A graph-based approach to investigating the influence of the landscape on population spread processes |
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| Institution: | 1. ThéMA UMR 6049 CNRS/University of Franche-Comté, 32, rue Mégevand, F-25030 Besançon, France;2. Chrono-Environnement UMR 6249 CNRS/University of Franche-Comté, Place Leclerc, F-25030 Besançon, France;1. Animal Ecology & Conservation, Biocenter Grindel, Universität Hamburg, Martin-Luther-King Platz 3, 20146 Hamburg, Germany;2. Organic Plant Production and Agroecosystems Research in the Tropics and Subtropics, University of Kassel, Steinstrasse 19, 37213 Witzenhausen, Germany;3. Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Invalidenstr. 43, 10115 Berlin, Germany;4. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany;1. Silas Little Experimental Forest, USDA Forest Service, New Lisbon, NJ 08064, USA;2. Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA;3. Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa (UNLPam), Santa Rosa, La Pampa 6300, Argentina;4. Instituto de Ciencias de la Tierra y Ambientales de La Pampa (INCITAP, CONICET-UNLPam), Mendoza 109 Santa Rosa, La Pampa 6300, Argentina;1. División Zoología Vertebrados, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, CONICET, Paseo del Bosque s/n, La Plata, Buenos Aires, Argentina;2. Grupo de Estudios sobre Biodiversidad en Agroecosistemas (GEBA), Departamento de Biodiversidad y Biología Experimental, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, and IEGEBA (CONICET- UBA), Argentina |
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| Abstract: | Modelling of landscape connectivity is a key point in the study of the movement of populations within a given landscape. For studies focused on the preservation of biodiversity, graph-based methods provide an interesting framework to investigate the landscape influence on population spread processes. Such an approach is described here, based on the mapping of landscape categories in habitat patches, including a diachronic data set describing the population spread within the habitat patches. A minimum planar graph was built by computing spatial distances between all pairs of neighbouring patches. From this structure, two types of analysis are proposed: one focused on the links of the graph and consists in correlating spatial distances and gap indicators computed from the diachronic data. The other was based on the correlations between population data and connectivity metrics at the patch level. As an example, this approach was applied to the spread of the fossorial water vole on the Jura plateau (France), with annual population data covering eleven years from 1989 to 2000. Link analysis allowed to find an optimal set of resistance values used in the least-cost distances computations, and thus to build a relevant graph. From this graph, patch analysis displayed a cyclic correlation between a metric based on potential dispersal flux and the population density, outlining the strong role of landscape connectivity in the population spread. The present study clearly shows that landscape modelling and graph-based approach can produce parameters which are consistent with field observations and thus pave the way to simulating the effect of landscape modification on population dynamics. |
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