Assessing the Potential Utility of Select State-owned Lands for Vertebrate 
Conservation 
 
by 
 
DeForrest Robertson Allgood Jr. 
 
 
 
 
A thesis submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Master of Science 
 
Auburn, Alabama 
May 9, 2011 
 
 
 
 
Keywords: conservation, kernel density estimation, decision support tool, 
Alabama. 
 
 
 
Copyright 2010 by DeForrest R. Allgood Jr. 
 
 
Approved by 
 
James B. Grand, Chair, Professor of Forestry and Wildlife Sciences 
Robert S. Boyd, Professor of Biological Sciences 
Edward F. Loewenstein, Associate Professor of Forestry and Wildlife Sciences 
 
Mark D. MacKenzie, Assistant Professor of Forestry and Wildlife Sciences (Posthumous) 
 
 
 
 
 
ii 
 
Abstract 
 
 
 With limited resources available for conservation, it is imperative that efficient 
state conservation plans be developed to protect species of greatest conservation need 
(GCN species).  Through the Inventory and Conservation Planning (ICP) project, the 
Alabama Department of Conservation and Natural Resources and the Alabama 
Cooperative Fish and Wildlife Research Unit have collected data on GCN species and 
habitat on select state-owned lands.  Using this information, we proposed development of 
decision support tools that will provide a spatial depiction of the most useful areas for 
conservation of each vertebrate GCN species and a relative conservation utility of each 
study area for the conservation of GCN species.  We first determined which species 
occurred on ICP lands and where it would be ecologically appropriate to manage for 
them.  Alabama GAP Analysis Project (AL-GAP) data were used to identify potential 
habitat in the Southeastern Plains ecoregion of Alabama.  We then identified those areas 
where appropriate management actions for these species are feasible.  Finally, we used 
the availability and arrangement of resources on the landscape to determine the relative 
utility of each property for each species.  Each of these objectives was incorporated into a 
geographic information system which was analyzed using kernel density estimation to 
generate estimates of potential conservation utility.  Through this research, we intend to 
provide land managers and conservation decision makers with additional information to 
augment field data and other resources used to make conservation decisions. 
 
 
iii 
 
Acknowledgments 
 
 
I am grateful to my wife, Lauren Allgood, for supporting me in every way 
imaginable during my research and coursework here at Auburn. 
 I would like to thank Dr. Mark MacKenzie for giving me the opportunity to 
pursue my graduate career at Auburn University.  I would also like to thank Dr. James B. 
Grand for taking me on as his student after Dr. MacKenzie?s passing, a courtesy and 
opportunity for which I am very grateful.  I am also grateful to my committee members 
Dr. Robert S. Boyd and Dr. Edward F. Loewenstein for their contributions to both my 
research and graduate education. 
 Allison Moody, Max Post van der Burg and Amy Silvano contributed helpful 
comments on drafts of this thesis.  Will McDearman, Eric Soehren, Val Johnson and 
Craig Guyer are all greatly appreciated for their input on soil classifications.  We thank 
Kevin Kleiner and Tyler Krepps for their assistance during the GIS components of this 
project.  This research was funded through the Alabama State Wildlife Grants Program as 
part of the Inventory and Conservation Planning project coordinated by the Alabama 
Cooperative Fish and Wildlife Research Unit.  The ACFWRU is a partnership between 
the U.S. Geologic Survey, the Wildlife Management Institute, the Alabama Department 
of Conservation and Natural Resources, Auburn University and the U.S. Fish and 
Wildlife Service. 
 
 
 
iv 
 
Table of Contents 
 
 
Abstract ............................................................................................................................... ii 
Acknowledgments.............................................................................................................. iii  
List of Figures ......................................................................................................................v  
List of Tables ..................................................................................................................... ix  
Chapter 1: Estimating Conservation Utility for Species of Greatest Conservation Need in  
            the Southeastern Plains of Alabama  .......................................................................1 
Chapter 2: Relative Conservation Utility of State-owned Lands for Species of Greatest      
            Conservation Need .................................................................................................60 
References ........................................................................................................................123 
Appendix A: Species Models...........................................................................................137 
Appendix B: Minimum Patch Size ..................................................................................140 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
v 
 
List of Figures 
 
 
Figure 1.1: Level IV Ecoregions of the Southeastern Plains of Alabama .........................19 
Figure 1.2: Current and Historic Distribution of American Black Bear in Alabama ........20 
Figure 1.3: Maps of Input Parameters................................................................................21 
Figure 1.4: Map of Ecoregion Utility for Alligator Snapping Turtle ................................26 
Figure 1.5: Map of Ecoregion Utility for American Black Bear .......................................27 
Figure 1.6: Map of Ecoregion Utility for American Kestrel .............................................28 
Figure 1.7: Map of Ecoregion Utility for American Woodcock ........................................29 
Figure 1.8: Map of Ecoregion Utility for Bachman?s Sparrow .........................................30 
Figure 1.9: Map of Ecoregion Utility for Black Pine Snake..............................................31 
Figure 1.10: Map of Ecoregion Utility for Coal Skink ......................................................32 
Figure 1.11: Map of Ecoregion Utility for Eastern Coral Snake .......................................33 
Figure 1.12: Map of Ecoregion Utility for Eastern Diamondback Rattlesnake .................34 
Figure 1.13: Map of Ecoregion Utility for Eastern Indigo Snake......................................35 
Figure 1.14: Map of Ecoregion Utility for Eastern Kingsnake ..........................................36 
Figure 1.15: Map of Ecoregion Utility for Eastern Spotted Skunk ...................................37 
Figure 1.16: Map of Ecoregion Utility for Flatwoods Salamander ...................................38 
Figure 1.17: Map of Ecoregion Utility for Florida Pine Snake .........................................39 
Figure 1.18: Map of Ecoregion Utility for Gopher Frog ...................................................40 
Figure 1.19: Map of Ecoregion Utility for Gopher Tortoise .............................................41
 
 
vi 
 
Figure 1.20: Map of Ecoregion Utility for Henslow?s Sparrow ........................................42 
Figure 1.21: Map of Ecoregion Utility for Kentucky Warbler ..........................................43 
Figure 1.22: Map of Ecoregion Utility for Least Bittern ...................................................44 
Figure 1.23: Map of Ecoregion Utility for Littlie Grass Frog ...........................................45 
Figure 1.24: Map of Ecoregion Utility for Long-tailed Weasel ........................................46 
Figure 1.25: Map of Ecoregion Utility for Mimic Glass Lizard ........................................47 
Figure 1.26: Map of Ecoregion Utility for Pine Barrens Treefrog ....................................48 
Figure 1.27: Map of Ecoregion Utility for Rainbow Snake...............................................49 
Figure 1.28: Map of Ecoregion Utility for Red-cockaded Woodpecker ...........................50 
Figure 1.29: Map of Ecoregion Utility for River Frog ......................................................51 
Figure 1.30: Map of Ecoregion Utility for Southeastern Pocket Gopher ..........................52 
Figure 1.31: Map of Ecoregion Utility for Southern Dusky Salamander ..........................53 
Figure 1.32: Map of Ecoregion Utility for Southern Hognose Snake ...............................54 
Figure 1.33: Map of Ecoregion Utility for Speckled Kingsnake .......................................55 
Figure 1.34: Map of Ecoregion Utility for Swainson?s Warbler .......................................56 
Figure 1.35: Map of Ecoregion Utility for Wood Thrush ..................................................57 
Figure 1.36: Map of Ecoregion Utility for Worm-eating Warbler ....................................58 
Figure 2.1: ICP Study Areas in the Southeastern Plains of Alabama ................................77 
Figure 2.2: Maps of Study Area and Neighborhood Utility for Alligator Snapping  
            Turtle ......................................................................................................................78 
Figure 2.3: Maps of Study Area and Neighborhood Utility for American Black Bear .....79 
Figure 2.4: Maps of Study Area and Neighborhood Utility for American Kestrel ...........80 
Figure 2.5: Maps of Study Area and Neighborhood Utility for American Woodcock ......81 
Figure 2.6: Maps of Study Area and Neighborhood Utility for Bachman?s Sparrow .......82 
 
 
vii 
 
Figure 2.7: Maps of Study Area and Neighborhood Utility for Black Pine Snake ...........83 
Figure 2.8: Maps of Study Area and Neighborhood Utility for Coal Skink ......................84 
Figure 2.9: Maps of Study Area and Neighborhood Utility for Eastern Coral Snake .......85 
Figure 2.10: Maps of Study Area and Neighborhood Utility for Eastern Diamondback       
            Rattlesnake .............................................................................................................86 
Figure 2.11: Maps of Study Area and Neighborhood Utility for Eastern Indigo Snake ...87 
Figure 2.12: Maps of Study Area and Neighborhood Utility for Eastern Kingsnake........88 
Figure 2.13: Maps of Study Area and Neighborhood Utility for Eastern Spotted Skunk .89 
Figure 2.14: Maps of Study Area and Neighborhood Utility for Flatwoods Salamander .90 
Figure 2.15: Maps of Study Area and Neighborhood Utility for Florida Pine Snake .......91 
Figure 2.16: Maps of Study Area and Neighborhood Utility for Gopher Frog .................92 
Figure 2.17: Maps of Study Area and Neighborhood Utility for Gopher Tortoise ...........93 
Figure 2.18: Maps of Study Area and Neighborhood Utility for Henslow?s Sparrow ......94 
Figure 2.19: Maps of Study Area and Neighborhood Utility for Kentucky Warbler ........95 
Figure 2.20: Maps of Study Area and Neighborhood Utility for Least Bittern .................96 
Figure 2.21: Maps of Study Area and Neighborhood Utility for Little Grass Frog ..........97 
Figure 2.22: Maps of Study Area and Neighborhood Utility for Long-tailed Weasel ......98 
Figure 2.23: Maps of Study Area and Neighborhood Utility for Mimic Glass Lizard ......99 
Figure 2.24: Maps of Study Area and Neighborhood Utility for Pine Barrens Treefrog 100 
Figure 2.25: Maps of Study Area and Neighborhood Utility for Rainbow Snake ..........101 
Figure 2.26: Maps of Study Area and Neighborhood Utility for Red-cockaded  
            Woodpecker .........................................................................................................102 
Figure 2.27: Maps of Study Area and Neighborhood Utility for River Frog ..................103 
Figure 2.28: Maps of Study Area and Neighborhood Utility for Southeastern Pocket  
            Gopher..................................................................................................................104 
 
 
viii 
 
Figure 2.29: Maps of Study Area and Neighborhood Utility for Southern Dusky  
            Salamander ...........................................................................................................105 
Figure 2.30: Maps of Study Area and Neighborhood Utility for Southern Hognose  
            Snake ....................................................................................................................106 
Figure 2.31: Maps of Study Area and Neighborhood Utility for Speckled Kingsnake ...107 
Figure 2.32: Maps of Study Area and Neighborhood Utility for Swainson?s Warbler ...108 
Figure 2.33: Maps of Study Area and Neighborhood Utility for Wood Thrush..............109 
Figure 2.34: Maps of Study Area and Neighborhood Utility for Worm-eating Warbler 110
 
 
ix 
 
List of Tables 
 
 
Table 1.1: Soil Classification  ............................................................................................59 
Table 2.1: Average Utility for Each Species in Each Study Area and Neighborhood  ...111 
Table 2.2: Relative Utility for Each Species in Each Study Area  ..................................114 
Table 2.3: Elasticity of Each Species Model in the State and Neighborhood  ................118 
Table 2.4: Elasticity of Three Species Models to Extreme Change  ...............................122 
 
 
 
1 
 
Chapter 1: Estimating Conservation Utility for Species of Greatest  
Conservation Need in the Southeastern Plains of Alabama  
Introduction: 
 The state of Alabama has one of the highest species diversity rankings in the 
continental United States (Stein 2002).  However, Alabama also ranks second in the 
nation in species extinctions and fourth in the nation in the percentage of species facing 
extinction (Stein 2002).  With limited resources available for conservation, it is 
imperative that efficient state conservation plans be developed to protect species of 
greatest conservation need (GCN species).  The state of Alabama owns and manages 
109,668 hectares of protected land, 89% of which is managed by the Alabama 
Department of Conservation and Natural Resources (Silvano et al. 2008).  Furthermore, 
per capita conservation and management funding by the state is less than would be 
expected based on the state?s natural resources as well as demographic and political 
characteristics (Adelaja et al. 2007).  However, Adelaja et al. (2007) do not provide the 
exact values resulting from their analysis.  The Alabama Department of Conservation and 
Natural Resources (ADCNR) hopes to address some of these concerns through the 
Inventory and Conservation Planning (ICP) project which will result in the production of 
inventories of GCN species on select state lands as well as conservation plans for these 
sites (ADCNR 2010).   
 
 
2 
 
Successful conservation strategies necessitate broad scale questions often asked 
by the landscape ecologist (Gutzwiller 2002).  However, these questions are inherently 
complex as landscape ecology is a discipline focused on heterogeneity in both time and 
space (Turner et al. 2001).  This spatial heterogeneity is often dealt with in terms of 
patches, which Forman and Godron (1981) defined as ?communities or species 
assemblages surrounded by a matrix with a dissimilar community structure or 
composition.?  Patch characteristics like size, shape, connectivity and arrangement are 
therefore some of the metrics of spatial heterogeneity.  Not surprisingly, these 
characteristics are also important to the process of conservation (Shafer 1997).  Patch size 
is obviously an important characteristic to conservation of biodiversity as larger patches 
typically harbor more species diversity (Schoener 1976).  However, characteristics like 
patch arrangement (Murphy et al. 1990) and patch quality (Moilanen and Hanski 1998) 
may supersede patch size when conserving specific species because of differing species? 
needs.  Connectivity is another well studied landscape metric pertinent to conservation, as 
connectivity is typically correlated with population persistence (Fahrig and Merriam 
1985).   
Aside from spatial heterogeneity, landscape ecology is also characterized by a 
concern with scale (Turner et al. 2001).  Many have noted effects of differing temporal 
and spatial scales on analyses of spatial heterogeneity (Turner et al. 1989, Benson and 
MacKenzie 1995), and processes or features observed at one scale may not translate to 
another scale.  The same phenomenon has been observed in organismal studies as well 
(Holland et al. 2004, Thogmartin and Knutson 2007), and it is therefore critical to 
determine the appropriate scale at which conservation strategies must be implemented.  
 
