National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Fort Collins, Colorado, USA.
Resources for the Future, Washington, District of Columbia, USA.
Ecol Appl. 2022 Sep;32(6):e2628. doi: 10.1002/eap.2628. Epub 2022 May 30.
Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.
扩散驱动着外来物种和病原体的入侵动态。将扩散知识应用于入侵管理的优化意味着控制计划的成败之间的差异，并显著提高控制工作的投资回报。识别入侵管理最佳解决方案的常用方法是优化包含扩散的动态空间模型。优化这些空间模型可能极具挑战性，因为时间、空间和不确定性的相互作用会迅速增加考虑的维度数量。解决这些问题需要在多个领域（包括生态学、决策分析、生物经济学、自然资源管理和优化）中应用技术的进步和整合。通过综合这些不同领域的最新进展，我们提供了一个应用生态理论来推进最佳管理科学的工作流程，并强调了优化入侵控制的优先事项。我们发现的一个显著差距是，即使扩散估计高度不确定且极大地影响入侵结果，但在最佳管理框架中，对扩散不确定性的考虑极其有限。此外，优化框架很少考虑多种类型的不确定性（我们描述了五种主要类型）及其相互关系。因此，管理或其他来源的反馈可能会放大扩散中的不确定性，而这些反馈很少被考虑。纳入不确定性对于提高决策风险的透明度和识别最佳管理策略至关重要。我们讨论了使用动态空间模型来优化面临的挑战和解决方案，以增加这些重要工具的实际应用，并提高入侵管理建议的一致性和稳健性。
