Species-specific differences in bumblebee worker body size between different elevations: Implications for pollinator community structure under climate change
Bibliographic record
Abstract
Pollinator populations face growing threats from global climate change, particularly in alpine environments with rapidly rising temperatures. Understanding how bumblebees, critical alpine pollinators, respond to these temperature changes is therefore an important goal. Predicting species’ responses to climate change requires several different approaches, one of which is to compare processes at different elevations, which experience different temperature regimes. Bumblebee body size is linked to fitness through its influence on nutritional requirements and foraging capacity. It is also a highly plastic trait that depends on ecological factors such as temperature. Thus, understanding how body size varies at different elevations may help predict bumblebee fitness under climate change. We collected bumblebee workers from five species in a single growing season, at two distinct elevations in the Swiss Alps. Our study aimed to examine whether body size responses differed among species and across functional traits related to foraging and nesting. Larger body size is thought to confer an advantage under cold conditions; we therefore expected greater body size with elevation, but with species-specific relationships. Contrary to our expectation, not all species were larger at high elevations. Specifically, while two species were significantly larger at high elevation, one (Bombus terrestris) was significantly smaller at high elevation, and two showed no size differences with elevation. Additionally, interspecific variation in body size was greater at low elevations. This suggests a divergence of body size with warming, although local factors may also play a role in shaping functional traits.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".