Species Distribution Modeling for Bog Turtles
(Glyptemys muhlenbergii) in North Carolina
Kevin Dick
Master of Environmental Management
December 6th, 2013
Masters Project submitted to the faculty of the Nicholas School of the Environment at Duke University in partial
fulfillment of the requirements for the degree of
Master of Environmental Management
Dr. Jennifer Swenson (jswenson@duke.edu)
Kevin Dick (kcdick@gmail.com)
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ABSTRACT
The bog turtle (Glyptemys muhlenbergii) is the smallest turtle species in North America
and is listed as a threatened species under the federal Endangered Species Act. Accurate
detection of its specialized wetland habitat and subsequent tagging of individuals for monitoring
purposes is critical for improving conservation efforts with this species. Parts of the Piedmont
region in North Carolina have historically served as habitat for bog turtles, but few populations
are now known to occur there. Increases in residential development, agricultural land use, and
the draining of wetland areas over the past several decades have likely contributed to their
current extirpation from this part of the state. Most wildlife managers no longer survey for bog
turtles in most of the Piedmont as efforts are both time and cost prohibitive, and funding
generally all allocated for work in counties where they have a better chance of locating bog
turtles during a given survey event. Several managers acknowledge that there may still be bog
turtles living in the Piedmont, but because of present limitations, there is currently no
conservation plan for them. GIS and predictive modeling were used as a low-cost method for
locating potential sites within four North Carolina counties that exhibit suitable habitat
characteristics for bog turtles. Such predictions may prove useful in documenting new
occurrences of bog turtles in both the Piedmont counties of Iredell, Davie, and Davidson, as well
as in the higher quality bog turtle habitat regions of Wilkes County. The Maxent distribution
model was used as it is capable of producing accurate habitat predictions for species with small
sample sizes. A total of 28 areas with species presence and 16 different environmental variables
were used in the analysis. The model returned several sites within Wilkes County exhibiting
higher levels of predicted suitability, and a smaller number of sites within Iredell County with
moderate levels of suitability. The predicted sites in Iredell County were previously unknown to
wildlife managers, and may help to direct future survey work in those locations. If these model
predictions can be translated to positive detection of turtles in the field, spatial modeling work of
this kind may begin to play a larger role in the conservation efforts for the species.
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Species Distribution Modeling for Bog Turtles
(Glyptemys muhlenbergii) in North Carolina
INTRODUCTION
The bog turtle (Glyptemys muhlenbergii) is one of the most threatened turtle species in
North America. The turtle’s geographic distribution is broken up into two distinct regions; one
found from upstate New York south to Maryland, and the other found in Georgia, North Carolina,
South Carolina, and Virginia.
The northern population is listed as threatened under the
Endangered Species Act. The southern population, despite being genetically identical, is not
federally protected under this law. (Somers,
2000). This is partly due to the northern
population being the better studied of the two
groups, and therefore having sufficient data
upon which to determine a specific legal status.
The southern population remains highly underdocumented however, and so is only listed as
“threatened due to similarity of appearance”
(USDA, 1997). This status divide has
fortunately not had major repercussions as of
yet, and these southern populations are
afforded almost as much protection as their
northern counterparts. Locating additional
Figure 1. Geographic distribution of the bog
turtle by county. Source: Dennis Herman.
populations of bog turtles in their southern
range is vital if they are ever to receive full protection under the Endangered Species Act
(Pittman & Dorcas, 2009).
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In North Carolina,
the large majority of
known bog turtle
populations are found
in the western
highlands. Mountain
conties such as Wilkes
have received a fair
amount of attention
from researchers who
are trying to document
new occurrences of the
Figure 2. North Carolina counties in the study region. Wilkes County is
mountainous, while the other three counties are in the lower elevation
Piedmont.
species as they offer the
highest density of probable habitat, and thus the greatest chance of locating new populations
(Figure 2). To the southeast of these highest-quality areas, the upper North Carolina Piedmont
currently contains a few remnant bog turtle sites, but was known to harbor more bog turtles in
the past (Herman, per. Comm.). A number of bog sites in the Piedmont exhibit some of the
same habitat characteristics as can be found in the mountain counties like Ashe and Wilkes,
though most of these remnant wetlands have now been drained for use as agricultural pasture
land or for residential development (Somers, 2000; NHP, 2010; USFWS, 2013). Despite this
situation, numerous wildlife experts believe it is quite possible that there are still remnant
populations of bog turtles in this part of the state (Beane, 2013). Bringing these lower elevation
Piedmont counties up-to-date by conducting a focused ecological niche modeling investigation
may produce more accurate predictions of where probable habitat is likely to be found, and
could even result in new records of bog turtle habitation for the Piedmont counties in question.
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Beyond the fact that they are a federally threatened species and any kind of further work
done on them could potentially aid in critical conservation work, the status of bog turtles in the
upper Piedmont region of
North Carolina are of special
interest to the conservation
community in this area. But
because the chances of
finding new populations of
turtles in this area are much
lower than in counties to the
west, very few management
personnel can afford to
Figure 3. The bog turtle, Glyptemmys muhlenbergii. (Photo by
Todd Pierson)
investigate whether new populations might still exist there. A now common way of thinking
about such isolated populations is that it is not practical to put resources into researching and
protecting them (Beane, per. Comm.). Isolated populations such as these are thought to have a
high likelihood of dying out because their numbers are now below an ecologically stable
threshold, and it is therefore not economical or realistic to put efforts into saving them. This
could be fair assessment in regard to any bog turtles that still exist in Iredell, Davie, or Davidson,
but it can also be argued that so little survey work has been done in the region that such a
determination isn’t supported by data or knowledge.
Goals of Analysis
This study conducts a spatial ecological niche analysis of areas in upland Wilkes County
and the lowland Piedmont counties of Iredell, Davie, and Davidson in an attempt to locate areas
whose environmental conditions are suitable for habitation by bog turtles. A species distribution
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model, Maxent (Phillips, 2006, 2008), was created to predict areas of probable bog turtle habitat
using geospatial environmental layers and known bog turtle field occurrences. In ArcGIS
(Version 10.1, ESRI, 2013), a rule-based model was generated in an attempt to map all suitable
bog turtle habitat in both study regions. The input variables and parameters of this model were
determined by expert opinion and specific findings in the relevant literature. This habitat
prediction map was then compared with the Maxent result in order to identify those sites
determined by both models to be suitable habitat. It was hypothesized that this analysis would
turn up a number of new sites in Wilkes County with highly suitable habitat conditions for bog
turtles. Because of the paucity of suitable environmental conditions present in the Piedmont
study area however, it was thought unlikely that many sites there would be identified as similarly
suitable. Identification of even a few areas of moderate suitability in these three Piedmont
counties would likely be of significant interest to wildlife managers. If any populations are
discovered here, they would represent an extension of the species range for the region and
could prove instrumental in establishing further conservation measures for both bog turtles and
other species in the Piedmont of North Carolina. This determination could also benefit local
land management entities such as The Landtrust for Central North Carolina since they manage
numerous properties in these counties. If one of their properties were found to contain viable
bog turtle habitat, it would undoubtedly be of interest to landowners and may influence longterm conservation plans for that particular property.
