Spatially Explicit Decision Support to Resolve Wildlife-Vehicle Conflict

ICOET 2023
Shilling, F

Conflict between transportation systems and ecosystems invariably has a spatial component and most decisions to resolve these conflicts revolve around where to act. Many resolutions involve establishing fencelines to reduce collisions, building or enhancing existing crossing structures, and land protection to support wildlife movement to these fixed structures. These methods depend on understanding the interacting and separate influences of: wildlife movement, wildlife resource selection, vegetation type and quality, topography, proximity to water, human development and activity, infrastructure curvature and dimensions, land ownership and management, temporal factors, animal lifestage, climatic variables and change, and other variables. Although prioritization of wildlife-vehicle conflict areas for resolution have been proposed, including at the state-scale (e.g., ID, CA, NV, NM) these have not been based on objective, repeatable, scientific, or engineering methods. I present here a repeatable method to include consideration of all of these factors in a transparent, science-based, and repeatable geographic information system (GIS). I use a familiar problem for many involved in resolving WVC – determining where fencing and/or wildlife-crossing structures (WCS) are needed. Although other questions could be addressed, I limit the example to: 1) where is fencing needed to reduce collisions, 2) where are new WCS needed and feasible, and 3) where do existing structures need to be enhanced to cost-effectively improve connectivity. I use as a case study SR 156 in California’s Coast Range. This highway parallels the proposed route of a high-speed train route, which will irreversibly and severely impact wildlife connectivity. Part of the mitigation for the rail project is to improve wildlife connectivity across the highway. I used available WVC, habitat suitability, topography, human disturbance, ownership, and infrastructure data and developed a fuzzy-logic based model in GIS to associate the highly disparate data types in a single decision-support framework. Fuzzy-logic uses precise rules and data to assist decisions in a context of imprecise, uncertain, incomplete, and sometimes conflicting information and needs. The model rules were based on a combination of binary and continuous use of variables. For example, I used the model to 1) identify stretches of highway where fencing could cost-effectively reduce collisions; 2) highlight stretches of highway where WCS were practical and could resolve connectivity; and 3) identify existing structures that could play or could be playing an important role in wildlife movement. The result of the modeling was a geographically-explicit identification of places for particular types of actions, which is critical for wildlife-vehicle conflict mitigation. The modeling approach could be used by anyone faced with similar types of decision-making.