A composite network approach for assessing multi-species connectivity: an application to road defragmentation prioritisation

One of the biggest challenges in large-scale conservation is quantifying connectivity at large geographic scales and for a large set of species. Since connectivity analyses can be computationally intensive, and the planning process quite complex when multiple taxa are involved, assessing connectivity at large spatial extents for many species turns to be often intractable. Such limitation results in that conducted assessments are often partial by focusing on a few key species only, or are generic by considering a range of dispersal distances and a fixed set of areas to connect that are not directly linked to the actual spatial distribution or mobility of particular species. By using a graph theory framework, here we propose an approach to reduce computational effort and effectively consider vast species assemblages in obtaining multi-species connectivity priorities. We demonstrate the potential of the approach by identifying defragmentation priorities in the Italian road network focusing on medium and large terrestrial mammals. We show that by combining probabilistic species graphs prior to conducting the network analysis (i) it is possible to analyse connectivity once for all species simultaneously, obtaining conservation or restoration priorities that apply for the entire species assemblage; and that (ii) those priorities are well aligned with the ones that would be obtained by aggregating the results of separate connectivity analysis for each of the individual species. This approach offers great opportunities to extend connectivity assessments to large species sets and broad geographic scales.

SANTINI Luca;  SAURA MARTINEZ DE TODA Santiago;  RONDININI Carlo; 

2016-10-31

PUBLIC LIBRARY SCIENCE

JRC101925

1932-6203,   

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164794,    https://publications.jrc.ec.europa.eu/repository/handle/JRC101925,   

10.1371/journal.pone.0164794,