Evaluation of the Use and Effectiveness of Wildlife Crossings (2008) / Chapter Skim
Currently Skimming:
Pages 30-97
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 30... ...
It is expected that results of this research project, specifically the predictive models developed, can be applied within SafetyAnalyst in undertaking tasks 1, 2, and 3 above with respect to wildlife–vehicle collisions. Aspect 2: Comparison of Wildlife–Vehicle Collision and Carcass Removal Data Reported WVC data may represent only a small portion of the large number of WVCs that occur61,201.
|
From page 31... ...
The activities completed as part of this aspect of NCHRP 2527 (e.g., plots, summary measures, and models) were used to investigate and compare the patterns of two databases (i.e., reported WVCs and deer carcass removals)
|
From page 32... ...
Several types of computer software were used to overlay, present, and summarize the WVC and deer carcass removal data within the GIS platform. Microsoft® Excel™ and TrueBasic™ were used to manage the deer carcass removal data.
|
From page 33... ...
WA AADT Average degree of curvature Shoulder width in feet Shoulder type Median barrier type Median width in feet Median type Number of lanes Speed limit in mph Surface width in feet Terrain (level, rolling, mountainous) Table 9.
|
From page 34... ...
. For this project it was only possible to plot the locations of the deer carcass removals by IaDOT personnel to the nearest mile marker (Figure 4)
|
From page 35... ...
Descriptive statistics for the 2001 to 2003 roadway length, AADT, WVC, and deer carcass removal data used in the model development are summarized in Table 11. The length of the segments evaluated and modeled was primarily defined by the changes in roadway cross section design (e.g., number of lanes)
|
From page 36... ...
36 Model Form: Total wildlife–vehicle collisions/mile-year = LANEWIDSPEEDHISURFWIDAADT 5432exp1State/ Model Terrain ln (s.e.)
|
From page 37... ...
HI: Average degree of curvature SPEED: Posted speed in North Carolina & design speed in California (mph) MEDWID: Median width (feet)
|
From page 38... ...
Aspect 2: Comparison of Wildlife–Vehicle Collision and Carcass Removal Data The findings from this aspect of the safety analysis focused on the challenges related to combining WVC and deer carcass removal data on a roadway network within a GIS platform. This information is useful because it helps define where the WVC and deer carcass removal data were reported or collected, and whether the occurrence of either is actually overor under-represented along roadways with particular characteristics.
|
From page 39... ...
WVC and deer carcass removal GIS activities. There are a number of advantages when information is incorporated into a GIS platform, including an increased ability to organize and integrate spatial data, the relatively easy presentation of the data, and the capability to quickly analyze and/or compare one or more datasets.
|
From page 40... ...
Figures 4 and 5 generally show that reported WVCs and deer carcass removal data (as available) likely have different spatial patterns.
|
From page 41... ...
WVC and deer carcass removal model development and comparison. Prediction models using WVC, deer carcass removal, and roadway cross section data from Iowa were developed to assist in the identification of potential hotspot roadway segments and are described next.
|
From page 42... ...
The models include one or more of the AADT, AVGSHLD, MEDTYPE, and MEDWID predictor variables. As with the two-lane models, the number of WVCs could also prove to be a useful predictor of deer carcass removal frequency.
|
From page 43... ...
0.6439 (0.0268) 1.0204 Deer Carcass Removals/ Mile-Year -5.5973 (0.2952)
|
From page 44... ...
The dispersion parameters of the deer carcass removal models show that these data are much more overdispersed than the WVC data. This difference reinforces the need for deer carcass removal data at the site level.
|
From page 45... ...
Cumulative residuals for multilane rural roadway volume-only WVC model recalibrated and applied to deer carcass removals. roads, WVC data are available and used, along with the model predictions in an empirical Bayes procedure to estimate the expected long-term mean collision frequency of a specific roadway segment.
|
From page 46... ...
The proposed methodology: • Properly accounts for regression-to-the-mean, • Overcomes the difficulties of using collision rates in normalizing for traffic volume differences between the before and after periods, • Reduces the level of uncertainty in the estimates of safety effects, • Provides a foundation for developing guidelines for estimating the likely safety consequences of installing a crossing and fencing, and • Properly accounts for differences in collision experience and reporting practice in amalgamating data and results from diverse jurisdictions. The task is to estimate what was the effect on safety of installing wildlife crossing measures.
|
From page 47... ...
