Mixed support for the idea that lower elevation animals are better competitors than their upper elevation relatives
Bibliographic record
Abstract
Abstract What factors set species’ range edges? One general hypothesis, often attributed to Darwin and MacArthur, is that interspecific competition prevents species from inhabiting the warmest portions along geographic gradients (i.e., low latitudes or low elevations). A prediction arising from the Darwin-MacArthur hypothesis is that lower elevation species are better competitors than are related upper elevation species. I tested this prediction by conducting a meta-analysis of studies that have measured behavioral competition between related species along elevational gradients. I found that (1) interspecific aggression appears to be a reliable indicator of interspecific competition; (2) as predicted, lower elevation species showed stronger interspecific aggression, but only for tropical species-pairs tested with playback experiments (nearly all songbirds); (3) for a broader range of taxa where authors directly observed behavioral interactions, upper elevation species showed stronger interspecific aggression; and (4) in general, larger species showed greater interspecific aggression. One potential explanation for why upper elevation species often show more interspecific aggression is that they tend to be larger (Bergmann’s rule; larger body sizes in colder environments). Supporting this possibility, tropical species tested with playback experiments, which do not follow Bergmann’s rule, were the only group that matched predictions arising from the Darwin-MacArthur hypothesis. Hence, available evidence does not consistently support the longstanding hypothesis that relative range position predicts the outcome of interspecific competition. Instead, body size is a better predictor of behavioral competition. Last, I consider these results in the context of the hypothesis that behavioral interactions may impact rates of upslope range shifts associated with recent warming.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".