Winners and Losers Among European Arthropods over the Last Half Century of Global Change
Shirey, V. M.; Branco, V. V.; Gillett, C. P. D. T.; Goula, M.; Hartung, V.; Hochkirch, A.; Kaila, L. J.; Merrien, T.; Milicic, M.; Paukkunen, J.; Tarasov, S.; Yrjola, V.; Guzman, L. M.; Cardoso, P.
Show abstract
Widespread concern over arthropod declines has raised questions about how global change is reshaping biodiversity, yet large-scale responses within the group remain poorly understood. Here, we analyse 50 years (1970-2019) of European arthropod occurrence data to quantify distributional changes across more than 700 species within 11 taxonomic groups. Using occupancy-detection models that account for imperfect detection, we compare time-only and environmentally mediated trends, including changes to temperature, precipitation, and agricultural and urban land cover. We find no universal signal of decline; responses are highly heterogeneous, revealing distinct winners and losers both within and among groups. Climate variables, particularly temperature and precipitation, are the strongest predictors of change, while agricultural intensification is broadly negative and urbanisation produces group-specific effects. Trait analyses indicate that warm- and dry-affiliated species and larger-bodied arthropods generally fare better. These results show that recent European arthropod change is dominated by redistribution rather than uniform collapse, with important implications for conservation and monitoring under ongoing global change.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Parallel evolution of urban-rural clines in melanism in a widespread mammal 95%
- Treeline ecotones shape the distribution of avian species richness and functional diversity in south temperate mountains 94%
- Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.