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.

中文翻译：

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确定入侵种群的帕累托有效根除策略


入侵物种是生物多样性丧失的主要原因，众所周知，其成本高昂且难以管理。我们开发了一个决策分析框架来评估入侵物种清除策略，其目标是最大限度地提高根除概率和最小化成本。该框架使用空间参考清除数据（最容易获得的入侵物种数据类型之一）的现有估计模型来获得种群增长率、移动概率和检测概率的估计值。我们在模拟中使用这些估计值，从一组提出的策略中确定帕累托有效策略——在不增加成本的情况下就无法增加根除概率的策略。我们在事后将框架应用于成功根除蒙面变色龙 （Chamaeleo calyptratus），并确定了大幅提高效率的潜力。我们的方法为管理人员和政策制定者提供了工具，以便仅使用先前的知识或初始物理清除的数据来确定针对一系列入侵物种的具有成本效益的策略。