Using rarefaction to isolate the effects of patch size and sampling effort on beta diversity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Beta diversity describes how species composition varies across space and through time. Current estimators of beta diversity typically ignore the effects of within‐patch sample size, determined jointly by local abundance and sampling effort. Many ecological processes such as immigration, predation, or nutrient limitation affect abundance and asymptotic beta diversity concurrently; thus, existing metrics may confound changes in asymptotic beta diversity with changes that result from differences in abundance or sampling. Results from a stochastic simulation model illustrate how decreasing within‐patch sample size may either increase or decrease observed beta diversity, depending on the type of metric, sample size, and community properties; these changes are easy to understand, and predict, by considering the effects of sampling on variance. A modified, patch‐level form of rarefaction controls for variation in within‐patch sample size; two case studies illustrate the utility of this approach. Studies seeking a mechanistic link between ecological process and beta diversity will continue to benefit from explicit consideration of sampling effects.
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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 | 0.000 | 0.000 |
| 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 it