Animal tracking moves community ecology: Opportunities and challenges.
Animal tracking moves community ecology: Opportunities and challenges.
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动物追踪推动社区生态：机遇与挑战。
DOI：
10.1111/1365-2656.13698
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发表时间：
2022-07
影响因子：
4.8
通讯作者：
Jetz, Walter
中科院分区：
文献类型：
--
作者：
Costa-Pereira, Raul;Moll, Remington J.;Jesmer, Brett R.;Jetz, Walter
关键词：
关于生物体如何、为什么以及何时相互作用以及与环境相互作用的个人决定，会扩大到塑造社区的模式和过程。最近的证据已经牢固地确立了种内变异在自然界中的普遍性及其在群落生态学中的相关性，但与收集大量个体同种和异种数据相关的挑战阻碍了个体变异与群落生态学的整合。然而，最近在GPS跟踪、遥感和行为生态学方面的技术和统计进展为将种内变异整合到群落过程中提供了一个工具箱。运动数据不仅仅是描述生物体的去向，还提供了关于相互作用和环境关联的独特信息，从中可以建立真正的个体到社区框架。通过将同种和异种的运动路径与环境数据联系起来，生态学家现在可以同时量化种内和种间的变化，关于Eltonian（生物相互作用）和Grinnellian（环境条件）因素支撑社区组合和动态，但必须解决大量的后勤和分析挑战，这些方法才能发挥其全部潜力。在整个社区中，Eltonian和Grinnellian因子的经验整合可以支持保护应用，并通过基于跟踪的分散数据揭示亚稳态动态。随着与多物种跟踪相关的后勤和分析挑战的克服，我们设想未来个体运动及其生态和环境特征将为社区生态学中许多持久的问题带来解决方案。从多物种追踪数据推断出Eltonian和Grinnelian动力学。小组（a）：来自三个不同物种的五个个体的轨迹揭示了种内和种间的相互作用，通过时间，从而使建设的相互作用拓扑结构，包括在个人层面上的同种和异种。图（B）：埃尔顿竞技场中的相互作用可以通过时间上明确的轨迹来映射，从而允许跨景观的相互作用的时空分析。因此，轨道与环境数据（例如遥感层）在空间和时间上的交叉可以量化环境关联，并有助于评估人口和社区范围内的格林内利生态位划分。
Individual decisions regarding how, why and when organisms interact with one another and with their environment scale up to shape patterns and processes in communities. Recent evidence has firmly established the prevalence of intraspecific variation in nature and its relevance in community ecology, yet challenges associated with collecting data on large numbers of individual conspecifics and heterospecifics have hampered integration of individual variation into community ecology. Nevertheless, recent technological and statistical advances in GPS‐tracking, remote sensing and behavioural ecology offer a toolbox for integrating intraspecific variation into community processes. More than simply describing where organisms go, movement data provide unique information about interactions and environmental associations from which a true individual‐to‐community framework can be built. By linking the movement paths of both conspecifics and heterospecifics with environmental data, ecologists can now simultaneously quantify intraspecific and interspecific variation regarding the Eltonian (biotic interactions) and Grinnellian (environmental conditions) factors underpinning community assemblage and dynamics, yet substantial logistical and analytical challenges must be addressed for these approaches to realize their full potential. Across communities, empirical integration of Eltonian and Grinnellian factors can support conservation applications and reveal metacommunity dynamics via tracking‐based dispersal data. As the logistical and analytical challenges associated with multi‐species tracking are surmounted, we envision a future where individual movements and their ecological and environmental signatures will bring resolution to many enduring issues in community ecology. Eltonian and Grinnelian dynamics inferred from multi‐species tracking data. Panel (a): Tracks of five individuals from three different species reveal intraspecific and interspecific interactions through time, thereby enabling the construction of interaction topologies including both conspecifics and heterospecifics at an individual level. Panel (b): Interactions in the Eltonian arena can be mapped via temporally explicit tracks, allowing for spatiotemporal analysis of interactions across landscapes. Therefore, the intersection of tracks with environmental data (e.g. remote sensing layers) in space and time quantifies environmental associations and facilitates assessments of population‐ and community‐wide Grinnellian niche partitioning.
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影响因子：
16.6
作者：
Carlson BS;Rotics S;Nathan R;Wikelski M;Jetz W
通讯作者：
Jetz W
影响因子：
8.8
作者：
Bastille-Rousseau, Guillaume;Wittemyer, George
通讯作者：
Wittemyer, George
影响因子：
56.9
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Davidson, Sarah C.;Bohrer, Gil;Hebblewhite, Mark
通讯作者：
Hebblewhite, Mark
影响因子：
4.8
作者：
Cusack, Jeremy J.;Kohl, Michel T.;MacNulty, Daniel R.
通讯作者：
MacNulty, Daniel R.
影响因子：
56.9
作者：
Atkins, Justine L.;Long, Ryan A.;Pringle, Robert M.
通讯作者：
Pringle, Robert M.