Land Use Change Consistently Reduces α‐ But Not β‐ and γ‐Diversity of Bees
Bibliographic record
Abstract
Land use change threatens global biodiversity and compromises ecosystem functions, including pollination and food production. Reduced taxonomic α-diversity is often reported under land use change, yet the impacts could be different at larger spatial scales (i.e., γ-diversity), either due to reduced β-diversity amplifying diversity loss or increased β-diversity dampening diversity loss. Additionally, studies often focus on taxonomic diversity, while other important biodiversity components, including phylogenetic diversity, can exhibit differential responses. Here, we evaluated how agricultural and urban land use alters the taxonomic and phylogenetic α-, β-, and γ-diversity of an important pollinator taxon-bees. Using a multicontinental dataset of 3117 bee assemblages from 157 studies, we found that taxonomic α-diversity was reduced by 16%-18% in both agricultural and urban habitats relative to natural habitats. Phylogenetic α-diversity was decreased by 11%-12% in agricultural and urban habitats. Compared with natural habitats, taxonomic and phylogenetic β-diversity increased by 11% and 6% in urban habitats, respectively, but exhibited no systematic change in agricultural habitats. We detected a 22% decline in taxonomic γ-diversity and a 17% decline in phylogenetic γ-diversity in agricultural habitats, but γ-diversity of urban habitats was not significantly different from natural habitats. These findings highlight the threat of agricultural expansions to large-scale bee diversity due to systematic γ-diversity decline. In addition, while both urbanization and agriculture lead to consistent declines in α-diversity, their impacts on β- or γ-diversity vary, highlighting the need to study the effects of land use change at multiple scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".