United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-49

Multiscale Habitat Suitability 

Index Models for Priority 

Landbirds in the Central 

Hardwoods and West Gulf 

Coastal Plain/Ouachitas Bird 

Conservation Regions

John M. Tirpak

D. Todd Jones-Farrand

Frank R. Thompson, III

Daniel J. Twedt

William B. Uihlein, III
Visit our homepage at: http://www.nrs.fs.fed.us/

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The Authors

JOHN M. TIRPAK is science coordinator for the Lower Mississippi Valley Joint Venture, 

Vicksburg, MS.

D. TODD JONES-FARRAND is a postdoctoral fellow with the Department of Fisheries and 

Wildlife Sciences, University of Missouri, Columbia, MO.

FRANK R. THOMPSON, III is a research wildlife biologist and Project Leader with the 

Northern Research Station, Columbia, MO.

DANIEL J. TWEDT is a research wildlife biologist with the U.S. Geological Survey Patuxent 

Wildlife Research Center, Vicksburg, MS.

WILLIAM B. UIHLEIN, III is coordinator for the Lower Mississippi Valley Joint Venture, 

Vicksburg, MS.

Abstract

Ecoregional conservation planning for priority landbirds requires methods that explicitly 

link populations to habitat conditions at multiple scales. We developed Habitat Suitability 

Index (HSI) models to assess habitat quality for 40 priority bird species in the Central 

Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions. The models 

incorporated both site and landscape environmental variables derived from one of six 

nationally consistent datasets: ecological subsections from the National Ecological Unit 

Hierarchy, National Land Cover Dataset, National Elevation Dataset, National Hydrography 

Dataset, State Soil Geographic Database, and Forest Inventory and Analysis data. We 

initially defi ned potential habitat for each species from unique landform, landcover, and 

successional age class combinations. Species-specifi c environmental variables identifi ed 

from the literature were used to refi ne initial habitat estimates. We verifi ed models by 

comparing subsection-level HSI scores and Breeding Bird Survey (BBS) abundance via 

Spearman rank correlation. To validate models, we developed generalized linear models 

that predicted BBS abundance as a function of HSI score and Bird Conservation Region. 

We considered models that included a signifi cant (P ≤ 0.100) positive coefficient on the 

BBS predictor to be valid and useful for conservation planning.

Cover Photos

Clockwise from top left: 

Cerulean warbler, U.S. Forest Service; Wood thrush, Steve Maslowski, U.S. Fish & Wildlife 

Service; Pileated woodpecker, U.S Forest Service; Painted bunting, Deanna K. Dawson, 

Patuxent Bird Identifi cation InfoCenter, Photo used with permission; Kentucky warbler, U.S. 

Fish & Wildlife Service; Bewick’s wren, Dave Menke, U.S. Fish & Wildlife Service.

Manuscript received for publication 27 August 2008
CONTENTS

Introduction..........................................................................................1

Study Areas ..........................................................................................3

Methods ................................................................................................4

 Priority Bird Species .........................................................................4

 HSI Model Development ..................................................................4

 Model Testing .................................................................................12

Model Accounts .................................................................................14

 Acadian Flycatcher.........................................................................14

 American Woodcock ......................................................................20

 Bachman’s Sparrow .......................................................................25

 Bell’s Vireo .....................................................................................29

 Bewick’s Wren ................................................................................33

 Black-and-white Warbler ................................................................36

 Blue-gray Gnatcatcher ...................................................................40

 Blue-winged Warbler ......................................................................44

 Brown Thrasher ..............................................................................47

 Brown-headed Nuthatch ................................................................51

 Carolina Chickadee ........................................................................55

 Cerulean Warbler ...........................................................................58

 Chimney Swift ................................................................................63

 Chuck-will’s-widow .........................................................................65

 Eastern Wood-pewee .....................................................................68

 Field Sparrow .................................................................................71

 Great Crested Flycatcher ...............................................................75

 Hooded Warbler .............................................................................78

 Kentucky Warbler ...........................................................................83

 Louisiana Waterthrush ...................................................................87

 Mississippi Kite...............................................................................92

 Northern Bobwhite .........................................................................96

 Northern Parula ............................................................................101

 Orchard Oriole..............................................................................105

 Painted Bunting ............................................................................108

 Pileated Woodpecker ................................................................... 112

 Prairie Warbler ............................................................................. 116

 Prothonotary Warbler ...................................................................120

 Red-cockaded Woodpecker .........................................................124
 Red-headed Woodpecker ............................................................129

 Swainson’s Warbler......................................................................133

 Swallow-tailed Kite .......................................................................137

 Whip-poor-will ..............................................................................141

 White-eyed Vireo ..........................................................................144

 Wood Thrush ................................................................................148

 Worm-eating Warbler ...................................................................153

 Yellow-billed Cuckoo ....................................................................157

 Yellow-breasted Chat ...................................................................161

 Yellow-throated Vireo ...................................................................165

 Yellow-throated Warbler ...............................................................169

Current Model Use And Future Directions ....................................173

Acknowledgments ...........................................................................173

Literature Cited ................................................................................174
1

INTRODUCTION

Th e primary goal of the North American Landbird Conservation Plan (Rich and others 2004) is 

to create landscapes that can sustain populations of the 448 native landbird species that breed in 

the United States and Canada. To attain this goal, the Plan advocates a three-phase approach:

1. Establish population objectives at the continental scale.

2. Allocate these population objectives to specifi c Bird Conservation Regions (BCRs).

3. Translate the regional population objectives to habitat goals within each BCR. 

Th e fi rst two steps of this process have been completed (Panjabi and others 2001, Rosenberg 

and Blancher 2005), and it is at this third step where the conservation community stands today.

Translating target population numbers into concrete habitat goals requires both knowledge of 

how landbird populations respond to changing habitat conditions and a method for quantifying 

this relationship. However, there are few data explicitly linking landbird abundance to specifi c 

habitat conditions, nor is there consensus on the optimal methodology to achieve this linkage. 

Th e goal of our research is to develop a comprehensive, replicable approach to ecoregional 

habitat assessment that links habitat conditions to the density of priority bird species. Specifi c 

objectives are to:

1. Assess the ability of landscapes to sustain priority species at prescribed population levels 

based on the extent and distribution of available habitats.

2. Monitor changes in the ability of landscapes to sustain species.

3. Predict how landscape suitability changes under alternative succession and disturbance 

patterns, land use, conservation strategies, management practices, and development 

pressures.

To create a replicable and transferable methodology, we selected a Habitat Suitability Index 

(HSI) modeling approach. HSI models were initially developed by the U.S. Department of 

the Interior (USDI) Fish and Wildlife Service (FWS) to evaluate habitat quality for a variety 

of species (Schamberger and others 1982). Th ese models identify and quantify the relationship 

between key environmental variables and habitat suitability on a scale from 0 to 1. HSI scores 

are calculated independently for each environmental factor and an appropriate weighting 

scheme is used to combine individual variables and determine a composite suitability index 

(SI) score for a particular location. Although the FWS developed HSI models solely with 

site-specifi c habitat variables (e.g., canopy cover) for assessing stand-level habitat suitability, 

researchers are increasingly developing HSI models that incorporate broad-scale metrics (e.g., 

percent forest in a 1-km radius) for application to large landscapes (Larson and others 2003). 

Th e continued use of the HSI approach by both researchers and managers likely is a result of 

the intuitive nature of these models as well as their scalability and portability to novel situations. 

HSI models easily incorporate existing information via a priori hypotheses but also allow 

generalization of habitat relationships across areas and species where empirical data are limited. 

Currently, few HSI models include environmental variables at both the site and landscape 

scale due to the limited site-specifi c data across areas that are large enough to exhibit strong 
2

diff erences in landscape structure or composition. Nevertheless, habitat selection by birds is a 

multiscale process (Villard and others 1998) and habitat models should refl ect conditions at 

multiple scales. Th is report begins fi lling this gap by documenting multiscale HSI models for 

40 priority landbird species (Table 1).

Table 1.—Partners in Flight regional combined score and USDI Fish and Wildlife Service Bird of Conservation 

Concern status for 40 priority landbird species in the Central Hardwoods and West Gulf Coastal Plain/

Ouachitas Bird Conservation Regions

Central Hardwoods West Gulf Coastal Plain/Ouachitas

Species

Alpha 

codea

Regional 

combined 

score

Bird of 

Conservation 

Concern

Regional 

combined 

score

Bird of 

Conservation 

Concern

Acadian fl ycatcher ACFL 16 No 17 Yes

American woodcock AMWO -- No -- No

Bachman’s sparrow BACS 20 Yes 20 Yes

Bell’s vireo BEVI 15 Yes 16 Yes

Bewick’s wren BEWR 15 Yes 16 Yes

Black-and-white warbler BAWW 13 No 16 No

Blue-gray gnatcatcher BGGN 14 No 13 No

Blue-winged warbler BWWA 19 Yes -- No

Brown thrasher BRTH 15 No 13 No

Brown-headed nuthatch BHNU 19 No 19 Yes

Carolina chickadee CACH 15 No 16 No

Cerulean warbler CERW 19 Yes 19 Yes

Chimney swift CHSW 16 No 14 No

Chuck-will’s-widow CWWI 14 No 16 Yes

Eastern wood-pewee EAWP 15 No 16 No

Field sparrow FISP 17 No 15 No

Great crested fl ycatcher GCFL 13 No 13 No

Hooded warbler HOWA 13 No 16 No

Kentucky warbler KEWA 18 No 19 Yes

Louisiana waterthrush LOWA 15 Yes 18 Yes

Mississippi kite MIKI 14 No 16 No

Northern bobwhite NOBO 16 No 15 No

Northern parula NOPA 12 No 13 No

Orchard oriole OROR 17 No 18 Yes

Painted bunting PABU 16 No 17 No

Pileated woodpecker PIWO 13 No 16 No

Prairie warbler PRAW 18 Yes 18 Yes

Prothonotary warbler PROW 14 No 17 Yes

Red-cockaded woodpecker RCWO 21 No 21 No

Red-headed woodpecker RHWO 16 Yes 17 Yes

Swainson’s warbler SWWA 20 Yes 20 Yes

Swallow-tailed kite STKI 19 No 18 Yes

Whip-poor-will WPWI 17 Yes 13 No

White-eyed vireo WEVI 15 No 16 No

Wood thrush WOTH 16 Yes 15 Yes

Worm-eating warbler WEWA 18 Yes 15 Yes

Yellow-billed cuckoo YBCU 13 No 15 No

Yellow-breasted chat YBCH 16 No 13 No

Yellow-throated vireo YTVI 16 No 15 No

Yellow-throated warbler YTWA 15 No 16 No

a

Pyle and DeSante (2003).
3

STUDY AREAS

We developed HSI models for landbirds identifi ed as priorities in the Central Hardwoods (CH) 

and West Gulf Coastal Plain/Ouachitas (WGCP) BCRs (Fig. 1). Th e CH, approximately 33 

million ha straddling the Mississippi River, is dominated by deciduous hardwood forest. Th is 

region is bordered to the north and west by the tallgrass prairie ecosystem, to the east by the 

Appalachian Mountains, and to the south by the southern pine belt along the Coastal Plain. 

Th e vast forests of the CH make it an important breeding area for many area-sensitive species, 

Figure 1.—Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions.
4

including the cerulean warbler, Kentucky warbler, Louisiana waterthrush, and worm-eating 

warbler (Panjabi and others 2001). Th e WGCP also is predominantly forested but consists 

primarily of pine: longleaf pine in the south transitioning to loblolly and shortleaf pine in the 

north. As a result, this region contains large populations of pine specialists (e.g., red-cockaded 

woodpecker, brown-headed nuthatch, and pine warbler). Th e WGCP also contains broad 

swaths of bottomland hardwood forest, particularly along the Arkansas, Ouachita, and Sabine 

Rivers, which support substantial populations of the hooded warbler, Kentucky warbler, and 

Swainson’s warbler (Conner and Dickson 1997).

METHODS

Priority Bird Species

We selected priority bird species for modeling by identifying a subset of the forest-breeding 

landbirds in the CH or WGCP with a Partners in Flight (PIF) regional combined score of at 

least 15 (Panjabi and others 2005) or an FWS designation as a Bird of Conservation Concern 

(USDI Fish and Wildl. Serv. 2002) (Table 1). Forty-nine species initially met these criteria. 

We eliminated Bachman’s warbler and the ivory-billed woodpecker from consideration due 

to limited habitat and validation data available within the CH and WGCP for these species. 

Also, we did not model habitat suitability for the ruff ed grouse, broad-winged hawk, eastern 

kingbird, scissor-tailed fl ycatcher, loggerhead shrike, summer tanager, or eastern towhee. We 

added American woodcock, blue-gray gnatcatcher, great crested fl ycatcher, and northern parula 

to ensure the species modeled were representative of a cross section of habitat associations 

(e.g., early successional forest, pine savanna, bottomland hardwoods) and conservation priorities 

(e.g., critical recovery, management attention, planning and responsibility) within these BCRs.

HSI Model Development

In our adaptation of the HSI approach, we assume that habitat suitability is a function of both 

composition and structure at the site and landscape scales. To characterize environmental 

variables at each of these scales, we relied on six nationally consistent datasets:

1. Ecological subsections from the National Ecological Unit Hierarchy.

2. National Landcover Dataset (NLCD) (30-m pixels).

3. National Elevation Dataset (NED) (30-m pixels).

4. National Hydrography Dataset (NHD).

5. State Soil Geographic Database (STATSGO).

6. Forest Inventory and Analysis (FIA) data. 

Th e fi rst fi ve datasets are widely available and commonly used to characterize landscape 

composition and structure. Th e sixth, FIA, provides information on the composition and 

structure of vegetation within forest patches (i.e., site scale) from a national fi eld survey of 

forest lands undertaken by the USDA Forest Service. A description of the methodology used 

to integrate these datasets in a spatially explicit framework is available in Tirpak and others 

(2009b).
5

Table 2.—Parameters and data sources for inputs in priority forest-breeding landbird Habitat Suitability Index 

models, Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions; numbers 

correspond to Suitability Index (SI) functions in text

Species codea

Data source ACFL AMWO BACS BEVI BEWR BAWW

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 1 11111

NLCD and FIA

 Early successional patch size (ha)

NLCD and NHD

 Occurrence of water

 Distance (m) to water 2

NLCD

 Forest patch size (ha) 42 2

 Landscape composition (percent forest in 1-km radius) 5 3

 Landscape composition (percent forest in 10-km radius)

 Occurrence of edge 3

 Distance (m) to edge

 Interspersion – 1 landcover class

 Interspersion – 2 landcover classes 3 22

 Connectivity (km) 4

 Grass-open landcover

FIA

 Basal area (m2

/ha)

 Hardwood basal area (m2

/ha)

 Pine basal area (m2

/ha)

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha)

 Large (> 50 cm d.b.h) tree density (trees/ha)

 Large (> 35 cm d.b.h) pine density (trees/ha)

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha)

 Midstory (11–25 cm d.b.h.) density (trees/ha)

 Snag density (snags/ha) 3

 Large (> 30 cm d.b.h.) snag density (snags/ha)

 Canopy cover (percent) 33 4

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 2 4

DEM

 Slope

NHD

 Distance (m) to stream

STATSGO

 Soil texture 4

 Soil moisture 5

continued

As a fi rst step in developing HSI models, we identifi ed key habitat factors for each species from 

the literature and compiled all pertinent data from these sources. In the interests of parsimony 

and processing time, we generally limited our HSI models to fi ve or fewer suitability indices 

(Table 2). Th e fi rst SI in all models (with the exception of chimney swift) was a function that 

assigned SI scores to unique combinations of landform, landcover, and successional age classes. 

Landform comprised three classes (fl oodplain-valley, terrace-mesic, and xeric-ridge) developed 

from the digital elevation model-derived metrics of aspect, slope, topographic position (the 

diff erence between the elevation value of an individual pixel and the average elevation in a 500- 

and 1,500-m-radius window around it), and relief. Landcover was classifi ed to seven forest types 

derived from the NLCD: low-density residential, transitional-shrubland, deciduous, evergreen, 
6

Species codea

Data source BGGN BWWA BRTH BHNU CACH CERW

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 1 11111

NLCD and FIA

 Early successional patch size (ha) 2

NLCD and NHD

 Occurrence of water

 Distance (m) to water

NLCD

 Forest patch size (ha) 2 2

 Landscape composition (percent forest in 1-km radius) 3

 Landscape composition (percent forest in 10-km radius) 3 4

 Occurrence of edge 4 2

 Distance (m) to edge

 Interspersion – 1 landcover class

 Interspersion – 2 landcover classes

 Connectivity (km)

 Grass-open landcover

FIA

 Basal area (m2

/ha) 5

 Hardwood basal area (m2

/ha) 4

 Pine basal area (m2

/ha)

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha)

 Large (> 50 cm d.b.h) tree density (trees/ha)

 Large (> 35 cm d.b.h) pine density (trees/ha)

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha) 4

 Midstory (11–25 cm d.b.h.) density (trees/ha)

 Snag density (snags/ha) 2 2

 Large (> 30 cm d.b.h.) snag density (snags/ha)

 Canopy cover (percent) 3 5

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 3 3

DEM

 Slope

NHD

 Distance (m) to stream

STATSGO

 Soil texture

 Soil moisture

continued

Table 2.—continued

mixed, orchard-vineyard, and woody wetlands. Finally, successional age class was delineated 

into fi ve classes based on the average diameter at breast height (d.b.h.) of dominant trees in each 

stand, ultimately derived from FIA data: grass-forb (trees < 2.5 cm d.b.h.), shrub-seedling (2.5 

to 7.5 cm), sapling (7.5 to 12.5 cm), pole (12.5 to 37.5 cm), and sawtimber (> 37.5 cm).

We assigned to each of the 105 unique landform, landcover, and successional age class 

combinations (three landform classes × seven forest type classes × fi ve successional age classes) 

an SI value based on the relative habitat suitability rankings reported in the bird habitat 

matrices in Hamel (1992). Th ese matrices qualitatively assess habitat suitability (marginal, 

suitable, optimal) for each bird species based on seral stage (4 classes) and forest type (23 

classes). To adapt these matrices to our purposes, we crosswalked these forest types to our 
7

Species codea

Data source CHSW CWWI EAWP FISP GCFL HOWA

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 11111

NLCD and FIA

 Early successional patch size (ha)

NLCD and NHD

 Occurrence of water

 Distance (m) to water

NLCD

 Forest patch size (ha) 4

 Landscape composition (percent forest in 1-km radius) 2 5

 Landscape composition (percent forest in 10-km radius)

 Occurrence of edge

 Distance (m) to edge 3

 Interspersion – 1 landcover class 1

 Interspersion – 2 landcover classes 2

 Connectivity (km)

 Grass-open landcover 4

FIA

 Basal area (m2

/ha)

 Hardwood basal area (m2

/ha)

 Pine basal area (m2

/ha)

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha) 3

 Large (> 50 cm d.b.h) tree density (trees/ha)

 Large (> 35 cm d.b.h) pine density (trees/ha)

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha)

 Midstory (11–25 cm d.b.h.) density (trees/ha)

 Snag density (snags/ha) 2

 Large (> 30 cm d.b.h.) snag density (snags/ha)

 Canopy cover (percent) 2 3

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 3 2

DEM

 Slope

NHD

 Distance (m) to stream

STATSGO

 Soil texture

 Soil moisture

continued

Table 2.—continued

landform-landcover classes and adapted the four seral stages to our fi ve successional age classes 

(Table 3). First, we identifi ed which of the 23 forest types occurred in the CH or WGCP (seven 

types: Sandhills longleaf pine, oak-gum-cypress, elm-ash-cottonwood, loblolly pine-shortleaf 

pine, mixed pine-hardwood, oak-hickory, and cove hardwoods). We then assigned these forest 

types to specifi c landform and landcover combinations based on the physiography associated 

with these forest communities.

However, not all NLCD landcovers have an analogous forest types in the Hamel classifi cation. 

For example, orchards-vineyards, low-density residential, and transitional-shrubland landcover 

types provide habitat for many priority species but do not have a specifi c forest type association. 

Th erefore, we assigned to orchards-vineyards and low-density residential sites the same SI scores 
8

Species codea

Data source KEWA LOWA MIKI NOBO NOPA OROR

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 1 11111

NLCD and FIA

 Early successional patch size (ha)

NLCD and NHD

 Occurrence of water

 Distance (m) to water

NLCD

 Forest patch size (ha) 3 52 2

 Landscape composition (percent forest in 1-km radius) 6 32

 Landscape composition (percent forest in 10-km radius) 4

 Occurrence of edge

 Distance (m) to edge

 Interspersion – 1 landcover class

 Interspersion – 2 landcover classes 3 5

 Connectivity (km)

 Grass-open landcover 4

FIA

 Basal area (m2

/ha) 3

 Hardwood basal area (m2

/ha) 2

 Pine basal area (m2

/ha) 3

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha)

 Large (> 50 cm d.b.h) tree density (trees/ha)

 Large (> 35 cm d.b.h) pine density (trees/ha)

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha) 4

 Midstory (11–25 cm d.b.h.) density (trees/ha)

 Snag density (snags/ha)

 Large (> 30 cm d.b.h.) snag density (snags/ha)

 Canopy cover (percent) 3 4

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 2 4

DEM

 Slope

NHD

 Distance (m) to stream 2

STATSGO

 Soil texture

 Soil moisture

continued

Table 2.—continued

as those for deciduous landcovers on the assumption that orchards are composed primarily of 

deciduous species and low-density residential sites typically are planted with deciduous shade 

trees. Similarly, we assumed that transitional-shrubland sites are regenerating forests. Where 

there were transitional-shrubland pixels in fl oodplain-valley landforms, we assumed that they 

were hardwood forest regeneration. Th us, we assigned to them the same SI scores associated with 

deciduous habitats. On the higher and drier landforms, transitional-shrubland sites likely are 

dominated by oak and redcedar in the CH and pine in the WGCP, so we assigned to these sites 

the same SI scores as those for mixed and evergreen forest in each BCR, respectively (Table 3).

To assign SI scores to specifi c age classes, we used the relative habitat quality values reported 

in Hamel (1992) for grass-forb, shrub-seedling, and sawtimber seral stages. However, Hamel 
9

Species codea

Data source PABU PIWO PRAW PROW RCWO RHWO

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 1 11111

NLCD and FIA

 Early successional patch size (ha) 3

NLCD and NHD

 Occurrence of water 2

 Distance (m) to water

NLCD

 Forest patch size (ha) 3 32

 Landscape composition (percent forest in 1-km radius) 4 4

 Landscape composition (percent forest in 10-km radius)

 Occurrence of edge 2 5

 Distance (m) to edge 2

 Interspersion – 1 landcover class

 Interspersion – 2 landcover classes 3

 Connectivity (km) 5

 Grass-open landcover

FIA

 Basal area (m2

/ha)

 Hardwood basal area (m2

/ha) 4

 Pine basal area (m2

/ha) 3

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha) 4

 Large (> 50 cm d.b.h) tree density (trees/ha)

 Large (> 35 cm d.b.h) pine density (trees/ha) 6

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha)

 Midstory (11–25 cm d.b.h.) density (trees/ha)

 Snag density (snags/ha) 5 2

 Large (> 30 cm d.b.h.) snag density (snags/ha) 2 3

 Canopy cover (percent) 5

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 4 4

DEM

 Slope

NHD

 Distance (m) to stream

STATSGO

 Soil texture

 Soil moisture

continued

Table 2.—continued

combined sapling- and pole-size trees into a single class, whereas we separated these two 

successional age classes (a segregation we believed was more appropriate for many of our 

species). To tease apart the SI scores for sapling and pole age classes, we averaged the value for 

sapling-pole with shrub-seedling (for sapling) or sawtimber (for pole). Th is approach assumes 

that sapling and pole stands have an equal weighting by Hamel in assessing the relative habitat 

quality for the aggregate age class, and that there is a linear relationship across age classes that 

allows us to discern the relative infl uence of each by simple averaging.

After crosswalking Hamel’s forest types and seral stages to our landform-landcover-successional 

age class matrix, we assigned SI scores to each unique combination based on Hamel’s qualitative 

assessments. Combinations considered optimal (Hamel 1992) were assigned a value of 1.000; 
10

Species codea

Data source SWWA STKI WPWI WEVI WOTH WEWA

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 1 11111

NLCD and FIA

 Early successional patch size (ha)

NLCD and NHD

 Occurrence of water

 Distance (m) to water

NLCD

 Forest patch size (ha) 2 2 23

 Landscape composition (percent forest in 1-km radius) 3 34

 Landscape composition (percent forest in 10-km radius)

 Occurrence of edge 2

 Distance (m) to edge

 Interspersion – 1 landcover class 3

 Interspersion – 2 landcover classes 2

 Connectivity (km)

 Grass-open landcover

FIA

 Basal area (m2

/ha)

 Hardwood basal area (m2

/ha)

 Pine basal area (m2

/ha)

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha)

 Large (> 50 cm d.b.h) tree density (trees/ha)

 Large (> 35 cm d.b.h) pine density (trees/ha)

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha) 4

 Midstory (11–25 cm d.b.h.) density (trees/ha)

 Snag density (snags/ha)

 Large (> 30 cm d.b.h.) snag density (snags/ha)

 Canopy cover (percent) 3 5

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 4 445

DEM

 Slope 2

NHD

 Distance (m) to stream

STATSGO

 Soil texture

 Soil moisture

Table 2.—continued

continued

those considered suitable were assizned a value of 0.667; and those considered marginal 

had a value of 0333. We assumed that forest types and age classes not assigned a qualitative 

habitat ranking were not used and assigned to these combinations an SI score of zero. Where a 

landform-landcover type was represented by more than one of Hamel’s forest types, SI values 

for the forest types were averaged. For example, deciduous landcover on fl oodplain-valley 

landforms are associated with cove hardwood and elm-ash-cottonwood forest communities. 

Cove hardwood is suitable (SI = 0.667) for the Acadian fl ycatcher but elm-ash-cottonwood 

is optimal (SI = 1.000). Th us, this landform-landcover type combination is assigned a base SI 

score of 0.834 (i.e., 1.667/2) prior to adjusting for successional age class (Table 4). Finally, we 

standardized all SI scores in the matrix to ensure that the maximum value was 1.000.
11

a

Pyle and DeSante 2003; see Table 1.

Species codea

Data source YBCU YBCH YTVI YTWA

DEM, NLCD, and FIA

 Landform, landcover, and successional age class 1 111

NLCD and FIA

 Early successional patch size (ha) 3

NLCD and NHD

 Occurrence of water

 Distance (m) to water 3

NLCD

 Forest patch size (ha) 5 2

 Landscape composition (percent forest in 1-km radius) 3 4

 Landscape composition (percent forest in 10-km radius) 4

 Occurrence of edge 2 2

 Distance (m) to edge

 Interspersion – 1 landcover class

 Interspersion – 2 landcover classes

 Connectivity (km)

 Grass-open landcover

FIA

 Basal area (m2

/ha)

 Hardwood basal area (m2

/ha)

 Pine basal area (m2

/ha)

 Sawtimber (> 28 cm d.b.h.) tree density (trees/ha)

 Large (> 50 cm d.b.h) tree density (trees/ha) 2

 Large (> 35 cm d.b.h) pine density (trees/ha)

 Dominant (> 76.2 cm d.b.h.) tree density (trees/ha)

 Midstory (11–25 cm d.b.h.) density (trees/ha) 3

 Snag density (snags/ha)

 Large (> 30 cm d.b.h.) snag density (snags/ha)

 Canopy cover (percent) 4

 Small stem (< 2.5 cm d.b.h.) density (stems/ha) 4

DEM

 Slope

NHD

 Distance (m) to stream

STATSGO

 Soil texture

 Soil moisture

Table 2.—continued

Similarly, we directly assigned SI scores to individual classes for other discrete environmental 

variables (e.g., occurrence of water). For continuous environmental variables (e.g., canopy 

cover), we used CurveExpert 1.38 software (Hyams 2001)1

 to fi t smoothed functions through 

known data points derived from the literature that quantify the relationship between each 

specifi c environmental factor and HSI scores for particular species. Information sources, 

assumptions, and functions (type and equation) are detailed in the model accounts.

1

Th e use of trade, fi rm, or corporation names in this publication is for the information and convenience 

of the reader. Such use does not constitute an offi cial endorsement or approval by the U.S. Department 

of Agriculture or Forest Service of any product or service to the exclusion of others that may be suitable.
12

To calculate the overall HSI score, we determined the geometric mean of SI scores for sitescale and landscape-scale variables separately and then the geometric mean of these means 

together. Use of the geometric mean follows recommendations from the published standards 

for development of HSI models (USDI Fish and Wildl. Serv. 1981). Th e equal weighting of 

individual functions within a spatial scale assumes that all variables are required for a habitat to 

be suitable and that all variables are nonsubstitutable. Further, the equal weighting of functions 

across scales assumes that site and landscape variables are equally important. Th e notable 

exception to use of the geometric mean was for species where both forest patch size and percent 

forest in the landscape are included as model parameters. In these cases, we used the maximum 

SI score from these two variables to account for the use of small forest patches by area-sensitive 

species when small patches are embedded in predominantly forested landscapes (Rosenberg and 

others 1999). For each species, we solicited at least fi ve reviewers with an intimate knowledge of 

the habitat requirements of at least one species. Each reviewer received a standard questionnaire 

requesting feedback on the appropriateness of the functions included in the model. We revised 

models based on reviewers’ comments.

Model Testing

To test the HSI models for reliability, we followed the three-stage framework (calibration, 

verifi cation, and validation) outlined by Brooks (1997). We fi rst ensured that the equations 

Table 3.—Crosswalk between landform-landcover class combinations and vegetation types 

defi ned in Hamel (1992)

Landform Landcover type Hamel vegetation typea

Floodplain-valley Low-density residential Same as deciduous

Transitional-shrubland Same as deciduous

Deciduous Cove hardwoods

Elm-ash-cottonwood

Evergreen Loblolly pine-shortleaf pine

Mixed Mixed pine-hardwood

Orchards-vineyards Same as deciduous

Woody wetlands Oak-gum-cypress

Elm-ash-cottonwood

Terrace-mesic Low-density residential Same as deciduous

Transitional-shrubland Same as mixed in Central Hardwoods, same as 

evergreen in West Gulf Coastal Plain/Ouachitas

Deciduous Oak-hickory

Cove hardwoods

Evergreen Loblolly pine-shortleaf pine

Mixed Mixed pine-hardwood

Orchards-vineyards Same as deciduous

Woody wetlands Elm-ash-cottonwood

Xeric-ridge Low-density residential Same as deciduous

Transitional-shrubland Same as Mixed in Central Hardwoods, same as 

evergreen in West Gulf Coastal Plain/Ouachitas

Deciduous Oak-hickory

Evergreen Loblolly pine-shortleaf pine. Also includes Sandhills 

longleaf pine in West Gulf Coastal Plain/Ouachitas

Mixed Mixed pine-hardwood

Orchards-vineyards Same as deciduous

Woody wetlands Elm-ash-cottonwood a

Hamel (1992).
13

used to predict SI scores resulted in the full potential range of SI scores given the habitat 

conditions within each BCR (i.e., calibration). We then used Spearman rank correlation 

to compare HSI scores to abundance estimates from Breeding Bird Survey (BBS) data 

summarized by ecological subsection (i.e., verifi cation). We ranked subsections by HSI score 

and BBS abundance for each species and within each BCR independently to compensate 

for geographical diff erences in these regions not explicitly incorporated in the HSI models. 

We assessed correlations between these variables based on all subsections and based solely on 

subsections within which each species was detected. Th e former analysis provides insight into 

the overall model performance; the latter addresses the potential bias associated with correctly 

predicting the absence of a rare species in many subsections.

Following verifi cation, we validated HSI models by developing species-specifi c generalized 

linear models that predicted abundance (as indexed by BBS data) from HSI and BCR 

predictor variables. We considered HSI models validated if the general linear model was 

signifi cant (P < 0.100) and the coeffi cient on the HSI predictor variable was both signifi cant 

(P < 0.100) and positive. Detailed results of these analyses are documented in Tirpak and 

others (2009a).

Table 4.—Initial assignment of suitability index scores for Acadian fl ycatcher habitat to landform, landcover 

type, and successional age classes based on Hamel (1992)

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.834 0.834 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.333 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 1.000 1.000 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.667 0.667 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.333 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.834 0.834 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.333 0.333 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.333 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.834 0.834 1.000
14

MODEL ACCOUNTS

Acadian Flycatcher

Status

Th e Acadian fl ycatcher (Empidonax virescens) is a 

long-distance migrant found throughout most of the 

eastern United States. While populations have declined 

in the northern portion of its range (particularly the 

Appalachians) over the last 40 years, populations in the 

South, particularly along the Atlantic and East Gulf 

Coastal Plains, have increased (Sauer and others 2005). 

However, the Acadian fl ycatcher has declined in the 

WGCP (Table 5), and the FWS classifi es this species as 

a Bird of Conservation Concern in the WGCP (Table 

1). Similarly, PIF considers the Acadian fl ycatcher as a planning and responsibility species in 

the CH (regional combined score of 16). In the WGCP, the fl ycatcher has a regional combined 

score of 17, warranting management attention (Table 1).

Natural History

Th e Acadian fl ycatcher is a forest-interior species associated with water throughout most of 

its range: bottomland hardwood and cypress forests in the Southeast and riparian forests and 

ravines in the deciduous forests of the Midwest and Northeast (Whitehead and Taylor 2002). 

Th is species is found in numerous forest types and uses a variety of tree species for nesting. 

However, this bird typically is associated with mesic forest stands and avoids upland oak-hickory 

sites (Klaus and others 2005). Breeding territories are small and average 1 ha (Woolfenden and 

others 2005). Th e Acadian fl ycatcher typically nests in midstory trees and large shrubs in mature 

forests. Canopy cover typically is dense (> 95 percent; Wilson and Cooper 1998), and the 

understory usually is sparse (Bell and Whitmore 2000, Wood and others 2004).

Th e Acadian fl ycatcher is particularly susceptible to forest fragmentation. Aquilani and Brewer 

(2004) found this species only in forest tracts larger than 55 ha in north-central Mississippi. 

Blake and Karr (1987) did not observe the Acadian fl ycatcher in woodlots smaller than 24 ha. 

In east Texas, the Acadian fl ycatcher was absent from riparian buff er strips less than 70 m wide 

(Conner and others 2004). Results were similar in Missouri (Peak and others 2004) and Indiana 

(Ford and others 2001).

Even in large forested tracts (> 600 ha), nest predation and parasitism rates may be 10 to 20 

percent higher if the surrounding landscape is highly fragmented. Nevertheless, Fauth and 

Cabe (2005) did not observe signifi cant eff ects of parasitism on a Blue Ridge study site where 

75 percent of the landscape was forested, including 45 percent more than 250 m from an 

edge. Disturbance, whether natural (e.g., tornado or pest outbreak) or anthropogenic (e.g., 

silvicultural treatments—thinning, selective harvesting, clearcutting, and prescribed burning) 

reduced the abundance and productivity of the Acadian fl ycatcher in most landscapes (Artman 

and others 2001, Duguay and others 2001, Robinson and Robinson 2001, Twedt and others 

2001, Prather and Smith 2003, Blake 2005).

John J. Mosesso, images.nbii.gov
15

Table 5.—Trend estimates (percent change per year) for 40 priority landbird species in the Central 

Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions, 1967 to 2004 (Sauer 

and others 2005)

Central Hardwoods West Gulf Coastal Plain/Ouachitas

Species Trend P na Trend P n

Acadian fl ycatcher -0.3 0.56 107 -2.0 0.05 67

American woodcock -9.1 0.35 3 --b -- --

Bachman’s sparrow -- -- -- -7.8 0.00 27

Bell’s vireo -3.2 0.49 18 -4.7 0.03 14

Bewick’s wren -6.5 0.00 61 0.8 0.88 11

Black-and-white warbler 2.3 0.21 50 -2.9 0.01 60

Blue-gray gnatcatcher -1.0 0.26 118 -0.9 0.36 75

Blue-winged warbler -4.0 0.01 62 -- -- --

Brown thrasher -1.4 0.00 125 -1.4 0.01 64

Brown-headed nuthatch -- -- -- -1.4 0.18 52

Carolina chickadee 0.2 0.70 123 -2.0 0.00 77

Cerulean warbler -6.3 0.00 34 -9.5 0.00 5

Chimney swift -2.6 0.00 124 -1.1 0.15 76

Chuck-will’s-widow -0.9 0.19 64 -1.3 0.04 60

Eastern wood-pewee -1.4 0.00 124 -4.9 0.00 75

Field sparrow -3.2 0.00 125 -3.7 0.01 45

Great crested fl ycatcher -0.8 0.09 123 -1.3 0.04 77

Hooded warbler 2.7 0.08 31 -3.1 0.35 60

Kentucky warbler -0.4 0.32 108 -2.2 0.00 73

Louisiana waterthrush 2.6 0.02 66 -1.3 0.49 28

Mississippi kite 16.3 0.16 2 6.4 0.21 16

Northern bobwhite -3.1 0.00 125 -4.4 0.00 75

Northern parula 3.7 0.00 95 -2.5 0.17 53

Orchard oriole -0.9 0.01 124 -3.0 0.01 75

Painted bunting 19.8 0.61 5 -0.6 0.48 63

Pileated woodpecker 1.8 0.01 112 -0.9 0.14 72

Prairie warbler -2.6 0.00 94 -4.4 0.00 60

Prothonotary warbler 0.0 0.98 52 -5.8 0.00 53

Red-cockaded woodpecker -- -- -- 9.0 0.00 6

Red-headed woodpecker -1.0 0.09 115 -3.2 0.00 68

Swainson’s warbler -- -- -- 23.5 0.23 26

Swallow-tailed kite -- -- -- -- -- --

Whip-poor-will -1.8 0.05 71 6.6 0.22 11

White-eyed vireo -0.4 0.20 120 -0.8 0.19 76

Wood thrush -0.7 0.05 118 -1.4 0.05 67

Worm-eating warbler 0.4 0.77 44 -2.3 0.51 28

Yellow-billed cuckoo -1.9 0.00 125 -1.1 0.00 77

Yellow-breasted chat -1.9 0.00 125 1.3 0.01 75

Yellow-throated vireo 0.9 0.25 99 1.1 0.38 62

Yellow-throated warbler 3.8 0.00 76 -0.9 0.65 43

a

Number of Breeding Bird Survey routes on which trend estimate is based.

b

No trend estimate available.
16

Model Description

Our Acadian fl ycatcher model includes seven variables related to density: landform, 

landcover type, successional age class, distance to water, canopy cover, forest patch size, and 

percent forest in a 1-km radius window.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 6). We directly 

assigned SI scores to these combinations on the basis of habitat suitability data from Hamel 

(1992) on the relative quality of diff erent vegetation types and successional stages for the 

Acadian fl ycatcher. However, we reduced SI scores for sapling and evergreen habitats on the 

basis of data from Hazler (1999).

Because the Acadian fl ycatcher typically is found near water (Whitehead and Taylor 2002), 

we fi t an inverse logistic function to describe the relationship between SI scores for this 

species and increasing distance to water (SI2; Fig 2). Th e fl ycatcher often aligns at least 

one edge of its 1-ha territory along a stream or wetland (Woolfenden and others 2005). 

Table 6.—Relationship of landform, landcover type, and successional age class to suitability index scores for 

Acadian fl ycatcher habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.050 0.917 1.000

Deciduous 0.000 0.000 0.050 0.917 1.000

Evergreen 0.000 0.000 0.017 0.167 0.333

Mixed 0.000 0.000 0.017 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.050 1.000 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.017 0.333 0.333

Deciduous 0.000 0.000 0.042 0.667 0.834

Evergreen 0.000 0.000 0.017 0.167 0.333

Mixed 0.000 0.000 0.017 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.050 1.000 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.017 0.333 0.333

Deciduous 0.000 0.000 0.033 0.500 0.667

Evergreen 0.000 0.000 0.017 0.167 0.333

Mixed 0.000 0.000 0.017 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.050 1.000 1.000
17

 

Distance to Water (m)

Suitability Index Score

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

Figure 2.—Relationship between distance to water and suitability 

index (SI) scores for Acadian fl ycatcher habitat. Equation: SI 

score = 1 - (1.049 / (1 + (1664.953 * e -0.021 * distance to water))).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Figure 3.—Relationship between canopy cover and suitability 

index (SI) scores for Acadian fl ycatcher habitat. Equation: SI 

score = 1.013 / (1.000 + (144082770 * e -0.248 * canopy cover)).

Assuming a circular home range, the diameter of the home range (112.8 m) represents the 

farthest distance from water a bird could be within the home range. On the basis of this 

assumption, we assigned all locations less than 120 m from water SI scores of 1.000 (Table 

7). Th e Acadian fl ycatcher also uses sites that are more than 120 m from water but generally 

are found at lower densities there. Th us, we considered areas 360 m from water (a distance 

of three home range diameters) as having an SI score that is one-quarter of the optimal value 

(0.250) and sites at least 480 m from water as nonhabitat (SI score of zero).

Th e habitat suitability model for the Acadian fl ycatcher also included canopy closure (SI3) as 

a variable because of the strong affi nity of this species for closed-canopy forests (Prather and 

Smith 2003). For this variable, we used a logistic function (Fig. 3) to extrapolate between 

known break points in the canopy cover-relative density relationship (Table 8).

Table 7.—Relationship between distance to water 

and suitability index (SI) scores for Acadian 

fl ycatcher habitat

Distance to water (m)a SI score

0b 1.00

120c 1.00

240b 0.75

360b 0.25

480b 0.00

a

Water defi ned as streams from the National Hydrography 

Dataset (medium resolution) or classifi ed as water, 

woody wetlands, or emergent herbaceous wetlands in the 

National Land Cover Dataset.

b

Assumed value.

c

Woolfenden and others (2005).

