Predicting reintroduction outcomes using data from multiple populations : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Palmerston North, New Zealand

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dc.contributor.authorParlato, Elizabeth
dc.date.accessioned2015-02-12T00:07:16Z
dc.date.available2015-02-12T00:07:16Z
dc.date.issued2014
dc.description.abstractPredicting reintroduction outcomes before populations are released is inherently challenging. Reintroductions typically involve small data sets from specific locations, making it difficult to know whether results from individual case studies are more widely applicable. However, a number of species have now been reintroduced to multiple sites, providing an opportunity to move beyond the inferences possible from single-site studies. I present a novel approach where data from multiple reintroduced populations are modelled simultaneously, allowing a priori predictions that account for random variation among sites to be made before new reintroductions are attempted. I construct models using data from multiple reintroductions of the North Island robin (Petroica longipes) to identify important factors influencing population establishment, vital rates and growth across existing reintroduction sites, and use the best supported models to make predictions for a candidate reintroduction site under alternative management scenarios. My results indicate that rat tracking rate (an index of rat density) and the surrounding landscape at reintroduction sites are important for both establishment and growth of reintroduced robin populations, and that sourcing founders from habitat similar to that at the reintroduction site (forest type and predators present) is also important for post-release establishment. I then extend the multi-population approach to integrate data from multiple species, and use the resulting model to predict growth of a reintroduced population at a range of predator densities when the candidate species for reintroduction (the North Island saddleback, Philesturnus rufusater) has never been observed in the presence of those predators. I predict saddleback population growth at different rat tracking rates using the relationship modelled for North Island robins, with the strength of the relationship adjusted to account for the greater vulnerability of saddlebacks to predation. The relative vulnerability to predation of saddlebacks (and 24 other New Zealand forest bird species) is estimated by measuring range contraction following the arrival of introduced mammalian predators on New Zealand’s mainland. My results suggest that saddlebacks could be successfully reintroduced to sites with very low rat densities. This study illustrates how an integrated approach to modelling reintroductions improves the information available to managers, providing guidance about site suitability and appropriate management measures. For species reintroduced to multiple sites, integrated models provide an ideal opportunity to develop understanding over time of the key drivers of reintroduction success.en_US
dc.identifier.urihttp://hdl.handle.net/10179/6217
dc.language.isoenen_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectRare birdsen_US
dc.subjectEndangered wildlifeen_US
dc.subjectReintroductionen_US
dc.subjectPredictionen_US
dc.subjectComputer modelingen_US
dc.subjectNorth Island robinen_US
dc.subjectPetroica longipesen_US
dc.subjectNorth Island saddlebacken_US
dc.subjectPhilesturnus rufusateren_US
dc.subjectResearch Subject Categories::NATURAL SCIENCES::Biology::Terrestrial, freshwater and marine ecology::Terrestrial ecologyen_US
dc.titlePredicting reintroduction outcomes using data from multiple populations : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Palmerston North, New Zealanden_US
dc.typeThesisen_US
massey.contributor.authorParlato, Elizabethen_US
thesis.degree.disciplineEcologyen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (Ph.D.)en_US
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