Evaluation of the Use and Effectiveness of Wildlife Crossings (2008) / Chapter Skim
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Appendix E - A Literature Review of Field Studies and Spatial Analyses for Hotspot Identification of WildlifeVehicle Collisions
Appendix E - A Literature Review of Field Studies and Spatial Analyses for Hotspot Identification of WildlifeVehicle Collisions
Pages 139-156
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2004. Modelling the spatial distribution of human-caused grizzly bear mortalities in the Central Rockies ecosystem of Canada.
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; level (not bank or gully) ; wooded; nonwooded; barren; distance to woodland; increasing slope; decreasing slope; no slope; angular visibility; in-line visibility; shortest visibility; speed limit; fencing; guardrails Data obtained by selecting a random point from within each 100m interval of site length and running a 100 m transect perpendicularly from each side of the road; at sites shorter than 100 m, two points were randomly selected Analysis: stepwise logistic regression used to test the importance of the variables used in the model; 5 pairs of sites randomly selected for a test of the model's predictive ability Results: 9 of 19 variables selected for inclusion in model (residences, commercial buildings, other buildings, shortest visibility, in-line visibility, speed limit, distance to woodland, fencing, non-wooded area)
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: elevation; distance to hydrology; distance to human use; road sinuosity ratio; change in elevation; habitat importance for deer, moose and elk; barrier Analysis: Spearman's rho correlations used to screen for multicollinearity, removed one highly correlated biophysical variable before model development; differences between seasons compared using χ2 tests • Model development: logistic regression, stepwise selection process using log likelihood ratio tests and a prob. value of 0.05 for entry and removal of variables to the model; selection process then repeated using only GIS-based variables; χ2 used as a goodness-of-fit test of model appropriateness; Wald stats to test the significance of independent variables; direction of predictor influence verified using Mann-Whitney U tests; odds ratios examined to assess contribution that a unit increase in predictor variable made to outcome probability • Model validation: 5 control and 5 kill sites randomly chosen to validate model's predictive ability; 0.29 chosen as classification cut-off for predicted group memberships based number of kill sites vs control sites; predicted probabilities classified into 3 groups: low, moderate and high risk of kill Results: Kill sites highly aggregated; highly significant seasonal differences (high in summer)
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: crops, forest, grass, water, developed, orchards; topographic physical features from aerial photographs and topographic maps Analyses: 0.8 km radius buffer zone around each road segment to quantify and compare landscape composition and pattern using FRAGSTATS • Simple correlation used to investigate relations among variables; highly correlated eliminated • t-tests to determine if variable means differed between hotspots and controls; those indices with |t| values ≥ 3.0 selected as predictor variables for logistic regression • Stepwise logistic regression model selection process to obtain a preliminary equation • AIC to compare models • Stepwise selection process repeated using only landscape indices, satellite imagery data only • 5 paired sites used to test models' predictive abilities Results: variables included in model 1: % distant woody cover, % adjacent gully; natural log of area of recreational land within buffer, natural log of width of corridors crossing road; of 10 samples to test model validity, 5 control and 4 hotspots correctly predicted • Variables included in model 2: Simpson's diversity index; natural log of woods mean proximity index; of 10 samples to test model validity, 4 control and 2 hotspots correctly predicted Discussion: study demonstrated that DVA site statistics and RS habitat and highway data can be used to predict DVA locations Gundersen, H., and H.P. Andreassen.
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which started when snow depth exceeded 30 cm and lasted until temp stabilized above 0 degrees C Number of days in new variable explained 83% of yearly variation in number of moose collisions; GLM including both accidental period and pop density explained 88% of yearly variation Spatial effects: significant negatively correlated between number of collisions and distance to nearest side valley; no association between number of collisions and topography; changes in food availability strongly associated to number of collisions Discussion: moose usually killed in winter on days with lots of snow and low temps; influenced by migratory routes to lower elevations and availability of food; temporal variation due to climatic factors, spatial variation due to migratory routes and food availability.