 
3 
 
Geographic information systems (GIS) have become a commonly used tool in the 
field of conservation (Santos et al. 2006, Friedlander et al. 2007, Brito et al. 2009, etc.), 
as they are particularly helpful in addressing the broad scale issues facing 
conservationists and natural resource managers.  We wanted to consider the conservation 
utility of patches according to the following four characteristics: site suitability, potential 
habitat, management potential and landscape context.   
First we wanted to examine site suitability, which we defined as sites where it 
would be ecologically appropriate to manage for a certain species.  Ecological 
appropriateness was determined by species range as well as abiotic factors such as soil 
suitability.  We hypothesized that it was important to distinguish suitable sites from 
unsuitable sites for a number of reasons.  First, establishment of habitat on unsuitable 
sites could lead to range expansions of native flora and fauna, a first step in becoming an 
unwanted invader (Tolley et al. 2008, Campos-Krauer and Wisely 2010).  Additionally, 
introduction and management of a species outside its historic range can compromise 
genetic integrity of species when a closely related species or subspecies occurs within the 
area of this range expansion (Storfer 1999, Delibes-Mateos et al. 2008).  Finally, debate 
continues over the use of assisted migration as a possible solution to conservation 
problems posed by climate change (McLachlan et al. 2007, Ricciardi and Simberloff 
2009, Minteer and Collins 2010), but translocation outside historic home range has been 
linked with decreased translocation success in reptiles and amphibians (Germano and 
Bishop 2008) as well as birds and mammals (Wolf et al. 1996).  For these reasons, we 
hypothesized that conservation utility would be maximized in areas within the range of 
 
 
4 
 
target species as they would avoid the costly mitigation actions associated with these 
pitfalls. 
The next characteristic which we felt needed to be identified was potential habitat, 
which we defined as the biotic conditions of a site.  Though potential habitat may not 
have been occupied by target species, it represented a management opportunity because 
of its potential contribution to cost minimization.  Westphal and Possingham (2003) 
noted the maximization of conservation value of a given area was dependent upon the 
cost of restoration of particular sites within that area.  If seeking to maximize the 
conservation utility of our conservation lands, we should obviously attempt to minimize 
our management costs.  In identifying biotic conditions on a site, we hoped to introduce 
an element of cost minimization into our decision support tools.  Though not traditionally 
within the scope of conservation research, many researchers have recently recognized the 
importance of minimizing costs and maximizing conservation utility obtained from these 
expenses in light of the limited resources available for conservation (Murdoch et al. 
2007, Goldstein et al. 2008). 
Management potential was defined as the economic, social, and political 
feasibility of management actions requisite for the persistence of a certain species.  For 
example, many species of longleaf pine ecosystems depend on frequent fires for 
maintenance of their preferred habitat type (Brennan et al. 1998, Russell et al. 1999).  
However, sites in close proximity to urban areas or with high road densities may not be 
burned for safety reasons and would therefore have poor management potential for a 
species like Bachman?s sparrow, which requires frequent fire (Tucker et al. 2004).  The 
association between longleaf pine ecosystems and fire is a particularly appropriate 
 
 
5 
 
example for discussing management potential, as research indicates that restoration of 
longleaf pine forests requires the use of fire (Outcalt and Brockway 2010).  Though fire 
surrogates such as disking and herbicide treatment may be used to create and maintain 
some habitat characteristics similar to those of a burned stand (Harrington and Edwards 
1999, Freeman and Jose 2009), the life cycles of some species found in this endangered 
ecosystem require the use of fire (Norden and Kirkman 2004).  Another example of a 
landscape feature that might determine management potential would be topography, 
which can also influence management options available for use on a site.  For example, if 
a site is characterized by steep slopes, it may not be feasible to conduct some 
management practices like forest thinning or extensive clearing.  These sites would 
therefore have poor management potential for a species like the eastern kingsnake which 
prefers open habitat (Means 2004). 
As previously discussed, successful conservation strategies must consider broad 
scale problems.  It was therefore imperative that we consider the landscape context of our 
study sites.  It is important to note that pertinent landscape features varied depending 
upon the species selected, and context analysis was therefore dependent upon the species.  
In general, patches of potential habitat within the state represented one of three 
possibilities: the same as the majority of the habitat in the region (i.e. matrix), an isolated 
patch, or a patch integrated into a habitat mosaic. 
 The final stage of our analysis was determination of the relative utility for 
conservation of a given species.  To accomplish this, we developed species? models that 
equated utility to resource concentration.  When developing these models, there were 
both limiting factors and compensatory factors.  Limiting factors were prohibitive in that 
 
 
6 
 
their absence resulted in the exclusion of a particular species from a given area.  Site 
suitability and management potential, in most cases, were considered limiting factors in 
the proposed models as their absence would result in unsuccessful management of a 
given species.  Compensatory factors were not prohibitive and high values in one can 
compensate for low values in another.  Potential habitat and landscape context, in most 
cases, were considered compensatory factors in our models.  By combining these factors 
using kernel density estimation (Silverman 1986), we developed our estimates of relative 
conservation utility. 
 Our objective was to determine the conservation utility for each vertebrate, 
terrestrial GCN species occurring in the Southeastern Plains ecoregion (Griffith et al. 
2001, see figure 1.1) of Alabama through the development of predictive models 
incorporating landscape scale parameters. 
Methods: 
We derived the model parameters for our analysis from numerous sources 
including expert opinion, available literature and land cover data.  Several of these 
parameters were generated during the Alabama Gap Analysis Project (AL-GAP).  Land 
cover data and information on study area boundaries were obtained from AL-GAP.  
Range information for each species was also generated through expert opinion during the 
AL-GAP and was used in this analysis to inform our models on the suitability of a site.  
The range data for one species, the American black bear (Ursus americanus), was 
generated in an ad hoc manner because the AL-GAP information was unsatisfactory for 
our purposes.  AL-GAP range maps for black bear indicated the current distribution is 
mostly concentrated in the southwest portion of the state.  Considering the historic 
 
 
7 
 
distribution of the species (see figure 1.2), we felt it would be ecologically appropriate to 
manage for the species anywhere in the state.  Habitat models were also generated for 
each species known to breed in Alabama during the AL-GAP, and these models were 
used as indicators of potential habitat.  Other sources were used for development of 
additional data parameters.  The soil survey geographic (SSURGO) database was used to 
generate all soil parameters, and a landform layer generated by Southeast Gap Analysis 
Project (McKerrow et al. Forthcoming) was used to determine additional site suitability 
characteristics.  Wetland parameters were derived from National Wetland Index data. 
Many species of concern in the Southeastern Plains ecoregion of Alabama are 
associated with sandy soils because they engage in fossorial behaviors (Mirarchi et al. 
2004).  To predict potential species use, we needed to incorporate soil data into our 
modeling.  SSURGO data were used to obtain information on the suitability of a site by 
distinguishing appropriate soils for each species.  We initially wanted to use soil texture, 
which we thought would be a very quantifiable feature, as our indicator of suitability.  
However, soil analyses for gopher tortoises (Gopherus polyphemus) have traditionally 
been conducted using soil series instead of texture values (McDearman 2005), and we 
were encouraged by scientists in the field to approach our analysis in the same way 
(Guyer Pers. Comm.).  The United States Fish and Wildlife Service has developed a 
three-tiered ranking system for soil series that is used to assess habitat for gopher tortoise 
projects (McDearman 2005).  This system ranks soil types as priority, suitable or 
marginal based on expert opinion of the quality of habitat provided by a certain soil type 
considering its texture, composition, depth, permeability and drainage (McDearman 
2005).  We augmented this system by incorporating information from Herman et al. 
 
 
8 
 
(2002) and a classification conducted by Val Johnson, Craig Guyer and Sharon Hermann 
(Guyer Pers. Comm.; see Table 1.1 for full list of soils and rankings).  Since many 
fossorial species of concern are often associated with gopher tortoise burrows (Landers 
and Speake 1980), we assumed that appropriate soils for gopher tortoises were 
appropriate for associated species such as Eastern indigo snake (Drymarchon couperi), 
gopher frog (Rana capito), etc. as well.  We ranked each soil group as priority, suitable or 
marginal based on the previously described ranking system.  The resulting data layer was 
submitted to two experts for review, and the ranking of one region was changed to reflect 
their knowledge of gopher tortoise presence. 
Several of the species we modeled also have particular landform requirements.  
For example, some species make burrows underground and therefore are unlikely to use 
areas that flood regularly.  Other species are typically found in wetlands; therefore it was 
important that we differentiate between such features on the landscape.  As previously 
discussed, we used a landform layer generated by the SE-GAP to obtain data on the 
location of particular landform parameters.  This layer had 16 classifications ranging 
from steep slopes to moist flats and open water.  We used these classifications to generate 
information representing bottomlands and uplands by demarcating the boundary between 
moist and dry flats.  We then used these parameters to predict site suitability for a given 
species. 
We also felt that many habitat models for these species would benefit from a 
parameter incorporating patch size requirements (see Appendix B).  For those species for 
which we could find explicit information on patch size requirements, we incorporated 
another parameter in the model to display these data.  The potential habitat layers for 
 
 
9 
 
these species were analyzed in ArcMap?  (ESRI, Inc., use of trade, product, or firm 
names is for descriptive purposes only and does not imply endorsement) using the Region 
Group tool to determine the contiguous patch size of potential habitats.  Any patch below 
the minimum patch requirement was excluded for this parameter, resulting in a layer that 
we hypothesized would identify potential source populations and potential reintroduction 
sites. 
In general, the following process was performed to create parameters usable in 
this analysis.  First, all layers were rasterized and processed to have a cell size of 30 
meters by 30 meters.  Layers were subsequently clipped to the ecoregion of interest 
(Southeastern Plains) in Alabama (Griffith et al. 2001).  We then converted these layers 
to ASCII so they could be read as matrices in MATLAB (Mathworks, Inc. ?, does not 
imply endorsement).  Within MATLAB, we created models based on literature review 
intended to generate utility maps based on species? habitat needs (see Appendix A).  This 
information was acquired from literature review and expert opinion.  We then used 
MATLAB to perform kernel density estimation for the models we created by layering our 
ASCII files over one another (Bearday and Baxter 1996).  The outputs from these 
analyses were in a text format which was read back into ArcGIS to create maps of 
relative conservation utility. 
Results: 
 Since many species models incorporated the same parameters, many of the 
species associated with certain habitat characteristics displayed similar trends.  For this 
reason, we will discuss species in groups based on habitat associations.  All of the maps 
resulting from our analysis are available in Figures 1.4 ? 1.36.  The first group we will 
 
 
10 
 
discuss are sandy soil specialists.  As previously noted several of the GCN species in this 
analysis are fossorial during at least one phase of their lives and are therefore dependent 
on friable soils.  This group included the following species: black pine snake (Pituophis 
melanoleucus lodingi), Eastern coral snake (Micrurus fulvius), Eastern diamondback 
rattlesnake (Crotalus adamanteus), Eastern indigo snake (Drymarchon couperi), 
flatwoods salamander (Ambystoma cingulatum), Florida pine snake (Pituophis 
melanoleucus mugitus), gopher frog (Rana capito), gopher tortoise (Gopherus 
polyphemus), Southeastern pocket gopher (Geomys pinetis) and Southern hognose snake 
(Heterodon simus).  In our models, this group was highly influenced by the soil 
classification parameter.  Priority soils were concentrated predominantly in the southern 
and southeastern portion of the Southeastern Plains, and conservation utility was as well.  
Many of these species? current ranges within Alabama were confined to thin slivers on 
the southern border with Florida; however, several species? models displayed high 
relative values in areas outside these ranges.  In addition, most of these species had high 
conservation utility north of the Blackland Prairies (Griffith et al. 2001, see Figure 1.1), 
areas traditionally outside of the range of all of these species in the most generous 
historical range maps.  Many of these species? models displayed high conservation utility 
in the large public land holdings of the southern portion of the state, particularly Conecuh 
National Forest, Fort Rucker and Barbour County Wildlife Management Area.  One 
notable exception was the black pine snake model, which displayed areas of high 
conservation utility on private lands west of the Tombigbee River.  Floodplains of major 
rivers were the areas of lowest conservation utility for most of these species. 
 
 
11 
 
 Species associated with hardwood forests were another prominent group from our 
analysis that displayed similar results.  These species included: Kentucky warbler 
(Oporonis formosus), Swainson?s warbler (Limnothlypis swainsonii), wood thrush 
(Hylocichla mustelina) and worm-eating warbler (Helmitheros vermivorus).  The models 
for these species identified bottomland areas in the southern part of the state as high 
utility areas.  In addition to these bottomlands, larger areas of the Fall Line Hills region 
(Griffith et al. 2001, see Figure 1.1) of the Southeastern Plains were also given relatively 
high values.  However, one species (Swainson?s warbler) did not have higher utility in 
the Fall Line.  All of these species? peak utility values were found at the confluence of the 
Alabama and Tombigbee Rivers in bottomland hardwoods of the Mobile Delta.  Lowest 
utilities for all of these species were found in upland areas of the southern portion of the 
state. 
 Riverine and floodplain species were another group that displayed similar patterns 
of high conservation utility.  As expected, high utilities for these species were centered on 
areas with many rivers and large bottomland areas.  These species included: alligator 
snapping turtle (Macrochelys temminckii), least bittern (Ixobrychus exilis), long-tailed 
weasel (Mustela frenata), river frog (Rana heckscheri) and rainbow snake (Farancia 
erytrogramma erytrogramma).  The long-tailed weasel and river frog models both 
display higher utility outside of floodplains.  As with the species associated with 
hardwood forests, most of these species? models highest values were found around the 
confluence of the Alabama and Tombigbee Rivers, though some also displayed high 
conservation utility in an area of the Alabama River floodplain directly west of 
Montgomery.  Low values were generally found in upland areas, particularly in the 
 