The exposure of animals to the roadway is not accounted for. In the EB procedure, the SPF is used to first estimate the number of collisions that would be expected during the before period at locations with traffic volumes and other characteristics similar to the one being analyzed.
|
From page 48... ...
48 Site No. of Lanes Length Years Before Years After AADT Before AADT After Crashes Before (x)
|
From page 49... ...
One or both of these two databases have been used in the past to describe the magnitude of the WVC problem and to propose and evaluate the effectiveness of WVC countermeasures. Overall, the visual and quantitative findings of the reported WVC and deer carcass removal comparison activities revealed that both their magnitudes and P = − =( )
|
From page 50... ...
In the following discussion, the focus is on the impact of the reported WVCs and deer carcass removal comparison results rather than the direct application of the plots, measures, and models calculated. Some of the challenges related to combining and presenting these data in a GIS platform are also discussed.
|
From page 51... ...
This situation is not surprising, but it did lead to some challenges related to their combination and comparison in a GIS platform. The WVC data from 2001 to 2003 was available by latitude and longitude, but the deer carcass removal locations were adjusted to the closest milepost and summed.
|
From page 52... ...
The total number and location of deer carcass removals, on the other hand, are rarely collected consistently statewide. For this type of situation, the research team recommends that reported AVC/DVC data should be used if safety improvements are the primary objective, and deer or animal carcass removal data (if not available by roadway location)
|
From page 53... ...
Thus, landscape spatial patterns would be expected to play an important role in determining locations where the probability of being involved in a wildlife–vehicle collision is higher compared to other locations.95 Explanatory factors of wildlife roadkill locations and rates vary widely among species and taxa. To properly mitigate road impacts for wildlife and increase motorist safety, transportation departments need to be able to identify where particular individuals, species, taxa, and vertebrate communities are susceptible to high roadkill rates along roads.
|
From page 54... ...
a 2005 annual average daily traffic volume. Data from Parks Canada; Banff National Park; and Alberta Transportation, Edmonton, Alberta.
|
From page 55... ...
To do this, each of the five highways in the study area was divided into 1.0-mile-marker segments using ArcView 3.3.77 All spatially accurate UVC data were plotted onto the road network and then moved to the nearest mile-marker reference point. Each observed data point was moved an average distance of 400.2 m ± 218.8 m (min.
|
From page 56... ...
Forest cover Mean percentage (%) of continuous forest cover (trees > 1 m height)
|
From page 57... ...
between high- and low-kill sites within the spatially accurate and mile-marker datasets. The significance of each differentiated class within the categorical variables was evaluated using Bailey's confidence intervals.48 Logistic regression analyses were used to identify which of the significant parameters best predicted the likelihood of UVC occurrence within the spatially accurate and mile-marker datasets.123 Stepwise (backward)
|
From page 58... ...
To reduce intercorrelation between the variables,252 the research team omitted the percentage of forest cover from further analyses because it was highly correlated (r > 0.70) with percentage of cleared ground.
|
From page 59... ...
In Table 25, results are presented from the logistic regression analyses for modeling the factors contributing to UVCs using two datasets. They include a spatially accurate dataset (n = 499 locations; 391 high- and 108 low-density points)
|
From page 60... ...
Many deer–vehicle collisions in Pennsylvania were concentrated around woodland-field interfaces in predominantly open habitat.15 On the other hand, some studies have not found this association between habitat type and UVCs.4,25 Wildlife tends to be associated with specific habitats that provide resources and environmental conditions that promote occupancy and survival.176 Thus, the spatial distribution of habitat types adjacent to or bisected by a highway transportation corridor would likely influence the extent, severity, and locations of vehicle collisions with wildlife. Landscape variables other than habitat and topography may also be important attributes determining UVCs.
|
From page 61... ...
Resource managers and transportation biologists have identified this lack as a severe shortcoming that needs immediate attention. A recent Transportation Research Board report highlighted the urgent need to better understand how wildlife respond to and are potentially impacted by highway barriers.233 Spatial accuracy and interpretation of results.
|
From page 62... ...
In the univariate analysis, 10 variables were significant in explaining UVCs; 8 were related to landscape, while only 2 were associated with the road environment. In the logistic regression analysis, three explanatory variables were significant; two were landscape based and one was from the road environment.
|
From page 63... ...
