samc: an R package for connectivity modeling with spatial absorbing Markov chains
samc: an R package for connectivity modeling with spatial absorbing Markov chains
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DOI：
10.1111/ecog.04891
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发表时间：
2020-01-13
期刊：
影响因子：
5.9
通讯作者：
Fletcher, Robert J., Jr.
中科院分区：
文献类型：
--
作者：
Marx, Andrew J.;Wang, Chao;Fletcher, Robert J., Jr.
量化景观连通性对于更好地理解和预测人口如何应对环境变化至关重要。目前，量化景观连通性的流行方法强调景观特征如何提供运动阻力。虽然有许多工具可用于量化景观阻力，但这些工具无法区分两种根本不同的阻力来源：运动行为和死亡率。为了解决这个问题，我们开发了 samc R 包，它使用吸收马尔可夫链理论来量化景观连通性。在这个数学框架内，运动被表示为马尔可夫链中的瞬态，而死亡率则由向吸收状态的转变表示。该框架不仅明确解释了这些不同的问题，还提供了一种概率方法，可以纳入短期和长期动态以及物种分布和丰度。该软件包包括量化预期寿命、长期访问率以及当地和景观尺度上死亡率和流动的各种空间和时间明确测量的功能。 Samc 中的这些函数已经过优化，可以在由 > 2 x 10(6) 个单元组成的景观中找到计算实用的解决方案。在这里，我们利用公开的濒危佛罗里达黑豹 Puma concolor coryi 的活动和死亡率数据来说明 samc 包的工作流程。该分析表明，除了在源位置扩散范围内的道路附近（高死亡风险区域）之外，流动和死亡率通常是相关的。使用当前量化运动阻力的方法无法检测到这种模式。总体而言，samc 包提供了一种实现空间吸收马尔可夫链的方法，可以区分运动行为和死亡率，从而产生更可靠的景观连通性测量。
Quantifying landscape connectivity is fundamental to better understand and predict how populations respond to environmental change. Currently, popular methods to quantify landscape connectivity emphasize how landscape features provide resistance to movement. While many tools are available to quantify landscape resistance, these do not discern between two fundamentally different sources of resistance: movement behavior and mortality. To address this issue, we developed the samc R package that quantifies landscape connectivity using absorbing Markov chain theory. Within this mathematical framework, movements are represented as transient states in the Markov chain, while mortality is represented by transitions to absorbing states. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate both short-term and long-term dynamics, as well as species distribution and abundance. The package includes functions to quantify life expectancy, long-term visitation rates, and various spatially and temporally explicit measures of mortality and movement at the local and landscape scales. These functions in samc have been optimized to find computationally practical solutions in landscapes comprised of > 2 x 10(6) cells. Here, we illustrate the workflow of the samc package with publicly available movement and mortality data on the endangered Florida panther Puma concolor coryi. This analysis showed that movement and mortality are generally correlated except for locations near roads (areas of high mortality risk) that are within the dispersal range of source locations. This pattern would have been undetectable with current methods that quantify movement resistance. Overall, the samc package provides a means for implementing spatial absorbing Markov chains that can distinguish between movement behavior and mortality resulting in more reliable landscape connectivity measures.