Reconciling topographic and climatic effects on widespread and range‐restricted species richness
Bibliographic record
Abstract
ABSTRACT Aim To identify the reasons behind differing geographical species richness patterns of range‐restricted and widespread species. Location The Western Hemisphere. Methods We used regression to determine the strongest environmental predictors of richness for widespread and range‐restricted mammal species in 10,000 km2 quadrats in the continental Americas. We then used range‐placement models to predict the expected correlation between range‐restricted and widespread species richness were they to be determined by identical, random, or contrasting environmental factors. Finally, to determine the reasons underlying deviations from these predictions, we divided the Americas into 5% quantiles based on temperature and topographic heterogeneity and correlated richness of these two assemblages across quantiles – an approach that avoids constraints on statistical testing imposed by low potential for range overlap among range‐restricted species. Results Minimum annual temperature was the strongest predictor of widespread species richness while topographic heterogeneity was the best, although weak, predictor of range‐restricted species richness in conventional regression analysis. Our models revealed that the observed correlation between range‐restricted and widespread species richness was similar to what would be observed if both range‐restricted and widespread species richness were determined by temperature. Patterns of range‐restricted and widespread species richness were highly correlated across temperature quantiles, but range‐restricted species uniquely showed an increasing pattern across heterogeneity quantiles. Main conclusions Species richness gradients among range‐restricted species differ from those of widespread species, but not as extensively or for the reasons reported previously. Instead, these assemblages appear to share some but not all underlying environmental determinants of species richness. Our new approach to examining species richness patterns reveals that range‐restricted and widespread species richnesses share a common response to temperature that conventional analyses have not previously revealed. However, topographic heterogeneity has assemblage‐specific effects on range‐restricted species.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".