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3.6 Interpretation of Research · Aid in the evaluation, selection, and prioritization of po-
Results tential mitigation measures; and
· Evaluate the effectiveness of mitigation measures already
The sections on the Phase 2 research studies (safety [3.1], ac- implemented.
curacy modeling [3.2], hotspot analysis [3.3], small mammals
and putative habitat degradation effects [3.4], allometric plac- An important caveat is that the safety approach does not
ing of wildlife crossings [3.5]) contain important information address any aspect of wildlife population response. As the
and suggestions for implementation. In particular, Sections 3.1, models stand, their primary application is for the safety man-
3.2, and 3.3 address different ways to achieve similar purposes agement of existing roads as opposed to design or planning
and therefore potentially may be confusing for the reader. For applications for new or newly built roads. Significantly, the
example, Section 3.1 involves analyses of WVCs and road envi- before-after analysis may be judged as successful from a road
ronment data from state DOT sources. Section 3.2 involves an safety perspective, while at the same time the wildlife popu-
investigation into the relative importance of factors associated lation concerned may be significantly reduced.
with wildlife killed on the road using two different datasets: one A second aspect of the safety effort was to investigate the
based on high-resolution, spatially accurate location data (<3 m hypothesis that roadside carcass removal data not only indi-
error) representing an ideal situation and a second dataset cate a different magnitude for the WVC problem, but may
created from the first that was characterized by lower resolution also show different spatial patterns than reported WVC data,
data (<_ 0.5 mi or 800 m, i.e., mile-marker data) and likely typi-
and lead to the identification of different roadway locations
cal of most transportation agency data. Section 3.3 investigates for potential WVC countermeasures. The magnitude and
several wildlife kill hotspot identification clustering techniques patterns of location-based WVC reports and deer carcass re-
within a GIS framework that can be used in a variety of land- moval datasets from Iowa were compared qualitatively
scapes. This section on the interpretation of the research results through visual GIS plots and quantitatively (e.g., frequency
will guide the reader in understanding these sections. per mile). Police-reported WVC information, deer carcass
The safety research (Section 3.1) is most effectively used removals, and deer salvage data were evaluated. Results
when the purpose is to assess if a specific mitigation has been showed that the number of deer carcasses removed from the
successful in reducing WVCs to improve public safety. It road was approximately 1.09 times greater than the number
employs the use of SPFs, predictive models for AVCs. SPFs of WVCs reported to the police. The number of salvaged and
typically relate the response variables (AVC data and/or unsalvaged deer carcasses, on the other hand, was approxi-
roadside carcass collection data) to the explanatory variables mately 1.66 times greater. Clearly, the choice of the database
(physical roadway and roadside characteristics; often impacts whether a particular roadway segment might be
referred to as road geometrics). Other explanatory variables identified for closer consideration. The message here is that
that animals respond to (e.g., topography, vegetative cover, the choice of the database used to define and evaluate the
and other off-road variables) are not among these variables WVC problem and its potential countermeasures requires
that are readily available within the typical DOT safety data- careful consideration. Recommendations are provided in this
bases. Hence, this approach will result in some unexplained report about how the databases might be used appropriately
variation, because the safety approach limits the explanatory and how the data can be most profitably collected.
variables to road geometrics. Regardless, this approach is To understand the important variables that account for
valuable because only these lower levels of data availability WVCs, then environmental variables must be considered that
may exist in some jurisdictions. are not normally included in datasets available from DOTs.