Bog Turtles: Ecology and Habitat Description
The bog turtle, Glyptemys muhlenbergii, is a terrestrial species of turtle found in the eastern
United States from upstate New York to the northeastern corner of Georgia. Reaching a
maximum carapace length 7-10 cm, the bog turtle (Figure 3) is the smallest turtle species in
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North America, and arguably the most
endangered. Bog turtles live in small, noncontiguous wetlands at elevations between 216
and 1,371 m of elevation in the Appalachian
Mountains. The northern and southern
populations are separated by a 563-km gap in
southern Virginia and Maryland (Figure 1). The
particular type of habitat in which they are found
are highland wetlands known as fens. Not
actually true bogs, fens are wetlands whose
defining characteristics are that they are largely
fed by groundwater, and not by rainfall, hillside
runoff, or though contact with neighboring
waterways (Beane, 2013; Figure 4). This allows
them to stay relatively wet during drier parts of the
Figure 4. Typical sedge meadow habitat of
the bog turtle
year as well as over longer periods of droughty conditions. Bog turtles are known to stay
relatively close to their home fens throughout their lives, with rare excursions by males to
nearby suitable habitat (Dorcas & Pittman, 2009). Other site characteristics include largely
hydric soils, low slope values in the wetland zone, and specific temperature requirements.
During the warmer months of the year, bog turtles are known to be most active when ambient
air temperatures are between 15.56 - 30.56 °C (NCWRC, 2013).
Vegetation type is also often fairly specific in such fen ecosystems. In the mountains, bog
turtle habitat is generally dominated by acid-loving bog species including peat mosses
(Sphagnum spp.), arrow arum (Peltandra virginica), skunk cabbage (Symplocarpus foetidus),
and jewelweed (Ernst and Lovich, 2009). In Piedmont bog habitat, numerous species of sedges
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and rushes can be found, along with box elder (Acer negundo), poison sumac (Toxicodendron
vernix), Crested Woodfern (Dryopteris cristata) and Southern purple pitcher plant (Sarracenia
purpurea) Sphagnum mosses are rare or absent from most Piedmont bog sites. (Somers, 2000;
Herman, 1994). Both in the mountains and Piedmont, ungrazed bog areas experiencing forest
encroachment often support successional species such as alder (Alnus serrulata) and red
maple (Acer rubrum).
In addition to naturally occurring habitats, bog turtles are also associated with agricultural
pasture land in many parts of their range. These areas often effectively mimic some of the
characteristics found in fens. Like fens, pasture will often be free of forest cover, an important
variable for bog turtles as they need such exposure in order to help regulate their internal
temperature. When temperatures get too high, the turtles also need water nearby to bring their
temperatures down. Active pastures are often dotted with small depressions that fill up with
water as a result of the cows or horses that graze in them (USFWS, 2013). Bog turtles will often
spend significant portions of their day resting in, or just under the surface of the water in such
depressions (USFWS, 2013). The fact that they rarely bask out in full view like some terrestrial
species makes survey work particularly difficult. Not only are they the smallest turtles in North
America, they also tend to spend most of their time submerged in muddy water. For this reason,
further integrating GIS and ecological niche modeling into survey planning may help make the
process more efficient by narrowing down the number of sites that have yet to be surveyed to
only those with the highest probability of suitable habitat characteristics.
Bog turtles are thought capable of living for 40 years or more in the wild (Herman, 1994).
Their longevity supports the idea that there may very well be populations in the Piedmont that
have been able to survive despite a decrease in habitat quality over the past several decades.
They lead largely sedentary lifestyles with an average home range of 0.5 acres. Males tend to
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travel farther than females and have been known to travel farther than this away from their
home bog (Pittman & Dorcas, 2009).
Species Decline
One of the major factors with their decline in the Piedmont as well as the mountains of North
Carolina is land conversion in the form of residential development, road construction, and the
draining of wetlands for agricultural purposes (Phu, 2008). This impacts reptile and amphibian
species, as well as hydric-loving vegetation. The Piedmont counties included in this study are
particularly susceptible to this kind of development pressure. Over the past ten years, parts of
the Piedmont have seen dramatic increases in the amount urbanization with the Raleigh
metropolitan region having the highest growth rate for any metropolitan region in the country
(Kotkin, 2013). In 2008 it was reported that of the 100 fastest growing counties in the US, 8 of
them were in North Carolina. One of these counties, Iredell, is known to have supported
populations of bog turtles in the past, but the last documented sighting of an animal was in 1968
(NCDENR). Despite an increasingly dire situation, numerous wildlife managers believe it is still
likely that there may still be remnant populations of bog turtles in certain isolated parts of the
Piedmont (Beane, J, pers. comm).
Another factor in the decline of quality bog turtle habitat is the encroachment of vegetation
into previously clear fens and wetland meadows. On grazed land where draining and ditching
have not been extensively implemented, suitable bog-like conditions can often be maintained to
a high degree. When the dominant land use type shifts away from agriculture however, tree
species can begin to move in and dry up once hydric soils. Invasive species such as multiflora
rose (Rosa multiflora) can also take advantage of such newly un-grazed areas, further pushing
the moisture regime on the land towards one that is increasingly dry and unsuitable to bog
turtles (Linh, 2008). Phenomena of this type have slowly degraded and finally exhausted most
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of the suitable bog turtle habitat in North Carolina’s upper Piedmont. In higher elevation bog
habitat in counties such as Wilkes, land conversion of this type is occurring, though not to as
great a degree as in the Piedmont.
Previous Work
There is a substantial number of studies on the biology and conservation of the northern and
southern bog turtle populations. A few have even looked at populations in the Piedmont of
North Carolina to try and
determine their status as
well as more effective
surveying techniques, but
few have conducted spatial
modeling analysis with the
goal of identifying new
habitat sites in the region
(Herman, 1994; USFWS,
2013). In 2011, the Southeast
Figure 5. SE-GAP Analysis predictive map of bog turtle habitat
near the study counties.
