Identifying Pareto-efficient eradication strategies for invasive populations

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.