Testing clustering strategies for metabarcoding-based investigation of community-environment interactions

The degradation of freshwater ecosystems has become a common ecological and environmental problem globally. Owing to the complexity of biological communities, there remain tremendous technical challenges for investigating influence of environmental stressors (e.g., chemical pollution) on biological communities. High-throughput sequencing-based metabarcoding provides a powerful tool to reveal complex interactions between environments and biological communities. Among many technical issues, the clustering strategies for operational taxonomic units (OTUs) which are crucial for assessing biodiversity of communities, may affect final conclusions. Here, we used zooplankton communities along an environmental pollution gradient in the Chaobai River in Northern China to test different clustering strategies, including nonclustering and clustering with varied thresholds. Our results showed that though the number of OTUs estimated by nonclustering strategies and clustering strategies with divergence thresholds of 99%-97% largely varied, they were able to identify the same set of significant environmental and spatial variables responsible for geographical distributions of zooplankton communities. In addition, the ecological conclusions obtained by clustering thresholds of 99%-97% were consistent with nonclustering strategies, where for all eight clustering scenarios we detected that species sorting predicted by environmental variables overrode dispersal as the dominant factor in structuring zooplankton communities. However, clustering with the divergence thresholds of <95% affected the environmental and spatial variables identified. We conclude that both newly developed nonclustering methods and traditional clustering methods with divergence thresholds ≥97% were reliable to reveal mechanisms of complex community-environment interactions, although different clustering strategies could lead to largely varied biodiversity estimates such as those for α-diversity.