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Graph models of habitat mosaics
Article first published online: 21 JAN 2009
DOI: 10.1111/j.1461-0248.2008.01271.x
© 2009 Blackwell Publishing Ltd/CNRS
Additional Information
How to Cite
Urban, D. L., Minor, E. S., Treml, E. A. and Schick, R. S. (2009), Graph models of habitat mosaics. Ecology Letters, 12: 260–273. doi: 10.1111/j.1461-0248.2008.01271.x
Publication History
- Issue published online: 18 FEB 2009
- Article first published online: 21 JAN 2009
- Editor, Gregor Fussmann Manuscript received 14 August 2008 First decision made 26 September 2008 Manuscript accepted 8 November 2008
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Keywords:
- Connectivity;
- conservation;
- graph theory;
- habitat;
- landscape;
- metapopulation;
- network analysis
Abstract
Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights.