School of Fisheries and Wildlife, Department of Biology and Blue Nile Water Institute, Bahir Dar University, Bahir Dar, Ethiopia.
Faculty of Social Science and Blue Nile Water Institute, Bahir Dar University, Bahir Dar, Ethiopia.
Environ Monit Assess. 2023 May 5;195(6):643. doi: 10.1007/s10661-023-11243-4.
This study aims to examine the physicochemical variables that influence macroinvertebrate assemblages in wetlands of the Fetam River watershed. Macroinvertebrates and water quality samples were collected from 20 sampling stations across four wetlands between February and May 2022. Principal component analysis (PCA) was used to elucidate the physicochemical gradients among datasets and canonical correspondence analysis (CCA) was applied to explore the relationship between taxon assemblages and physicochemical variables. Aquatic insects such as Dytiscidae (Coleoptera), Chironomidae (Diptera), and Coenagrionidae (Odonata) were the most abundant families, and they comprised 20-80% of the macroinvertebrate communities. As demonstrated by cluster analysis, three site groups including slightly disturbed (SD), moderately disturbed (MD), and heavily disturbed (HD) sites were identified. PCA showed a clear separation of slightly disturbed sites from moderately and highly impacted sites. Differences in physicochemical variables, taxon richness and abundance, and Margalef diversity indices were observed along the SD to HD gradient. Phosphate concentration was an important predictor that influenced richness and diversity. The extracted two CCA axes of physicochemical variables accounted for 44% of the variability in macroinvertebrate assemblages. Nutrient concentration (nitrate, phosphate, and total phosphorus), conductivity, and turbidity were the main drivers of this variation. This suggested the need for sustainable wetland management intervention at the watershed level, ultimately benefiting invertebrate biodiversity.
本研究旨在检验影响费塔姆河流域湿地大型无脊椎动物群落的物理化学变量。2022 年 2 月至 5 月,从四个湿地的 20 个采样站采集大型无脊椎动物和水质样本。主成分分析(PCA)用于阐明数据集之间的物理化学梯度,典范对应分析(CCA)用于探索分类群组合与物理化学变量之间的关系。水生昆虫如蜉蝣科(鞘翅目)、摇蚊科(双翅目)和豆娘科(蜻蜓目)是最丰富的科,它们占大型无脊椎动物群落的 20-80%。聚类分析表明,确定了三个包括轻度干扰(SD)、中度干扰(MD)和重度干扰(HD)的站点组。PCA 显示轻度干扰站点与中度和高度干扰站点之间有明显的分离。在 SD 到 HD 梯度上观察到物理化学变量、分类丰富度和丰度以及玛氏多样性指数的差异。磷酸盐浓度是影响丰富度和多样性的重要预测因子。提取的物理化学变量的两个 CCA 轴解释了大型无脊椎动物组合的 44%的可变性。养分浓度(硝酸盐、磷酸盐和总磷)、电导率和浊度是这种变化的主要驱动因素。这表明需要在流域层面进行可持续的湿地管理干预,最终有利于无脊椎动物生物多样性。