Locating wildlife crossings for multispecies connectivity across linear infrastructures
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Linear transportation infrastructures traverse and separate wildlife populations, potentially leading to their short- and long-term decline at local and regional scales. To attenuate such effects, we need wildlife crossings suitable for a wide range of species.
We propose a method for identifying the best locations for wildlife crossings along linear infrastructures so as to improve the connectivity of species with varying degrees of mobility and living in different habitats. We evaluate highway impacts on mammal species.
The study area is the Grésivaudan Valley, France. We used allometric relationships to create eight virtual species and model their connectivity networks, developing a nested method defining populations by daily travel distances and connecting them by dispersal. We tested the gain in connectivity for each species produced by 100 and 600 crossing locations respectively in crossable, i.e. with crossing infrastructures, and uncrossable highway scenarios. We identified the crossings that optimize the connectivity of the maximum number of species combining the results in multivariate analyses.
Highly mobile species needing a large habitat area were the most sensitive to highways. The importance of locomotive performance in structuring the graphs decreased with highway impermeability. Depending on the species, the best locations improved connectivity by 0–10 and 2–75 % respectively in the crossable and uncrossable scenarios. Compromise locations were found for seven of the eight species in both scenarios.
This method could guide planners in identifying crossing locations to increase the connectivity of different species at regional scales over the long term.
KeywordsMultispecies Connectivity Allometric relationships Highway Population Dispersal Daily distance Slope Local and regional impacts
We wish to thank Jean-Louis Michelot and Laurent Simon from the Ecosphere design office for the constructive discussions we had about this work, including the search for land cover and biodiversity data and the location of the study area. We also thank Gilles Vuidel for his technical improvement to the Graphab software by including slope, new habitat patches, and metapatch functions. We are grateful to Damien Roy for his extensive work on the land-cover map combination. This work was funded by the Franche-Comté region. It is a part of the Graphab 2 project managed by USR 3124 MSHE C.N. Ledoux and funded by the French Ministry of Ecology (ITTECOP program). Computations were performed on the supercomputer facilities of the “Mésocentre de calcul de Franche-Comté”.
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