology for each

species (Pyle 1997). 350 ± 10µg of material was cut from the distal end of the shaft of

each feather. These cut samples were weighed on a Sartorius microbalance and placed in

107

isotope-grade silver capsules (3.5 x 5 mm Costech). Sample weights were recorded and

capsules were closed, folded into small packets, and stored in 96-position Elisa plates.

STABLE ISOTOPE ANALYSES

Stable hydrogen isotope analysis was performed between September 2002 and February

2003 at the Stable Isotope Hydrology and Ecology Laboratory of the National Water

Research Institute in Saskatoon, Saskatchewan, Canada. Analyses were made by online

continuous-flow isotope-ratio mass spectrometry (CF-IRMS) using a Micromass

IsoPrime EuroVector pyrolysis mass spectrometer. Methods described by Wassenaar and

Hobson (2003) were utilized for this analysis. These methods used calibrated keratin

working standards and comparative equilibration to correct for the effects of ambient

atmospheric moisture on exchangeable hydrogen. All sample values are given in parts

per thousand (‰) deviation from the Vienna Standard Mean Ocean Water standard

(VSMOW).

I compared the resulting values to a passerine feather δD contour map (Fig. 2) for

North America that is based on mean growing season precipitation δD values (Hobson

and Wassenaar 1997). This map uses a correction factor of -25‰ to account for mean

isotopic fractionation between rainfall, diet, and feather keratin (Wassenaar and Hobson

2001). From this δD map known breeding ranges for each of the four species were

superimposed and breeding origins were then interpolated using the resulting values for

each corresponding set of samples.

108

RESULTS

STABLE ISOTOPE ANALYSIS

I observed a broad range of δD values (Table 2, Fig. 3) for all four species of wintering

sparrows sampled in the lower Mississippi River alluvial valley. Three species,

Savannah, Le Conte’s, and Song sparrows, had δD values that were highly negative,

indicating the origins of some birds corresponded with extreme northern latitudes.

No statistically significant differences existed for the mean δD values between

sites for Savannah, Le Conte’s, and Song sparrows (t-tests, P > 0.05 for each test)

indicating a regional pooling of populations in the LMAV from common approximate

northern breeding latitudes. Swamp Sparrow samples showed mean δD values from two

locations (Quitman County, MS and Catahoula Parish, LA) that were significantly

different from the other four sites. However, the sample size from these two sites is very

small (n = 2) and the means are greatly influenced by two outlying values that have

isotopic values representative of the study area latitude. These two low values likely

represent feathers that were regrown on the winter grounds.

I detected no discernable patterns regarding the distribution of δD values for

individual species across the LMAV, between the two years of sample collection. No

patterns in δD values occurred between species and sites sampled throughout the study

region.

GEOGRAPHIC BREEDING ORIGINS

Ranges in δD values showed overlap between species, but mean values varied between

species (Fig. 3). Savannah Sparrow δD values showed a mean of -121.7‰ which

109

corresponds with a very broad isobar across the continent from the central Rocky

Mountains in the western United States northeastward through southern Alberta,

Saskatchewan, Manitoba, northern Ontario and Quebec, and through central Labrador in

Newfoundland, based on the δD feather map of Wassenaar and Hobson (2001; Fig. 2)

and the known breeding range of the species. Le Conte’s Sparrow feather samples

showed a mean δD value of -110.4‰ which, based on isobar restrictions from known

breeding range, can be interpolated as originating from a fairly restricted area in northern

Minnesota and North Dakota into southernmost Manitoba. Song Sparrow mean δD

values were -75.3‰ indicating a more southerly natal origin compared to the previous

species. Song Sparrow δD values indicated that, based on the species’ known breeding

range, birds may have originated from a region in the United States that ranged from

southern Minnesota and northern Iowa eastward across the Great Lakes states to southern

New England. Swamp Sparrow feathers had the most negative δD values with a mean of

-133.9‰ which corresponds with the region of central Alberta and Saskatchewan.

Some highly negative values for Swamp Sparrow (-166‰) and Savannah

Sparrow (-164‰) indicate that individuals from extreme northern latitudes may be

traveling significant distances to wintering grounds in the LMAV. In the case of the

Swamp Sparrow, these localities correspond to the southern regions of the Northwest

Territories. In contrast, three samples, from Le Conte’s Sparrow and Swamp Sparrow,

had feather δD values between -47‰ and -59‰ indicating that those feathers were likely

regrown in the study region, probably as a result of feather loss in those areas rather than

actual molt. However, Pyle (1997) does indicate that molt may be arrested for migration

110

and completed on the breeding grounds in these species. This may also be an explanation

for the less negative values observed.