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: Light condition and posted speed limit related to severity, and mutually independent -- risk 2.1x greater at night and 2x higher at highway (high)
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; number of buildings in buffer, speed limit; number of lanes; distance from road to nearest forest cover patch; ROW topography based on presence or absence of ditches Analysis: univariate procedure used to reduce 66 variable set to smaller group; removed variables correlated at r ≥ 0.70; left with number of buildings, number of forest cover patches, proportion of forest cover, Shannon's diversity index for further analysis • Logistic regression analysis to determine which variables best explained difference between DVA areas and control areas; built one global model and 10 a priori models; used AIC and Akaike's weights to rank and select best model; used relative weight of evidence to compare parameter importance; model averaging to incorporate model-selection uncertainty into final unconditional parameter estimates and standard errors • 40 sites retained to validate best-fit model Results: global model was significant; areas with DVA contained fewer buildings, more patches and higher proportion of forest cover, more public land patches and higher Shannon's diversity index of landscape; Akaike's weights indicated number of buildings and number of public land patches most important variables • 7 models necessary to compile a 95% confidence set; best-fit model correctly classified 77.5% of test sites Discussion: study unique because assessed landscape factors influencing DVA in an urban environment; pooled data over 7-year period so pop growth or land-use change may have affected data Nielsen, S.E., Herrero, S., Boyce, M.S., Mace, R.D., Benn, B., Gibeau, M.L., and S Jevons.
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perpendicular) ; observable area from highway every 0.10 mile; ROW width and slope; ROW vegetation; vegetation composition; road type Analysis: stereoscopic aerial photography used to describe habitat features; transparent grid placed over photos to determine percent cover and topographic features at deer-highway mortality locations beginning at the road and extending 1.2 km distant; identified roadkill and live deer locations, as well as descriptive roadside features to 0.10 mile Results: 397 deer roadkills during 2 years of study; deer kills averaged < 20 before roads relocated; 19 deer kill zones identified; deer spotlight counts not significantly correlated with kill sites; kill zones had higher mean % cover Discussion: traffic volume significantly influenced deer mortality; higher kill levels occurred along drainages; ROW topography may funnel deer to the ROW and encourage movement along highway corridor Seiler, A
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• Validation results: combined model gave best results predicting 72.4% of all MVC sites and 79.8% of all control sites; traffic model concordance = 77.9%; landscape model concordance = 62.0%; all results are significant • Identified 72.7% of all accident sites • Other parameters were important in distinguishing between accident and control sites within a given road category including amount of and distance to forest cover, density of intersections between forest edges, private roads and the main accident road, moose abundance indexed by harvest statistics • Together, road traffic and landscape parameters produced an overall concordance in 83.6% of the predicted sites and identified 76.1% of all test road sections correctly • Speed reduction appeared to be most effective measure to reduce MVC risk at any given traffic volume; modified by fencing, moose abundance and forest proximity Discussion: spatial distribution of MVC not random; collisions a product of environmental factors quantified from RS landscape info, road traffic data and estimates of animal abund.; parameters used to identify high risk roads (traffic data) different from parameters used to identify high risk road segments (landscape data)
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: as originally developed, several limitations -- inaccuracy in interpretation in some situations and edge effects 2-D study areas (not roads) : defined as distance between point a and the nearest other point in the pattern Distances other than those between a point and its closest neighbor are referred to as second, third, or "higher order neighbor distances" NNA in 1-D study areas (roads)
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, with the distribution function of expected nearest neighbor distances for CSR P(di)
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– Hawaii Pointstat – S-Plus – Venables and Ripley Spatial Statistics Functions – SASP: A 2-D Spectral Analysis Package for Analyzing Spatial Data – SpaceStat: A Program for the Statistical Analysis of Spatial Data • Variables may be described spatially as either – Occurring at unique point locations (incidents, buildings, people) – Aggregated to areas (census tracts, traffic analysis zones, city boundaries)
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– 2-D spectral analysis seen as exploratory guide for examining repeating spatial patterns. • SpaceStat program designed to spatially analyze areal distribution (Anselin 1992a)
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* Number of linear nearest neighbors e.
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Expected nearest neighbor distance if CSR = the mean random distance. Mean random distance = d(ran)
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– Kth linear NNI is ratio of observed Kth linear nearest neighbor distance to the Kth linear mean random distance. – Expected linear nearest neighbor distance is Ld(ran)
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. • K-function uses all point-to-point distances not just nearest neighbor distances • When k-function used for point patterns constrained by linear road networks, can overdetect clustering patterns possibly leading to Type 1 errors.
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• Combo of using graphical Kernel (for visual) and network k-function was helpful, but must be realized that kernel estimations do not compensate for spatial differences I road networks and their effect on point patterns observed.
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