Using network connectivity to prioritise sites for the control of invasive species.

Published online
11 Oct 2017
Content type
Journal article
Journal title
Journal of Applied Ecology

Perry, G. L. W. & Moloney, K. A. & Etherington, T. R.
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Habitat connectivity is a crucial determinant of population dynamics in fragmented landscapes. The corollary of the emphasis on maintaining connectivity to enhance the movement of organisms is that disrupting connectivity should minimise it. Here, we evaluate the efficiency of an invasive species control strategy that targets the most connected habitats in a landscape. A network (spatial graph) provides an intuitive representation of a landscape, and the topology of this network can be used to identify the most connected patches. We implemented a simulation model of the spread of an invasive species on a network and used it to evaluate whether targeting the better-connected components of the landscape enhances control effectiveness. Control strategies based on network topology consistently outperformed both a null strategy of random habitat selection and one based on separation distance alone. The advantages of the connectivity-based strategy were strongest in the early phases of the invasion process, when a small number of habitats are occupied at low population density. However, if long-distance dispersal events were common, the advantages of the connectivity approach weakened. The performance of the connectivity-based strategy is robust to habitat-level demographic stochasticity. In fact, connectivity-based targeting outperforms a strategy focussing on source habitats, with the additional benefit that it requires less information to be implemented. Synthesis and applications. Our simulation model outcomes demonstrate that deliberately targeting the best-connected components of a landscape is an efficient control strategy for invasive species when long-distance dispersal is infrequent, and it is likely to be cheaper than other alternatives such as targeting population sources. Network scientists have developed a range of methods designed to identify the minimal set of nodes on a graph that will disrupt the network as a whole; these tools have potential to aid in the design of more effective control strategies.

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