CORRELATIONS WITH POPULATION TRENDS

I compared delineated breeding origins of the four species of sparrows to recent (1980-

2002) population trend data from the BBS (Sauer et al. 2003b). Population trend

estimates from the BBS for each species and the respective estimated breeding range,

separated by state and/or province, are shown in Table 3.

BBS trend estimates and corresponding interpolated breeding-ground origins

indicate that sampled Savannah Sparrows wintering in the LMAV may have originated

from populations that are in decline in all states and provinces except Montana (2.7%

year-1) and Manitoba (2.7% year-1). These declining trends ranged from -0.7% year-1 in

Saskatchewan to -2.1% year-1 in Ontario.

Le Conte’s Sparrow samples indicated that these birds were possibly derived from

locations where the population trend is positive: 5.2% year-1 in Manitoba, 10.4% year-1 in

North Dakota. One negative trending state, Minnesota (-3.3% year-1), was detected as a

possible source for LMAV wintering Le Conte’s Sparrows.

Song Sparrow interpolated breeding ranges experienced mixed population trends

(Table 3) with six states, mostly in the eastern United States, showing negative

population trends (-0.5% year-1 to -2.4% year-1). More westerly states hosted an

increasing population (0.1% year-1 to 3.5% year-1).

Population trends for Swamp Sparrows were negative for the two provinces that

have available data. Alberta and Saskatchewan have declining Swamp Sparrow

111

populations at the rates of -0.6% year-1 and -8.5% year-1, respectively. No data exist with

the BBS for Swamp Sparrow breeding populations in southern Northwest Territories.

This region lies north of the area covered by the BBS (Fig. 2).

DISCUSSION

Banding data have shown exceedingly small returns compared to the number of birds

banded of the four species involved in this study. The following number of birds and

associated encounters (recoveries) have been recorded by the U.S. Geological Survey

Bird Banding Laboratory (BBL) between the years 1914-2002: Savannah Sparrow –

119,025 banded : 556 encounters (0.005%); Le Conte’s Sparrow – 1,564 banded : 1

encounter (0.0006%); Song Sparrow – 712,876 banded : 16,639 encounters (0.02%); and

Swamp Sparrow – 162,631 banded : 263 encounters (0.002%). With such scant

information on migratory connectivity for these birds, the use of stable isotope methods

provides information on these wintering birds’ origins much more rapidly than that

provided by traditional banding studies.

The interpolated breeding ranges of Savannah, Le Conte’s, Song, and Swamp

sparrows wintering in the lower Mississippi River alluvial valley and associated

comparisons with breeding population trend data from the North American Breeding Bird

Survey demonstrate how stable isotope analysis can be used in establishing connectivity

between breeding and wintering ranges of birds to ascertain potential relationships with

trends of the breeding populations from which they originate. Use of this new

information and further implementation and refinement of this methodology should be

112

helpful to land managers in making decisions to increase amounts of habitat for wintering

birds that originate from declining populations.

I found that breeding-range origin estimates based on stable isotope analyses

were, for the most part, consistent with findings from the few band encounters from the

LMAV of all species except Le Conte’s Sparrow (BBL data). Because only a single band

encounter exists for Le Conte’s Sparrow, a recovery on the breeding grounds, no

relationships could be established from band records of birds wintering in the LMAV.

Using stable isotope analysis has proven useful in determining breeding origins of

populations of passerine birds thus allowing for more informed management decision to

be made (Hobson 1999, Hobson and Wassenaar 2001, Wassenaar and Hobson 2001,

Hobson et al. 2001). The use of δD isotopes is a significant improvement over

conventional banding and observation methods because of the quick results received that

are not dependent on the chance recapture of a short-lived songbird. However, a problem

with the precision of this method still remains. Limitations still exist in that the δD

isotope analysis provides only longitudinal precision to locality estimates. The use of

other isotopes, namely δ13C and δ15N, have been used in other studies (Hobson et al.

2001, Graves et al. 2002) to add more precision to locality estimates. Use of an

additional stable isotope or naturally occurring chemical that can be proven to occur in a

latitudinal gradient would undoubtedly enhance the ability to add precision and

discrimination to estimates of the origins of bird populations (Wassenaar and Hobson

2001).

My study helps to establish broad lines of migratory connectivity to an extent

previously unknown for four species of sparrows wintering in the LMAV. This research

113

further demonstrates the vast distances these species traveled to reach suitable wintering

habitats. The distances traveled by these individuals are much greater than previously

realized and documented through other methods.

