‘Structured' beta diversity increases with climatic productivity in a classic dataset
Bibliographic record
Abstract
Despite a surge of interest in the measurement of beta diversity, there remain only a modest number of well-supported explanations for variation in naturally occurring levels of beta diversity. Among the few emerging generalizations is that beta diversity tends to increase with productivity; it remains to be determined whether the mechanism(s) involves habitat specialization or random factors in community assembly. We examined this question using the classic dataset of Whittaker (1960), who first defined beta diversity in a study of plant communities along multiple abiotic gradients related to productivity. With increasing productivity along climatic gradients (elevation or topography), though not a soil fertility gradient, we found increases in the levels of ‘structured' beta diversity, i.e., the turnover associated with each of the other gradients, consistent with greater habitat specialization. ‘Unstructured' beta diversity, i.e., the among-site variation not associated with gradients, varied idiosyncratically among different combinations of environmental factors. These results were robust to the use of either presence-absence or relative abundance data. We conclude that habitat specialization along gradients may tend to increase either with productivity itself, or with regional (gamma) diversity, which tends to be higher in more productive climates and conceivably ‘spills over' in ecological or evolutionary time to enhance structured beta 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".