A framework for species conservation by managed relocation: quantifying risks, uncertainties, and alternatives

如果环境变化，如栖息地的丧失，或温度和降水的变化，威胁到一个物种在给定地理区域茁壮成长的能力，保护生物学家可能会通过将该物种的个体转移到更合适的环境来促进它们的生存。然而，这种被称为受控重新安置的方法会带来一些风险，例如在错误的时间或错误的位置移动生物体，或者引入可能在新环境中出现问题的物种。该项目将研究将物种转移到更合适的地点的风险和好处，而不是简单地保护它们目前的栖息地。该项目的一个关键部分将是探索现有环境信息的不确定性可能如何影响野生动物管理者使用的决策过程。这项研究的结果将使社会受益，为各种动植物物种的保护规划决策提供信息，并通过将基础研究与直接应用相结合来促进科学教育。该项目的目标是量化在不同的不确定性来源和决策过程下，有管理的搬迁战略与保护和恢复方案相比的潜在好处和风险。将使用一套动态建模框架来模拟受控重新安置，受控重新安置被定义为物种(辅助迁移)和基因型(辅助基因流动)有目的地超越其历史范围，目的是支持在不同环境条件变化模型下的种群持久性。竞争性集合群落模型将模拟辅助迁徙，考虑到意外引入入侵物种对接受者群落产生负面影响的风险，而耦合的遗传-人口模型将研究局部适应和集合种群水平遗传多样性丧失的风险。所有模型都将量化风险和收益如何依赖于所使用的人口和环境指标，以及相关的不确定性，以决定何时、何地和多少人重新安置。反过来，这项研究还将探讨在确定连通性对多样性的影响时，当地规模的救援和全球规模的同质化如何进行权衡。最后，该项目将促进对如何在持续的全球变化和不同程度的不确定性下进行与保护相关的决策的理解。

If environmental changes such as habitat loss, or changes in temperature and precipitation threaten a species' ability to thrive in a given geographic area, conservation biologists might promote their survival by moving individuals of that species to more appropriate environments. However, this approach, called managed relocation, entails a number of risks, such as moving organisms at the wrong time or to the wrong location or introducing species that may become problematic in their new environments. This project will examine the risks and benefits of moving species to more appropriate locations as opposed to simply protecting their current habitats. A key part of this project will be to explore how the uncertainty of available environmental information might influence the decision making process used by wildlife managers. The outcomes from this study will benefit society by informing conservation planning decisions across a variety of plant and animal species and will advance science education by combining basic research with its direct application. The goal of this project is to quantify how the potential benefits and risks of managed relocation strategies compare to protection and restoration programs under different sources of uncertainty and decision-making processes. A suite of dynamical modeling frameworks will be employed to model managed relocation, which is defined as the purposeful movement of both species (assisted migration) and genotypes (assisted gene flow) beyond their historical range, with the intention of supporting population persistence under different models of changing environmental conditions. Competitive metacommunity models will model assisted migration accounting for the risk of accidentally introducing invasive species that negatively impact recipient communities, and coupled genetic-demographic models will examine the risk of loss of local adaptation and metapopulation-level genetic diversity. All models will quantify how risks and benefits depend on the demographic and environmental indicators used, with associated uncertainty, to decide when, where, and how many individuals to relocate. In turn, this study will also address how local-scale rescue and global-scale homogenization trade-off in determining the effects of connectivity on diversity. Finally, this project will advance the understanding of how conservation associated decision-making should be approached given ongoing global change and different levels of uncertainty.