Table 8.—Relationship between canopy cover 

and suitability index (SI) scores for Acadian 

fl ycatcher habitat

Canopy cover (percent) SI score

0a 0.00

31b 0.00

73b 0.33

91b 1.00

100a 1.00

a

Assumed value.

b

Prather and Smith (2003).
18

We also included forest patch size (SI4) as a variable because of the sensitivity of the 

Acadian fl ycatcher to fragmentation (Robbins and others 1989) and increasing edge 

density (Parker and others 2005). We used a logarithmic function (Fig. 4) to describe the 

relatively quick increase in suitability of a forest patch with increasing area (Robbins and 

others 1989) (Table 9). We assumed that 312 ha, the minimum forest patch size on which 

Wallendorf and others (2007) always observed the Acadian fl ycatcher, was representative 

of optimal habitat (SI score = 1.000). Nevertheless, the eff ects of forest patch size on 

suitability are infl uenced by the percentage of forest in the landscape. In predominantly 

forested landscapes, small forest patches that may not be used in predominantly nonforested 

landscapes may provide habitat due to their proximity to large forest blocks (Rosenberg 

and others 1999). To capture this relationship, we fi t a logistic function (Fig. 5) to data 

(Table 10) derived from Donovan and others (1997), who observed diff erences in predator 

and brood parasite communities among highly fragmented (< 15 percent), moderately 

fragmented (45 to 50 percent), and lightly fragmented (> 90 percent forest) landscapes. We 

assumed that the midpoints between these classes (30 and 70 percent forest) defi ned the 

specifi c cutoff s for poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) habitat, respectively. 

We used the maximum value of SI4 or SI5 to assess area sensitivity and to account for small 

patches in predominantly forested landscapes and large patches in predominantly nonforested landscapes.

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1 and SI3) and landscape attributes (maximum value of SI4 or SI5 

and SI2) separately and then the geometric mean of these means together.

Overall HSI = ((SI1 * SI3)0.500 * (Max(SI4 or SI5) * SI2)0.500)

0.500

Verifi cation and Validation

Th e Acadian fl ycatcher was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.47) between 

average HSI score and mean BBS abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the Acadian fl ycatcher was 

signifi cant (P = 0.095; R2

 = 0.054), and the coeffi cient on the HSI predictor variable was 

both positive (β = 4.250) and signifi cantly diff erent from zero (P = 0.043). Th erefore, we 

considered the HSI model for the Acadian fl ycatcher both verifi ed and validated (Tirpak and 

others 2009a).
19

 

Forest Patch Size (ha)

Suitability Index Score

0 100 200 300

0.0

0.2

0.4

0.6

0.8

1.0

Figure 4.—Relationship between forest patch size and suitability 

index (SI) scores for Acadian fl ycatcher habitat. Equation: 

SI score = 0.174 * ln(forest patch size) + 0.010.

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Figure 5.—Relationship between landscape composition and 

suitability index (SI) scores for Acadian fl ycatcher habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * 

(landscape composition)).

Table 9.—Relationship between forest patch 

size and suitability index (SI) scores for Acadian 

fl ycatcher habitat

Forest patch size (ha) SI score

0.2a 0.0

15a 0.5

312b 1.0

a

Robbins and others (1989).

b

Wallendorf and others (2007).

Table 10.—Relationship between local 

landscape composition (percent forest in 1-km 

radius) and suitability index (SI) scores for 

Acadian fl ycatcher habitat

Local landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed that value.

b

Dononvan and others (1997).
20

American Woodcock

Status

Th e American woodcock (Scolopax minor) is a popular 

gamebird found throughout the eastern United States 

and southeastern Canada. Although this species breeds 

primarily in the northern portion of its continental 

range, small numbers breed regularly throughout the 

wintering range in the Southeast. Singing ground 

surveys and wing collections from northern latitudes in 

the Central United States document annual 1.8 percent 

declines in woodcock since 1968 (Kelley 2003). Th e status of the relatively small breeding 

population in the Southeast is unknown.

Natural History

Th e American woodcock breeds in early successional habitat throughout its range (Keppie 

and Whiting 1994). Typically, these young forest stands are on moist, uncompacted soils 

that allow the woodcock to probe for earthworms, the bird’s preferred food (Steketee 2000). 

Equally important is an interspersion of the forest with openings that provide sites for both 

courtship displays and roosting (Sepik and Derleth 1993). Openings used by woodcock 

in Maine generally were at least 1.2 ha (Dunford and Owen 1973). Given the affi nity of 

the woodcock for openings and early successional habitat, Sprankle and others (2000) 

recommended even-age forest management in rotational blocks to ensure that both habitat 

requirements are met.

Most of the available quantitative information on breeding habitat for the American 

woodcock is from the Northeast, particularly Maine and Pennsylvania (Straw and others 

1986, McAuley and others 1996). Shrub cover generally is high (75 to 87 percent; 

Morgenweck 1977), while overstory cover typically is moderate (50 to 64 percent; Dunford 

and Owen 1973, Gregg and others 2000). Nests are in young forest stands (Morgenweck 

1977). McAuley and others (1996) compared nest sites to random sites and found lower 

basal area and fewer coniferous saplings, but higher densities of deciduous saplings and 

shrub stems around nests sites. Young broods inhabit young to mid-age forest interspersed 

with openings; older broods occupy sites with greater basal area but fewer mature trees 

(Morgenweck 1977).

Many habitat variables have been associated with the presence of woodcock (Storm and 

others 1995; Klute and others 2002). Landcover variables were the best predictors at fi ne 

scales whereas indices of landscape heterogeneity were the most important predictors at large 

spatial scales (Klute and others 2000). Murphy and Th ompson (1993) developed a model 

to predict the density of males on singing grounds in central Missouri that contained small 

stem density (≤ 2.5 cm d.b.h.), tree density (> 2.5 cm d.b.h.), and fi eld size as predictor 

variables.

U.S. Fish & Wildlife Service
21

Model Description

Th e American woodcock HSI model includes seven variables: landform, landcover, 

successional age class, small stem density (< 2.5 cm d.b.h.), composition of appropriately 

sized foraging-nesting and courtship-roosting habitat patches in the landscape, soil moisture, 

and soil texture.

Th e fi rst suitability function combines landform, landcover type, and successional age class 

into a single matrix (SI1) that defi nes unique combinations of these classes (Table 11). 

Because the woodcock prefers moist habitats with high deciduous stem densities, we assigned 

the highest SI scores to sapling-aged transitional, deciduous, and woody wetland cover types 

in fl oodplain-valley landforms. We considered mixed and evergreen forests as well as xericridge landforms as poor habitat for the American woodcock.

We included small stem density (SI2) as a model function because the woodcock relies on 

vertical structure to provide security from predators as it forages, nests, and loafs during the 

day. McAuley and others (1996) summarized habitat attributes around woodcock nest sites 

from seven studies in which stem density ranged from 5,051 to 49,250 stems per ha. Due to 

the relatively small sample size and the lack of geographic representation within the samples 

Table 11.—Relationship of landform, landcover type, and successional age class to suitability index scores for 

American woodcock habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 1.000 0.667 0.333

Deciduous 0.000 0.000 1.000 0.667 0.333

Evergreen 0.000 0.000 0.500 0.250 0.125

Mixed 0.000 0.000 0.667 0.333 0.167

Orchard-vineyard 0.000 0.000 0.667 0.333 0.167

Woody wetlands 0.000 0.000 1.000 0.667 0.333

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.834 0.500 0.250

Deciduous 0.000 0.000 0.834 0.500 0.250

Evergreen 0.000 0.000 0.400 0.200 0.100

Mixed 0.000 0.000 0.500 0.250 0.125

Orchard-vineyard 0.000 0.000 0.500 0.250 0.125

Woody wetlands 0.000 0.000 0.834 0.500 0.250

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.750 0.400 0.167

Deciduous 0.000 0.000 0.750 0.400 0.167

Evergreen 0.000 0.000 0.333 0.167 0.083

Mixed 0.000 0.000 0.400 0.200 0.100

Orchard-vineyard 0.000 0.000 0.500 0.417 0.000

Woody wetlands 0.000 0.000 0.750 0.400 0.167
22

(both New York and Pennsylvania are represented twice), we used the midpoint of this 

range rather than the average to summarize these data. With three of the studies observing 

stem densities of at least 44,000 and three observing densities of approximately 14,000 

stems per ha (+/- 600 stems/ha), we believed there was adequate evidence to assign to the 

midpoint of this range (27,125 stems/ha) a higher SI score than average (0.500). Th erefore, 

we assigned 27,125 stems per ha an SI score of 0.900, the maximum stem density (49,250) 

an SI score of 1.000 and the minimum density (3,767 stems/ha, as reported by Murphy and 

Th ompson [1993]) an SI score of 0.250 (Table 12). We fi t a logistic function through these 

data points to quantify the small stem density-SI score relationship (Fig. 6).

Th e next two variables relate to the minimum size of habitat patches used by the American 

woodcock. Movement rates within diurnal foraging and nesting habitats often are low, 

resulting in small diurnal home ranges (≤ 0.3 ha; Hudgins and others 1985). Conversely, 

the woodcock displays and roosts in relatively large openings at night (≥ 1.6 ha; Keppie and 

Whiting 1994). We used these data to establish minimum area thresholds for forests and 

openings, respectively. Nevertheless, the ultimate suitability of either of these habitat types 

is related to their interspersion with one another, as the woodcock requires both. Ideally, 

these habitats should be separated by less than 400 m (Hudgins and others 1985) even 

though the average home range may be at least 74 ha (485-m radius; Keppie and Whiting 

1994). Because home ranges may encompass areas of nonhabitat, the American woodcock 

sometimes is found where the proportion of these habitat types within a typical home range 

is relatively small (e.g., 0.1; Table 13). We assumed that the woodcock derives greater 

benefi t from increasing proportions of early successional forest habitat than fi eld habitat 

within its home ranges due to greater foraging opportunities and increased protection from 

predators. Th us, our table defi ning the relationship between landscape composition (SI3) 

Table 12.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems*1,000/ha) on suitability index (SI) 

scores for American woodcock habitat

Small stem density SI score

0accc 0.00

3.767b 0.25

27.125c 0.90

49.250d 1.00

a

Assumed value.

b

Murphy and Thompson (1993).

c

McAuley and others (1996).

d

Coon and others (1982).

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0

Figure 6.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems*1000/ha) and suitability index (SI) scores for 

American woodcock habitat. Equation: SI score = 1.029 * 

(0.998 – e -0.076 * (small stem density / 1000)).
23

and SI scores shows greater increases in suitability with relatively modest increases in diurnal 

habitat compared to the increases in suitability associated with similar proportional increases 

in openings.

Soil properties also infl uence American woodcock habitat suitability. Th is species feeds 

nearly exclusively on earthworms, which it probes for preferentially in moist loamy soils 

(Rabe and others 1983). Because soils with excessive clay or sand contain insuffi cient, 

accessible earthworms with which to support a foraging woodcock, we included both soil 

texture (SI4) and soil drainage (SI5) as variables in the habitat suitability model. We used 

the STATSGO database to defi ne soil characteristics. Soil texture classes from STATSGO 

were crosswalked to soil texture classes from the soil triangle (Table 14) and then assigned 

SI scores on the basis of texture descriptions in Rabe and others (1983) (Table 15). We 

also assumed that soil drainage class was associated with soil moisture content and similarly 

assigned SI scores to these drainage classes (Table 16) based on observations from Rabe 

and others (1983), who documented higher probing rates in soils with greater moisture 

contents.

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI2) and landscape factors (SI3, SI4 and SI5) separately and then the 

geometric mean of these means together.

Overall HSI = ((SI1 * SI2)0.500 * (SI3 * SI4 * SI5)0.333)

0.500

Table 13.—Suitability index scores for American woodcock habitat based on composition of open and forest 

habitat within 500-m radius 

Proportion opena

Proportion 

forestb 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.10 0.05 0.05 0.05 0.05 0.05 0.00 0.00 0.00 0.00 0.00

0.20 0.10 0.10 0.10 0.10 0.10 0.05 0.05 0.05 0.00

0.30 0.20 0.20 0.20 0.20 0.20 0.10 0.10 0.05

0.40 0.40 0.40 0.40 0.40 0.40 0.20 0.10

0.50 0.60 0.60 0.60 0.60 0.60 0.40

0.60 0.80 0.80 0.80 0.80 0.80

0.70 1.00 1.00 1.00 1.00

0.80 1.00 1.00 1.00

0.90 1.00 1.00

1.00 1.00

a

Merged grasslands, pasture/hay, fallow, urban/recreational grasses, emergent herbaceous wetlands, grass-forb, and shrubseedling forests ≥1.6 ha.

b

Sites with a positive SI1 score (Table 11) and ≥ 0.3 ha.
24

Verifi cation and Validation

Th e American woodcock was observed only in 50 of the 88 subsections within the CH and 

WGCP. Spearman rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship 

(rs = 0.36) between average HSI score and mean BBS route abundance across all subsections. 

When the 38 subsections in which the American woodcock was not found were removed 

from the analysis, the correlation not only remained signifi cant (P ≤ 0.001) but also was 

more strongly positive (rs = 0.68). Th us, the HSI model is predicting habitat for this species 

in subsections where it was not detected on BBS routes. Th e generalized linear model 

predicting BBS abundance from BCR and HSI for the American woodcock was signifi cant 

(P ≤ 0.001; R2

 = 0.218), and the coeffi cient on the HSI predictor variable was both positive 

(β = 0.090) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the 

HSI model for the American woodcock both verifi ed and validated (Tirpak and others 

2009a).

Table 14.—Crosswalk of soil texture classes defi ned 

in STATSGO soil database to soil texture triangle 

classes

STATSGO soil texture class

Soil texture 

triangle class

Clayey Clay

Clayey over loamy Clay

Clayey-skeletal Clay

Coarse-loamy Sandy loam

Coarse-silty Sandy loam

Fine Silt

Fine-loamy Silt loam

Fine-loamy over clayey Silty clay loam

Fine-loamy over sandy or sandyskeletal Silt loam

Fine-silty Silt

Fine-silty over clayey Silt

Loamy Loam

Loamy-skeletal Loam

Loamy-skeletal over clayey Loam

Not used None

Sandy Sand

Very-fi ne Silty clay

All others None

Table 15.—Suitability index (SI) scores for American 

woodcock habitat based on soil texture triangle 

classes

Soil texture triangle class SI score

Clay 0.0a

Silty clay 0.0a

Silty clay loam 0.2a

Silt loam 0.4a

Silt 0.0a

Loam 1.0b

Sandy loam 0.8b

Loamy sands 0.0a

Sands 0.0b

Sandy clay loam 0.4a

Sandy clay 0.0a

Clay loam 0.1b

None 0.0a

 aAssumed value.

b

Rabe and others (1983).

Table 16.—Suitability index (SI) scores for American 

woodcock habitat based on soil moisture, as defi ned 

by drainage class in the STATSGO soil database

Soil moisture SI score

Very poorly 1.0a

Poorly 1.0a

Somewhat poorly 0.5a

Moderately well 0.1a

Well 0.0a

Somewhat excessively 0.0a

Excessively 0.0a

a

Rabe and others (1983).
25

Bachman’s Sparrow

Status

Bachman’s sparrow (Aimophila aestivalis) is a resident bird 

associated with pine savannas and other open habitats 

throughout the Southeastern United States. Although its 

range expanded north to include Illinois, Indiana, and 

Ohio at the turn of the 20th century (likely in response 

to widespread land clearing), the range of this species 

has contracted steadily over the last 100 years. Today, 

the Bachman’s sparrow is restricted to the extreme 

Southeast. BBS data from the central United States 

indicates signifi cant annual declines (8.1 percent) over 

the past 40 years; declines have been particularly steep 

since 1980 (20.8 percent/year). Th is species is a Bird of Conservation Concern in both the 

CH and WGCP (Table 1). Similarly, this bird has a regional combined score of 20 in both 

regions, and PIF considers this species in need of critical recovery in the CH and immediate 

management in the WGCP (Table 1).

Natural History

Bachman’s sparrow occupies two primary habitats in the Southeast: mature (> 80 year old) 

pine stands that are frequently burned (< 3-year burn interval) and recently cutover areas (< 

5 year old; Dunning and Watts 1990). However, productivity is lower in these latter habitats 

(one vs. three off spring/pair/year; Liu and others 1995, Perkins and others 2003a). On the 

basis of this lower productivity and the poor colonizing ability of this species—suitable 

clearcut habitats more than 3 km from a source population generally remained unoccupied 

in South Carolina (Dunning and others 1995)—Tucker and others (2004) considered 

Bachman’s sparrow as endemic to mature longleaf pine stands.

In all studies of Bachman’s sparrow habitat, two features are identifi ed repeatedly: a dense 

grass understory and an open overstory, both of which are maintained through frequent fi res 

(Haggerty 1998, Plentovich and others 1998, Tucker and others 2004, Wood and others 

2004). Stands managed for the red-cockaded woodpecker via prescribed burning typically 

provide excellent habitat for the Bachman’s sparrow as well because the fi res are frequent 

enough to suppress dense woody understories and maintain sparse canopies (Wilson and 

others 1995, Plentovich and others 1998, Provencher and others 2002, Wood and others 

2004).

Model Description

Our habitat suitability model for the Bachman’s sparrow includes six variables: landform, 

landcover type, successional age class, forest patch size, canopy cover, and connectivity.

Th e fi rst suitability function combines landform, landcover type, and successional age class 

into a single matrix (SI1) that defi nes unique combinations of these classes (Table 17). We 

directly assigned SI scores to these combinations on the basis of data from Hamel (1992) on 

the relative quality of diff erent vegetation types in diff erent successional stages for this species.

U.S. Forest Service
26

We also included forest patch size (SI2) as a variable because of the relatively large home 

range for this species (mean = 2.5 ha; Haggerty 1998). Home ranges varied among regions 

and habitat types (reviewed in Mitchell 1998). Th ey were slightly larger in evergreen stands 

(4.8 ha) than in ephemeral, early successional habitats (2.2 ha). We fi t a logistic function 

(Fig. 7) through these data points, assuming that the former represented a stand area that 

would be occupied reliably and that the latter value was a minimum below which the 

sparrow would be absent (Table 18).

We included canopy cover (SI3) as a third suitability function to satisfy the two-fold 

requirement for open canopies and dense understories, two habitat components often well 

correlated (Table 19). Haggerty (1998) observed an average canopy cover of 9.5 percent at 

sites occupied by the Bachman’s sparrow and 40 percent canopy cover at unoccupied sites. 

Wood and others (2004) observed 20 times more Bachman’s sparrows in habitats with 25 

to 50 percent canopy cover than sites with 50 to 75 percent cover. We fi t an inverse logistic 

function to these data to extrapolate values between these known points (Fig. 8).

Because this resident species is restricted to a specialized habitat, occupancy of a site by 

the Bachman’s sparrow is aff ected by the ability of dispersers to colonize it. Th is ability is 

Table 17.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for Bachman’s sparrow habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 1.000 0.333 0.000 0.000 1.000

Deciduous 1.000 0.333 0.000 0.000 0.000

Evergreen 1.000 0.333 0.000 0.000 1.000

Mixed 1.000 0.333 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 1.000 0.333 0.000 0.000 1.000

Deciduous 1.000 0.333 0.000 0.000 0.000

Evergreen 1.000 0.333 0.000 0.000 1.000

Mixed 1.000 0.333 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 1.000 0.333 0.000 0.000 1.000

Deciduous 1.000 0.333 0.000 0.000 0.000

Evergreen 1.000 0.333 0.000 0.000 1.000

Mixed 1.000 0.333 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000
27

directly aff ected by the connectivity (or conversely the isolation) of habitat patches (SI4). 

Birds are unable to colonize clearcuts more than 3 km distant before succession renders 

habitat conditions within them unsuitable (Dunning and others 1995). Although isolation 

also may aff ect the occupancy of mature evergreen stands, habitat conditions within them 

are less ephemeral. Th us, the Bachman’s sparrow has a potentially longer time to colonize 

these stands. To compensate for this diff erential temporal window in accessibility, we used a 

15-km distance threshold to fi t a longer tail to the function relating connectivity of patches 

to their suitability as Bachman’s sparrow habitat (Table 20, Fig. 9). We also assumed that 

source populations were restricted to mature evergreen forest stands with a preliminary 

overall SI score (calculated from SI1, SI2, and SI3) that was greater than 0.8.

Figure 7.—Relationship between forest patch size and suitability 

index (SI) scores for Bachman’s sparrow habitat. Equation: 

SI score = 1.000 / (1 + (699817.120 * e -3.845 * forest patch size)).

Figure 8.—Relationship between canopy cover and suitability 

index (SI) scores for Bachman’s sparrow habitat. Equation: 

SI score = 1 - (1.000 / (1 + (126024970 * e -0.3455 * canopy cover))).

 

Forest Patch Size (ha)

Suitability Index Score

0.0 2.5 5.0 7.5

0.0

0.2

0.4

0.6

0.8

1.0

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 18.—Relationship between forest patch size 

and suitability index (SI) scores for Bachman’s 

sparrow habitat

Forest patch size (ha) SI score

0.0a 0.0

2.2b 0.0

3.5b 0.5

4.8b 1.0

6.0a 1.0

a

Assumed value.

b

Stober (1996), reviewed in Mitchell (1998).

Table 19.—Relationship between canopy cover 

and suitability index (SI) scores for Bachman’s 

sparrow habitat

Canopy cover (percent) SI score

0.0a 1.00

9.5b 1.00

37.5c 1.00

62.5c 0.05

100.0a 0.00

a

Assumed value.

b

Haggerty (1998).

c

Wood and others (2004).
28

To calculate the overall HSI score, we calculated the geometric mean of the two SIs related 

to forest structure (SI1 and SI3) and landscape attributes (SI2 and SI4) separately and then 

the geometric mean of these values together.

Overall HSI = ((SI1 * SI3)0.500 * (SI2 * SI4)0.500)

0.500

Verifi cation and Validation

Bachman’s sparrow was found only in 29 of the 88 subsections within the CH and WGCP. 

Spearman rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.62) 

between average HSI score and mean BBS route abundance across all subsections. However, 

when subsections where the Bachman’s sparrow was not found were removed from the 

analysis, the relationship was not signifi cant (rs

 = 0.24; P = 0.208). Th us, the HSI model 

predicts the absence of the Bachman’s sparrow better than its abundance in subsections 

where it is found. Th e generalized linear model predicting BBS abundance from BCR and 

HSI for the Bachman’s sparrow was signifi cant (P ≤ 0.001; R2

 = 0.567), and the coeffi cient 

on the HSI predictor variable was both positive (β = 0.908) and signifi cantly diff erent from 

zero (P = 0.079). Th erefore, we considered the HSI model for the Bachman’s sparrow both 

verifi ed and validated (Tirpak and others 2009a).

Figure 9.—Relationship between distance to nearest evergreen 

sawtimber habitat with initial suitability index (SI) score >0.8 

and SI scores for Bachman’s sparrow habitat. Equation: 

SI score = 1 / (1.000 + (0.002 * (distance to evergreen 

sawtimber habitat with initial SI score >0.8)4.066)).

Table 20.—Relationship between distance to 

nearest evergreen sawtimber habitat with initial 

suitability index (SI) score > 0.8 and SI scores for 

Bachman’s sparrow habitat

Habitat connectivity (km) SI score

0a 1.00

6b 0.25

15b 0.00

a

Dunning and others (1995).

b

Assumed value.

 

Distance to Mature Evergreen Stand (km)

Suitability Index Score

0 5 10 15

0.0

0.2

0.4

0.6

0.8

1.0
29

Bell’s Vireo

Status

Bell’s vireo (Vireo bellii) is a scrubland specialist that 

reaches the eastern limit of its range in the CH and 

WGCP. Th roughout both regions this species has declined 

over the past 40 years, with the most severe declines in the 

southern portion of the eastern range (-4.7, -6.6, and -10.1 

percent annually in Missouri, Oklahoma, and the OzarkOuachita Plateau, respectively; Sauer and others 2005). 

Bell’s vireo has a regional combined score of 15 in the 

CH and 16 in the WGCP, and PIF considers the species as 

requiring management attention in both regions (Table 1). Th e FWS also recognizes Bell’s 

vireo as a Bird of Conservation Concern in both BCRs (Table 1).

Natural History

Bell’s vireo is a small, Neotropical migrant associated with dense, low, shrubby vegetation 

(Brown 1993). It uses a variety of early successional scrubland habitats that meet these 

requirements (e.g., riparian woods, brushy fi elds, and regenerating forest). Most of the 

research on this species was conducted in the West, where Bell’s vireo is alternately described 

as a riparian specialist (particularly the federally endangered subpopulation of least Bell’s 

vireo in California) or a scrub-shrub generalist. Th is bird nests in dense shrub or understory 

vegetation 0.5 to 1.5 m above the ground, making its nests susceptible to both terrestrial 

and avian predators. Predation and brood parasitism are the primary causes of nest failure 

(Budnik and others 2000, 2002; Powell and Steidl 2000). Increasing the density of large 

shrub patches may improve Bell’s vireo habitat in Missouri (Budnik and others 2002).

Model Description

Th e model for Bell’s vireo includes six variables: landform, landcover, successional age class, 

interspersion of forest and open areas, edge, and small stem density.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 21). We directly 

assigned SI values to these combinations on the basis of data from Hamel (1992) relating 

vegetation types and successional age class to habitat suitability estimates for Bell’s vireo.

Both landcover and age class data were used to identify upland shrublands in grassland 

landscapes, the preferred habitat for this species in its eastern range (Budnik and others 

2000). We used a 10-ha moving window (an average home range; Budnik and others 2000) 

to assess the interspersion of shrubland and grassland habitats (SI2). We assumed that an 

area containing 50 percent of each habitat type was ideal (Table 22). To extrapolate from 

this point we used broad incremental changes in habitat suitability (20 percent) and applied 

these symmetrically to 10-percent incremental changes in the proportion of scrubland or 

grassland. Landscapes lacking shrublands or grasslands were unsuitable and assigned an SI 

score of zero.

Steve Maslowski, U.S. Fish & Wildlife Service
30

Table 21.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for Bell’s vireo habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.500 0.250 0.125 0.000 0.000

Deciduous 0.500 0.250 0.125 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.500 0.250 0.125 0.000 0.000

Woody wetlands 0.500 0.500 0.250 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.500 1.000 0.750 0.000 0.000

Deciduous 0.250 0.500 0.375 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.250 0.500 0.375 0.000 0.000

Woody wetlands 1.000 0.500 0.250 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.500 1.000 0.750 0.000 0.000

Deciduous 0.500 1.000 0.750 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.500 1.000 0.750 0.000 0.000

Woody wetlands 1.000 0.500 0.250 0.000 0.000

Table 22.—Relative composition of scrubland and grassland within 10-ha moving window on suitability 

index scores for Bell’s vireo habitat

Proportion grasslanda

Proportion 

scrubland b 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.1 0.0 0.0 0.0 0.1 0.2 0.2 0.2 0.2 0.2 0.2

0.2 0.0 0.0 0.1 0.2 0.4 0.4 0.4 0.4 0.4

0.3 0.0 0.1 0.2 0.4 0.6 0.6 0.6 0.6

0.4 0.0 0.2 0.4 0.6 0.8 0.8 0.8

0.5 0.0 0.2 0.4 0.6 0.8 1.0c

0.6 0.0 0.2 0.4 0.6 0.8

0.7 0.0 0.2 0.4 0.6

0.8 0.0 0.2 0.4

0.9 0.0 0.2

1.0 0.0

a

Grasslands/herbaceous, pasture/hay, and grass-forb successional age class.

b

Shrub-seedling and sapling successional age classes.

c

Budnik and others (2000); all other values assumed.
31

Bell’s vireo uses a variety of young woody habitats (Brown 1993); however, birds also nest 

along the edges of sapling stands and in hedgerows (Budnik and others 2002). Th erefore, 

we included edge (SI3) as a parameter in the Bell’s vireo HSI model. To identify edges, we 

examined the eight pixels surrounding each sapling age class pixel to determine whether 

any were classifi ed as shrub-seedling or grass-forb age class forest or as a nonforest landcover 

class. If so, the central pixel in the 3 × 3 pixel window (90 x 90 m) was assigned an SI score 

of 1.000; if not, it was assigned a zero. We assigned to grass-forb and shrub-seedling pixels 

an SI score of 1.000 regardless of edge (Table 23). Similarly, we always assigned to pole and 

sawtimber pixels an SI score of zero regardless of edge.

We also included small stem density (SI4) as a component of the overall Bell’s vireo HSI 

model because of the importance of dense woody shrub cover for this species. Farley (1987) 

measured an average of 9.8 stems greater than 2 mm per 1-m diameter plot (approximately 

392,000 stems/ha) in Bell’s vireo territories. Th is relatively high stem value included woody 

and nonwoody stems of all sizes greater than 2 mm; therefore, we assumed that that only 

one-eighth of these stems (49,000 = 1⁄8 * 392,000) were woody and less than 2.5 cm d.b.h. 

and that this value represented optimal habitat (Table 24, Fig. 10).

To calculate the overall HSI score for Bell’s vireo, we fi rst determined the geometric mean 

of the suitability indices related to forest structure (SI1 and SI4) and landscape attributes 

(SI2 and SI3) separately and then determined the geometric mean of these values together. 

Because SI3 applies only to sapling habitats, HSI scores were calculated diff erently for sapling 

Figure 10.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores 

for Bell’s vireo habitat. Equation: SI score = 1.001 / (1.000 + 

(85.005 * e -0.222 * (small stem density / 1000))).

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0

Table 23.—Infl uence of edge occurrence on 

suitability index (SI) scores for Bell’s vireo habitat

3 × 3 pixel window around forest 

pixel includes fi elda SI score

Yesb 1.0

No 0.0

a

Field defi ned as any shrub-seedling or grass-forb age 

class pixel, natural grasslands/herbaceous, or pasture/

hay. Forest defi ned as any used sapling age class pixel 

of transitional, shrublands, deciduous, orchard, or woody 

wetlands.

b

Grass-forb and seedling-shrub habitats used regardless 

of edge.

Table 24.—Relationship between small stem 

(< 2.5 cm d.b.h.) density (stems * 1,000/ha) and 

suitability index (SI) scores for Bell’s vireo habitat

Small stem density SI score

0a 0.00

10a 0.10

25a 0.75

49 b 1.00

a

Assumed value.

b

Farley (1987).
32

successional age class stands than for grass-forb or shrub-seedling successional age class 

stands. To determine the overall SI score across the entire BCR, we added suitability scores 

from individual age classes across the entire landscape.

For grass-forb and shrub-seedling habitats:

HSIGF and SS = (((SI1 * SI4)0.500) * (SI2))0.500

For sapling habitats:

HSISap = ((SI1 * SI4)0.500 * (SI2 * SI3)0.500)

0.500

Overall HSI = HSIGF and SS + HSISap 

Verifi cation and Validation

Bell’s vireo was found in 54 of the 88 subsections within the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs = 0.44) between 

average HSI score and mean BBS route abundance across all subsections. Removing 

subsections in which Bell’s vireo was not observed had a minimal eff ect on these results (rs

= 0.46; P ≤ 0.001). Th e generalized linear model predicting BBS abundance from BCR and 

HSI for the Bell’s vireo was signifi cant (P = 0.042; R2

 = 0.072); however, the coeffi cient on 

the HSI predictor variable was negative (β = -19.906) and not signifi cantly diff erent from 

zero (P = 0.544). Th erefore, we considered the HSI model for the Bell’s vireo verifi ed but not 

validated (Tirpak and others 2009a).
33

Bewick’s Wren

Status

Bewick’s wren (Th ryomanes bewickii) was once a common 

resident throughout the Southeast and mid-Atlantic. 

However, its range has contracted steadily over the last 

century and today this species is virtually absent east of 

the Mississippi River (Kennedy and White 1997). BBS 

data from FWS Region 4 indicates that populations have 

declined by 12.8 percent per year over the last 40 years 

(Sauer and others 2005). Th e decline of this species coincided 

with the range expansion of the house wren, which often destroys Bewick’s wren nests in 

areas where the species’ ranges overlap (Kennedy and White 1996). Bewick’s wren is a Bird 

of Conservation Concern in both the CH and WGCP (Table 1). PIF identifi es the species as 

requiring both critical recovery in the WGCP (regional combined score = 16) and immediate 

management attention in the CH (regional combined score = 15).

Natural History

Bewick’s wren is a small resident passerine that breeds in a variety of vegetation types, including 

brushy areas, scrub and thickets in open country, and open and riparian woodlands (Kennedy 

and White 1997). Th is plasticity has produced confl icting reports of habitat associations in 

the literature (e.g., dry vs. riparian, open woodlands vs. shrub thickets). However, this species 

likely responds most strongly to the availability of nest sites. Bewick’s wren nests in cavities 

or opportunistically in crevices up to 10 m high. In the eastern portion of its range, this bird 

often lives near human habitation, particularly farmland. As mentioned, population declines 

of this species may be partly the result of competition with the house wren (Kennedy and 

White 1996). Bewick’s wren is found primarily in grassland scrub while the house wren occurs 

primarily in secondary growth on abandoned agricultural land and in residential areas. Both 

species exploit the full range of these habitat types, and populations of both expanded as these 

latter types increased. However, as scrub habitats declined, Bewick’s wren may have declined 

because its primary source habitat no longer was abundant.

Model Description

Our model for Bewick’s wren includes fi ve variables: landform, landcover, successional age 

class, interspersion of forest and open habitats, and snag density.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 25). We then 

directly assigned an SI score to these combinations on the basis of data from Hamel (1992) on 

the relative quality of Bewick’s wren habitat based on vegetation type and successional age class.

We also considered as important for this species the interspersion of forest and grassland 

habitats (SI2), as Bewick’s wren is most abundant in semi-open areas containing about 40 

percent woodland (Pogue and Schnell 1994; Table 26). We relied on data from Pogue and 

Schnell to defi ne SI values along the diagonal axis of our interspersion table (where forest and 

grassland totaled 100 percent) and completed the table from these values.

Dave Menke, U.S. Fish & Wildlife Service
34

Table 25.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for Bewick’s wren habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 1.000 1.000 0.500 0.000 0.000

Transitional-shrubland 1.000 1.000 0.500 0.000 0.000

Deciduous 0.500 0.500 0.250 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 1.000 1.000 0.500 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Terrace-mesic Low-density residential 1.000 1.000 0.500 0.000 0.000

Transitional-shrubland 1.000 1.000 0.500 0.000 0.000

Deciduous 0.500 0.500 0.250 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 1.000 1.000 0.500 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Xeric-ridge Low-density residential 1.000 1.000 0.500 0.000 0.000

Transitional-shrubland 1.000 1.000 0.500 0.000 0.000

Deciduous 0.500 0.500 0.250 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 1.000 1.000 0.500 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Table 26.—Infl uence of interspersion between forest and open habitats (as indexed by relative composition 

within 10-ha moving window) on suitability index scores for Bewick’s wren habitat

Proportion opena

Proportion 

forestb 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00c

0.1 0.00 0.00 0.05 0.10 0.10 0.20 0.20 0.20 0.20 0.20c

0.2 0.00 0.05 0.10 0.15 0.20 0.25 0.40 0.40 0.40c

0.3 0.00 0.05 0.20 0.25 0.60 0.80 0.80 0.80c

0.4 0.00 0.05 0.20 0.40 0.80 1.00 1.00c

0.5 0.00 0.05 0.20 0.40 0.80 1.00c

0.6 0.00 0.10 0.20 0.40 0.80c

0.7 0.00 0.10 0.20 0.40c

0.8 0.00 0.10 0.20c

0.9 0.00 0.10c

1.0 0.00c

a

Open = grasslands, herbaceous planted (pasture-hay, fallow, and urban-recreational grasses), emergent herbaceous wetlands. 

b

Forest = forested upland, low-density residential, shrubland, transitional, and woody wetlands.

c

Pogue and Schnell (1994); all other values assumed.
35

We also included snag density (SI3) in our model of Bewick’s wren habitat because as a 

secondary cavity nester, this species responds strongly to nest-site availability. We assumed 

that higher snag densities would decrease competition with other cavity nesters, improving 

habitat quality. Specifi c data relating snag density to Bewick’s wren habitat suitability were 

not available, so we assumed that the average snag density observed by Sedgwick and Knopf 

(1990) (16.4 snags/ha) within home ranges of the house wren, a secondary cavity nester of 

similar size, represented average habitat suitability (SI score = 0.500) for the Bewick’s wren. 

We coupled this information with data from Rumble and Gobeille (2004) (Table 27) on the 

relative density of the house wren in habitats with diff erent snag densities to build a logistic 

function quantifying the relationship between habitat suitability and snag density (Fig. 11).

To calculate the overall HSI score, we fi rst calculated the geometric mean of the two 

suitability indices related to forest structure attributes (SI1 and SI3), and then the geometric 

mean of this result and the SI related to interspersion (SI2).

Overall HSI = ((SI1 * SI3)0.500 * SI2)0.500

Verifi cation and Validation

Bewick’s wren was found in 74 of the 88 subsections within the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs = 0.40) between 

average HSI score and mean BBS route abundance across subsections. However, this 

relationship was weaker (rs = 0.35; P = 0.002) when subsections in which the Bewick’s wren 

was not detected were removed from the analysis. Th e generalized linear model predicting 

BBS abundance from BCR and HSI for the Bewick’s wren was not signifi cant (P = 0.517; R2

= 0.015), and the coeffi cient on the HSI predictor variable was negative (β = -3.193) and not 

signifi cantly diff erent from zero (P = 0.857). Th erefore, we considered the HSI model for the 

Bewick’s wren verifi ed but not validated (Tirpak and others 2009a).

Table 27.—Infl uence of snag density on suitability 

index scores for Bewick’s wren habitat

Snag density (snags/ha) SI score

6.2a 0.128

16.4b 0.500

52.8a 1.000

a

Rumble and Gobeille (2004).

b

Sedgwick and Knopf (1990).

Figure 11.—Relationship between snag density and suitability 

index (SI) score for Bewick’s wren habitat. Equation: SI score = 

1.0011 / (1 + (21.9129 * e -0.1881 * snag density)).

 

Snag Density (snags/ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0
36

Black-and-white Warbler

Status

Th e black-and-white warbler (Mniotilta varia) is a neotropical 

migrant found throughout the eastern United States and 

southern Canada. Th is is a forest-interior species and the 

annual declines of 1.2 percent observed in the United States 

over the last 40 years likely are the result of increasing forest 

fragmentation (Sauer and others 2005). Th is species has a 

regional combined score of 16 in the WGCP, where it is a 

species requiring management attention (Table 1). Th e blackand-white warbler has a regional combined score of only 13 

in the CH. Th e FWS does not recognize the black-and-white 

warbler as a Bird of Conservation Concern in either BCR (Table 1).

Natural History

As a forest-interior specialist, the black-and-white warbler is found in the mature deciduous 

hardwood forests of the eastern United States and Canada (Kricher 1995). It is highly sensitive 

to fragmentation in the landscape (Robbins and others 1989) and typically is absent from 

small woodlots (< 7.5 ha; Galli and others 1976). Hamel (1992) suggested that 550 ha was the 

minimum tract size for this species in the Southeast.

Few studies have focused exclusively on the habitat ecology of this bird, though Conner and 

others (1983) found that the black-and-white warbler is associated with mature forest stands 

with high densities of large (> 32 cm d.b.h.) trees. Although a ground-nesting bird, this species 

is associated with high densities of hardwood saplings. Conversely, pine saplings negatively 

aff ect both the presence and abundance of the black-and-white warbler.

Th is bird occupies upland and bottomland forests but reaches greater densities in the former, 

with oak-hickory and cove forests considered optimal (Hamel 1992). Nevertheless, successional 

age may be the most critical habitat factor aff ecting the black-and-white warbler. Dettmers 

and others (2002) validated Hamel’s (1992) habitat suitability model for the black-and-white 

warbler, fi nding the model performed well due to the restriction of the black-and-white warbler 

to older age class forests. However, Th ompson and others (1992) and Annand and Th ompson 

(1997) observed the black-and-white warbler in sapling and clearcut stands in Missouri.

Model Description

Our HSI model for the black-and-white warbler includes six variables: landform, landcover, 

successional age class, forest patch size, percent forest in a 1-km radius, and canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 28). We directly 

assigned SI scores to these combinations based on vegetation type and age class associations of 

the black-and-white warbler reported by Hamel (1992). However, we assigned higher values 

to shrub-seedling stands based on data from Th ompson and others (1992) and Annand and 

Th ompson (1997).

