A Wildlife Movement Approach to Optimally Locate Wildlife Crossing Structures

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animal movement, location modeling, optimization, roads, time-geography

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Transportation networks negatively impact wildlife populations by limiting the physical movement of the individual animal. In extreme cases road presence can lead to collisions between vehicles and animals, resulting in direct mortality if an animal attempts to cross the road. Crossing structures are one commonly used method for reducing wildlife–vehicle collisions. However, limited funding often reduces the amount of structures that may be constructed in practice. Therefore, areas that have the highest probability for animal interactions with roads should be targeted for locating new structures to provide the best possible outcome. This research uses a probabilistic time-geographic strategy coupled with a site selection phase handled by a classical optimization model to site wildlife crossing structures. To achieve optimal site selection, crossing locations are first identified where wildlife frequently cross roads, and then a maximum covering location problem is applied to these areas as demand nodes. The objective is to cover the largest area having the highest probability of interaction given a finite number of crossing structures available to be located. Coverage is defined in terms of fencing distance associated with a particular structure. The approach was demonstrated using Florida panther telemetry data identifying potential crossing structures across two counties in south Florida. The maximal covering location problem (MCLP) was solved for four coverage distances using radio telemetry tracking data, which captured frequent contact with roads. The results identify that the most effective coverage distance is 2000 m, which incrementally covers more total animal–road interaction probability than that of lower fencing distances in the case of the Florida panther. The results illustrate how this new time-geographic approach, combined with location modeling, measures animal–road interactions probabilistically for finding the optimum placement of wildlife crossing structures.

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Citation / Publisher Attribution

International Journal of Geographical Information Science, v. 30, issue 1, p. 74-88