发表机构
Universidad Politécnica de Madrid; CEAB-CSIC（马德里理工大学; 西班牙国家研究委员会生态与生物多样性研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个统计框架，利用动力学平均场理论从丰度时间序列直接推断自然生态群落的稳定性状态，无需重建相互作用网络，并定量估计物种灭绝风险。
AI 中文摘要
生态群落的稳定性通常通过物种相互作用来定义，这些相互作用量化了物种之间的相互影响。在物种丰富的群落中，相互作用强度是出了名的难以测量。然而，对物种丰富群落的长期监测提供了物种丰度时间序列，这些数据在栖息地和分类群中日益可得，但尚未与其所描述的群落的稳定性属性联系起来。在此，我们开发了一个统计框架，直接从丰度时间序列推断大型生态群落中的稳定性状态。我们利用动力学平均场理论研究具有随机相互作用和环境波动的随机广义Lotka--Volterra动力学，将多物种系统简化为一个代表性物种的有效随机过程。该理论预测了三个稳定性状态（稳定共存、接近灭绝的间歇性动力学和无界增长），它们由解析边界分隔，并表明环境随机性通过促进间歇性低丰度动力学而系统地破坏稳定共存。由此产生的稳态物种丰度分布是一个伽马定律，它将动力学相与经验研究中使用的基于变异性的稳定性度量联系起来。将有效动力学重构为多元回归模型，我们从群落数据中推断相互作用统计量、环境变异性和动力学的特征时间尺度，而无需重建相互作用网络。将该方法应用于涵盖广泛栖息地和分类群的自然群落，它解析了对比鲜明的稳定性状态，并提供了物种灭绝风险的定量估计。
英文摘要
The stability of an ecological community is conventionally defined through species interactions, which quantify how species affect one another. Interaction strengths are notoriously difficult to measure in species-rich assemblages. Long-term monitoring of species-rich communities, however, provides species abundance time series, increasingly available across habitats and taxa but not yet connected to the stability properties of the communities they describe. Here we develop a statistical framework that infers stability regimes in large ecological communities directly from abundance time series. We study stochastic Generalized Lotka--Volterra dynamics with random interactions and environmental fluctuations using dynamical mean-field theory, reducing the multispecies system to an effective stochastic process for a representative species. The theory predicts three stability regimes (stable coexistence, intermittent dynamics close to extinction, and unbounded growth) separated by analytical boundaries, and shows that environmental stochasticity systematically destabilizes coexistence by promoting intermittent low-abundance dynamics. The resulting steady-state species abundance distribution is a Gamma law that ties the dynamical phases to the variability-based stability metrics used in empirical studies. Recasting the effective dynamics as a multivariate regression model, we infer interaction statistics, environmental variability and the characteristic timescale of the dynamics from community data, without reconstructing the interaction network. Applied to natural communities spanning a broad range of habitats and taxa, the method resolves contrasting stability regimes and yields quantitative estimates of species extinction risk.
Comments27 pages (including supplemental material with 15 pages), 13 figures (4 figures in the main text and 9 figures in the supplemental material), 1 table in the main text
