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Distribution, demography and dispersal model of spatial spread of invasive plant populations with limited data

Adams, Vanessa M., Petty, Aaron M., Douglas, Michael M., Buckley, Yvonne M., Ferdinands, Keith B., Okazaki, Tomoko, Ko, Dongwook W. and Setterfield, Samantha A. (2015). Distribution, demography and dispersal model of spatial spread of invasive plant populations with limited data. Methods in Ecology and Evolution,6(7):782-794.

Document type: Journal Article
Citation counts: Altmetric Score Altmetric Score is 16
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IRMA ID 84376995xPUB210
Title Distribution, demography and dispersal model of spatial spread of invasive plant populations with limited data
Author Adams, Vanessa M.
Petty, Aaron M.
Douglas, Michael M.
Buckley, Yvonne M.
Ferdinands, Keith B.
Okazaki, Tomoko
Ko, Dongwook W.
Setterfield, Samantha A.
Journal Name Methods in Ecology and Evolution
Publication Date 2015
Volume Number 6
Issue Number 7
ISSN 2041-210X   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-84937072965
Start Page 782
End Page 794
Total Pages 13
Place of Publication United Kingdom
Publisher Wiley-Blackwell Publishing Ltd.
HERDC Category C1 - Journal Article (DIISR)
Abstract 1. Invasive weeds are a major cause of biodiversity loss and economic damage world-wide. There is often a limited understanding of the biology of emerging invasive species, but delay in action may result in escalating costs of control, reduced economic returns from management actions and decreased feasibility of management. Therefore, spread models that inform and facilitate on-ground control of invasions are needed.

We developed a spatially explicit, individual-based spread model that can be applied to both data-poor and data-rich situations to model future spread and inform effective management of the invasion. The model is developed using a minimum of two mapped distributions for the target species at different times, together with habitat suitability variables and basic population data. We present a novel method for internally calibrating the reproduction and dispersal distance parameters. We use a sensitivity analysis to identify variables that should be prioritized in future research to increase robustness of model predictions.

3. We apply the model to two case studies, gamba grass and para grass, to provide management advice on emerging weed priorities in northern Australia. For both species, we find that the current extent of invasion in our study regions is expected to double in the next 10 years in the absence of management actions. The predicted future distribution identifies priority areas for eradication, control and containment to reduce the predicted increase in infestation.

4. The model was built for managers and policymakers in northern Australia working on species where expert knowledge and environmental data are often lacking, but is flexible and can be easily adapted for other situations, for example where good data are available. The model provides predicted probability of occurrence over a user-specified, typically short-term, time horizon. This output can be used to direct surveillance and management actions to areas that have the highest likelihood of rapid invasion and spread. Directing efforts to these areas provides the greatest likelihood of management success and maximizes the return on investment in management response.
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