Scaling pairwise β‐diversity and α‐diversity with area
Bibliographic record
Abstract
Abstract Aim The relationship between species richness (α‐diversity) and area is well studied; however, the way in which compositional dissimilarity between pairs of sites (β‐diversity) scales with area has only recently attracted research attention. The aim of this study was to improve the understanding of how both α‐ and β‐diversity scale with area, to illuminate ecological processes structuring the distribution of biodiversity and enable prediction of α‐ and β‐diversity for large regions from much smaller samples. Location We examined both simulated spatial community data and measurements from tropical forest tree plots in P anama. Methods We applied the simulated and measured community data to assess how both α‐ and β‐diversity scale with area. Then we examined how accurately community α‐diversity and pairwise β‐diversity can be extrapolated from small sample areas of different size within each community, using the species–area power relationship. Results For both the simulated and tree plot data, pairwise β‐diversity scaled with area in a corresponding manner to the much more familiar species–area relationship. By altering the attributes of the simulated communities, we found that α‐ and β‐diversity saturated at smaller areas where abundances were more even, species distributions were less aggregated and regional richness was lower. Estimates of α‐ and β‐diversity for a pair of communities generally increased in accuracy with the size of the local sample areas from which extrapolations were made. Main conclusions These analyses suggest that the most robust estimates of α‐ and β‐diversity for a larger area will be obtained by local samples that are greater than 10% the size of that larger area. Our results emphasize the fundamental link in how both α‐ and β‐diversity scale with area, and demonstrate how simple knowledge of these scaling relationships can be used to predict the diversity of larger areas from smaller samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".