Because of the extensive amount of agricultural clearing in the LMAV’s past, and

considering the current increasing rates of afforestation (Schoenholtz et al. 2001), the

value of extensive early-successional habitats to these wintering bird communities is

significant. It is estimated that these four species have traveled thousands of kilometers

to reach suitable habitats for winter residency. Therefore, it is likely that the LMAV

hosts, or will host with the continuation of reforestation programs, an extensive amount

of appropriate habitat, based on estimates of the overall effort of these ecological

restoration programs (currently 71,000 ha; Schoenholtz et al. 2001). With the potential

for further increases in the quantity of these habitats, the LMAV is a major source of

winter refugia for the above four species of sparrows, in addition to many other early-

successional habitat specialist bird species. Managing for the continued presence of

early-successional habitats throughout the LMAV should be coordinated with overall

goals of forest restoration and regional biodiversity objectives. The availability of early-

successional habitat is especially important for wintering birds that are experiencing

population declines on their breeding grounds.

114

TABLE 1. Locality and sample size of feathers collected for stable hydrogen isotope (δD) analysis in winter 2000-2001 and 2001-2002.

a Corresponding sample size in winter 2000-2001/2001-2002. Abbreviations: LCSP = Le Conte’s Sparrow, SAVS = Savannah Sparrow, SOSP = Song Sparrow, SWSP = Swamp Sparrow.

State Locality Species and sample size LCSP SAVS SOSP SWSP Arkansas Clay County 6/1a 4/1 3/2 4/2 Arkansas Crittenden County - - 14/0 6/0

Louisiana East Carroll Parish - 0/3 2/3 1/9

Louisiana Catahoula Parish - - - 2/0

Mississippi Quitman County 7/0 12/0 - 4/0

Mississippi Bolivar County 0/3 - - - Mississippi Holmes County -

24 28

0/1 - -

Total 17 21

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TABLE 2. Summary of stable-hydrogen isotope (δD) values of central rectrices from four species of sparrows wintering in the lower Mississippi River alluvial valley.

Species n δD ‰ ( x ± SE) 95% CI Q1/Q3a

Savannah Sparrow 21 -121.7 ± 6.8 14.2 -149.0/-91.0

Le Conte’s Sparrow 17 -110.4 ± 7.1 15.0 -135.0/-90.5

Song Sparrow 24 -75.3 ± 3.9 8.0 -81.0/-63.5

Swamp Sparrow 28 -133.9 ± 5.3 10.9 -152.0/-127.0

a First (Q1) and third (Q3) quartiles for δD.

116

TABLE 3. Population trend estimates for four sparrow species wintering in the lower Mississippi River alluvial valley. Estimates based on Breeding Bird Survey data (Sauer et al. 2003) from the period 1980-2002 for regions where breeding origins have been interpolated through stable hydrogen isotope (δD) ratios from feathers. Trends were kept at the state/province level to allow for adequate sample sizes.

Species Location

Trenda

Pb

nc

Savannah Sparrow Idaho -1.6 0.09 34 Montana 2.7 0.05 42 British Columbia -0.1 0.89 59 Alberta -0.9 0.11 100 Saskatchewan -0.7 0.26 56 Manitoba 2.8 0.00 40 Ontario -2.1 0.00 101 Quebec -1.9 0.00 61 Le Conte’s Sparrow Manitoba 5.2 0.01 33 North Dakota 10.4 0.00 23 Minnesota -3.3 0.24 23 Song Sparrow Minnesota -0.3 0.72 76 Iowa 3.5 0.05 36 Illinois 1.1 0.01 92 Wisconsin 0.2 0.37 93 Michigan 0.1 0.74 76 Ontario -0.5 0.20 121 New York -0.5 0.24 112 Massachusetts -2.2 0.01 24 Vermont -2.4 0.00 24 New Hampshire -1.0 0.03 24 Swamp Sparrow Northwest Territories (no data) Alberta -0.6 0.89 14 Saskatchewan -8.5d 0.31 8 a Estimated trend summarized as % change year-1. b Statistical significance of the trend. c Number of survey routes in the analysis. d Estimate considered deficient due to small sample size.

117

FIGURE 1. Locations (circles) where feather samples were collected for stable hydrogen isotope (δD) analysis in winter 2000-2001 and 2001-2002. Shaded area depicts the lower Mississippi River alluvial valley.

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FIGURE 2. Map of δD values for feathers grown in North America (from figure 1 in Wassenaar and Hobson 2001). Dashed line shows approximate northern limit of the Breeding Bird Survey. Shaded region is approximate location of the lower Mississippi River alluvial valley. Isobars are based on those by Hobson and Wassenaar (1997) with a correction factor of -25‰ that accounts for isotopic fractionation between rainfall and feathers.

119

120

FIGURE 3. Stable hydrogen isotope values from central retrices of four sparrow species from the lower Mississippi River alluvial valley in winter 2000-2001 and 2001-2002. Box plots indicate 25th and 75th quartile range of data (at each end). Median sample value is indicated by the line across the box while horizontal lines crossing beyond the boxes indicate the mean of the sample. Dashed line across the figure indicates the approximate center of latitude for the study region where samples were taken. More negative δD values correspond to more northerly latitudes. Abbreviations: LCSP = Le Conte’s Sparrow; SAVS = Savannah Sparrow; SOSP = Song Sparrow; SWSP = Swamp Sparrow.