GAP Analysis Project released a predictive model for southern populations of bog turtles (NCSU,
2011; Figure 5). Environmental variables used in this model included vegetation type, elevation,
and distance from water features. This particular analysis is strongest with regard to the
vegetative cover inputs used. Researchers were not limited to the SE-GAP cover types, and
instead created hybrid classes and subcategories out of the GAP cover types in order to better
capture a more accurate description of the ecological cover types turtles were most likely to
inhabit (Williams, S, pers. comm.). A limitation of this model though may be the fact that it
integrates very few environmental variables. Potentially critical factors such as soil type, aspect,
and temperature were not included in the analysis. As a result, the predictive map likely
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encompasses most sites that would support bog turtles, but individual sites, especially in the
Piedmont, will be reported as having a low probability of containing viable bog turtle habitat. For
this reason, a model that is spatially-specific to these areas in question is needed, one that
utilizes only nearby presence data, and whose inputs include a wide variety of environmental
variables specific to the Piedmont region.
Conservation efforts in the mountain counties of North Carolina also stand to benefit from
new predictive models. Wilkes County in particular is of great interest to wildlife managers as a
number of new bog turtle sites have been discovered there in recent years. The amount of
surveying time that goes into each new record is oftentimes substantial, with sites in one
Piedmont study having an average capture rate of 0.038 turtles per hour of visual search
(USFWS, 2013). Generating focused predictive maps for specific areas in the Piedmont as well
as the mountains is likely to increase the effectiveness of such survey efforts. In 2009,
Elizabeth Walton finished up a multi-year analysis of bog turtle populations in Ashe County,
North Carolina, examining how remote sensing and ecological niche modeling can help in
identifying new sites for surveying. Walton used the ecological niche modeling program GARP
(Genetic Algorithm for Rule Set Prediction) and included over 40 different environmental
variables as inputs (Walton, 2009). Unfortunately, the GARP model was unable to process
some of the categorical and binary datasets including soils, some hydrology inputs, and aspect
(ibid). Alternative means of representing these variables in the model were developed, but the
fact that the software itself was not able to easily incorporate critical environmental
characteristics such as these is likely to negatively impact the accuracy of the predictions. The
choice to use the Maxent ecological niche model in this investigation was made based partly on
its ability to easily process these kinds of datasets and its success relative to other models (Elith
et al. 2006).
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In her 2009 study, Walton also utilized remote sensing tools in determining her site
predictions. While it added an important layer to the analysis, it also proved to have a number
of complications. Spectral signatures for the small, non-contiguous wetland sites that she
hoped to detect were largely missed by the coarse 30-m cell size in the Landsat 7 imagery
(Walton, 2009). Many bog turtle sites in North Carolina are 3 ha or less in size. Some sites in
the Piedmont are as small as 0.3 ha (Somers, 2000). With satellite imagery of this resolution, it
can be very difficult to detect spectral signatures for such small habitat areas. Based on these
findings, I determined not to incorporate spectral analysis into the new predictive maps for the
Wilkes County and the Piedmont counties.
METHODS
Study Region
The primary study region of Wilkes County is a largely mountainous region in the
northwestern part of North Carolina totaling 196,839 ha in size (Figure 2). It has a moderately
sparse human population of 69,340 (Wilkes County, 2013). The eastern slopes of the Blue
Ridge Mountains dominate the varied topography of Wilkes with elevations ranging from 274 m
in the east to 1,243 m in the northwest. The foothills and valleys of the mountains make up the
majority of the landscape, but its eastern edge falls within the Piedmont of North Carolina.
Wilkes is known for several parks such as Stone Mountain State Park, that offer excellent
opportunities for outdoor activities such as rock climbing and trout fishing. The secondary study
region consists of Iredell, Davie, and Davidson counties in the Piedmont region of North
Carolina. This area is significantly lower in elevation than Wilkes, with a range between 98 and
536 m. The combined total area of all three Piedmont counties is 370,627 ha (Iredell County,
2013). The Piedmont is a much flatter terrain than the mountains, with gently rolling hills that
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gradually get higher towards the west edge as it approaches the Blue Ridge escarpment.
Temperatures in both the summer and winter are significantly milder than in Wilkes County
(Wilkes County Economic Development Corporation; Iredell County, 2013 ).
Data and Software
One of the first datasets sought for the analysis was soil types in the study region. Bog
turtles are known to be associated with hydric soils, the characteristics of which include low
slope, close proximity to depressions or floodplains, and fine, alluvial deposits (Herman, per.
comm). SSURGO soil data was obtained from the United States Department of Agriculture’s
Geospatial Data Gateway website lists nine hydric soil units within the study region that exhibit
these characteristic (USDA, Figure 6). Element occurrence data for bog turtles were obtained
from the North Carolina Natural Heritage Program, and used in the generation of a presence
point dataset for the Maxent model (North Carolina Natural Heritage Program, 2013). The
digital Elevation Model (DEM) data came from the North Carolina Floodplain Mapping Program
(Figure 7). Precipitation and temperature data in the form of average precipitation, daily high
temperatures, and daily low temperatures during the month of July over the interval 1981-2010
was from the PRISM Climate Group (Oregon State University, 2013). Detailed land cover and
habitat data came in the form of the SE-GAP dataset, a product of the 2006 Southeast GAP
Analysis Project (North Carolina State University, 2011).
Approach
Maxent was the species distribution model used for the habitat prediction component of
the analysis (Princeton, 2004; Phillips 2006, 2008).
This model uses a maximum entropy
approach to species distribution modeling and requires environmental input data and species
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presence data. ArcGIS (version 10.1, ESRI) was used for all other spatial analysis and
processing.
While studies have modeled bog turtle habitat from the broad scale large scale down to that
of an individual county, none have conducted a predictive analysis specifically targeting either
the Piedmont counties of Iredell, Davie and Davidson, or the upland habitats of Wilkes County.
This study is designed to create focused predictions that are practical and relevant to the needs
of managers surveying these regions in the future. This analysis utilizes species presence
points in the North Carolina counties of Ashe, Wilkes, Alexander, Surry, Forsyth, Iredell, Davie,
Davidson, and Gaston to predict bog turtle habitat using two different modeling approaches.
The Maxent model was chosen because of its ability to generate accurate predictions under the
constraint of small sample sizes; studies comparing the accuracy of different species distribution
models (SDMs) have shown that Maxent is capable of producing accurate predictions of habitat
with as few as 5 presence points (Hernandez et al., 2006). A 2003 study of chameleons in
Madagascar that incorporated Maxent predictions saw great success with the predictive maps
directly aiding in the discovery of seven new chameleon species (Raxworthy et al., 2003). A
result like this is relevant with this analysis since bog turtles, like the chameleon species in this
study, have specialized habitats and are similarly difficult to locate during field survey work. It is
the intent of this investigation that the Maxent predictions be used in future bog turtle surveys in
hopes that new populations might be found.