 
12 
 
southern part of the state, though some (like the rainbow snake model) displayed low 
utility in upland habitats across the entire state. 
 Two other catchall groups can also be discussed though they do not portray 
similar conservation utility patterns.  The first group, upland generalists, includes species 
such as the American black bear, American kestrel (Falco sparverius), eastern kingsnake 
(Lampropeltis getula getula), eastern spotted skunk (Spilogale putorius), southeastern 
five-lined skink (Eumeces inexpectatus) and speckled kingsnake (Lampropeltis getula 
holbrooki).  These species generally had elevated conservation utilities across larger areas 
and were characterized by high conservation utilities on public lands.  Another group 
would be the species dependent on longleaf pine ecosystems that are not dependent on 
specific soil types.  Models for species such as red-cockaded woodpecker (Picoides 
borealis) and Henslow?s sparrow (Ammodramus henslowii) had some similarities with 
the soil dependent group, but these models also displayed high values within the 
Oakmulgee Ranger District of the Talladega National Forest.  However, the red-cockaded 
woodpecker model also displayed areas of high conservation utility outside of public 
lands in rural areas flanking the Mobile Delta.   
Discussion: 
 Several of the patterns observed in our outputs were expected prior to our 
analysis.  The highest relative utilities for the suite of species associated with sandy soils, 
for example, were found in the southern half of the state because the highest densities of 
these soils are found in that region.  We also expected that conservation utility would be 
low for these species in floodplain areas given the flooding in these areas would not be 
conducive to fossorial lifestyles.  However, we did not expect that areas north of the 
 
 
13 
 
Blackland Prairie would have such high conservation utility for these fossorial species.  
These northern areas fall outside of the natural range of all of the species within this 
group, with the possible exception of the southern hognose snake, depending on which 
range map is used (Jensen 2004, Silvano et al. 2008, etc.).  These high values in the Fall 
Line Hills region reflect the predominantly sandy soils found in the area (Shaw et al. 
2004).  The black pine snake model also displayed a conservation utility distribution 
unlike others in this group.  Many of the species have high values in the large public land 
holdings in the eastern part of the Southeastern Plains.  However, black pine snake utility 
was highest to the west of the Tombigbee River.  This difference is to be expected since 
the black pine snake?s range is more limited than other species in this group. 
 Species associated with hardwood forests also displayed some expected trends.  
They had relatively high values in the northern part of the state because of the large area 
of hardwood forests there and high values in the bottomlands of the southern portion of 
the state because of the predominantly hardwood canopy in these bottomlands.  
Swainson?s warbler, despite sharing many similarities with other species in this group, 
did not show the increased conservation utilities that were observed for other species in 
the northern uplands of the Southeastern Plains.  This can probably be attributed to the 
high affinity of Swainson?s warbler for floodplain forests and associated cane stands 
(Graves 2002, Brown et al. 2009).  One interesting result of the patch size parameter can 
be observed from differences in the wood thrush, worm-eating warbler and Swainson?s 
warbler models.  All three species are found across the state and have similar habitat 
preferences, though Swainson?s warblers are more attracted to floodplains.  Despite these 
 
 
14 
 
similarities, the utility maps for these species are different because of their different patch 
size requirements. 
 The trends we observed with the riverine and floodplain associated species were 
some of the most predictable.  As would be expected, highest conservation utilities were 
concentrated around large wetlands and the confluences of major rivers.  The two 
species? models that did display higher values outside of floodplains (long-tailed weasel 
and river frog) were less restrictive in their dependence on bottomlands, having 
considerable areas of potential habitat disassociated from floodplains.  As previously 
mentioned, most of these species? highest values were found around the confluence of the 
Alabama and Tombigbee Rivers.  This area undoubtedly represented the highest density 
of potential habitat for this species while also benefitting from an increase in utility 
because of some smaller public land holdings in the region. 
 Though the rest of the species were not easily characterized as a single group, 
there were several notable features of these outputs.  The American black bear model 
displayed high utility in the Southwestern portion of the state where large patches of 
potential habitat in their current distribution increased conservation utility.  Another of 
these species? models that displays high conservation utilities enhanced by our patch size 
parameter is the red-cockaded woodpecker.  In this case, the large patches of existing 
habitat located in Conecuh and Talladega National Forests are quite obvious on our 
outputs, but other large patches are apparent flanking the Tombigbee River in the 
Southwestern part of the state.  Two other species? models are good examples of one 
problem with our analysis, though they present the same problem in different ways.  The 
eastern spotted skunk model is characterized by relatively high values across most of the 
 
 
15 
 
state and particularly high values on public lands.  In contrast, the little grass frog model 
displays almost exclusively low utility across most of the state with high values on public 
lands.  Both of these utility distributions can be linked to a lack of information on habitat 
associations, distributions, etc.  Whenever information on a species? habitat associations 
was lacking, the resulting models tended to generalize utility across the state (either high 
or low) and increase the value of public lands. 
 Despite the relatively successful application of the model, there are several areas 
in which we could improve.  Range maps are important components of these types of 
analyses because without them, our models could indicate high utility for a species in an 
area where it would be totally inappropriate to manage for them.  However, many range 
maps are highly contentious and some of the range maps we used do not necessarily 
represent a consensus. As previously mentioned in the case with the southern hognose 
snake, there are occasionally multiple range maps available in the literature.  Since our 
model incorporates species range as a component of conservation utility, it is possible 
that more restrictive range maps may depress the value of certain lands.  We also 
encountered problems when historic ranges differed drastically from current distributions.  
As previously described, we felt it was necessary for us to generate a new potential 
habitat layer for the American black bear model since the species historically ranged 
across the entire state (Laliberte and Ripple 2004).  Other species, such as the Southern 
hognose snake, have experienced range contractions (Gibbons et al. 2000) presenting 
problematic questions over where it is ecologically appropriate to manage for them.  
These factors could contribute uncertainty to our statewide estimates by restricting or 
 
 
16 
 
expanding the areas we deemed appropriate for management.  We therefore recommend 
that inclusive range maps be used in future analyses such as this.   
Though public conservation lands should be an important component of 
conservation utility, some models tended to overvalue public land.  As previously noted, 
this typically resulted when there was little information available to inform our models on 
a species? habitat associations or other mappable parameters.  For example, the coal skink 
(Plestiodon anthracinus), flatwoods salamander and little grass frog (Pseudacris 
ocularis) models incorporated few parameters and consequently had very little relative 
utility outside of the boundaries of public lands.  This effect is compounded, particularly 
in the case of the flatwoods salamander and little grass frog, by restrictive range maps 
that did not allow for much potential habitat data.  Alternatively, there could be situations 
where additional parameters become extraneous, particularly if there are redundant 
parameters.  This situation could result in artificially elevated utility estimates in some 
areas while simultaneously suppressing utility estimates on public lands.  For these 
reasons, parameters should not be thrown into these models without justification, but 
incorporation of several pertinent parameters should provide the most explicit indications 
of utility. 
One final potential problem is the use of GAP data, which has come under 
scrutiny from several sources for its inherent assumptions and shortcomings, most of 
which revolve around issues of scale and assumed accuracy of data (Conroy and Noon 
1996, Flather et al. 1997).  It has also been noted that broad scale analyses often cannot 
account for certain fine scale features and that fine scale management resulting from 
broad scale analyses may sometimes be somewhat misguided (Conroy and Noon 1996, 
 
 
17 
 
Rouget 2003).  Considering the broad extent at which this analysis was conducted, it is 
quite possible that decisions based solely on this information could be misguided due to 
scaling issues.  Regardless of the implications of these assumptions, at the time of this 
research, the satellite imagery used to develop AL-GAP data is over ten years old.  
Undoubtedly, many landscape features have changed since these data were generated, 
introducing unwanted uncertainty into our analysis. 
 Keeping these caveats in mind, this method presents an attempt to use available 
data to generate relative conservation utility estimates across a fairly broad extent.  Future 
analyses would absolutely benefit from more current land cover data and better 
information on species specific habitat preferences and habitat quality indicators.  
However this information would require further quantitative estimates of habitat use, 
studies on the cost and effectiveness of management techniques and newer land cover 
analyses.  These models appear to work best when parameters such as habitat 
associations and ranges are well understood and well documented. 
Acknowledgements: 
 First and foremost, we thank Dr. Mark MacKenzie for initiating and inspiring this 
research.  We would like to thank Allison Moody, Max Post ? Van der Burg and Amy 
Silvano for their contributions to earlier drafts.  Will McDearman, Eric Soehren Val 
Johnson and Craig Guyer are all greatly appreciated for their input on soil classifications.  
We thank Kevin Kleiner and Tyler Krepps for their assistance during the GIS 
components of this project.  This research was funded through the Alabama State 
Wildlife Grants Program as part of the inventory and conservation planning project 
coordinated by the Alabama Cooperative Fish and Wildlife Research Unit.  The 
 
 
18 
 
ALCFWRU is a partnership between the U.S. Geological Survey, the Wildlife 
Management Institute, the Alabama Department of Conservation and Natural Resources, 
Auburn University and the U.S. Fish and Wildlife Service.
 
 
 
Figure 1.1: Level IV Ecoregions of the Southeastern Plains of Alabama, From Griffith et 
al. 2001 
 
19 
 
 
 
 
Figure 1.2: Current and historic distribution of American black bears in Alabama 
 
20 
 
 
 
 
Figure 1.3: Maps of the parameters used as inputs in our models 
 
21 
 
 
 
 
 
 
Figure 1.3: Maps of the parameters used as inputs in our models 
 
22 
 
 
 
 
 
 
Figure 1.3: Maps of the parameters used as inputs in our models 
 
23 
 
 
 
 
 
Figure 1.3: Maps of the parameters used as inputs in our models 
 
24 
 
 
 
 
 
 
Figure 1.3: Maps of the parameters used as inputs in our models 
 
25 
 
 
 
 
 
Figure 1.4: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for alligator snapping turtle. For mean value across study extent, see Table 2.1. 
 
26 
 
 
 
 
Figure 1.5: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for American black bear.  For mean value across study extent, see Table 2.1. 
 
27 
 
 
 
 
Figure 1.6: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for American kestrel.  For mean value across study extent, see Table 2.1. 
 
28 
 
 
 
 
Figure 1.7: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for American woodcock.  For mean value across study extent, see Table 2.1. 
 
29 
 
 
 
 
 
Figure 1.8: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Bachman?s sparrow.  For mean value across study extent, see Table 2.1. 
 
30 
 
 
 
 
Figure 1.9: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for black pine snake.  For mean value across study extent, see Table 2.1. 
 
31 
 
 
 
 
Figure 1.10: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for coal skink.  For mean value across study extent, see Table 2.1. 
 
32 
 
 
 
 
Figure 1.11: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Eastern coral snake.  For mean value across study extent, see Table 2.1. 
 
33 
 
 
 
 
Figure 1.12: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Eastern diamondback rattlesnake.  For mean value across study extent, see 
Table 2.1. 
 
34 
 
 
 
 
Figure 1.13: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Eastern indigo snake.  For mean value across study extent, see Table 2.1. 
 
35 
 
 
 
 
Figure 1.14: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Eastern kingsnake.  For mean value across study extent, see Table 2.1. 
 
36 
 
 
 
 
Figure 1.15: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Eastern spotted skunk.  For mean value across study extent, see Table 2.1. 
 
37 
 
 
 
 
Figure 1.16: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for flatwoods salamander.  For mean value across study extent, see Table 2.1. 
 
38 
 
 
 
 
 
Figure 1.17: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Florida pine snake.  For mean value across study extent, see Table 2.1. 
 
39 
 
 
 
 
 
Figure 1.18: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for gopher frog.  For mean value across study extent, see Table 2.1. 
 
40 
 
 
 
 
Figure 1.19: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for gopher tortoise.  For mean value across study extent, see Table 2.1. 
 
41 
 
 
 
 
Figure 1.20: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Henslow?s sparrow.  For mean value across study extent, see Table 2.1. 
 
42 
 
 
 
 
Figure 1.21: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Kentucky warbler.  For mean value across study extent, see Table 2.1. 
 
43 
 
 
 
 
Figure 1.22: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for least bittern.  For mean value across study extent, see Table 2.1. 
 
44 
 
 
 
 
Figure 1.23: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for little grass frog.  For mean value across study extent, see Table 2.1. 
 
45 
 
 
 
 
Figure 1.24: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for long-tailed weasel.  For mean value across study extent, see Table 2.1. 
 
46 
 
 
 
 
Figure 1.25: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for mimic glass lizard.  For mean value across study extent, see Table 2.1. 
 
47 
 
 
 
 
Figure 1.26: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for pine barrens treefrog.  For mean value across study extent, see Table 2.1. 
 
48 
 
 
 
 
Figure 1.27: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for rainbow snake.  For mean value across study extent, see Table 2.1. 
 
49 
 
 
 
 
Figure 1.28: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for red-cockaded woodpecker.  For mean value across study extent, see Table 
2.1. 
 
50 
 
 
 
 
Figure 1.29: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for river frog.  For mean value across study extent, see Table 2.1. 
 
51 
 
 
 
 
Figure 1.30: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Southeastern pocket gopher.  For mean value across study extent, see Table 
2.1. 
 
52 
 
 
 
 
Figure 1.31: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Southern dusky salamander.  For mean value across study extent, see Table 
2.1. 
 
53 
 
 
 
 
Figure 1.32: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Southern hognose snake.  For mean value across study extent, see Table 2.1. 
 
54 
 
 
 
 
Figure 1.33: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for speckled kingsnake.  For mean value across study extent, see Table 2.1. 
 
55 
 
 
 
 
Figure 1.34: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for Swainson?s warbler.  For mean value across study extent, see Table 2.1. 
 
56 
 
 
 
 
Figure 1.35: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for wood thrush.  For mean value across study extent, see Table 2.1. 
 
57 
 
 
 
 
Figure 1.36: Map of conservation utility across the Southeastern Plains ecoregion of 
Alabama for worm-eating warbler.  For mean value across study extent, see Table 2.1. 
 
58 
 
 
 
 
59 
 
Table 1.1: Soil classification to determine site suitability for gopher tortoise and other 
fossorial herpetofauna.  Unless otherwise noted, soil class from McDearman (2005). 
Priority 
 
Suitable 
 
Marginal 
Alaga 
 
Bama Lucedale 
 
Alapaha * Petal 
Alpin * 
 
Benndale Lucy 
 
Basin * Poarch 
Bigbee 
 
Bonneau * Maxton ? 
 
Baxterville Prentiss 
Blanton * 
 
Carnegie ? McLaurin * 
 
Bibb * Quitman * 
Eustis 
 
Chisolm ? Nankin ? 
 
Boswell Saucier 
Lakeland 
 
Cowarts ? Norfolk * 
 
Falkner * Savannah 
Lakewood * 
 
Cuthbert* Orangeburg * 
 
Freestone Susquehanna 
Troup * 
 
Dothan * Ruston 
 
Grady * 
 Wadley 
 
Esto ? Shubuta 
 
Leefield * 
 
  
Faceville * Smithdale 
 
Lorman 
 
  
Florala * Stilson ? 
 
