Control of invasive species is receiving increasing attention, both in terms of the development of methodological tools and the documentation of negative effects of such species on native flora. A recurrent question focuses on the problem of optimal spatial allocation of resources in order to minimize the invasive population, while respecting practical constraints. Several theoretical frameworks exist to compute the optimal management strategy when the spatial distribution of the species is known; however, knowledge on spatial distribution is not often available. We propose to use a method from the field of image analysis, where the problem of images segmentation from noisy data is common and several statistical methods are available. We adapted these methods to the management of invasive species where only a rough estimate of the invasive spatial distribution is known. This first estimate is then used to compute the probability of presence of the species over space. These probabilities are in turn used to set-up an integer linear programming problem to compute the optimal allocation of control effort under cost constraints. We illustrate our method with the example of melaleuca (Melaleuca quinquenervia) control.
Results/Conclusions
Our approach is particularly relevant to the melaleuca case study where a first rough estimate of its spatial distribution can be obtained by aerial survey or satellite imaging. This first estimation can then help to prioritize the controlled locations in a cost-effective way. We show the superiority of our approach in terms of number of trees removed compared to common ‘search and destroy’ strategies. We also implemented an adaptive strategy, where observations that are acquired during control are used to update the probability of presence. Based on this updated information we can then compute the set of locations to control in an iterative way. We parameterized the model for melaleuca using both on-ground and aerial observations, but our approach is general and can be applied to a large variety of management problems. This approach can greatly improve control efficacy in practice.