Optimizing Habitat Protection Using Demographic Models of Population Viability

Publisher Website
Google Scholar
Abstract
Expanding habitat protection is a common tactic for species conservation. When unprotected habitat is privately owned, decisions must be made about which areas to protect by land purchase or conservation easement. To address this problem, we developed an optimization framework for choosing the habitat‐protection strategy that minimizes the risk of population extinction subject to an upper bound on funding. The framework is based on the idea that an extinction‐risk function that predicts the relative effects of varying the quantity and quality of habitat can be estimated from the results of a demographic model of population viability. We used the framework to address the problem of expanding the protected habitat of a core population of the endangered San Joaquin kit fox ( Vulpes macrotis mutica) in the Panoche area in central California. We first developed a stochastic demographic model of the kit fox population. Predictions from the simulation model were used to estimate an extinction‐risk function that depended on areas of good‐ and fair‐quality habitat. The risk function was combined with costs of habitat protection to determine cost‐efficient protection strategies and risk‐cost curves showing how extinction risk could be reduced at minimum cost for increasing levels of funding. One important result was that cost‐efficient shares of the budget used to protect different types of habitat changed as the budget increased and depended on the relative costs of available habitat and the relative effects of habitat protection on extinction risk. Another important finding was the sensitivity of the location and slope of the risk‐cost curve to assumptions about the spatial configuration of available habitat. When the location and slope of the risk‐cost curve are sensitive to model assumptions, resulting predictions of extinction risk and risk reduction per unit cost should be used very cautiously in ranking conservation options among different species or populations. The application is an example of how the results of a complex demographic model of population viability can be synthesized for use in optimization analyses to determine cost‐efficient habitat‐protection strategies and risk‐cost tradeoffs.