Abstract
ContextInvasive species cause widespread species extinction and economic loss. There is an increasing need to identify ways to efficiently target control efforts from local to regional scales.ObjectivesOur goal was to test whether prioritizing managed habitat using different treatments based on spatial measures of connectivity, including graph-theoretic measures, can improve management of invasive species and whether the level of control effort affects treatment performance. We also explored how uncertainty in biological variables, such as dispersal ability, affects measures performance.MethodsWe used a spatially-explicit, individual-based model (sIBM) based on the American bullfrog (Lithobates catesbeianus), a globally pervasive invasive species. Simulations were informed by geographic data from part of the American bullfrog’s non-native range in southeastern Arizona, USA where they are known to pose a threat to native species.ResultsWe found that total bullfrog populations and occupancy declined in response to all treatments regardless of effort level or patch prioritization methods. The most effective spatial prioritization was effort-dependent and varied depending on spatial context, but frequently a buffer strategy was most effective. Treatments were also sensitive to dispersal ability. Performance of treatments prioritizing habitat patches using betweenness centrality improved with increasing dispersal ability, while performance of eigenvalue centrality improved as dispersal ability decreased.ConclusionsWith the careful application of connectivity measures to prioritize control efforts, similar reductions in invasive species population size and occupancy could be achieved with less than half the effort of sub-optimal connectivity measures at higher effort rates. More work is needed to determine if trait-based generalities may define appropriate connectivity measures for specific suites of dispersal abilities, demographic traits, and population dynamics.
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