USING A DYNAMIC LANDSCAPE MODEL FOR PLANNING THE MANAGEMENT OF ALIEN PLANT INVASIONS

E-ISSN： 1939-5582|10|6|1833-1848

ISSN： 1051-0761

Source： Ecological Applications, Vol.10, Iss.6, 2000-12, pp. : 1833-1848

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Abstract

Biological invasions are widespread phenomena that threaten the integrity and functioning of natural ecosystems. In this paper we develop a model that is designed to be a decision‐making tool for planning and managing alien plant control operations. Most decision tools adopt a static approach; in this application we integrate a dynamic simulation model of alien plant spread with decision‐making tools commonly used for reserve design. The model is a landscape‐scale implementation of a fine‐grained individual‐based spread model. We first describe the scaling up of the fine‐scaled model into a landscape extent model. Comparisons between the fine‐grained local‐scale and coarse‐grained landscape‐scale model show that the scaling‐up process did not introduce significant artifacts into the behavior of the model. The landscape model is used to explore a range of strategies and funding schedules for clearing alien plants. These strategies are evaluated in terms of the cost of the clearing operation, the time it takes to eradicate the plants, and the impact the plants have on three components of native plant diversity (all species, rare and threatened species, endemic species). Clearing strategies that prioritize low‐density sites dominated by juvenile alien plants proved to be the most cost effective. Strategies that used information on the distribution of plant diversity were not much more expensive than the most cost‐effective strategy, and they substantially reduced the threat to native plant diversity. Delaying the initiation of clearing operations had a strong effect on both the eventual costs of the clearing operation and the threat to native plant diversity. We conclude that the integration of dynamic modeling with decision‐making tools, as illustrated here, will be useful for the management of biodiversity under global change.