Adaptation and latitudinal gradients in species interactions: nest predation in birds
Bibliographic record
Abstract
Abstract The “biotic interactions” hypothesis—that stronger interspecific interactions in the tropics drive faster evolution and speciation, giving rise to the latitudinal diversity gradient—has inspired many tests of whether certain biotic interactions are indeed stronger in the tropics. However, the possibility that populations have adapted to latitudinal differences in species interactions, blunting effects on evolutionary rates, has been largely ignored. Here we show that mean rates of nest predation experienced by land birds vary minimally with latitude in the Western Hemisphere. This result is surprising because nest predation in birds is a canonical example of a strong tropical biotic interaction. We explain our finding by demonstrating that (1) rates of nest predation are in fact higher in the tropics, but only when controlling for the length of the nesting period, (2) long nesting periods are associated with reduced predation rates, and (3) tropical birds have evolved particularly long nesting periods. We suggest this is a case example of how adaptation to a biotic interaction can alter observed latitudinal gradients in interaction strength, potentially equalizing evolutionary rates among latitudes. More broadly, we advocate for tests of the biotic interactions hypothesis to consider both latitudinal patterns in interaction strength and evolutionary responses to these interactions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".