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Potential Distribution and Risk Assessment of an Invasive Plant Species: A Case Study of Hymenachne amplexicaulis in Australia
Authors:L J Wearne  D Ko  M Hannan-Jones  M Calvert
Institution:1. RPS Group , Brisbane , Queensland , Australia;2. Charles Darwin University, Research Institute for Environment and Livelihoods , Darwin , Northern Territory , Australia;3. University of Queensland, Spatial Ecology Laboratory , St Lucia , Queensland , Australia;4. Biosecurity Queensland, Invasive Plants and Animals, Department of Employment, Economic Development and Innovation , Brisbane , Queensland , Australia
Abstract:Given the limited resources available for weed management, a strategic approach is required to give the “best bang for your buck.” The current study incorporates: (1) a model ensemble approach to identify areas of uncertainty and commonality regarding a species invasive potential, (2) current distribution of the invaded species, and (3) connectivity of systems to identify target regions and focus efforts for more effective management. Uncertainty in the prediction of suitable habitat for H. amplexicaulis (study species) in Australia was addressed in an ensemble-forecasting approach to compare distributional scenarios from four models (CLIMATCH; CLIMEX; boosted regression trees BRT]; maximum entropy Maxent]). Models were built using subsets of occurrence and environmental data. Catchment risk was determined through incorporating habitat suitability, the current abundance and distribution of H. amplexicaulis, and catchment connectivity. Our results indicate geographic differences between predictions of different approaches. Despite these differences a number of catchments in northern, central, and southern Australia were identified as high risk of invasion or further spread by all models suggesting they should be given priority for the management of H. amplexicaulis. The study also highlighted the utility of ensemble approaches in indentifying areas of uncertainty and commonality regarding the species’ invasive potential.
Keywords:species distribution models  risk assessment framework  climate envelope models  machine learning models  catchment risk  
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