Development of the model was further focused to the study region by limiting the number of
presence points being used to only those within the four counties. For the study region, there
were 28 field localities for the bog turtle of the 124 available for the entire southern population.
The reasoning to restrict the study to only points within the study region is practical from a data
processing standpoint, but also is anticipated to return more geographically-relevant predictions.
This limiting of presence points will help direct the model to be more adapted to the bog turtles’
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unique habitat needs within the study region, rather than a more general characterization of all
habitat in the southern range (Hernandez, 2006).
Assumptions with the Analysis
A key assumption with this analysis was that species presence data from one geographic
region could be used to predict potential habitat in another geographic region. The Maxent
model is designed to be most effective when predicting habitat within the boundary of the
presence points (Peterson et al., 2007). The presence data in this analysis will be almost
entirely from the mountain counties of Ashe and Wilkes. I have used these points to predict
new habitat locations within Wilkes County, as well as in the three Piedmont counties of Iredell,
Davie, and Davidson. This analysis also involves a small sample size of 28 bog turtle presence
points, which may result in a model with lower predictive accuracy than one run with a larger
dataset (Wisz et al., 2008). Additionally, extra points were generated and placed near the
actual occurrence records to act as additional points in the model. This was an attempt to
increase the sample size with points whose environmental characteristics very closely resemble
those of the actual species occurrences. These extra datapoints were generated with the help
of Dennis Herman, an expert on bog turtles in the southern range. The process of creating the
points involved locating each known bog turtle site in an aerial image, and placing 5-10
additional points within the delineated boundary of the site. Determination of how many points
added was contingent upon the site’s size, with smaller sites receiving fewer points. Through
this process, the number of presence points used in the model was increased to 216.
With this analysis, I included a wide range of environmental variables. Inputs such as TCI
(Topographic Convergence Index), NWI features (National Wetland Inventory), soil pH, and
Normalized Difference Vegetation Index (NDVI) were not included. TCI helps identify wet areas
across a landscape based on where drainage channels are most likely to form. This is
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something that is important for bog turtles, but that can likely be accounted for by using a
variable such as Distance to Sink features. This is similarly the case with the NWI input. Soil
pH was not included as I felt that the basic Soil Map Unit attribute in the SUURGO dataset
would help to identify what particular types of soils are present at bog turtle sites. The NDVI
variable was not included as vegetative cover in habitat areas would be sufficiently represented
by the SE-GAP dataset. Because variables such as these have not been included, it is possible
the modeling process will not be as exhaustive as it should be to capture the true realized niche
of the species.
GIS Analysis Procedures: Rule-Based Model
An elevation raster was used to create several of the environmental layers used in the
modeling analysis (Figure 7). The Rule- Based Model was based on elevation, focal land cover,
distance to sink features, distance to hydric soils, July minimum temperature, and July
maximum temperature (Figure 8). The spatial data used in the analysis had resolutions varying
from 800 to 6 m, but for the analysis were scaled to 6 m. Elevation most appropriate for bog
turtles was determined to be between 216 m and below 1,371 m (Herman, 1994). Bog turtles
are known to inhabit the SE-GAP land cover types Pasture/Hay, Southern Piedmont/Ridge and
Valley Upland Depression Swamp, and Southern and Central Appalachian Bog and Fen (NCSU,
2006). They are also known to live in close proximity to water features such as depressions or
sinks (Walton, 2009). A raster for these Sink features was generated in ArcGIS from the DEM
raster. A distance of < 60 m from such sink features was used as the rule (Dorcas & Pittman,
2009). Close proximity to hydric soil features is a known habitat characteristic and so a distance
of < 18 m was decided upon as a reasonable measure of that limit (Herman, pers. Comm.)
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For the air temperature
variable, the PRISM climate
dataset was used as it
included average high and
low ambient air
temperatures for different
months of the year. In a
study on wood turtles, a
proxy species for bog turtles, Figure 8. Environmental parameters for Rule-Based Model.
Ernst, (1986) found individuals basking and feeding at temperatures upwards of 33 °C . In a
previous study (Ernst, 1977), researchers recorded the cloacal temperatures of Pennsylvania
bog turtles, with one individual having a reading of 31 °C (Ernst, 1977). Surrounding air
temperatures were not recorded as part of this study, but are likely to have been of a similar
level, if not slightly higher. From such studies, it is assumed that bog turtles could be active at
such high temperatures, though their optimal temperature range is likely to be closer to 15.56 –
30 °C (USDA, 2006). Informed by these studies, a probable range for average high
temperatures in the month of July was determined to be 15.56 – 31 °C.
GIS Analysis Procedures : Maxent Model
In addition to those used in the Rule-Based Model, a number of other environmental inputs
were generated for use within the Maxent model portion of the analysis. The rationale behind
why more types of variables could be used in this part of the analysis lies with the lack of expert
opinion on what range of values, for each respective environmental variable, are known to be
associates with suitable bog turtles habitat. Variables such as Topographic Position Index (TPI),
may in fact have a specific range of values associated with bog turtle sites, but such a
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correlation has never been formally documented, and so expert opinion does not yet exist for
this particular habitat metric. Since this is also the case with Aspect, Slope, and Hillshade, it is
similarly not possible to include them in a Rule-Based Model.
A number of the environmental datasets used were further processed in ArcGIS to create
additional variables for use in Maxent. One of these, the SE-GAP land cover dataset, was used
to generate two additional rasters, SEGAP_450 and SEGAP_1200. These were created as a
way to potentially better capture the specific land cover characteristics at each species
presence point, allowing the model to then make the best possible prediction of where similar
characteristics would be found across the landscape. The process used to generate these
additional rasters calculates a new value for each cell in the original SE-GAP raster that is both
reflective of the original value, but also reflective of values close to it. This can be important as
land use class boundaries in the original SE-GAP layer are rarely exact, and the generation of
additional rasters that may help compensate for this are often useful inputs (Fay, pers. comm.).
Furthermore, one raster calculates this for the area within a 137 m radius of each cell, and the
other calculates it for within 365 m of each cell. This same neighborhood analysis procedure
was applied to both the Soils data and the Topographic Position Index (TPI) data. As a result,
neighborhood analysis rasters with 137 and 365 m focal radii were generated for the Soils data
as well as the TPI data. An Insolation raster was also generated from the DEM, and estimates
areas of high and low exposure to solar radiation. Two final DEM-derived datasets, Aspect and
Slope, were also generated from the DEM layer. These layers were then clipped to the primary
study area of Wilkes, Iredell, Davie, and Davidson counties.