Lynchburg * 
 
  
Fuquay * Suffolk * 
 
Malbis 
 
  
Greenville ? Sunsweet * 
 
Mashulaville * 
 
  
Harleston * Tifton ? 
 
Myatt * 
 
  
Heidel Varina ? 
 
Ocilla * 
 
  
Izagora * Wadley * 
 
Osier * 
 
  
Kershaw ? Wagram * 
 
Pelham * 
        * From Guyer Pers. Comm. 2010 
? From Herman et al. 2002 
 
60 
 
Chapter 2: Relative Conservation Utility of State-owned  
Lands for Species of Greatest Conservation Need 
Introduction: 
 Publicly owned conservation lands are frequently the primary focus of 
conservation efforts, though private lands can often prove to be valuable contributors to 
species conservation (Wallace et al. 2008, Wilson et al. 2010).  Most reserve networks 
are composed of public lands designed to contribute to the conservation of all species 
(Rodrigues et al. 2004, Tognelli et al. 2008).  However, the contribution of reserve 
networks to biodiversity targets is greatly affected by privately owned areas surrounding 
the reserves (Hansen and DeFries 2007, Ewers and Rodrigues 2008).  Management of 
public lands should consider the conservation contributions of surrounding areas, as some 
species may be low priorities if their populations are adequately maintained by 
management on private lands.  Conversely, some species may not choose to occupy a site 
within a reserve if landscape cues, such as forest or non-forest area, water, etc., are absent 
(Bergin 1992, McLoughlin et al. 2004, Lagory et al. 2009).  Thus, landscape context is 
an important consideration when managing for species conservation, though its 
significance often depends on the species of interest (Gagn? and Fahrig 2007, Heden?s 
and Ericson 2008).  This suggests that managers may have to make trade-offs among 
species, especially in regions with high species diversity. 
 The state of Alabama has one of the highest species diversity rankings in the 
continental United States (Stein 2002).  However, Alabama has also ranked second in the 
 
61 
 
nation in species extinctions and fourth in the nation in the percentage of species facing 
extinction (Stein 2002).  With limited resources available for conservation, it is 
imperative that efficient state conservation plans be developed to protect species of 
greatest conservation need (GCN species) as designated in the state wildlife conservation 
strategy.  The state of Alabama owns and manages 109,668 hectares of protected land, 
89% of which is managed by the Alabama Department of Conservation and Natural 
Resources (Silvano et al. 2008).  Furthermore, per capita conservation and management 
funding by the state is less than would be expected based on the state?s natural resources 
as well as demographic and political characteristics (Adelaja et al. 2007).  Despite the 
fact that many GCN species once ranged extensively in the state, many, like the Southern 
hognose snake (Jensen 2004) and flatwoods salamander (Means 2004), have not been 
encountered in Alabama for over twenty years (Mirarchi et al. 2004).  Furthermore, 
conservation practitioners in the state face knowledge gaps regarding the locations of 
populations, habitat associations and other key features of many GCN species.  The 
Alabama Department of Conservation and Natural Resources (ADCNR) hopes to address 
some of these concerns through the Inventory and Conservation Planning (ICP) project 
which will result in the production of inventories of GCN species and conservation plans 
on select public lands (ADCNR 2010).   
 Successful conservation plans necessitate a broad perspective (Gutzwiller 2002).  
However, this broad perspective leads to inherently complex questions as it incorporates 
increasing spatial heterogeneity as the extent broadens (Turner et al. 2001).  Spatial 
heterogeneity is often dealt with in terms of patch characteristics like size, shape, 
connectivity and arrangement.  Not surprisingly, these characteristics are also important 
 
62 
 
to the process of conservation network design (Shafer 1997).  Aside from spatial 
heterogeneity, broad perspectives require increased attention to scale (Turner et al. 2001).  
Processes or features important to a species at one scale may not be pertinent at another 
scale, and it is therefore critical to determine the appropriate scale at which conservation 
strategies must be implemented.  Furthermore, broad scale conservation planning has the 
ability to increase management efficiency (Kark et al. 2009).  In an effort to address 
many of these issues, geographic information systems (GIS) have become a commonly 
used tool in the field of conservation (Santos et al. 2006, Friedlander et al. 2007, Brito et 
al. 2009, etc.) because they are particularly helpful in addressing the broad scale issues 
facing natural resource managers.   
Through this research, we sought to assess the conservation utility of managing 
ICP lands in the Southeastern Plains of Alabama for GCN species.  Unlike other studies 
that only focus on the utility or value of individual parcels of land, we assessed the value 
of ICP lands within a neighborhood.  This allowed us to measure the management 
potential of each ICP property relative to other properties, the surrounding neighborhood 
and the entire Southeastern Plains ecoregion for the conservation of vertebrate species.  
Our method required development of multiple GIS data layers pertinent to the 
conservation of each species.  Site suitability was determined to gauge ecological 
appropriateness of managing for a species on a certain site.  This objective determined if 
a certain species should be managed on a site and was gleaned from available literature, 
soil maps, species range maps, etc.  Potential habitat was determined to identify areas 
where biotic conditions required for a particular species could potentially be met.  For the 
purposes of this analysis, biotic conditions on a site predominantly focused on the 
 
63 
 
distribution and arrangement of land cover types.  Land cover was assessed using data 
provided by the Alabama GAP Analysis Project (AL-GAP) (Silvano et al. 2008).  We 
sought to inform decisions related to the management of study areas for GCN species by 
accomplishing two objectives.  First, we wanted to determine the relative utility of each 
area for GCN species given the distribution and abundance of requisite resources within 
the state.  Second, we wanted to determine the relative utility of each ICP property for 
GCN species given the distribution and abundance of requisite resources within the areas 
surrounding the properties.  This analysis will provide information to assist managers in 
confronting the inherent trade-offs in conserving biodiversity.   
Methods 
Study Areas 
 Our study focused on public lands within the Southeastern Plains ecoregion of 
Alabama (Griffeth et al. 2001, see Figure 2.1).  As part of the larger ICP project, a 
steering committee identified study areas that represented the highest diversity of GCN 
species on properties in excess of 1200 hectares.  After this initial cut, the committee was 
left with 27 properties which were further reduced to include only areas for which the 
state had little information.  From the remaining 13 properties that were used for the ICP 
project, we selected four study areas for our research: Barbour Wildlife Management 
Area, the Perdido River tracts, the Sipsey River tracts and Stimpson Wildlife Sanctuaries.  
For the sake of clarity, these study areas will henceforth be referred to as Barbour, 
Perdido, Sipsey and Stimpson respectively.  These properties are managed for a variety 
of objectives and outputs.  Unless otherwise noted, all management objectives were 
obtained from ADCNR (ADCNR 2010), and all ecoregion classifications were obtained 
 
64 
 
from Griffith et al. (2001).  Barbour included the Barbour Wildlife Management Area 
(WMA) and the adjacent Wehle tract of the state?s Forever Wild program encompass 
8,187 hectares and are located within Barbour and Bullock Counties on the Southern 
Hilly Gulf Coastal Plain ecoregion.  The WMA is managed for big and small game 
hunting, and the Wehle tract is managed for habitat conservation.  Perdido consisted of 
the Perdido River Longleaf Hills tracts, the adjacent Forever Wild Perdido Nomination 
and the Nature Conservancy?s Perdido River parcel, which are all located along the 
Perdido River in Baldwin County within the Southern Pine Plains and Hills ecoregion of 
Alabama.  Comprising 7,333 hectares, the state-owned portions of these properties are 
managed for habitat conservation and big and small game hunting.  The timber rights on 
these properties are owned by a private corporation.  Sipsey was composed of the Sipsey 
Sullivan, Sipsey Robertson and Sipsey Randolph tracts of the state?s Forever Wild 
Program and are found along the floodplain of the Sipsey River in the Fall Line Hills 
ecoregion.  The specific management goals for this 1,379 hectare area are habitat 
conservation, outdoor recreation and scientific research and education.  Stimpson was 
made up of Fred T. Stimpson Sanctuary and the nearby Upper State Sanctuary and was 
located along the Tombigbee River in Clark County, Alabama.  Together, the sanctuaries 
encompass 2,943 hectares, providing both timber production and wildlife sanctuaries.  
Since the 1970s, Stimpson has been the source for many translocated game animals 
(Stimpson Pers. Comm.). 
Model Development 
We derived the model parameters for our analysis from numerous sources 
including expert opinion, available literature and land cover data (see Chapter 1 for more 
 
65 
 
information and Appendix A for a full list of species models).  For this analysis we used a 
GIS consisting of the utility values for species of greatest conservation need in the 
Southeastern Plains of Alabama that were expected or observed to occur on our study 
areas. A map of the study area boundaries was derived from the AL-GAP stewardship 
layer (Silvano et al. 2008).  Layers were subsequently clipped to the ecoregion of interest 
(Southeastern Plains) in Alabama and exported as binary ASCII format.  We then used 
MATLAB? (Mathworks, Inc., use of trade product or firm name is for descriptive 
purposes and does not imply endorsement) to estimate a kernel density surface from the 
binary data (Bearday and Baxter 1996).  Because density indicated where the resources 
important to species conservation were concentrated, we used it as an indicator of the 
conservation value of a site.  We then combined the density surfaces using a model that 
treated each surface as either limiting or compensatory to the utility of a site for each 
species.  The outputs from these analyses were imported back into ArcGIS to create maps 
of relative conservation utility.  Statistical analyses were then conducted on these maps 
using the Zonal Statistics tool of the Spatial Analyst toolbox in ArcGIS.  All ICP study 
areas of interest were buffered by 20 kilometers to determine value within a 
?neighborhood? surrounding the property.  Utility of each study area and the surrounding 
neighborhood was determined by summing the values within the area of interest.  We 
determined the elasticity of our utility estimates for all 33 species by incrementally 
increasing the extents and calculating the percentage change in utility relative to the 
percentage change in area.  Finally, because our choice of neighborhood size was 
arbitrary, we examined relative changes in utility at several different neighborhood 
(buffer) sizes by determining the change in utility in proportion to the change in 
 
66 
 
neighborhood area.  Three species were selected for this analysis to represent wide 
ranging species (wood thrush), intermediately ranging species (gopher tortoise and 
narrowly ranging species (Eastern kingsnake). 
Results 
 All of the maps resulting from our analysis are available in Figures 2.2 ? 2.34. 
Average utility values for each property and neighborhood are also available in Table 2.1, 
and Relative values are available in Table 2.2.  Statewide conservation utility varied 
considerably among species, though in general values were fairly low across the state 
when compared to local areas of high resource concentration.  American kestrel (Falco 
sparverius), American black bear (Ursus americanus), Eastern spotted skunk (Spilogale 
putorius) and American woodcock (Scolopax minor) had the highest statewide utilities.  
Conversely, little grass frog (Pseudacris ocularis), Pine Barrens treefrog (Hyla 
andersonii), Southern dusky salamander (Desmognathus auriculatus) and flatwoods 
salamander (Ambystoma cingulatum) had negligible utilities across the state.  Other 
species, such as Southern hognose snake (Heterodon simus) and red-cockaded 
woodpecker (Picoides borealis), displayed fairly low average utility across the state 
compared with other species.  Our results also indicate that though all species included in 
this analysis had value within the extent of the Southeastern Plains, no ICP study areas in 
our analysis were appropriate for flatwoods salamander, little grass frog and Southern 
dusky salamander because all of the study properties are located outside the current 
distributions of these species. 
At the considerably smaller extent of ICP study areas, several species displayed 
higher average utility values than others (see Table 2.1).  As would be expected, the 
 
67 
 
species with the highest utility values within a property often had high utility in the 
surrounding neighborhood, though there were a few notable exceptions.  Within Barbour, 
Southeastern pocket gopher (Geomys pinetis), gopher tortoise (Gopherus polyphemus), 
Eastern kingsnake (Lampropeltis getula getula) and American kestrel had the highest 
utility values.  These species all had high neighborhood values as well, though Florida 
pine snake (Pituophis melanoleucus mugitus) and Bachman?s sparrow (Aimophila 
aestivalis) both displayed high neighborhood values despite mid-range values within the 
property.  Southeastern pocket gopher, mimic glass lizard (Ophisaurus mimicus), gopher 
tortoise, Florida pine snake, Eastern coral snake (Micrurus fulvius) and black pine snake 
(Pituophis melanoleucus lodingi) had the highest average conservation utility values in 
Perdido.  Again, these species had the highest utility values in the surrounding 
neighborhood, but conservation utility values for Eastern diamondback rattlesnake 
(Crotalus adamanteus) and river frog (Rana hecksheri) were among the highest in the 
neighborhood.  Only American black bear and speckled kingsnake (Lampropeltis getula 
holbrooki) displayed high utility values within the Stimpson Sanctuaries, and only the 
black bear maintained those high values in the surrounding neighborhood.  Within 
Sipsey, utility values were highest for wood thrush (Hylocichla mustelina), worm-eating 
warbler (Helmitheros vermivorus) and Kentucky warbler (Oporornis formosus), and 
these species maintained high utilities in the neighborhood.  
Several species displayed high utility values relative to the surrounding 
neighborhood and state on multiple properties (see Table 2.2).  Red-cockaded 
woodpecker displayed particularly high relative values on all ICP study areas but 
Stimpson.  Southern hognose snake also had high relative utility on both Barbour and 
 
68 
 
Perdido.  Other species had high relative values on only one property.  The models for 
mimic glass lizard and coal skink both indicated high relative utility on Perdido.  In 
general, Sipsey and the Stimpson did not provide high relative utility for any species and 
most species highest relative utility values were located on either Barbour or Perdido.  
One notable exception to that generalization is red-cockaded woodpecker which had its 
highest relative utility in Sipsey.   
 Elasticity analysis revealed that most utility estimates were fairly close to unit 
elastic (see Table 2.3).  There were, however, a few notable exceptions.  Less than one 
percent increase in area resulted in approximately four percent increase in utility for 
mimic glass lizard and coal skink in the Stimpson and Barbour neighborhoods, 
respectively.  Similarly, less than one percent increase in area resulted in approximately 
two percent increase in utility for Eastern kingsnake in the Stimpson neighborhood.  
State-wide utility for all species was particularly insensitive to increases in area size, 
though little grass frog proved most sensitive at this extent.  The fact that the frog?s range 
barely extends into the state presumably contributed heavily to the fact that less than one 
percent increase in area resulted in approximately one percent increase in utility.  Several 
species ranges did not extend into some neighborhoods, resulting in exclusion of these 
species from sensitivity analysis on those neighborhoods. 
 Our examination of changes in neighborhood size revealed similar results (See 
Table 2.4).  Most species (including the narrowly ranging species in most neighborhoods) 
were either fairly inelastic or close to unit elastic; that is, the change in utility was either 
less than or proportionate to the change in neighborhood area.  However, as with our 
initial elasticity analysis, there were exceptions.  Eastern kingsnake utility estimates were 
 