Charles H. Warren, images.nbii.gov
37

Table 28.—Relationship between landform, landcover type, age class, and suitability index scores for blackand-white warbler habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.167 0.333 0.333 0.667

Deciduous 0.000 0.167 0.333 0.333 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.167 0.333 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.333 0.333 0.333

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.167 

(0.000)

0.333 

(0.000)

0.333 

(0.000)

0.333 

(0.000)

Deciduous 0.000 0.167 0.333 0.333 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.167 0.333 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.333 0.333 0.333

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.167 

(0.000)

0.333

(0.000)

0.333 

(0.000)

0.333 

(0.000)

Deciduous 0.000 0.167 0.333 0.333 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.167 0.333 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.333 0.333 0.333

Forest patch size (SI2) aff ects occurrence of this species as it is notably absent from small 

forest blocks. Th erefore, we fi t a logarithmic function (Fig. 12) relating forest patch size 

to SI scores derived from probability of occurrence data from Robbins and others (1989) 

(Table 29). Th e relative value of a forest block of a specifi c size is infl uenced by its landscape 

context. In predominantly forested landscapes, small forest patches that may not be used in 

predominantly nonforested landscapes may provide habitat due to their proximity to large 

forest blocks (Rosenberg and others 1999). To capture this relationship, we fi t a logistic 

function (Fig. 13) to data (Table 30) derived from Donovan and others (1997), who 

observed diff erences in predator and brood parasite communities among highly fragmented 

(< 15 percent), moderately fragmented (45 to 50 percent), and lightly fragmented (> 90 

percent forest) landscapes. Because of the extreme sensitivity of the black-and-white warbler 

to fragmented landscapes, we assumed that the midpoint between moderately and lightly 

fragmented forest defi ned the specifi c cutoff for average (SI score = 0.500) hatitat. We used 

the maximum value of SI2 or SI3 to account for small patches in predominantly forested 

landscapes and large patches in predominantly nonforested landscapes.
38

Canopy cover (SI4) also may aff ect the quality of black-and-white warbler habitat. Th us, we 

included it as a factor in our HSI model. Prather and Smith (2003) reported higher densities 

of the black-and-white warbler in forests with relatively open canopies, so we used their data 

(Table 31) to derive an inverse logistic function (Fig. 14) that quantifi ed the relationship 

between canopy cover and SI scores.

We calculated the overall HSI score as the geometric mean of the geometric mean of 

individual SI functions related to forest structure (SI1 and SI4) multiplied by the maximum 

SI score for forest patch size or percent forest in the 1-km radius landscape.

Overall HSI = ((SI1 * SI4)0.500 * Max(SI2 or SI3))0.500

Figure 12.—Relationship between forest patch size and 

suitability index (SI) scores for black-and-white warbler habitat. 

Equation: SI score = 0.1731 * ln(forest patch size) – 0.4096.

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

Table 29.—Infl uence of forest patch size on 

suitability index (SI) scores for black-and-white 

warbler habitat 

Forest patch size (ha) SI score

10a 0.0

220b 0.5

3,200b 1.0

a

Assumed value.

b

Robbins and others (1989).

Table 30.—Relationship between landscape 

composition (proportion forest in 1-km radius) 

and suitability index (SI) scores for black-andwhite warbler habitat

Landscape compositiona

 SI score

0.00a 0.00

0.10a 0.00

0.20a 0.00

0.30a 0.00

0.40a 0.00

0.50a 0.10

0.60a 0.25

0.70b 0.50

0.80a 0.75

0.90a 0.90

1.00a 1.00

a

Assumed value.

b

Donovan and others (1997).

Figure 13.—Relationship between landscape composition and 

suitability index (SI) scores for black-and-white warbler habitat. 

Equation: SI score = 1.047 / (1.000 + (1991.516 * e -10.673 * 

landscape composition)).

 

Landscape Composition (proportion forest in 1-km radius)

Suitability Index Score

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0
39

Verifi cation and Validation

Th e black-and-white warbler was found in 85 of the 88 subsections within the CH and 

WGCP. Not surprisingly, Spearman rank correlations based on all subsections and only 

subsections in which this species was found produced similar results: signifi cant (P ≤ 0.001 

for both analyses) positive relationships (rs

 = 0.54 and 0.53, respectively) between average 

HSI score and mean BBS route abundance. Th e generalized linear model predicting BBS 

abundance from BCR and HSI for the black-and-white warbler was signifi cant (P ≤ 0.001; 

R2

 = 0.380), and the coeffi cient on the HSI predictor variable was both positive (β = 3.194) 

and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the HSI model 

for the black-and-white warbler both verifi ed and validated (Tirpak and others 2009a).

Table 31.—Infl uence of canopy cover on 

suitability index (SI) scores for black-and-white 

warbler habitat.

Canopy cover (percent)a SI score

31 1.000

73 0.866

91 0.627

a

Prather and Smith (2003).

Figure 14.—Relationship between canopy cover and suitability 

index (SI) scores for black-and-white warbler habitat. Equation: 

SI score = 1 - (-4.190 / (1 + (-1890.213 * e -0.055 * canopy cover))).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0
40

Blue-gray Gnatcatcher

Status

Th e blue-gray gnatcatcher (Polioptila caerulea) is a shortdistance migrant found throughout eastern North America and 

the Southwest. Populations are relatively stable in both the CH 

and WGCP (Table 5). Th e FWS does not recognize this species 

as a Bird of Conservation Concern in either region (Table 1). 

Th is bird requires management attention in the CH (regional 

combined score = 14) but does not have any special designation 

in the WGCP (regional combined score =13; Table 1).

Natural History

Th e blue-gray gnatcatcher is a small passerine that inhabits woodland types ranging from 

shrubland to mature forest (Ellison 1992). It prefers deciduous habitats and is rare or absent 

in evergreen forests. Th is species attains its highest numbers in mesic and low-lying areas, but 

is also found in xeric forests and along ridges.

Kershner and others (2001) did not identify specifi c microhabitat requirements for this 

species in Illinois, and considerable variation in nest height (0.8 to 24.4 m) and territory size 

(0.5 to 8 ha) has been documented across the range.

Although often associated with edges, this bird may be area sensitive (Knutson 1995, Kilgo 

and others 1998). Nest success was greater for nests placed higher and farther from an edge 

in Illinois (Kershner and others 2001) but did not diff er between bottomland hardwood 

stands and cottonwood plantations in the Mississippi Alluvial Valley (Twedt and others 

2001). Th e abundance of the blue-gray gnatcatcher was higher in bottomland hardwood 

stands surrounded by fi elds than those surrounded by pine forest (Kilgo and others 1998).

Model Description

Th e HSI model for the blue-gray gnatcatcher includes seven variables in fi ve functions: 

landform, landcover, successional age class, forest patch size, percent forest in a 1-km radius 

landscape, edge, and basal area.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 32). We 

directly assigned SI scores to these combinations on the basis of data from Hamel (1992) 

on the relative quality of vegetation associations and successional age classes for this species. 

We adjusted Hamel’s values for shrub-seedling and sapling-aged stands to account for the 

higher densities observed in young forests by Th ompson and others (1992) and Annand and 

Th ompson (1997).

We included forest patch size (SI2) as a variable to account for the area sensitivity of the 

blue-gray gnatcatcher. We fi t a logarithmic function (Fig. 15) to data from Robbins and 

others (1989) on the probability of occurrence for this bird in stands of various sizes (Table 

33). Nevertheless, the actual use of a forest patch refl ects both its area and its landscape 

Charles H. Warren, images.nbii.gov
41

context (SI3). In predominantly forested landscapes, a small forest patch that otherwise may 

not be suitable may be occupied due to its proximity to a larger forest block (Rosenberg 

and others 1999). Because the gnatcatcher also is associated with edges, it may not be as 

abundant in predominantly forested landscapes that lack signifi cant edge habitat. Th us, we 

assumed that the relationship between habitat suitability of the blue gray gnatcatcher and the 

amount of forest in the landscape followed a Gaussian function (Fig. 16), with landscapes 

containing 70 to 80 percent forest as optimal and suitability declining as the proportion of 

forest in the landscape moved from this ideal (Table 34). We used the maximum suitability 

score of SI2 or SI3 to simultaneously account for patch area and landscape composition.

We also included edge (SI4) in our HSI model because of the association of the blue-gray 

gnatcatcher with edges within large forest blocks. Th is species nests along both hard and 

soft edges (typically within 30 m; Kershner and others 2001). Th erefore, we defi ned edge 

as the interface among sapling, pole, and sawtimber stands and herbaceous and nonforest 

landcovers (hard edge) or seedling and grass-forb stands (soft edge). We used a 7 × 7 pixel 

moving window (210 x 210 m) to identify where these adjacencies occurred but recognized 

that the blue-gray gnatcatcher is not restricted to edge habitats and applied a residual SI score 

(0.010) to sites that did not meet this criterion (Table 35).

Table 32.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for blue-gray gnatcatcher habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.333 0.667 0.667 1.000

Transitional-shrubland 0.000 0.333 0.667 0.667 1.000

Deciduous 0.000 0.333 0.667 0.667 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.083 0.167 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.333 0.333 1.000

Terrace-mesic Low-density residential 0.000 0.333 0.667 0.667 1.000

Transitional-shrubland 0.000 0.333 

(0.000)

0.667

(0.000)

0.667 

(0.000)

1.000 

(0.000)

Deciduous 0.000 0.333 0.667 0.667 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.083 0.167 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.333 0.333 1.000

Xeric-ridge Low-density residential 0.000 0.333 0.667 0.667 1.000

Transitional-shrubland 0.000 0.333 

(0.000)

0.667

(0.000)

0.667 

(0.000)

1.000 

(0.000)

Deciduous 0.000 0.333 0.667 0.667 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.083 0.167 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.333 0.333 1.000
42

We fi t a quadratic function to data from Annand and Th ompson (1997) on the response of 

the blue-gray gnatcatcher to basal area (SI5; Table 36, Fig. 17), refl ecting the preference of 

this species for open forest conditions.

Figure 15.—Relationship between forest patch size and 

suitability index (SI) scores for blue-gray gnatcatcher habitat. 

Equation: SI score = 0.137 * ln(forest patch size) + 0.186.

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

Figure 16.—Relationship between landscape composition and 

suitability index (SI) scores for blue-gray gnatcatcher habitat. 

Equation: SI score = 1.002 * e ((0 – ((landscape composition) – 74.165) ^ 2) / 

1064.634).

 

Landscape Composition (% forest in 10-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 33.—Infl uence of forest patch size on 

suitability index (SI) scores for blue-gray 

gnatcatcher habitat 

Forest patch size (ha)a SI score

6.8 0.0

15 0.5

3,200 1.0

a

Robbins and others (1989).

Table 34.—Relationship between landscape 

composition (percent forest in 10-km radius) 

and suitability index (SI) scores for blue-gray 

gnatcatcher habitat 

Landscape composition SI score

0a 0.00

10a 0.10

20a 0.20

30b 0.30

40a 0.40

50b 0.50

60a 0.75

70b 1.00

80a 1.00

90b 0.75

100a 0.50

a

Assumed value.

b

Dononvan and others (1997).

Table 35.—Infl uence of edge on suitability index 

(SI) scores for blue-gray gnatcatcher habitat

7 × 7 pixel window around forest 

pixel includes fi elda SI score

Yes 1.00

No 0.01

a

Field defi ned as any shrub-seedling or grass-forb age 

class forest, or natural grasslands, pasture-hay, fallow, 

urban-recreational grasses, emergent herbaceous 

wetlands, open water, high intensity residential, 

commercial-industrial-transportation, bare rock-sand-clay, 

quarries-strip mines-gravel pits, row crops, or small grains. 

Forest defi ned as any used sapling, pole, or sawtimber 

age class pixel of low-density residential, transitional, 

shrublands, deciduous, mixed, evergreen, orchard, or 

woody wetlands (i.e., SI1 > 0).
43

To calculate the HSI score for sapling, pole, and sawtimber age classes, we determined the 

geometric mean of SI scores for forest structure (SI1 and SI5) and landscape composition 

attributes (Max(SI2 or SI3) and SI4) separately and then the geometric mean of these 

means together. Because edge occurrence (SI4) was not applicable to the shrub-seedling age 

class, we calculated HSI scores separately for this age class and summed across age classes to 

determine the overall HSI score for the landscape.

Sapling, pole, and sawtimber successional age classes:

HSIOld = (((SI1 * SI5)0.500) * ((Max (SI2 or SI3)) * SI4)0.500)

0.500

Shrub-seedling successional age classes:

HSIShrub = ((SI1 * SI5)0.500 * (Max (SI2 or SI3)))0.500

Overall HSI = HSIOld + HSIShrub

Verifi cation and Validation

Th e blue-gray gnatcatcher was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation analysis on average HSI score and mean BBS route abundance across 

subsections resulted in a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.58) between these 

variables. Th e generalized linear model predicting BBS abundance from BCR and HSI for 

the blue-gray gnatcatcher was signifi cant (P ≤ 0.001; R2

 = 0.210), and the coeffi cient on the 

HSI predictor variable was both positive (β = 19.625) and signifi cantly diff erent from zero (P

≤ 0.001). Th erefore, we considered the HSI model for the blue-gray gnatcatcher both verifi ed 

and validated (Tirpak and others 2009a).

Figure 17.—Relationship between basal area and suitability 

index (SI) scores for blue-gray gnatcatcher habitat. Equation: SI 

score = 0.3863 + 0.1105 * (basal area) – 0.0049 * (basal area)2

.

 

Basal Area (m^2/ha)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

Table 36.—Infl uence of basal area (m2/ha) on 

suitability index (SI) scores for blue-gray 

gnatcatcher habitat 

Basal areaa SI score

3.41 0.706

12.33 1.000

22.20 0.412

a

Annand and Thompson (1997).
44

Blue-winged Warbler

Status

Th e blue-winged warbler (Vermivora pinus) is 

a neotropical migrant found from southern 

New England west to the Lake States and 

south through the southern Appalachians and 

Ozarks. Across most of its range, this species has 

been stable and has even increased in some areas (possibly to the detriment of the goldenwinged warbler, with which it sometimes interbreeds; Gill 1980). Once limited to a mostly 

Midwestern range, this bird expanded into southern New England as forests were cleared and 

farms were abandoned. However, as the forest has matured in this region, the blue-winged 

warbler has experienced declines (3.3 and 5.3 percent annually from 1966 to 2004 in the 

increasingly residential Connecticut and New Jersey, respectively). A similar phenomenon 

has occurred in the Southeast and BBS data indicate a 3.7 percent decline in FWS Region 

4 during this same period (Sauer and others 2005). Th is species is designated a Bird of 

Conservation Concern in the CH but not in the WGCP (Table 1), where it rarely breeds. It 

has a regional combined score of 19 in the CH and requires management attention in that 

region (Table 1).

Natural History

Th e blue-winged warbler is an early successional species (Gill and others 2001) that benefi ted 

from European settlement by expanding its range following the initial clearing of forests 

for agriculture and the subsequent abandonment of farms. Breeding habitat includes early 

to midsuccessional forest containing dense low growth (shrubs, young trees, thickets). Th is 

species makes use of a variety of landform conditions from wetland edges to dry uplands, 

though mated males have more xeric territories than unmated males. Territories range from 

0.2 to 5 ha, with boundaries often aligned along edges. Nests typically are within 30 m of 

a forest edge in grassy areas with high numbers of small (< 10 cm d.b.h.) trees. Density is 

inversely related to successional age class, fragmentation, and the abundance of the goldenwinged warbler and brown-headed cowbird.

Model Description

Th e blue-winged warbler model includes fi ve variables: landform, landcover, successional age 

class, early successional patch size, and canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 37). We directly 

assigned SI scores to these combinations based on habitat associations reported in Hamel 

(1992) for the blue-winged warbler. We modifi ed Hamel’s data to maximize SI scores in the 

transitional-shrubland landcover class in the xeric landform.

We also included early successional patch size (SI2) in our model on the basis of data from 

Rodewald and Vitz (2005) on the relative abundance of the blue-winged warbler in small 

and large clearcuts (Table 38; Fig. 18). We defi ned early successional forest by age class and 

included only grass-forb, shrub-seedling, and sapling age classes in the calculation of patch 

area.

Chandler S. Robbins, Patuxent Bird Identifi cation InfoCenter

Photo used with permission 
45

Table 37.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for blue-winged warbler habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.333 0.167 0.000 0.000

Deciduous 0.000 0.333 0.167 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.333 0.167 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.083 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.667 0.333 0.000 0.000

Deciduous 0.000 0.667 0.333 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.333 0.167 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.167 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 1.000 0.500 0.000 0.000

Deciduous 0.000 1.000 0.500 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.333 0.167 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.167 0.000 0.000

Figure 18.—Relationship between early successional patch size 

and suitability index (SI) scores for blue-winged warbler habitat. 

Equation: SI score = 1.000 / (1 + (14353.617 * e -2.788 * forest patch size)).

 

Early Successional Patch Size (ha)

Suitability Index Score

0 5 10 15

0.0

0.2

0.4

0.6

0.8

1.0

Table 38.—Infl uence of early successional patch 

size on suitability index scores for blue-winged 

warbler habitat; early successional patches 

include all adjacent grass-forb, shrub-seedling, 

and sapling successional age class forest

Early successional patch size (ha) SI score

0a 0.000

4b 0.786

13b 1.000

a

Assumed value.

b

Rodewald and Vitz (2005).
46

We used an inverse logistic function (Fig. 19) to quantify the relationship between canopy 

cover (SI3) and SI scores to refl ect the lower densities of the blue-winged warbler in forests 

with increasingly closed canopies. We defi ned this function by fi tting a curve to data from 

Annand and Th ompson (1997) on the relative density of this bird in forest stands with 

diff erent estimates of canopy cover (Table 39).

To calculate the overall HSI score for this species , we determined the geometric mean of SI 

scores for forest structure attributes (SI1 and SI3) and then calculated the geometric mean of 

this value and early successional patch size (SI2).

Overall HSI = ((SI1 * SI3)0.500 * SI2)0.500

Verifi cation and Validation

Th e blue-winged warbler was found in 64 of the 88 subsections within the CH and WGCP. 

We used Spearman rank correlations between average HSI score and mean BBS route 

abundance at the subsection scale to verify this model. We observed signifi cant positive 

relationships when analyses included all subsections (rs

 = 0.26; P = 0.014) or only those 

subsections where this species was detected (rs

 = 0.28; P = 0.026). Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the blue-winged warbler was 

signifi cant (P ≤ 0.001; R2

 = 0.232), and the coeffi cient on the HSI predictor variable was 

positive (β = 1.717) but not signifi cantly diff erent from zero (P = 0.334). Th erefore, we 

considered the HSI model for the blue-winged warbler verifi ed but not validated (Tirpak and 

others 2009a).

Figure 19.—Relationship between canopy cover and suitability 

index (SI) scores for blue-winged warbler habitat. Equation: SI 

score = 1 - (1.0381 / (1 + (16277.383 * e -0.1327 * canopy cover))).

Table 39.—Infl uence of canopy cover on suitability 

index (SI) scores for blue-winged warbler habitat

Canopy cover (percent)a SI score

29.26 1.000

71.86 0.523

93.38 0.034

95.58 0.000

96.59 0.011

a

Annand and Thompson (1997).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0
47

Brown Thrasher

Status

Th e brown thrasher (Toxostoma rufum) is a shortdistance migrant found throughout eastern North 

America. Although populations in the CH and WGCP 

declined by 1.4 percent per year between 1966 and 

2004 (Table 5), this species is not considered a Bird 

of Conservation Concern in either BCR (Table 1). 

Th e brown thrasher has a regional combined score 

of 13 and 15 in the WGCP and CH, respectively, and is a species warranting management 

attention in the CH (Table 1).

Natural History

A ground-foraging passerine, the brown thrasher is associated with edge habitats throughout 

the eastern United States and Canada (Cavitt and Haas 2000). Breeding habitat includes 

a variety of vegetation types, but this species reaches its highest densities in shrublands and 

midsuccessional forests. Grand and Cushman (2003) found that thrashers in Massachusetts 

were associated predominately with the amount of scrub oak in the landscape. Rumble and 

Gobeille (2004) found no signifi cant diff erence in brown thrasher occurrence among seral 

stages of cottonwood fl oodplains in South Dakota, though this bird was detected most often 

in younger forest classes. Savanna restoration eff orts increase thrasher abundance by reducing 

tree density (Davis and others 2000).

Nests are typically low in a tree or shrub but some may be on the ground. Territory size 

and thrasher density vary according to habitat quality (0.5 to 1.1 ha and 0.1 to 0.4/ha, 

respectively). Th e FWS (Cade 1986) developed an HSI model for this species that included 

three site-specifi c variables: density of woody stems, canopy cover, and litter cover.

Model Description

Our brown thrasher model includes six variables: landform, landcover, successional age class, 

edge occurrence, small stem density (<2.5 cm d.b.h.), and forest composition in a 10-km 

radius.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 40). We directly 

assigned SI scores to these combinations on the basis of habitat associations reported by 

Hamel (1992) for the brown thrasher in the Southeast.

Th is edge species inhabits thickets and hedgerows in deciduous forests. Because the brown 

thrasher uses both hard and soft edges, we defi ned edge (SI2) as the interface between pole 

age forest and herbaceous or non-forest landcovers (hard edge) and seedling or grass-forb age 

forest (soft edge). To be suitable, we required pole age forest sites to be adjacent to an edge 

(Table 41). However, we relaxed this requirement for seedling-shrub and sapling stands, 

which we considered suitable regardless of edge.

Jeffrey A Spendelow, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
48

Table 40.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for brown thrasher habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.500 0.333 0.083 0.000

Transitional-shrubland 0.000 0.500 0.333 0.083 0.000

Deciduous 0.000 0.500 0.333 0.083 0.000

Evergreen 0.000 0.667 0.500 0.167 0.000

Mixed 0.000 1.000 0.667 0.167 0.000

Orchard-vineyard 0.000 0.500 0.333 0.083 0.000

Woody wetlands 0.000 0.667 0.417 0.083 0.000

Terrace-mesic Low-density residential 0.000 0.667 0.417 0.083 0.000

Transitional-shrubland 0.000 1.000 

(0.667)

0.667 

(0.500)

0.167 0.000

Deciduous 0.000 0.667 0.417 0.083 0.000

Evergreen 0.000 0.667 0.500 0.167 0.000

Mixed 0.000 1.000 0.667 0.167 0.000

Orchard-vineyard 0.000 0.667 0.417 0.083 0.000

Woody wetlands 0.000 0.667 0.500 0.167 0.000

Xeric-ridge Low-density residential 0.000 1.000 0.667 0.167 0.000

Transitional-shrubland 0.000 1.000 

(0.334)

0.667 

(0.250)

0.167 

(0.083)

0.000

Deciduous 0.000 1.000 0.667 0.167 0.000

Evergreen 0.000 0.667 

(0.334)

0.500 

(0.250)

0.167 

(0.083)

0.000

Mixed 0.000 1.000 0.667 0.167 0.000

Orchard-vineyard 0.000 1.000 0.667 0.167 0.000

Woody wetlands 0.000 0.667 0.500 0.167 0.000

Table 41.—Infl uence of edge on suitability index (SI) 

scores for brown thrasher habitat

3 × 3 pixel window around forest 

pixel includes fi elda SI score

Yesb 1.0

No 0.0

a

Field defi ned as any shrub-seedling or grass-forb age class 

pixel, or natural grasslands, pasture-hay, fallow, urbanrecreational grasses, emergent herbaceous wetlands, open 

water, high intensity residential, commercial-industrialtransportation, bare rock-sand-clay, quarries-strip minesgravel pits, row crops, or small grains. Forest defi ned as 

any used pole age class pixel of low-density residential, 

transitional, shrublands, deciduous, mixed, evergreen, 

orchard, or woody wetlands.

b

Seedling-shrub and sapling habitats used regardless of edge.
49

Th e brown thrasher occupies habitats with numerous small stems (SI3). We fi t a smoothed 

quadratic function (Fig. 20) to HSI cutoff values from the FWS HSI model for this species 

(Cade 1986; Table 42) to quantify the relationship between small stem density and habitat 

suitability.

Although the brown thrasher is associated with edges, it prefers modestly forested landscapes 

(Haas 1997). We included forest composition (SI4) in our model, assuming that habitat 

suitability would be low if there were no woodland (i.e., 0 percent forest, the left side of 

the function; Fig. 21) or no edges (i.e., 100 percent forest, the right side of the function). 

Haas (1997) observed higher reproductive success for birds in more isolated shelterbelts and 

Robbins and others (1989) observed negative relationships between the occurrence of the 

gray catbird and American robin (species that share similar habitat preferences to those of 

the brown thrasher) and forest patch size. Further, Perkins and others (2003b) observed an 

increase in abundance of edge-associated birds as the total amount of woody cover decreased. 

However, the brown thrasher responded positively to the amount of forest cover in the 

study area. We interpreted these observations as evidence that this species would exhibit 

a preference for landscapes with moderate forest landcover. We fi t a Gaussian function 

to landscape proportions refl ecting this pattern and assumed that landscapes that were 70 

percent forested were associated with the maximum SI score (Table 43).

Figure 20.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

brown thrasher habitat. Equation: SI score = (0.1 + (0.165 

* (small stem density / 1000))) / (1 + (-0.003 * (small stem 

density / 1000)) + (0.0078 * ((small stem density / 1000))2

)).

Table 42.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for brown thrasher habitat 

Small stem densitya SI score

0 0.1

10 1.0

40 0.5

a

Cade (1986).

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0
50

We assumed that the brown thrasher used edge as a surrogate to early successional habitat, 

so we calculated HSI scores separately for young (seedling-shrub and sapling) and old 

(pole) age class forests. In the former, the geometric mean of forest structure and landscape 

composition variables defi nes the suitability score. For the latter, we included edge 

occurrence in the calculation. We summed the age class-specifi c HSI scores to determine the 

overall HSI score for all sites.

Seedling-shrub and sapling successional age classes:

HSIYoung: ((SI1 * SI3)0.500 * SI4)0.500

Pole successional age class: 

HSIPole: ((SI1 * SI3)0.500 * SI4)0.500 * SI2

Overall SI = HSIYoung + HSIPole

Verifi cation and Validation

Th e brown thrasher was found in all 88 subsections of the CH and WGCP. Spearman rank 

correlation did not identify a positive relationship between average HSI score and mean BBS 

route abundance across subsections. Th e generalized linear model predicting BBS abundance 

from BCR and HSI for the brown thrasher was signifi cant (P ≤ 0.001; R2

 = 0.719); however, 

the coeffi cient on the HSI predictor variable was negative (β = -7.087). Th erefore, we 

considered the HSI model for the brown thrasher neither verifi ed nor validated (Tirpak and 

others 2009a).

Figure 21.—Relationship between landscape composition 

and suitability index (SI) scores for brown thrasher habitat. 

Equation: SI score = 0.998 * e ((0 – ((landscape composition) – 70.304) ^ 2) / 

1253.402).

 

Landscape Composition (% forest in 10-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 43.—Relationship between landscape 

composition (percent forest in 10-km radius) and 

suitability index (SI) scores for brown thrasher habitat

Landscape compositiona

 SI score

0 0.00

10 0.05

20 0.10

30 0.25

40 0.50

50 0.75

60 0.90

70 1.00

80 0.90

90 0.75

100 0.50

a

Assumed value.
51

Brown-headed Nuthatch

Status

Th e brown-headed nuthatch (Sitta pusilla) is a resident species 

of mature pine forests along the Piedmont and Coastal Plains 

of the southeastern United States. Although this species has 

experienced modest declines throughout most of its range over 

the last 40 years (1.2 percent per year), only in Florida has the 

decline been signifi cant (4.2 percent annually from 1966 to 

2004; Sauer and others 2005). Th is species is an FWS Bird of 

Conservation Concern in the WGCP (Table 1), where it has a 

regional combined score of 19. Th e brown-headed nuthatch is a 

rare breeder in the CH (regional combined score = 19), and 

PIF considers this species one that warrants critical recovery in 

that region.

Natural History

Th e brown-headed nuthatch is closely associated with pine: it breeds in mature pine forests 

and forages almost exclusively in pine trees (> 98 percent of observations; Withgott and 

Smith 1998). Although often associated with the longleaf pine savanna characteristic of the 

habitat for red-cockaded woodpecker and Bachman’s sparrow, the brown-headed nuthatch 

has a broader niche than these species (Hamel 1992, Dornak and others 2004). Th e habitat 

of this species is defi ned by two habitat elements: mature pines for foraging and cavities 

for nesting (Wilson and Watts 1999, Dornak and others 2004). Specifi c composition 

of pine species is not as critical as d.b.h., with an average d.b.h. of 25.6 cm considered 

optimal (O’Halloran and Conner 1987 cited in Dornak and others 2004). Th e brownheaded nuthatch nests primarily in large-diameter snags < 3 m tall and may require seven 

to eight snags per ha to ensure adequate nest and roost sites, particularly in the presence of 

interspecifi c competition for cavities. In urban areas, the brown-headed nuthatch readily 

adopts nest boxes and may use other manmade cavities, such as streetlights.

Th is species prefers open pine stands with few hardwoods (≤ 17.4 stems/ha and basal area ≤ 

5 m2

/ha) and an open midstory (Wilson and Watts 1999). Optimal canopy cover is highly 

variable (15 to 85 percent) but stands with closed canopies are not preferred (O’Halloran 

and Conner 1987, Wilson and Watts 1999). Undergrowth typically is sparse (roughly 

35 percent; Dornak and others 2004). Th e nuthatch regularly breeds at low densities in 

suboptimal habitats, including stands with small pines, a large fraction of hardwoods, and 

dense understories (Withgott and Smith 1998). Area sensitivity apparently is not an issue for 

this species, which is not an acceptable host for the brown-headed cowbird (Withgott and 

Smith 1998).

Model Description

Th e HSI model for the brown-headed nuthatch includes six variables: landform, landcover, 

successional age class, snag density, small stem (< 2.5 cm d.b.h.) density, and hardwood 

basal area.

Fernbank Science Center

Photo used with permission
52

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 44). We directly 

assigned SI scores to these combinations on the basis of habitat associations of the brownheaded nuthatch described by Hamel (1992).

We included snag density (SI2) in our HSI model because of the importance of cavities to 

this species. We assumed that the SI score was zero when eight or fewer snags of any size 

were present (Dornak and others 2004). We fi t a logistic function (Fig. 22) to data from 

Wilson and Watts (1999) (Table 45) to quantify the relationship between snag density and 

SI scores.

We also used small stem density as a function (SI3) in the HSI model to account for the 

preference of the brown-headed nuthatch for open understories. We fi t an inverse logistic 

function (Fig. 23) to hypothetical data refl ecting this preference (Table 46). Th e shape of 

this function is supported by observations from Wilson and others (1995), who observed a 

higher abundance of the brown-headed nuthatch in stands immediately following wildlife 

stand improvements and prescribed burns (when stem density was lowest) with subsequent 

declines in abundance as stem density increased through time.

Table 44.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for brown-headed nuthatch habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000
53

Finally, we incorporated hardwood basal area (SI4) as a model variable as birds are less 

abundant in habitats with a greater hardwood component (Wilson and others 1995, 

Withgott and Smith 1998, Wilson and Watts 1999). Again, we relied on data from 

Wilson and Watts (1999) (Table 47) to develop an inverse logistic function to describe the 

relationship between hardwood basal area and SI score (Fig. 24).

To determine the overall HSI score for the brown-headed nuthatch, we calculated the 

geometric mean of the four individual functions related to forest structure attributes.

Overall HSI = (SI1 * SI2 * SI3 * SI4)0.250

Figure 22.—Relationship between snag density and suitability 

index (SI) scores for brown-headed nuthatch habitat. Equation: 

SI score = 1.000 / (1 + (49.165 * e (-0.073 * snag density))).

Figure 23.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores 

for brown-headed nuthatch habitat. Equation: SI score = 1 - 

(1.010 / (1 + (79.565 * e (-0.217 * (small stem density / 1000))))).

 

Snag Density (snags/ha)

Suitability Index Score

0 25 50 75 100 125

0.0

0.2

0.4

0.6

0.8

1.0

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0

Table 45.—Infl uence of snag density on suitability 

index (SI) scores for brown-headed nuthatch habitat

Snag density (snags/ha) SI score

8a 0.000

40b 0.286

66.67b 0.715

106.67b 1.000

a

Dornak and others (2004).

b

Wilson and Watts (1999).

Table 46.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for brown-headed nuthatch habitat

Small stem densitya SI score

01 1.0

101 0.9

201 0.5

301 0.1

401 0.0

a

Assumed value.
54

Verifi cation and Validation

Th e brown-headed nuthatch was found in 37 of the 88 subsections within the CH and 

WGCP. Spearman rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship 

(rs

 = 0.58) between average HSI score and mean BBS route abundance across subsections. 

Th is relationship was even stronger (rs

 = 0.80) when subsections in which the brownheaded nuthatch was not detected were removed from the analysis. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the brown-headed nuthatch 

was signifi cant (P ≤ 0.001; R2

 = 0.738), and the coeffi cient on the HSI predictor variable 

was both positive (β = 4.712) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, 

we considered the HSI model for the brown-headed nuthatch both verifi ed and validated 

(Tirpak and others 2009a).

Figure 24.—Relationship between hardwood basal area and 

suitability index (SI) scores for brown-headed nuthatch habitat. 

Equation: SI score = 1 - (1.018 / (1 + (29.747 * e (-0.441 * hardwood 

basal area)))).

 

Hardwood Basal Area (m^2/ha)

Suitability Index Score

0 5 10 15 20

0.0

0.2

0.4

0.6

0.8

1.0

Table 47.—Infl uence of hardwood basal area on 

suitability index (SI) scores for brown-headed 

nuthatch habitat

Hardwood basal area (m2

/ha) SI score

0.0a 1.000

4.6a 0.778

10.5a 0.222

15.0b 0.000

20.0b 0.000

a

Wilson and Watts (1999).

b

Assumed value.
55

Carolina Chickadee

Status

Th e Carolina chickadee (Parus carolinensis) is a 

resident species of the southeastern United States. 

Although populations have been stable in the CH, 

this species has declined by about 2 percent annually 

over the last 40 years in the WGCP (Table 5). Th is 

bird is a planning and responsibility species in both 

the CH (regional combined score = 15) and WGCP 

(regional combined score = 16; Table 1).

Natural History

Th e Carolina chickadee is a generalist species that breeds in a variety of forest types across 

a broad spectrum of landforms (Mostrom and others 2002). It nests in cavities of live and 

dead trees within multilayered forests containing well developed shrub, midstory, and 

overstory canopies (Hamel 1992). Abundance declines following reduction of hardwoods 

in pine stands, likely as a result of the loss of midstory trees (Provencher and others 2002). 

Nest success and adult survival is positively correlated with woodlot area but is lower on 

edges regardless of patch size (Doherty and Grubb 2002). Nest destruction by the house 

wren is a major cause of nest failure in areas where the ranges of these species overlap. 

Territory size ranges from 1.6 to 2.4 ha.

Model Description

Th e Carolina chickadee model includes four variables: landform, landcover, successional age 

class, and snag density.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 48). We directly 

assigned SI scores to these combinations on the basis of vegetation and successional age class 

associations of the Carolina chickadee reported in Hamel (1992).

We included snag density (SI2) as a variable because of the importance of nest and roost 

cavities for the chickadee, a secondary cavity nester. Data for the Carolina chickadee were 

not available but Rumble and Gobeille (2004) and Sedgwick and Knopf (1990) observed 

the black-capped chickadee in habitats with six snags per hectare (Table 49). Th erefore, we 

assumed that stands with six or more snags per ha were representative of optimal habitat. 

Because the chickadee can use cavities in live trees, we assumed that stands with no snags 

were not necessarily nonhabitat and assigned to them a small but non-zero SI score (0.03). 

We fi t a logistic function through these data points to quantify the relationship between 

snag density and habitat suitability (Fig. 25).

We calculated the overall HSI score as the geometric mean of the two individual functions: 

Overall HSI = (SI1 * SI2)0.500

Charles H. Warren, images.nbii.gov
56

Table 48.—Relationship of landform, landcover type, and successional age class to SI scores for Carolina 

chickadee habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.167 0.500 0.667

Transitional-shrubland 0.000 0.000 0.167 0.500 0.667

Deciduous 0.000 0.000 0.167 0.500 0.667

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.167 0.500 0.667

Woody wetlands 0.000 0.000 0.167 0.500 0.667

Terrace-mesic Low-density residential 0.000 0.000 0.167 0.500 0.667

Transitional-shrubland 0.000 0.000 0.334 0.834 1.000

Deciduous 0.000 0.000 0.167 0.500 0.667

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.167 0.500 0.667

Woody wetlands 0.000 0.000 0.167 0.500 0.667

Xeric-ridge Low-density residential 0.000 0.000 0.167 0.500 0.667

Transitional-shrubland 0.000 0.000 0.334 

(0.250)

0.834 

(0.667)

1.000 

(0.834)

Deciduous 0.000 0.000 0.167 0.500 0.667

Evergreen 0.000 0.000 0.334 

(0.250)

0.834 

(0.667)

1.000 

(0.834)

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.167 0.500 0.667

Woody wetlands 0.000 0.000 0.167 0.500 0.667

Figure 25.—Relationship between snag density and suitability 

index (SI) scores for Carolina chickadee habitat. Equation: 

SI score = 1.007 / (1.000 + (32.567 * e (-1.403 * snag density))).

 

Snag Density (snags/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 49.—Infl uence of snag density on suitability 

index (SI) scores for Carolina chickadee habitat

Snag density 

 (snags/ha) SI score

0a 0.03

4b 0.90

6a, c 1.00

a

Rumble and Gobeille (2004).

b

Assumed value.

c

Sedgwick and Knopf (1990).
57

Verifi cation and Validation

Th e Carolina chickadee was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.55) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized 

linear model predicting BBS abundance from BCR and HSI for the Carolina chickadee was 

signifi cant (P ≤ 0.001; R2

 = 0.473), and the coeffi cient on the HSI predictor variable was 

both positive (β = 5.142) and signifi cantly diff erent from zero (P = 0.038). Th erefore, we 

considered the HSI model for the Carolina chickadee both verifi ed and validated (Tirpak 

and others 2009a).
58

Cerulean Warbler

Status

Th e cerulean warbler (Dendroica cerulea) is a long-distance 

migrant to the eastern United States. Densities are highest in 

the Ohio River Valley and along the Cumberland Plateau. 

Th is species has declined across most of its range, including 

the CH and WGCP (6.3 and 9.5 percent per year from 

1966 to 2004, respectively; Table 5). Th e cerulean warbler 

is classifi ed as a Bird of Conservation Concern requiring 

critical recovery in the WGCP (regional combined score 

= 19) and immediate management in the CH (regional combined score = 19) (Table 1). 

Concern for this species culminated in a petition to the FWS to list the cerulean warbler as 

threatened. However, this action was deemed unwarranted on the basis of current scientifi c 

information (Federal Register 71:234 [6 December 2006] p. 70717).

Natural History

A forest interior specialist, the cerulean warbler has experienced some of the most dramatic 

declines of any songbird over the last 30 years (Hamel 2000). Th is species has a broad 

geographic range but is abundant only locally. It may nest semi-colonially, with territories 

in good habitat highly clumped. Th e cerulean warbler seems to be highly sensitive to forest 

fragmentation. Robbins and others (1989) found a 50 percent reduction in observations of 

this species as forest patch size declined from 3,000 to 700 ha. No birds were detected on 

forest patches less than 138 ha. Estimates from other researchers suggest that forest tracts 

as large as 8,000 ha may be required to ensure sustainable populations in the Mississippi 

Alluvial Valley (summarized in Hamel [2000]).

Although it requires large forest tracts, the cerulean warbler establishes territories near 

interior forest gaps. Weakland and Wood (2005) observed a positive association between 

this species and forest roads or snags that created small canopy openings. Aside from canopy 

gaps (a measure of horizontal canopy structure), the cerulean warbler also may respond to 

the vertical canopy profi le. Canopy cover of 6 to 12 m and more than 24 m was preferred 

in West Virginia (Weakland and Wood 2005). In Ontario, canopy cover of 12 to 18 m 

and more than 18 m was preferred (Jones and Robertson 2001). Th e diff erence in preferred 

canopy heights between these studies likely refl ects diff erences in local vegetation structure 

rather than an absolute diff erence in preferred canopy height. Th e key habitat feature in both 

is the multilayered character of the overstory canopy.

Closed-canopy stands with large trees (both in height and d.b.h.) are commonly associated 

with the cerulean warbler but likely are a crude proxy for the aforementioned canopy features 

that provide the true selection criteria for this bird (Hamel 2000). Th is species is associated 

with bottomland hardwoods in the Southeast and ridges in West Virginia (Hamel 2000, 

Weakland and Wood 2005). Again, specifi c landforms probably are not directly selected for 

but are correlated with the location of large tracts of deciduous forest containing large trees 

and favorable canopy conditions in these landscapes.

U.S. Forest Service
59

In “Birds of North America,” Hamel (2000) stated: “Important habitat elements for this 

species thus appear to be large tracts with big deciduous trees in mature to old-growth forest 

with horizontal heterogeneity of the canopy. Th e pattern of vertical distribution of foliage in 

the canopy is also important.”

Model Description

Th e HSI model for the cerulean warbler includes seven variables: landform, landcover, 

successional age class, forest patch size, percent forest in a 1-km radius, dominant tree 

density, and canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 50). We directly 

assigned SI scores to these combinations on the basis of habitat associations of the cerulean 

warbler outlined in Hamel (1992).

We derived the suitability function for forest patch size (SI2) by fi tting a logistic curve 

(Fig. 26) to data from Robbins and others (1989) and Rosenberg and others (2000), who 

Table 50.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for cerulean warbler habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.500 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.400 0.800

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.500 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.400 0.800

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.400 0.800

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.400 0.800
60

observed that about 95 percent of all birds in FWS Region 4 were on tracts of at least 400 ha 

(Table 51). Recognizing the suitability of a forest patch is aff ected by its landscape context 

(Rosenberg and others 1999), we fi t a logistic function (Fig. 27) to data (Table 52) derived 

from Donovan and others (1997), who observed diff erences in predator and brood parasite 

communities among highly fragmented (< 15 percent), moderately fragmented (45 to 50 

percent), and lightly fragmented (> 90 percent forest) landscapes. We assumed that the 

midpoint between moderately and lightly fragmented forest defi ned the specifi c cutoff for 

average (SI score = 0.500) habitat. We used the maximum value from SI2 or SI3 to account 

for the suitability of small patches in predominantly forested landscapes.