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RESULTS
The Rule-Based Model revealed suitable habitat characteristics present in Wilkes County,
as well as in two small regions of Iredell County (Figure 9). There were no suitable areas in
Davie or Davidson counties; this agrees with the consensus among wildlife managers that few,
if any, suitable turtle sites still exist in this part of the state. With this rule-based analysis, the
parameter of High Temperature was a major factor in the model not returning results in this
region. Much of Davie and Davidson counties were above the threshold of 30.56 °C and so
were not included in the model’s output. The lower temperatures in Wilkes County allowed
many areas there to be selected as potential habitat by the model.
The Maxent model returned habitat predictions for all of the counties that contributed
presence point data for bog turtles. A raw raster image of Ashe, Wilkes, Iredell, Davie,
Davidson, Forsyth, Surry, Gaston, and Alexander counties shows values for probability of
suitable habitat characteristics in this larger region with this analysis focusing in on only those
values returned for Wilkes, Iredell, Davie, and Davidson (Figure 20). Within these counties, the
Maxent model identified a number of locations with moderate to high probability of suitable
habitat characteristics (Figure 10). Locations with the highest probability values are displayed
as bright red, with the lowest probabilities shown in blue. The upper inset map shows an area in
northern Wilkes County displaying a region of high probability, with several cells returning
probability values greater than or equal to 0.7. Most bog turtle occurrence sites returned high
values similar to this, but one site (lower inset) did contain a lower value of 0.35. Other
presence points that were utilized to run the model, but that are located outside the primary
study region, are displayed within the map as well. Very few locations in either Davie or
Davidson County returned probability values higher than 0.4. In Iredell County though, there
are a group of isolated sites with probability values above 0.6.
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A number of thresholds were applied to the resulting Maxent suitability values to enable
managers to gauge the sensitivity of the model. A commonly used threshold with this particular
model is the “Balance Training Omission, Predicted Area and Threshold Value” (Giovanelli et al.,
2008; Buermann et al., 2008). This was treated as the low threshold for this analysis, with a
value of 0.019. The next threshold used was the Lowest Presence Threshold (LPT), which can
be interpreted as representing those pixels whose environmental characteristics are at least as
suitable as those found at a site where the species is known to exist (Pearson et al., 2007).
Here, the LPT value was 0.074. In addition to these, three higher thresholds at 0.3, 0.6, and 0.8
were applied as a way of depicting locations with higher degrees of predicted suitability for bog
turtles.
DISCUSSION
Rule-Based Model
Comparing the Rule-Based Model (Figure 9) to the predictive SE-GAP map (Figure 5), it is
clear that the SE-GAP model highlighted more area as potentially suitable habitat. This was
expected as the SE-GAP analysis involved fewer criteria than the RBM. The SE-GAP
parameters included parameters such as elevations < 1,400 meters, freshwater areas of slow
current, and a 120-meter buffer away from water features (NCSU, 2006). The land cover
parameter it used was somewhat different in that it consisted of not only the three distinct SEGAP land cover categories included in the RBM, but also a number of small, hybrid ecological
systems developed by biologists for the analysis, and not represented in the distinct SE-GAP
classes (Williams, per. comm.). In addition to vegetative cover types, the Rule-Based Model
includes the additional criteria on Temperature and Soils, and is a more comprehensive
estimate of locations that contain suitable conditions for bog turtles. One important component
of the SE-GAP map not included in the RBM map is a buffer from streams (Figure 4). Stream
20
channels cannot themselves be considered potential habitat, and this could be incorporated into
any future versions of the RBM.
Overlaying these two models reveals more about the areas each one selected as suitable
habitat (Figure 11). The area highlighted in this map is located in southeastern Wilkes County
and shows both models selecting locations alongside streams. Importantly, the SE-GAP model
does not select areas within stream channels as habitat. Such a perspective on the two models
is valuable as it helps validate some of the parameters I used in the Rule-Based Model. Criteria
such as close proximity water features, and particular vegetation cover types, are clearly similar
to those used in the SE-GAP analysis. The fact that overall, the Rule-Based Model selects less
area in the study region than the SE-GAP, suggests that it is better able to select areas most
likely to serve as potential habitat.
One possible limitation with the Rule-Based Model is the particular temperature datasets
used. The PRISM climate data was incorporated as it seemed a straightforward was to account
for temperature differences throughout the study region. Unfortunately, this data is likely not a
precise proxy for this species’ temperature requirements as it is merely limited to one month of
the year, July, and one particular kind of metric; average daily high and low air temperatures,
and not surface water temperature for example. Despite uncertainty surrounding the use of this
definition of temperature, the RBM does highlight many of the same locations selected by the
SE-GAP map, including almost all of the species presence points. The Rule-Based Model also
appears more conservative than the SE-GAP analysis map, as it includes a stricter set of
criteria resulting in less land selected as suitable habitat. This could be more practical to
managers in the field because it narrows down the potential areas they could survey for the
animal.
21
Maxent Model
Many of the highest suitability values predicted by Maxent are associated with known
bog turtle presence (Figure 10). Yet of interest are a number of locations which were predicted
to have high suitability of habitat, but that were not associated with any presence points. One
such location of high suitability but with unknown bog turtle presence was found in northwestern
Wilkes County (Figure 12). This site appears to have high levels of suitability, > 0.8, that are
comparable to the Maxent values at any of the known bog turtle occurrence sites. This same
site with an aerial photo inset map (Figure 13), indicates a property that contains cleared
pasture land and numerous water features including drainage ditches and a creek, as well as
grazing cattle (Figure 13). Conditions such as these, where saturated meadows are kept open
and wet through both the use of machinery and cattle, are known to support bog turtle
populations at other locations.
I examined a number of these high suitability sites with high resolution aerial imagery
(National Agricultural Imagery Program, 2010). Figure 14 compares a known bog turtle site in
northern Wilkes County with an unknown site to the southwest that has a comparably high
suitability value. Both sites appear to have open pasture land with individual cows detectable in
the images. As with the previous figure’s aerial image, the presence of livestock within the site
can be a strong indicator that favorable habitat is present. The known site in Figure 14 appears
to have a drainage feature on the right side visible as a darker patch of pasture. The predicted
site appears to have a drainage feature, albeit a less-defined one, in the upper-right corner of
the frame. The area of lower suitability at this site is indicated with the blue hues, and whose
predicted suitability value is supported by the positive visual detection of buildings there.