69 
 
extremely elastic (i.e., change in utility was greater than proportionate change in area) 
when the neighborhood size was increased to a 50 km buffer around Stimpson.  
Alternatively, there was no utility for Eastern kingsnake calculated in the neighborhood 
when the buffer was reduced to 5 km.  These results are what we would expect with areas 
close to the edge of a species? range: large increase in relative utility as the size of the 
neighborhood increases when there is little to no utility within the property. 
Discussion 
Utility estimates across the Southeastern Plains ecoregion reflect the availability 
of resources that are potentially important to the conservation of GCN species at a broad 
extent. These estimates could, therefore, be used to prioritize conservation effort among 
species within the region.  Decisions based on these utility values would be affected by 
the accuracy of the range maps we used.  If our range maps over-predicted the actual 
range of a species in the state, then we will have over-estimated the value of the region to 
the overall conservation of that species and vice versa. Fortunately, our sensitivity 
analysis indicates that range inaccuracies would not significantly alter those statewide 
estimates for most species as elasticity was negligible for most species models at the 
extent of a species? range.  For prioritizing actions at local extents, however, the 
inaccuracy of range maps could have caused us to assign value to study areas that were 
outside of the actual range of some species and to assign no value to study areas that were 
actually within the range of others.  This was most likely to occur when study areas were 
on the margins of a species? range. These observations highlight the significance of the 
range maps used in analyses like this and suggest that one way of making better informed 
decisions using this tool would be by developing more accurate range maps.  It is also 
 
70 
 
important to note that, since range map inaccuracies would affect utility estimates 
differently, decisions at broad extents should use our statewide estimates while decisions 
at more local levels should incorporate additional information.   
Utility estimates at the neighborhood level indicate the available resources 
immediately surrounding our ICP study areas.  These estimates could therefore be used to 
choose between competing conservation needs on a more local scale.  For instance, high 
utility values in the neighborhood adjacent to a study area might be helpful in identifying 
potential land acquisitions.  If accompanied by stable populations on ICP study areas, 
high neighborhood utility might also indicate an opportunity for existing populations to 
act as source populations that could colonize neighboring suitable habitat.  However, if 
low or unstable population numbers accompany high neighborhood values, that could 
indicate that either our potential habitat models are inadequate or habitat resources are 
not being managed appropriately.  Low neighborhood values could also be important in 
guiding conservation decision making.  Low neighborhood values for a species with a 
large or stable population on an ICP study area might indicate a study area that is very 
important to the conservation of that particular species.  However, ICP study areas with a 
small, unstable population of a particular species with low utility in the surrounding area 
could represent an area that might be better used by devoting its resources to other 
species, particularly if that species is well represented elsewhere in the state?s reserve 
system.  A key point to remember from each of these examples is that without the context 
provided by population vital rates, population viability analyses, or inventories, 
interpretation of neighborhood utility can be misleading.  However utility estimates could 
prove quite informative when paired with the appropriate contextual data. 
 
71 
 
Utility estimates on ICP study areas reflect concentrations of important resources 
for the conservation of GCN species in areas that will be managed for the preservation of 
those species in perpetuity.  This continuity gives these study areas added utility because 
most private lands cannot offer the same stability.  According to our models, Barbour and 
Perdido presented the most conservation utility for fossorial GCN species such as black 
pine snake, Florida pine snake, gopher tortoise and Southeastern pocker gopher.  
Considering the large area encompassed by each of these properties, they could prove to 
be very valuable for the conservation of these species within their region over a long 
period of time.  In some cases, one part of a study area might have high utility for a 
particular species or suite of species, while having low utility for these species in other 
parts of the study area.  For example, consider the situation of the Florida pine snake on 
Barbour.  According to our estimates, the southeastern portion of Barbour harbors the 
highest concentration of resources needed by Florida pine snake in the area (see Figure 
2.15).  This information could be used to justify concentrating management and resources 
for that species in the southeastern portion of the property while managing other species 
elsewhere in Barbour.   
Of course, conservation management should never take place in a vacuum, as 
indeed we have noted that surrounding lands can contribute to conservation.  This point 
highlights the significance of our relative utility values which indicate the conservation 
utility for a given species found on a particular study area relative to the utility available 
in the state and neighborhood.  Though these estimates are all quite small, they vary 
greatly between species and between ICP study areas.  Thus, they can be used to inform 
decisions when multiple species are in competition with one another for resources 
 
72 
 
(money, management effort, area, etc.) on a particular property.  These estimates can also 
be used to compare the relative importance of each study area for a particular species.  
However, estimates of relative conservation utility can seem confusing without context.  
For example, our red-cockaded woodpecker model indicated that the Sipsey had high 
relative utility for red-cockaded woodpecker.  These findings make little sense 
considering the fact that the property does not meet the species? minimum patch size 
requirement (see Appendix B) and does not seem to provide suitable habitat.  However, 
we suspect that since all conservation lands received positive value in our models, even 
public lands with little to no appropriate habitat could have higher utility than the 
surrounding neighborhood.  In such a case, that property would represent a high 
proportion of the utility in that area relative to what is available in the neighborhood 
without actually being very important to a particular species.  This appears to be the case 
with our red-cockaded woodpecker model on Sipsey.  This situation highlights the fact 
that these relative values should not be considered alone.  Our relative values should 
always be informed by population models and other contextual information when 
available.  
For the species we examined, the choice of neighborhood sizes from the area 5 ? 
50 km surrounding each study area had little effect on wide or moderately wide-ranging 
species.  However, for species with restricted distributions, and those whose range 
extents were largely outside of our region of interest the results were equivocal or 
misleading.  Thus the results of this type of analysis should be viewed with caution in 
those cases. 
 
73 
 
Considering these guidelines, it becomes possible for us to make some 
conclusions about the importance of each study area for the conservation of GCN species.  
According to our estimates, Barbour and the Perdido have high relative utility for red-
cockaded woodpecker (see Table 2.26).  There simply are not many properties of this size 
within the state, and our models indicate that because of this these two properties could 
be very important for the conservation of red-cockaded woodpecker across the region and 
within their neighborhood.  Additionally, because both study areas have similar soil 
qualities, our models indicate that both Barbour and Perdido have high utility for many of 
the fossorial GCN species, particularly Florida pine snake, gopher tortoise and 
Southeastern pocket gopher on Barbour and black pine snake, Eastern coral snake, 
Florida pine snake, mimic glass lizard and Southeastern pocket gopher on Perdido (see 
Table 2.1).  The neighborhood values for these species and properties are also fairly high, 
resulting in rather low relative utility (see Table 2.2).  This indicates that the importance 
of these properties for these particular species may not be very high within the region or 
the neighborhood, but they still represent excellent opportunities to manage for these 
threatened species. 
Because of its smaller size, interpreting our relative utility estimates on Stimpson 
is difficult.  In our example with the red-cockaded woodpecker, smaller properties with 
low values in both the study area and the surrounding neighborhood can result in 
unexpectedly high relative utility.  However, by checking relative utility estimates against 
study area and neighborhood utility values, the interpretation becomes clearer.  For 
example, our estimates indicated fairly high relative utility for speckled kingsnake on 
Stimpson (see Table 2.2) because, contrary to our red-cockaded woodpecker example 
 
74 
 
from this property, the available utility for speckled kingsnakes is actually fairly high (see 
Table 2.1).  For this reason, our models indicate that Stimpson could be an important 
property for speckled kingsnakes within the region as well as the surrounding 
neighborhood.  We should also point out we found very high utility values for American 
black bear on Stimpson.  Though high neighborhood values for the species diminish the 
relative utility of the property, Stimpson could prove to be an important stepping stone 
for black bears attempting to disperse from their current stronghold in the southeastern 
portion of the state. 
Sipsey is another case where relative utility estimates are best used when 
informed by our study area and neighborhood utility estimates.  Our models indicate that 
the property represents fairly high relative utility for Swainson?s warbler and a few other 
species (see Table 2.2).  However, Swainson?s warbler is the only species with fairly high 
study area utility estimates (see Table 2.1), indicating that the property is likely important 
for this warbler but not the other species.  Study area utility estimates are also particularly 
high for Kentucky warbler, worm-eating warbler and wood thrush.  As with the black 
bear on Stimpson, neighborhood values for these species are also high and the relative 
importance of the property is somewhat low across the region.  However, these maps 
indicate that Sipsey represents an excellent management opportunity for these species.  
One potential shortcoming of this research is the use of GAP data, which has 
come under scrutiny from several sources for its inherent assumptions and shortcomings 
most of which revolve around issues of scale and assumed accuracy of data (Conroy and 
Noon 1996, Flather et al. 1997).  Conroy and Noon (1996) also point out that the 
uncertainty associated with these assumptions should always be explained to resource 
 
75 
 
managers.  Finally, it has been noted that broad scale analyses often cannot account for 
certain fine scale features and that fine scale management resulting from broad scale 
analyses may sometimes be somewhat misguided (Conroy and Noon 1996, Rouget 
2003).  In our study, there were several resources important to the management of GCN 
species that could not be mapped.  For example, though information on long-tailed 
weasels is scarce, some sources indicate that prey density is a good indicator of long-
tailed weasel habitat (Mitchell and Sievering 2004).  We were unable to model prey 
density because those data were not available at the time of our analysis.  Plentovich et 
al. (1999) found that Henslow?s sparrows in Alabama preferred pitcher plant bogs, a 
habitat feature that is below the spatial resolution of our data and therefore could not be 
mapped for our analysis. These issues could result in difficulty when attempting to scale 
down our findings for on-the-ground management actions.  Regardless of the 
implications of these assumptions, at the time of this research, the satellite imagery used 
to develop AL-GAP data are over ten years old.  Undoubtedly, many landscape features 
have changed since these data were generated, introducing unwanted uncertainty into our 
analysis. 
This study focused on vertebrate GCN species occurring in the Southeastern 
Plains of Alabama, but there is no reason it could not be extended to encompass other 
regions or other states.  In fact, the neighborhood estimates conducted in this analysis 
could be quite helpful in any number of conservation situations.  Furthermore, this type 
of analysis could be useful with other species, not just species of greatest conservation 
need.  Management decisions are made regarding common species as well as GCN 
species, and our utility estimates could easily be used to inform those decisions.  That 
 
76 
 
being said, we would probably not recommend that these types of analyses take place 
across several ecoregions because it might become difficult to accurately apply our utility 
estimates to a decision making process across such varied landscapes. 
Acknowledgements: 
 First and foremost, we thank Dr. Mark MacKenzie for initiating and inspiring this 
research.  We would like to thank Max Post ? Van der Burg for his contributions to 
earlier drafts.  Will McDearman, Eric Soehren, Val Johnson and Craig Guyer are all 
greatly appreciated for their input on soil classifications.  We thank Kevin Kleiner and 
Tyler Krepps for their assistance during the GIS components of this project.  This 
research was funded through the Alabama State Wildlife Grants Program as part of the 
Inventory and Conservation Planning project coordinated by the Alabama Cooperative 
Fish and Wildlife Research Unit.  The ALCFWRU is a partnership between the U.S. 
Geological Survey, the Wildlife Management Institute, the Alabama Department of 
Conservation and Natural Resources, Auburn University and the U.S. Fish and Wildlife 
Service.
Figure 2.1: ICP study areas in the Southeastern Plains of Alabama 
77 
 
Figure 2.2:  Utility Maps for alligator snapping turtle on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
78 
 
 
  
A 
B 
C 
D 
E 
Figure 2.3:  Utility Maps for American black bear on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
79 
 
A 
B 
C 
D 
E 
Figure 2.4:  Utility Maps for American kestrel on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
80 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.5:  Utility Maps for American woodcock on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
81 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.6:  Utility Maps for Bachman?s sparrow on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
82 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.7:  Utility Maps for black pine snake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
83 
 
 A 
B 
C 
D 
E 
Figure 2.8:  Utility Maps for coal skink on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management Area and 
D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
84 
 
 A 
B 
C 
D 
E 
Figure 2.9:  Utility Maps for Eastern coral snake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
85 
 
 A 
B 
C 
D 
E 
Figure 2.10:  Utility Maps for Eastern diamondback rattlesnake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour 
Wildlife Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by 
dotted line. 
 
86 
 
 A 
B 
C 
D 
E 
Figure 2.11:  Utility Maps for Eastern indigo snake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
87 
 
 A 
B 
C 
D 
E 
Figure 2.12:  Utility Maps for Eastern kingsnake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
88 
 
 A 
B 
C 
D 
E 
Figure 2.13:  Utility Maps for Eastern spotted skunk on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
89 
 
A 
B 
C 
D 
E 
Figure 2.14:  Utility Maps for flatwoods salamander on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
90 
 
 A 
B 
C 
D 
E 
Figure 2.15:  Utility Maps for Florida pine snake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
91 
 
 A 
B 
C 
D 
E 
Figure 2.16:  Utility Maps for gopher frog on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management Area 
and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
92 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.17:  Utility Maps for gopher tortoise on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
93 
 
A 
B 
C 
D 
E 
Figure 2.18:  Utility Maps for Henslow?s sparrow on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
94 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.19:  Utility Maps for Kentucky warbler on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
95 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.20:  Utility Maps for least bittern on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management Area 
and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
96 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.21:  Utility Maps for little grass frog on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
97 
 
A 
B 
C 
D 
E 
Figure 2.22:  Utility Maps for long-tailed weasel on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
98 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.23:  Utility Maps for mimic glass lizard on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
99 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.24:  Utility Maps for pine barrens treefrog on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
100 
 
 A 
B 
C 
D 
E 
Figure 2.25:  Utility Maps for rainbow snake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management 
Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
101 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.26:  Utility Maps for red-cockaded woodpecker on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
102 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.27:  Utility Maps for river frog on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management Area and 
D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
103 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.28:  Utility Maps for Southeastern pocket gopher on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
104 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.29:  Utility Maps for Southern dusky salamander on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
105 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.30:  Utility Maps for Southern hognose snake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
106 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.31:  Utility Maps for speckled kingsnake on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
107 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.32:  Utility Maps for Swainson?s warbler on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
108 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.33:  Utility Maps for wood thrush on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife Management Area 
and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
109 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
Figure 2.34:  Utility Maps for worm-eating warbler on A) the Sipsey Tracts, B) Stimpson Sanctuaries, C) Barbour Wildlife 
Management Area and D) Perdido Tracts and neighborhoods with E) legend.  Neighborhood boundaries represented by dotted line. 
 