Figure 26.—Relationship between forest patch size and suitability 

index (SI) scores for cerulean warbler habitat. Equation: 

SI score = 1.000 / (1.000 + (524.457 * e -0.0089 * forest patch size)).

Figure 27.—Relationship between landscape composition and 

suitability index (SI) scores for cerulean warbler habitat. Equation: 

SI score = 1.047 / (1.000 + (1991.516 * e -10.673 * landscape composition)).

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (proportion forest in 1-km radius)

Suitability Index Score

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 51.—Infl uence of forest patch size on 

suitability index (SI) scores for cerulean warbler 

habitat

Forest patch size (ha) SI score

400a 0.064

700b 0.500

3,000b 1.000

5,000c 1.000

a

Rosenberg and others (2000).

b

Robbins and others (1989).

c

Assumed value.

Table 52.—Relationship between landscape 

composition and suitability index (SI) scores for 

cerulean warbler habitat

Landscape composition SI score

0.00a 0.00

0.10a 0.00

0.20a 0.00

0.30a 0.00

0.40a 0.00

0.50a 0.10

0.60a 0.25

0.70b 0.50

0.80a 0.75

0.90a 0.90

1.00a 1.00

a

Assumed value.

b

Donovan and others (1997).
61

We used the density of dominant trees (SI4) in the HSI model and assumed that trees 

with a d.b.h. greater than 76.2 cm would produce the heterogeneous vertical canopy 

structure preferred by the cerulean warbler. On the basis of qualitative habitat descriptions 

by Rosenberg and others (2000), we assumed that the cerulean warbler reached its highest 

density in stands containing at least one dominant tree per ha. Because this bird nests 

almost exclusively in these trees (Weakland and Wood 2005), we also assumed that it would 

be absent from stands with a uniform canopy height (i.e., no dominant trees). We fi t an 

exponential function (Fig. 28) to these data points and assumed that stands with at least 

14 dominant trees per ha (the maximum number observed in the WGCP during the FIA 

surveys of the 1990s) were associated with maximum habitat suitability (Table 53).

We used data from Rosenberg and others (2000), Jones and others (2001), and Weakland 

and Wood (2005) to derive an inverse quadratic function (Fig. 29) that predicted habitat 

suitability for the cerulean warbler from canopy cover (SI5; Table 54). Canopy cover of 50 

percent or less is associated with failed reproduction by this species (Jones and others 2001), 

so we considered these values as nonhabitat (SI score = 0.000). Rosenberg and others (2000) 

identifi ed “a tall, but broken, canopy” as one of the few common denominators of cerulean 

warbler habitat rangewide, and we maximized the SI score at 90 percent canopy closure. 

However, Weakland and Wood (2005) observed the cerulean warbler selecting internal 

edges, so we also discounted habitat suitability for closed canopies. Nonetheless, we recognize 

that a dense upper canopy is needed by this species (Hamel 2000) and assigned to sites with 

80 and 100 percent canopy cover an average SI score (0.500).

To calculate overall HSI scores for cerulean warbler habitat, we calculated the geometric 

mean of the three suitability indices related to forest structure (SI1, SI4, and SI5) and the 

maximum value for the two suitability indices related to landscape composition (SI2 and 

SI3) separately and then the geometric mean of these values together.

Overall SI = ((SI1 * SI4 * SI5)0.333 * Max(SI2 or SI3))0.500

Figure 28.—Relationship between dominant tree density 

and suitability index (SI) scores for cerulean warbler habitat. 

Equation: SI score = 1 – e -8.734 * dominant tree density.

 

Dominant Tree Density (trees/ha)

Suitability Index Score

0 2 4

0.0

0.2

0.4

0.6

0.8

1.0

Table 53.—Infl uence of dominant tree density on 

suitability index (SI) scores for cerulean warbler 

habitat

Dominant tree density (trees/ha)a SI score

0 0.0

1 1.0

14 1.0

a

Assumed value.
62

Verifi cation and Validation

Th e cerulean warbler was found in 60 of the 88 subsections within the CH and WGCP. 

Spearman rank correlation identifi ed a signifi cant positive relationship between average 

HSI score and mean BBS route abundance across all subsections (P ≤ 0.001; rs

 = 0.44) and 

those in which this species was detected (P ≤ 0.001; rs

 = 0.42). Th e generalized linear model 

predicting BBS abundance from BCR and HSI for the cerulean warbler was signifi cant (P ≤ 

0.001; R2

 = 0.205), and the coeffi cient on the HSI predictor variable was both positive (β = 

0.627) and signifi cantly diff erent from zero (P = 0.023). Th erefore, we considered the HSI 

model for the cerulean warbler both verifi ed and validated (Tirpak and others 2009a).

Table 54.—Infl uence of canopy cover on suitability 

index (SI) scores for cerulean warbler habitat

Canopy cover (percent) SI score

50a 0.00

70b 0.25

80b 0.50

90c 1.00

100d 0.50

a

Jones and others (2001).

b

Hamel (2000).

c

Rosenberg and others (2000).

d

Weakland and Wood (2005).

Figure 29.—Relationship between canopy cover and suitability 

index (SI) scores for cerulean warbler habitat. Equation: 

SI score = 1 / (62.548 – (1.369 * canopy cover) + (0.007612 * 

(canopy cover)2

)).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0
63

Chimney Swift

Status

Th e chimney swift (Chaetura pelagica) is a familiar bird 

found across most of North America east of the Rocky 

Mountains. Populations have declined in both the CH 

and WGCP over the last 40 years (2.6 and 1.1 percent 

per year). However, the high annual variability in 

abundance for this species prevents the identifi cation of 

signifi cant trends (Sauer and others 2005; Table 5). Th is 

bird has a regional combined score of 16 and requires 

management attention in the CH. However, in the WGCP, the chimney swift is only a 

planning and responsibility species with a regional combined score of 14 (Table 1).

Natural History

Th e range of the chimney swift, a small, long-distance migrant, expanded dramatically with 

European settlement and the increase in artifi cial nest structures (e.g., chimneys) that followed 

(Cink and Collins 2002). Prior to European settlement, this species probably was distributed 

thinly and relied on tree cavities for nesting. Nesting in trees is now rare (Graves 2004) and 

most nests and roosts are concentrated in urban areas (Cink and Collins 2002). Th is species 

is weakly territorial (typically one nest per cavity), and population declines may be due to the 

loss of nest sites as large, open chimneys become scarce. Home ranges are largely unknown.

Model Description

For a bird that occurs in such close association with humans, few data are available on the 

habitat preferences of the chimney swift. We assumed that habitat suitability for this species 

was primarily a function of the availability of nest and roost sites within the proper landscape 

context (i.e., open chimneys near foraging areas). To identify these locations, we estimated the 

proportion of foraging habitats in a 1-km buff er around each pixel of developed landcover. 

We assumed that this bird could travel 1 km from nesting-roosting areas to foraging habitats 

(defi ned as water, grassland, pasture-hay, recreational grasses, or forest landcover classes) and 

that these habitats had to be more than 1 ha to accommodate the aerial foraging maneuvers 

of this species. Because the chimney swift is semi-colonial, we also assumed that that as 

foraging habitat increased in the 1-km buff er, developed pixels were increasingly isolated and 

would be of lower suitability (Table 55). We used a quadratic curve (Fig. 30) to quantify the 

relationship between landscape composition and habitat suitability for this species.

Verifi cation and Validation

Th e chimney swift occurred in all 88 subsections of the CH and WGCP. Spearman rank 

correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.50) between average 

HSI score and mean BBS route abundance across subsections. Th e generalized linear model 

predicting BBS abundance from BCR and HSI for the chimney swift was signifi cant (P ≤ 

0.001; R2

 = 0.208), and the coeffi cient on the HSI predictor variable was positive (β = 5.043) 

but not signifi cantly diff erent from zero (P = 0.524). Th erefore, we considered the HSI model 

for the chimney swift verifi ed but not validated (Tirpak and others 2009a).

Ron Austing, used with permission
64

Table 55.—Infl uence of proportion of foraging 

habitata

 within 1-km buffer around potential 

nesting-roosting sitesb

 on suitability index (SI) 

scores for chimney swift habitat

Proportionc

 of foraging habitat 

around potential nestingroosting sites SI score

0.0 0.00

0.1 0.25

0.2 0.50

0.3 0.75

0.4 1.00

0.5 1.00

0.6 1.00

0.7 1.00

0.8 0.75

0.9 0.25

1.0 0.25

a

Foraging habitat = water, grassland, pasture-hay, 

recreational grasses, forest > 1 ha.

b

Nesting-roosting site = any developed landcover.

c

Assumed value.

Figure 30.—Relationship between proportion of foraging 

habitat within 1-km buffer around potential nesting/roosting 

sites on suitability index (SI) scores for chimney swift habitat. 

Equation: SI score = (-0.0769 + (4.0734 * proportion foraging 

cover) - (3.8462 * (proportion foraging cover2

))).

 

Foraging Habitat in 1-km Radius Around Nest Habitat

Suitability Index Score

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0
65

Chuck-will’s-widow

Status

Th e chuck-will’s-widow (Caprimulgus carolinensis) is 

a neotropical migrant that breeds in the southeastern 

United States. It has experienced small yet signifi cant 

declines in the WGCP over the last 40 years (1.3 

percent per year; Sauer and others 2005). Populations 

in the CH have remained relatively stable during the 

same period (Table 5). Chuck-will’s-widow is as 

Bird of Conservation Concern and a PIF species in 

need of management attention in the WGCP (regional combined score = 16). Th is species has 

no special conservation status in the CH (regional combined score = 14; Table 1).

Natural History

Th e chuck-will’s-widow, like all nightjars, is nocturnal and most active on moonlit nights. 

Because of this behavior and its cryptic coloration, this species is diffi cult to study and few 

systematic investigations of its habitat, demography, or population status have been conducted. 

Most of the information on chuck-will’s-widow is anecdotal and coincident to studies of other 

species (Straight and Cooper 2000).

Th e chuck-will’s-widow occupies woodland habitats interspersed with large openings in which 

the bird forages at night. Calling males are equally abundant among suburban, pasture, and 

forested landscapes (Cooper 1981). Urban habitats are unsuitable (Straight and Cooper 2000). 

Th e chuck-will’s-widow prefers more open habitats than the whip-poor-will (Cooper 1981) and 

is unaff ected by forest fragmentation (it may even benefi t from it). Drier sites also are preferred.

Model Description

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 56). We directly 

assigned SI scores to these combinations on the basis of data from Hamel (1992) on the habitat 

associations of the chuck-will’s-widow in the Southeast.

Th e realized suitability of the sites identifi ed in SI1 depends largely on landscape context. 

Cooper (1981) found that the abundance of chuck-will’s-widow was highest in areas with equal 

amounts of forest and agriculture. Th erefore, we used the proportion of these two habitats 

in a 500-m radius window (SI2) in the HSI model. We assigned the maximum SI score to 

landscapes characterized by 50 percent forest and 50 percent agriculture. We reduced these 

scores as landscapes varied from this optimal confi guration towards a more open or a more 

forested composition with a stronger reduction in suitability for increasingly forested landscapes 

(Table 57).

Th e overall HSI score for chuck-will’s-widow is based solely on SI2, which incorporates the 

results from SI1.

Overall HSI = SI2

Chandler S. Robbins, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
66

Verifi cation and Validation

Th e chuck-will’s-widow was found in 86 of the 88 subsections within the CH and WGCP. 

Spearman rank correlations yielded similar results when analysis included all subsections 

and only those subsections in which this species was detected: signifi cant (P ≤ 0.001 and 

0.003, respectively) positive associations (rs

 = 0.34 and 0.32, respectively) between average 

HSI score and mean BBS route abundance. Th e generalized linear model predicting BBS 

abundance from BCR and HSI for the chuck-will’s-widow was signifi cant (P ≤ 0.001; R2

 = 

0.312), and the coeffi cient on the HSI predictor variable was positive (β = 0.569) but not 

signifi cantly diff erent from zero (P = 0.415). Th erefore, we considered the HSI model for the 

chuck-will’s-widow verifi ed but not validated (Tirpak and others 2009a).

Table 56.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for chuck-will’s-widow habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.334 0.834 1.000

Deciduous 0.000 0.000 0.083 0.167 0.167

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.334 0.834 1.000

Deciduous 0.000 0.000 0.083 0.167 0.167

Evergreen 0.000 0.000 0.334 0.834 1.000

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.334 

(0.250)

0.834 

(0.583)

1.000 

(0.667)

Deciduous 0.000 0.000 0.167 0.333 0.333

Evergreen 0.000 0.000 0.334 

(0.250)

0.834 

(0.583)

1.000 

(0.667)

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000
67

Table 57.—Suitability index scores for chuck-will’s-widow habitat based on proportion of nesting-roosting 

and foraging habitat within 500-m radius landscape

Proportion foraginga

Proportion nest 

and roostb 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.1 0.0 0.0 0.0 0.1 0.2 0.2 0.2 0.2 0.2 0.2

0.2 0.0 0.0 0.1 0.2 0.4 0.6 0.6 0.6 0.5

0.3 0.0 0.1 0.2 0.4 0.6 0.6 0.8 0.8

0.4 0.0 0.2 0.4 0.6 0.8 0.8 1.0

0.5 0.0 0.2 0.4 0.6 0.8 1.0c

0.6 0.0 0.2 0.4 0.6 0.8

0.7 0.0 0.2 0.4 0.6

0.8 0.0 0.2 0.4

0.9 0.0 0.2

1.0 0.0

a

Foraging = pasture-hay, recreational grasses, grasslands, and emergent herbaceous wetland landcovers or grass-forb and 

shrub-seedling successional age classes.

b

Nest and roost = habitats identifi ed in SI1 (Table 56). c

Cooper (1981).
68

Eastern Wood-pewee

Status

Th e eastern wood-pewee (Contopus virens) is a longdistance neotropical migrant that breeds throughout the 

temperate regions of eastern North America (McCarty 

1996). Th is species reaches its highest densities in the 

Ozark Mountain region of the CH, where it has a 

regional combined score of 15 (Table 1). In the WGCP, 

the eastern wood-pewee has a regional combined score of 

16. Th is bird is one requiring management attention in 

both BCRs, with declining populations in both regions 

(Sauer and others 2005) (Table 5).

Natural History

Th e eastern wood-pewee is a common species in woodlands of all types (deciduous, mixed, 

and evergreen). However, this species consistently selects open park-like conditions on xeric 

sites with limited canopy cover and low shrub densities (Robbins and others 1989; McCarty 

1996). Th e eastern wood-pewee is positively associated with increasing density of sawtimber 

trees, reaching a threshold at 100 trees per ha where a negative relationship develops (Best 

and Stauff er 1986, Robbins and others 1989).

Th e eastern wood-pewee, common in both forest interiors and edges, generally is areainsensitive, and may occupy fragments as small as 0.3 ha (Blake and Karr 1987, Robbins 

and others 1989). Its cryptic nests high in the canopy may limit predation and parasitism, 

allowing the pewee to occupy small fragments without the adverse eff ects on reproduction 

common to other open-cup nesters (McCarty 1996, Knutson and others 2004, Underwood 

and others 2004). Th is species is not found in riparian corridors with less than 24 percent 

forest cover in the landscape (Perkins and others 2003b).

Model Description

Th e HSI model for the eastern wood-pewee includes fi ve variables: landform, landcover, 

successional age class, percent forest in a 1-km radius, and density of sawtimber trees (> 28 

cm d.b.h.).

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 58). We directly 

assigned SI scores to these combinations on the basis of habitat associations of the eastern 

wood-pewee reported by Hamel (1992).

Th is species can occupy small forest fragments but may require a minimum amount of forest 

in the landscape. Th erefore, our model did not include a forest patch size function but relied 

solely on landscape composition (SI2). We used a logistic function (Fig. 31) to predict SI 

scores from the percentage of forest in the landscape (Table 59). 

Jeffrey A Spendelow, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
69

We included density of sawtimber trees in the HSI model and used the threshold of 100 

trees per ha observed by Best and Stauff er (1986) as the optimal value in a quadratic 

function (Fig. 32) that links density of sawtimber trees (SI3) to habitat suitability. Because 

Best and Stauff er (1986) observed a reduction in wood-pewee abundance at sawtimber 

tree densities less than 100 trees per ha and Robbins and others (1989) observed a negative 

relationship between occurrence and tree density, we assumed a symmetrical decline in 

habitat quality as sawtimber tree density increased or decreased above or below the optimum 

(Table 60).

To calculate the overall HSI score, we determined the geometric mean of individual SI 

functions relating to forest structure (SI1 and SI3) and then calculated the geometric mean 

of this value and landscape composition (SI2). 

Overall HSI = ((SI1 * SI3)0.500 * SI2)0.500

Table 58.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for eastern wood-pewee habitat. Values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.167 0.250 0.500 0.667

Transitional-shrubland 0.000 0.167 0.250 0.500 0.667

Deciduous 0.000 0.167 0.250 0.500 0.667

Evergreen 0.000 0.250 0.333 0.667 1.000

Mixed 0.000 0.000 0.167 0.667 1.000

Orchard-vineyard 0.000 0.167 0.250 0.500 0.667

Woody wetlands 0.000 0.250 0.333 0.417 0.500

Terrace-mesic Low-density residential 0.000 0.000 0.167 0.583 0.834

Transitional-shrubland 0.000 0.000 

(0.333)

0.167 

(0.333)

0.667 1.000

Deciduous 0.000 0.000 0.167 0.583 0.834

Evergreen 0.000 0.250 0.333 0.667 1.000

Mixed 0.000 0.000 0.167 0.667 1.000

Orchard-vineyard 0.000 0.000 0.167 0.583 0.834

Woody wetlands 0.000 0.250 0.333 0.500 0.667

Xeric-ridge Low-density residential 0.000 0.000 0.167 0.667 1.000

Transitional-shrubland 0.000 0.000 

(0.167)

0.167 

(0.250)

0.667 1.000

Deciduous 0.000 0.000 0.167 0.667 1.000

Evergreen 0.000 0.250 

(0.167)

0.333 

(0.250)

0.667 1.000

Mixed 0.000 0.000 0.167 0.667 1.000

Orchard-vineyard 0.000 0.000 0.167 0.667 1.000

Woody wetlands 0.000 0.250 0.333 0.500 0.667
70

Verifi cation and Validation

Th e eastern wood-pewee was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation on average HSI score and mean BBS route abundance identifi ed a 

signifi cant (P ≤ 0.001) positive association (rs

 = 0.46) between these two variables at the 

subsection scale. Th e generalized linear model predicting BBS abundance from BCR and 

HSI for the eastern wood-pewee was signifi cant (P ≤ 0.001; R2

 = 0.472), and the coeffi cient 

on the HSI predictor variable was both positive (β = 5.183) and signifi cantly diff erent from 

zero (P ≤ 0.001). Th erefore, we considered the HSI model for the eastern wood-pewee both 

verifi ed and validated (Tirpak and others 2009a).

Figure 31.—Relationship between landscape composition and 

suitability index (SI) scores for eastern wood-pewee habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Figure 32.—Relationship between sawtimber tree (≥ 28 cm 

d.b.h.) density and suitability index (SI) scores for eastern 

wood-pewee habitat. Equation: SI score = (0.0200 * sawtimber 

tree density) - (0.0001 * (sawtimber tree density2

)).

 

Sawtimber Tree Density (trees/ha)

Suitability Index Score

0 50 100 150 200

0.0

0.2

0.4

0.6

0.8

1.0

Table 59.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for eastern woodpewee habitat 

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Dononvan and others (1997).

Table 60.—Infl uence of sawtimber tree (≥ 28 cm 

d.b.h.) density (trees/ha) on suitability index (SI) 

scores for eastern wood-pewee habitat

Sawtimber tree density SI score

0a 0.0

100b

 1.0

200a 0.0

a

Assumed value.

b

Best and Stauffer (1986).
71

Field Sparrow

Status

Th e fi eld sparrow (Spizella pusilla) is a shortdistance migrant found throughout North 

America east of the Rocky Mountains. 

Associated with early successional habitats, 

this species has experienced the sharp declines 

typical of many scrub-shrub and grassland 

species in the East. BBS data indicate declines in populations of the fi eld sparrow in both 

the CH and WGCP (Sauer and others 2005; Table 5). Th e fi eld sparrow has a regional 

combined score of 17 and 15 in the CH and WGCP, respectively, but is not a Bird of 

Conservation Concern in either BCR (Table 1). About 20 percent of the continental 

population occurs in the CH (Panjabi and others 2001). 

Natural History

Th e fi eld sparrow breeds in a variety of vegetation types, including brushy pastures, secondgrowth scrub, forest openings and edges, Christmas tree farms, orchards, nurseries, and 

roadsides and railroads near open fi elds (Carey and others 1994). Abundance increases in 

forested landscapes managed for early successional habitat (Yahner 2003), and this bird 

commonly occupies reclaimed mines (DeVault and others 2002) and savanna restoration 

sites (Davis and others 2000). Abundance is positively related to the size of old fi elds in 

Arkansas (Bay 1994). Th e fi eld sparrow nests on or near the ground in early spring but may 

nest in saplings or shrubs later in the year. Brood parasitism rates vary geographically but the 

fi eld sparrow generally is a poor cowbird host. Parasitism rates are higher in thinned forest 

stands than in regenerating plantations (Barber and others 2001).

Th is species also uses grasslands, though at lower densities than in shrub-scrub habitats 

(Horn and others 2002). Grass type aff ects habitat suitability, with warm-season grasses 

supporting higher abundance (Giuliano and Daves 2002, Walk and Warner 2000), nest 

density (Farrand 2005), and productivity than cool-season grasses (Giuliano and Daves 

2002). Conservation Reserve Program fi elds serve as source habitat for the fi eld sparrow in 

Missouri (McCoy and others 1999).

Model Description

Th e model predicting habitat suitability for the fi eld sparrow includes six variables: landform, 

land cover, successional age class, canopy cover, density of small stems (< 2.5 cm d.b.h.), and 

the presence of grassy landcover.

Th e fi rst suitability function of the fi eld sparrow HSI model combines landform, landcover, 

and successional age class into a single matrix (SI1) that defi nes unique combinations of 

these classes (Table 61). We used habitat associations of the fi eld sparrow reported by Hamel 

(1992) to assign SI scores to these combinations.

Deanna K. Dawson, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
72

We included canopy cover (SI2) and small stem density (SI3) as SIs in our model to account 

for the absence of the fi eld sparrow from closed-canopy forests or forested sites with an 

open understory. We used data from Annand and Th ompson (1997) (Tables 62 and 63) to 

fi t a quadratic function to canopy cover and a Gaussian function to small stem density for 

predicting SI scores (Fig. 33 and 34). Th e negative relationship between the fi eld sparrow 

and stem density is supported by Carey and others (1994), who observed a reduction in 

habitat suitability as “thickets of trees spread in the habitat.” Sousa (1983) constructed an 

HSI model that contained a negative relationship between habitat suitability and percent 

shrub cover. Suitability of habitat for the fi eld sparrow declined from optimal at 50 percent 

shrub cover (defi ned as the percentage of ground shaded by a vertical projection of the 

canopies of woody vegetation less than 5 m) to unsuitable at 75 percent shrub cover. We did 

not have a quantitative estimate of the relationship between small stem density and shrub 

cover, so we assumed that 40,000 stems per ha would shade 75 percent of the ground. We 

were conservative with this estimate; lacking quantitative data, we did not want to exclude 

stands that might provide habitat for this species.

Table 61.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for fi eld sparrow habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.333 0.000 0.000 0.000

Deciduous 0.000 0.333 0.000 0.000 0.000

Evergreen 0.667 1.000 0.000 0.000 0.000

Mixed 0.667 1.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.333 0.000 0.000 0.000

Woody wetlands 0.000 0.167 0.000 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.667 1.000 0.000 0.000 0.000

Deciduous 0.000 0.667 0.000 0.000 0.000

Evergreen 0.667 1.000 0.000 0.000 0.000

Mixed 0.667 1.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.667 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.000 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.667 1.000 0.000 0.000 0.000

Deciduous 0.000 1.000 0.000 0.000 0.000

Evergreen 0.667 1.000 0.000 0.000 0.000

Mixed 0.667 1.000 0.000 0.000 0.000

Orchard-vineyard 0.000 1.000 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.000 0.000 0.000
73

Th e fi eld sparrow often is associated with grasslands with suffi cient perches (Carey and others 

1994, Kahl and others 1985). Th erefore, we included an SI function related to grasslands 

(SI4) in the model. Many useable grassland sites may have insuffi cient woody cover to be 

classifi ed as shrublands in the NLCD, so we required all grassland types (natural as well as 

pasture and hayfi elds) to be within 170 m of a wooded edge—a distance approximating a 

large fi eld sparrow territory (Best 1974)—to be considered useable. Natural grasslands also 

are more likely to contain dense grass nesting sites than pastures and hayfi elds (Giuliano and 

Daves 2002, Farrand 2005), so we assigned to useable natural grasslands an SI score of 1.000 

and to useable pasture-hayfi elds a score of 0.500 (Table 64).

Figure 33.—Relationship between canopy cover and suitability 

index (SI) scores for fi eld sparrow habitat. Equation: SI score = 

1.0038 + 0.0040 * (canopy cover) – 0.0001475 * (canopy cover)2

.

Figure 34.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

fi eld sparrow habitat. Equation: SI score = 1.003 * e (-((small stem 

density / 1000) – 8.461)^2 )/ 31.0472.

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30 40

0.0

0.2

0.4

0.6

0.8

1.0

Table 62.—Infl uence of canopy cover on suitability 

index (SI) scores for fi eld sparrow habitat

Canopy cover (percent) SI score

0.00a 1.000

29.26b 1.000

71.86b 0.555

93.38b 0.000

100.00a 0.000

1

Assumed value.

2

Annand and Thompson (1997).

Table 63.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for fi eld sparrow habitat

Small stem density SI score

0a 0.1

3.812b 0.5

8.148b 1.0

40.000a 0.0

a

Sousa (1983).

b

Annand and Thompson (1997).

Table 64.—Relationship between grass landcover 

and suitability index (SI) scores for fi eld sparrow 

habitat

Landcover SI score

Grassland-herbaceousa 1.0

Pasture-haya 0.5

a

Must occur ≤ 170 meters from forested landcover.
74

To calculate the HSI score for fi eld sparrow habitat in forested landcovers, we calculated the 

geometric mean of the SI scores relating to forest structure (SI1, SI2, and SI3). We added the 

SI score for grasslands (SI4) to this value to determine the overall HSI score.

Overall HSI = ((SI1 * SI2 * SI3)0.333 + SI4)

Verifi cation and Validation

Th e fi eld sparrow was found in 87 of the 88 subsections within the CH and WGCP. 

Spearman rank correlation on average HSI score and mean BBS route abundance identifi ed 

a signifi cant (P ≤ 0.001) positive association (rs = 0.55) between these two variables within 

subsections where this species was detected. Th e generalized linear model predicting BBS 

abundance from BCR and HSI for the fi eld sparrow was signifi cant (P ≤ 0.001; R2

 = 

0.690), and the coeffi cient on the HSI predictor variable was both positive (β = 37.060) and 

signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the HSI model for the 

fi eld sparrow both verifi ed and validated (Tirpak and others 2009a).
75

Great Crested Flycatcher

Status

Th e great crested fl ycatcher (Myiarchus crinitus), a 

neotropical migrant, is found throughout the forests of 

eastern North America and the riparian habitats of the 

Mississippi River watershed. Populations have remained 

relatively stable across most of its range, though in the 

WGCP they have declined by 1.3 percent per year since 

1966 (Sauer and others 2005) (Table 5). Th is species has a 

regional combined score of 13 in both the CH and WGCP 

(Table 1).

Natural History

Th e great crested fl ycatcher is an obligate cavity nester in deciduous forest habitats of the 

eastern United States; it generally is absent in pure evergreen stands (Lanyon 1997). Th is 

species is not area sensitive but does require a minimum amount of forested habitat in the 

landscape. It may nest in patches as small as 0.2 ha and abundance may decline in forest 

interiors (Robbins and others 1989). Th e great crested fl ycatcher does not occupy riparian 

corridors surrounded by less than 14.7 percent forest (Perkins and others 2003b), and 

detection probabilities steadily increase with increasing corridor width (Groom and Grubb 

2002).

Th e great crested fl ycatcher forages by sallying from exposed perches (Lanyon 1997), so open 

forest stands are preferred. Holmes and others (2004) found that abundance was highest 

in heavily cut stands where one-third or more of the basal area was removed. Similarly, 

Moorman and Guynn (2001) found that the great crested fl ycatcher was associated with 

large (0.5 ha) canopy gaps in bottomland hardwood forest in South Carolina. Snags not 

only provide exposed perches for foraging but also cavities for nesting, and the great crested 

fl ycatcher is negatively aff ected by the removal of snags associated with certain forestry 

practices (Lohr and others 2002). Where snags are lacking, this species will use nest boxes 

and other artifi cial cavities; this enables it to occupy cemeteries, suburban parks, and wooded 

pastures. Wakeley and Roberts (1996) found that this bird is associated with mesic sites, 

but this may refl ect a preference for bottomland hardwoods over evergreen uplands in the 

Southeast.

Model Description

Th e HSI model for great crested fl ycatcher includes fi ve variables: landform, landcover, 

successional age class, snag density, and distance to edge.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 65). We directly 

assigned SI scores to these combinations on the basis of relative habitat quality associations 

reported by Hamel (1992) for the great crested fl ycatcher.

Deanna K. Dawson, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
76

Th e great crested fl ycatcher relies on snags (SI2) for nesting and foraging. We fi t a logistic 

function (Fig. 35) through average snag values (8.5/ha) observed by Lohr and others (2002), 

assuming that this value represented average habitat suitability (SI score = 0.500) and that a 

higher abundance of snags would not be detrimental but increase the likelihood that this bird 

will use a site (Table 66).

Th is species is associated with edges (Lanyon 1997), and its abundance declines with 

increasing distance from an edge (SI3). Small and Hunter (1989) found that more than 60 

percent of all fl ycatchers were less than 60 m from an edge. We assumed maximum habitat 

suitability at the edge and modeled the relationship between distance to edge and SI score as 

an inverse logistic function through these data points (Fig. 36, Table 67).

To calculate the overall HSI, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI2) and then calculated the geometric mean of this value with the edge 

function (SI3). 

Overall HSI = ((SI1 * SI2)0.500 * SI3)0.500

Table 65.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for great crested fl ycatcher habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.167 0.500 0.667

Transitional-shrubland 0.000 0.000 0.167 0.500 0.667

Deciduous 0.000 0.333 0.333 0.500 0.667

Evergreen 0.000 0.333 0.333 0.500 0.667

Mixed 0.000 0.333 0.333 0.667 1.000

Orchard-vineyard 0.000 0.000 0.167 0.500 0.667

Woody wetlands 0.000 0.333 0.333 0.667 1.000

Terrace-mesic Low-density residential 0.000 0.333 0.333 0.583 0.834

Transitional-shrubland 0.000 0.333 0.333 0.667 

(0.500)

1.000 

(0.667)

Deciduous 0.000 0.333 0.333 0.583 0.834

Evergreen 0.000 0.333 0.333 0.500 0.667

Mixed 0.000 0.333 0.333 0.667 1.000

Orchard-vineyard 0.000 0.000 0.167 0.583 0.834

Woody wetlands 0.000 0.333 0.333 0.667 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.167 0.667 1.000

Transitional-shrubland 0.000 0.333 

(0.250)

0.333 

(0.250)

0.667 

(0.500)

1.000 

(0.667)

Deciduous 0.000 0.333 0.333 0.667 1.000

Evergreen 0.000 0.333 

(0.250)

0.333 

(0.250)

0.500 0.667

Mixed 0.000 0.333 0.333 0.667 1.000

Orchard-vineyard 0.000 0.333 0.333 0.667 1.000

Woody wetlands 0.000 0.333 0.333 0.667 1.000
77

Verifi cation and Validation

Th e great crested fl ycatcher was found in all 88 subsections within the CH and WGCP. 

Spearman rank correlation on average HSI score and mean BBS route abundance failed 

to identify a signifi cant (P ≤ 0.001) association (rs = 0.55) between these two variables. 

Th e generalized linear model predicting BBS abundance from BCR and HSI for the great 

crested fl ycatcher was not signifi cant (P = 0.152; R2

 = 0.043), and the coeffi cient on the 

HSI predictor variable was negative (β = -2.740) and not signifi cantly diff erent from zero (P

= 0.151). Th erefore, we considered the HSI model for the great crested fl ycatcher neither 

verifi ed nor validated (Tirpak and others 2009a).

Figure 35.—Relationship between snag density and suitability 

index (SI) scores for great crested fl ycatcher habitat. Equation: 

SI score = 1.001 / (1 + (18.704 * e (-0.346 * snag density))).

Figure 36.—Relationship between distance to edge and suitability 

index (SI) scores for great crested fl ycatcher habitat. Equation: 

SI score = 1 - (1.000 / (1 + (28.950 * e -0.049 * distance to edge))).

 

Snag Density (snags/ha)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

 

Distance to Edge (m)

Suitability Index Score

0 50 100 150 200

0.0

0.2

0.4

0.6

0.8

1.0

Table 66.—Infl uence of snag density on suitability 

index (SI) scores for great crested fl ycatcher habitat 

Snag density (snags/ha) SI score

0.0a 0.000

1.9a 0.133

8.5a 0.500

20.0b 1.000

25.0b 1.000

a

Lohr and others (2002).

b

Assumed value.

Table 67.—Infl uence of distance (m) to edgea on 

suitability index (SI) scores for great crested 

fl ycatcher habitat 

Distance to edge SI score

0b 1.0

60c 0.6

120b 0.1

150b 0.0

a

Edge defi ned by nonhabitat pixels adjacent to 

habitat pixels (defi ned by SI1). b

Assumed value.

c

Small and Hunter (1989).
78

Hooded Warbler

Status

Th e hooded warbler (Wilsonia citrina) is a long-distance 

migrant found throughout the deciduous forests of 

eastern North America. Because of area sensitivity, it 

is restricted to forested landscapes and disappears from 

the forest-prairie ecotone at the western edge of its 

range faster than other silvicolous species (e.g., eastern 

wood-pewee). Populations in the WGCP declined prior 

to 1990 but have since remained stable. Conversely, 

populations in the CH have increased (Sauer and 

others 2005) (Table 5). Th is species is not a Bird of 

Conservation Concern in either BCR (Table 1) but it is a planning and responsibility species 

in the WGCP (regional combined score = 16; Table 1). Nearly 30 percent of the continental 

population of the hooded warbler breeds in the WGCP (Panjabi and others 2001).

Natural History

Th e hooded warbler breeds in a variety of habitats, from mixed-hardwood forests in the 

northern portion of its range to cypress-gum swamps in the South. Regardless of forest 

type, it prefers mesic sites in large forest tracts (> 15 ha; Evans-Ogden and Stutchbury 

1994). Although nest success in small forest patches is not signifi cantly lower than in large 

patches (Buehler and others 2002), females may avoid small fragments and males use edge 

less than its availability (Norris and Stutchbury 2002, Norris and others 2000). Occupancy 

of a site by a nesting pair increases with shrub height and the percentage of vegetation 

between 1 and 2 m.

Th is species nests in shrubs within small forest clearings or in the dense understories of 

closed-canopied forests. As a result, territories often include a mix of open and closed 

canopies. Gaps created by tree fall or selective logging are particularly attractive (≤ 0.5 ha; 

Annand and Th ompson 1997, Moorman and others 2002, Whittam and others 2002), and 

the hooded warbler colonizes these sites within 1 to 5 years. Nest sites in Canada had denser 

ground vegetation, fewer tree stems, lower basal area of small trees, and greater basal area 

of large trees than control sites (Whittam and others 2002). Bisson and Stutchbury (2000) 

concluded that canopy gaps and density of understory vegetation were the most important 

factors aff ecting site selection. Repeated burning, which removed understory vegetation, 

reduced hooded warbler abundance in Ohio (Artman and others 2001). Th is species is a 

common cowbird host, which may explain its sensitivity to fragmentation (Donovan and 

Flather 2002).

Model Description

Th e HSI model for the hooded warbler includes seven variables: landform, land cover, 

successional age class, small stem (< 2.5 cm d.b.h.) density, canopy cover, forest patch size, 

and percent forest in a 1-km landscape.

U.S. Fish & Wildlife Service
79

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 68). We directly 

assigned SI scores to these combinations on the basis of relative habitat quality rankings from 

Hamel (1992) for the hooded warbler in the Southeast.

Th is species occupies dense understories in mature forested habitats, so we included both small 

stem density (SI2) and canopy cover (SI3) in our model. We fi t a logistic function (Fig. 37) 

that links small stem density to SI scores on the basis of data from Annand and Th ompson 

(1992) and Moorman and others (2002) (Table 69). We assumed that the average stem density 

measured at nest sites by Moorman and others (2002) (4,700 stems/ha) was representative of 

ideal habitat conditions for the hooded warbler and that there was no upper threshold above 

which habitat suitability declined. We also fi t a logistic function (Fig. 38) to data from Annand 

and Th ompson (1997) (Table 70) to link canopy cover values to SI scores.

We included forest patch size (SI4) as a model predictor because of the negative eff ect of 

fragmentation on this species. We used an exponential curve (Fig. 39) to predict habitat 

Table 68.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for hooded warbler habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.167 0.667 1.000

Deciduous 0.000 0.000 0.167 0.667 1.000

Evergreen 0.000 0.000 0.000 0.334 0.667

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.167 

(0.000)

0.500 

(0.334)

0.667

Deciduous 0.000 0.000 0.167 0.667 1.000

Evergreen 0.000 0.000 0.000 0.334 0.667

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.167 

(0.000)

0.500 

(0.167)

0.667 

(0.334)

Deciduous 0.000 0.000 0.167 0.667 1.000

Evergreen 0.000 0.000 0.000 0.334 

(0.167)

0.667 

(0.334)

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000
80

suitability from forest patch size on the basis of data from Evans-Ogden and Stutchbury 

(1994) and Kilgo and others (1998). To convert riparian widths reported by Kilgo and 

others (1998) to forest patch sizes, we assumed that all riparian strips were 10 km long 

(Table 71). Th e suitability of a specifi c forest patch is infl uenced by the percentage of forest 

in the landscape (SI5). Small patches that otherwise would be unsuitable may be occupied 

when in close proximity to a large forest block or in a predominantly forested landscape 

(Rosenberg and others 1999). To capture this relationship, we fi t a logistic function (Fig. 

40) to data (Table 72) derived from Donovan and others (1997), who observed diff erences 

in predator and brood parasite communities among highly fragmented (< 15 percent), 

moderately fragmented (45 to 50 percent), and lightly fragmented (> 90 percent forest) 

landscapes. We assumed that the midpoints between these classes (30 and 70 percent 

Figure 37.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) on suitability index (SI) scores for 

hooded warbler habitat. Equation: SI score = 1.000 / (1.000 + 

(102634.340 * e -4.017 * (small stem density / 1000))).

Figure 38.—Relationship between canopy cover on suitability 

index (SI) scores for hooded warbler habitat. Equation: 

SI score = 1.024 / (1.000 + (3823.776 * e -0.120 * canopy cover)).

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

Table 69.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for hooded warbler habitat

Small stem density SI score

0.000a 0.000

2.077b 0.039

4.700c 1.000

4.717b 1.000

10.000a 1.000

a

Assumed value.

b

Annand and Thompson (1992).

c

Moorman and others (2002).

Table 70.—Infl uence of canopy cover on suitability 

index (SI) scores for hooded warbler habitat

Canopy cover (percent) SI score

0.00a 0.0

29.26b 0.0

71.86b 0.6

93.38b 1.0

95.58b 1.0

96.59b 1.0

a

Assumed value.

b

Annand and Thompson (1997).
81

forest) defi ned the specifi c cutoff s for poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) 

habitat, respectively. We used the maximum SI score from SI4 or SI5 to account for the higher 

suitability of small forest patches in a heavily forested landscape. 

Th e overall HSI score was calculated as the geometric mean of the geometric mean of the SI 

values from the landform, landcover, and successional age class matrix, small stem density, and 

canopy cover functions (SI1, SI2, and SI3) multiplied by the maximum value of either the 

forest patch size or percent forest in the 1-km radius landscape functions (SI4 and SI5).

Overall HSI = ((SI1 * SI2 * SI3)0.333 * Max(SI4 or SI5))0.500

Figure 39.—Relationship between forest patch size and 

suitability index (SI) scores for hooded warbler habitat. 

Equation: SI score = 0.994 * (1 - e -0.024 * forest patch size).

Figure 40.—Relationship between landscape composition 

and suitability index (SI) scores for hooded warbler habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 71.—Infl uence of forest patch size on suitability 

index (SI) scores for hooded warbler habitat

Forest patch size (ha) SI score

15a 0.00

25b 0.65

50b 0.74

100b 0.86

200b 0.97

500b 1.00

1,000b 1.00

2,500b 1.00

a

Evans-Ogden and Stutchbury (1994).

b

Kilgo and others (1998).

Table 72.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for hooded warbler habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Dononvan and others (1997).
82

Verifi cation and Validation

Th e hooded warbler was found in 84 of the 88 subsections within the CH and WGCP. 

