Connectivity Analysis Toolkit License Version history Getting Started Overview of Methods Three Methods for Connectivity Analysis Subset Centrality: Mapping Linkages All-pairs Centrality: Mapping Landscape Connectivity Network Flow Analysis of Time Series Input files Output files Running the Toolkit from the GUI Hexmap Tab Graphs Tab Connectivity Tab Output Tab Running the Toolkit from the Command Prompt Flowchart of analysis process Examples Example 1: Shortest-path betweenness centrality (BC) Example 2: Shortest-path betweenness subset centrality Example 3: Comparison of subset and all-pairs current flow BC Example 4: Comparison of shortest-path and current flow BC Tutorial Exercise 1 - Mapping linkages with min-cost-max-flow subset centrality Exercise 2 - Mapping linkages with shortest-path subset centrality Exercise 3 - Mapping landscape connectivity with all-pairs centrality Exercise 4 - Mapping connectivity under climate change: analysis of time series Troubleshooting Related Resources System requirements Contacts & Credits
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# Overview of Methods The connectivity analysis methods in this Toolkit are based on the concept of 'centrality'. Centrality analysis encompasses a diverse group of metrics that provide information on the relative role of nodes in facilitating movement across a graph, i.e., as ‘gatekeepers’ for functional connectivity (Bunn et al. 2000; Borgatti 2005). Metrics that consider the role of a node in mediating flow between all other nodes must consider all possible pairwise combinations of nodes, and thus increase in computational complexity at a polynomial rate (typically quadratic to cubic) as the number of nodes increases (Ahuja et al. 1993). Thus their previous use in connectivity analysis has been limited to contexts where a landscape can be represented a system of patches (numbering at most in the hundreds) embedded in a matrix of non-habitat. Computationally-efficient algorithms for analysis of large networks, which have recently been developed for purposes such as ranking web pages on the internet, now allows analysis of landscape connectivity at a resolution that makes simplifying assumptions less necessary (Hagberg et al. 2008). This facilitates application of centrality metrics to landscape and species contexts where a continuous habitat gradient is more ecologically relevant than a binary patch-matrix framework (Cushman et al. 2010). Within the limits of computational feasibility, resolution of the landscape lattice can be scaled to match the scale of habitat selection of the species of interest. Below we first describe three alternative methods of delineating habitat linkages between a single source and target patch (least-cost path, current flow, and network flow). We then describe analogous centrality metrics that analyze connectivity across a landscape without reference to specific source and target patches. For more information on the methods used in the this software, see Carroll, C., B. McRae, and A. Brookes. 2012. Use of Linkage Mapping and Centrality Analysis Across Habitat Gradients to Conserve Connectivity of Gray Wolf Populations in Western North America. Conservation Biology 26:78-87. Available at the journal website.
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