Analytical models for <i>β</i> ‐diversity and the power‐law scaling of <i>β</i> ‐deviation
Bibliographic record
Abstract
Abstract β ‐diversity is a primary biodiversity pattern for inferring community assembly. A randomized null model that generates a standardized β ‐deviation has been widely used for this purpose. However, the null model has been much debated and its application is limited to abundance data. Here we derive analytical models for β ‐diversity to address the debate, clarify the interpretation and extend the application to occurrence data. The analytical analyses show unambiguously that the standardized β ‐deviation is a quantification of the effect size of non‐random spatial distribution of species on β ‐diversity for a given species abundance distribution. It robustly scales with sampling effort following a power law with exponent of 0.5. This scaling relationship offers a simple method for comparing β ‐diversity of communities of different sizes. Assuming log‐series distribution for the metacommunity species abundance distribution, our model allows for calculation of the standardized β ‐deviation using occurrence data plus a datum on the total abundance. Our theoretical model justifies and generalizes the use of the β null model for inferring community assembly rules.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".