Spearman rank correlations identifi ed signifi cant positive associations between average 

HSI score and mean BBS route abundance across all subsections (P ≤ 0.001; rs

 = 0.49) and 

subsections within which this species was detected (P ≤ 0.001; rs

 = 0.42). Th e generalized 

linear model predicting BBS abundance from BCR and HSI for the hooded warbler was 

signifi cant (P ≤ 0.001; R2

 = 0.551), and the coeffi cient on the HSI predictor variable was 

both positive (β = 8.190) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we 

considered the HSI model for the hooded warbler both verifi ed and validated (Tirpak and 

others 2009a).
83

Kentucky Warbler

Status

Th e Kentucky warbler (Oporornis formosus) breeds 

throughout the southeastern United States; densities are 

highest west of the Appalachian front. Populations have 

been stable in the CH over the last 40 years, but have 

declined in the WGCP by 2.2 percent per year during 

this period (Table 5). Th is species requires management 

attention in both regions (regional combined score = 

18 and 19 in the CH and WGCP, respectively). A high 

percentage of the continental population breeds in both 

BCRs (28 and 22 percent, respectively; Panjabi and others 

2001). Th e species is an FWS Bird of Conservation Concern in the WGCP (Table 1).

Natural History

Th e Kentucky warbler, a long-distance migrant, breeds in mature moist deciduous forests 

of the Southeast. It is a forest-interior specialist, primarily because of low productivity and 

survival in edge and early successional habitats (Morse and Robinson 1999; Robinson 

and Robinson 2001). Th e Kentucky warbler occupies fragments as small as 2.4 ha (Blake 

and Karr 1987) but tracts larger than 500 ha are considered the minimum size necessary 

to support sustainable populations (McDonald 1998). A dense understory is a common 

feature of nesting sites. Ground cover averaged 46 percent in Kentucky warbler territories 

in Missouri (Wenny and others 1993), and vegetation of less than 1.5 m was denser around 

nests than random sites in South Carolina (Kilgo and others 1996). Dense vegetation (0.3 

to 1 m) was also associated with higher numbers of the Kentucky warbler in Maryland 

(Robbins and others 1989). Mesic sites are universally selected (McShea and others 1995, 

McDonald 1998, Gram and others 2003).

Model Description

Th e habitat suitability model for the Kentucky warbler includes six variables: landform, 

landcover, successional age class, small stem (< 2.5 cm d.b.h.) density, forest patch size, and 

percent forest in the landscape.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 73). We relied 

on relative habitat quality associations reported by Hamel (1992) to assign SI scores to these 

combinations. However, we increased SI scores for shrub-seedling stands on the basis of data 

from Th ompson and others (1992).

Th e Kentucky warbler nests at the base of shrubs and occupies habitats containing high 

densities of small stems (SI2). We used data on the relative abundance of this species from 

Wenny and others (1993), Kilgo and others (1996), and Annand and Th ompson (1997) to 

derive a logistic function (Fig. 41) that predicts habitat suitability from small stem density 

(Table 74).

U.S. Fish & Wildlife Service
84

We used a logarithmic function (Fig. 42) to quantify the relationship between forest patch 

size (SI3) and habitat suitability on the basis of minimum patch size observations by Hayden 

and others (1985) and occupancy rates in diff erent patch sizes reported by Robbins and 

others (1989) (Table 75). However, the suitability of a specifi c forest patch is infl uenced 

by its landscape context (SI4). Because the Kentucky warbler is particularly sensitive to 

fragmentation (Lynch and Whigham 1984), we used a 10-km window to characterize the 

landscape. We fi t a logistic function (Fig. 43) to data (Table 76) derived from Donovan and 

others (1997), who observed diff erences in predator and brood parasite communities among 

highly fragmented (< 15 percent), moderately fragmented (45 to 50 percent), and lightly 

fragmented (> 90 percent forest) landscapes. We assumed that the midpoints between these 

classes (30 and 70 percent forest) defi ned the specifi c cutoff s for poor (SI score ≤ 0.10) and 

excellent (SI score ≥ 0.90) habitat, respectively. 

Table 73.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for Kentucky warbler habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.667 0.417 0.667 0.667

Deciduous 0.000 0.667 0.417 0.667 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.333 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 1.000 0.667 1.000 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.333 

(0.000)

0.167 

(0.000)

0.333 

(0.000)

0.333 

(0.000)

Deciduous 0.000 0.667 0.334 0.667 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.333 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 1.000 0.667 1.000 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.333 

(0.000)

0.167 

(0.000)

0.333 

(0.000)

0.333 

(0.000)

Deciduous 0.000 0.500 0.250 0.500 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.333 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 1.000 0.667 1.000 1.000
85

To calculate the overall HSI score, we determined the geometric mean of SI scores for 

functions relating to forest structure (SI1 and SI2) and landscape composition (SI3 and SI4) 

separately and then the geometric mean of these means together.

Overall HSI = ((SI1 * SI2)0.500 * (SI3 * SI4)0.500)

0.500

Figure 41.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

Kentucky warbler habitat. Equation: SI score = 1.026 / (1.000 + 

(111.558 * e -1.707 * (small stem density / 1000))).

Figure 42.—Relationship between forest patch size and 

suitability (SI) scores for Kentucky warbler habitat. Equation: SI 

score = 0.248 * ln(forest patch size) – 0.377.

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

 

Forest Patch Size (ha)

Suitability Index Score

0 100 200 300

0.0

0.2

0.4

0.6

0.8

1.0

Table 74.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems/ha) on suitability index (SI) scores 

for Kentucky warbler habitat

Small stem density SI score

0.000a 0.000

2.077b 0.316

3.000c 0.500

3.812b 1.000

8.148b 1.000

47.600d 1.000

a

Assumed value.

b

Annand and Thompson (1997).

c

Wenny and others (1993).

d

Kilgo and others (1996).

Table 75.—Infl uence of forest patch size on suitability 

index (SI) scores for Kentucky warbler habitat 

Forest patch size (ha) SI score

8a 0.0

17b 0.5

300b 1.0

a

Hayden and others (1985).

b

Robbins and others (1989).
86

Verifi cation and Validation

Th e Kentucky warbler was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlations identifi ed a signifi cant positive association between average HSI score and 

mean BBS route abundance across all subsections (P ≤ 0.001; rs

 = 0.71). Th e generalized 

linear model predicting BBS abundance from BCR and HSI for the Kentucky warbler was 

signifi cant (P ≤ 0.001; R2

 = 0.346), and the coeffi cient on the HSI predictor variable was 

both positive (β = 6.351) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we 

considered the HSI model for the Kentucky warbler both verifi ed and validated (Tirpak and 

others 2009a).

Figure 43.—Relationship between landscape composition 

and suitability index (SI) scores for Kentucky warbler habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

Table 76.—Relationship between landscape 

composition (percent forest in 10-km radius) and 

suitability index (SI) scores for Kentucky warbler 

habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Dononvan and others (1997).

 

Landscape Composition (% forest in 10-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0
87

Louisiana Waterthrush

Status

Th e Louisiana waterthrush (Seirus motacilla) is a longdistance neotropical migrant found throughout the 

deciduous forests of the eastern and central United 

States. Th e small population in the WGCP has remained 

relatively stable since 1966 while the larger population 

in the CH has increased by 2.6 percent annually (Sauer 

and others 2005) (Table 5). Th is species is a Bird of 

Conservation Concern in both regions (Table 1). 

However, PIF diff erentiates the priority for this species 

in the CH (planning and responsibility, regional combined score = 15) and WGCP 

(management attention, regional combined score = 18; Table 1).

Natural History

As its name implies, the Louisiana waterthrush is associated with water throughout its range 

(Robinson 1995). Densities are highest along gravel-bottomed, fi rst- and second-order 

streams fl owing through large (> 350 ha) tracts of mature deciduous forest (Robbins and 

others 1989, Robinson 1995). Birds also breed at lower densities along mud-bottomed 

streams in cypress swamps and bottomland hardwood forests (Hamel 1992, Robinson 1995).

Prosser and Brooks (1998) developed and validated an HSI model for the Louisiana 

waterthrush in central Pennsylvania that included eight variables: canopy cover (> 80 percent 

considered ideal), shrub cover (< 25 percent), ratio of deciduous to conifer cover (30 to 69 

percent, mostly refl ecting hemlock dominance along streams in the Northeast), herbaceous 

cover (< 25 percent), stream order (fi rst- orsecond-order with well developed pools and 

riffl es), water clarity and substrate (clear and rocky or sandy), nesting cover (presence of 

uprooted trees or creviced, steep banks), and forest area (> 350 ha).

Model Description

Our HSI model for the Louisiana waterthrush included eight variables: landform, landcover, 

successional age class, distance to stream, canopy cover, small stem (< 2.5 cm d.b.h.) density, 

forest patch size, and percent forest in a 1-km radius.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 77). We directly 

assigned SI scores to these combinations on the basis of vegetation and successional age class 

associations outlined in Hamel (1992).

We included distance to stream (SI2) as a variable because the waterthrush uses streams 

and creeks for foraging and nesting. Th e Louisiana waterthrush restricts its foraging to the 

streambed and bank, so we assumed a sharp decline in suitability with increasing distance 

to a stream (Table 78). We used an inverse logistic function to characterize this relationship 

(Fig. 44).

Charles H. Warren, images.nbii.gov
88

Table 77.—Relationship of landform, landcover type, and successional age class to suitability index (SI) 

scores for Louisiana waterthrush habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.500 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.500 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.500 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.500 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.250 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.334 0.667

Figure 44.—Relationship between distance to stream and 

suitability index (SI) scores for Louisiana waterthrush habitat. 

Equation: SI score = 1 - (1.0015 / (1 + (104411.5 * e -0.1926 * 

distance to stream))).

 

Distance to Stream (m)

Suitability Index Score

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Table 78.—Relationship between distance to stream 

and suitability index (SI) scores for Louisiana 

waterthrush habitat.

Distance to stream (m)a SI score 

0 1.0

30 1.0

60 0.5

90 0.0

120 0.0

a

Assumed value.
89

We also included canopy cover (SI3) and small stem density (SI4) as variables based on 

the preference of this species for mature forested sites with closed canopies and open 

understories. We fi t logistic (Fig. 45) and inverse logistic (Fig. 46) functions to data adapted 

from the HSI model of Prosser and Brooks (1998) for canopy cover (Table 79) and small 

stem density (Table 80), respectively. 

Forest patch size (SI5) aff ects the occupancy of habitats by the Louisiana waterthrush. 

To predict habitat suitability from forest patch size, we fi t a logarithmic function (Fig. 

47) to data from Hayden and others (1985) and Robbins and others (1989) (Table 81) 

Figure 45.—Relationship between canopy cover and suitability 

index (SI) scores for Louisiana waterthrush habitat. Equation: 

SI score = (1.0313 / (1 + (175.8083 * e -0.0864 * canopy cover))).

Figure 46.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

Louisiana waterthrush habitat. Equation: SI score = 1 - (1.000 / 

(1 + (113.261 * e -0.592 * (small stem density / 1000)))).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

Table 79.—Relationship between canopy cover 

and suitability index (SI) scores for Louisiana 

waterthrush habitat

Canopy cover (percent)a SI score

0 0.0

10 0.0

20 0.0

30 0.0

40 0.2

50 0.2

60 0.7

70 0.7

80 0.7

90 1.0

a

Prosser and Brooks (1998).

Table 80.—Relationship between small stem (< 2.5 

cm d.b.h.) density (stems * 1,000/ha) and suitability 

index (SI) scores for Louisiana waterthrush habitat

Small stem density SI score

0a 1.000

2.519a 1.000

5.803a 0.767

9.086a 0.349

25.000b 0.000

a

Prosser and Brooks (1998).

b

Assumed value.
90

on the detection probabilities of the Louisiana waterthrush in patches of varying size. 

However, forest patch size alone may not be an appropriate measure of a site’s suitability. 

In predominantly forested landscapes, small patches otherwise not suitable may be occupied 

due to their proximity to large forest blocks (Rosenberg and others 1999). To capture this 

relationship, we fi t a logistic function (Fig. 48) to data (Table 82) derived from Donovan 

and others (1997), who observed diff erences in predator and brood parasite communities 

among highly fragmented (< 15 percent), moderately fragmented (45 to 50 percent), and 

lightly fragmented (> 90 percent forest) landscapes. We assumed the midpoints between 

these classes (30 and 70 percent forest) defi ned the specifi c cutoff s for poor (SI score ≤ 0.10) 

and excellent (SI score ≥ 0.90) habitat, respectively. We used the maximum SI score from 

Figure 47.—Relationship between forest patch size and 

suitability index (SI) scores for Louisiana waterthrush habitat. 

Equation: SI score = 1.000 – (1.010 * e -0.0003 * (forest patch size ^ 1.321)).

Figure 48.—Relationship between landscape composition and 

suitability index (SI) scores for Louisiana waterthrush habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))). 

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 81.—Relationship between forest patch size 

and suitability index (SI) scores for Louisiana 

waterthrush habitat

Forest patch size (ha) SI score

42.2a 0.0

350b 0.5

3,200b 1.0

a

Hayden and others (1985).

b

Robbins and others (1989).

Table 82.—Relationship between landscape 

composition (percent forest in 1-km radius) 

and suitability index (SI) scores for Louisiana 

waterthrush habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).
91

SI5 or SI6 to ensure that small forest blocks in predominantly forested landscapes were 

assigned an appropriate suitability score.

To calculate the overall HSI, we determined the geometric mean of SI scores for forest 

structure (SI1, SI3, and SI4) and landscape composition (Max (SI5 or SI6) and SI2) 

separately and then the geometric mean of these means together.

Overall HSI = ((SI1 * SI3 * SI4)0.333 * (Max (SI5 or SI6) * SI2)0.500)

0.500

Verifi cation and Validation

Th e Louisiana waterthrush was found in all 88 subsections of the CH and WGCP. 

Spearman rank correlation on average HSI score and mean BBS route abundance per 

subsection identifi ed a signifi cant (P ≤ 0.001) positive association (rs

 = 0.56) between these 

two variables. Th e generalized linear model predicting BBS abundance from BCR and HSI 

for the Louisiana waterthrush was signifi cant (P ≤ 0.001; R2

 = 0.263), and the coeffi cient on 

the HSI predictor variable was both positive (β = 3.664) and signifi cantly diff erent from zero 

(P ≤ 0.001). Th erefore, we considered the HSI model for the Louisiana waterthrush both 

verifi ed and validated (Tirpak and others 2009a).
92

Mississippi Kite

Status

Th e Mississippi kite (Ictinia mississippiensis), a neotropical 

migrant raptor, is restricted to the Coastal Plains as well 

as the lower Mississippi and Red River Valleys. Like many 

birds of prey, this species has exhibited dramatic recoveries 

over the last 25 years from historical lows in the 1970s. 

However, its general scarcity prevents BBS from detecting 

statistically signifi cant trends (Sauer and others 2005; 

Table 5). Th e Mississippi kite is not a Bird of Conservation 

Concern in the CH or WGCP (Table 1). It has a regional 

combined score of 14 in the CH and 16 in the WGCP.

Natural history

Th e Mississippi kite exhibits two breeding strategies within its range. In the southern Great 

Plains, it is a colonial nester that often inhabits urban areas. In the Mississippi Valley and 

farther east, this bird is less colonial and nests singly in large trees in bottomland forest and 

riparian woodlands. Nests from birds within the eastern population generally are located in 

large (> 22 ha) unfragmented forest near open habitats where birds forage aerially (Parker 

1999).

Model Description

Th e HSI model for the Mississippi kite includes six variables: landform, land cover, 

successional age class, forest patch size, interspersion of forest and open habitats, and density 

of dominant trees.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 83). We directly 

assigned SI scores to these combinations on the basis of relative habitat quality ranks reported 

by Hamel (1992) for this species. However, we restricted the Mississippi kite to sawtimber 

stands based on its preference for mature forest stands (Parker 1999).

We also included forest patch size (SI2) in the model and used the range and mean of patch 

sizes reported by Barber and others (1998) to defi ne the minimum, maximum, and average 

patch sizes associated with nonhabitat, optimal, and average habitat suitability for this 

function, respectively (Table 84; Fig. 49).

Th e Mississippi kite requires large patches of forest and grassland in a specifi c landscape 

context (Parker 1999, Coppedge and others 2001). We used the relative amount of these 

habitats within a 1-km radius as an index to their interspersion at the landscape scale (SI3). 

We assumed that habitat suitability was optimal in open habitats with few trees (70 to 90 

percent agriculture or grassland) or landscapes containing moderate forest cover interspersed 

with open habitats (60 to 70 percent forest; Table 85).

Peter S. Weber, www.wildbirdphotos.com

Photo used with permission
93

Th e Mississippi kite nests in dominant trees (SI4) that extend above the canopy. Parker 

(1999) identifi ed old-growth stands and isolated trees as preferred nesting substrates for this 

species, and Barber and others (1998) observed the Mississippi kite using nest trees that were 

higher and larger in d.b.h. than those in the surrounding overstory. We assumed that a tree 

with a d.b.h. greater than 76.2 cm in a sawtimber stand would extend above the canopy and 

provide an adequate nest substrate for this species. We further assumed that one dominant 

tree per ha would satisfy this requirement and that the Mississippi kite would be absent from 

stands with a uniform canopy (zero dominant trees/ha). We fi t an exponential function 

(Fig. 50) to the values between these data points. Stands with 14 dominant trees per ha (the 

maximum observed in the WGCP during the FIA surveys of the 1990s) were associated with 

maximum habitat suitability (Table 86).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI4) and landscape composition (SI2 and SI3) separately and then the 

geometric mean of these means together.

Overall HSI = ((SI1 * SI4)0.500 * (SI2 * SI3)0.500)

0.500

Table 83.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for Mississippi kite habitat. Values in parentheses apply to West Gulf Coastal Plain/Ouachitas.

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.500

Transitional-shrubland 0.000 0.000 0.000 0.000 0.500

Deciduous 0.000 0.000 0.000 0.000 0.500

Evergreen 0.000 0.000 0.000 0.000 0.333

Mixed 0.000 0.000 0.000 0.000 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.500

Woody wetlands 0.000 0.000 0.000 0.000 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.500

Transitional-shrubland 0.000 0.000 0.000 0.000 0.333

Deciduous 0.000 0.000 0.000 0.000 0.500

Evergreen 0.000 0.000 0.000 0.000 0.333

Mixed 0.000 0.000 0.000 0.000 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.500

Woody wetlands 0.000 0.000 0.000 0.000 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.500

Transitional-shrubland 0.000 0.000 0.000 0.000 0.333 

(0.167)

Deciduous 0.000 0.000 0.000 0.000 0.500

Evergreen 0.000 0.000 0.000 0.000 0.333 

(0.167)

Mixed 0.000 0.000 0.000 0.000 0.333

Orchard-vineyard 0.000 0.000 0.000 0.000 0.500

Woody wetlands 0.000 0.000 0.000 0.000 1.000
94

Figure 49.—Relationship between forest patch size and 

suitability index (SI) scores for Mississippi kite habitat. 

Equation: SI score = 1.002 – (1.000 * e -0.0002 * (forest patch size ^ 1.278)).

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

Table 84.—Infl uence of forest patch size on 

suitability index (SI) scores for Mississippi kite 

habitat

Forest patch size (ha)a SI score

22 0.0

683 0.5

3,000 1.0

a

Barber and others (1998).

Table 85.—Suitability index scores for Mississippi kite habitat based on proportion of cells providing roosting 

and nesting habitat within 1-km radius

Proportion foresta

Proportion 

agriculturegrasslandb 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.50 0.50 0.50

0.1 0.00 0.00 0.00 0.00 0.20 0.20 0.40 0.60 0.60 0.60

0.2 0.00 0.00 0.00 0.00 0.40 0.40 0.60 0.80 0.80

0.3 0.00 0.00 0.00 0.00 0.60 0.60 0.80 1.00

0.4 0.35 0.40 0.40 0.60 0.80 0.80 1.00

0.5 0.50 0.50 0.55 0.70 0.70 0.60

0.6 0.60 0.70 0.75 0.90 0.80

0.7 0.70 0.75 1.00 1.00

0.8 0.80 0.90 1.00

0.9 0.80 1.00

1.0 0.80

a

Woody wetlands, deciduous forest, low-density residential.

b

Open water, open fi elds (natural or cultivated), emergent herbaceous wetland.
95

Verifi cation and Validation

Th e Mississippi kite was found in 49 of the 88 subsections within the CH and WGCP. 

Spearman rank correlations based on all subsections yielded a signifi cant (P = 0.003) positive 

association (rs

 = 0.31) between average HSI score and mean BBS route abundance. However, 

this association was not evident when the correlation considered only subsections in which 

this species was found. Th e generalized linear model predicting BBS abundance from BCR 

and HSI for the Mississippi kite was signifi cant (P ≤ 0.001; R2

 = 0.287); however, the 

coeffi cient on the HSI predictor variable was negative (β = -0.176). Th erefore, we considered 

the HSI model for the Mississippi kite verifi ed but not validated (Tirpak and others 2009a).

Figure 50.—Relationship between dominant tree (> 76.2 cm 

d.b.h.) density and suitability index (SI) scores for Mississippi 

kite habitat. Equation: SI score = 1 – e -8.734 * dominant tree density.

 

Dominant Tree Density (trees/ha)

Suitability Index Score

0 2 4

0.0

0.2

0.4

0.6

0.8

1.0

Table 86.—Infl uence of dominant tree (d.b.h. > 76.2 

cm) density (trees/ha) on suitability index (SI) scores 

for Mississippi kite habitat

Dominant tree densitya

 SI score

0 0.0

1 1.0

14 1.0

a

Assumed value.
96

Northern Bobwhite

Status

Th e northern bobwhite (Colinus virginianus) is a resident 

gamebird found throughout the eastern United States and 

Great Plains. Populations have declined by 3 percent per 

year since 1966 (Sauer and others 2005). Declines in the 

CH and WGCP have been equally dramatic (3.1 and 4.4 

percent per year, respectively) during this period (Table 

5). As a resident gamebird, this species is not aff orded 

special status by the FWS (protection is relegated to state 

wildlife agencies). Nevertheless, PIF has designated this 

bird as one requiring management attention in both the 

CH and WGCP (regional combined scores = 16 and 15, respectively) (Table 1). To address 

rangewide declines in populations, the Northern Bobwhite Conservation Initiative was 

established in 2002.

Natural History

Th e northern bobwhite is an economically important gamebird in the southern and central 

United States (Brennan 1999). It is associated with early successional vegetation, making 

use of agricultural fi elds, grasslands, grass-shrub rangelands, park-like pine forests and mixed 

pine-hardwood forests. At the county scale in Texas, the area in cultivated land and livestock 

density show curvilinear relationships to bobwhite population indices (Lusk and others 

2002a). In Oklahoma, bobwhite indices decrease with the proportion of the landscape in 

mature woodland, but increase with the proportion of brushy prairie or early successional 

habitat (Guthery and others 2001). Guthery and others (2001) found that populations 

were highest in areas lacking cropland agriculture. However, Williams and others (2000) 

found that the bobwhite selected cropland when it accounted for a small proportion of 

the landscape. Patterns of use and survival diff er between crop-dominated and rangelanddominated areas during the hunting season in Kansas (Williams and others 2000). Bobwhite 

densities vary across the range depending on habitat quality but are highest in areas with 

small (0.5 to 5.0 ha) interspersed patches of habitat. 

Frequency and intensity of disturbance are important for this species, especially in southern 

pine forests where prescribed burning is a useful management tool. Cram and others 

(2002) reported higher bobwhite abundance in pine-grassland restoration areas in Arkansas 

as conifer and hardwood basal area decreased and woody structure less than 2 m tall 

increased. Th e bobwhite also occupies cottonwood reforestation plots less than 4 years old 

in Mississippi and Louisiana (Twedt and others 2002). Most management for this species 

has been at the local scale, but Guthery (1999) showed that optimal confi guration of patch 

types and sizes has variability (slack), and Williams and others (2004) promoted a regional 

management strategy that focused on useable space (i.e., more patches of native prairies, 

savanna, and other favored vegetation types). 

Weather aff ects bobwhite populations, including positive eff ects of summer temperature 

and fall precipitation (Lusk and others 2002a) and negative eff ects of spring fl ooding and 

U.S. Forest Service
97

low winter temperatures (Applegate and others 2002). Bridges and others (2001) found a 

negative correlation between drought indices in dry regions and bobwhite abundance, but 

this pattern did not hold in wetter regions of Texas. Lusk and others (2002b) also found 

that climatic variables were more important than landscape variables for predicting bobwhite 

abundance in Oklahoma.

Nests are constructed of litter (grass or pine needles) in areas of high structural complexity 

(Townsend and others 2001); brood cover is found in open areas with dense forbs that still 

permit mobility at ground level. Nevertheless, Taylor and others (1999) did not fi nd any 

habitat attributes associated with higher probabilities of adult survival or nest success. White 

and others (2005) examined multiple landscape buff ers (radii of 250 to 1,000 m) around 

nest sites and random points to examine landscape eff ects on nest site selection. Bobwhite 

responded to both composition and confi guration of landscapes, including proportions of 

open-canopy planted pine and fallow fi elds, interspersion-juxtaposition index, and patch 

density. A model containing all four of these variables applied at the largest landscape had 

the best predictive ability, but was closely followed by a model containing only proportion 

of open-canopy planted pine applied at the smallest landscape size. Several other types of 

habitat models have been developed for the bobwhite: HSI (Schroeder 1985), PATREC 

(Roseberry and Sudkamp 1998), and logistic regression (Burger and others 2004). Tests of 

these models showed that they perform poorly (Roseberry and Sudkamp 1998, Burger and 

others 2004, Jones-Farrand and Millspaugh 2006).

Model Description

Habitat quality for bobwhite is aff ected by many parameters that are not measured easily at 

any scale: the proportion of forbs or open areas in grasslands, herbaceous vegetation height, 

grasslands and crop-fi eld management, and intra- and inter-annual climatic variations. 

Th erefore, we restricted our habitat suitability model to aspects of landscape composition 

and forest structure that were quantifi able from available datasets. Our fi nal model includes 

seven variables: landform, landcover, successional age class, hardwood basal area, evergreen 

basal area, grass landcover, and interspersion of open and forest habitats.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 87). We directly 

assigned SI scores to these combinations on the basis of habitat associations for the northern 

bobwhite outlined in Hamel (1992).

Forested sites used by the northern bobwhite typically are woodlands with low hardwood 

and pine basal area (SI2 and SI3, respectively). We used data from Cram and others (2002) 

and Palmer and Wellendorf (2006) to inform inverse logistic functions that predict SI scores 

for the bobwhite at various basal area levels (Tables 88-89; Figs 51-52).

We directly assigned SI scores to grass landcover (SI4) classes based on their potential to 

provide feeding, nesting, and brood-rearing habitat (Guthery 1997) (Table 90). We assumed 

that natural grassland-herbaceous landcovers had the greatest potential to provide these 

habitats, though it is likely that a given patch can satisfy only two of the three requisites 
98

at any point in time (Stoddard 1931). We assumed that areas in small grain production 

provided foraging opportunities but had little residual value for nesting or brood rearing. 

Similarly, fallow fi elds provide marginal nest and brood habitat but little forage. Finally, 

pasture-hay and row crops may provide foraging, nesting, and brood-rearing habitat but 

their value likely is limited due to management practices that produce unsuitable vegetative 

structure during most of the breeding season.

Th e bobwhite relies on landscapes comprised of interspersed vegetation types (White and 

others 2005, Guthery 2000). We used the composition of open and forest landcovers within 

a 1-km landscape (SI5) to index the interspersion of these cover types. Guthery (1999, 2000) 

and others before him (see Schroeder 1985 and references therein) have noted that this 

species can tolerate a broad range of landscape confi gurations. On the basis of suggestions 

from Fred Guthery (2006, Oklahoma State University, pers. commun.), we assumed that 

high quality habitat was characterized by 10 to 40 percent forest land and 60 to 90 percent 

open habitat (Table 91).

Table 87.—Relationship of landform, landcover type, and successional age class to suitability index (SI) 

scores for northern bobwhite habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.167 0.167 0.083 0.000 0.000

Deciduous 0.167 0.167 0.083 0.000 0.000

Evergreen 1.000 1.000 0.667 0.500 0.667

Mixed 0.667 1.000 0.667 0.333 0.333

Orchard-vineyard 0.167 0.167 0.083 0.000 0.000

Woody wetlands 0.334 0.334 0.250 0.250 0.334

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.667 

(1.000)

1.000 0.667 0.333 

(0.500)

0.333 

(0.667)

Deciduous 0.333 0.667 0.333 0.000 0.000

Evergreen 1.000 1.000 0.667 0.500 0.667

Mixed 0.667 1.000 0.667 0.333 0.333

Orchard-vineyard 0.333 0.667 0.333 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.667 

(0.834)

1.000 

(0.834)

0.667 0.333 

(0.667)

0.333 

(0.667)

Deciduous 0.333 1.000 0.500 0.000 0.000

Evergreen 1.000 

(0.834)

1.000 

(0.834)

0.667 0.500 

(0.667)

0.667

Mixed 0.667 1.000 0.667 0.333 0.333

Orchard-vineyard 0.333 1.000 0.500 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000
99

We calculated the overall HSI score by fi rst determining the geometric mean of SI scores 

for forest structure attributes (SI1, SI2, and SI3). Open habitats lacking forest structure 

were assigned SI score independently (SI4). Th e landscape context of these forest and open 

habitats were incorporated into the HSI calculation by determining the geometric mean of 

these site-level and landscape-level variables (SI5) together.

Overall HSI = (((SI1 * SI2 * SI3)0.333 + SI4) * SI5)0.500

Figure 51.—Relationship between hardwood basal area and 

suitability index (SI) scores for northern bobwhite habitat. 

Equation: SI score = 1/ (1.000 + (0.053 * (hardwood basal 

area)5.068)).

Figure 52.—Relationship between pine basal area and 

suitability index (SI) scores for northern bobwhite habitat. 

Equation: SI score = 1 - (0.984 / (1 + (83605490 * e -1.305 * pine 

basal area))).

 

Hardwood Basal Area (m^2/ha)

Suitability Index Score

0 5 10 15

0.0

0.2

0.4

0.6

0.8

1.0

 

Pine Basal Area (m^2/ha)

Suitability Index Score

0 5 10 15 20

0.0

0.2

0.4

0.6

0.8

1.0

Table 88.—Infl uence of hardwood basal area on 

suitability index (SI) scores for northern bobwhite 

habitat

Hardwood basal area (m2

/ha) SI score

0.0a 1.000

2.6b 1.000

3.3b 0.439

5.0a 0.100

6.5b 0.000

10.0a 0.000

a

Assumed value.

b

Cram and others (2002).

Table 89.—Infl uence of pine basal area on suitability 

index (SI) scores for northern bobwhite habitat

Pine basal area (m2

/ha) SI score

0.00a 1.000

9.20b 1.000

12.30a 1.000

13.78b 0.500

15.40c 0.228

17.20c 0.000

18.37b 0.000

a

Assumed value.

b

Palmer and Wellendorf (2006).

c

Cram and others (2002).
100

Verifi cation and Validation

Th e northern bobwhite was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation support a signifi cant (P = 0.006) positive association (rs

 = 0.29) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized 

linear model predicting BBS abundance from BCR and HSI for the northern bobwhite was 

signifi cant (P ≤ 0.001; R2

 = 0.440); however, the coeffi cient on the HSI predictor variable 

was negative (β = -37.119). Th erefore, we considered the HSI model for the northern 

bobwhite verifi ed but not validated (Tirpak and others 2009a).

Table 90.—Relationship between open and 

grassy landcover and suitability index (SI) scores 

for northern bobwhite habitat

Landcover typea SI score

Grassland-herbaceous 1.0

Pasture-hay 0.1

Row crops 0.1

Small grains 0.4

Fallow 0.2

a

Assumed value.

Table 91.—Suitability index scores for northern bobwhite habitat based on the proportion of cells providing: 

1) good nesting, feeding, and brood-rearing habitat (open landcovers); 2) escape and thermal cover (forest 

landcovers) within 1-km radius

Proportion 

openb 

Proportion foresta

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.1 0.00 0.00 0.10 0.15 0.25 0.25 0.25 0.20 0.15 0.10

0.2 0.00 0.10 0.15 0.25 0.35 0.35 0.30 0.25 0.20

0.3 0.00 0.30 0.35 0.45 0.45 0.45 0.40 0.30

0.4 0.00 0.50 0.50 0.50 0.50 0.50 0.50

0.5 0.00 0.70 0.70 0.70 0.70 0.70

0.6 0.00 0.90 0.90 0.90 0.90

0.7 0.00 0.90 1.00 1.00

0.8 0.00 0.90 1.00

0.9 0.00 0.90

1.0 0.00

a

Forest = landcovers with positive SI1 score (Table 87).

b

Open = landcovers identifi ed in SI4 (Table 90).
101

Northern Parula

Status

Th e northern parula (Parula americana), a longdistance neotropical migrant, breeds in two disjunct 

zones of eastern North America: New Englandsouthern Canada and the southeastern United States. 

Th is species is notably absent from the southern Great 

Lakes. It depends on epiphytes—Spanish moss in the 

south and old man’s beard in the north—as a nesting 

substrate. Parula populations have been stable in most 

regions during the last 40 years and have increased in 

some areas including the CH (Table 5). Th is species is not considered a Bird of Conservation 

Concern in the CH or WGCP (regional combined score = 12 and 13, respectively; Table 1).

Natural History

Th e northern parula is common in the bottomland hardwood and riverine forests of the 

Southeastern United States (Moldenhauer and Regelski 1996). It also occupies mixed pinehardwoods, though at lower densities (Moldenhauer and Regelski 1996). Th e northern 

parula has two competing habitat requirements: a preference for canopy gaps and large forest 

blocks. Moorman and Guynn (2001) found that this species is more abundant near canopy 

gaps than forest-interior sites with an unbroken canopy in bottomland hardwoods, and 

Annand and Th ompson (1997) observed the highest northern parula densities in forests with 

canopy gaps resulting from single-tree selection. However, the probability of detecting the 

northern parula increases with riparian buff er width (Kilgo and others 1998) and forest patch 

size (Robbins and others 1989).

Th e northern parula forages in the mid- to upper canopy layers (Moldenhauer and Regelski 

1996), so it is not surprising that it prefers microsites with high basal area (Robbins and 

others 1989), high canopy cover, and tall canopies (James 1971), and avoids areas with 

dense understories (often associated with open canopies) (Torres and Leberg 1996). In the 

Southeast, this species nests almost exclusively in Spanish moss (Moldenhauer and Regelski 

1996). However, no studies have identifi ed Spanish moss as limiting.

Model Description

Th e HSI model for the northern parula includes six variables: landform, landcover, 

successional age class, forest patch size, percent forest in a 1-km radius, and canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 92). We directly 

assigned SI scores to these combinations on the basis of habitat associations of northern 

parulas reported by Hamel (1992) for the Southeast.

We derived a logarithmic function (Fig. 53) from data on the occupancy rate of northern 

parulas in forest blocks of varying size (SI2; Hayden and others 1985, Robbins and others 

1989) (Table 93) to predict habitat suitability from patch area. However, small forest 

Chandler S. Robbins, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
102

patches in predominantly forested landscapes may provide habitat due to their proximity 

to large forest blocks (Rosenberg and others 1999). To capture this relationship, we fi t a 

logistic function (Fig. 54) to data (Table 94) derived from Donovan and others (1997), who 

observed diff erences in predator and brood parasite communities among highly fragmented 

(< 15 percent), moderately fragmented (45 to 50 percent), and lightly fragmented (> 90 

percent forest) landscapes. We assumed that the midpoints between these classes (30 and 70 

percent forest) defi ned the specifi c cutoff s for poor (SI score ≤ 0.10) and excellent (SI score 

≥ 0.90) habitat, respectively. We used the maximum SI score from SI2 or SI3 to account for 

small patches in predominantly forested landscapes.

We included canopy cover (SI4) in our model to capture the preference of the northern 

parula for interior edges. James (1971), Collins and others (1982), and Morgan and 

Freedman (1986) found that the northern parula is associated with increased canopy 

cover. Nonetheless, there seems to be a threshold above which suitability declines. Robbins 

and others (1989) observed an inverse relationship between canopy cover and northern 

parula abundance, and Annand and Th ompson (1997) observed a threefold increase of 

parulas in single-tree selection stands characterized by a heterogeneous canopy than in 

mature forest habitats with closed canopies. On the basis of these studies, we assumed that 

Table 92.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for northern parula habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.083 0.500 0.834

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.250 0.750 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.250 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.167 0.333

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000
103

habitat suitability was optimal at 90 percent canopy cover and decreased as the canopy 

became increasingly open or closed. We fi t an inverse quadratic function (Fig. 55) to data 

demonstrating this relationship (Table 95).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1 and SI4) and then calculated the geometric mean of this value and 

landscape composition (Max of SI2 or SI3).

Overall HSI = ((SI1 * SI4)0.500 * Max(SI2 or SI3))0.500

Figure 53.—Relationship between forest patch size and 

suitability index (SI) scores for northern parula habitat. 

Equation: SI score = 0.199 * ln(forest patch size) – 0.661.

Figure 54.—Relationship between local landscape composition 

and suitability index (SI) scores for northern parula habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (local 

landscape composition))).

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 93.—Infl uence of forest patch size on suitability 

index (SI) scores for northern parula habitat

Forest patch size (ha) SI score 

23.6a 0.0

520b 0.5

3,200b 1.0

a

Hayden and others (1985).

b

Robbins and others (1989).

Table 94.—Relationship between local landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for northern parula 

habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).
104

Verifi cation and Validation

Th e northern parula was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.51) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the northern parula was signifi cant 

(P ≤ 0.001; R2

 = 0.276), and the coeffi cient on the HSI predictor variable was both positive 

(β = 5.250) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the 

HSI model for the northern parula both verifi ed and validated (Tirpak and others 2009a).

Figure 55.—Relationship between canopy cover and suitability 

index (SI) scores for northern parula habitat. Equation: SI score 

= 1 / (37.3645 – (0.8127 * canopy cover) + (0.00454 * (canopy 

cover2

))).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 95.—Infl uence of canopy cover on suitability 

index (SI) scores for northern parula habitat

Canopy cover (percent)a SI score

60 0.2

70 0.4

80 0.8

85 0.9

90 1.0

95 0.9

100 0.8

a

Assumed value.
105

Orchard Oriole

Status

Th e orchard oriole (Icterus spurius), a neotropical 

migrant, is found throughout most of the United States 

east of the Rocky Mountains except for New England 

and the northern Great Lakes. Although this species has 

experienced increases along the edges of its distribution, 

populations have declined in the core of its range where 

densities are highest. In the WGCP, populations have 

declined by 3 percent per year since 1967 (Table 5). 

Populations in the adjacent Mississippi Alluvial Valley have declined 4 percent. Th e orchard 

oriole is a Bird of Conservation Concern in the WGCP and has been identifi ed as a species 

requiring management attention in both the CH and WGCP (regional combined score = 17 

and 18, respectively; Table 1).

Natural History

Th e orchard oriole breeds in wooded riparian zones, fl oodplains, marshes, and shorelines 

(Scharf and Kren 1996) but also in open shrublands and low-density human-dominated 

areas (e.g., farms and parklands). It is semi-colonial in optimal habitat but relatively solitary 

in marginal areas. Th is species is a common host of the brown-headed cowbird.

Model Description

Th e HSI model for the orchard oriole includes fi ve variables: landform, landcover, 

successional age class, forest within a 1-km radius, and basal area.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 96). We 

directly assigned SI scores to these combinations based on vegetation and successional age 

class associations in Hamel (1992). However, we adjusted Hamel’s values to account for 

the preference of the orchard oriole for mesic habitats (e.g., riparian zones, fl oodplains, and 

marshes; Scharf and Kren 1996).

Th e orchard oriole is not area sensitive but generally is restricted to forested landscapes. 

Th erefore, we included only local forest composition (SI2) in our model to discount forest 

patches that were isolated within a matrix of nonforest landcover. Conversely, this is an 

edge species whose abundance declines in heavily forested regions (Scharf and Kren 1996). 

Th erefore, we assumed that landscapes with 70 to 80 percent forest provided optimal habitat 

suitability and reduced suitability symmetrically as landscape composition shifted from these 

optima (Table 97, Fig. 56).

Th is species is most abundant in areas with scattered trees. Heltzel and Leberg (2006) 

observed signifi cantly fewer orioles in stands with an average basal area of 25 m2

 per ha 

than in recently harvested stands with an average basal area of 18 m2

 per ha. We assumed 

that habitat suitability was optimal for the orchard oriole at lower basal areas and modeled 

Deanna K. Dawson, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
106

the basal area (SI3)-habitat suitability relationship as a quadratic function (Fig. 57) that 

maximized SI scores at intermediate basal area values (12.5 m2

/ha; Table 98).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure indices (SI1 and SI3) and then determined the geometric mean of this value and 

landscape composition (SI2). 

Overall HSI = ((SI1 * SI3)0.500 * SI2)0.500

Verifi cation and Validation

Th e orchard oriole was found in all 88 subsections of the CH and WGCP. Spearman rank 

correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.34) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the orchard oriole was signifi cant 

(P = 0.088; R2

 = 0.056), and the coeffi cient on the HSI predictor variable was positive (β = 

2.442) but not signifi cantly diff erent from zero (P = 0.221). Th erefore, we considered the 

HSI model for the orchard oriole verifi ed but not validated (Tirpak and others 2009a).

Table 96.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for orchard oriole habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.500 1.000 1.000

Transitional-shrubland 0.000 0.000 0.500 1.000 1.000

Deciduous 0.000 0.000 0.500 1.000 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.500 1.000 1.000

Woody wetlands 0.000 0.000 0.500 1.000 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.333 0.667 0.667

Transitional-shrubland 0.000 0.000 0.250 0.500 0.500

Deciduous 0.000 0.000 0.250 0.500 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.333 0.667 0.667

Woody wetlands 0.000 0.000 0.500 1.000 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.333 0.667 0.667

Transitional-shrubland 0.000 0.000 0.250 0.500 0.500

Deciduous 0.000 0.000 0.250 0.500 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.333 0.667 0.667

Woody wetlands 0.000 0.000 0.500 1.000 1.000
107

Figure 56.—Relationship between landscape composition and 

suitability index (SI) scores for orchard oriole habitat. Equation: 

SI score = 1.011 * e ((0 – ((landscape composition * 100) – 74.945) ^ 2) / 863.949).

