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Graph theory as an invasive species management tool: case study in the Sonoran Desert

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ContextBiodiversity in arid regions is usually concentrated around limited water resources, so natural resource managers have constructed artificial water catchments in many areas to supplement natural waters. Because invasive species… Click to show full abstract

ContextBiodiversity in arid regions is usually concentrated around limited water resources, so natural resource managers have constructed artificial water catchments in many areas to supplement natural waters. Because invasive species may also use these waters, dispersing into previously inaccessible areas, the costs and benefits of artificial waters must be gauged and potential invasion- and climate change-management strategies assayed.ObjectivesWe present a network analysis framework to identify waters that likely contribute to the spread of invasive species.MethodsUsing the Sonoran Desert waters network and the American bullfrog (Lithobates catesbeianus)—a known predator, competitor, and carrier of pathogens deadly to other amphibians—as an example, we quantified the structural connectivity of the network to predict regional invasion potential under current and two future scenarios (climate change and management reduction) to identify waters to manage and monitor for invasive species.ResultsWe identified important and vulnerable waters based on connectivity metrics under scenarios representing current conditions, projected climate-limited conditions, and conditions based on removal of artificial waters. We identified 122,607 km2 of land that could be used as a buffer against invasion and 67,745 km2 of land that could be augmented by artificial water placement without facilitating invasive species spread.ConclusionsStructural connectivity metrics can be used to evaluate alternative management strategies for invasive species and climate mitigation.

Keywords: invasive species; management; species management; graph theory; theory invasive; sonoran desert

Journal Title: Landscape Ecology
Year Published: 2017

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