While Wilkes County returned a larger number of high suitability sites than the Piedmont
counties, there were still several areas, particularly in Iredell County, where suitable habitat
characteristics were predicted by the model (Figure 15). Some of the cells within these sites
22
have probability values as high as 0.64. While not as high as some sites in Wilkes County, they
may be of great interest to some wildlife managers as most of the Piedmont is generally
considered to be of such low habitat quality that very few surveys are ever conducted there. It
is also interesting to note the strikingly isolated nature of these sites. Almost all of the land
surrounding them returned very low habitat values, suggesting that these locations contain
conditions markedly different from those in the rest of the county.
A seemingly close association between predicted habitat suitability and close proximity
to hydric soils is represented in the Maxent model’s Variable Contribution table (Figure 18).
This table reflects the relative importance of each variable to the final output, and is therefore
the best overall indicator of what variables are the most important in determining locations of
suitable habitat. The table indicates that one of the soil inputs, soils_450_all, contributed 33.5%
to the final depiction of potential habitat for the turtle. This provides evidence that soil type,
particularly soil types of a hydric nature, is the most significant variable in the model’s
determination of suitable habitat locations. Other important variables included slope, which
contributed 22.7% to the final output, and minimum temperature (tmin_p) which contributed
13.3%.
Response Curves and Variable Contribution
For the most important variables, the Dependence Response Curves reflect how an
individual variable is related to the overall prediction of suitability in a model. Looking at the
curve for the Precipitation variable (Figure 16a), the graph indicates that as precipitation
increases, probability of suitable habitat also increases. This reflects that for this particularly
variable in isolation, suitable habitat conditions generally improve with increasing rainfall
amounts. The dependence response curve for the soils variable, Soils_450 (Figure 16b), had a
particularly strong positive correlation with suitability of habitat. In this curve, close proximity to
23
hydric soil types is represented by the higher values on the x-axis. As the value increases
(shorter distance to a hydric soil area), probability of suitable habitat increases on the y-axis.
Another kind of graphical output is Marginal Response Curves (Figure 17). These reflect
how predicted habitat suitability is related to a particular range of values with an environmental
variable. For the Minimum Temperature Variable (Figure 17a), there is a moderate chance,
around 50%, of suitable conditions being found when average daily low temperatures remain
within a certain range. Once daily low temperatures during the summer no longer fall below
about 18 °C, the likelihood of suitable habitat drops off markedly. In a similar way, the Slope
variable reflects a higher likelihood of suitable habitat being found when the angle of the ground
is less than about 10 degrees (Figure 17b). In terrain where the slope values are much higher,
there is a significantly lower chance of suitable bog turtle habitat characteristics being found.
A confusion matrix was generated for the 216 presence points used in the Maxent model
(Figure 19). Most of the bog turtle presence points returned values between 0.6 – 0.8 for
probability of suitable habitat characteristics. The highest probability value associated with a
presence point was 0.79, with the lowest being 0.074. At this lowest threshold, all 216 of the
presence points are selected, but it also results in a very large amount of land in the study
region being identified as potentially suitable habitat, and so is likely inaccurate. Selecting a
threshold around 0.7 would encompass 80% of the presence points while limiting the predicted
area; it appears a reasonable measure of suitable habitat locations within the study region.
Limitations
One limitation with the Maxent predictions in the Piedmont, including those in Iredell
County, is that the model is designed to give the most accurate predictions for areas that are
encompassed by the presence points (Peterson et al., 2007). Most of the presence points
used in this analysis were from Ashe and Wilkes counties, and therefore of significant distance
24
from Iredell, Davie, and Davidson counties. The predictions of potential habitat in the Piedmont
counties should therefore be treated with caution if they are to be thought of as direct indicators
of habitat suitability. Predictions in Wilkes or Ashe counties are likely to contain a higher degree
of accuracy.
Generating pseudo-absence points and running an ROC curve test is one commonly
used metric for determining the strength of the model’s predictive power (Philips, 2007). A
presence-only ROC curve analysis determines the probability that a randomly chosen presence
point will have a higher probability value assigned to it than a random pseudo-absence point.
This probability is called the AUC (Area Under the Curve) score; models with high scores
indicate that the model fits the training data well relative to random. In this bog turtle analysis,
true absence data is unavailable, but the same kind of test can be performed with the use of
pseudo-absence points. Running this test returned an AUC score of 0.988, which reflects that
the model fit the data points well to a very high degree. This high score is not necessarily a
sure indication of high accuracy in the model, and may partially be the result of the bog turtle
being a rare species with limited habitat ranges. Such strong results can be useful though when
looking to determine the overall strength of a model’s predictions.
Sample selection bias is another potential problem with a predictive model such as this.
It is unknown whether the occurrence locations used can be seen as a strong representative
sampling of all potential habitat areas in the study region. More than likely, some of the
occurrences are biased by being areas that were easy for researchers to access. The points
that make up this dataset may not be as representative as they should be, and therefore the
predictions they produced are not likely to capture a fully accurate characterization of bog turtle
habitat.
Threshold analysis of the kind used here can also potentially lead to inaccurate
conclusions. Depicting the Maxent results through such suitability classes can be a helpful way
25
to visually display the somewhat abstract information of habitat suitability probabilities. Among
other possible benefits, it aids in narrowing down results to those sites with the highest
probability of suitable habitat conditions, which can then be overlaid with aerial imagery to see
whether the landscape contains features often associated with bog turtle habitat. While it may
be tempting to draw conclusions from spatial information when it is displayed in dynamic fashion
such as this, it is important to remember that these probability classes, and the Maxent results
they depict, merely represent the probability of these chosen habitat characteristics being
present within the area of analysis. In Figure 14 for example, there is an area of high predicted
suitability within the site of “Known Suitability” that is represented by a thin band of orange.
Using bold coloration for the different probability thresholds can generate a sharp visual contrast
between them, suggesting that there is a strong difference in habitat suitability between the
orange and light blue regions. It isn’t likely however that such a strong divide always exists, and
surveys of the blue areas may reveal habitat conditions that are as suitable, if not more suitable,
than that found within the orange areas.
RECOMMENDATIONS
This analysis generated a number of results that could be of great interest to wildlife
managers in the study region. In part because of the broader scale of the investigation, there
are numerous areas within the study counties that were predicted to have high probability of bog
turtle habitat suitability. It is my intention to share these results with wildlife managers at the
North Carolina Wildlife Resources Commission, as well as researchers with Project Bog Turtle,
in hopes that the results may be able to help direct future survey work in Wilkes and Iredell
counties. Developing maps for these counties of high predicted suitability areas along with
locations of State Natural History Areas (SNHAs), land trust properties, or other
conservation/park lands, could be an interesting product for such groups to have on hand when
26
planning new conservation objectives. If any of these properties had boundaries that
overlapped areas of high suitability for example, it would be interesting to contact the
appropriate manager in order to see if they are aware of any potential bog turtle habitat on their
lands. If the model were to be seen as a useful tool for predicting bog turtle habitat, it would
also be necessary to inform managers of the model’s development process. In general, most
bog turtle survey work in the past has not been based on habitat model predictions, and
therefore some managers may not be familiar with these methods. It would be vital for
managers to understand the thinking behind the model, and whether they saw any faults with it
and the variables included in it.