110 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 
B 
C 
D 
E 
 
111 
 
 
Table 2.1 Mean conservation utility for each species within each study area and the surrounding neighborhood 
  
State Barbour 
Barbour 
Neighborhood 
Perdido 
Perdido 
Neighborhood 
Sipsey 
Sipsey 
Neighborhood 
Stimpson 
Stimpson 
Neighborhood 
Alligator Snapping Turtle 0.2640 0.4058 0.2309 0.4522 0.2730 0.4514 0.3695 0.4173 0.2825 
American Black Bear 0.3922 0.5950 0.3635 0.6151 0.5163 0.8477 0.7004 0.3323 0.2394 
American Kestrel 0.4552 0.6605 0.4954 0.6058 0.3842 0.3918 0.2801 0.4278 0.3967 
American Woodcock 0.3513 0.5270 0.3332 0.5782 0.3545 0.5244 0.3875 0.4803 0.3528 
Bachman?s Sparrow 0.3324 0.6313 0.4365 0.4004 0.2553 0.4231 0.2587 0.2798 0.2702 
Black Pine Snake 0.2758 0.5216 0.3252 0.8043 0.5529 0.5615 0.4037 0.2538 0.2436 
Coal Skink 0.1630 0.3746 0.1129 0.2594 0.0566 0.3630 0.2580 0.5438 0.3244 
Eastern Coral Snake 0.2013 0.5337 0.2435 0.7918 0.4940 0.5081 0.3633 0.2192 0.1622 
Eastern Diamondback 
Rattlesnake 0.2605 0.3519 0.1961 0.6770 0.4908 0.5152 0.4273 0.1693 0.1552 
Eastern Indigo Snake 0.1289 0.4719 0.2153 0.4520 0.2411 0.3418 0.2817 0.1938 0.1434 
Eastern Kingsnake 0.1808 0.6648 0.4261 0.6792 0.4082 0.0000 0.0141 0.0000 0.0000 
 
112 
 
Table 2.1 Mean conservation utility for each species within each study area and the surrounding neighborhood 
  
State Barbour 
Barbour 
Neighborhood 
Perdido 
Perdido 
Neighborhood 
Sipsey 
Sipsey 
Neighborhood 
Stimpson 
Stimpson 
Neighborhood 
Eastern Spotted Skunk 0.3570 0.6470 0.3477 0.4736 0.2368 0.4610 0.2783 0.5445 0.4841 
Flatwoods Salamander 0.0259 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 
Florida Pine Snake 0.2852 0.6406 0.4747 0.7911 0.5545 0.3231 0.2253 0.2367 0.2272 
Gopher Frog 0.1693 0.5169 0.2954 0.5473 0.3467 0.3610 0.2048 0.2069 0.1531 
Gopher Tortoise 0.2339 0.6936 0.4640 0.7602 0.4920 0.4522 0.3438 0.1957 0.1448 
Henslow?s Sparrow 0.1619 0.4478 0.2479 0.4560 0.2004 0.2466 0.1132 0.0840 0.0396 
Kentucky Warbler 0.2730 0.3985 0.2066 0.3181 0.2054 0.4699 0.3780 0.6490 0.5353 
Least Bittern 0.2436 0.3428 0.2003 0.4370 0.2682 0.4589 0.3658 0.3866 0.2778 
Little Grass Frog 0.0018 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 
Long-tailed Weasel 0.3017 0.5407 0.3026 0.5690 0.3202 0.5385 0.3755 0.4478 0.3334 
Mimic Glass Lizard 0.0979 0.0000 0.0000 0.7889 0.4905 0.0000 0.1121 0.0000 0.0000 
Pine Barrens Treefrog 0.0048 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 
 
113 
 
Table 2.1 Mean conservation utility for each species within each study area and the surrounding neighborhood 
  
State Barbour 
Barbour 
Neighborhood 
Perdido 
Perdido 
Neighborhood 
Sipsey 
Sipsey 
Neighborhood 
Stimpson 
Stimpson 
Neighborhood 
Rainbow Snake 0.2065 0.2903 0.1693 0.3201 0.2040 0.4107 0.3513 0.3815 0.2400 
Red-cockaded Woodpecker 0.0815 0.2613 0.0989 0.4945 0.2452 0.2156 0.1373 0.0528 0.0042 
River Frog 0.2783 0.4693 0.2329 0.6693 0.4483 0.4066 0.2983 0.3832 0.2638 
Southeastern Pocket 
Gopher 0.2733 0.7200 0.4474 0.7185 0.4609 0.4037 0.2246 0.1847 0.1389 
Southern Dusky 
Salamander 0.0172 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 
Southern Hognose Snake 0.1052 0.3974 0.1813 0.3656 0.1811 0.2165 0.1191 0.1632 0.1208 
Speckled Kingsnake 0.1585 0.0000 0.0000 0.0000 0.0000 0.6871 0.3902 0.0000 0.0000 
Swainson?s Warbler 0.1846 0.2564 0.1446 0.2970 0.1610 0.4063 0.3307 0.3897 0.2117 
Wood Thrush 0.3079 0.4349 0.2480 0.3383 0.2356 0.4876 0.4006 0.6467 0.5408 
Worm-eating Warbler 0.2416 0.2957 0.1697 0.2983 0.1615 0.4040 0.3421 0.6540 0.5383 
 
114 
 
Table 2.2: Conservation utility for each species within each property relative to the utility in the 
state and surrounding neighborhood 
  Barbour Perdido Sipsey Stimpson 
     Alligator Snapping 
Turtle 1.294E-06 1.492E-06 2.376E-07 2.859E-07 
American Black Bear 8.112E-07 7.223E-07 1.503E-07 1.906E-07 
American Kestrel 5.693E-07 8.224E-07 1.006E-07 1.899E-07 
American Woodcock 8.751E-07 1.104E-06 1.646E-07 2.380E-07 
Bachman?s Sparrow 8.457E-07 1.122E-06 1.323E-07 3.040E-07 
Black Pine Snake 1.131E-06 1.254E-06 1.604E-07 3.116E-07 
Coal Skink 3.957E-06 6.686E-06 4.369E-07 5.334E-07 
Eastern Coral Snake 2.116E-06 1.893E-06 2.852E-07 4.293E-07 
Eastern Diamondback 
Rattlesnake 1.340E-06 1.259E-06 1.778E-07 2.859E-07 
Eastern Indigo Snake 3.305E-06 3.458E-06 4.453E-07 5.814E-07 
Eastern Kingsnake 1.678E-06 2.188E-06 0.000E+00 0.000E+00 
 
115 
 
Table 2.2: Conservation utility for each species within each property relative to the utility in the 
state and surrounding neighborhood 
  Barbour Perdido Sipsey Stimpson 
     Eastern Spotted 
Skunk 1.013E-06 1.332E-06 1.338E-07 2.867E-07 
Flatwoods 
Salamander 0.000E+00 0.000E+00 0.000E+00 0.000E+00 
Florida Pine Snake 9.198E-07 1.189E-06 1.551E-07 3.107E-07 
Gopher Frog 2.008E-06 2.216E-06 3.389E-07 6.430E-07 
Gopher Tortoise 1.242E-06 1.570E-06 2.453E-07 3.474E-07 
Henslow?s Sparrow 2.169E-06 3.343E-06 5.566E-07 8.310E-07 
Kentucky Warbler 1.373E-06 1.349E-06 1.886E-07 2.813E-07 
Least Bittern 1.366E-06 1.591E-06 2.427E-07 3.182E-07 
Little Grass Frog 0.000E+00 0.000E+00 0.000E+00 0.000E+00 
Long-tailed Weasel 1.151E-06 1.400E-06 1.890E-07 2.936E-07 
Mimic Glass Lizard 0.000E+00 3.905E-06 0.000E+00 0.000E+00 
 
116 
 
Table 2.2: Conservation utility for each species within each property relative to the utility in the 
state and surrounding neighborhood 
  Barbour Perdido Sipsey Stimpson 
     Pine Barrens Treefrog 0.000E+00 0.000E+00 0.000E+00 0.000E+00 
Rainbow Snake 1.614E-06 1.807E-06 3.268E-07 3.498E-07 
Red-cockaded 
Woodpecker 6.295E-06 5.881E-06 6.582E-06 1.190E-06 
River Frog 1.403E-06 1.271E-06 2.210E-07 3.016E-07 
Southeastern Pocket 
Gopher 1.144E-06 1.356E-06 2.066E-07 4.061E-07 
Southern Dusky 
Salamander 0.000E+00 0.000E+00 0.000E+00 0.000E+00 
Southern Hognose 
Snake 4.049E-06 4.562E-06 5.455E-07 1.067E-06 
Speckled Kingsnake 0.000E+00 0.000E+00 0.000E+00 6.862E-07 
Swainson?s Warbler 1.867E-06 2.376E-06 4.234E-07 4.112E-07 
Wood Thrush 1.107E-06 1.109E-06 1.650E-07 2.443E-07 
 
117 
 
Table 2.2: Conservation utility for each species within each property relative to the utility in the 
state and surrounding neighborhood 
  Barbour Perdido Sipsey Stimpson 
     Worm-eating Warbler 1.402E-06 1.818E-06 2.136E-07 3.020E-07 
 
118 
 
Table 2.3: Elasticity of each species model for the ecoregion and each neighborhood.  No value 
indicates an area where the species did not occur. 
  State Barbour Perdido Sipsey Stimpson 
Alligator Snapping 
Turtle 1.0011 1.0097 1.0002 1.0049 1.0043 
American Black Bear 1.0012 1.0057 1.0004 1.0057 1.0041 
American Kestrel 1.0013 1.0066 1.0002 1.0063 1.0043 
American Woodcock 1.0012 1.0069 1.0002 1.0050 1.0042 
Bachman?s Sparrow 1.0012 1.0027 1.0003 1.0071 1.0049 
Black Pine Snake 1.0013 1.0039 1.0003 1.0063 1.0041 
Coal Skink 1.0013 1.0406 1.0003 1.0074 1.0092 
Eastern Coral Snake 1.0013 1.0041 1.0002 1.0066 1.0083 
Eastern Diamondback 
Rattlesnake 1.0012 1.0012 1.0002 1.0064 1.0041 
Eastern Indigo Snake 1.0013 1.0039 1.0000 1.0048 1.0040 
Eastern Kingsnake 1.0011 1.0042 1.0003 1.0246 - 
 
119 
 
Table 2.3: Elasticity of each species model for the ecoregion and each neighborhood.  No value 
indicates an area where the species did not occur. 
  State Barbour Perdido Sipsey Stimpson 
Eastern Spotted 
Skunk 1.0013 1.0062 1.0002 1.0061 1.0050 
Flatwoods 
Salamander 1.0015 - - - - 
Florida Pine Snake 1.0012 1.0022 1.0002 1.0070 1.0041 
Gopher Frog 1.0013 1.0043 1.0001 1.0059 1.0083 
Gopher Tortoise 1.0012 1.0022 1.0001 1.0065 1.0083 
Henslow?s Sparrow 1.0013 1.0106 1.0004 1.0063 1.0029 
Kentucky Warbler 1.0012 1.0072 1.0001 1.0049 1.0040 
Least Bittern 1.0012 1.0111 1.0002 1.0046 1.0043 
Little Grass Frog 1.0096 - - - - 
Long-tailed Weasel 1.0012 1.0073 1.0003 1.0051 1.0042 
Mimic Glass Lizard 1.0014 - 1.0001 1.0391 - 
 
120 
 
Table 2.3: Elasticity of each species model for the ecoregion and each neighborhood.  No value 
indicates an area where the species did not occur. 
  State Barbour Perdido Sipsey Stimpson 
Pine Barrens Treefrog 1.0008 - - - - 
Rainbow Snake 1.0011 1.0087 1.0002 1.0045 1.0042 
Red-cockaded 
Woodpecker 1.0012 1.0046 1.0007 1.0057 1.0021 
River Frog 1.0012 1.0095 1.0001 1.0049 1.0041 
Southeastern Pocket 
Gopher 1.0012 1.0024 1.0001 1.0078 1.0070 
Southern Dusky 
Salamander 1.0014 - - - - 
Southern Hognose 
Snake 1.0013 1.0039 1.0000 1.0060 1.0040 
Speckled Kingsnake 1.0009 - - 1.0052 - 
Swainson?s Warbler 1.0011 1.0078 1.0002 1.0045 1.0043 
Wood Thrush 1.0012 1.0074 1.0001 1.0050 1.0040 
 
121 
 
Table 2.3: Elasticity of each species model for the ecoregion and each neighborhood.  No value 
indicates an area where the species did not occur. 
  State Barbour Perdido Sipsey Stimpson 
Worm-eating Warbler 1.0012 1.0080 1.0002 1.0046 1.0040 
 
122 
 
Table 2.4: Elasticity of three species models to extreme changes in neighborhood 
size surrounding each study area.  No value indicates an area where the species did 
not occur. 
 
