Zhou Mingliang, Xu Yan, Zhu Keyi, Teng Tong, Huo Xumeng, Chen Xinyue, Hou Weili, Liu Jiaqiang, Yan Shengjun, Khan Imran
College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China.
College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China.
J Environ Manage. 2025 Sep;392:126873. doi: 10.1016/j.jenvman.2025.126873. Epub 2025 Aug 9.
Ecosystem stability is a central focus in ecological research. Despite the proliferation of stability measurement indices in recent years, they tend to have specific conditions and do not provide a widely applicable network index for measuring stability. In this research, we established an indicator to assess network stability, and compared the network stability features in estuarine phytoplankton networks from a temporal and spatial perspective. Based on microscopic examination, we acquired phytoplankton community data of 15 sampling points at three hydrological periods in the Bohai Bay. In addition, we analyzed the spatial pattern of phytoplankton biodiversity in the estuary of Bohai Bay by using α-diversity indices (e.g. Shannon, Simpson and Evenness). Then, we developed an optimized co-occurrence network using Random Matrix Theory (RMT). We also selected 12 topological indices to describe the attributes of networks. According to the eigenvector centrality algorithm, we built an indicator of Key Nodes Concentration (K), and also compared the Geodesic Efficiency (E) to verify the network stability. Our findings reveal distinct spatial and temporal patterns: the Shannon and Chao indices peaked at different locations across hydrological periods. The highest Shannon index was at F14(Yellow River old course) in the normal-flow period, while it was at F4(Shahe River estuary) in the high-flow period. Additionally from the aspect of topological indices, we can see the distribution in the low-flow period is the most uniform (degree = 0.076, betweenness centrality = 0.224) and the average degree in the high-flow period was significantly higher than that in the normal-flow period and the low-flow period. The calculation results show that the network's stability is highest during the high-flow period, with K = 0.406 and E = 0.332, and both K and E values decrease across the three periods. And 73.3 % of the esturary were more stable during the common-flow period. This research (a) provides a fundamental network-based topological indices for quantifying network stability.; (b) improving a co-occurrence network establishment approach by using RMT theory; (c) reveals the spatial-temporal variability of estuarine ecological networks. These findings provide a scientific foundation for the management and conservation of phytoplankton ecological networks in Bohai Bay.
生态系统稳定性是生态学研究的核心焦点。尽管近年来稳定性测量指标不断涌现，但它们往往具有特定条件，并未提供一个广泛适用的衡量稳定性的网络指标。在本研究中，我们建立了一个评估网络稳定性的指标，并从时空角度比较了河口浮游植物网络的稳定性特征。基于显微镜检查，我们获取了渤海湾三个水文时期15个采样点的浮游植物群落数据。此外，我们使用α多样性指数（如香农指数、辛普森指数和均匀度指数）分析了渤海湾河口浮游植物生物多样性的空间格局。然后，我们利用随机矩阵理论（RMT）构建了一个优化的共现网络。我们还选择了12个拓扑指数来描述网络的属性。根据特征向量中心性算法，我们构建了关键节点浓度（K）指标，并比较了测地效率（E）以验证网络稳定性。我们的研究结果揭示了明显的时空格局：香农指数和 Chao 指数在不同水文时期的不同位置达到峰值。正常流量时期，最高香农指数出现在F14（黄河故道）；高流量时期，最高香农指数出现在F4（沙河河口）。此外，从拓扑指数方面来看，我们可以看到低流量时期的分布最为均匀（度 = 0.076，介数中心性 = 0.224），高流量时期的平均度显著高于正常流量时期和低流量时期。计算结果表明，高流量时期网络稳定性最高，K = 0.406，E = 0.332，且K和E值在三个时期均呈下降趋势。并且73.3%的河口在常流量时期更为稳定。本研究（a）提供了一个基于网络的基本拓扑指数来量化网络稳定性；（b）通过使用RMT理论改进了共现网络建立方法；（c）揭示了河口生态网络的时空变异性。这些发现为渤海湾浮游植物生态网络的管理和保护提供了科学依据。
