Details

Autor(en) / Beteiligte

• Puyravaud, J.‐P.
• Cushman, S. A.
• Davidar, P.
• Madappa, D.

Titel

Predicting landscape connectivity for the Asian elephant in its largest remaining subpopulation

Ist Teil von

• Animal conservation, 2017-06, Vol.20 (3), p.225-234

Ort / Verlag

London: Wiley Subscription Services, Inc

Erscheinungsjahr

2017

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Landscape connectivity between protected areas is crucial for the conservation of megafauna. But often, corridor identification relies on expert knowledge that is subjective and not spatially synoptic. Landscape analysis allows generalization of expert knowledge when satellite tracking or genetic data are not available. The Nilgiri Biosphere Reserve in southern India supports the largest wild populations of the endangered Asian elephant Elephas maximus. Current understanding of connectivity in this region is based on corridors identified by experts, which are not empirically validated and incongruent with each other. To more rigorously assess population connectivity for the Asian elephant, we evaluated a combination of three resistance layers and three dispersal abilities. The resistance models were based on the combined contributions of land cover, topographical slope, elevation, roads and buildings. A spatially explicit connectivity modeling tool predicted optimal movement corridors as a function of factorial least‐cost routes across the resistance maps. A resistant kernel approach produced maps of the expected frequency of elephant movement through each cell to define core areas. We conducted a sensitivity analysis to determine the influence of resistance and dispersal. We selected the resistance surface and dispersal ability that produced the highest correlation with observed elephant densities. We evaluated the optimality of expert corridors by using a path randomization method. Eleven out of 24 expert corridors had connectivity values significantly higher than expected by chance, while only two corridors were spatially congruent between expert teams. Areas with the highest connectivity corresponded well with priority areas identified by conservationists and elephant density predicted by the resistant kernel connectivity model correlated significantly with surveys (Spearman's ρ = 0.85, n = 500, P ≪ 0.001). The results provide the first rigorous, spatially synoptic and empirically validated evaluation of the connectivity of the elephant population across the reserve. To better assess population connectivity for the Asian elephant in the Nilgiri Biosphere Reserve, we evaluated a combination of three resistance layers and three dispersal abilities with a connectivity modeling tool over the landscape. We evaluated the optimality of 24 expert corridors and obtained a much better agreement between the calculated corridors and the expert corridors than the expert teams achieved. Our results provide the first rigorous, spatially synoptic and empirically validated evaluation of the connectivity of the elephant population across the reserve.