Study Area 50 km Buffer 5 km Buffer 
 
Barbour 
  Eastern Kingsnake 
 
0.9277 1.1531 
Gopher Tortoise 
 
0.7469 1.2201 
Wood Thrush 
 
1.0198 1.2038 
 
Perdido 
      Eastern Kingsnake 
 
0.8370 1.1867 
Gopher Tortoise 
 
0.7800 1.2368 
Wood Thrush 
 
1.0635 1.0907 
    
 
Sipsey 
      Eastern Kingsnake 
 
- - 
Gopher Tortoise 
 
1.0338 1.1950 
Wood Thrush 
 
0.9079 1.1084 
    
 
Stimpson 
   Eastern Kingsnake 
 
6.8060 - 
Gopher Tortoise 
 
1.1240 0.9926 
Wood Thrush 
 
0.7377 1.1439 
 
123 
 
References 
Adelaja, S., Y.G. Hailu, R. Kuntzsch, M. Lake, M. Fulkerson, C. McKeown, L. 
 Racevskis, N.  Griswold.  2007.  State Conservation Spending in the United 
 States: A Political Economy Analysis.  Land Policy Institute, Michigan State 
 University, East Lansing, MI. 
ADCNR.  Accessed February 2010.  http://www.outdooralabama.com.   
Beardah, C.C. and M.J. Baxter.  1996.  MATLAB Routines for Kernel Density 
 Estimation and the Graphical Presentation of Archaeological Data.  In H. 
 Kammermans and K. Fennema, eds.  Analecta Prehistorica Leidensia 28, 
 Interfacing the Past, Computer Applications and Quantitative Methods in 
 Archaeology.  Leiden. 
Benson, B.J. and M.D. MacKenzie.  1995.  Effects of Sensor Spatial Resolution on 
 Landscape Structure Parameters.  Landscape Ecology 10(2): 113 ? 120. 
Bergin, T.M.  1992.  Habitat Selection by the Western Kingbird in Western Nebraska: a 
 Hierarchical Analysis.  The Condor 94: 903 ? 911. 
Brennan, L.A., R.T. Engstrom, W.E. Palmer, S.M. Hermann, G.A. Hurst, L.W. Burger 
 and C.L. Hardy.  1998.  Whither Wildlife without Fire?  Transactions of the 63rd 
 
124 
 
 North American Wildlife and Natural Resources Conference: 1998 March 20-25; 
 Orlando, FL.  Washington, DC: Wildlife Management Institute: 402 ? 414. 
Brown, J.D., T.J. Benson and J.C. Bednarz.  2009.  Vegetation Characteristics of 
 Swainson?s Warbler Habitat at the White River National Wildlife Refuge, 
 Arkansas.  Wetlands 29(2): 586 ? 597. 
Brito, J.C., A.L. Acosta, F. Alvares and F. Cuzin.  2009.  Biogeography and Conservation 
 of Taxa from Remote Regions: An Application of Ecological-niche Based Models 
 and GIS to North African Canids.  Biological Conservation 142(12): 3020 ? 3029. 
Campos-Krauer, J.M. and S.M. Wisely.  2010.  Deforestation and Cattle Ranching Drive 
 Rapid  Range  Expansion of Capybara in the Gran Chaco Ecosystem.  Global 
 Change Biology: In Print. 
Carignan, V. and M. Villard.  2002.  Selecting Indicator Species to Monitor Ecological 
 Integrity: A Review.  Environmental Monitoring and Assessment 78: 45 ? 61. 
Comer, P., D. Faber-Langendoen, R. Evans, S. Gawler, C. Josse, G. Kittel, S. Menard, M. 
 Pyne, M. Reid, K. Shultz, K. Snow and J. Teague.  2003.  Ecological Systems of 
 the United States: A Working Classification of U.S. Terrestrial Systems. 
 NatureServe, Arlington, Virginia. 
Conroy, M.J. and B.R. Noon.  1996.  Mapping of Species Richness for Conservation of 
 Biological Diversity: Conceptual and Methodological Issues.  Ecological 
 Applications 6(3): 763 ? 773. 
 
125 
 
Delibes-Mateos, M., E. Ramirez, P. Ferreras and R. Villafuerte.  2008.  Translocations as 
 a Risk  for the  Conservation of European Wild Rabbit Oryctolagus cuniculus 
 Lineages.  Oryx 42: 259 ? 264. 
Duran, C.M. 1998.  A Radio-telemetric Study of the Black Pine Snake (Pituophis 
 melanoleucus lodingi) on Camp Shelby Training Site, Camp Shelby, Mississippi. 
 Final Report to the Mississippi Natural Heritage Program and the Mississippi 
 Army National Guard. 
Eddleman, W.R. K.E. Evans and W.H. Elder.  1980.  Habitat Characteristics and 
 Management of Swainson?s Warbler in Southern Illinois.  Wildlife Society 
 Bulletin 8(3): 228 ? 233. 
Ewers, R.M. and A.S.L. Rodrigues.  2008.  Estimates of Reserve Effectiveness Are 
 Confounded by Leakage.  Trends in Ecology & Evolution 23(3):113 ? 116. 
Fahrig, L. and G. Merriam.  1985.  Habitat Patch Connectivity and Population Survival.  
 Ecology 66(6): 1762 ? 1768. 
Flather, C.H., K.R. Wilson, D.J. Dean and W.C. McComb.  1997.  Identifying Gaps in 
 Conservation Networks: Of Indicators and Uncertainty in Geographic-Based 
 Analyses.  Ecological Applications 7(2): 531 ? 542. 
Forman, R.T.T. and M. Godron.  1981.  Patches and Structural Components for a 
 Landscape Ecology.  BioScience 31(10): 733 ? 740. 
 
126 
 
Fortin, M.J., T.H. Keitt, B.A. Maurer, M.L. Taper, D.M. Kaufman and T.M. Blackburn.  
 2005.  Species? Geographic Ranges and Distributional Limits: Pattern Analysis 
 and Statistical Issues.  OIKOS 108: 7 ? 17. 
Franz, R. 2005.  Up Close and Personal: a Glimpse into the Life of the Florida Pine 
 Snake in a North Florida Sand Hill.  In W.E. Meshaka, Jr. and K.J. Babbitt, eds.  
 Amphibians and Reptiles: Status and Conservation in Florida.  Krieger 
 Publishing, Malabar, FL. 
Freeman, J.E. and S. Jose.  2009.  The Role of Herbicide in Savanna Restoration: Effects 
 of Shrub Reduction Treatments on the Understory and Overstory of a Longleaf 
 Pine Flatwoods.  Forest Ecology and Management 257(3): 978 ? 986. 
Friedlander, A.M., E.K. Brown and M.E. Monaco.  2007.  Coupling Ecology and GIS to 
 Evaluate Efficacy of Marine Protected Areas in Hawaii.  Ecological Applications 
 17(3): 715 ? 730. 
Gagne, S.A. and L. Fahrig.  2007.  Effect of Landscape Context on Anuran Communities 
 in Breeding Ponds in the National Capital Region, Canada.  Landscape Ecology 
 22: 205 ? 215. 
Germano, J.M. and P.J. Bishop.  2008.  Suitability of Amphibians and Reptiles for 
 Translocation.  Conservation Biology 23(1): 7 ? 15. 
 
127 
 
Gibbons, J.W., D.E. Scott, T.J. Ryan, K.A. Buhlmann, T.D. Tuberville, B.S. Metts, J.L. 
 Greene, T. Mills, Y. Leiden, S. Poppy and C.T. Winne.  2000.  The Global 
 Decline of Reptiles, D?j? vu Amphibians.  BioScience 50(8): 653 ? 666. 
Gibbs, J.P. and J. Faaborg.  1990.  Estimating the Viability of Ovenbird and Kentucky 
 Warbler Populatins in Forest Fragments.  Conservation Biology 4: 193 ? 196. 
Goldstein J.H., L. Pejchar and G.C. Daily.  2008.  Using Return-on-investment to Guide 
 Restoration: a  Case Study from Hawaii.  Conservation Letters 1: 236 ? 243. 
Graves, G.R. 2002.  Habitat Characteristics in the Core Breeding Range of the 
 Swainson?s Warbler.  Wilson Bulletin 114(2): 210 ? 220. 
Griffith, G.E., J.M. Omernik, J.A. Comstock, G. Martin, A. Goddard and V.J. Hulcher.  
 2001.  Ecoregions of Alabama.  U.S. Environmental Protection Agency, National 
 Health and Environmental Effects Research Laboratory, Corvallis, OR. 
Gutzwiller, K.J.  2002.  Applying Landscape Ecology in Biological Conservation.  
 Springer Science+Business Media, LLC, New York, NY. 
Guyer, C.  2010.  Pers. Comm.  
Hallam, C.O. K. Wheaton and R.A. Fischer.  1998.  Species Profile: Eastern Indigo 
 Snake (Drymarchon coarais couperi) on Military Installations in the Southeastern 
 United States.  Prepared for U.S. Army Corp of Engineers. 
Hansen, A.J. and R. DeFries.  2007.  Ecological Mechanisms Linking Protected Areas to 
 Surrounding Lands.  Ecological Applications 17(4): 974 ? 988. 
 
128 
 
Harrington, T.B. and M.B. Edwards.  1999.  Understory Vegetation, Resource 
 Availability, and Litterfall Responses to Pine Thinning and Woody Vegetation 
 Control in Longleaf Pine Plantations.  Canadian Journal of Forest Research 29: 
 1055 ? 1064. 
Heden?s, H. and L. Ericson.  2008.  Species Occurences at Stand Level Cannot Be 
 Understood without Considering the Landscape Context: Cyanolichens on Aspen 
 in Boreal Sweden.  Biological Conservation 141(3): 710 ? 718. 
Hermann, S.M., C. Guyer, J.H. Waddle and M.G. Nelms.  2002.  Sampling on Private 
 Property to Evaluate Population Status and Effects of Land Use Practices on the 
 Gopher Tortoise, Gopherus polyphemus.  Biological Conservation 108(3): 289 ? 
 298. 
Holland, J.D., D.G. Bert and L. Fahrig.  2004.  Determining the Spatial Scale of Species? 
 Response to Habitat.  BioScience 54(3): 227 ? 233. 
Jensen, J.B.  2004.  Southern hognose snake Heterodon Simus.  Pp.  42-43.  In R.E. 
 Mirarchi, M.A. Bailey, T.M. Haggerty and T.L. Best, eds.  Alabama Wildlife. 
 Volume 3. Imperiled Amphibians, Reptiles, Birds and Mammals.  The University 
 of Alabama Press, Tuscaloosa, AL. 
Kleiner, K.J.  2007.  A Satellite Derived Map of Ecological Systems in the East Gulf 
 Coastal Plain, USA. Master?s Thesis, Auburn University. Auburn, AL.  
 
129 
 
Lagory, K.E., L.J. Walston, C. Goulet, R.A. Van Lonkhuyzen, S. Najjar and C. Andrews.  
 2009.  An Examination of Scale-dependent Resource Use by Eastern Hognose 
 Snakes in Southcentral New Hampshire.  Journal of Wildlife Management 73(8): 
 1387 ? 1393. 
Laliberte, A.S. and W.J. Ripple.  2004.  Range Contractions of North American 
 Carnivores and Ungulates.  BioScience 54(2): 123 ? 138. 
Landers, J.L. and D.W. Speake.  1980.  Management Needs of Sandhill Reptiles in 
 Southern Georgia.  Proceedings of the Annual Conference of the Southeastern 
 Association of Fish and Wildlife Agencies.  34: 515 ? 529. 
Linhan, J.M., L.L. Smith and D.A. Steen.  2010.  Ecology of the Eastern Kingsnake 
 (Lampropeltis getula getula) in a Longleaf Pine (Pinus palustris) Forest in 
 Southwestern Georgia.  Herpetological Conservation and Biology 5(1): 94 ? 101. 
Kark, S., N. Levin, H.S. Grantham and H.P. Possingham.  Between-country 
 Collaboration and Consideration of Costs Increase Conservation Planning 
 Efficiency in the Mediterranean Basin.  Proceedings of the National Academy of 
 Sciences of the United States of America 106: 15360 ? 15365. 
McDearman, W.  2005.  Gopher Tortoise (Gopherus polyphemus) Soil Classification in 
 the Federally Listed Range.  File Report.  U.S. Fish and Wildlife Service, 
 Ecological Services, Jackson, MS. 
 
130 
 
McKerrow, A.J., S.G. Williams, A.L. Silvano, E.A. Kramer, K.J. Kleiner, T.S. Earnhardt, 
 J.W. Lee, M.J. Rubino, M. Pyne, K.W. Samples, C.M. Belyea, A.E. Ernst, J.B. 
 Grand, M.D. MacKenzie and J.A. Collazo.  Forthcoming.  Southeast Gap 
 Analysis Final Report.  U.S. Geological Survey, Gap Analysis Program, Moscow, 
 ID.  Forthcoming. 
McLachlan, J.S., J.J. Hellmann and M.W. Schwartz.  2007.  A Framework for Debate of 
 Assisted Migration in an Era of Climate Change.  Conservation Biology 21: 297 ? 
 302. 
McLoughlin, P.D., L.R. Walton, H.D. Cluff, P.C. Paquet and M.A. Ramsay.  2004.  
 Hierarchical Habitat Selection by Tundra Wolves.  Journal of Mammalogy 85(3): 
 576 ? 580. 
Means, D.B.  2004.  Eastern kingsnake Lampropeltis getula getula.  Pp.  65-66.  In R.E. 
 Mirarchi, M.A. Bailey, T.M. Haggerty and T.L. Best, eds.  Alabama Wildlife. 
 Volume 3. Imperiled  Amphibians, Reptiles, Birds and Mammals.  The 
 University of Alabama Press, Tuscaloosa, AL. 
Means, D.B.  2004.  Flatwoods salamander Ambystoma cingulatum.  Pp.  65-66.  In R.E. 
 Mirarchi, M.A. Bailey, T.M. Haggerty and T.L. Best, eds.  Alabama Wildlife. 
 Volume 3. Imperiled  Amphibians, Reptiles, Birds and Mammals.  The 
 University of Alabama Press, Tuscaloosa, AL. 
Mitchell, M.S. and M. Sievering.  2004.  Long-tailed Weasel Mustela frenata.  Pp. 208-
 209.  In R.E. Mirarchi, M.A. Bailey, T.M. Haggerty and T.L. Best, eds.  Alabama 
 
131 
 
 Wildlife. Volume 3. Imperiled Amphibians, Reptiles, Birds and Mammals.  The 
 University of Alabama Press, Tuscaloosa, AL. 
Minteer, B.A. and J.P. Collins.  2010.  Move It Or Lose It?  The Ecological Ethics of 
 Relocating Species under Climate Change.  Ecological Applications 20(7): 1801 ? 
 1804. 
Mirarchi, R.E., M.A. Bailey, T.M. Haggerty and T.L. Best, eds.  2004.  Alabama 
 Wildlife.  Volume 3.  Imperiled Amphibians, Reptiles, Birds and Mammals.  The 
 University of Alabama Press, Tuscaloosa, AL. 
Moilanen, A. and I. Hanski.  1998.  Metapopulation Dynamics: Effects of Habitat Quality 
 and Landscape Structure.  Ecology 79(7): 2503 ? 2515. 
Murdoch, W., S. Polasky, K.A. Wilson, H.P. Possingham, P. Kareiva and R. Shaw.  
 2007.   Maximizing Return on Investment in Conservation.  Biological 
 Conservation 139: 375 ? 388. 
Murphy, D.D, K.E. Freas and S.B.Weiss.  1990.  An Environment-Metapopulation 
 Approach to Population Viability Analysis for a Threatened Invertebrate.  
 Conservation Biology 4(1): 41 ? 51. 
Norden, A.H. and L.K. Kirkman.  2004.  Factors Controlling the Fire-induced Flowering  
 Response of the Federally Endangered Schwalbea americana L. 
 (Scrophulariaceae).  Journal of the Torrey Botanical Society 131(1): 16 ? 22. 
 