. The model-based clustering techniques that are demonstrated include a linear nearest neighbor analysis used initially to measure if the WVC locations were random and then Ripley's K statistic, nearest-neighbor measurements, and density measures to identify hotspots.
|
From page 64... ...
Data from Parks Canada; Banff National Park; and Alberta Transportation, Edmonton, Alberta. b 1999 summer average daily traffic volume.
|
From page 65... ...
The research team used a linear nearest neighbor analysis, cluster analysis, Ripley's K analysis, and density measures to identify collision hotspots at different scales of application. Linear nearest neighbor analysis.
|
From page 66... ...
66 Figure 12. Spatially accurate locations of WVC locations on each road in each of the watersheds.
|
From page 67... ...
The NNI used in this analysis is only an indicator of first order spatial randomness; a K-order nearest neighbor distance (e.g., second or third order) would likely better describe the overall spatial distribution of WVCs.145 Sample sizes were small on the TCH in Yoho and Banff, and on Highway 40 in Alberta (n < 100)
|
From page 68... ...
68 Figure 13. Clusters or hotspots derived from CrimeStat III software on each road in each of the watersheds in Alberta, Canada.
|
From page 69... ...
Neighbor K statistics are well suited for the description of one-dimensional spatial distributions.200,104,192 The range of scales over which clustering appears significant is dependent on the intensity of the distribution of roadkills.52,192 Peaks in L(t) (i.e., the intensity of clustering)
|
From page 70... ...
The Ripley's K analysis clearly shows the spatial distribution of WVCs along each segment of highway. The largescale aggregation evident on Highway 93 South in Kootenay shows the importance of broad-scale landscape variables such as elevation and valley bottoms in a mountain environment.
|
From page 71... ...
Comparison of Hotspot Identification Techniques Visual analysis and observation versus analytical techniques. The pros and cons of the simple visual analysis of WVC versus more complex or analytical methods were discussed earlier ("Simple graphic techniques, one dataset")
|
From page 72... ...
The nearest neighbor CrimeStat technique was more conservative compared to the mile-marker density analysis; it identified less length of highway as a WVC hotspot. Additionally, the average length of WVC clusters was shorter than the density-based high-kill aggregations; however
|
From page 73... ...
. The nearest neighbor CrimeStat clusters followed a spatial distribution similar to the mile-marker high-kill zones (Figure 13)
|
From page 74... ...
With their wildlife carcass information they performed a GIS-based wildlife linkage habitat analysis using landscape-scale data to identify or predict the location of potentially significant Wildlife Linkage Habitats (WLHs) associated with state roads throughout Vermont.
|
From page 75... ...
The nearest neighbor CrimeStat method essentially pinpoints the location of WVC hotspots, whereby the segmental analyses of WVC densities provide a more comprehensive evaluation of mitigation options and prioritization of mitigation schemes based on cost-benefit, scheduling of transportation projects, or severity of motorist safety concerns. Collection of WVC data (both reported vehicle collision and carcass collection data)
|
From page 76... ...
There is tremendous variation across the North American continent in terms of vegetation cover, topography, levels of urban development, land use practices, road density, and traffic volume, as well as differences in the typical species diversity, richness, and abundance in local areas. Yet, it was impossible to capture that entire variation in one study.
|
From page 77... ...
Indirect effects have been suggested to operate within 100 m of a road; however, as a precaution, we designed our sampling protocol to detect changes that may occur up to 600 m or more from the road. Small mammals have relatively small home ranges and limited mobility, and the research team expected that results should be evident within 600 m from the road.
|
From page 78... ...
safety reasons, the ROW verge between the road edge and the 2.4 m deer exclusion fence was not sampled because of very high traffic volume. All traps in both sampling schemes were baited with a mixture of horse grain and peanut butter, and checked on three consecutive mornings and afternoons (lethal traps only)
|
From page 79... ...
Sites were not randomly selected. Rather, the research team used 1:20,000 orthophotos and field inspections to locate all points along the transmission line.
|
From page 80... ...
80 Figure 21. Schematic of site layout for a highway site.
|
From page 81... ...
. Results of Shannon–Wiener diversity index (H)
|
From page 82... ...
The low sample sizes and clumpy, among-site distribution of captures prevented within-species comparisons of spatial distribution in relation to transect, with the exception of deer mice (Figure 27)
|
From page 83... ...
