Identifying Pareto-efficient eradication strategies for invasive populations

Abstract

Invasive species are a major cause of biodiversity loss and are notoriously expensive and challenging to manage. We developed a decision-analytic framework for evaluating invasive species removal strategies, given objectives of maximizing eradication probability and minimizing costs. The framework uses an existing estimation model for spatially referenced removal data—one of the most accessible types of invasive species data—to obtain estimates of population growth rate, movement probability, and detection probability. We use these estimates in simulations to identify Pareto-efficient strategies—strategies where increases in eradication probability cannot be obtained without increases in cost—from a set of proposed strategies. We applied the framework post hoc to a successful eradication of veiled chameleons (Chamaeleo calyptratus) and identified the potential for substantial improvements in efficiency. Our approach provides managers and policymakers with tools to identify cost-effective strategies for a range of invasive species using only prior knowledge or data from initial physical removals.