Measuring Connectivity Putting It All Together

The preceding discussion should have highlighted that connectivity metrics differ in their ease of measurement and the amount and type of data required to use them. There is a tradeoff between the ease of measurement of

Data requirements

Figure 2 A representation of the information contained in different metrics of connectivity as a function as the amount of data required for their measurement. IF = incidence function model approaches. Modified from Calabrese JM and Fagan WF (2004) A comparison-shopper's guide to connectivity metrics. Frontiers in Ecology and the Environment 2: 529-536.

connectivity metrics and the amount of detail they capture. This is illustrated in Figure 2. Hence, we face a choice between metrics in which we are uncertain about their utility or accuracy and investing the time and resources to measure metrics that are more information rich. The choice of metric is likely to depend on the spatial scale of the problem which we are attempting to solve, be it a conservation strategy for a region, or the need for a migration route for deer across a highway, and so on. It should be noted that the metrics that have been discussed can often be improved by including additional information. Most commonly, information about the number of individuals within patches is used, or patch size is taken as a surrogate for information about local population size. Alternatively, immigration and emigration may scale differently with patch size, such that individuals are more likely to remain in or colonize smaller or larger habitat patches. Movement can also be adjusted for variation in the type of habitat being crossed or landscape features present in the area traversed. Such modifications to metrics have been shown to improve the accuracy in specific studies, but their general utility is unknown.

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