Strategically Locating Wildlife Crossing Structures for Florida Panthers Using Maximal Covering Approaches

Abstract

Crossing structures are an effective method for mitigating habitat fragmentation and reducing wildlifevehicle collisions, although high construction costs limit the number that can be implemented in practice. Therefore, optimizing the placement of crossing structures in road networks is suggested as a strategic conservation planning method. This research explores two approaches for using the maximal covering location problem (MCLP) to determine optimal sites to install new wildlife crossing structures. The first approach is based on records of traffic mortality, while the second uses animal tracking data for the species of interest. The objective of the first is to cover the maximum number of collision sites, given a specified number of proposed structures to build, while the second covers as many animal tracking locations as possible under a similar scenario. These two approaches were used to locate potential wildlife crossing structures for endangered Florida panthers (Puma concolor coryi) in Collier, Lee, and Hendry Counties, Florida, a population whose survival is threatened by excessive traffic mortality. Historical traffic mortality records and an extensive radio-tracking dataset were used in the analyses. Although the two approaches largely select different sites for crossing structures, both models highlight key locations in the landscape where these structures can remedy traffic mortality and habitat fragmentation. These applications demonstrate how the MCLP can serve as a useful conservation planning tool when traffic mortality or animal tracking data are available to researchers.

Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods

“…Specifically, resistant kernels appear to be the preferred choice when the goal is quantifying differences in connectivity between different landscapes or overtime, while factorial least cost paths would be best suited for the analysis of resistance maps where there is high certainty in resistance values and where researchers wish to localize predictions to prioritize specific locations for protection rather than evaluating landscape-wide patterns of connectivity. It would be interesting to compare the methods tested here with other spatial methods to identify where animals cross highways (e.g., [22][23][24][25])…”

Effects of Road Density and Pattern on the Conservation of Species and Biodiversity

“…For example, one model was constructed specifically to identify ideal locations for crossing structures for the endangered Florida panther (Puma concolor coryi) using GPS collar data and wildlife-vehicle collision reports [140]. Another model was developed to identify habitat patches that could be restored and define wildlife corridor locations that would have the best chance of increasing landscape connectivity [141].…”

Optimizing the positioning of wildlife crossing structures using GPS telemetry

“…Previous approaches typically summarized the general crossing behaviour, habitat selection or simply record intensity of use without subsequent optimization (Horne et al., ; Sawyer, Kauffman, Nielson, & Horne, ; Schuster, Römer, & Germain, ). Other approaches proposed optimization algorithms but without regard to sampling bias and spatial spread (Downs et al., ; Loraamm & Downs, ). Intensity of use may not be the only metric of value when quantifying the importance of a segment for wildlife movement.…”

“…GPS telemetry can present analytical challenges when applied to identifying wildlife crossings; one issue being that the degree to which the sample of GPS monitored individuals is spatially and behaviourally representative of the population. Additionally, the limited application of GPS telemetry may be related to the lack of objective tools to optimize crossing structure positioning (see Downs et al., ; Loraamm & Downs, ).…”

“…Additionally, the limited application of GPS telemetry may be related to the lack of objective tools to optimize crossing structure positioning (see Downs et al, 2014;Loraamm & Downs, 2016).…”