One of the biggest challenges in large-scale conservation is quantifying connectivity at broad geographic scales and for a large set of species. Because 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 large assemblages of species 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 assemblages of species and broad geographic scales.

A composite network approach for assessing multi-species connectivity: An application to road defragmentation prioritisation / Santini, Luca; Saura, Santiago; Rondinini, Carlo. - In: PLOS ONE. - ISSN 1932-6203. - ELETTRONICO. - 11:10(2016). [10.1371/journal.pone.0164794]

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

SANTINI, LUCA;RONDININI, CARLO
2016

Abstract

One of the biggest challenges in large-scale conservation is quantifying connectivity at broad geographic scales and for a large set of species. Because 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 large assemblages of species 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 assemblages of species and broad geographic scales.
2016
Medicine (all); Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)
01 Pubblicazione su rivista::01a Articolo in rivista
A composite network approach for assessing multi-species connectivity: An application to road defragmentation prioritisation / Santini, Luca; Saura, Santiago; Rondinini, Carlo. - In: PLOS ONE. - ISSN 1932-6203. - ELETTRONICO. - 11:10(2016). [10.1371/journal.pone.0164794]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/910594
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