δD (‰

)

-170

-160

-150

-140

-130

-120

-110

-100

-90

-80

-70

-60

-50

-40

LCSP SAVS SOSP SWSP

Species

LITERATURE CITED

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GRAVES, G. R., C. S. ROMANEK, AND A. R. NAVARRO. 2002. Stable isotope signature of

plilopatry and dispersal in a migratory songbird. Proceedings of the National Academy of Sciences 99:8096-8100.

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Neotropical migrant songbirds using stable hydrogen isotopic analysis of feathers. Oecologia. 109:142-148.

HOBSON, K. A., AND L. I. WASSENAAR. 2001. Isotopic delineation of North American

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HOBSON, K. A. 1999. Tracing origins and migration of wildlife using stable isotopes: a

review. Oecologia. 120:314-326. HOBSON, K. A. 2002. Incredible journeys. Science. 295:981-982. KING, S. L., AND B. D. KEELAND. 1999. Evaluation of reforestation in the lower

Mississippi River alluvial valley. Restoration Ecology 7:348-359. LOWTHER, P. E. 1996. Le Conte’s Sparrow (Ammodramus leconteii). In A. Poole and F.

Gill [EDS.], The birds of North America, No. 224. The Academy of Natural Sciences, Philadelphia, PA, and The American Ornithologist’s Union, Washington, DC.

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Ploceidae. Slate Creek Press, Bolinas, CA. SAUER, J. R., AND S. DROEGE. 1992. Geographic patterns in population trends of

Neotropical migrants in North America. p. 26-42 In J. M. Hagan and D. W. Johnston [EDS.], Ecology and conservation of Neotropical migrant landbirds. Smithsonian Institution Press, Washington, DC.

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SAUER, J. A., J. E. HINES, AND J. FALLON. 2003b. The North American breeding bird

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ECOLOGY AND CONSERVATION OF WINTERING

MIGRATORY BIRDS IN EARLY-SUCCESSIONAL HABITATS OF THE

LOWER MISSISSIPPI RIVER ALLUVIAL VALLEY

Abstract of dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

By

ROBERT HUNTER DOSTER, B.A., M.S. Hendrix College, 1989

University of Arkansas, 1991

May 2005 University of Arkansas

This abstract is approved by: Dissertation Co-Directors:

ABSTRACT

Grassland and shrubland birds have declined throughout North America. I

studied habitat use and community dynamics by these species in the lower Mississippi

River alluvial valley (LMAV). In winters 1999-2000, 2000-2001, and 2001-2002, I

surveyed birds at 69 locations in the LMAV of Arkansas, Louisiana, and Mississippi. I

produced bird density and species richness estimates for these sites. I measured

corresponding habitat characteristics and landscape features. I used regression models to

explore relationships of density and species richness to habitat and landscape measures.

Density models indicated decreases with increasing distance to forest; decreases with

increasing distance from the Mississippi River; and increases with rising vegetation

height. Species richness models showed an increase in richness with increasing

vegetation height; declined with increasing distance from the Mississippi River; and

smaller sites close to the river were likely to hold mores species than distant sites.

Sedge Wren (Cistothorus platensis) and Le Conte’s Sparrow (Ammodramus

leconteii) are of conservation concern. In 2002 I quantified their winter habitat at 20

locations. Partitions of Mahalanobis D2 were used to discern least variable habitat

features across study sites. Sedge Wrens preferred uniformity in vegetation height;

selected a balance between plant litter depth and vertical vegetation diversity; and

favored corresponding increases in composition of plant litter and forbs coverage. Sedge

Wrens preferred areas dominated by grass. Le Conte’s Sparrows favored corresponding

increases in grass and plant litter; occurred when a parallel increase between % forbs and

% grass was present; and were present when mean vegetation height increase along with

1

an increase in % plant litter and % forbs. Le Conte’s Sparrows preferred short

vegetation.

Feathers from 90 birds of four species, Savannah (Passerculus sandwichensis), Le

Conte’s, Song (Melospiza melodia), and Swamp (M. georgiana) sparrows were analyzed

for stable hydrogen isotope ratios. Results were compared to North American hydrogen

isotope maps to determine breeding origins. I contrasted delineated breeding ranges with

North American Breeding Bird Survey data. Savannah Sparrows originated from

declining populations. Le Conte’s Sparrows originated from increasing populations.

Song Sparrows originated from regions experiencing mixed population trends. Swamp

Sparrows came from declining populations in northwestern Canada.

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