132 
 
Outcalt, K.W. and D.G. Brockway.  2010.  Structure and Composition Changes 
 Following Restoration Treatments of Longleaf Pine Forests on the Gulf Coastal 
 Plain of Alabama.  Forest Ecology and Management 259(8): 1615 ? 1623. 
Plentovich, S., N.R. Holler and G.E. Hill.  1999.  Habitat Requirements of Henslow?s 
 Sparrows Wintering in Silvicultural Lands of the Gulf Coastal Plain.  The Auk 
 116(1): 109 ? 115. 
Plummer, M.V. and N.E. Mills.  2000.  Spatial Ecology and Survivorship of Resident and 
 Translocated Hognose Snakes (Heterodon platirhinos). Journal of Herpetology 
 34(4): 565 ? 575. 
Polderboer, E.B., L.W. Kuhn and G.O. Hendrickson. 1941.  Winter and Spring Habits of 
 Weasels in Central Iowa.  Journal of Wildlife Management.  5: 115 ? 119. 
Ricciardi, A. and D. Simberloff.  2009.  Assisted Colonization is not a Viable 
 Conservation Strategy.  Trends in Ecology and Evolution 24(5): 248 ? 253. 
Rittenhouse, C.D., W.D. Dijak, F.R. Thompson, III and J.J. Millspaugh.  2007.  
 Development of Landscape- level Habitat Suitability Models for Ten Wilidlife 
 Species in the Central Hardwoods Region.  U.S. Department of Agriculture, 
 Forest Service General Technical Report NRS-4, Northern Research Station, 
 Newton Square, Pennsylvania, USA. 
Rodrigues, A.S.L., H. Resit Akcakaya, S.J. Andelman, M.I. Bakarr, L. Boitani, T.M. 
 Brooks, J.S. Chanson, L.D.C. Fishpool, G.A.B. Da Fonseca, K.J. Gaston, M. 
 
133 
 
 Hoffmann, P.A. Marquet, J.D. Pilgrim, R.L. Pressey, J. Schipper, W. Sechrest, 
 S.N. Stuart, L.G. Underhill, R.W. Waller, M.E.J. Watts and X. Yan.  2004.  
 Global Analysis: Priority Regions for Expanding the Global Protected-Area 
 Network.  BioScience 54(12): 1092 ? 1100. 
Rouget, M.  2003.  Measuring Conservation Value at Fine and Broad Scales: Implications 
 for a Diverse and Fragmented Region, the Agulhas Plain.  Biological 
 Conservation 112: 217 ? 232.  
Rudis, V.A. and B. Tansey.  1995.  Regional Assessment of Remote Forests and Black 
 Bear Habitat from Forest Resource Surveys.  Journal of Wildlife Management 59: 
 170 ? 180. 
Russell, K.R., D.H. Van Lear and D.C. Guynn Jr.  1999.  Prescribed Fire Effects on 
 Herpetofauna: Review and Management Implications.  Wildlife Society Bulletin 
 27(2):  374 ? 384. 
Santos, X., J.C. Brito, N. Sillero, J.M. Pleguezuelos, G.A. Llorente, S. Fahd and X. 
 Parellada.  2006.  Inferring Habitat-suitablity Areas with Ecological Modelling 
 Techniques and GIS: A Contribution to Assess the Conservation Status of Vipera 
 latastei.  Biological Conservation 130(3): 416 ? 425. 
Schoener, T.W.  1976.  The Species-Area Relations within Archipelagos: Models and 
 Evidence from Island Land Birds.  Pp. 629-642. In H.J. Frith and J.H. Calaby, 
 eds.  Proceedings of the 16th International Ornithological Conference. 
 Australian Academy of Sciences, Canberra, Australia. 
 
134 
 
Shafer, C.L.  1997.  Terrestrial Nature Reserve Design at the Urban/Rural Interface.  
 Pp. 345-378.  In M.W. Schwartz, ed.  Conservation in Highly Fragmented 
 Landscapes.  Chapman and Hall, New York, NY. 
Shaw, J.N., L.T. West, D.D. Bosch, C.C. Truman and D.S. Leigh.  2004.  Parent Material 
Influence on Soil Distribution and Genesis in a Paleudult and Kandiudult 
Complex, Southeastern USA.  Catena 57: 157 ? 174. 
Silvano, A., K. Kleiner, J.B. Grand, M.D. MacKenzie, M.S. Mitchell, and E.R. Irwin. 
2008.  The Alabama Gap Analysis Project:  Draft Final Report.  U.S. Geological 
Survey, Gap Analysis Program, Moscow, ID.  
Silverman, B.W. 1986.  Density Estimation for Statistics and Data Analysis.  Chapman & 
 Hall, London, UK. 
Smallwood, J.A., P. Winkler, G.I. Fowles and M.A. Craddock.  2009.  American Kestrel 
 Breeding Habitat: the Importance of Patch Size.  Journal of Raptor Research 
 43(4): 308 ? 314. 
Sollins, P. and G. Spycher.  1983.  Processes of Soil Organic-Matter Accretion at a 
 Mudflow Chronosequence, Mt. Shasta, California.  Ecology 64(5): 1273 ? 1282. 
Stein, B.A.  2002.  States of the Union: Ranking America?s Biodiversity.  NatureServe,  
 Arlington, Virginia. 
Storfer, A.  1999.  Gene Flow and Endangered Species Translocations: a Topic Revisited.  
 Biological Conservation 87: 173 ? 180. 
 
135 
 
Thogmartin, W.E. and M.G. Knutson.  2007.  Scaling Local Species-Habitat Relations to 
 the Larger Landscape with a Hierarchical Spatial Count Model.  Landscape 
 Ecology 22: 61 ? 75. 
Timmerman, W.W. 1989.  Home Range, Habitat Use and Behavior of the Eastern 
 Diamondback Rattlesnake.  University of Florida, Gainsville, Florida.  M.S. 
 Thesis. 
Togneilli, M.F., P.I. Ramirez de Arellano and P.A. Marquet.  2008.  How Well Do the 
 Existing and Proposed Reserve Networks Represent Vertebrate Species in Chile?  
 Diversity and Distributions 14: 148 ? 158. 
Tolley, K.A., S.J. Davies and S.L. Chown.  2008.  Deconstructing a Controversial Local 
 Range  Expansion: Conservation Biogeography of the Painted Reed Frog 
 (Hyperolius marmoratus) in South Africa.  Diversity and Distributions 14: 400 ? 
 411. 
Tucker Jr., J.W., W.D. Robinson and J.B. Grand.  2004.  Influence of Fire on Bachman?s 
 Sparrow, an Endemic North American Songbird.  Journal of Wildlife 
 Management 68(4): 1114 ? 1123. 
Turner, M.G., R.V.O?Neill, R.H. Gardner and B.T. Milne.  1989.  Effects of Changing 
 Spatial Scale on the Analysis of Landscape Pattern.  Landscape Ecology 3: 153 ? 
 162. 
 
 
136 
 
Turner, M.G., R.H. Gardner and R.V. O?Neill.  2001.  Landscape Ecology in Theory and 
 Practice: Pattern and Process.  Springer Science+Business Media, LLC, New 
 York, NY. 
 
U.S. Fish and Wildlife Service.  2003.  Recovery Plan for the Red-cockaded Woodpecker 
 (Picoides borealis): Second Revision.  U.S. Fish and Wildlife Service, Atlanta, 
 GA. 
Wallace, G.N., D.M. Theobald, T. Ernst and K. King.  2008.  Assessing the Ecological 
 and Social Benefits of Private Land Conservation in Colorado.  Conservation 
 Biology 22(2): 284 ? 296. 
 
Westphal, M.I. and H.P. Possingham.  2003.  Applying a Decision-Theory Framework to 
 Landscape Planning for Biodiversity: Follow-up to Watson et al.  Conservation 
 Biology 17(1): 327 ? 329. 
 
Wilson, K.A., E. Meijaard, S. Drummond, H.S. Grantham, L. Boitani, G. Catullo, L. 
 Christie, R. Dennis, I. Dutton, A. Falcucci, L. Maiorano, H.P. Possingham, C. 
 Rondinini, W.R. Turner, O. Venter and M. Watts.  2010.  Conserving Biodiversity 
 in Production Landscapes.  Ecological Applications 20(6): 1721 ? 1732. 
 
Wolf, C.M., B. Griffith, C. Reed and S.A. Temple.  1996.  Avian and Mammalian 
 Translocations: Update and Reanalysis of 1987 Survey Data.  Conservation 
 Biology 10(4): 1142 ? 1154.
 
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Appendix A: Models used to predict conservation utility 
Species Model 
  Alligator Snapping Turtle = (Expected Habitat + Public Land + Bottomland)*Urban 
American Black Bear  = (Expected Habitat + Minimum Patch + Public Land)*Urban 
American Kestrel  = (Expected Habitat + Minimum Patch + Public Land)*Urban 
American Woodcock  = (Expected Habitat + Public Land + Wetlands)*Urban 
Bachman's Sparrow  = (Expected Habitat + Minimum Patch + Public Land + Upland)*Urban 
Black Pine Snake  = (Expected Habitat + Minimum Patch + Public Land + Upland + Sandy Soils)*Urban 
Coal Skink  = (Expected Habitat + Public Land)*Urban 
Eastern Coral Snake  = (Expected Habitat + Public Land + Sandy Soils)*Urban 
Eastern Diamondback Rattlesnake  = (Expected Habitat + Minimum Patch + Public Land + Upland)*Urban 
Eastern Indigo Snake  = (Expected Habitat + Minimum Patch + Public Land + Sandy Soils)*Urban 
Eastern Kingsnake  = (Expected Habitat + Public Land)*Urban 
Eastern Spotted Skunk  = (Expected Habitat +Public Land)*Urban 
 
138 
 
Appendix A: Models used to predict conservation utility 
Species Model 
  Flatwoods Salamander = (Expected Habitat + Public Land + Isolated Wetlands + Sandy Soils)*Urban 
Florida Pine Snake  = (Expected Habitat + Minimum Patch + Public Land + Upland + Sandy Soils)*Urban 
Gopher Frog  = (Expected Habitat + Public Land + Sandy Soils)*Urban 
Gopher Tortoise  = (Expected Habitat + Public Land + Sandy Soils)*Urban 
Henslow's Sparrow  = (Expected Habitat + Public Land + Isolated Wetlands)*Urban 
Kentucky Warbler  = (Expected Habitat + Minimum Patch + Public Land + Bottomland)*Urban 
Least Bittern  = (Expected Habitat + Public Land + Wetlands)*Urban 
Little Grass Frog  = (Expected Habitat + Public Land + Isolated Wetlands)*Urban 
Long-tailed Weasel  = (Expected Habitat + Public Land + Wetlands)*Urban 
Mimic Glass Lizard  = (Expected Habitat + Public Land + Sandy Soils)*Urban 
Pine Barrens Treefrog  = (Expected Habitat + Public Land + Isolated Wetlands)*Urban 
Rainbow Snake  = (Expected Habitat + Public Land + Bottomland)*Urban 
 
139 
 
Appendix A: Models used to predict conservation utility 
Species Model 
  Red-cockaded Woodpecker = (Expected Habitat + Minimum Patch + Public Land)*Urban 
River Frog  = (Expected Habitat + Public Land + Bottomland)*Urban 
Southeastern Pocket Gopher  = (Expected Habitat + Public Land + Sandy Soils)*Urban 
Southern Dusky Salamander  = (Expected Habitat + Public Land + Isolated Wetlands)*Urban 
Southern Hognose Snake  = (Expected Habitat + Minimum Patch + Public Land + Sandy Soils)*Urban 
Speckled Kingsnake  = (Expected Habitat + Public Land)*Urban 
Swainson's Warbler  = (Expected Habitat + Minimum Patch + Public Land + Bottomland)*Urban 
Wood Thrush  = (Expected Habitat + Minimum Patch + Public Land + Bottomland)*Urban 
Worm-eating Warbler  = (Expected Habitat + Minimum Patch + Public Land + Bottomland)*Urban 
 
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Appendix B: Minimum patch size requirements for select species.  If a species is not on this 
list, minimum patch was not incorporated into its model because the information was 
unavailable in literature. 
Species 
Minimum 
Patch Source 
American Kestrel 250 ha Smallwood et al. 2009 
Wood Thrush  80 ha Rittenhouse et al. 2007 
Worm-eating Warbler 150 ha Rittenhouse et al. 2007 
Swainson's Warbler 350 ha Eddleman et al 1980 
American Black Bear 1000 ha Rudis and Tansey 1995 
Eastern Indigo Snake 1000 ha Hallam et al. 1998 
Eastern Diamondback Rattlesnake 8500 ha * Timmerman 1989 
Kentucky Warbler 500 ha Gibbs and Faaborg 1990 
Eastern Kingsnake 4900 ha * Linhan et al. 2010 
Southern Hognose Snake 5000 ha # Plummer and Mills 2000 
Black Pine Snake 4800 ha * Duran 1998 
Florida Pine Snake 5700 ha * Franz 2005 
Bachman's Sparrow 2000 ha Silvano et al. 2008 
Red-cockaded Woodpecker 80000 ha ? US Fish and Wildlife Service 2003 
* home range size multiplied by population of 100 individuals 
# home range size of similar subspecies multiplied by 100 individuals 
? home range size on "sites of low productivity" multiplied by 100 individuals