The SPF approach is statistically correct and accounts for The safety research recognized that variables other than road-
"regression to the mean" problems. It makes use of three dif- related variables might be important. In the accuracy model-
ferent levels of road data commonly available. The first level re- ing (Section 3.2), the research team used 14 ecological field
quires data on (1) road length and (2) ADDT. The second level variables, 3 distance-to-landscape-feature variables, and 5 GIS-
adds the requirement that road segments be classified as flat, generated buffer variables as explanatory variables to assess
rolling, or mountainous terrain. The third level incorporates their relative importance in explaining where ungulates were
the data used in levels 1 and 2, but includes additional roadway killed on the road. Further, the research team assessed whether
variables such as average lane width. The safety approach has the spatial accuracy of these datasets was important in identi-
several applications and can be used to: fying the significant explanatory variables. Spatially accurate
data were discovered to make a difference in the ability of
· Identify collision-prone locations for existing or proposed models to provide not just statistically significant results but
roads for all collision types combined or for specific target more importantly, biologically meaningful results for trans-
collision types portation and resource managers responsible for reducing
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WVCs and improving motorist safety. Hence, these models The research team used Ripley's K statistic of roadkills, near-
are especially applicable when it is important to locate hotspot est neighbor measurements (using CrimeStat software), and
areas of WVCs and hence wildlife crossings during the design density measures to more formally identify WVC hotspot
and planning of new roads. locations, once roadkill locations were found to be unevenly
The hotspot analysis (Section 3.3) investigated WVC dispersed. The Ripley's K analysis clearly shows the spatial
hotspot identification techniques, taking into account differ- distribution of WVCs and the importance of broad-scale
ent scales of application and transportation management landscape variables (such as elevation and valley bottoms
concerns. Studies of WVCs have demonstrated that they are in a mountain environment). Further, the locations of high-
not random occurrences but are spatially clustered. Data on intensity roadkill clustering within each area can help to focus
hotspots of WVCs can aid transportation managers in in- or prioritize the placement of mitigation activities, such as
creasing motorist safety and habitat connectivity for wildlife. wildlife crossings or other countermeasures, on each highway
Knowledge of the geographic location and severity of WVCs segment. The research team found that the nearest neighbor
is a prerequisite for devising mitigation schemes that can be (CrimeStat) approach was useful for identifying key hotspot
incorporated into future infrastructure projects (e.g., bridge areas on highways with many roadkills because it, in essence,
reconstruction, highway expansion). Many of the studies filters through the roadkill data to extract where the most prob-
characterizing WVCs have appeared in scientific and lematic areas lay. The density analysis approach identified
management-focused journals and often include different more hotspot clusters on longer sections of highway. Although
conclusions or recommendations for managers to consider in the density analysis approach appears to be less useful to man-
designing wildlife-friendly highways. However, lacking are agement, it may be a preferred option where managers are
best management practices for identifying WVC hotspots interested in taking a broader, more comprehensive view of
based on current knowledge and technology to help guide wildlifevehicle conflicts within a given area. Such a broader
planning and decision making. Few studies specifically view may be necessary not only to prioritize areas of conflicts
address the nature of WVC hotspots or their use and appli- but also to plan a suite of mitigation measures. The location of
cation in transportation planning. Because WVCs represent the larger clusters produced by the density analysis could be
a distribution of points, recently developed and refined clus- tracked each year to determine how stable they are or whether
tering techniques can be used to identify hotspots. there is a notable amount of shifting between years or over
As an initial step, the researchers used the linear nearest longer time periods. This type of information will be of value
neighbor index (a simple plotting technique) to assess to managers in addressing the type of mitigation and intended
whether the location of dead ungulates found on roads as a duration (e.g., short-term vs. long-term applications).
result of WVCs was random. The results, however, are only The identification and delineation of WVC clusters, which
an indicator of first order spatial randomness, i.e., an indi- often vary widely in length depending on distribution and
cator to what extent the animal kill locations may be intensity of collisions, facilitates between-year or multiyear
clumped. Simple plotting most often results in collision analyses of the stability or dynamics of WVC hotspot loca-
points being tightly packed together, in some cases directly tions. The WVC data that transportation departments
overlapping with neighboring WVC carcass locations, thus currently possess are suitable for meeting the primary objec-
making it difficult to identify distinct clusters, i.e., where the tive of identifying hotspot locations at a range of geographic
real high-risk collision areas occurred. Modeling or analyt- scales, from project-level (<50 km of highway) to larger
ical techniques permit a more detailed assessment of where district-level or state-wide assessments on larger highway
WVCs occur, their intensity, and the means to begin network systems. The spatial accuracy of WVCs is not of crit-
prioritizing highway segments for potential mitigation ical importance for the relatively coarse-scale analysis of where
applications. Hence, more definitive analytical clustering hotspots are located. Any of the analytical clustering tech-
techniques are needed. niques can be used when more detailed information is needed.
