Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost‐effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

中文翻译：

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控制资源的最佳空间优先次序，以消除人口不确定性下的入侵物种。

入侵物种的种群通常在景观中异质分布，其中包括在空间中聚集但通过扩散相互联系的局部种群。入侵物种控制的一个基本难题是如何在局部人群中最佳分配有限的财政资源。基于对人口连通性的全面了解的理论工作表明，针对移民是来自哪里的本地人口（来源）可能是最佳的。但是，人口分布和分布等人口统计过程可能非常不确定，并且当地人口密度与破坏成本（破坏函数）之间的关系鲜为人知。我们使用了种群模型来了解预算，丰度，连通性和损害函数的不确定性，共同影响最佳控制策略的投资回报（ROI）。预算，观测不确定性和损害函数对最佳资源分配策略有很大影响。分散概率的不确定性是ROI的最不重要的决定因素。损害函数确定了当连接对称时而不是非对称时哪种资源优先化策略最佳。当连通性是不对称的时，无论损害函数如何，对源种群进行优先级排序要比在本地人口中平均分配努力要有更高的投资回报率，但是根据控制预算的不同，连通性结构和数量的不确定性会使最佳优先级排序策略的投资回报率平均降低了57％ 。预算较低（每月清除率为人口的6.7％），优先分配资源几乎没有优势，尤其是在连通性很高或对称的情况下，并且观测不确定性对ROI的影响很小。当预算适中（每月撤出13％至20％的人口）时，分配资金用于改善监控似乎是最重要的。我们的结果表明，应同时考虑多种观测不确定性源，以优化ROI。准确估计连通性方向和丰度比准确估计扩散率更为重要。开发具有成本效益的监视方法以减少观测不确定性，