1. River assessments are predominantly based on biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality.
2. Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various symptoms (macroinvertebrate metrics) and probabilistically link them to various potential causes of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented.
3. Water quality‐related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53%–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application.
4. Synthesis and applications. Bayesian inference can greatly assist the diagnosis of potential causes of ecosystem deterioration based on a selection of diagnostic biological metrics. If integrated into a Bayesian network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve biological and ecological status. Diagnostic Bayesian networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures.

中文翻译：

---

利用生物学症状诊断河流生态系统恶化原因的框架

1. 河流评估主要基于生物指标和指标，这些指标和指标的选择或设计旨在整合在各种空间尺度上运行的多种恶化原因（压力源）的影响。但是，许多生物评估系统的综合性质无法追溯单个压力源及其对总体评估结果的影响。因此，河流管理者常常无法将生物评估与管理措施计划联系起来以改善生态质量。
2. 在这里，我们提出了一种新颖的诊断方法，该方法可以估算单个应激源在单个河流生态系统规模上造成生物降解的原因。与医学诊断类似，我们使用各种症状（无脊椎动物指标），并通过概率将其与生态状态恶化的各种潜在原因（压力源）联系起来。症状和原因是由来自欧洲中部低地河流的157个样本（应激源，分类群）的训练数据集提供的，并通过贝叶斯网络（BN）进行链接。提出了针对三个不同空间尺度（集水区，覆盖范围和地点）的三个独立的BN。
3. 与水质有关的原因在流域尺度上影响最大，而水形态学原因在较小尺度上占主导地位。指示河岸退化的原因在影响范围内影响最大。许多症状显示出与病因的紧密联系，并揭示出具有生态意义的关系，因此指出了所选症状的潜在诊断用途。使用47个样本的独立数据集验证BN。总体而言，三个BN的模型精度范围为53％– 58％，而对于单个节点（原因和症状），在验证数据中可以达到100％的预测节点状态与实际节点状态一致性。BN被实现为交互式在线诊断工具，以允许最终用户轻松进行应用。
4. 综合与应用。贝叶斯推断可以根据诊断生物学指标的选择，极大地帮助诊断生态系统恶化的潜在原因。如果将其整合到贝叶斯网络中，则可以将症状和潜在原因联系起来，并为管理决策提供适当措施的信息，以改善生物和生态状况。贝叶斯诊断网络因此支持最终用户弥合生物监测与适当的管理措施计划之间的鸿沟。