Using landscape graphs to delineate ecologically functional areas
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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.
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.
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).
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.
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.
KeywordsLandscape connectivity Compartmentalization Modularity Ecological network Montane water vole
This research has been funded by the French Ministry of Ecology (Campagraphe project in DIVA3 Program). Computations were performed on the supercomputer facilities of the “Mésocentre de calcul de Franche-Comté”. This research is part of the Jurassian Arc long-term ecological research site (http://zaaj.univ-fcomte.fr/?lang=en).
- Berthier K, Chaval Y, Galan M, Charbonnel N, Cosson JF (2009) Dispersion individuelle: conséquences. In: Delattre P, Giraudoux P (eds) Le campagnol terrestre: prévention et contrôle des populations. QUAE Edition, Versailles, pp 39–47Google Scholar
- Blant MA, Beuret B, Poitry R, Joseph E (2009) Influence of landscape and soil on the intensity of pullulations of vole (Arvicola terrestris scherman) in Swiss Jura. Rev Suisse Agric 41:301–307Google Scholar
- Everitt BS, Dunn G (1991) Applied multivariate data analysis. Edward Arnold, LondonGoogle Scholar
- McLachlan GJ (2004) Discriminant analysis and statistical pattern recognition. Wiley, HobokenGoogle Scholar
- Schuetz P, Caflisch A (2008) Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Phys Rev E 77(046):112Google Scholar
- Webster R (1972) Wilks’s criterion: a measure for comparing the value of general purpose soil classifications. J Soil Sci 11:254–260Google Scholar