Identifying functionally connected habitat compartments with a novel regionalization technique
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Landscape ecologists have increasingly turned to the use of landscape graphs in which a landscape is represented as a set of nodes (habitat patches) connected by links representing inter-patch-dispersal. This study explores the use of a graph-based regionalization method, Graph-based REgionalization with Clustering And Partitioning (GraphRECAP), to detect structural groups of habitat patches (compartments) in a landscape graph such that the connections (i.e. the movement of individual organisms) within the groups are greater than those across groups. Specifically, we mapped compartments using habitat and dispersal data for ring-tailed lemurs (Lemur catta) in an agricultural landscape in southern Madagascar using both GraphRECAP and the widely-used Girvan and Newman method. Model performance was evaluated by comparing compartment characteristics and three measures of network connectivity and traversability: the connection strength of habitat patches in the compartments (modularity), the potential ease of individual organism movements (Harary index), and the degree of alternative route presence (Alpha index). Compartments identified by GraphRECAP had stronger within-compartment connections, greater traversability, more alternative routes, and a larger minimum number of habitat patches within compartments, all of which are more desirable traits for ecological networks. Our method could thus facilitate the study of ecosystem resilience and the design of nature reserves and landscape networks to promote the landscape-scale dispersal of species in the fragmented habitats.
KeywordsEcoregionalization Graph theory Habitat connectivity Fragmentation Ring-tailed lemur
This work was supported by a Dean’s Dissertation Fellowship from the College of Arts and Sciences at the University of South Carolina to the lead author. This work was also supported in part by the National Science Foundation under Grant No. 0748813. The lead author would like to thank Hai Jin’s help with programming. The authors would especially like to thank Dr. Örjan Bodin for the generous offer of the dataset of his published work.
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