According to metacommunity theories, the structure of natural communities is the result of both environmental filtering and spatial processes, with their relative importance depending on factors including local habitat characteristics, functional features of organisms, and the spatial scale considered. However, few studies have explored environmental and spatial processes in riverine systems at local scales, explicitly incorporating spatial coordinates into multi-taxa distribution models. To address this gap, we conducted a small-scale study to discriminate between abiotic and biotic factors affecting the distribution of aquatic macroinvertebrates, applying metacommunity concepts. We studied a mountain section in each of three perennial streams within the Po River Basin (northern Italy). We sampled macroinvertebrates both in summer and winter, using specific in situ 50-point random sampling grids. Environmental factors, including benthic organic matter (BOM), flow velocity, water depth, and substrate were recorded together with spatial coordinates for each sampling point. The relationships between community metrics (taxon richness, abundance, biomass, biomass–abundance ratio, and functional feeding groups) and explanatory variables (environmental and spatial) were assessed using generalised additive models. The influence of the explanatory variables on community structure was analysed with joint species distribution models. Environmental variables—primarily BOM—were the main drivers affecting community metrics, whereas the effects of spatial variables varied among metrics, streams, and seasons. During summer, community structure was strongly affected by BOM and spatial position within the riverbed, the latter probably being a proxy for mass effects mediated by biotic and stochastic processes. In contrast, community structure was mainly shaped by hydraulic variables in winter. Using macroinvertebrate communities as a model group, our results demonstrate that metacommunity concepts can explain small-scale variability in community structure. We found that both environmental filtering and biotic processes shape local communities, with the strength of these drivers depending on the season. These insights provide baseline knowledge that informs our understanding of ecological responses to environmental variability in contexts including restoration ecology, habitat suitability modelling, and biomonitoring.
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