Spatial patterning of benthic macroinvertebrate communities using Geo-self-organizing map (Geo-SOM): A case study in the Nakdong River, South Korea

Abstract

Characterizing community responses to environmental disturbances is difficult because of the complexity of heterogeneous ecosystems. A geographical self-organizing map (Geo-SOM) was applied to present the spatial distribution patterns of benthic communities in a river. The benthic macroinvertebrate communities were collected in the mainstream of the Nakdong River in South Korea. Geo-SOM is a machine learning technique that extracts spatial patterns of given data across spatial weight k values (0–5), which control the vicinity of the map, to extract geographical information effectively. In the results, clusters were formed mainly according to the topography on a large scale and anthropogenic impacts on a small-scale showing consistency in spatial patterning for benthic communities in the gradient across different degrees of spatial weight. Geo-SOM provided both comprehensive and detailed views for presenting species-space relationships. Corresponding to the decrease in k value (more weight in geographical information), we accumulated data variations to present a comprehensive view of spatial species distributions. Overall, correlations between species were more associated with latitude rather than longitude. The feasibility of spatial clustering was also demonstrated with the effective differentiation of community indices. Community indices were effectively differentiated into clusters in the Geo-SOM. Finally, Geo-SOM is a useful tool for extracting the spatial distribution patterns of communities in a comprehensible manner for the monitoring and management of communities in aquatic ecosystems.