A framework for linking dispersal biology to connectivity across landscapes
A framework for linking dispersal biology to connectivity across landscapes
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DOI：
10.1007/s10980-023-01741-8
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发表时间：
2023-07
影响因子：
5.2
通讯作者：
R. Fletcher;M. E. Iezzi;R. Guralnick;Andrew J. Marx;S. Ryan;D. Valle
中科院分区：
文献类型：
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作者：
R. Fletcher;M. E. Iezzi;R. Guralnick;Andrew J. Marx;S. Ryan;D. Valle
ContextDispersal通常由三个组成部分组成-离开，短暂性和定居-每个都可以受到景观的影响。扩散的一个基本方面是扩散核，它描述了定居的可能性如何作为离出发地点的距离的函数而变化。分散的概念往往与景观连通性的解释密切相关，然而，景观连通性的模型往往不产生扩散核，也没有明确地捕捉扩散的三个组成部分。步行扩散核的基础上的三个组成部分的扩散，以更好地联系扩散过程的景观连接。方法我们扩展了空间吸收马尔可夫链（SAMC）框架，旨在解决广泛的问题，在景观连接，明确模型的扩散内核，承认每个组成部分的扩散过程中，以及如何景观可以改变这些组件。我们提供了一个例子与佛罗里达黑熊（Ursus americanus floridanus），一个物种的保护和管理的关注，在那里我们对比预期的关键亚群之间的连接时，模型做，不考虑随机行走的扩散kernels.ResultsOur扩展显示如何SAMC可以产生不同类型的随机行走内核，包括信息的景观如何改变出发，瞬变和结算过程。重要的是，这个框架还可以很容易地将死亡率纳入预测，并应用于跨景观进行时间明确的预测。连通性的佛罗里达黑熊被预测为低得多时，承认分散内核，并建议解决过程可能是更有影响力的连接性预测比景观resistance.ConclusionThese结果提供了一个基础，应用SAMC分散内核。这些扩展不仅提供了连接性与扩散生物学中概念的正式联系，而且还有助于将来自常见连接性模型的概念（例如，电路理论和最低成本抵抗内核），以促进跨景观的连接预测。
ContextDispersal typically consists of three components—departure, transience and settlement—each of which can be influenced by the landscape. A fundamental aspect of dispersal is the dispersal kernel, which describes how the likelihood of settlement varies as a function of the distance from the departure location. Dispersal concepts are often closely connected to the interpretation of landscape connectivity, yet models of landscape connectivity often do not generate dispersal kernels nor explicitly capture the three components of dispersal.ObjectivesWe apply Markov chain theory for the generation of random-walk dispersal kernels that are based on the three components of dispersal to better link dispersal processes to landscape connectivity.MethodsWe extend the spatial absorbing Markov chain (SAMC) framework, which is aimed at addressing a broad range of problems in landscape connectivity, to explicitly model dispersal kernels that acknowledge each component of the dispersal process and how the landscape can alter each of these components. We provide an example with the Florida black bear (Ursus americanus floridanus), a species of conservation and management concern, where we contrast expected connectivity between key subpopulations when models do and do not consider random-walk dispersal kernels.ResultsOur extensions show how the SAMC can generate different types of random-walk kernels that include information on how the landscape alters departure, transience and settlement processes. Importantly, this framework can also readily incorporate mortality into predictions and be applied to make time-explicit predictions across landscapes. Connectivity for the Florida black bear is predicted to be much lower when acknowledging dispersal kernels and suggests that the settlement process may be more influential to connectivity predictions than landscape resistance.ConclusionThese results provide a foundation for applying the SAMC to dispersal kernels. Not only do these extensions provide a formal linkage of connectivity to concepts in dispersal biology, but also help to bring together concepts from common connectivity models (e.g., circuit theory and least-cost resistant kernels) to facilitate predicting connectivity across landscapes.