U.S. Geological Survey, Upper Midwest Environmental Sciences Center, La Crosse, Wisconsin, United States of America.
University of Wisconsin La-Crosse, Department of Mathematics and Statistics, La Crosse, Wisconsin, United States of America.
PLoS Comput Biol. 2023 Jun 7;19(6):e1011147. doi: 10.1371/journal.pcbi.1011147. eCollection 2023 Jun.
Aquatic systems worldwide can exist in multiple ecosystem states (i.e., a recurring collection of biological and chemical attributes), and effectively characterizing multidimensionality will aid protection of desirable states and guide rehabilitation. The Upper Mississippi River System is composed of a large floodplain river system spanning 2200 km and multiple federal, state, tribal and local governmental units. Multiple ecosystem states may occur within the system, and characterization of the variables that define these ecosystem states could guide river rehabilitation. We coupled a long-term (30-year) highly dimensional water quality monitoring dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years in the river to guide conservation. Across the entire system, TDA identified five ecosystem states. State 1 was characterized by exceptionally clear, clean, and cold-water conditions typical of winter (i.e., a clear-water state); State 2 had the greatest range of environmental conditions and contained most the data (i.e., a status-quo state); and States 3, 4, and 5 had extremely high concentrations of suspended solids (i.e., turbid states, with State 5 as the most turbid). The TDA mapped clear patterns of the ecosystem states across several riverine navigation reaches and seasons that furthered ecological understanding. State variables were identified as suspended solids, chlorophyll a, and total phosphorus, which are also state variables of shallow lakes worldwide. The TDA change detection function showed short-term state transitions based on seasonality and episodic events, and provided evidence of gradual, long-term changes due to water quality improvements over three decades. These results can inform decision making and guide actions for regulatory and restoration agencies by assessing the status and trends of this important river and provide quantitative targets for state variables. The TDA change detection function may serve as a new tool for predicting the vulnerability to undesirable state transitions in this system and other ecosystems with sufficient data. Coupling ecosystem state concepts and TDA tools can be transferred to any ecosystem with large data to help classify states and understand their vulnerability to state transitions.
全球的水生系统可以存在于多种生态系统状态（即生物和化学属性的反复集合）中，有效地描述多维性将有助于保护理想状态并指导恢复。密西西比河上游系统由一个长达 2200 公里的大型洪泛平原河流系统和多个联邦、州、部落和地方政府单位组成。该系统内可能存在多种生态系统状态，对定义这些生态系统状态的变量进行描述可以指导河流恢复。我们将一个长期（30 年）高维水质监测数据集与多种拓扑数据分析（TDA）技术相结合，以分类生态系统状态、识别状态变量，并在 30 年内检测河流中的状态变化，以指导保护。在整个系统中，TDA 识别出了五种生态系统状态。状态 1 的特点是异常清澈、清洁和冷水条件，这是冬季的典型特征（即清水状态）；状态 2 具有最大的环境条件范围，包含了大部分数据（即现状状态）；而状态 3、4 和 5 的悬浮物浓度极高（即浑浊状态，状态 5 最浑浊）。TDA 绘制了生态系统状态在几个河流导航段和季节中的清晰模式，进一步增进了对生态系统的了解。状态变量被确定为悬浮物、叶绿素 a 和总磷，它们也是全球浅湖的状态变量。TDA 的变化检测功能根据季节性和偶发性事件显示了短期的状态转变，并提供了由于三十年来水质改善而导致的长期渐进变化的证据。这些结果可以为决策提供信息，并为监管和恢复机构提供指导，评估这条重要河流和其他具有足够数据的生态系统的状况和趋势，并为状态变量提供定量目标。TDA 的变化检测功能可能成为预测该系统和其他具有足够数据的生态系统中不良状态转变脆弱性的新工具。将生态系统状态概念和 TDA 工具相结合，可以应用于任何具有大数据的生态系统，以帮助分类状态并了解其对状态转变的脆弱性。
