Niche breadth and range area in North American trees
Bibliographic record
Abstract
Identifying factors affecting species distribution is a longstanding goal in ecology and evolution that is accentuated by our need to anticipate climate change impacts. We sought to test whether any phylogenetic effect can be detected in either the environmental characteristics or range attributes of North American trees, and to explore the existence of a general interspecific pattern in the environmental factors influencing species range size. To do so we tested prevailing hypotheses relating climatic and edaphic characteristics to species range size in the North American arboflora (n = 598), using spatial null models to test for the relevance of observed patterns. We found that interspecific variation in the range area of North American trees is strongly related to the environmental regimes characteristic of the species range. Linear models and phylogenetic regressions involving six environmental characteristics explained 83% of the variance in species range area, and affirmed a positive relationship between niche breadth and range size. Tree species that can tolerate a larger variability in local climatic conditions, deal with harsher edaphic conditions, and weak levels of environmental energy tend to have larger range area; this can account for the greater geographic range of species at higher latitudes, the Rapoport effect. There is a significant phylogenetic signal for both range area and limits in North American trees, and for climatic limits, but not for energy or edaphic characteristics associated with species range. These findings highlight the possibility that species with small geographic ranges may be more sensitive to the effects of climate change.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".