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Abstract Background Recent studies indicate a partial recovery of European stream macroinvertebrate diversity. However, the key determinants shaping the overall community trends are only partly explored, owing to insufficient long-term environmental data collected in parallel with community responses. We investigate long-term trends in stream macroinvertebrate communities (i.e., taxonomic and trait composition and metrics), and explore their relationships to diverse environmental drivers (i.e., land-use, runoff, water temperature, and in-stream chemicals). We use macroinvertebrate data collected annually in spring and summer between 2007 and 2021 at four sampling sites within the Rhine-Main-Observatory Long-Term Ecological Research site. These sampling sites encompass a gradient from less-disturbed to disturbed conditions. Results Over time, shifts in taxonomic and trait composition and metrics indicated an improvement in environmental conditions. Long-term trends of biological trait metrics mirrored those for taxonomic metrics; for example, increases over time in taxonomic richness were paralleled by increases in functional richness and functional dispersion. Meanwhile, trends of ecological trait metrics were particularly driven by changes in environmental drivers. Land-use, water temperature, and runoff explained around 20% of the overall variance in long-term trends of macroinvertebrate communities. Water temperature and land-use played relatively equal roles in shaping taxonomic and trait composition and metric responses in spring, while water temperature emerged as the most influential driver in summer. However, when incorporating long-term chemical data as a more direct measurement of changes in land-use, the overall variance explained in macroinvertebrate community trends increased to c.a. 50% in both seasons. Conclusions Examining more relevant driver variables beyond land-use and climate improves insights into why biodiversity exhibits long-term trends. We call for an increase in initiatives to link biodiversity monitoring with parallel sampling of relevant environmental drivers. |
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