In any future versions of this particular model, it is recommended that the presence
points used in the Maxent program be limited to those within the boundaries of the study region
in question. This was an experimental part of the analysis as there were no presence points in
the Piedmont counties, and so other records were required to generate a prediction for the
study region. Maxent has been shown to generate accurate results with small sample sizes
though, so this may not be a necessary, or even desirable, path to taken with the analysis.
Similarly, there may not be much benefit with adding additional presence points to each bog
turtle occurrence site. An interesting comparison study could be to run the model with the same
environmental parameters, but limit the presence points to the 28 individual bog turtle sites in
the region, instead of the 216 datapoints that were included in this investigation.
In this analysis, temperature data was seen as a valid environmental input to include, but
the use of temperature for this species is not well understood or known to have been applied in
a similar modeling approach. Thus, it is recommended that the temperature variable potentially
be excluded in future versions of the Rule-Based model, or, an attempt made to find a dataset
that can more accurately reflect air temperature values at specific bog turtle sites. Other
datasets, including the soils data used here, should be more carefully investigated before
27
inclusion into a model. Some wildlife professionals believe the inclusion of SSURGO soils data
can generate misleading conclusions as some soil characteristics listed in the dataset do not
match conditions on the ground (Floyd, pers. comm.).
Analyses of this kind, where reliability and accuracy of prediction can be compromised at
many different stages of the process, should have the accuracy of their outputs tested in
multiple ways. Testing the model in a different area having additional presence points, a portion
of which could be held back for model testing could be useful in the future. In addition, running
the model additional times, both with the same criteria and with altered parameters, and with an
exploration of Maxent’s “clamping” settings, may generate different predictions and better
understanding of the model. It is also recommended that the model be run with just 28
occurrence points instead of 216, as well as with only those presence points found in Wilkes
County. Comparing predictions from these runs with my results may lead to a more
comprehensive prediction of suitability in Wilkes County. Results from any and all versions of
the model should then be ground-truthed by managers in order to further determine how well
the predictions match habitat suitability they see in the field.
CONCLUSION
Through the use of both a Rule-Based Model as well as the Maxent Species Distribution
Model, a number of sites in Wilkes and Iredell counties were predicted to contain habitat
characteristics suitable for bog turtles. The most accurate predictions of habitat are likely to be
those within Wilkes County, and a number of them appear to be locations where surveys for bog
turtles have not yet been conducted. Some of the sites returned predictions of habitat suitability
and subsequent analysis of aerial imagery revealed landscape characteristics very similar to
those found at sites with known populations of bog turtles.
28
In Iredell County, an isolated group of sites retuned moderately high values of predicted
suitability, suggesting that viable bog turtle habitat may still be present in the Piedmont. As this
species has not been seen in this part of the state in over 40 years, the possibility that remnant
populations still exist among these sites may be of great interest to wildlife managers. It is
recommended that sites with the highest predicted suitability be investigated by bog turtle
researchers in order to help determine the accuracy of the predictions. Additional work on the
Maxent model is recommended as a way of potentially improving accuracy with the predictions.
The use of GIS and habitat modelling programs such as Maxent are an exciting set of new tools
available to researchers in the conservation ecology field. While these methods are not
intended to be replacements for traditional on-the-ground methods, they do provide new ways of
thinking about the question of species conservation and how best to address the many
challenges it presents.
29
APPENDIX:
Figure 5. SE-GAP Analysis Project predicted species distribution map for the Bog turtle (Glyptemys muhlenbergii)
near the study region
30
Figure 6. Hydric soil types associated with bog turtle habitat within
the study region.
31
Data
Variable
Source
Spatial Resolution,
(meters)
Elevation
Elevation
North Carolina Floodplain Mapping Program.
http://www.ncfloodmaps.com/
6
·
·
·
·
·
·
Land Cover
Precipitation
Temperature
Soil
Aspect
Sinks
Insolation
Slope
TPI_250
TPI_1000
SE-GAP Land Cover
· SE-GAP_450
· SE-GAP_1200
Average July
Precipitation, (1981 2010)
Average July High
Temperature, (1981 2010)
Average July Low
Temperature, (1981 2010)
Soil Map Unit
6
6
6
6
6
6
Southeast GAP Analysis Project.
http://www.basic.ncsu.edu/segap/
PRISM Climate Group. Oregon State University.
http://www.prism.oregonstate.edu/
PRISM Climate Group. Oregon State University.
http://www.datagateway.nrcs.usda.gov
30
30
30
800
800
800
SSURGO Soils Data.
http://www.datagateway.nrcs.usda.gov
· Soil Map Unit_450
· Soil Map Unit_1200
30
30
30
Figure 7. Spatial environmental data used in both the rule-based and Maxent models.
32
Figure 9. Rule-Based Model for the Bog Turtle (Glyptemys muhlenbergii) in Ashe, Wilkes, Iredell, Davie and Davidson counties,
North Carolina.
33
Figure 10. Maxent output for study counties of Wilkes, Iredell, Davie, and Davidson counties, North Carolina.
34
Figure 11. Comparison of the SE-GAP and Rule-Based Model (RBM) habitat maps for bog turtles.
35
Figure 12. Maxent output for bog turtle habitat in Wilkes County. A site of high probability in northeastern part of the
county contains no bog turtle presence data associated with it, yet exhibits environmental conditions comparable with the
highest quality bog turtle sites in Wilkes County.
36
Figure 13. Maxent output for bog turtle habitat. Site of high habitat probability in northeastern Wilkes County. This site has no bog turtle
presence data associated with it, yet exhibits environmental conditions comparable with the highest quality bog turtle sites in Wilkes County.
37
Figure 14. Maxent output for bog turtle habitat. Habitat probability values at sites with known suitability and predicted
suitability in Wilkes County.
38
Figure 15. Maxent output for bog turtle habitat. Potential sites of high environmental suitability within Iredell County.
39
Figure 16. Dependence Response Curves for Precipitation and Soil inputs in the Maxent model.
Figure 17. Marginal Response Curves for Precipitation and Soil inputs in the Maxent model.
40
Figure 18. Variable contributions of Maxent inputs. The
percent contribution column indicates which variable
contributes most to the model’s gain; it’s ability to separate
suitable habitat from all other areas within a landscape.
