Beyond Rapoport's rule: evaluating range size patterns of New World birds in a two‐dimensional framework
Bibliographic record
Abstract
ABSTRACT Aim To evaluate Rapoport's rule for New World birds in two‐dimensional geographical space. We specifically test for a topography × climate interaction that predicts little difference in range sizes between lowlands and mountains in cold climates, whereas in the tropics, montane species have narrow ranges and lowland species have broad ranges. Location The western hemisphere. Methods We used digitized range maps of breeding birds to generate mean range sizes in grids of 27.5 × 27.5 km and 110 × 110 km across North and South America. We examined the geographical pattern with respect to range in elevation, mean temperature in the coldest month, their interaction, biome size and continental width, using model II analysis of variance, multiple regression and simple correlation. Results In northern latitudes species have broad ranges in both mountainous and flat areas. However, range sizes in the mountains and lowlands diverge southwards, with the most extreme differences in the tropics. Further, there are minimal differences in range sizes across latitudes in lowlands. The smallest mean ranges occur in the tropical Andes. Mean range sizes in north‐central Canada, Central America and Argentina/Chile are also small, reflecting the narrowing of the continents in these areas. The best regression model explained 51% of the variation in mean range size. Main conclusions The two‐dimensional range size pattern indicates that neither winter temperature nor annual variability in temperature strongly influences the distribution of range sizes directly; rather, climate influences bird range sizes indirectly via effects on habitat size. Also, macroclimate interacts with topographic relief across latitudes, generating sharp mesoscale habitat gradients in tropical mountains but not in high latitude mountains or in lowlands at any latitude. Birds respond to these habitat gradients, resulting in ‘latitudinal’ range size gradients in topographically complex landscapes but not in simple landscapes.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".