Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in Southwestern France

Artificial neural networks were used to quantify the distribution of macroinvertebrate functional feeding groups (FFGs) in relation to physical variables and to land-cover in the Adour–Garonne stream system (SW France; 116,000 km²). The relative abundances of 5 FFGs were calculated from macroinvertebrate data recorded at 165 sampling sites. Each site was characterized using 5 physical variables (elevation, stream order, stream width, distance from the source, slope) and 3 land-cover variables (% forested, % urban areas, % agricultural areas). The sites were first classified using the Self-Organizing Map algorithm (SOM), according to the physical and land-cover variables. Two major clusters of sites corresponded to anthropogenically modified and natural areas, respectively. Anthropogenically modified areas were clearly divided into agricultural and urban landscapes. Each major cluster was divided into 3–4 subsets of sites according to a topographic gradient of physical variables. To examine the variability of the communities, FFG proportions at the 165 sites were examined on the SOM trained with physical and land-cover variables. When the riverine landscape was natural, FFG patterns responded to the upstream–downstream gradient in physical variables. When the landscape was altered by agriculture or urbanization, the effects of land-cover on FFGs overcame the influence of the physical variables. The categorization of the landscape into forested, agricultural, and urban areas was relevant to detect changes in FFG patterns. In light of increasing development along riparian zones, the use of SOMs to detect responses of FFGs to landscape alterations at regional scales exemplifies an effective technique for assessing river health based on ecological indicator groups.