Using rarefaction to isolate the effects of patch size and sampling effort on beta diversity
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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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 | 0.032 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".