41
Probability Threshold
# of presence points included
% of presence points
0.019
216
100
0.074
0.4
0.6
0.7
0.8
216
195
135
42
0
100
90.3
62.5
19.4
0
Figure 19. Confusion matrix for the 216 presence points (localities) used in the Maxent analysis. Most presence
points returned probability values in the 0.6 – 0.8 range.
42
Figure 20. Maxent output showing probability of predicted habitat values for the nine counties (Ashe, Ashe,
Wilkes, Iredell, Davie, Davidson, Forsyth, Surry, Gaston, Alexander) that contributed presence data to the
analysis. Only values from Wilkes, Iredell, Davie, and Davidson counties are included in this modeling
investigation.
43
REFERENCES
Giovanelli, Joao G.R., Haddad, Célio F. B.,
Alexandrino, Joao. (2008) Predicting the
potential distribution of the alien invasive
American bullfrog (Lithobates
catesbeianus) in Brazil. Biological
Invasions 5: 585-590.
Beane, J. (2013) North Carolina State Museum
of Natural Resources. Personal
communication.
Buermann, W., Saatchi, S., Smith, T. B., Zutta, B.
R., Chaves, J. A., Milá, B. and Graham, C. H.
(2008), Predicting species distributions
across the Amazonian and Andean regions
using remote sensing data. Journal of
Biogeography, 35: 1160–1176.
Herman, D. W. (1994) The bog turtle, Clemmys
muhlenbergii, in North Carolina.
An action plan for its conservation and
management. Report submitted to
the North Carolina Resources Commission.
156 p.
Dorcas, M.E., and Pittman, S.E. (2009)
Movements, Habitat Use, and Thermal
Ecology of an Isolated Population of Bog
Turtles (Glyptemys muhlenbergii) Shannon.
Copeia 4, 781-790.
Hernandez, P.A., Graham, C.H., master, L.L., &
Albert, D.L. (2006) The effect of sample
size and species characteristics on
performance of different specie distribution
modeling methods. Ecography, 29: 773–785.
Elith, J., Graham, C.H., Anderson, R.P. et al.
(2006) Novel methods improve prediction
of species’ distributions from occurrence
data. Ecography, 29, 129–151.
Iredell County Governmental Center.
www.co.iredell.nc.us. Accessed on October
2nd, 2013.
Ernst, C. (1977) Biological Notes on the Bog
Turtle, Clemmys muhlenbergii.
Herpetologica 33: 241-246.
Kotkin, J. “America’s Fastest and Slowest
Growing Cities.” Forbes. March 18th 2013.
Ernst, C. (1986) Environmental Temperatures
and Activities in the Wood Turtle, Clemmys
insculpta. Journal of Herpetology 20: 222229.
Maxent. Princeton University.
http://www.cs.princeton.edu/~schapire/ma
xent/. 2004.
Ernst, C. H., & Lovich, J.E. (2009) Turtles of the
United States and Canada. Johns Hopkins
University Press; second edition.
National Agricultural Imagery Program. 2010.
United States Department of Agriculture.
http://www.fsa.usda.gov/FSA/apfoapp?are
a=home&subject=prog&topic=nai
Fay, John. (2013) Nicholas School of the
Environment. Duke University. Personal
communication.
North Carolina Department of Environment and
Natural Resources. North Carolina Natural
Heritage Program.
44
North Carolina Department of Transportation.
Contour and Elevation Data.
http://www.ncdot.gov/. 2013.
Phillips, S. J., and Dudik, M. (2008) Modeling
of species distributions with Maxent: new
extensions and a comprehensive evaluation.
Ecography, 31, 161-175.
North Carolina State University. SE-GAP
Analysis Project.
http://www.basic.ncsu.edu/segap/. 2011.
Phu, L. D. (2008) Restoring Bog Turtle Wetlands
With the use of Prescribed Grazing: First
Year Results. Technical Memorandum.
Maryland Department of Natural Resources.
North Carolina Wildlife Resources Commission.
Bogs and associated wetlands in the
Southern Blue Ridge.
http://www.ncwildlife.org/Conserving/Habi
tats/Mountain.aspx. 2013.
Pittman, S.E., & Dorcas, M. (2009) Movements,
Habitat Use, and Thermal Ecology of an
Isolated Population of Bog Turtles
(Glyptemys muhlenbergii). Copeia 4, 781790.
Oregon State University. PRISM Climate Group.
http://www.prism.oregonstate.edu/. 2013.
Raxworthy, C.J., Martinez-Meyer, E., Horning,
N., Nussbaum, R.A., Schneider, G.E., OrtegaHuerta, M.A. & Peterson, A.T. (2003)
Predicting distributions of known and
unknown reptile species in Madagascar.
Nature, 426, 837–841.
Pearson, R. G., Raxworthy, C.J., Nakamura, M.,
& Peterson, A. T. (2007) Predicting species
distributions from small numbers of
occurrence records: a test case using cryptic
geckos in Madagascar. Journal of
Biogeography 34, 102-117.
Somers, AB. (2000) A Population of Bog Turtles
in the Piedmont of North Carolina: Habitat
Preferences, Capture Method Efficacy,
Conservation Initiatives, and Site
Enhancement. Unpubl. Department of
Biology. University of North Carolina at
Greensboro.
Pearson, R. G., Raxworthy, C. J., Nakamura, M.
and Townsend Peterson, A. (2007),
ORIGINAL ARTICLE: Predicting species
distributions from small numbers of
occurrence records: a test case using cryptic
geckos in Madagascar. Journal of
Biogeography 34, 102–117.
United States Fish and Wildlife Service. 1997.
Proposed rule to list the northern
population of the bog turtle as threatened
and the southern population as threatened
due to similarity of appearance. Federal
Register 62:4229–4239.
Peterson, A.T., Papeş, M. and Eaton, M. (2007),
Transferability and model evaluation in
ecological niche modeling: a comparison of
GARP and Maxent. Ecography, 30: 550–560.
Phillips, S. J., Anderson, R. P., and Schapire , R.
E. (2006) Maximum entropy modeling of
species geographic distributions. Ecological
Modelling, 190, 231-259.
United States Fish and Wildlife Service.
www.fws.gov/northeast/nyfo/es/bogturtle.
pdf. Check site for full reference info.
45
Wilkes County
http://www.wilkesedc.com/demographics/
geography-climate/. 2013.
Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T.,
Graham, C. H., Guisan, A. and NCEAS
Predicting Species Distributions Working
Group (2008), Effects of sample size on the
performance of species distribution models.
Diversity and Distributions, 14: 763–773.
Wilkes County Governmental Center.
http://wilkescounty.net/Accessed on
October 2nd, 2013.
46