Department of Environmental Solution Technology, Faculty of Science and Technology, Ryukoku University, Otsu 520-2194, Japan.
Joint Research Center for Science and Technology, Ryukoku University, Otsu 520-2194, Japan.
Nature. 2018 Feb 15;554(7692):360-363. doi: 10.1038/nature25504. Epub 2018 Feb 7.
Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.
生态理论表明，群落稳定性等大规模模式可能会受到种间相互作用变化的影响，而这些变化则源于物种个体行为和/或生理反应随时间的变化。尽管该理论得到了实验支持，但由于跟踪种间相互作用的快速变化（已知这种变化发生在比一个世代时间短得多的时间尺度上）以及确定这种变化对大规模群落动态的影响存在挑战，因此自然生态系统中缺乏证据。在这里，我们使用分析非线性时间序列的工具和日本舞鹤湾一个为期 12 年的半月一次的自然海洋鱼类群落观测数据集，表明短期的相互作用网络变化会影响整体群落动态。在 15 个主要物种中，我们确定了 14 种种间相互作用来构建动态相互作用网络。我们表明，相互作用的强度，甚至类型，都会随时间而变化；我们还基于非平衡非线性系统中的局部李雅普诺夫稳定性开发了一种时变稳定性度量方法，用于吸引子动力学。我们使用这种动态稳定性度量来研究时变相互作用网络与群落稳定性之间的联系。我们发现这个鱼类群落的动态稳定性存在季节性模式，这广泛支持了当前生态理论的预期。具体来说，夏季弱相互作用和更高物种多样性的主导地位与更高的动态稳定性和更小的种群波动有关。我们认为，种间相互作用、群落网络结构和群落稳定性是动态属性，将波动的相互作用网络与群落水平的动态属性联系起来是理解自然界中生态群落维持的关键。
