National Wildlife Research Center, USDA-APHIS, Wildlife Services, 4101 Laporte Avenue, Fort Collins, Colorado, 80521, USA.
Centers for Epidemiology and Animal Health, USDA-APHIS, Veterinary Services, 2150 Centre Avenue, Fort Collins, Colorado, 80526, USA.
Ecol Appl. 2020 Sep;30(6):e02126. doi: 10.1002/eap.2126. Epub 2020 Apr 15.
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.
入侵物种的种群经常在景观中不均匀地扩散，由空间上聚集的局部种群组成，但通过扩散连接。入侵物种控制的一个基本困境是如何在局部种群之间最优地分配有限的财政资源。基于对人口连通性的完美了解的理论工作表明，针对移民来源的局部种群（来源）可以是最优的。然而，丰度和扩散等人口过程可能高度不确定，并且局部种群密度与损害成本（损害函数）之间的关系很少为人所知。我们使用了一个复合种群模型来理解预算以及丰度、连通性和损害函数的不确定性如何共同影响最优控制策略的投资回报率 (ROI)。预算、观测不确定性和损害函数对最优资源分配策略有强烈影响。扩散概率的不确定性是 ROI 最重要的决定因素。当连通性对称时，损害函数决定了哪种资源优先级策略是最优的，但当连通性不对称时则不是。当连通性不对称时，优先考虑源种群比在局部种群之间平均分配资源具有更高的 ROI，无论损害函数如何，但连通性结构和丰度的不确定性将最优优先级策略的 ROI 降低了 57%，具体取决于控制预算。在预算较低的情况下（每月去除种群的 6.7%），资源优先级没有太大优势，特别是在连通性较高或对称的情况下，观测不确定性对 ROI 的影响也很小。在预算适中（每月去除 13-20%的种群）时，为改善监测而分配资金似乎最为重要。
我们的研究结果表明，在优化 ROI 时，应该同时考虑多个观测不确定性来源。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测方法以降低观测不确定性，以及确定如何在多个监测和控制活动之间分配资源的定量框架，是优化 ROI 以优化入侵物种控制计划的重要且具有挑战性的未来方向。
