Environmental filtering explains a U‐shape latitudinal pattern in regional β‐deviation for eastern North American trees
Bibliographic record
Abstract
The underlying drivers of β-diversity along latitudinal gradients have been unclear. Previous studies have focused on β-diversities calculated at a local scale and shed limited light on regional β-diversity. We tested the much-debated effects of range size vs. environmental filtering on the β-gradient using data from the US Forest Inventory Analysis Program. We showed that the drivers of the β-gradient were scale dependent. At the local scale species spatial patterns contributed little to the β-gradient, whereas at the regional scale spatial patterns dominated the gradient and a U-shape latitudinal relationship for the standardised β-diversity deviation was revealed. The relationship can be explained by spatial variation in climate and soil texture, thus supporting the environmental filtering hypothesis. But it is inconsistent with Rapoport's rule about the effect of range size on β-gradient. These results resolve the debate on whether species spatial distributions contribute to β-gradient and attest the importance of environmental filtering in determining regional β-diversity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".