Forest bees are replaced in agricultural and urban landscapes by native species with different phenologies and life‐history traits
Bibliographic record
Abstract
Abstract Anthropogenic landscapes are associated with biodiversity loss and large shifts in species composition and traits. These changes predict the identities of winners and losers of future global change, and also reveal which environmental variables drive a taxon's response to land use change. We explored how the biodiversity of native bee species changes across forested, agricultural, and urban landscapes. We collected bee community data from 36 sites across a 75,000 km 2 region, and analyzed bee abundance, species richness, composition, and life‐history traits. Season‐long bee abundance and richness were not detectably different between natural and anthropogenic landscapes, but community phenologies differed strongly, with an early spring peak followed by decline in forests, and a more extended summer season in agricultural and urban habitats. Bee community composition differed significantly between all three land use types, as did phylogenetic composition. Anthropogenic land use had negative effects on the persistence of several life‐history strategies, including early spring flight season and brood parasitism, which may indicate adaptation to conditions in forest habitat. Overall, anthropogenic communities are not diminished subsets of contemporary natural communities. Rather, forest species do not persist in anthropogenic habitats, but are replaced by different native species and phylogenetic lineages preadapted to open habitats. Characterizing compositional and functional differences is crucial for understanding land use as a global change driver across large regional scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".