Using Bayesian population viability analysis to define relevant conservation objectives

Abstract/Summary

Adaptive management provides a useful framework for managing natural resources in the
face of uncertainty. An important component of adaptive management is identifying clear,
measurable conservation objectives that reflect the desired outcomes of stakeholders. A common
objective is to have a sustainable population, or metapopulation, but it can be difficult to quantify
a threshold above which such a population is likely to persist. We performed a Bayesian
metapopulation viability analysis (BMPVA) using a dynamic occupancy model to quantify the
characteristics of two wood frog (Lithobates sylvatica) metapopulations resulting in sustainable
populations, and we demonstrate how the results could be used to define meaningful objectives
that serve as the basis of adaptive management. We explored scenarios involving
metapopulations with different numbers of patches (pools) using estimates of breeding
occurrence and successful metamorphosis from two study areas to estimate the probability of
quasi-extinction and calculate the proportion of vernal pools producing metamorphs. Our results
suggest that >50 pools are required to ensure long-term persistence with approximately 16% of
pools producing metamorphs in stable metapopulations. We demonstrate one way to incorporate
the BMPVA results into a utility function that balances the trade-offs between ecological and
financial objectives, which can be used in an adaptive management framework to make optimal,
transparent decisions. Our approach provides a framework for using a standard method (i.e.,
PVA) and available information to inform a formal decision process to determine optimal and
timely management policies.

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