Data from: Regime shifts in marine communities: a complex systems perspective on food web dynamics

Species composition and habitats are changing at unprecedented rates in the world’s oceans, potentially causing entire food webs to shift to structurally and functionally different regimes. Despite the severity of these regime shifts, elucidating the precise nature of their underlying processes has remained difficult. We address this challenge with a new analytic approach to detect and assess the relative strength of different driving processes in food webs. Our study draws on complexity theory, and integrates the network-centric Exponential Random Graph Modeling (ERGM) framework developed within the social sciences with community ecology. In contrast to previous research, this approach makes clear assumptions of direction of causality and accommodates a dynamic perspective on the emergence of food webs. We apply our approach in analysing food webs of the Baltic Sea before and after a previously reported regime shift. Our results show that the dominant food web processes have remained largely the same, although we detect changes in their magnitudes. The results indicate that the reported regime shift may not be a system-wide shift, but instead involve a limited number of species. Our study emphasizes the importance of community-wide analysis on marine regime shifts and introduces a novel approach to examine food webs.全球海洋中，物种组成与栖息地正以前所未有的速率发生变化，或导致整个食物网转变为结构与功能均截然不同的生态系统稳态。尽管这类生态系统状态跃迁的影响极为严峻，但阐明其背后驱动过程的精确本质依旧困难重重。为此，我们提出一种全新的分析方法，用于检测食物网中不同驱动过程并评估其相对强度。本研究依托复杂性理论，将社会科学领域提出的以网络为中心的指数随机图模型（Exponential Random Graph Modeling, ERGM）框架与群落生态学相结合。与以往研究不同，该方法明确设定了因果关系方向的假设，并为食物网的形成提供了动态视角。我们将该方法应用于分析波罗的海在已报道的一次生态系统状态跃迁前后的食物网。研究结果显示，尽管我们检测到各驱动过程的强度发生了变化，但主导食物网的核心过程整体上并未发生显著改变。该结果表明，此次已报道的状态跃迁或许并非全系统范围的转变，而仅涉及有限的部分物种。本研究强调了群落尺度分析在海洋生态系统状态跃迁研究中的重要性，并为食物网分析提供了一种全新的研究范式。