Department of Wildlife Ecology and Conservation, University of Florida, PO Box 110430, 110 Newins-Ziegler Hall, Gainesville, FL, 32611-0430, USA.
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Ecol Lett. 2019 Oct;22(10):1680-1689. doi: 10.1111/ele.13333. Epub 2019 Jul 25.
Predicting connectivity, or how landscapes alter movement, is essential for understanding the scope for species persistence with environmental change. Although it is well known that movement is risky, connectivity modelling often conflates behavioural responses to the matrix through which animals disperse with mortality risk. We derive new connectivity models using random walk theory, based on the concept of spatial absorbing Markov chains. These models decompose the role of matrix on movement behaviour and mortality risk, can incorporate species distribution to predict the amount of flow, and provide both short- and long-term analytical solutions for multiple connectivity metrics. We validate the framework using data on movement of an insect herbivore in 15 experimental landscapes. Our results demonstrate that disentangling the roles of movement behaviour and mortality risk is fundamental to accurately interpreting landscape connectivity, and that spatial absorbing Markov chains provide a generalisable and powerful framework with which to do so.
预测连通性，即景观如何改变动物的运动方式，对于理解物种在环境变化下的持续存在范围至关重要。尽管众所周知，动物的运动是有风险的，但连通性模型通常将动物扩散过程中对基质的行为反应与死亡率风险混为一谈。我们使用随机游走理论，基于空间吸收马尔可夫链的概念，推导出新的连通性模型。这些模型分解了基质对运动行为和死亡率风险的作用，可以结合物种分布来预测流动量，并为多种连通性指标提供短期和长期的分析解决方案。我们使用昆虫食草动物在 15 个实验景观中的运动数据来验证该框架。我们的结果表明，区分运动行为和死亡率风险的作用对于准确解释景观连通性至关重要，而空间吸收马尔可夫链为实现这一目标提供了一个具有普遍适用性和强大功能的框架。