Figure 57.—Relationship between basal area and suitability 

index (SI) scores for orchard oriole habitat. Equation: 

SI score = (0.16 * basal area) - (0.00639 * (basal area2

)).

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

 

Basal Area (m^2/ha)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

Table 97.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for orchard oriole habitat

Landscape compositiona SI score

0 0.00

10 0.00

20 0.05

30 0.10

40 0.25

50 0.50

60 0.75

70 1.00

80 1.00

90 0.75

100 0.50

a

Assumed value.

Table 98.—Infl uence of basal area (m2/ha) on 

suitability index (SI) scores for orchard oriole habitat

Basal area (m2

/ha) SI score

0.0a 0.0

12.5a 1.0

25.0b 0.0

a

Assumed value.

b

Heltzel and Leberg (2006).
108

Painted Bunting

Status

Th e painted bunting (Passerina cyanea) occurs as two 

allopatric populations that may represent separate species 

(Lowther and others 1999). Th e western population 

inhabits the southern Great Plains and the western edges 

of the CH and WGCP, while the eastern population 

inhabits the Atlantic Coastal Plain from North Carolina 

to Florida. Populations have been relatively stable across 

the WGCP as a whole (Table 5), but populations have 

declined in Arkansas (5.8 percent per year from 1967 to 

2004), Louisiana (3.5 percent), and Texas (2.4 percent) 

but increased in Oklahoma (1.3 percent; Sauer and others 2005). Th e painted bunting is not 

an FWS Bird of Conservation Concern but is a PIF management attention priority in both 

the CH and WGCP (regional combined score = 16 and 17, respectively; Table 1).

Natural History

Th e habitat requirements of the painted bunting are poorly understood. Th is species 

generally occupies areas of scattered woody vegetation. Kopachena and Crist (2000a) 

characterized painted bunting habitat in northeast Texas as “wooded areas in otherwise 

open habitat” as opposed to the indigo bunting, which occurs in “open areas in otherwise 

wooded habitat.” Th e painted bunting use smaller, more heterogeneous groups of trees than 

the indigo bunting, but microhabitats diff er little between these species (Kopachena and 

Crist 2000b). Th e painted bunting occupies narrow riparian strips in eastern Texas and its 

abundance decreases quickly as widths exceed 70 m (Conner and others 2004).

Th e painted bunting nests in low, woody vegetation (Lowther and others 1999) and its 

territory size varies with its population density. In Missouri, territories ranged from 0.64 to 

6.66 ha and included 80 percent pasture and 20 percent woodland. Th is species is a common 

host of both the brown-headed and bronzed cowbird.

Model Description

Th e HSI model for the painted bunting includes six variables: landform, landcover, 

successional age class, distance to edge, interspersion of open and forested lands, and small 

stem (< 2.5 cm d.b.h.) density.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 99). We 

directly assigned SI scores to these combinations on the basis of relative habitat rankings for 

vegetation and successional age class associations of painted buntings reported by Hamel 

(1992). We assigned higher values to the shrub-seedling age class than Hamel (1992) on the 

basis of qualitative descriptions in Lowther and others (1999).

An early-successional species, the painted bunting is associated with edges. We used data on 

territory density from Lanyon and Th ompson (1986; Table 100) to defi ne an inverse logistic 

function linking SI scores to distance from an edge (SI2; Fig. 58).

Deanna K. Dawson, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
109

Table 99.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for painted bunting habitat 

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.500 0.500 0.250 0.000

Deciduous 0.000 0.500 0.500 0.250 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.500 0.500 0.250 0.000

Woody wetlands 0.000 1.000 0.750 0.500 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.500 0.500 0.250 0.000

Deciduous 0.000 0.500 0.500 0.250 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.500 0.500 0.250 0.000

Woody wetlands 0.000 1.000 0.750 0.500 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.500 0.500 0.250 0.000

Deciduous 0.000 0.500 0.500 0.250 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.500 0.500 0.250 0.000

Woody wetlands 0.000 1.000 0.750 0.500 0.000

Figure 58.—Relationship between distance to edge and 

suitability index (SI) scores for painted bunting habitat. Equation: 

SI score = 1 - (1.034 / (1 + (39.685 * e -0.301 * (distance to edge / 10 m)))).

 

Distance to Edge (m/10)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

Table 100.—Infl uence of distance to edge on 

suitability index (SI) scores for painted bunting 

habitat

Distance to edge (m) SI score

0a 1.0

90a 0.7

150a 0.3

210a 0.0

270b 0.0

a

Lanyon and Thompson (1986).

b

Assumed value.
110

Th e presence of both forest and open landcovers in the landscape (SI3) is perhaps the most 

important component of painted bunting habitat. We maximized SI scores for this species in 

landscapes containing 50 percent forest and 50 percent open habitats based on unpublished 

data (Jeff rey Kopachena, 2006, Texas A&M University—Commerce, pers. commun.). 

Norris and Elder (1982, cited in Lowther and others 1999) observed the painted bunting in 

landscapes with forest cover of 20 to 80 percent forest. We used these values as cutoff s for 

forest cover in our interspersion function for the painted bunting (Table 101).

As an early successional species, the painted bunting occupies habitats containing high 

densities of small stems (SI4). We assumed that the mean stem density values (6,400 

stems/ha) reported by Kopachena and Crist (2000b) were characteristic of average habitat 

suitability (SI score = 0.500). However, because of the high standard error (6,300 stems/ha) 

associated with this estimate, we assumed that a stem density that was twice the mean was 

necessary to ensure optimal habitat (Table 102). We fi t a smoothed logistic function through 

these data points (Fig. 59) to quantify the relationship between small stem density and SI 

scores for painted bunting habitat.

To calculate the HSI score for sapling and pole successional age class stands, we determined 

the geometric mean of SI scores for forest structure (SI1 and SI4) and landscape composition 

(SI2 and SI3) separately and then the geometric mean of these means together. 

HSISap-pole = ((SI1 * SI4)0.500 * (SI2 * SI3)0.500)

0.500

Table 101.—Suitability index scores for painted bunting habitat based on the proportion of open and forest 

landcovers within 5-ha area

Proportion 

forestb

Proportion opena

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.2 0.0 0.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5

0.3 0.0 0.0 0.5 0.7 0.7 0.7 0.7 0.7

0.4 0.0 0.0 0.5 0.7 0.9 0.9 0.9

0.5 0.0 0.0 0.5 0.7 0.9 1.0c

0.6 0.0 0.0 0.5 0.7 0.9

0.7 0.0 0.0 0.5 0.7

0.8 0.0 0.0 0.5

0.9 0.0 0.0

1.0 0.0

a

Open = herbaceous natural, cultivated, and emergent herbaceous wetland.

b

Forest = upland forested, transitional, woody wetland, and orchard/vineyard.

c

Unpublished data.
111

We assumed that shrub-seedling successional age class stands were suitable regardless of edge 

or landscape composition. Th us, we calculated the HSI score as the geometric mean of forest 

structure attributes alone (SI1 and SI4).

HSIShrub = (SI1 * SI4)0.500

Th e overall HSI score is the sum of the two age class specifi c SIs:

Overall HSI = SISap-pole + SIShrub

Verifi cation and Validation

Th e painted bunting was found in only 38 of the 88 subsections within the CH and WGCP. 

Nevertheless, Spearman rank correlations based on either all subsections or only subsections 

in which the painted bunting occurred produced similar results: signifi cant (P ≤ 0.001 in 

both analyses) positive associations (rs

 = 0.56 and 0.58, respectively) between average HSI 

score and mean BBS route abundance at the subsection scale. Th e generalized linear model 

predicting BBS abundance from BCR and HSI for the painted bunting was signifi cant (P ≤ 

0.001; R2

 = 0.480), and the coeffi cient on the HSI predictor variable was both positive (β = 

70.737) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the HSI 

model for the painted bunting both verifi ed and validated (Tirpak and others 2009a).

Figure 59.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores 

for painted bunting habitat. Equation: SI score = (1.000 / (1 + 

(1178.674 * e -1.105 * (small stem density / 1000)))).

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

Table 102.—Infl uence of small stem density (stems * 

1,000/ha) on suitability index (SI) scores for painted 

bunting habitat

Small stem density SI score

0.0a 0.0

6.4b 0.5

12.8a 1.0

25.0a 1.0

a

Assumed value.

b

Kopachena and Crist (2000b).
112

Pileated Woodpecker

Status

Th e pileated woodpecker (Dryocopus pileatus) breeds 

throughout eastern North America, southern Canada, 

and the montane forests of the West. Populations 

have been stable across most of its range, including the 

WGCP, over the last 40 years and have increased along 

the northern limit of this bird’s distribution. In the CH, 

populations have increased by 1.8 percent per year since 

1967 (Sauer and others 2005) (Table 5). Th is species 

is a management attention priority in the WGCP 

(regional combined score = 16) but has no special 

conservation status in the CH (regional combined score = 13; Table 1).

Natural History

Th e pileated woodpecker uses a variety of forest types across its range but typically is associated 

with older successional age classes (Bull and Jackson 1995, Annand and Th ompson 1997). 

Th e key component to pileated woodpecker habitat is an abundance of large snags—the 

more the better. Diff erent researchers defi ne “large” diff erently (Renken and Wiggers 1989, 

Savignac and others 2000, Showalter and Whitmore 2002) but the pileated woodpecker is 

invariably associated with the largest available size class. In Missouri, this species is associated 

with bottomland hardwood forest (Renken and Wiggers 1993); in east Texas, the pileated 

woodpecker is equally abundant in bottomland hardwoods, longleaf pine savanna, and mixed 

pine-hardwood stands, so long as suitable snags are available (Shackelford and Conner 1997). 

Closed canopies (canopy cover of 75 to 96 percent) are the norm (Renken and Wiggers 

1989). Because it has a large home range (53 to 160 ha), it is not surprising that the pileated 

woodpecker is sensitive to forest area. Robbins and others (1989) did not detect this species 

in woodlots less than 42 ha and larger areas likely are required for breeding pairs. Schroeder 

(1982) considered 130 ha as the minimum forest patch size for this species.

Model Description

Th e pileated woodpecker model includes six variables: landform, land cover, successional age 

class, large snag (> 30 cm d.b.h.) density, forest patch size, and percentage of forest in a 1-km 

radius.

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 103). We used the 

habitat associations of the pileated woodpecker outlined in Hamel (1992) to assign SI scores 

to these combinations.

Large snags (SI2) are used for roosting, nesting, and foraging and are an important component 

of pileated woodpecker habitat. We fi t a logistic function (Fig. 60) to data from Renken and 

Wiggers (1989) on the relative density of this species on sites with varying large snag densities 

to predict SI scores based on this habitat feature (Table 104).

U.S Forest Service
113

Table 103.—Relationship of landform, landcover type, and successional age class to suitability index 

scores for pileated woodpecker habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.042 0.083 0.167

Transitional-shrubland 0.000 0.000 0.083 0.583 1.000

Deciduous 0.000 0.000 0.083 0.583 1.000

Evergreen 0.000 0.000 0.167 0.333 0.333

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.042 0.083 0.167

Transitional-shrubland 0.000 0.000 0.167 0.500 

(0.333)

0.667 

(0.333)

Deciduous 0.000 0.000 0.000 0.500 1.000

Evergreen 0.000 0.000 0.167 0.333 0.333

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.042 0.083 0.167

Transitional-shrubland 0.000 0.000 0.167 

(0.083)

0.500 

(0.167)

0.667 

(0.167)

Deciduous 0.000 0.000 0.000 0.500 1.000

Evergreen 0.000 0.000 0.167 

(0.083)

0.333 

(0.167)

0.333 

(0.167)

Mixed 0.000 0.000 0.167 0.500 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.667 1.000

Figure 60.—Relationship between large snag (> 30 cm 

d.b.h.) density and suitability index (SI) scores for pileated 

woodpecker habitat. Equation: SI score = (1.0054 / (1 + 

(747.0936 * e -0.8801 * large snag density))).

 

Large Snag Density (snags/ha)

Suitability Index Score

0 10 20

0.0

0.2

0.4

0.6

0.8

1.0

Table 104.—Infl uence of large snag (> 30 cm d.b.h.) 

density (snags/ha) on suitability index (SI) scores 

for pileated woodpecker habitat 

Large snag density SI score

0. a 0.0

2.5a 0.0

6.1b 0.1

7.6b 0.5

10.0b 1.0

15.0a 1.0

12.5a 1.0

a

Assumed value.

b

Renken and Wiggers (1989).
114

We incorporated forest patch size (SI3) and percent forest in the local landscape (SI4) as 

predictors of habitat suitability. Large home ranges for the pileated woodpecker necessitate 

large forest patches. We fi t a logarithmic function (Fig. 61) to data from Robbins and 

others (1989) on the eff ect of forest patch size on occupancy rates (Table 105). We also 

included percent forest in the landscape because small forest patches that may not be used 

in predominantly nonforested landscapes may provide habitat in predominantly forested 

landscapes due to their proximity to large forest blocks (Rosenberg and others 1999). To 

capture this relationship, we fi t a logistic function (Fig. 62) to data (Table 106) derived 

from Donovan and others (1997), who observed diff erences in predator and brood parasite 

Figure 61.—Relationship between forest patch size and 

suitability index (SI) scores for pileated woodpecker habitat. 

Equation: SI score = 0.230 * ln(forest patch size) – 0.877.

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

Table 105.—Infl uence of forest patch size 

on suitability index (SI) scores for pileated 

woodpecker habitat 

Forest patch size (ha)a SI score

42.2 0.0

165 0.5

3,200 1.0

a

Robbins and others (1989).

Table 106.—Relationship between landscape 

composition (percent forest in 1-km radius) 

and suitability index (SI) scores for pileated 

woodpecker habitat 

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).

Figure 62.—Relationship between landscape composition and 

suitability index (SI) scores for pileated woodpecker habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (local 

landscape composition))).

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0
115

communities among highly fragmented (< 15 percent), moderately fragmented (45 to 50 

percent), and lightly fragmented (> 90 percent forest) landscapes. We assumed that the 

midpoints between these classes (30 and 70 percent forest) defi ned the specifi c cutoff s for 

poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) habitat, respectively. We used the 

maximum SI score from SI3 or SI4 to account for the higher suitability of small forest 

patches in predominantly forested landscapes.

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1 and SI2) and multiplied that by the maximum value of forest patch 

size (SI3) or percent forest in the 1-km radius landscape (SI4) and calculated the geometric 

mean of that product.

Overall HSI = ((SI1 * SI2)0.500 * Max(SI3 or SI4))0.500

Verifi cation and Validation

Th e pileated woodpecker was observed in all 88 subsections of the CH and WGCP. 

Spearman rank correlation identifi ed a signifi cant (P ≤ 0.002) positive association (rs

 = 

0.33) between average HSI score and mean BBS route abundance across subsections. Th e 

generalized linear model predicting BBS abundance from BCR and HSI for the pileated 

woodpecker was signifi cant (P ≤ 0.001; R2

 = 0.313), and the coeffi cient on the HSI predictor 

variable was both positive (β = 8.852) and signifi cantly diff erent from zero (P ≤ 0.001). 

Th erefore, we considered the HSI model for the pileated woodpecker both verifi ed and 

validated (Tirpak and others 2009a).
116

Prairie Warbler

Status

Th e prairie warbler (Dendroica discolor), a neotropical 

migrant, occupies early successional habitats throughout 

the eastern United States. Like many early successional 

species, populations of this bird have declined 

throughout the eastern and central United States since 

1967, including a drop of 2.6 percent per year in the CH 

and 4.4 percent per year in the WGCP (Table 5). Th e 

prairie warbler is an FWS Bird of Conservation Concern 

and a management attention priority in both BCRs 

(regional combined score = 18 in the CH and WGCP; Table 1).

Natural History

Th e prairie warbler breeds in shrubby vegetation under an open canopy (Nolan and 

others 1999). Typical associations in the CH and WGCP include shrubby southern pine 

forest, pine barrens, scrub oak barrens, abandoned fi elds and pastures, regenerating forest, 

abandoned orchards, grassland-forest edge, Christmas tree farms, and reclaimed strip mine 

spoils. Th e prairie warbler uses a variety of landforms from xeric uplands in Arkansas to 

palustrine swamps in Virginia. In comparison to other early successional warblers, this bird 

occupies sites with fewer dense shrubs than the blue-winged warbler, more dense vegetation 

and drier areas than the yellow warbler, and less dense vegetation and higher vegetation strata 

than the common yellowthroat or yellow-breasted chat (Nolan and others 1999).

Th e prairie warbler nests in shrubs and small trees that are more than 20 m from a fi eldforest edge (Nolan and others 1999, Woodward and others 2001). However, in eastern 

Texas this species typically occurs in narrow riparian zones, with abundance decreasing 

quickly as widths increase (Conner and others 2004). Mean territory size varies inversely 

with population density, ranging from 0.2 to 3.5 ha in Indiana (Nolan and others 1999). 

Territory size also varies with shape of forest patch; it is larger in more linear patches. 

Although males do not limit movements to their defended territory, a female’s home range 

usually is contained within a male’s defended territory. Th is species is a cowbird host. 

Although parasitism has little eff ect on hatching success, it can signifi cantly reduce fl edging 

rates.

Model Description

Our HSI model for the prairie warbler includes seven variables: landform, landcover, 

successional age class, early-successional patch size, small stem (< 2.5 cm d.b.h.) density, edge 

occurrence, and canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 107). We 

directly assigned SI scores to these combinations on the basis of habitat associations for the 

prairie warbler documented in Hamel (1992).

Deanna K. Dawson, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
117

Both Woodward and others (2001) and Rodewald and Vitz (2005) observed edge avoidance 

by this species. Th us, we used a 3 × 3 pixel (90 x 90 m) window to identify early successional 

habitats (i.e., grass-forb, shrub-seedling, or sapling successional age class forest) adjacent to 

mature forest stands (i.e., pole or sawtimber successional age class) and reduced the suitability 

of locations adjacent to edges by half (SI2; Table 108).

We also included early successional patch size (SI3) as an explanatory variable because the 

prairie warbler is absent from small clearings and edge habitats. We used data from Larson 

and others (2003) (Table 109) to fi t a logistic function (Fig. 63) that characterized the 

relationship between habitat suitability and early successional patch size.

We also included small stem density (SI4) as a variable because the prairie warbler is 

associated with dense understory vegetation. We used point count and habitat data reported 

by Annand and Th ompson (1997) (Table 110) to derive a logistic function (Fig. 64) that 

predicted habitat suitability for the prairie warbler from small stem density.

Table 107.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for prairie warbler habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.333 0.167 0.000 0.000

Deciduous 0.000 0.333 0.167 0.000 0.000

Evergreen 0.000 0.667 0.334 0.000 0.000

Mixed 0.000 1.000 0.500 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.167 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 1.000 

(0.667)

0.500 

(0.334)

0.000 0.000

Deciduous 0.000 0.667 0.333 0.000 0.000

Evergreen 0.000 0.667 0.334 0.000 0.000

Mixed 0.000 1.000 0.500 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.167 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 1.000 

(0.500)

0.500 

(0.250)

0.000 0.000

Deciduous 0.000 1.000 0.500 0.000 0.000

Evergreen 0.000 0.667 

(0.500)

0.334 

(0.250)

0.000 0.000

Mixed 0.000 1.000 0.500 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.333 0.167 0.000 0.000
118

Finally, we used data from Sheffi eld (1981) to inform an inverse logistic function (Fig. 65) 

that discounted SI scores at increasingly high canopy closures (SI5; Table 111). 

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1, SI4, and SI5) and landscape composition (SI2 and SI3) separately 

and then the geometric mean of these means together. 

Overall HSI = ((SI1 * SI4 * SI5)0.333 * (SI2 * SI3)0.500)

0.500

Figure 63.—Relationship between early successional patch 

size and suitability index (SI) scores for prairie warbler habitat. 

Equation: SI score = (1.002 / (1 + (1207.332 * e -3.757 * forest patch size))).

Figure 64.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores 

for prairie warbler habitat. Equation: SI score= (1.000 / (1 + 

(99.749 * e -1.001 * (small stem density / 1000)))).

 

Early Successional Patch Size (ha)

Suitability Index Score

0 2 4 6

0.0

0.2

0.4

0.6

0.8

1.0

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 108.—Infl uence of edge on suitability index 

(SI) scores for prairie warbler habitat 

3 × 3 pixel window around early 

successional habitat includes 

mature foresta SI score

Yes 0.5

No 1.0

a

Early successional = grass-forb, shrub-seedling, and 

sapling successional age classes; mature forest = pole or 

sawtimber successional age classes.

Table 109.—Infl uence of early successional patch 

size on suitability index (SI) scores for prairie 

warbler habitat; early successional patches only 

include grass-forb, shrub-seedling, and sapling 

successional age classes

Early successional patch size (ha)a SI score

0.18 0.0

0.36 0.0

1.89 0.5

3.42 1.0

5.00 1.0

a

Larson and others (2003).

Table 110.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for prairie warbler habitat 

Small stem density SI score

0.0a 0.00

3.8b 0.31

8.1b 1.00

a

Assumed value.

b

Annand and Thompson (1997).
119

Verifi cation and Validation

Th e prairie warbler was found in all 88 subsections of the CH and WGCP. Spearman rank 

correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.41) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the prairie warbler was signifi cant 

(P = 0.005; R2

 = 0.117), and the coeffi cient on the HSI predictor variable was both positive 

(β = 15.317) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the 

HSI model for the prairie warbler both verifi ed and validated (Tirpak and others 2009a).

Figure 65.—Relationship between canopy cover and suitability 

index (SI) scores for prairie warbler habitat. Equation: SI score 

= 1 - (1.003 / (1 + (26950.420 * e -0.204 * canopy cover))).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 111.—Infl uence of canopy cover on suitability 

index (SI) scores for prairie warbler habitat

Canopy cover (percent)a SI score

0 1.0

25 1.0

50 0.5

75 0.0

100 0.0

a

Sheffi eld (1981).
120

Prothonotary Warbler

Status

Th e prothonotary warbler (Protonotaria citrea) is a longdistance neotropical migrant associated with bottomland 

hardwood and fl oodplain forests of the Southeast. 

Densities are highest in the Mississippi Alluvial Valley; 

this species is notably absent from the central and 

southern Appalachians. Populations in the CH have 

remained relatively stable while those in the WGCP, 

where the prothonotary warbler is a Bird of Conservation 

Concern (Table 1), have declined by 5.8 percent per 

year since 1967 (Table 5). Th is bird is a planning and 

responsibility species in the CH (regional combined 

score = 14) and a management attention species in the WGCP (regional combined score = 17). 

Natural History

Because it nests in cavities and readily accepts nest boxes, the prothonotary warbler has been 

well-studied.

Petit (1999) provided an excellent, detailed description of this bird’s habitat requirements:

Key (and nearly universal) features are presence of water near wooded area with 

suitable cavity nest sites. Nest usually placed over or near large bodies of standing 

or slow-moving water, including seasonally fl ooded bottomland hardwood forest, 

baldcypress swamps, and large rivers or lakes (Walkinshaw 1953, Blem and Blem 

1991). Many other forms of water also chosen, such as creeks, streams, backyard 

ponds, and even swimming pools. Nests located away from permanent water are 

usually in low-lying, temporarily fl ooded spots (Walkinshaw 1953).

Other important habitat correlates include low elevation, fl at terrain, shaded forest 

habitats with sparse understory, and in some places, presence of baldcypress (Kahl and 

others 1985, Robbins and others 1989). Common overstory trees in nesting habitat 

include willows, maples, sweet gum, willow oak, ashes, elms, river birch, black gum, 

tupelo, cypress, and other species associated with wetlands. Buttonbush is the most 

common subcanopy species. Canopy height 12-40 m (usually 16-20), canopy cover 

usually 50-75 percent; ground vegetation usually very sparse and of low stature (< 0.5 

m; Kahl and others 1985).

Exhibits area sensitivity, avoiding forests <100 ha in area and avoiding waterways with 

wooded borders <30 m wide (Kahl and others 1985).

Model Description

Th e HSI model for prothonotary warbler includes seven variables: landform, landcover, 

successional age class, water, forest patch size, percentage of forest in the local (1-km radius) 

landscape, and snag density.

John and Karen Hollingsworth, 

U.S. Fish & Wildlife Service
121

Th e fi rst suitability function combines landform, landcover, and successional age class into a 

single matrix (SI1) that defi nes unique combinations of these classes (Table 112). We directly 

assigned SI scores to these combinations on the basis of relative rankings of habitat associations 

reported by Hamel (1992) for the prothonotary warbler in the Southeast.

Th is species is rarely found more than 200 m from water during the breeding season, so we used 

a 9 × 9 pixel window (270 x 270 m) to examine whether water was close enough to each site 

to make it suitable (SI2). If water was present in any of the 81 pixels comprising the window, 

we assigned the center pixel a value of 1.000. If water was absent, we assigned the center pixel a 

value of zero (Table 113).

We also included forest patch size (SI3) as a variable in the HSI model because prothonotary 

warbler abundance is lower in small isolated fragments and thin riparian buff er strips (Table 

114; Fig. 66). However, this species occupies small forest fragments within heavily forested 

landscapes so we included the percentage of forest in the local landscape as a variable (SI4). 

To capture this relationship, we fi t a logistic function (Fig. 67) to data (Table 115) derived 

Table 112.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for prothonotary warbler habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.100 0.300 0.400

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.100 0.300 0.400

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.300 0.800 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.200 0.600 0.800

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.200 0.600 0.800
122

from Donovan and others (1997), who observed diff erences in predator and brood parasite 

communities among highly fragmented (< 15 percent), moderately fragmented (45 to 50 

percent), and lightly fragmented (> 90 percent forest) landscapes. We assumed that the 

midpoints between these classes (30 and 70 percent forest) defi ned the specifi c cutoff s for 

poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) habitat, respectively. We applied the 

maximum value of SI3 or SI4 to all sites to compensate for the higher suitability of small 

forest blocks in predominantly forested landscapes.

Figure 66.—Relationship between forest patch size and 

suitability index (SI) scores for prothonotary warbler habitat. 

Equation: SI score = 1.002 – 1.001 * e -0.031 * (forest patch size ^ 0.968).

Figure 67.—Relationship between landscape composition and 

suitability index (SI) scores for prothonotary warbler habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

 

Forest Patch Size (ha)

Suitability Index Score

0 100 200 300

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 113.—Infl uence of occurrence of water on 

suitability index (SI) scores for prothonotary 

warbler habitat

9 × 9 pixel window contains water SI score

Yes 1.0

No 0.0

Table 114.—Infl uence of forest patch size on 

suitability index (SI) scores for prothonotary 

warbler habitat

Forest patch area (ha)a SI score

0 0.00

50 0.75

200 1.00

500 1.00

a

Assumed value.

Table 115.—Relationship between local landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for prothonotary 

warbler habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).
123

Th e prothonotary warbler is a cavity nester and uses snags (SI5) for nesting. McComb and 

others (1986) recommended 212 snags per 40 ha to satisfy the requirements of the primary 

cavity-nesting bird guild. We assumed that fi ve snags per ha (Table 116) was suffi cient for 

this bird (a secondary cavity-nesting species), but we recognized that this species also uses 

both cavities in live trees and crevices as nest sites. Th erefore, we assigned a residual SI score 

(0.25) to sites lacking snags. We fi t a logistic function through these points to quantify the 

snag density-habitat suitability relationship (Fig. 68).

To calculate the overall HSI, we calculated the geometric mean of the two SIs related to 

forest structure (SI1 and SI5) and the product of the maximum of the two SIs related to 

landscape composition (SI3 or SI4) and SI2 separately and then the geometric mean of these 

values together.

Overall HSI = ((SI1 * SI5)0.500 * (Max(SI3 or SI4) * SI2))0.500

Verifi cation and Validation

Th e prothonotary warbler was found in 83 of the 88 subsections within the CH and 

WGCP. Spearman rank correlations identifi ed signifi cant positive associations between 

average HSI score and mean BBS route abundance across all subsections (P ≤ 0.001; rs

 = 

0.39) and subsections within which the prothonotary warbler were detected (P ≤ 0.001; rs

 = 

0.41). Th e generalized linear model predicting BBS abundance from BCR and HSI for the 

prothonotary warbler was signifi cant (P ≤ 0.001; R2

 = 0.249), and the coeffi cient on the HSI 

predictor variable was both positive (β = 2.271) and signifi cantly diff erent from zero (P = 

0.002). Th erefore, we considered the HSI model for the prothonotary warbler both verifi ed 

and validated (Tirpak and others 2009a).

Figure 68.—Relationship between snag density and suitability 

index (SI) scores for prothonotary warbler habitat. Equation: 

SI score = 1.000 / (1 + (3.113 * e -3.689 * snag density)).

 

Snag Density (snags/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 116.—Infl uence of snag density on suitability 

index (SI) scores for prothonotary warbler habitat

Snag density (snags/ha) SI score

0a 0.25

5b 1.00

20a 1.00

a

Assumed value.

b

McComb and others (1986).
124

Red-cockaded Woodpecker

Status

Th e red-cockaded woodpecker (Picoides borealis) is a federally 

endangered, nonmigratory resident of old-growth pine forest 

(particularly longleaf pine) throughout the Southeast (Jackson 

1994). Due to the low detection rate for this species (0.05 bird/

route in the WGCP), BBS data poorly estimates population 

trends (Table 5). Th e red-cockaded woodpecker is designated as 

a species warranting critical recovery in both the WGCP and CH 

(regional combined score = 21), though it is extirpated from the 

latter region.

Natural History

Due to the limited availability of suitable habitat, the red-cockaded 

woodpecker lives in loose family groups and engages in cooperative breeding (Jackson 1994). 

Home ranges are large (average = 76.1 ha) but highly variable (17.2 to 159.5 ha; reviewed in 

Doster and James 1998).

Suitable habitat is defi ned by two primary habitat components. Th e fi rst is the presence of 

large pines. Pines at least 35 cm d.b.h. generally are required for a stand to be occupied by 

the red-cockaded woodpecker (Davenport and others 2000, James and others 2001, Walters 

and others 2002). However, once large pine density exceeds 80 per ha, family group size (a 

demographic parameter related to productivity; Heppell and others 1994) declines (Walters 

and others 2002). Similarly, as the average d.b.h. of overstory pines increases above 35 cm, 

habitat quality declines (Davenport and others 2000), though these declines likely are linked 

to the maturation of the forests rather than to the negative eff ects of large trees directly. 

Similar patterns have been observed for overstory pine basal area and small pine tree density 

in occupied stands, where values for these habitat attributes are lower than local maxima 

(James and others 2001, Rudolph and others 2002, Walters and others 2002).

Open midstory is the second notable feature of high-quality habitat for the red-cockaded 

woodpecker. Hardwood midstory trees should be less than 3.26 m tall and ideally less than 

1.8 m (Davenport and others 2002, Walters and others 2002). Th e open midstory typically 

is maintained through periodic fi re (burn interval of 1 to 3 years), which also facilitates a 

wiregrass understory (James and others 2001). Because this species is nonmigratory and 

suitable habitat is disjunct, connectivity of patches is critical for the long-term persistence of 

this species across the landscape.

Model Description

Th e HSI model for the red-cockaded woodpecker includes eight variables: landform, 

landcover, successional age class, forest patch size, pine basal area, hardwood basal area, 

connectivity, and large pine (> 35 cm d.b.h.) density.

Th e fi rst suitability function combines landform, landcover, and successional age class 

into a single matrix (SI1) that defi nes unique combinations of these classes (Table 117). 

John and Karen Hollingsworth, 

U.S. Fish & Wildlife Service
125

We directly assigned SI scores to these combinations on the basis of relative rankings of 

vegetation types and successional age classes for red-cockaded woodpeckers reported by 

Hamel (1992).

We included forest patch size (SI2) as a variable because of the large home ranges of the 

red-cockaded woodpecker. We assumed that the minimum and maximum home range 

sizes reported by Doster and James (1998) represented patch size thresholds for nonsuitable 

and optimal habitat, respectively. To inform the shape of the curve between these points, 

we assumed that the minimum area requirement of habitat identifi ed in the red-cockaded 

woodpecker recovery plan (USDI Fish and Wildl. Serv. 2003) defi ned average (SI score = 

0.500) habitat suitability. We used these data (Table 118) to defi ne a logarithmic function to 

predict SI scores from forest patch size (Fig. 69). 

Pine basal area (SI3) is a key component of red-cockaded woodpecker habitat, and sites with 

pine basal areas that are too low or too high are of poor quality. We fi t a quadratic function 

(Fig. 70) to data from Conner and others (1995) and Walters and others (2002; Table 119) 

on the relative abundance of this species in habitats with varying levels of pine basal area.

Table 117.—Relationship between landform, landcover type, age class, and suitability scores for redcockaded woodpecker habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.200 0.600 0.800

Mixed 0.000 0.000 0.200 0.400 0.400

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.200 0.600 0.800

Mixed 0.000 0.000 0.200 0.400 0.400

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.200 0.600 

(0.700)

0.800 

(1.000)

Mixed 0.000 0.000 0.200 0.400 0.400

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.000
126

Mid- and overstory hardwoods reduce habitat suitability for red-cockaded woodpeckers. We 

fi t an inverse logistic function (Fig. 71) to data from Kelly and others (1993) and Wilson and 

others (1995) (Table 120) on the amount of hardwood basal area (SI4) around woodpecker 

nest cavities to predict habitat suitability based on this habitat feature.

As a resident species occupying disjunct habitat patches, the red-cockaded woodpecker 

exists in metapopulations. Th erefore, dispersal between suitable forest patches is critical for 

the persistence of this species on the landscape. Isolated patches lacking a breeding female 

have no productivity, so we used the median dispersal distance for females (3.2 km; Jackson 

Figure 69.—Relationship between forest patch size and 

suitability index (SI) scores for red-cockaded woodpecker habitat. 

Equation: SI score = 0.4334 * ln(forest patch size) – 1.2133.

Figure 70.—Relationship between pine basal area and 

suitability index (SI) scores for red-cockaded woodpecker 

habitat. Equation: SI score = 0.0367 + 0.2006 * (pine basal 

area) – 0.009507 * (pine basal area)2

.

 

Forest Patch Size (ha)

Suitability Index Score

0 50 100 150 200

0.0

0.2

0.4

0.6

0.8

1.0

 

Pine Basal Area (m^2/ha)

Suitability Index Score

0 10 20

0.0

0.2

0.4

0.6

0.8

1.0

Table 118.—Relationship between forest patch size 

and suitability index (SI) scores for red-cockaded 

woodpecker habitat

Forest patch size (ha) SI score

17a 0.0

49b 0.5

170a 1.0

a

Doster and James (1998).

b

USDI Fish and Wildl. Serv. (2003).

Table 119.—Relationship between basal area of 

pines and suitability index (SI) scores for redcockaded woodpecker habitat

Pine basal area (m2/ha) SI score

0.0a 0.00

2.3b 0.50

12.7c 1.00

14.2c 1.00

20.0a 0.25

a

Assumed value.

b

Walters and others (2000).

c

Conner and others (1995).
127

1994) to defi ne average SI score (0.500). However, long-distance dispersal does occur (Larry 

Hedrick, 2006, U.S. Forest Service, pers. commun.), so we assigned to patches isolated more 

than 20 km from any other suitable site at least some residual suitability (0.010). We fi t an 

exponential relationship (Fig. 72) through these data points (Table 121) to describe how the 

connectivity of patches infl uences habitat suitability.

Large pines (SI6) are a necessary component of red-cockaded woodpecker habitat because 

this bird disproportionately forages and nests in large pines. However, there is a threshold 

above which habitat suitability declines and increasingly large trees reduce the preferred open 

Figure 71.—Relationship between hardwood basal area and 

suitability index (SI) scores for red-cockaded woodpecker 

habitat. Equation: SI score = 1 - (1.001 / (1 + (5745.304 * e 

-1.006 * hardwood basal area))).

Figure 72.—Relationship between habitat connectivity and 

suitability index (SI) scores for red-cockaded woodpecker 

habitat. Equations: SI score = e -0.0002 * distance to nearest habitat patch.

 

Hardwood Basal Area (m^2/ha)

Suitability Index Score

0 10 20

0.0

0.2

0.4

0.6

0.8

1.0

Table 120.—Relationship between basal area of 

hardwoods (m2

/ha) and suitability index (SI) scores 

for red-cockaded woodpecker habitat

Hardwood basal area (m2

/ha) SI score

0.0a 1.0

3.9b 1.0

8.6c 0.5

14.6c 0.0

20.0a 0.0

a

Assumed value.

b

Wison and others (1995).

c

Kelly and others (1993).

Table 121.—Relationship between distance to 

nearest habitat patch and suitability index (SI) 

scores for red-cockaded woodpecker habitat

Distance to nearest habitat 

patch (m) SI score

0a 1.00

3,200b 0.50

20,000a 0.01

a

Assumed value.

b

Jackson (1994).

 

Distance to Nearest Patch (m)

Suitability Index Score

0 10000 20000 30000

0.0

0.2

0.4

0.6

0.8

1.0
128

character of the forest. We fi t a quadratic function (Fig. 73) to data from Walters and others 

(2002), who identifi ed this threshold at 60 to 90 large pines per ha (Table 122).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1, SI3, SI4, and SI6) and landscape composition (SI2 and SI5) separately and 

then the geometric mean of these means together. 

Overall HSI = ((SI1 * SI3 * SI4 * SI6)0.250 * (SI2 * SI5)0.500)

0.500

Verifi cation and Validation

Th e red-cockaded woodpecker was found in only 10 of the 88 subsections within the 

CH and WGCP. Spearman rank correlation identifi ed a signifi cant (P ≤ 0.001) positive 

relationship (rs

 = 0.49) between average HSI score and mean BBS route abundance across all 

subsections. However, when subsections where the red-cockaded woodpecker was not found 

were removed from the analysis, the relationship was not signifi cant (P = 0.645; rs = 0.17). 

Th us, the HSI model predicts the absence of the red-cockaded woodpecker better than its 

abundance in subsections where it is found. Th e generalized linear model predicting BBS 

abundance from BCR and HSI for the red-cockaded woodpecker was signifi cant (P ≤ 0.001; 

R2

 = 0.203), and the coeffi cient on the HSI predictor variable was both positive (β = 0.094) 

and signifi cantly diff erent from zero (P = 0.042). Th erefore, we considered the HSI model 

for the red-cockaded woodpecker both verifi ed and validated (Tirpak and others 2009a).

Figure 73.—Relationship between large pine tree (> 35 

cm d.b.h.) density and suitability index (SI) scores for redcockaded woodpecker habitat. Equation: SI score = 0.0269 * 

(pine tree density) – 0.000193 * (pine tree density)2

 + 0.1127.

 

Large Pine Density (trees/ha)

Suitability Index Score

0 50 100 150

0.0

0.2

0.4

0.6

0.8

1.0

Table 122.—Relationship between large pine (> 35 

cm d.b.h.) density (trees/ha) and suitability index 

(SI) scores for red-cockaded woodpecker habitat

Large pine density SI score

0a 0.000

15b 0.647

30b 0.765

45b 0.882

60b 1.000

75b 1.000

90b 1.000

105b 0.824

a

Assumed value.

b

Walters and others (2002).
129

Red-headed Woodpecker

Status

Th e red-headed woodpecker (Melanerpes erythrocephalus) 

is found throughout North America east of the Rocky 

Mountains; however, it is absent from New England and the 

higher elevations of the central and southern Appalachians. 

Since 1967, populations have declined by 3.2 percent per 

year in the WGCP and by 1 percent in the CH (Sauer 

and others 2005) (Table 5). Th is species is a Bird of 

Conservation Concern and a management attention priority 

in both the CH and WGCP (regional combined score = 16 

and 17, respectively; Table 1).

Natural History

Th e red-headed woodpecker is one of the most recognizable birds of the eastern United 

States and southern Canada, but few in-depth studies of this species have been conducted 

(Smith and others 2000). Nesting habitat consists of deciduous woodlands, including upland 

and bottomland hardwoods, riparian strips, open woods, open wooded swamps, groves of 

dead and dying trees, orchards, shelterbelts, parks, open agricultural lands, savannas, forest 

edges, roadsides, and utility poles (Smith and others 2000). It prefers xeric sites with large, 

tall trees, high basal area, and a sparse understory.

Th e red-headed woodpecker exhibits seasonal shifts in habitat use. Population dynamics 

are linked to annual fl uctuations in oak acorn crops, and migration occurs in northern and 

western populations when hard mast is limited (Rodewald 2003). More locally, winter 

territories are established around small food caches within forest interiors; breeding territories 

are larger (3.1 to 8.5 ha in Florida) and concentrated along edges (Smith and others 2000).

Occurrence of the red-headed woodpecker varies with mean patch dimension, edge density 

of agricultural land, and the area of urban landcover (Lukomski 2003). It is a primary cavity 

excavator and snag availability may drive habitat selection (Giese and Cuthbert 2003). Th is 

species often is associated with high snag densities (Conner and others 1994) in mature 

stands near openings (Conner and Adkisson 1977, Brawn and others 1984). Snag density 

and basal area of dead elm distinguish nest sites from random sites in Minnesota (Giese and 

Cuthbert 2003). Similarly, loblolly pine stands with both standing and down dead woody 

debris removed contain fewer birds (Lohr and others 2002). Snags retained as groups provide 

multiple snags for roosting and foraging. Hardwood snags are used predominantly for 

foraging, whereas pine snags are more commonly used for nesting (Smith and others 2000). 

