The movement of organisms rather than materials is also a critically important ecological process. Dispersal of individuals within and among populations helps to maintain genetic diversity, rescue declining populations, and reco-lonize extirpated habitat patches (dispersal/migration). The potential to decrease geographic isolation (Isolation) by increasing dispersal success of organisms in patchy environments has been used to justify management actions that improve landscape connectivity, such as the construction of habitat corridors. Connectivity is a poorly understood concept in landscape ecology, however, and the value of wildlife corridors remains a topic of considerable debate (connectance and connectivity).
In general, the quantification of landscape connectivity has proved elusive. A variety of spatial pattern indices (e.g., proximity, contagion, and nearest neighbor distance) have been proposed, but these have shown very little correlation with actual dispersal success. Ecologists, therefore, have turned to more detailed models of dispersal (e.g., individual-based models), which provide lots of information on movement pathways but have correspondingly high data demands. Recently, ecological models using graph theory have been applied to landscapes to assess the consequences of habitat modification and change (Figure 3). The network approach makes use of well-developed algorithms optimized in fields such as communication, transportation, and operations research to provide estimates of landscape connectivity with minimal data requirements (application of algorithms in modeling). In addition to providing basic information about the overall landscape structure, graph theory analysis can be used to identify individual habitat patches of special importance for dispersal success. These
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