Tropical bee species abundance differs within a narrow elevational gradient
Bibliographic record
Abstract
Insect pollination is among the most essential ecosystem services for humanity. Globally, bees are the most effective pollinators, and tropical bees are also important for maintaining tropical biodiversity. Despite their invaluable pollination service, basic distributional patterns of tropical bees along elevation gradients are globally scarce. Here, we surveyed bees at 100 m elevation intervals from 800 to 1100 m elevation in Costa Rica to test if bee abundance, community composition and crop visitor assemblages differed by elevation. We found that 18 of 24 bee species spanning three tribes that represented the most abundantly collected bee species showed abundance differences by elevation, even within this narrow elevational gradient. Bee assemblages at the two crop species tested, avocado and squash, showed community dissimilarity between high and low elevations, and elevation was a significant factor in explaining bee community composition along the gradient. Stingless bees (Tribe Meliponini) were important visitors to both crop species, but there was a more diverse assemblage of bees visiting avocado compared to squash. Our findings suggest that successful conservation of tropical montane bee communities and pollination services will require knowledge of which elevations support the highest numbers of each species, rather than species full altitudinal ranges.
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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 | 0.000 |
| 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) | 0.001 | 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".