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.
Adams VA, AM Petty, MM Douglas, YM Buckley, KB Ferdinands, T Okazaki, DW Ko and SA Setterfield (2015), Distribution, demography and dispersal model of spatial spread of invasive plant populations with limited data. Methods in Ecology and Evolution, 6: 782–794. doi: 10.1111/2041-210X.12392
In the paper above, 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.
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.
The publication below is for those interested in projecting invasive species distributions under climate change when empirical data are lacking.
Martin, TG, H Murphy, A Liedloff, C Thomas, I Chades, G Cook, R Fensham, J McIvor, R van Klinken (2015) Buffel grass and climate change: a framework for projecting invasive species distributions when data are scarce. Biological Invasions doi:10.1007/s10530-015-0945-9.
Invasive species pose a substantial risk to native biodiversity. As distributions of invasive species shift in response to changes in climate so will management priorities and investment. To develop cost-effective invasive species management strategies into the future it is necessary to understand how species distributions are likely to change over time and space. For most species however, little data are available on their current distributions, let alone projected future distributions. We demonstrate the benefits of Bayesian Networks (BNs) for projecting distributions of invasive species under various climate futures, when empirical data are lacking.
Using the introduced pasture species, buffel grass (Cenchrus ciliaris) in Australia as an example, we employ a framework by which expert knowledge and available empirical data are used to build a BN. The framework models the susceptibility and suitability of the Australian continent to buffel grass colonization using three invasion requirements; the introduction of plant propagules to a site, the establishment of new plants at a site, and the persistence of established, reproducing populations.
Our results highlight the potential for buffel grass management to become increasingly important in the southern part of the continent, whereas in the north conditions are projected to become less suitable. With respect to biodiversity impacts, our modelling suggests that the risk of buffel grass invasion within Australia’s National Reserve System is likely to increase with climate change as a result of the high number of reserves located in the central and southern portion of the continent. In situations where data are limited, we find BNs to be a flexible and inexpensive tool for incorporating existing process-understanding alongside bioclimatic and edaphic variables for projecting future distributions of species invasions.