Population viability analyses in New Zealand: a review
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
- School of Environment, University of Auckland, Private Bag 92019, Auckland, New Zealand
- Department of Ecosystem Modelling, Georg-August University of Göttingen, Büsgenweg 4, 37077, Göttingen, Germany
Biodiversity assets often require conservation management, which, in turn, necessitates decisions about which ecosystem, community or species should be prioritised to receive resources. Population viability analysis (PVA) uses a suite of quantitative methods to estimate the likelihood of population decline and extinction for a given species, and can be used to assess a population's status, providing useful information to decision-makers. In New Zealand, a range of taxa have been analysed using the PVA approach, but the scope of its implementation has not previously been reviewed. We compiled a database of 78 published PVAs for New Zealand indigenous fauna and flora, along with details of the species considered, the data used to parametrise the model, and the technical details of their implementation. We assessed the taxa and threat status of the species for which PVA were conducted relative to the distribution of taxa across threat classes in the New Zealand Threat Classification System database. There were clear biases in the species selected for analysis, notably an over-representation of birds and threatened species in general, and an under-representation of invertebrates and plants. Model parameterisation and implementation were often not reported in a transparent or standardised way, which hinders model communication and reconstruction. To maximise the benefit of PVAs, we suggest that more attention should be given to the ecosystem-level importance of species, and to species whose threat status is changing rapidly or are not yet threatened. More clearly describing the parameterisation, underlying assumptions and implementation of PVAs will help to better contextualise their results and support reproducible ecological science and decision-making.