Optimization of spatial resource allocation is crucial for the successful control of invasive species under a limited budget but requires labor‐intensive surveys to estimate population parameters. In this study, we devised a novel framework for the spatially explicit optimization of capture effort allocation using state‐space population models from past capture records. We applied it to a control program for invasive snapping turtles to determine effort allocation strategies that minimize the population density over the whole area. We found that spatially heterogeneous density dependence and capture pressure limit the abundance of snapping turtles. Optimal effort allocation effectively improved the control effect, but the degree of improvement varied substantially depending on the total effort. The degree of improvement by the spatial optimization of allocation effort was only 3.21% when the total effort was maintained at the 2016 level. However, when the total effort was increased by two, four, and eight times, spatial optimization resulted in improvements of 4.65%, 8.33%, and 20.35%, respectively. To achieve the management goal for snapping turtles in our study area, increasing the current total effort by more than four times was necessary, in addition to optimizing the spatial effort. The snapping turtle population is expected to reach the target density one year after the optimal management strategy is implemented, and this rapid response can be explained by high population growth rate coupled with density‐dependent feedback regulation. Our results demonstrated that combining a state‐space model with optimization makes it possible to adaptively improve the management of invasive species and decision‐making. The method used in this study, based on removal records from an invasive management program, can be easily applied to monitoring data for wildlife and pest control management using traps in a variety of ecosystems.

中文翻译：

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通过管理实践实现入侵物种控制的空间优化


空间资源配置的优化对于在有限的预算下成功控制入侵物种至关重要，但需要进行劳动密集型调查来估计种群参数。在这项研究中，我们设计了一个新颖的框架，使用来自过去捕获记录的状态空间种群模型对捕获努力分配进行空间显式优化。我们将其应用于入侵鳄龟的控制计划，以确定尽​​量减少整个地区种群密度的工作分配策略。我们发现空间异质密度依赖性和捕获压力限制了鳄龟的丰度。最优的努力分配有效地提高了控制效果，但改善的程度根据总努力的不同而有很大差异。当总努力量维持在2016年水平时，空间优化配置努力量的改善程​​度仅为3.21%。然而，当总工作量增加两倍、四倍和八倍时，空间优化分别提高了 4.65%、8.33% 和 20.35%。为了实现我们研究区域鳄龟的管理目标，除了优化空间工作量之外，还需要将当前的总工作量增加四倍以上。实施最佳管理策略一年后，鳄龟种群预计将达到目标密度，这种快速反应可以通过高种群增长率和密度依赖性反馈调节来解释。我们的结果表明，将状态空间模型与优化相结合可以自适应地改进入侵物种的管理和决策。 本研究中使用的方法基于入侵管理计划的清除记录，可以轻松应用于各种生态系统中使用陷阱的野生动物和害虫控制管理的监测数据。