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Predicting reintroduction outcomes for highly vulnerable species that do not currently coexist with their key threats
Elizabeth H Parlato1, Doug P Armstrong1
1Wildlife Ecology Group, Massey University, Palmerston North, Private Bag, 11 222, New Zealand.
Abstract:
Predicting reintroduction outcomes before populations are released is inherently challenging. It becomes even more difficult when the species being considered for reintroduction no longer coexists with the key threats limiting its distribution. However, data from other species facing the same threats can be used to make predictions under these circumstances. We used an integrated Bayesian modeling approach to predict growth of a reintroduced population at a range of predator densities when no data are available for the species in the presence of that predator. North Island Saddlebacks (Philesturnus rufusater) were extirpated from mainland New Zealand by exotic mammalian predators, particularly ship rats (black rats [Rattus rattus]) but are now being considered for reintroduction to sites with intensive predator control. We initially modeled data from previous saddleback reintroductions to predator-free sites to predict population growth at a new predator-free site while accounting for random variation in vital rates among sites. We then predicted population growth at different rat-tracking rates (an index of rat density) by incorporating a previously modeled relationship between rat-tracking and vital rates of another predator-sensitive species, the North Island Robin (Petroica longipes), and accounted for greater vulnerability of saddlebacks to rat predation based on information on historical declines of both species. The results allowed population growth to be predicted as a function of management effort while accounting for uncertainty, which means formal decision analysis could be used to decide whether to proceed with a reintroduction. Similar approaches could be applied to other situations where data on the species of interest are limited and provide an alternative to decision making based solely on expert judgment.
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