University of Duisburg Essen, Department of Aquatic Ecology, D-45141 Essen, Universitätsstr. 5, Germany.
University of Duisburg Essen, Department of Aquatic Ecology, D-45141 Essen, Universitätsstr. 5, Germany; University of Duisburg Essen, Centre for Water and Environmental Research, D-45141 Essen, Universitätsstr. 5, Germany.
Sci Total Environ. 2017 Dec 15;603-604:148-154. doi: 10.1016/j.scitotenv.2017.06.092. Epub 2017 Jun 15.
Interactions of multiple stressors in lotic systems have received growing interest and have been analysed in a growing number of studies using experiment and survey data. In this study, we present a protocol to identify, display and analyse stressors of rivers and their interactions (additive, synergistic or antagonistic). We used a dataset of 125 samples of central European lowland rivers comprising hydromorphological, physico-chemical and land use stressor and pressure variables as well as benthic macroinvertebrate traits as biological response variables. To identify and visualise multiple stressor combinations jointly operating in the data set, we applied social network analysis. The main co-occurring stressor combination was fine sediment accumulation (hydromorphological stress) and enhanced phosphorus concentration (nutrient stress). Agricultural (cropland) and urban land use were identified as the main large scale environmental pressures. Stressor interactions were analysed using generalised linear regression modelling (GLM) including pairwise interaction terms. Altogether, 14 macroinvertebrate response variables were tested on six stressor combinations and revealed predominantly additive effects (80% of all significant models with absolute standardised effect sizes >0.1). Significant antagonistic and synergistic interactions occurred in almost 20% of the models. Fine sediment stress was more influential and frequent than nutrient stress. The methodology presented here is standardisable and thus could help inform practitioners in aquatic ecosystem monitoring about prominent combinations of multiple stressors and their interactions. Yet, further understanding of the mechanisms behind the biological responses is required to be able to derive appropriate guidance for management. This applies to rather complex stressors and pressures, such as land use, for which more detailed data (e.g. nutrient concentrations, fine sediment entry, pesticide pollution) is often missing.
多胁迫在流水系统中的相互作用受到了越来越多的关注，并且已经在越来越多的研究中使用实验和调查数据进行了分析。在本研究中，我们提出了一种方案来识别、展示和分析河流及其相互作用（加性、协同或拮抗）的胁迫因素。我们使用了一个包含中欧低地河流的 125 个样本数据集，这些样本包括水力学形态学、物理化学和土地利用胁迫和压力变量以及底栖大型无脊椎动物特征作为生物响应变量。为了识别和可视化共同作用于数据集的多个胁迫组合，我们应用了社会网络分析。主要共同发生的胁迫组合是细沉积物积累（水力学形态学胁迫）和增强的磷浓度（营养胁迫）。农业（耕地）和城市土地利用被确定为主要的大尺度环境压力。使用广义线性回归模型（GLM）分析了胁迫相互作用，包括成对交互项。总共对六个胁迫组合测试了 14 个大型无脊椎动物响应变量，结果显示主要是加性效应（所有具有绝对标准化效应大小>0.1 的显著模型中，有 80%）。几乎 20%的模型中存在显著的拮抗和协同作用。细沉积物胁迫比营养胁迫更有影响力且更频繁。本文提出的方法是标准化的，因此可以帮助水生生态系统监测的从业者了解多个胁迫及其相互作用的主要组合。然而，需要进一步了解生物响应背后的机制，以便能够为管理提供适当的指导。这适用于土地利用等更复杂的胁迫和压力，对于这些压力，通常缺乏更详细的数据（例如养分浓度、细沉积物输入、农药污染）。
