Characterizing macroinvertebrate communities across China: Large-scale implementation of a self-organizing map

Understanding the geographical patterns and divisions of communities is a fundamental step in achieving the sustainable management of ecosystems, especially in deteriorating global and local environments. The idea of geographical division has been applied on all continents but Antarctica, but it has never been rigorously tested for stream ecosystems in China, leaving a gap in knowledge for many basic and applied research questions regarding, for example, diversity patterns, conservation issues or climate change effects. To fill this gap, we aimed to (1) evaluate the geographical divisions of the macroinvertebrate communities in Chinese streams using the self-organizing map (SOM) method and (2) to characterize the distribution patterns in relation to different environmental variables. Macroinvertebrates were collected from 57 relatively clean stream sites covering a south-north gradient along the boundary of the geographic ladder (or altitudinal divide) in China. SOM was used to analyze large-scale biogeographical divisions of the macroinvertebrate communities. The sampling sites were divided into six clusters, distinguishing the samples from northern, central, and southern China. This pattern was also reflected by biotic metrics (abundance, biomass, taxa and sum of Ephemeroptera, Plecoptera, and Trichoptera richness, and diversity). The gradient of environmental variables, particularly water quality variables, was similar between the clusters, with the exceptions of two clusters from southwestern China when considering altitude and one cluster from northern China when considering conductivity and TN. The different clusters from the SOM were associated with indicator species, with clean-water adapted species dominating in southwestern China and pollution tolerant species in northern China. However, there were no significant correlations between environmental variables and biotic metrics. The overall combination of environmental variables and organism data suggests that spatial variation was the main predictor determining the composition of the macroinvertebrate communities on a large-scale, and the trained SOM appeared to be efficient at classifying streams on a broad geographic scale.