Using landscape graphs to delineate ecologically functional areas

Abstract : Context Landscape graphs are widely used to model connectivity and to support decision-making in conservation planning. Compartmentalization methods applied to such graphs aim to define clusters of highly interconnected patches. Recent studies show that compartmentalization based on modularity is suitable, but it applies to non-weighted graphs whereas most landscape graphs involve weighted nodes and links. Objectives We propose to adapt modularity computation to weighted landscape graphs and to validate the relevance of the resulting compartments using demographic or genetic data about the patches. Methods A weighted adjacency matrix was designed to express potential fluxes, associating patch capacities and inter-patch distances. Eight weighting scenarios were compared. The statistical evaluation of each compartmentalization was based on Wilks’ Lambda. These methods were performed on a grassland network where patches are documented by annual densities of water voles in the Jura massif (France). Results The scenarios in which patch capacity is assigned a small weight led to the more relevant results, giving high modularity values and low Wilks’ Lambda values. When considering a fixed number of compartments, we found a significant negative correlation between these two criteria. Comparison showed that compartments are ecologically more valid than graph components. Conclusions The method proposed is suitable for designing ecologically functional areas from weighted landscape graphs. Maximum modularity values can serve as a guide for setting the parameters of the adjacency matrix.
Liste complète des métadonnées
Contributor : Théoriser Et Modéliser Pour Aménager (umr 6049) Université de Bourgogne Franche-Comté <>
Submitted on : Monday, January 23, 2017 - 1:54:53 PM
Last modification on : Friday, July 13, 2018 - 11:31:42 AM




Jean-Christophe Foltête, Gilles Vuidel. Using landscape graphs to delineate ecologically functional areas. Landscape Ecology, Springer Verlag, 2017, 32 (2), pp.249-263. ⟨10.1007/s10980-016-0445-z⟩. ⟨hal-01443809⟩



Record views