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Long-term ecological reorganization of forest carabid beetle assemblages following reductions in sulfur dioxide pollution

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Zenodo2026-01-04 更新2026-05-26 收录
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Long-term industrial pollution has severely impacted forest ecosystems around the globe, yet the trajectories of biological reorganization following pollution reductions remain complex and poorly understood. We analysed a 26-year time series (1989–2015) of epigeic carabid beetles from the Děčín Sandstone Uplands, Czech Republic, a region historically subjected to extreme sulfur dioxide pollutions. Based on 54,359 specimens representing 92 species sampled across seven forest stands, we examined taxonomic, trait-based, and phylogenetic responses to declining SO₂ concentrations with generalized additive models and multivariate analyses. Although SO₂ declined markedly, following the air policy update in 2002, carabid activity density followed a non-linear trajectory rather than returning to pre-impact levels. The year 2002 was used solely as a temporal reference to structure long-term trends, not as a presumed causal driver. We detected pronounced compositional shifts between high-pollution (before-2002) and lower-pollution periods. Elevated SO₂ levels historically filtered assemblages toward large-bodied, low-dispersal, and regionally distributed species, whereas directional changes in trait composition largely ceased following pollution abatement. Despite stable species richness across periods, phylogenetic structure shifted significantly, with stronger clustering after 2002 indicating increasing dominance of closely related lineages. This suggests continued lineage-level environmental filtering and species replacement rather than functional diversification. Overall, declining atmospheric pollution was associated with long-lasting reorganization of species composition and trait and phylogenetic community structure, rather than simple ecological recovery. Our findings underscore the importance of multidimensional biomonitoring frameworks, as phylogenetic and trait-based metrics reveal cryptic recovery dynamics that remain undetected by abundance- or richness-based approaches alone.

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Zenodo
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2026-01-04
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