83 difference was evident for deer mice between treatments only for the 600 m transect (ROW P = 0.32, 50m P = 0.47, 300m P = 0.83, 600m P = 0.05)
|
From page 84... ...
84 preferred habitats, which are more typically associated with ROWs than forest (i.e., rich meadows with abundant forbs) .178 Had there been a strong effect of highway proximity, differences between the highway and transmission-line sites should have been found for the ROW transects.
|
From page 85... ...
When the percentage composition they report for deer mice is converted to absolute numbers, abundance was consistently higher near interstate than county highways. Whether this phenomenon was related to the larger area of grassy habitat along interstate highways is not clear, but it does suggest that large highway size and volume did not have an overwhelmingly negative effect on deer mice.
|
From page 86... ...
An animal's movement capabilities define in large part its abilities to find resources necessary for survival. The development of allometric equations that relate the home range sizes of species to movement ability allows the calculation of scaling properties for individual species.
|
From page 87... ...
effects on wildlife populations and on ecological patterns and processes.26,30 In particular, animal movement is hindered as road density and traffic volume increase. Spatial linkage, accomplished by animal movement, is critical because the arrays of resources that are essential to population viability are usually distributed heterogeneously across the habitat network.168 Animal movement can be seasonal migrations120 that tend to be cyclic, dispersal events227 that are usually unidirectional,180 or ranging behavior144,228,216 characterized by shorter exploratory movement within a home range or territory.
|
From page 88... ...
They argue that the residual variance in the body size versus home range, and the body size versus dispersal distance relationships represent real differences in vagility independent of body size and therefore the relationship between dispersal distance and home range size should co-vary across mammal species after the effects of body size are removed. The Dispersal Distance Connection Dispersal is a fundamental element of demography,7 colonization,117 and gene flow182 but dispersal movements are perhaps the least well understood of ecological phenomena.227 Bowman et al.33 showed that dispersal distance is actually more closely related to home range size (R2 = 0.74)
|
From page 89... ...
increase as transitions between domains are approached. If possible, then the recognition of a few groups or guilds composed of similarly sized species with similar home range domains is an important first step in determining the spatial location for effective crossings for most species.
|
From page 90... ...
Because median dispersal distance and linear home range distances are derived from home range area, if there was a relationship, it should apply to any of these measures. Finally, the research team compared the options for spacing wildlife crossings and presented the most feasible scaling domains for large mammals that are most likely to be involved in serious animal–vehicle collisions.
|
From page 91... ...
Median dispersal (7 * Home Range)
|
From page 92... ...
provides a scaling that more closely approximates the majority of movements made by mammalian species, which typically move within their home range for most of the year. During spring and fall of course, juvenile animalsusually make longer migratory movements.227 When linear movement domains were used to place multiple wildlife crossings according to a mile-marker spacing, approximately 12% of species would be likely to cross at a distance of 7 mi, approximately 30% at 3.0 mi, and approximately 64% at crossing distances of 1 mi.
|
From page 93... ...
Using the square root of the home range to establish scaling domains to inform the placement of wildlife crossings is most reasonable because shorter dispersal distances by juveniles are more frequent.227 Additionally, animal fidelity to 7 *
|
From page 94... ...
To the extent that daily movement data are available for species, allometric domains can be developed to inform the placement of wildlife crossings. The sample given in Table 35, however, suggests that a large sample will be needed to extract the relationship, if it exists.
|
From page 95... ...
However, placing wildlife crossings using the LHRD domain for white-tailed deer and mule deer at about 1 mi (1.6 km) apart in areas where these animals cross the road frequently, and are often hit by vehicles, would certainly improve highway safety and help ensure ease of movement, improving landscape permeability for these animals.
|
From page 96... ...
Significantly, the before-after analysis may be judged as successful from a road safety perspective, while at the same time the wildlife population concerned may be significantly reduced. A second aspect of the safety effort was to investigate the hypothesis that roadside carcass removal data not only indicate a different magnitude for the WVC problem, but may also show different spatial patterns than reported WVC data, and lead to the identification of different roadway locations for potential WVC countermeasures.
|
From page 97... ...
Because WVCs represent a distribution of points, recently developed and refined clustering techniques can be used to identify hotspots. As an initial step, the researchers used the linear nearest neighbor index (a simple plotting technique)
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.