Th innings and prescribed fi res that open the understory and create snags are benefi cial.

Model Description

Th e HSI model for the red-headed woodpecker includes seven variables: landform, 

landcover, successional age class, snag density, large snag (> 20 cm d.b.h.) density, sawtimber 

tree (> 28 cm d.b.h.) density, and the occurrence of edge.

Dave Menke, U.S. Fish & Wildlife Service
130

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 123). We 

directly assigned SI scores to these combinations on the basis of data from Hamel (1992) 

on the relative value of various vegetation types and successional age classes as red-headed 

woodpecker habitat in the Southeast.

Th is species relies heavily on snags for nesting, foraging, and roosting. King and others (2007) 

observed 31.8 snags per ha in savanna habitat used by the red-headed woodpecker, though 

basal area was only 0.9 m2

 per ha in that study. Th erefore, we adjusted snag densities to refl ect 

the intermediate basal area values (12 to 15 m2

/ha; Heltzel and Leberg 2006) characteristic 

of stands used by the red-headed woodpecker in the WGCP and CH BCRs. We assumed 

that 500 snags per ha represented an upper threshold above which maximal suitability was 

achieved and that 200 snags per ha represented a threshold below which sites were unsuitable 

(Table 124). We fi t a logistic function (Fig. 74) through these data to predict how habitat 

suitability varied with snag density (SI2). Because the snag density in SI2 includes all dead 

trees greater than 2.5 cm d.b.h., we also included large snag (> 20 cm d.b.h.) density (SI3) 

as a variable. Th is additional requirement ensured the presence of snags suitable for nesting 

Table 123.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for red-headed woodpecker habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.125 0.250 0.250

Transitional-shrubland 0.000 0.000 0.125 0.250 0.250

Deciduous 0.000 0.000 0.125 0.250 0.250

Evergreen 0.000 0.000 0.250 0.500 0.500

Mixed 0.000 0.000 0.250 0.500 0.500

Orchard-vineyard 0.000 0.000 0.125 0.250 0.250

Woody wetlands 0.000 0.000 0.250 0.625 0.750

Terrace-mesic Low-density residential 0.000 0.000 0.125 0.375 0.500

Transitional-shrubland 0.000 0.000 0.250 0.500 0.500

Deciduous 0.000 0.000 0.125 0.375 0.500

Evergreen 0.000 0.000 0.250 0.500 0.500

Mixed 0.000 0.000 0.250 0.500 0.500

Orchard-vineyard 0.000 0.000 0.125 0.375 0.500

Woody wetlands 0.000 0.000 0.250 0.500 0.500

Xeric-ridge Low-density residential 0.000 0.000 0.250 0.750 1.000

Transitional-shrubland 0.000 0.000 0.250 0.500 

(0.750)

0.500 

(1.000)

Deciduous 0.000 0.000 0.250 0.750 1.000

Evergreen 0.000 0.000 0.250 0.500 

(0.750)

0.500 

(1.000)

Mixed 0.000 0.000 0.250 0.500 0.500

Orchard-vineyard 0.000 0.000 0.250 0.750 1.000

Woody wetlands 0.000 0.000 0.250 0.750 1.000
131

in high-quality habitats. We relied on data from Lohr and others (2002) to inform an inverse 

logistic function (Fig. 75) that linked habitat suitability to large snag density (Table 125).

Th e red-headed woodpecker breeds in relatively open habitats with widely spaced large trees 

near openings (King and others 2007). Th erefore, we included sawtimber tree density (SI4) 

and edge occurrence (SI5) as variables. We assumed that habitat suitability was highest when 

sawtimber tree density was 20 or fewer trees per ha and lowest when sawtimber tree density 

exceeded 50 trees per ha (Table 126). We fi t a logistic function (Fig. 76) through these data 

points to quantify the relationship between sawtimber tree density and SI scores. To identify 

edges, we used a 7 × 7 pixel moving window (210 x 210 m) to locate the transitions between 

Figure 74.—Relationship between snag density (snags * 

100/ha) and suitability index (SI) scores for red-headed 

woodpecker habitat. Equation: SI score = 1.006 / (1 + 

(249051.2 * e (-0.0338 * snag density))).

Figure 75.—Relationship between large snag (> 20 cm 

d.b.h.) density and suitability index (SI) scores for redheaded woodpecker habitat. Equation: SI score = 1.006 / (1 + 

(90614077 * e (-1.899 * large snag density))).

 

Snag Density (snags/ha)

Suitability Index Score

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

 

Large Snag Density (snags/ha)

Suitability Index Score

0 10 20

0.0

0.2

0.4

0.6

0.8

1.0

Table 124.—Infl uence of snag density on suitability 

index (SI) scores for red-headed woodpecker habitat

Snag density (snags/ha)a SI score

0 0.00

200 0.00

400 0.75

500 1.00

a

Assumed value.

Table 125.—Infl uence of large snag (> 20 cm d.b.h.) 

density (snags/ha) on suitability index (SI) scores for 

red-headed woodpecker habitat

Large snag density SI score

0.0a 0.0

8.5b 0.1

12.0a 1.0

a

Assumed value.

b

Lohr and others (2002).
132

forest and non-forest landcovers or sapling-pole-sawtimber and grass-forb-shrub-seedling 

successional age class stands. We assigned to edge habitats the maximal SI score and 

discounted areas with no edge (Table 127).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1, SI2, SI3, and SI4) and multiplied this product by the SI score for 

edge occurrence (SI5).

Overall HSI = ((SI1 * SI2 * SI3 * SI4)0.250) * SI5

Verifi cation and Validation

Th e red-headed woodpecker was found in all 88 subsections of the CH and WGCP. 

Spearman rank correlation failed to identify a positive association between average HSI 

score and mean BBS abundance. Th e generalized linear model predicting BBS abundance 

from BCR and HSI for the red-headed woodpecker was signifi cant (P ≤ 0.001; R2

 = 0.225); 

however, the coeffi cient on the HSI predictor variable was negative (β = -3.359). Th erefore, 

we considered the HSI model for the red-headed woodpecker neither verifi ed nor validated 

(Tirpak and others 2009a).

Figure 76.—Relationship between sawtimber tree (≥ 28 cm 

d.b.h.) density (trees * 10/ha) and suitability index (SI) scores 

for red-headed woodpecker habitat. Equation: SI score =1 – 

(1.000 / (1 + (1615169 * e (-0.4398 * sawtimber tree density)))).

 

Sawtimber Tree Density (trees/ha)

Suitability Index Score

0 25 50 75

0.0

0.2

0.4

0.6

0.8

1.0

Table 126.—Infl uence of sawtimber tree (> 28 cm 

d.b.h.) density (trees/ha) on suitability index (SI) 

scores for red-headed woodpecker habitat

Sawtimber tree densitya

 SI score

0 1.00

20 1.00

30 0.75

35 0.25

50 0.00

70 0.00

a

Assumed value.

Table 127.—Infl uence of edge on suitability index 

(SI) scores for red-headed woodpecker habitat

7 × 7 window around forest 

pixel includes fi elda SI score

Yes 1.0

No 0.1

a

Field defi ned as any shrub-seedling or grass-forb age 

class pixel, or natural grasslands, pasture-hay, fallow, 

urban-recreational grasses, emergent herbaceous 

wetlands, open water, high intensity residential, 

commercial-industrial-transportation, bare rock-sand-clay, 

quarries-strip mines-gravel pits, row crops, or small grains. 

Forest defi ned as any used sapling, pole, or sawtimber 

age class pixel of low-density residential, transitional, 

shrublands, deciduous, mixed, evergreen, orchard, or 

woody wetlands.
133

Swainson’s Warbler

Status

Th e Swainson’s warbler (Limnothlypis swainsonii) is a neotropical 

migrant that breeds in dense thickets across the Southeast. Due to 

its overall low density and occurrence in habitats not well sampled 

by BBS, estimates of population trends based on this dataset are not 

reliable (Sauer and others 2005) (Table 5). Nonetheless, this species 

is a Bird of Conservation Concern and has a regional combined 

score of 20 in both the CH and WGCP (Table 1). An estimated 

46 percent of the continental population of the Swainson’s warbler 

breeds in the WGCP (Panjabi and others 2001).

Natural History

Th e Swainson’s warbler is distributed locally across the Southeast 

(Brown and Dickson 1994). Once believed to be restricted to 

canebrakes in bottomland hardwood and swamp forests of the Atlantic and Gulf Coastal 

Plains, it now has been documented breeding at low densities in regenerating clearcuts in 

Texas and rhododendron-mountain laurel thickets in the southern Appalachians (Graves 

2002). Territory size is large for a wood warbler (3.2 ha) (Brown and Dickson 1994), and 

this species demonstrates area sensitivity. In Illinois, the Swainson’s warbler is not observed 

on tracts smaller than 350 ha (Eddleman and others 1980).

Th is species does not use canopy height, basal area, successional age class, or species 

composition as habitat cues (Eddleman and others 1980, Graves 2002), but selects habitat 

based on understory characteristics. Dense thickets are required, and stem densities of 

about 35,000 stems per ha are optimal (Graves 2002). Canopy gaps are important for 

encouraging this dense growth, and canopy cover typically is high (70 to 80 percent) but 

rarely closed (> 90 percent) (Eddleman and others 1980, Graves 2001, Somershoe and 

others 2003). Understory vegetation is primarily woody; herbaceous cover is typically 

sparse (< 25 percent) (Eddleman and others 1980, Brown and Dickson 1994). Leaf litter 

is abundant and provides an important foraging substrate (Graves 2001, Somershoe and 

others 2003).

Hydrology is a critical factor infl uencing the habitat suitability for this warbler. In 

bottomland and fl oodplain habitats, birds select areas that typically are drier than 

surrounding sites (Graves 2001, Somershoe and others 2003). Inundation of otherwise 

suitable habitat from March - September negatively aff ects the quality of an otherwise 

suitable site (Graves 2002). Th is species occasionally breeds in xeric uplands with 

appropriate understory characteristics (Carrie 1996).

Model Description

Th e HSI model for the Swainson’s warbler includes six variables: landform, landcover, 

successional age class, forest patch size, proportion of forest in a 1-km radius, and small 

stem (< 2.5 cm d.b.h.) density.

Chandler S. Robbins, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
134

Th e fi rst suitability function combines landform, landcover, and successional age class 

into a single matrix (SI1) that defi nes unique combinations of these classes (Table 128). 

We adjusted the relative habitat quality rankings of Hamel (1992) for Swainson’s warbler 

vegetation and successional age class associations to maximize habitat suitability in woody 

wetland habitats along fl oodplains, and to ensure that transitional sapling stands that may be 

used in the WGCP were assigned SI scores (Carrie 1996).

We included forest patch size (SI2) in the model because of the preference of the Swainson’s 

warbler for interior sites within large forest tracts. We assumed that the minimum patch size 

in which Eddleman and others (1980) observed this species (350 ha) represented optimal 

habitat. Because this study was at the northern limit of the range of the Swainson’s warbler, 

we assumed that birds would occupy signifi cantly smaller tracts (Table 129). We based a 

logistic function on these assumptions to predict the impact of forest patch size on habitat 

suitability (Fig. 77). Nevertheless, the suitability of a specifi c patch size also is infl uenced by 

its landscape context (SI3). In predominantly forested landscapes, small forest patches that 

otherwise may not be suitable may be occupied due to their proximity to large forest blocks 

(Rosenberg and others 1999). To capture this relationship, we fi t a logistic function (Fig. 

78) to data (Table 130) derived from Donovan and others (1997), who observed diff erences 

Table 128.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for Swainson’s warbler habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.400 0.000 0.000

Deciduous 0.000 0.000 0.400 0.900 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.400 0.900 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.200 0.000 0.000

Deciduous 0.000 0.000 0.200 0.500 0.600

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.400 0.800 0.800

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.200 0.000 0.000

Deciduous 0.000 0.000 0.200 0.500 0.600

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.400 0.800 0.800
135

in predator and brood parasite communities among highly fragmented (< 15 percent), 

moderately fragmented (45 to 50 percent), and lightly fragmented (> 90 percent forest) 

landscapes. We assumed that the midpoint between moderately and lightly fragmented forest 

defi ned the specifi c cutoff for average (SI score = 0.500) habitat. We used the maximum 

score from SI2 or SI3 to account for the higher suitability of small patches in predominantly 

forested landscapes relative to their size alone.

Th e Swainson’s warbler breeds in dense thickets and stem densities of approximately 35,000 

stems per ha are optimal (SI score = 1.000) (Graves 2002). Stem densities can be even 

higher in early-successional bottomland hardwoods (> 200,000/ha), but we assumed habitat 

Figure 77.— Relationship between forest patch size and suitability 

index (SI) scores for Swainson’s warbler habitat. Equation: 

SI score = (1.001 / (1 + (31096.960 * e -0.041 * (forest patch size)))).

Figure 78.—Relationship between landscape composition and 

suitability index (SI) scores for Swainson’s warbler habitat. 

Equation: SI score = 1.047 / (1.000 + (1991.516 * e -10.673 * 

landscape composition)).

 

Landscape Composition (proportion forest in 1-km radius)

Suitability Index Score

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 129.—Infl uence of forest patch size on 

suitability index (SI) score for Swainson’s warbler 

habitat

Forest patch size (ha) SI score

0a 0.00

35a 0.01

250a 0.50

350b 1.00

500a 1.00

a

Assumed value.

b

Eddleman and others (1980).

Table 130.—Relationship between landscape 

composition (proportion forest in 1-km radius) 

and suitability index (SI) scores for Swainson’s 

warbler habitat

Landscape composition SI score

0.00a 0.00

0.10a 0.00

0.20a 0.00

0.30a 0.00

0.40a 0.00

0.50a 0.10

0.60a 0.25

0.70b 0.50

0.80a 0.75

0.90a 0.90

1.00a 1.00

a

Assumed value.

b

Donovan and others (1997).

 

Forest Patch Size (ha)

Suitability Index Score

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0
136

suitability was not negatively aff ected by stem density. Th erefore, we fi t a logistic function 

(Fig. 79) to data from Graves (2002) that captured the eff ect of varying stem density on 

habitat suitability (Table 131).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI4) and multiplied that by the maximum SI score for forest patch size 

(SI2) or percent forest in the 1-km landscape (SI3) and fi nally calculated the geometric mean 

of that product.

Overall HSI = ((SI1 * SI4)0.500 * Max(SI2 or SI3))0.500

Verifi cation and Validation

Th e Swainson’s warbler was found only in 31 of the 88 subsections within the CH and 

WGCP. Spearman rank correlation identifi ed a signifi cant (P ≤ 0.010) positive relationship 

(rs

 = 0.31) between average HSI score and mean BBS route abundance across all subsections. 

However, when subsections where this species was not found were removed from the 

analysis, the relationship was not signifi cant (P = 0.893; rs

 = -0.03). Th us, the HSI model 

better predicts the absence of the Swainson’s warbler than its abundance in subsections where 

this species is found. Th e generalized linear model predicting BBS abundance from BCR 

and HSI for the Swainson’s warbler was signifi cant (P ≤ 0.001; R2

 = 0.260); however, the 

coeffi cient on the HSI predictor variable was negative (β = -0.298). Th erefore, we considered 

the HSI model for the Swainson’s warbler verifi ed but not validated (Tirpak and others 

2009a).

Figure 79.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

Swainson’s warbler habitat. Equation: SI score = 1.008 / (1.000 

+ (59.233 * e -0.235 * (small stem density / 1000))).

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0

Table 131.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for Swainson’s warbler habitat

Small stem density SI score

0.000a 0.0

7.550b 0.1

17.365b 0.5

34.773b 1.0

72.999b 1.0

a

Assumed value.

b

Graves (2002).
137

Swallow-tailed Kite

Status

Th e swallow-tailed kite (Elanoides forfi catus) is a 

neotropical raptor that reaches the northern limit of 

its distribution in the Unites States. Once ranging 

throughout the Mississippi River drainage as far north 

as Minnesota, this species now is restricted to seven 

states in the Southeast. Th ere are too few swallowtailed kites detected on BBS routes in the WGCP 

to estimate a population trend; however, this species 

is a Bird of Conservation Concern and immediate 

management attention priority in this BCR (regional 

combined score = 18; Table 1). Th e swallow-tailed kite 

no longer breeds in the CH and this species warrants critical recovery eff orts in this region 

(regional combined score = 19).

Natural History

Th e swallow-tailed kite is a rare breeder in the continental United States. Th e current 

restriction of this species to seven southern states (with limited distributions in all but 

Florida) represents a signifi cant contraction of its former range. Most of the information on 

this bird in the United States is from Florida (Meyer 1995).

Th e swallow-tailed kite has a large home range (500 to 1800 ha) that increases substantially 

(> 20,000 ha) when the long but regular foraging forays characteristic of this species are 

included. With such a large home range, the important role of landscape structure on habitat 

suitability is not surprising. Critical habitat elements are large, tall trees for nesting and open 

habitats containing prey (Meyer 1995, Sykes and others 1999). Any interspersion of these 

features is useable (e.g., trees adjacent to prairie, wetlands, or marsh). Landscapes containing 

bottomland hardwood forest interspersed with scattered openings are particularly attractive. 

Th e edges of pine forests along swamps and riparian zones also are commonly used along 

the Coastal Plains. Th e Mississippi kite typically occupies habitats that are drier and contain 

more contiguous forest than the habitats of the swallow-tailed kite.

Model Description

Th e HSI model for the swallow-tailed kite includes six variables: landform, landcover, 

successional age class, forest patch size, landscape composition, and dominant tree density.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 132). We 

then directly assigned SI scores to these combinations on the basis of relative habitat quality 

rankings from Hamel (1992) for the swallow-tailed kite. However, we assumed that only 

stands in the sawtimber successional age class provided suitable habitat for this species

We also included forest patch size (SI2) as a variable because of this bird’s large home range 

and association with large blocks of forested wetlands. We fi t a logarithmic function (Fig. 80) 

D.A. Rintoul, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
138

to data (Table 133) from Zimmerman (2004) on the mean value of forest in 5-km buff ers 

around swallow-tailed kite nest sites and the maximum home range size reported by Cely 

and Sorrow (1990) to assess the impact of forest patch size on habitat suitability scores for 

the swallow-tailed kite.

Like the Mississippi kite, the swallow-tailed kite forages aerially in open habitats, so it 

requires both forested sites for nesting and open areas for foraging (SI3). We based the ideal 

composition of vegetation types in the landscape on data from Sykes and others (1999), who 

observed 20 percent open habitat within 200-ha core areas in Florida. We maximized habitat 

suitability at this threshold and reduced SI scores in landscapes containing greater or lower 

proportions of open habitat (Table 134, Fig. 81).

Th e swallow-tailed kite nests in dominant trees (SI4) that extend above the canopy. We 

assumed that trees with a d.b.h. greater than 76.2 cm would extend above the canopy in 

the sawtimber stands that provide the exclusive habitat for this species. We assumed that 

one dominant tree per ha would satisfy this requirement and that the swallow-tailed kite 

would be absent from stands with a uniform canopy (zero dominant trees/ha). We fi t an 

exponential function (Fig. 82) to the values between these data points and assumed that 

Table 132.—Relationship of landform, landcover type, and successional age class to SI scores for swallowtailed kite habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.500

Evergreen 0.000 0.000 0.000 0.000 0.500

Mixed 0.000 0.000 0.000 0.000 0.500

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.800

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.000 0.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.000 0.800
139

stands with 14 dominant trees per ha (the maximum value from the WGCP during the FIA 

surveys of the 1990s) were associated with maximum habitat suitability (Table 135).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1 and SI4) and landscape composition (SI2 and SI3) separately and 

then the geometric mean of these means together. 

Overall HSI = ((SI1 * SI4)0.500 * (SI2 * SI3)0.500)

0.500

Figure 80.—Relationship between forest patch size and 

suitability index (SI) scores for swallow-tailed kite habitat. 

Equation: SI score = 0.224 * ln(forest patch size) – 1.376.

Figure 81.—Relationship between landscape composition 

and suitability index (SI) scores for swallow-tailed kite habitat. 

Equation: SI score = (0.001 * 0.885(percent open habitat)) * (percent 

open habitat)3.065.

 

Landscape Composition (% open habitat in 1,200-ha area)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 133.—Infl uence of forest patch size on 

suitability index (SI) scores for swallow-tailed 

kite habitat

Forest patch size (ha) SI score

4,300a 0.5

40,000b 1.0

a

Zimmerman (2004).

b

Cely and Sorrow (1990).

Table 134.—Suitability index scores for swallowtailed kite habitat based on landscape composition 

(percent of open habitat) within 1,200-ha landscape

Landscape compositiona SI score

6b 0.1

20c 1.0

25b 1.0

75b 0.1

a

Water, grasslands, cultivated lands, and emergent wetlands.

b

Assumed value.

c

Sykes and others (1999).

 

Forest Patch Size (ha)

Suitability Index Score

0 10000 20000 30000 40000

0.0

0.2

0.4

0.6

0.8

1.0
140

Verifi cation and Validation

Th e swallow-tailed kite was found in 8 of the 88 subsections of the CH and WGCP. 

Spearman rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs = 0.73) 

between average HSI score and mean BBS route abundance across all subsections. However, 

when subsections where this species was not found were removed from the analysis, the 

relationship was not signifi cant (P = 0.432; rs = 0.33). Th us, the HSI model better predicts 

the absence of the swallow-tailed kite than its abundance in subsections where this species is 

found. Th e generalized linear model predicting BBS abundance from BCR and HSI for the 

swallow-tailed kite was signifi cant (P ≤ 0.001; R2

 = 0.522), and the coeffi cient on the HSI 

predictor variable was both positive (β = 0.725) and signifi cantly diff erent from zero (P ≤ 

0.001). Th erefore, we considered the HSI model for the swallow-tailed kite both verifi ed and 

validated (Tirpak and others 2009a).

Figure 82.—Relationship between dominant tree density and 

(SI) scores for swallow-tailed kite habitat. Equation: SI score = 

1 – e -8.734 * dominant tree density.

 

Dominant Tree Density (trees/ha)

Suitability Index Score

0 2 4

0.0

0.2

0.4

0.6

0.8

1.0

Table 135.—Infl uence of dominant tree (> 76.2 cm 

d.b.h.) density (trees/ha) on suitability index (SI) 

scores for swallow-tailed kite habitat

Dominant tree densitya SI score

0 0.0

1 1.0

14 1.0

a

Assumed value.
141

Whip-poor-will

Status

Th e whip-poor-will (Caprimulgus vociferus) is a 

neotropical migrant with a more northerly range than 

the chuck-will’s-widow, though the ranges of the two 

are not exclusive and overlap broadly across the CH. 

Th e whip-poor-will has declined by 1.8 percent per 

year since 1967 in the CH (Sauer and others 2005) 

(Table 5), where this species is a Bird of Conservation 

Concern and has a regional combined score of 17 

(Table 1). A large proportion of the continental 

population (35.5 percent) breeds in the CH (Panjabi and others 2001). Th is species is a rare 

breeder in the WGCP (regional combined score = 13).

Natural History

Owing to its cryptic coloration and crepuscular activity pattern, the whip-poor-will is one 

of the least studied birds in North America (Cink 2002). Breeding habitat in the CH and 

WGCP consists of xeric deciduous and mixed forests with a sparse understory. Th is species 

also is associated with open areas, such as rural farmland, powerline and roadway rights-ofway, clearcuts and selectively logged forest, old fi elds, and reclaimed surface mines. Shaded 

forest stands with limited ground cover adjacent to open areas for foraging provide ideal 

whip-poor-will habitat. Th is species usually is absent from extensive areas of closed canopy 

forest, but there are no data on minimum or maximum thresholds for forest patch size. 

Small, isolated woodlots in a Maryland agricultural landscape are not used (Reese 1996, cited 

in Cink 2002). In Massachusetts, Grand and Cushman (2003) found that the whip-poorwill is strongly associated with complex patch shapes and high contrast edges. Th is species 

nests on the forest fl oor and hatching is synchronized with the full moon to optimize the 

foraging time of adults. Whip-poor-wills are not strongly territorial; home range varies from 

2.8 to 11.1 ha.

Model Description

Th e HSI model for whip-poor-will includes four variables: landform, landcover, successional 

age class, and the relative composition of forest and open habitats in the landscape.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 136). We 

directly assigned SI scores to these combinations on the basis of relative habitat rankings for 

vegetation and successional age class associations of the whip-poor-will reported by Hamel 

(1992).

Th e whip-poor-will nests in forest and forages in openings. As a result, it requires landscapes 

with an interspersion (SI2) of these landcover types. We assumed that a landscape with 70 

percent forest and 30 percent open habitat was optimal (Michael Wilson, 2006, College of 

William & Mary, pers. commun.) and that landscapes with a greater proportion of forest 

Chandler S. Robbins, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
142

were more suitable than those with less forest cover so long as some openings were present 

(Table 137; sensu Cooper 1981).

We calculated the overall HSI score as the geometric mean of the two component variables.

Overall HSI = (SI1 * SI2)0.500

Verifi cation and Validation

Th e whip-poor-will was found in 76 of the 88 subsections within the CH and WGCP. 

Spearman rank correlation identifi ed a signifi cant (P = 0.005) positive relationship (rs

 = 0.30) 

between average HSI score and mean BBS route abundance across subsections. Th is relationship 

was even stronger (rs

 = 0.47) when subsections in which the whip-poor-will was not detected 

were removed from the analysis. Th e generalized linear model predicting BBS abundance 

from BCR and HSI for the whip-poor-will was signifi cant (P = 0.002; R2

 = 0.139), and the 

coeffi cient on the HSI predictor variable was positive (β = 1.270) but not signifi cantly diff erent 

from zero (P = 0.229). Th erefore, we considered the HSI model for the whip-poor-will verifi ed 

but not validated (Tirpak and others 2009a).

Table 136.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for whip-poor-will habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.334 0.667 0.667

Deciduous 0.000 0.000 0.334 0.667 0.667

Evergreen 0.000 0.000 0.334 0.667 0.667

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.333 0.333

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.334 0.834 1.000

Deciduous 0.000 0.000 0.334 0.667 0.667

Evergreen 0.000 0.000 0.334 0.667 0.667

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.333 0.333

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.334 0.834 1.000

Deciduous 0.000 0.000 0.334 0.667 0.667

Evergreen 0.000 0.000 0.334 0.667 0.667

Mixed 0.000 0.000 0.334 0.834 1.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.167 0.333 0.333
143

Table 137.—Suitability index scores for whip-poor-will habitat based on the relative proportion of cells 

providing open and forest landcover within 500-m radius

Proportion 

forestb

Proportion opena

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0

0.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.3 0.00 0.10 0.10 0.10 0.10 0.10 0.10 0.10

0.4 0.00 0.25 0.25 0.25 0.25 0.25 0.25

0.5 0.00 0.50 0.50 0.50 0.50 0.50

0.6 0.00 0.70 0.90 0.90 0.90

0.7 0.00 0.80 0.90 1.00

0.8 0.00 0.80 0.90

0.9 0.00 0.80

1.0 0.00

a

Open = pasture/hay, recreational grasses, grasslands/herbaceous, and emergent herbaceous wetland landcovers or 

grass-forb and shrub-seedling successional age class stands.

b

Forest = any habitats with positive SI1 values (Table 136).
144

White-eyed Vireo

Status

Th e white-eyed vireo (Vireo griseus) is a neotropical 

migrant that breeds throughout the southeastern United 

States. Populations have been stable in both the CH and 

WGCP over the last 40 years, but have been increasing 

in the WGCP by 1.6 percent annually since 1980 

(Sauer and others 2005; Table 5). Th is species requires 

management attention in both the CH and WGCP 

(regional combined score = 15 and 16, respectively) but 

is not a Bird of Conservation Concern in either BCR 

(Table 1).

Natural History

A small secretive songbird, the white-eyed vireo is associated with dense vegetation in 

secondary deciduous scrub-shrub, wood margins, overgrown pastures, abandoned farmlands, 

streamside thickets, and even mid- to late successional forests (Hopp and others 1995). 

Th is species shares habitats with the blue-gray gnatcatcher, Carolina wren, gray catbird, and 

brown thrasher, but prefers later successional forest than the yellow-breasted chat, prairie 

warbler, and Bell’s vireo.

In Texas, the white-eyed vireo breeds in areas of shrubby vegetation (0 to 1 m) with dense 

foliage (Conner and Dickson 1997). Similarly, in Virginia, it prefers habitats with an 

extensive undergrowth of shrubs, brambles, and saplings interspersed with taller trees (10 

to 20 percent of area). Vireo densities are higher in glade and regenerating forest habitat 

than edges in Missouri (Fink and others 2006). Densities also are inversely related to 

vegetation height, foliage density at 12 to 15 m, density of pole trees, and percent canopy 

closure (Conner and others 1983). Prather and Smith (2003) found that this species was 

more abundant in tornado-damaged forest in Arkansas than in undamaged areas. In South 

Carolina, abundance was positively related to gap size in bottomland forest that had been 

harvested by group-selection (Moorman and Guynn 2001). Territory size (0.1 to 1.8 ha) and 

population density vary with habitat quality. Brood parasitism aff ects nearly half of all nests 

and may signifi cantly reduce productivity. Th e white-eyed vireo is more abundant in wide 

riparian strips of bottomland hardwood forest than in narrow strips (Kilgo and others 1998). 

Model Description

Th e HSI model for the white-eyed vireo includes six variables: landform, landcover, 

successional age class, edge occurrence, canopy cover, and small stem (< 2.5 cm d.b.h.) 

density.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 138). We 

directly assigned SI scores to these combinations on the basis of data from Hamel (1992) on 

the habitat associations of the white-eyed vireo in the Southeast.

David Arbour, U.S. Forest Service
145

In older forest stands, the white-eyed vireo 

concentrates on edges (SI2) and other areas with 

dense vegetation (Conner and Dickson 1997). 

We used a 3 × 3 pixel window (90 x 90 m) 

to identify the interfaces between pole and 

sawtimber successional age class forest and 

herbaceous and nonforest landcovers (hard 

edge) or shrub-seedling, grass-forb, and sapling 

successional age class forest (soft edge). We 

assumed that pole and sawtimber stands 

adjacent to these edges would have the highest SI score but applied a residual suitability 

value (0.01) to areas not identifi ed as edge habitats to compensate for small forest gaps and 

openings that may be used. Shrub-seedling and sapling stands were suitable habitat regardless 

of edge (Table 139).

Table 138.—Relationship of landform, landcover type, and successional age class to suitability index (SI) 

scores for white-eyed vireo habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 1.000 0.834 0.500 0.333

Deciduous 0.000 1.000 0.834 0.500 0.333

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.000 0.000

Orchard-vineyard 0.000 1.000 0.834 0.500 0.333

Woody wetlands 0.000 1.000 0.834 0.500 0.333

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.667 0.500 0.333 0.167

Deciduous 0.000 0.667 0.500 0.333 0.167

Evergreen 0.000 0.667 0.500 0.333 0.167

Mixed 0.000 0.667 0.500 0.333 0.167

Orchard-vineyard 0.000 0.667 0.500 0.333 0.167

Woody wetlands 0.000 1.000 0.834 0.500 0.333

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.667 0.500 0.333 0.167

Deciduous 0.000 0.667 0.500 0.333 0.167

Evergreen 0.000 0.667 0.500 0.333 0.167

Mixed 0.000 0.667 0.500 0.333 0.167

Orchard-vineyard 0.000 0.667 0.500 0.333 0.167

Woody wetlands 0.000 1.000 0.834 0.500 0.333

Table 139.—Infl uence of edge on suitability index (SI) 

scores for white-eyed vireo habitat

3 × 3 pixel window around 

forest pixel includes fi eld? a SI score

Yesb 1.00

No 0.01

a

Field defi ned as any sapling, shrub-seedling, or grass-forb 

age class pixel, or natural grasslands, pasture-hay, fallow, 

urban-recreational grasses, emergent herbaceous wetlands, 

open water, high-intensity residential, commercial-industrialtransportation, bare rock-sand-clay, quarries-strip mines-gravel 

pits, row crops, or small grains. Forest defi ned as any pole or 

sawtimber age class pixel of low-density residential, transitional, 

shrublands, deciduous, mixed, evergreen, orchard, or woody 

wetlands.

b

Seedling-shrub and sapling habitats used regardless of edge.
146

To refi ne the association of the white-eyed vireo with canopy gaps, we modeled the eff ect 

of canopy cover (SI3) on SI scores as an inverse logistic function (Fig. 83) that captured the 

absence of this species in closed-canopy forests (Table 140).

Finally, we fi t a logistic function (Fig. 84) to data from Annand and Th ompson (1997) (Table 

141) on the infl uence of small stem (< 2.5 cm d.b.h.) density (SI4) on the relative density of the 

white-eyed vireo to quantify the relationship between SI scores and this habitat feature. 

Assuming that this species uses edge as a surrogate to its preferred shrub-seedling and sapling 

habitats, we calculated HSI scores separately for shrub-seedling-sapling and pole-sawtimber 

Figure 83.—Relationship between canopy cover and suitability 

index (SI) scores for white-eyed vireo habitat. Equation: SI 

score = 1 - (1.0101 / (1 + (127952.58 * e -0.1629 * canopy cover))).

Figure 84.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

white-eyed vireo habitat. Equation: SI score = (1.000 / (1 + 

(14512.121 * e -2.396 * (small stem density / 1000)))).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 140.—Infl uence of canopy cover on suitability 

index (SI) scores for white-eyed vireo habitat

Canopy cover (percent) SI score

29.26a 1.000

31.00b 1.000

71.86a 0.482

73.00b 0.493

91.00b 0.000

93.38a 0.024

95.58a 0.036

96.59b 0.012

a

Annand and Thompson (1997).

b

Prather and Smith (2003).

Table 141.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for white-eyed vireo habitat

Small stem densitya SI score

2 0.01

4 0.50

8 1.00

a

Annand and Thompson (1997).
147

forest stands. In the former, the geometric mean of forest structure variables alone defi nes the 

suitability score. For the latter, landscape composition (edge occurrence) also was a factor in 

the calculation.

Shrub-seedling and sapling (young) successional age classes: 

HSIYoung: (SI1 * SI3 * SI4)0.333

Pole and sawtimber (old) successional age classes: 

HSIOld: ((SI1 * SI3 * SI4)0.333 * SI2)0.500

To determine the overall HSI score, we summed the age class specifi c HSIs:

Overall HSI = HSIYoung + HSIOld

Verifi cation and Validation

Th e white-eyed vireo was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P = 0.002) positive association (rs

 = 0.33) between 

average HSI score and mean BBS route abundance across all subsections. Th e generalized 

linear model predicting BBS abundance from BCR and HSI for the white-eyed vireo was 

signifi cant (P ≤ 0.001; R2

 = 0.529); however, the coeffi cient on the HSI predictor variable 

was negative (β = -9.070). Th erefore, we considered the HSI model for the white-eyed vireo 

verifi ed but not validated (Tirpak and others 2009a).
148

Wood Thrush

Status

Th e wood thrush (Hylocichla mustelina) is a familiar 

woodland migrant to the forests of the eastern and 

central United States. Population declines for this 

species in the Midwest are linked to higher predation 

and parasitism rates in fragmented landscapes (Robinson 

and others 1995, Sauer and others 2005) (Table 

5). Th e wood thrush is both a Bird of Conservation 

Concern and a management attention priority in the 

CH and WGCP (regional combined score = 16 and 15, 

respectively; Table 1).

Natural History

Th e wood thrush is a long-distance neotropical migrant that exemplifi es the decline in 

songbirds due to forest fragmentation. Due to its general abundance, ease of nest location 

and monitoring, and area sensitivity, the wood thrush is easy to study and there is a 

large body of knowledge on this bird (Roth and others 1996). Th is species is common in 

deciduous and mixed forests but rare in pure evergreen stands (Roth and others 1996). 

Mesic, upland forests with a moderate density of midcanopy trees and shrubs for nesting 

and an open understory with abundant leaf litter for foraging are optimal (Roth and others 

1996). Closed overstory canopies are commonly used (Roth and others 1996, Bell and 

Whitmore 2000).

Th e wood thrush displays area sensitivity in productivity but not in its occupancy of habitats. 

It nests in forest fragments as small as 0.3 ha, albeit at low densities (Tilghman 1987, 

Weinberg and Roth 1998), and in narrow (< 150 m wide) riparian strips (Sargent and others 

2003). However, nest predation and parasitism rates are extremely high in fragments of less 

than 80 ha and in riparian buff ers less than 530 m wide (Donovan and others 1995, Hoover 

and others 1995, Peak and others 2004). Landscapes with greater amounts of forest cover 

(particularly unfragmented forest) mitigate some of these eff ects in small woodlots (Donovan 

and others 1997, Driscoll and Donovan 2004, Driscoll and others 2005). Nest success is 

predicted better by the amount of forest in the landscape than by the structural characteristics 

of microhabitat around nests (Hoover and Brittingham 1998, Driscoll and others 2005).

Model Description

Th e HSI model for the wood thrush includes seven variables: landform, landcover, 

successional age class, forest patch size, percent forest in the local (1-km radius) landscape, 

small stem (< 2.5 cm d.b.h.) density, and canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 142). We 

directly assigned SI scores to these combinations on the basis of habitat associations reported 

by Hamel (1992) but made minor adjustments to increase SI scores for sapling stands on the 

basis of data from Th ompson and others (1992).

Steve Maslowski, U.S. Fish & Wildlife Service
149

Although the wood thrush will occupy small forest fragments, its density may be lower 

within them. Th erefore, we included forest patch size (SI2) in the HSI model. We fi t an 

exponential function (Fig. 85) to data from Robbins and others (1989) and Kilgo and others 

(1998) (riparian strips in this study were assumed to be 10 km long) that documented 

changes in relative occurrence with changes in patch size (Table 143). Nevertheless, the 

suitability of a forest patch is infl uenced not only by its size but also by its landscape context 

(SI3). To capture this relationship, we fi t a logistic function (Fig. 86) to data (Table 144) 

derived from Donovan and others (1997), who observed diff erences in predator and brood 

parasite communities among highly fragmented (< 15 percent), moderately fragmented (45 

to 50 percent), and lightly fragmented (> 90 percent forest) landscapes. We assumed that 

the midpoints between these classes (30 and 70 percent forest) defi ned the specifi c cutoff s 

for poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) habitat, respectively. We used the 

maximum SI score from SI2 or SI3 to increase the suitability of small patches in heavily 

forested landscapes.

Table 142.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for wood thrush habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.250 0.750 0.750 1.000

Transitional-shrubland 0.000 0.250 0.750 0.750 1.000

Deciduous 0.000 0.250 0.750 0.750 1.000

Evergreen 0.000 0.167 0.000 0.000 0.000

Mixed 0.000 0.167 0.333 0.333 0.667

Orchard-vineyard 0.000 0.250 0.333 0.333 0.667

Woody wetlands 0.000 0.250 0.500 0.500 1.000

Terrace-mesic Low-density residential 0.000 0.250 0.500 0.500 0.834

Transitional-shrubland 0.000 0.167 

(0.000)

0.333 

(0.000)

0.333 

(0.000)

0.667 

(0.000)

Deciduous 0.000 0.250 0.500 0.500 0.834

Evergreen 0.000 0.167 0.000 0.000 0.000

Mixed 0.000 0.167 0.333 0.333 0.667

Orchard-vineyard 0.000 0.250 0.333 0.333 0.667

Woody wetlands 0.000 0.334 0.667 0.667 1.000

Xeric-ridge Low-density residential 0.000 0.334 0.667 0.667 1.000

Transitional-shrubland 0.000 0.167 

(0.000)

0.333 

(0.000)

0.333 

(0.000)

0.667 

(0.000)

Deciduous 0.000 0.334 0.667 0.500 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.167 0.333 0.333 0.667

Orchard-vineyard 0.000 0.334 0.333 0.333 0.667

Woody wetlands 0.000 0.334 0.667 0.667 1.000
150

Th e wood thrush forages in leaf litter on the forest fl oor and is most common in stands 

with an open understory. We included small stem density (SI4) in the model as a proxy to 

understory cover. Although some researchers suggest that the wood thrush selects habitats 

with higher stem densities than generally are available, the controls in these studies typically 

are in mature forest and the wood thrush may simply be selecting habitats with locally high 

stem densities (Artman and Downhower 2003). We assumed that the average stem density 

(1,988 stems/ha) observed by Hoover and Brittingham (1998) around wood thrush nests 

was representative of optimal habitat. We discounted habitat suitability as small stem density 

increased due to presumed reductions in leaf litter, the preferred foraging substrate (Roth 

and others 1996). Nonetheless, Hoover and Brittingham (1998) observed wood thrush 

Figure 85.—Relationship between forest patch size and 

suitability index (SI) scores for wood thrush habitat. Equation: 

SI score = 1.000 – (1.017 * e -0.710 * (forest patch size ^ 0.797)).

Figure 86.—Relationship between landscape composition and 

suitability index (SI) scores for wood thrush habitat. Equation: SI 

score = 1.005 / (1.000 + (221.816 * e -0.108 * landscape composition)).

 

Forest Patch Size (ha)

Suitability Index Score

0 10 20 30

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 143.—Infl uence of forest patch size on 

suitability index (SI) scores for wood thrush habitat 

Forest patch size (ha) SI score

0a 0.0

1a 0.5

25b 1.0

500a 1.0

a

Robbins and others (1989).

b

Kilgo and others (1998).

Table 144.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for wood thrush habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).
151

utilizing sites with extraordinarily high small stem densities (58,500 stems/ha, no doubt 

localized). Th erefore, we assigned residual SI scores to sites with these characteristics. We 

fi t an inverse logistic function (Fig. 87) to small stem density numbers that refl ected this 

relationship (Table 145).

