Wildlife Linkage Habitat Analysis
Document source : www.aot.state.vt.us
Discussion
a.
GIS and WLH Identification:
The WLH analysis was designed to objectively consider the suitability of habitats
associated with state highways for wildlife movement. This analysis relied on several
basic, landscape level databases including: (1) land cover and land use; (2) development
density; and (3) "core" or contiguous habitat, hereinafter referred to as "core" habitat for
purposes of consistency with the GIS data layer from Vermont Center for Geographic
Information (VCGI). Conserved land GIS data was also included as a feasibility
component to the analysis so that we could examine the extent to which potentially
significant WLHs were associated with conserved lands, and whether conserved lands
were already providing a positive benefit for WLHs. This information may prove
beneficial for future decision-making regarding locations for wildlife passage structures
and their long-term success, among others. The model identifies areas associated with
the state road system that intersect critical or important wildlife corridors.
The landscape level GIS data used to identify potential WLH is expected to account for
the broad, general habitat requirements of many species of wildlife ranging from wide
ranging mammals such as black bear, otter and moose to smaller animals such as reptiles
and amphibians. This analysis was also correlated to a statewide wildlife road mortality
database to examine the extent to which road mortality data informs the identification of
WLH. Though the model does not identify the best possible habitat for each individual
species, it attempts to link large, undeveloped areas with relatively low human
disturbance in association with conducive land use and land cover types. In addition, it
does not implicate areas with a high frequency of road crossings, but rather areas with the
highest probability of wildlife crossing at that location.
Other states and countries have conducted GIS based assessments to identify and
prioritize important wildlife linkage habitat. Montana (Craighead 2001, Ruediger et al.
2004), Florida (Endries et al 2003) California (Penrod et al. 2001), Washington
(Singleton et al. 2001), Iowa(Hubbard et al. 2000), New Mexico, and Utah (Carr et al.
2002, Ruediger et al 2005) represent some of the states that have conducted similar
investigations. The Canadian provinces of Alberta, and British Columbia have also
conducted similar investigations (Gibeau et al. 2001; Tremblay 2001). Some of these
states and provinces have advanced beyond the planning and evaluation process and have
modified their highway infrastructure based on their analysis of wildlife movement and
habitat suitability data.
While GIS analytical techniques vary among WLH projects in other states, a common
theme among these models is a process termed cost weighted coverage or least cost
analysis (Singleton et al. 2001, Craighead 2001, Endries et al 2003, Gibeau et all 2001,
Tremblay 2001, Carr et al. 2002). Cost weighted coverage is created through the
reclassification of common landscape variables based on their relative impediment or
benefit to wildlife movement. Setting these landscape variables to a common scale
normalizes the data so that each variable is represented in the model or analysis based on
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Summary :
Montana (Craighead 2001, Ruediger et al. 2004), Florida (Endries et al 2003) California (Penrod et al. 2001), Washington (Singleton et al. 2002, Ruediger et al 2005) represent some of the states that have conducted similar investigations. While GIS analytical techniques vary among WLH projects in other states, a common theme among these models is a process termed cost weighted coverage or least cost analysis (Singleton et al. 2001, Craighead 2001, Endries et al 2003, Gibeau et all 2001, Tremblay 2001, Carr et al.
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wildlife,2001,analysis,habitat,data,gis,land,wlh,hae,based,states,landscape,road
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