Effects of neutrality, geometric constraints, climate, and habitat quality on species richness and composition of <scp>A</scp>tlantic <scp>F</scp>orest small‐mammals
Bibliographic record
Abstract
Abstract Aim To compare the fit of models of climate, habitat quality, neutral processes, and geometric constraints to species richness and composition of small mammal assemblages. Location The South American Atlantic Forest biome. Methods Using neutral models and mid‐domain effect models, we simulated species spread in a spatially explicit array of grid cells representing the Atlantic Forest domain. We compared empirical patterns of species richness and composition with predictions of the neutral and mid‐domain effect models. We also modeled individual species responses to climatic conditions and forest integrity, a measure of habitat quality. Results Habitat quality was the single best predictor of local species richness (α‐diversity), but was a poor predictor of local species composition and of the decay in species similarity with distance (β‐diversity). The neutral and mid‐domain models generated very similar predictions, and were better predictors of species composition than of species richness. Climate variables were also strongly associated with overall species composition, but not with species richness. Main Conclusions The species richness of small‐mammal assemblages in the Atlantic Forest is best explained by variation in habitat quality. In contrast, the composition of small‐mammal assemblages is best explained by models of limited dispersal (neutral and mid‐domain) and effects of climate on local species composition. Collectively, these results suggest that regional patterns of species richness may be uncoupled from patterns of species composition. Both species richness and composition should be considered when evaluating the predictions of neutral and mid‐domain effect models, and of correlations of community structure with climatic or habitat variables.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".