Th e wood thrush also is associated with closed-canopied forests, so we included canopy cover 

(SI5) as a variable and fi t a logistic function (Fig. 88) to data from Annand and Th ompson 

(1997) and Hoover and Brittingham (1998) to predict SI scores from canopy cover values 

(Table 146).

Figure 87.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 100/ha) and suitability index (SI) scores for 

wood thrush habitat. Equation: SI score = 1 - (0.963 / (1 + 

(243.780 * e -0.116 * (small stem density / 100))).

Figure 88.—Relationship between canopy cover and suitability 

index (SI) scores for wood thrush habitat. Equation: SI score = 

1.032 / (1 + (141241.64 * e -0.153 * canopy cover)).

 

Small Stem Density (stems * 100/ha)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 145.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 100/ha) on suitability index (SI) 

scores for wood thrush habitat 

Small stem densitya SI score

0 1.0

20 1.0

40 0.7

80 0.1

100 0.0

a

Assumed value.

Table 146.—Infl uence of canopy cover (percent) on 

suitability index (SI) scores for wood thrush habitat 

Canopy cover (percent) SI score

25a 0.00

70b 0.25

90b 0.90

100b 1.00

a

Hoover and Brittingham (1998).

b

Annand and Thompson (1997).
152

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure attributes (SI1, SI4, and SI5) and then calculated the geometric mean of this value 

and the maximum of SI scores from forest patch size or percent forest in the landscape 

(Max(SI2 or SI3)). 

Overall HSI = ((SI1 * SI4 * SI5)0.333 * Max(SI2 or SI3))0.500

Verifi cation and Validation

Th e wood thrush was found in all 88 subsections of the CH and WGCP. Spearman rank 

correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.52) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the wood thrush was signifi cant (P

≤ 0.001; R2

 = 0.311), and the coeffi cient on the HSI predictor variable was both positive (β

= 9.992) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered the HSI 

model for the wood thrush both verifi ed and validated (Tirpak and others 2009a).
153

Worm-eating Warbler

Status

Th e worm-eating warbler (Helmitheros vermivorus) 

breeds on forested slopes of the eastern deciduous forest. 

It is notably absent from the Mississippi fl oodplain and 

the relatively fl at forest-prairie ecotone immediately 

east of the Great Plains. Its preference for rugged 

terrain and its high-pitched, insect-like song result in 

underestimations of its density from roadside surveys. 

As a result, there are no credible trends from BBS data 

for this species (Table 5). Nevertheless, this species is 

a Bird of Conservation Concern in both BCRs. However, PIF designates the worm-eating 

warbler as a management attention priority in the CH (regional combined score = 18) and a 

planning and responsibility species in the WGCP (regional combined score = 15; Table 1).

Natural History

Th e worm-eating warbler is a neotropical migrant that breeds in forest interiors of the 

Eastern United States (Hanners and Patton 1998). Minimum area requirements range from 

21 ha in the mid-Atlantic (Robbins and others 1989) to more than 800 ha in Missouri 

(Wenny and others 1993). Th is species nests on the ground along moderate to steep slopes 

(≥ 20 percent) with dense (≥ 48 percent) shrub understories in mature deciduous and mixed 

deciduous-coniferous forests (Gale and others 1997). Both Artman and others (2001) and 

Blake (2005) found that the worm-eating warbler was less abundant in recently burned 

stands due to the loss of leaf litter, a preferred nesting and foraging substrate. Canopy closure 

exceeded 95 percent in both Missouri (Wenny and others 1993) and Connecticut (Gale and 

others 1997).

Model Description

Th e HSI model for the worm-eating warbler includes seven variables: landform, landcover, 

successional age class, slope, forest patch size, percent forest in the landscape, and small stem 

(< 2.5 cm d.b.h.) density.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 147). We 

directly assigned SI scores to these combinations on the basis of habitat associations reported 

by Hamel (1992).

We included slope (SI2) in our model because of the prevalence of steep slopes in the 

territories of the worm-eating warbler. We defi ned slope classes on the basis of data from 

Gale and others (1997) who identifi ed the relative preference of various slopes for this species 

(Table 148).

We also included forest patch size (SI3) as a variable to account for the preference of the 

worm-eating warbler for forest interiors. We fi t a modifi ed exponential function (Fig. 89) 

to data from Robbins and others (1989) to quantify the relationship between patch size 

Charles H. Warren, images.nbii.gov
154

and habitat suitability (Table 149). Th e suitability of a forest patch is infl uenced by its size 

and landscape context (SI4). To capture this relationship, we fi t a logistic function (Fig. 90) 

to data (Table 150) derived from Donovan and others (1997), who observed diff erences 

in predator and brood parasite communities among highly fragmented (< 15 percent), 

moderately fragmented (45 to 50 percent), and lightly fragmented (> 90 percent forest) 

landscapes. We assumed that the midpoints between these classes (30 and 70 percent forest) 

defi ned the specifi c cutoff s for poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) habitat, 

respectively. We assigned the maximum SI score of SI3 or SI4 to each site to account for the 

higher suitability of small forest patches in heavily forested landscapes.

We relied on data from Wenny and others (1993) and Annand and Th ompson (1997) 

(Table 151) to quantify the relationship between SI scores and small stem density (SI5; Fig. 

91). We assumed that the worm-eating warbler occupied forests with low stem densities, 

but these sites had lower suitability scores than sites with well developed understories 

characterized by dense stems.

Table 147.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for worm-eating warbler habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.300 0.700 0.800

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.200 0.400 0.400

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.200 0.500 0.600

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.300 0.800 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.200 0.400 0.400

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.200 0.400 0.400

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.200 0.600 0.800

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.200 0.400 0.400

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.200 0.400 0.400
155

Figure 89.—Relationship between forest patch size and 

suitability index (SI) scores for worm-eating warbler habitat. 

Equation: SI score = 1.035 * e -109.238 / (forest patch size).

Figure 90.—Relationship between landscape composition and 

suitability index (SI) scores for worm-eating warbler habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

 

Forest Patch Size (ha)

Suitability Index Score

0 1000 2000 3000

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 148.—Infl uence of slope on suitability index 

(SI) scores for worm-eating warbler habitat

Slope (percent) a SI score

< 5 0.0

5-20 0.5

21 1.0

a

Gale and others (1997).

Table 149.—Infl uence of forest patch size on 

suitability index (SI) scores for worm-eating 

warbler habitat

Forest patch size (ha) SI score

21a 0.0

120b 0.5

3,200a 1.0

a

Robbins and others (1989).

b

Assumed value.

Table 150.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for worm-eating 

warbler habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).
156

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI5) and landscape composition (Max(SI3 or SI4) and SI2) separately and 

then the geometric mean of these means together.

Overall HSI = ((SI1 * SI5)0.500 * (Max(SI3 or SI4) * SI2)0.500)

0.500

Verifi cation and Validation

Th e worm-eating warbler was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.66) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the worm-eating warbler was 

signifi cant (P ≤ 0.001; R2

 = 0.408), and the coeffi cient on the HSI predictor variable was both 

positive (β = 1.798) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered 

the HSI model for the worm-eating warbler both verifi ed and validated (Tirpak and others 

2009a).

Figure 91.—Relationship between small stem (< 2.5 cm d.b.h.) 

density and suitability index (SI) scores for worm-eating warbler 

habitat. 

Equation: SI score = 1.000 / (1 + e 18.707 – 0.006 * (small stem density))

 ^ 1 / 26.989

Equation takes the general form: y = a/(1 + eb-cx)

1/d.

 

Small Stem Density (stems/ha)

Suitability Index Score

0 1000 2000 3000 4000 5000

0.0

0.2

0.4

0.6

0.8

1.0

Table 151.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems/ha) on suitability index (SI) scores 

for worm-eating warbler habitat

Small stem density SI score

0a 0.500

2,077b 0.773

4,200c 1.000

4,717b 1.000

a

Assumed value.

b

Annand and Thompson (1997).

c

Wenny and others (1993).
157

Yellow-billed Cuckoo

Status

Th e yellow-billed cuckoo (Coccyzus americanus) is a neotropical 

migrant that breeds throughout North America east of the Rocky 

Mountains. Th e yellow-billed cuckoo is abundant in the CH and 

WGCP (10.43 and 12.93 birds/route, respectively), but populations 

in these BCRs have declinded slightly (Table 5). Although the 

yellow-billed cuckoo is not a Bird of Conservation Concern in 

either BCR, it is a management attention priority in both due to 

the importance of these regions (the core of this bird’s range) for the 

sustainability of the continental population (Table 1).

Natural History

A long-distance migrant, the yellow-billed cuckoo breeds in low, 

dense scrub near streams, marshes, and wetlands within otherwise 

open woodlands (Hughes 1999). It is among the most common birds in fl oodplain habitats 

along the Mississippi River and occupies both young cottonwood-willow stands and mature 

silver maple forests (Knutson and others 2005). Th is species exhibits some area sensitivity. 

Conner and others (2004) found that the yellow-billed cuckoo was most abundant in 

riparian strips more than 70 m wide, and Aquilani and Brewer (2004) recorded highest 

abundances in forest tracts larger than 55 ha.

Breeding success is correlated with insect outbreaks, particularly those of hairy caterpillars, 

and population densities vary greatly with food supply. Nests are located in dense, broadleaved, deciduous shrubs or trees within 10 m of the ground. Twedt and others (2001) 

reported no diff erence in nest success between bottomland hardwoods and cottonwood 

plantations, nor did Wilson (1999) report a diff erence in nest success among stands subject 

to alternative thinning rates in Arkansas. On the basis of anticipated harvest scenarios, Klaus 

and others (2005) predicted that populations of the yellow-billed cuckoo would decline by 

approximately 37 percent on the Cherokee National Forest over the next 60 years. 

Model Description

Th e HSI model for the yellow-billed cuckoo includes seven variables: landform, landcover, 

successional age class, edge occurrence, midstory tree (11 to 25 cm d.b.h.) density, percent 

forest in the landscape (10-km radius), and forest patch size.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 152). We 

directly assigned SI scores to these combinations on the basis of habitat associations of the 

yellow-billed cuckoo reported by Hamel (1992). We increased SI scores within fl oodplainvalley and terrace-mesic landforms to account for the higher abundance of the yellow-billed 

cuckoo on these sites in the CH and WGCP.

U.S. Forest Service
158

Th is species is more abundant within edge (SI2) habitats 

than within forest interiors (Kroodsma 1984). We used 

a 9 × 9 pixel moving window (270 x 270 m) to identify 

habitat edges and assumed that these locations represented 

optimal habitat. Nevertheless, nonedge habitats also are 

used by the yellow-billed cuckoo so we assigned to these 

sites only a slightly lower SI score (0.667; Table 153).

Th e yellow-billed cuckoo breeds in forest stands with welldeveloped midstories (SI3). We fi t a quadratic function 

(Fig. 92) to data from Annand and Th ompson (1997) on 

the relative densities of this species in stands with diff erent 

midstory tree densities (Table 154) to predict how SI 

scores responded to changes in this habitat variable.

Table 152.—Relationship of landform, landcover type, and successional age class to suitability index 

scores for yellow-billed cuckoo habitat. Values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.500 0.667 1.000

Transitional-shrubland 0.000 0.000 0.500 0.667 1.000

Deciduous 0.000 0.000 0.500 0.667 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.500 0.667 1.000

Woody wetlands 0.000 0.000 0.333 0.667 0.667

Terrace-mesic Low-density residential 0.000 0.000 0.500 0.667 1.000

Transitional-shrubland 0.000 0.000 0.500 

(0.000)

0.667 

(0.000)

1.000 

(0.000)

Deciduous 0.000 0.000 0.500 0.667 1.000

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.167 0.333 0.333

Orchard-vineyard 0.000 0.000 0.500 0.667 1.000

Woody wetlands 0.000 0.000 0.333 0.667 0.667

Xeric-ridge Low-density residential 0.000 0.000 0.250 0.333 0.500

Transitional-shrubland 0.000 0.000 0.250 

(0.000)

0.333 

(0.000)

0.500 

(0.000)

Deciduous 0.000 0.000 0.250 0.333 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.083 0.167 0.167

Orchard-vineyard 0.000 0.000 0.250 0.333 0.500

Woody wetlands 0.000 0.000 0.167 0.333 0.333

Table 153.—Infl uence of edge on suitability 

index (SI) scores for yellow-billed cuckoo 

habitat

9 × 9 pixel window around 

forest pixel includes fi elda SI score

Yes 1.000

No 0.667

a

Field defi ned as any shrub-seedling or grass-forb 

age class pixel, or natural grasslands, pasturehay, fallow, urban-recreational grasses, emergent 

herbaceous wetlands, open water, high-intensity 

residential, commercial-industrial-transportation, 

bare rock-sand-clay, quarries-strip mines-gravel 

pits, row crops, or small grains. Forest defi ned as 

any used sapling, pole, or sawtimber age class pixel 

of low-density residential, transitional, shrublands, 

deciduous, mixed, evergreen, orchard, or woody 

wetlands.
159

Although a forest-breeding species, the yellow-billed cuckoo is associated with fragmented 

landscapes (Robbins and others 1989, Hughes 1999). We assumed that 70 to 80 percent 

forest in a 10-km landscape (SI4) was characteristic of ideal habitat (Table 155) and fi t a 

function that reduced SI scores symmetrically as forest compositions departed from these 

ideal proportions (Fig. 93). Nevertheless, the cuckoo exhibits area sensitivity and may be 

absent or at low densities in small fragments (Robbins and others 1989, Bancroft and others 

1995, Hughes 1999). Th erefore, we used data from these sources to derive a logistic function 

(Fig. 94) that quantifi ed the relationship between habitat suitability and forest patch size 

(SI5; Table 156).

Figure 92.—Relationship between midstory tree (11–25 cm 

d.b.h.) density and suitability index (SI) scores for yellow-billed 

cuckoo habitat. Equation: SI score = 0.0078 * (midstory tree 

density) – 0.00001 * (midstory tree density)2

 – 0.0355.

Figure 93.—Relationship between landscape composition and 

suitability index (SI) scores for yellow-billed cuckoo habitat. 

Equation: SI score = 1.002 * e((0 – ((landscape forest composition * 100) – 74.165) 

^ 2) / 1064.634).

 

Midstory Tree Density (trees/ha)

Suitability Index Score

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 10-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 154.—Infl uence of midstory tree (11–25 cm 

d.b.h.) density (trees/ha) on suitability index (SI) 

scores for yellow-billed cuckoo habitat 

Midstory tree densitya SI score

70 0.439

320 1.000

361 0.902

506 0.244

a

Annand and Thompson (1997).

Table 155.—Relationship between landscape 

composition (percent forest in 10-km radius) 

and suitability index (SI) scores for yellow-billed 

cuckoo habitat 

Landscape compositiona SI score

0 0.00

10 0.10

20 0.20

30 0.30

40 0.40

50 0.50

60 0.75

70 1.00

80 1.00

90 0.75

100 0.50

a

Assumed value.
160

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI3) and landscape composition (SI2, SI4, and SI5) separately and then the 

geometric mean of these means together. 

Overall HSI = ((SI1 * SI3)0.500 * (SI2 * SI4 * SI5)0.333)

0.500

Verifi cation and Validation

Th e yellow-billed cuckoo was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P = 0.024) positive relationship (rs

 = 0.24) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized 

linear model predicting BBS abundance from BCR and HSI for the yellow-billed cuckoo 

was signifi cant (P ≤ 0.001; R2

 = 0.190), and the coeffi cient on the HSI predictor variable 

was positive (β = 5.265) but not signifi cantly diff erent from zero (P = 0.302). Th erefore, we 

considered the HSI model for the yellow-billed cuckoo verifi ed but not validated (Tirpak and 

others 2009a).

Figure 94.—Relationship between forest patch size and 

suitability index (SI) scores for yellow-billed cuckoo habitat. 

Equation: SI score = 1.000 / (1.000 + (20350.850 * e -0.401 * forest 

patch size)).

 

Forest Patch Size (ha)

Suitability Index Score

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0

Table 156.—Infl uence of forest patch size on 

suitability index (SI) scores for yellow-billed 

cuckoo habitat 

Forest patch size (ha) SI score

0a 0.00

7.5b 0.00

22c 0.25

50d 1.00

a

Assumed value.

b

Bancroft and others (1995).

c

Hughes (1999).

d

Robbins and others (1989).
161

Yellow-breasted Chat

Status

Th e yellow-breasted chat (Icteria virens) is 

a neotropical migrant that breeds in early 

successional habitats across the eastern United 

States. Th e distribution of this species in the 

West is patchy. Populations have responded to 

the loss of early successional habitat and have 

declined sharply across the northern edge of 

this bird’s distribution (Sauer and others 2005). 

Within the CH, where this species has a regional combined score of 16 and is a management 

attention priority, populations have declined by approximately 2 percent per year during the 

last 40 years (Table 5). Conversely, at the southern limit of their range, populations have 

increased (1.3 percent annual increases in the WGCP from 1966 to 2005; Table 5).

Natural History

Th e yellow-breasted chat breeds in low, dense, deciduous and evergreen vegetation within 

forests lacking a closed canopy (Eckerle and Th ompson 2001). Habitat associations include 

forest edges and openings, regenerating forest, powerline rights-of-way, fencerows, upland 

thickets, abandoned farms, and shrubby areas along streams, swamps, and ponds. Chats 

are most abundant in 6- to 9-year-old cottonwood plantations in the Mississippi Alluvial 

Valley (Twedt and others 1999). However, Annand and Th ompson (1997) observed similar 

abundance across stands subject to alternative forest management prescriptions. In east 

Texas, density is positively correlated with foliage density at 0 to 3 m, the percentage of 

saplings that are pine, and the number of shrub species. Densities are negatively aff ected by 

increasing vegetation height, percent canopy cover, foliage density at 12 to 15 m, and density 

of pole trees (Conner and others 1983).

In Missouri, the yellow-breasted chat nests more than 20 m from the edge of large early 

successional patches characterized by high densities of small stems (Burhans and Th ompson 

1999). Nest success increases with patch size; territories range from 0.5 to 1.6 ha.

Model Description

Th e HSI model for the yellow-breasted chat includes six variables: landform, landcover, 

successional age class, edge, early successional patch size, and small stem (< 2.5 cm d.b.h.) 

density.

Th e fi rst suitability function combines landform, landcover, and successional age class 

into a single matrix (SI1) that defi nes unique combinations of these classes (Table 157). 

We directly assigned SI scores to these combinations based on data from Hamel (1992). 

However, we assumed that shrub-seedling habitats were optimal and that pole stands were 

nonhabitat. We ignored landform eff ects in assessing habitat suitability for this species.

Chats prefer to nest more than 20 m from the edge of mature forest (SI2) (Woodward and 

others 2001). Th us, we used a 3 × 3 pixel window (90 x 90 m) to identify suitable early 

Chandler S. Robbins, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
162

successional forest sites immediately adjacent to pole 

or sawtimber successional age class forest. We reduced 

the suitability of these sites by half (SI score = 0.500; 

Table 158).

Th e yellow-breasted chat is associated with large 

patches of early successional forest (SI3). We 

aggregated all grass-forb, shrub-seedling, and sapling 

successional age class sites to calculate patch sizes for 

this species. We fi t a logarithmic function (Fig. 95) to data from Rodewald and Vitz (2005) on 

the relative abundance of the yellow-breasted chat in early successional patches of various sizes 

to quantify the relationship between patch size and habitat suitability (Table 159).

Th is species occupies sites with high small stem densities (SI4). Th erefore, we fi t a logistic 

function (Fig. 96) to data from Annand and Th ompson (1997) relating the relative density of 

the yellow-breasted chat to small stem densities (Table 160) to predict the eff ect of this habitat 

characteristic on habitat suitability.

Table 157.—Relationship of landform, landcover type, and successional age class to suitability index scores 

for yellow-breasted chat habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.000 1.000 0.500 0.000 0.000

Deciduous 0.000 1.000 0.500 0.000 0.000

Evergreen 0.333 0.667 0.500 0.000 0.000

Mixed 0.333 0.667 0.334 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.667 0.334 0.000 0.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.333 1.000 0.500 0.000 0.000

Deciduous 0.167 1.000 0.500 0.000 0.000

Evergreen 0.333 0.667 0.500 0.000 0.000

Mixed 0.333 0.667 0.334 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.667 0.334 0.000 0.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.000 0.000

Transitional-shrubland 0.333 1.000 0.500 0.000 0.000

Deciduous 0.333 1.000 0.500 0.000 0.000

Evergreen 0.333 0.667 0.500 0.000 0.000

Mixed 0.333 0.667 0.334 0.000 0.000

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.667 0.334 0.000 0.000

Table 158.—Infl uence of edge on suitability index 

(SI) scores for yellow-breasted chat habitat

3 × 3 pixel window around 

early successional pixel 

includes mature foresta SI score

Yes 0.5

No 1.0

a

Early successional = grass-forb, shrub-seedling, and 

sapling successional age classes; mature forest = pole or 

sawtimber successional age classes.
163

To calculate the overall HSI score for the yellow-breasted chat, we determined the geometric 

mean of the SI scores for forest structure attributes (SI1 and SI4) and the SI score for 

landscape composition (SI2 and SI3) separately and then the geometric mean of these values 

together.

Overall HSI = ((SI1 * SI4)0.500 * (SI2 * SI3)0.500)

0.500

Figure 95.—Relationship between early successional patch size 

and suitability index (SI) scores for yellow-breasted chat habitat. 

Equation: SI score = -0.212 + 0.453 * ln(forest patch size).

Figure 96.—Relationship between small stem (< 2.5 cm d.b.h.) 

density (stems * 1000/ha) and suitability index (SI) scores for 

yellow-breasted chat habitat. Equation: SI score = (1.000 / (1 + 

(1148216.200 * e -3.689 * (small stem density / 1000)))).

 

Early Successional Patch Size (ha)

Suitability Index Score

0 5 10 15

0.0

0.2

0.4

0.6

0.8

1.0

 

Small Stem Density (stems * 1,000/ha)

Suitability Index Score

0.0 2.5 5.0 7.5 10.0

0.0

0.2

0.4

0.6

0.8

1.0

Table 159.—Infl uence of early successional patch 

size on suitability index (SI) scores for yellowbreasted chat habitat; early successional patches 

only include grass-forb, shrub-seedling, and 

sapling successional age classes

Early successional patch size (ha)a SI score

6 0.6

14.5 1.0

a

Rodewald and Vitz (2005).

Table 160.—Infl uence of small stem (< 2.5 cm d.b.h.) 

density (stems * 1,000/ha) on suitability index (SI) 

scores for yellow-breasted chat habitat

Small stem densitya SI score

0.0 0.000

3.8 0.516

8.1 1.000

a

Annand and Thompson (1997).
164

Verifi cation and Validation

Th e yellow-breasted chat was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation identifi ed a signifi cant (P ≤ 0.001) positive relationship (rs

 = 0.40) between 

average HSI score and mean BBS route abundance across subsections. Th e generalized linear 

model predicting BBS abundance from BCR and HSI for the yellow-breasted chat was 

signifi cant (P ≤ 0.001; R2

 = 0.379), and the coeffi cient on the HSI predictor variable was both 

positive (β = 93.367) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we considered 

the HSI model for the yellow-breasted chat both verifi ed and validated (Tirpak and others 

2009a).
165

Yellow-throated Vireo

Status

Th e yellow-throated vireo (Vireo fl avifrons) is a neotropical 

migrant found throughout North America east of the Great 

Plains. Populations in both the CH and WGCP are stable 

(Sauer and others 2005) (Table 5). Th is species is not a 

Bird of Conservation Concern in either region (Table 1) 

but is a planning and responsibility species in both the 

CH (regional combined score = 16) and WGCP (regional 

combined score = 15). Approximately 20 percent of the 

continental population breeds in these two BCRs (Panjabi 

and others 2001).

Natural History

Th e yellow-throated vireo breeds along the edges of mature forest stands; its abundance 

may even decline within forest interiors (Rodewald and James 1996). Appropriate edges 

include streams, rivers, swamps, and roads. Parks, orchards, and suburban habitats also 

may be used (Rodewald and James 1996). Th is species uses both bottomland and upland 

sites but is restricted to deciduous and mixed-forest habitats. As a forest edge species, it is 

not area sensitive and may benefi t from canopy gaps. However, Robbins and others (1989) 

observed a positive relationship between the abundance of the yellow-throated vireo and 

forest cover within a 2-km buff er. Similarly, this bird did not use riparian forests strips that 

were less than 70 m wide in east Texas (Conner and others 2004). Th us, the yellow-throated 

vireo prefers canopy gaps within forested landscapes. Th e key component of its habitat is 

canopy structure, and this species selects taller trees (> 20 m) than other vireos (James 1976). 

Robbins and others (1989) also noted a positive relationship between abundance and canopy 

height. Specifi c tree species do not aff ect selection (Gabbe and others 2002).

Model Description

Our HSI model for the yellow-throated vireo includes six variables: landform, landcover, 

successional age class, forest patch size, percent forest in the landscape (1-km radius), and 

canopy cover.

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 161). We 

directly assigned SI scores to these combinations on the basis of relative rankings of habitat 

associations for the yellow-throated vireo described in Hamel (1992).

Although a forest edge species, the yellow-throated vireo is aff ected by forest area (SI2) and 

the percentage of forest in the landscape (SI3). We fi t a logarithmic function (Fig. 97) to 

data from Blake and Karr (1987) and Kilgo and others (1998) to describe the relationship 

between forest patch size and habitat suitability (Table 162). Similarly, we used a logistic 

function to predict habitat suitability from percent forest cover in a 1-km radius landscape 

(Fig. 98) based on data (Table 163) derived from Donovan and others (1997), who observed 

diff erences in predator and brood parasite communities among highly fragmented (< 15 

Chandler S. Robbins, 

Patuxent Bird Identifi cation InfoCenter

Photo used with permission
166

percent), moderately fragmented (45 to 50 percent), and lightly fragmented (> 90 percent 

forest) landscapes. We assumed that the midpoints between these classes (30 and 70 percent 

forest) defi ned the specifi c cutoff s for poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) 

habitat, respectively.

Th e affi nity of the yellow-throated vireo for canopy gaps led us to incorporate canopy cover 

in the HSI model for this species (SI4). We fi t a smoothed quadratic function (Fig. 99) 

to data from Kahl and others (1985) (Table 164) on the relative density of this species at 

varying canopy closures, and assumed that Kahl’s optimal designation of canopy cover (80 

to 90 percent) was associated with maximum SI scores. Further, we assumed that habitat 

suitability declined symmetrically as canopy cover departed from this optimum.

Table 161.—Relationship of landform, landcover type, and successional age class to SI scores for yellowthroated vireo habitat

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.333 0.667

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.333 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.333 0.667

Woody wetlands 0.000 0.000 0.000 0.417 0.834

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.250 0.500

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.250 0.500

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.250 0.500

Woody wetlands 0.000 0.000 0.000 0.500 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.334 0.667

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.334 0.667

Evergreen 0.000 0.000 0.000 0.000 0.000

Mixed 0.000 0.000 0.000 0.167 0.333

Orchard-vineyard 0.000 0.000 0.000 0.334 0.667

Woody wetlands 0.000 0.000 0.000 0.500 1.000
167

Figure 97.—Relationship between forest patch size and 

suitability index (SI) scores for yellow-throated vireo habitat. 

Equation: SI score = 0.180 * ln(forest patch size) – 0.323.

Figure 98.—Relationship between landscape composition and 

suitability index (SI) scores for yellow-throated vireo habitat. 

Equation: SI score = 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

 

Forest Patch Size (ha)

Suitability Index Score

0 250 500 750 1000

0.0

0.2

0.4

0.6

0.8

1.0

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 162.—Infl uence of forest patch size on 

suitability index (SI) scores for yellow-throated 

vireo habitat

Forest patch size (ha) SI score

6.5a 0.000

25b 0.365

50b 0.381

100b 0.429

200b 0.524

500b 0.794

1000b 1.000

a

Blake and Karr (1987).

b

Kilgo and others (1998).

Table 163.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for yellow-throated 

vireo habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI4) and landscape composition attributes (SI2 and SI3) separately and 

then the geometric mean of these means together.

Overall HSI = ((SI1 * SI4)0.500 * (SI2 * SI3)0.500)

0.500
168

Verifi cation and Validation

Th e yellow-throated vireo was found in all 88 subsections of the CH and WGCP. Spearman 

rank correlation on average HSI score and mean BBS route abundance per subsection identifi ed 

a signifi cant (P ≤ 0.001) positive association (rs

 = 0.51) between these two variables. Th e 

generalized linear model predicting BBS abundance from BCR and HSI for the yellow-throated 

vireo was signifi cant (P = 0.002; R2

 = 0.133), and the coeffi cient on the HSI predictor variable 

was both positive (β = 2.811) and signifi cantly diff erent from zero (P ≤ 0.001). Th erefore, we 

considered the HSI model for the yellow-throated vireo both verifi ed and validated (Tirpak and 

others 2009a).

Figure 99.—Relationship between canopy cover and suitability 

index (SI) scores for yellow-throated vireo habitat. Equation: 

SI score = 1.011 * e (0-((canopy cover – 82.319)^2 / 508.869)).

 

Canopy Cover (%)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 164.—Infl uence of canopy cover (percent) 

on suitability index (SI) scores for yellow-throated 

vireo habitat

Canopy cover (percent) SI score

0a 0.00

70b 0.75

80b 1.00

90a 0.90

a

Assumed value.

b

Kahl and others (1985).
169

Yellow-throated Warbler

Status

Th e yellow-throated warbler (Dendroica 

dominica) is a neotropical migrant that breeds 

in the southeastern United States and reaches 

its highest densities in the Ohio River Valley. 

Th is species has remained relatively stable 

in the WGCP over the past 40 years but has 

increased considerably in the CH (3.8 percent per year since 1967; Table 5). Th e yellowthroated warbler is not a Bird of Conservation Concern in either BCR but is a planning and 

responsibility species in the CH (regional combined score = 15; Table 1).

Natural History

Th e yellow-throated warbler breeds in two distinct habitat types: mature bottomland 

hardwood forest and dry, upland oak-pine forest (Hall 1996). It is more common in the 

former. Th is species shows a strong affi nity for cypress along the Coastal Plains, but prefers 

sycamore along inland rivers (Hall 1996, Gabbe and others 2002). Where Spanish moss is 

found, it is used for both foraging and nesting (Hall 1996). Elsewhere, the warbler forages 

by creeping along limbs and probing leaf clusters and pinecones. Th is bird is both an interior 

and edge species and may occupy woodlots as small as 6 ha (Blake and Karr 1987). Robbins 

and others (1989) associated this species with large tree (> 38 cm d.b.h.) density, forest in a 

2-km buff er, and coniferous canopy cover.

Model Description

Our HSI model for the yellow-throated warbler includes six variables: landform, landcover, 

successional age class, large tree (> 50 cm d.b.h.) density, distance to water, and percent 

forest in the landscape (1-km radius).

Th e fi rst suitability function combines landform, landcover, and successional age class into 

a single matrix (SI1) that defi nes unique combinations of these classes (Table 165). We 

directly assigned SI scores to these combinations on the basis of habitat associations outlined 

by Hamel (1992) for the yellow-throated warbler in the Southeast.

We also incorporated large tree density (SI2) into the HSI model for the yellow-throated 

warbler because of its affi nity for nesting and foraging in large trees (Hamel 1992, Robbins 

and others 1989). Lacking data points from the literature to fi t a curve, we assumed that 

SI scores were logistically related to large tree density up to 50 trees per ha and remained 

optimal above this threshold (Fig. 100, Table 166).

Th e yellow-throated warbler typically nests near water (Hall 1996, Hamel 1992). Th us, we 

included distance to water (SI3) in the HSI model. We assumed that sites closer to water 

had a higher suitability. Lacking quantitative data on the potential eff ect of water on habitat 

suitability, we assumed that the size of the yellow-throated warbler’s territory is similar 

to that of the Acadian fl ycatcher but that the warbler is not as dependent on water as the 

Deanna K. Dawson, Patuxent Bird Identifi cation InfoCenter

Photo used with permission
170

fl ycatcher. Th erefore, we assumed that all sites less than 100 m from water were optimal 

but reduced SI more slowly for the yellow-throated warbler than the Acadian fl ycatcher as 

distance to water increased (Fig. 101; Table 167).

Th e yellow-throated warbler responds to the percentage of forest in the landscape (SI4). To 

capture this relationship, we fi t a logistic function (Fig. 102) to data (Table 168) derived 

from Donovan and others (1997), who observed diff erences in predator and brood parasite 

communities among highly fragmented (< 15 percent), moderately fragmented (45 to 50 

percent), and lightly fragmented (> 90 percent forest) landscapes. We assumed that the 

midpoints between these classes (30 and 70 percent forest) defi ned the specifi c cutoff s for 

poor (SI score ≤ 0.10) and excellent (SI score ≥ 0.90) habitat, respectively.

Table 165.—Relationship of landform, landcover type, and successional age class to suitability index (SI) 

scores for yellow-throated warbler habitat; values in parentheses apply to West Gulf Coastal Plain/Ouachitas

Successional age class

Landform Landcover type Grass-forb

Shrubseedling Sapling Pole Saw

Floodplain-valley Low-density residential 0.000 0.000 0.000 0.250 0.500

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.250 0.500

Evergreen 0.000 0.000 0.000 0.333 0.667

Mixed 0.000 0.000 0.000 0.333 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.834 1.000

Terrace-mesic Low-density residential 0.000 0.000 0.000 0.167 0.167

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.167 0.167

Evergreen 0.000 0.000 0.000 0.333 0.667

Mixed 0.000 0.000 0.000 0.333 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.500 1.000

Xeric-ridge Low-density residential 0.000 0.000 0.000 0.167 0.333

Transitional-shrubland 0.000 0.000 0.000 0.000 0.000

Deciduous 0.000 0.000 0.000 0.167 0.333

Evergreen 0.000 0.000 0.000 0.333 

(0.167)

0.667 

(0.334)

Mixed 0.000 0.000 0.000 0.333 0.667

Orchard-vineyard 0.000 0.000 0.000 0.000 0.000

Woody wetlands 0.000 0.000 0.000 0.500 1.000
171

To calculate the overall HSI score, we determined the geometric mean of SI scores for forest 

structure (SI1 and SI2) and landscape composition attributes (SI3 and SI4) separately and then 

the geometric mean of these means together.

Overall HSI = ((SI1 * SI2)0.500 * (SI3 * SI4)0.500)

0.500

Figure 100.—Relationship between large tree (> 50 cm d.b.h.) 

density and suitability index (SI) scores for yellow-throated 

warbler habitat. Equation: SI score = 1.000 / (1.0000 + (38.185 

* e -0.123 * large tree density)).

Figure 101.—Relationship between distance to water and 

suitability index (SI) scores for yellow-throated warbler habitat. 

Equation: SI score = 1 - (1.050 / (1 + (1661.322 * e -0.021 * distance 

to water))).

 

Large Tree Density (trees/ha)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

 

Distance to Water (m)

Suitability Index Score

0 100 200 300 400 500

0.0

0.2

0.4

0.6

0.8

1.0

Table 166.—Infl uence of large tree (> 50 cm d.b.h.) 

density (trees/ha) on suitability index (SI) scores 

for yellow-throated warbler habitat

Large tree densitya SI score

0 0.00

20 0.25

40 0.75

50 1.00

75 1.00

a

Assumed value.

Table 167.—Relationship between distance to water 

and suitability index (SI) scores for yellow-throated 

warbler habitat 

Distance to water (m) a SI score

100b 1.00

300b 0.75

400b 0.25

500b 0.00

a

Water defi ned as NHD streams or NLCD water, woody 

wetlands, and emergent herbaceous wetlands classes.

b

Assumed value.
172

Verifi cation and Validation

Th e yellow-throated warbler was found in 87 of the 88 subsections within the CH and WGCP. 

Spearman rank correlation on average HSI score and mean BBS route abundance identifi ed 

a signifi cant (P ≤ 0.001) positive association (rs

 = 0.48) between these two variables within 

subsections where this species was detected. Th e generalized linear model predicting BBS 

abundance from BCR and HSI for the yellow-throated warbler was signifi cant (P = 0.003; R2

= 0.125), and the coeffi cient on the HSI predictor variable was both positive (β = 2.870) and 

signifi cantly diff erent from zero (P = 0.020). Th erefore, we considered the HSI model for the 

yellow-throated warbler both verifi ed and validated (Tirpak and others 2009a).

Figure 102.—Relationship between landscape composition and 

suitability index (SI) scores for yellow-throated warbler habitat. 

Equation: SI score= 1.005 / (1.000 + (221.816 * e -0.108 * (landscape 

composition))).

 

Landscape Composition (% forest in 1-km radius)

Suitability Index Score

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Table 168.—Relationship between landscape 

composition (percent forest in 1-km radius) and 

suitability index (SI) scores for yellow-throated 

warbler habitat

Landscape composition SI score

0a 0.00

10a 0.00

20a 0.05

30b 0.10

40a 0.25

50b 0.50

60a 0.75

70b 0.90

80a 0.95

90b 1.00

100a 1.00

a

Assumed value.

b

Donovan and others (1997).
173

CURRENT MODEL USE AND FUTURE DIRECTIONS

For species with verifi ed and validated models, we developed geospatial datasets that 

summarize the habitat suitability and estimated population size of these species within 

each subsection for two periods (1992 and 2001). Th ese datasets are being used to assess 

changes in habitats through time and identify which model variables are associated with 

these changes. We also are using these datasets as conservation design tools to identify the 

specifi c location and type of management practice that may most eff ectively increase the 

habitat quality and population size of target species. Population estimates explicitly tied to 

habitat suitability are allowing the refi nement of landbird population objectives and spatial 

depiction of these objectives at the ecological subsection scale. We are developing a decisionsupport tool based on these model outputs that will estimate the magnitude of management 

that may be required to achieve population objectives for a particular species and will assess 

the simultaneous impacts of diff erent management options on populations of multiple 

species.

With conservation informed by these models in both the CH and WGCP, these models 

are informing the status at the continental scale of species with a signifi cant portion of their 

populations in these BCRs (e.g., Kentucky warbler; Panjabi and others 2005). Adoption 

and application of these models in other BCRs (the East Gulf Coastal Plain Joint Venture 

references the use of these models in its Implementation Plan [East Gulf Coastal Plain Joint 

Venture 2008]) may provide a framework for assessing the status of additional species at the 

continental scale. However, the use of these models outside the CH and WGCP will require 

careful scrutiny and additional testing to ensure that the habitat associations remain valid as 

diff erences in forest types among regions (particularly outside the Southeast) likely will aff ect 

the SI scores in the landform, forest type, and successional age class matrix derived from 

Hamel (1992).

ACKNOWLEDGMENTS

We thank the following reviewers who provided invaluable feedback for improving our HSI 

models: Eric Baka, Laurel Moore Barnhill, Charles Baxter, Michelle Beck, Jim Bednarz, 

Jeff Buler, Wes Burger, Dirk Burhans, Bob Cooper, Dean Demarest, Randy Dettmers, Jim 

Dickson, Th erese Donovan, Rob Doster, John Dunning, Jane Fitzgerald, Jim Giocomo, Bill 

Giuliano, Gypsy Gooding, Fred Guthery, Carola Haas, Tom Haggerty, Paul Hamel, Kirsten 

Hazler, Larry Hedrick, Mark Howery, Pamela Hunt, Chuck Hunter, James Ingold, Brad 

Jacobs, Christopher Kellner, Eric Kershner, John Kilgo, Melinda Knutson, Jeff Kopachena, 

David Krementz, Scott Lanyon, Chester Martin, Dan McAuley, Joe Meyers, Warren 

Montague, Christopher Moorman, Rua Mordecai, Allan Mueller, Wayne Norling, Brainard 

Palmer-Ball, David Pashley, Bruce Reid, Rochelle Renken, Amanda Rodewald, Scott Rush, 

Kristin Schaumburg, Brian Smith, Phil Stauff er, Jeff rey Stratford, Wayne Th ogmartin, Bill 

Vermillion, Shawchyi Vorisek, R. Montague Whiting, Jr., Mike Wilson, Randy Wilson, and 

Doug Zollner.
174

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(voice) or (202)720-6382 (TDD). USDA is an equal opportunity provider and employer. 

Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R., III; Twedt, Daniel J.; 

Uihlein, William B., III. 2009. Multiscale habitat suitability index models for 

priority landbirds in the Central Hardwoods and West Gulf Coastal Plain/

Ouachitas Bird Conservation Regions. Gen. Tech. Rep. NRS-49. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service Northern Research Station. 195 p.

Habitat Suitability Index (HSI) models were developed to assess habitat quality 

for 40 priority bird species in the Central Hardwoods and West Gulf Coastal Plain/

Ouachitas Bird Conservation Regions. The models incorporated both site and 

landscape environmental variables from one of six nationally consistent datasets. 

Potential habitat was fi rst defi ned from unique landform, landcover, and successional 

age class combinations. Species-specifi c environmental variables identifi ed from the 

literature were used to refi ne initial habitat estimates. Models were verifi ed by comparing 

subsection-level HSI scores and Breeding Bird Survey (BBS) abundance via Spearman 

rank correlation. Generalized linear models that predicted BBS abundance as a function 

of HSI were used to validate models.

KEY WORDS: Conservation planning, ecoregion, forest, Forest Inventory and Analysis, 

National Landcover Dataset, validation. 
Capitalizing on the strengths of existing science 

capacity in the Northeast and Midwest to attain a more 

integrated, cohesive, landscape-scale research program

www.nrs.fs.fed.us