Towards a unified framework for connectivity that disentangles movement and mortality in space and time
Towards a unified framework for connectivity that disentangles movement and mortality in space and time
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DOI：
10.1111/ele.13333
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发表时间：
2019-07-25
期刊：
影响因子：
8.8
通讯作者：
Acevedo, Miguel A.
中科院分区：
文献类型：
--
作者：
Fletcher, Robert J., Jr.;Sefair, Jorge A.;Acevedo, Miguel A.
预测连通性，或景观如何改变运动，对于理解环境变化对物种持久性的影响至关重要。虽然众所周知，移动是有风险的，但连通性建模往往将动物对矩阵的行为反应与死亡风险混为一谈。我们推导出新的连接模型，使用随机游走理论，基于空间吸收马尔可夫链的概念。这些模型分解了矩阵对运动行为和死亡风险的作用，可以结合物种分布来预测流量，并为多个连接性指标提供短期和长期的分析解决方案。我们验证的框架，在15个实验景观的食草昆虫的运动数据。我们的研究结果表明，解开运动行为和死亡风险的作用是根本的，以准确地解释景观连接，空间吸收马尔可夫链提供了一个通用的和强大的框架，这样做。
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.