Organismal movement is ubiquitous and facilitates important ecological mechanisms that drive community and metacommunity composition and hence biodiversity. In most existing ecological theories and models in biodiversity research, movement is represented simplistically, ignoring the behavioural basis of movement and consequently the variation in behaviour at species and individual levels. However, as human endeavours modify climate and land use, the behavioural processes of organisms in response to these changes, including movement, become critical to understanding the resulting biodiversity loss. Here, we draw together research from different subdisciplines in ecology to understand the impact of individual‐level movement processes on community‐level patterns in species composition and coexistence. We join the movement ecology framework with the key concepts from metacommunity theory, community assembly and modern coexistence theory using the idea of micro–macro links, where various aspects of emergent movement behaviour scale up to local and regional patterns in species mobility and mobile‐link‐generated patterns in abiotic and biotic environmental conditions. These in turn influence both individual movement and, at ecological timescales, mechanisms such as dispersal limitation, environmental filtering, and niche partitioning. We conclude by highlighting challenges to and promising future avenues for data generation, data analysis and complementary modelling approaches and provide a brief outlook on how a new behaviour‐based view on movement becomes important in understanding the responses of communities under ongoing environmental change.

中文翻译：

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运动介导的社区集会与共存

生物体运动无处不在，促进了重要的生态机制，推动了群落和元群落组成，从而推动了生物多样性。在生物多样性研究的大多数现有生态理论和模型中，运动被简单地表示，忽略了运动的行为基础，从而忽略了物种和个体层面的行为变化。然而，随着人类努力改变气候和土地利用，生物体响应这些变化的行为过程，包括运动，对于理解由此导致的生物多样性丧失变得至关重要。在这里，我们汇集了生态学不同分支学科的研究，以了解个体层面的运动过程对物种组成和共存的社区层面模式的影响。我们将运动生态学框架与元社区理论、社区组装和现代共存理论的关键概念相结合，使用微观宏观链接的思想，其中紧急运动行为的各个方面扩展到物种流动和移动链接的本地和区域模式-在非生物和生物环境条件下产生的模式。这些反过来又会影响个体运动，并在生态时间尺度上影响扩散限制、环境过滤和生态位分区等机制。最后，我们强调了数据生成、数据分析和补充建模方法面临的挑战和有希望的未来途径，并简要介绍了基于行为的新运动观如何在理解持续环境变化下社区的反应方面变得重要。