Reconstruction and prediction of invasive mongoose population dynamics from history of introduction and management: a Bayesian state-space modelling approach.

Published online
10 Apr 2013
Content type
Journal article
Journal title
Journal of Applied Ecology

Fukasawa, K. & Hashimoto, T. & Tatara, M. & Abe, S.
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An understanding of the underlying processes and comprehensive history of invasive species is necessary to assess the long-term effectiveness of invasive species management. However, continuous, long-term labour-intensive population surveys on invasive species are often not feasible. Thus, it is important to learn about their dynamics through management action and its consequences. Amami Island, Japan, has an ongoing large-scale and long-term eradication programme of invasive small Indian mongooses. To estimate the long-term pattern of population size and the parameters determining the dynamics, including anthropogenic removal, we applied a surplus-production model within a Bayesian state-space formulation incorporating the initial population size, number of captures and capture effort. Using the estimated process model directly, we conducted stochastic simulations to evaluate the feasibility of eradication. Estimated 32-year annual capture probability of mongooses has increased since their introduction. The population size started to decline in 2001; mean population size in 2000 was 6141 (95% CI: 5415-6817), and declined to 169 (95% CI: 42-408) by 2011. Parameter estimates of a Weibull catchability model indicated that there was large individual heterogeneity in the probability of being captured, and per-effort capture probability declined with an increase in annual capture effort. The simulation study indicated that the eradication feasibility in 2023 would be over 90% if the same annual capture effort is upheld as in 2010 (2 075 760 corrected trap-days). However, increasing annual capture effort would have little effect on shortening the time to eradication. Synthesis and applications. A hierarchical model that incorporates multiple types of data to reveal long-term population dynamics has the potential to be updated with the outcomes of control efforts, and will enhance adaptive management of invasive species. This approach will offer valuable information about trade-offs between time to eradication success and effort per unit time in a long-term eradication project, and the length of time needed to continue management actions to achieve eradication success.

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