Mammal species richness and biogeographic structure at the southern boundaries of the Nearctic region
Bibliographic record
Abstract
Abstract We analyzed whether the spatial variation in mammal species richness reflects the southern boundaries of the Nearctic region as previously established by endemism patterns. Records from 710 mammal species were drawn on a map of North America (from Canada and Alaska to Panama) gridded at 4° latitude-longitude. We evaluated the probable existence of unknown species through three richness estimators (Chao2, ICE, and Jack1), modeled the potential distribution of species, and mapped the predicted pattern of species richness through the number of coexisting potential distributions. The poorest grid cells are in the northern areas, whereas the richest ones are in the southern areas, coinciding with the pattern of collecting points. The average richness of 4° grid cells comprising the Nearctic region was 18 species, and the richest 4° grid cells had 150 species, coinciding with the 26° latitude. From the 406 mammal species of the Nearctic region, 104 are restricted to it, and 305 species situated south of it are not distributed in the region. The map of predicted richness shows the classical latitudinal diversity gradient, with the number of species increasing to the tropics. We conclude that the Nearctic region has a low mammal richness, with a richness pattern corresponding with previously described patterns of endemism, with a boundary situated at 26°–30° latitude.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".