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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".