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Estimating the influence of land management change on weed invasion potential using expert knowledge

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Abstract

Aim  To develop and test a general framework for estimating weed invasion potential (suitability and susceptibility) that utilized expert knowledge of dispersal, establishment and persistence and considered the influence of land management.

Location  The semi‐arid Desert Channels Region of Queensland, Australia (476,000 km2).

Methods  We developed a general framework that integrated knowledge and empirical data of the environmental and land management variables influencing the dispersal, establishment and persistence of the invasive shrub parkinsonia (Parkinsonia aculeata) using a Bayesian network linked to a Geographic Information System (GIS). We evaluated the influence of different land management scenarios on landscape suitability for parkinsonia. Model performance was assessed by comparing predicted landscape suitability with mapped parkinsonia locations and estimated parkinsonia density.

Results  Our predictions of moderate to high suitability corresponded reasonably well with mapped parkinsonia locations (71% match) and areas of common to abundant estimated density (92% match). They also suggested that parkinsonia has not reached its potential distribution within the study region. Under current land management conditions, 77,000 km2 of land was found to be highly or moderately suitable for parkinsonia. Scenario analysis indicated that maintaining moderate herbaceous ground cover levels, and using sheep to browse juvenile parkinsonia, reduced the predicted moderate to high suitability area to 27,000 km2, offering a potential management strategy for limiting parkinsonia invasion.

Main conclusions  Weed invasion potential can be reasonably estimated using expert knowledge of dispersal, establishment and persistence, integrated using a Bayesian network linked to a GIS. This modelling approach can be an alternative to process‐based and phenomenological modelling, which can be problematic for modelling new and emerging weed invasions, particularly where data are patchy. The modelling approach also allows the influence of land management change on invasion potential to be investigated through scenario analysis.
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Document Type: Research Article

Affiliations: 1: School of Agriculture and Food Sciences, The University of Queensland, Qld 4072, Australia 2: CSIRO Ecosystem Sciences, EcoSciences Precinct, PO Box 2583, Brisbane, Qld 4001, Australia 3: Landscape Ecology and Conservation Group, Centre for Spatial Environmental Research, School of Geography, Planning and Environmental Management, The University of Queensland, Qld 4072, Australia

Publication date: 2012-08-01

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