Habitat type strongly influences the structural benthic invertebrate community composition in a landscape characterized by ubiquitous, long-term agricultural stress

The importance of small waterbodies for biodiversity and ecosystem processes has long been neglected in research and policy development. However, because of their heterogeneous characteristics and habitat structures, these waterbodies are biodiversity hotspots for various aquatic and terrestrial organisms, as previously shown for rotifers, aquatic birds, and amphibians. Knowledge of benthic invertebrate community structures in small waterbodies is scarce. Thus, we performed a comprehensive field study of these structures in an agricultural landscape in the northeastern German lowlands. We obtained a dataset containing biological (taxon abundances) and chemical (pesticide residue and nutrient concentration) data as well as data on environmental parameters (e.g., conductivity and habitat type) for 111 samples. The dataset was analyzed using generalized linear models, permutational multivariate analysis of variance, and partial redundancy analysis to understand the relationships among benthic invertebrate communities, agricultural stressors, and environmental parameters. The factor that best explained structural community composition was habitat type. Pesticide and nutrient concentrations were of minor importance in our analysis. Because of the ubiquitous and long-term occurrence of the agricultural stressors, the structural indices used in benthic invertebrate biomonitoring were not sufficiently sensitive to provide information on the main environmental variables that shape macroinvertebrate communities.

长期以来，小型水体在生物多样性与生态系统过程中的重要性，在科研与政策制定领域始终未得到足够重视。然而，得益于其异质性特征与多样的生境结构，这类小型水体乃是众多水生与陆生生物的生物多样性热点区域——此前针对轮虫、水鸟及两栖类的相关研究已证实了这一点。目前针对小型水体内底栖无脊椎动物（benthic invertebrate）群落结构的研究仍较为匮乏。为此，我们在德国东北部低地的农业景观区域内，针对这类群落结构开展了全面的野外调研。本研究共获取了111份样本的数据集，涵盖生物学（类群丰度）、化学（农药残留与营养盐浓度）数据，以及电导率、生境类型等环境参数数据。本研究采用广义线性模型（Generalized Linear Models）、置换多元方差分析（Permutational Multivariate Analysis of Variance）与偏冗余分析（Partial Redundancy Analysis）对该数据集进行分析，以厘清底栖无脊椎动物群落、农业胁迫因子与环境参数三者间的关联。在所有影响因子中，生境类型对群落结构组成的解释力最强。农药残留与营养盐浓度在本研究的分析中仅发挥次要作用。由于农业胁迫因子普遍且长期存在，当前用于底栖无脊椎动物生物监测的结构类指标，其灵敏度不足以揭示塑造大型无脊椎动物（macroinvertebrate）群落的核心环境变量。