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Keywords:

  • Amphibian;
  • Bayesian;
  • metapopulation;
  • regression;
  • risk;
  • SPOM ;
  • viability

Abstract

Aim

Population viability analysis (PVA) is used to quantify the risks faced by species under alternative management regimes. Bayesian PVAs allow uncertainty in the parameters of the underlying population model to be easily propagated through to the predictions. We developed a Bayesian stochastic patch occupancy model (SPOM) and used this model to assess the viability of a metapopulation of the growling grass frog (Litoria raniformis) under different urbanization scenarios.

Location

Melbourne, Victoria, Australia.

Methods

We fitted a Bayesian model that accounted for imperfect detection to a multiseason occupancy dataset for L. raniformis collected across northern Melbourne. The probability of extinction was modelled as a function of effective wetland area, aquatic vegetation cover and connectivity, using logistic regression. The probability of colonization was modelled as a function of connectivity alone. We then simulated the dynamics of a metapopulation of L. raniformis subject to differing levels of urbanization and compensatory wetland creation. Uncertainty was propagated by conducting simulations for 5000 estimates of the parameters of the models for extinction and colonization.

Results

There was considerable uncertainty in both the probability of quasi-extinction and the minimum number of occupied wetlands under most urbanization scenarios. Uncertainty around the change in quasi-extinction risk and minimum metapopulation size increased with increasing habitat loss. For our focal metapopulation, the analysis revealed that significant investment in new wetlands may be required to offset the impacts of urbanization.

Main conclusions

Bayesian approaches to PVA allow parametric uncertainty to be propagated and considered in management decisions. They also provide means of identifying parameters that represent critical uncertainties, and, through the use of informative priors, can easily assimilate new data to reduce parametric uncertainty. These advantages, and the ready availability of software to run Bayesian analyses, will ensure that Bayesian approaches are used increasingly for PVAs.