<scp>rarestR</scp> : An R Package Using Rarefaction Metrics to Estimate α‐ and β‐Diversity for Incomplete Samples
Bibliographic record
Abstract
ABSTRACT Aim Species abundance data is commonly used to study biodiversity patterns. In this context, comparing α‐ and β‐diversity across incomplete samples can lead to biases. Therefore, it is essential to employ methods that enable standardised and accurate comparisons of α‐ and β‐diversity across varying sample sizes. In addition, biodiversity studies also often require robust estimates of the total number of species within a community and the number of species shared by two communities. Innovation Rarefaction methods are commonly used to calculate α‐diversity for standardised sample sizes, and they can also serve as the basis for calculating β‐diversity. In this application note, we present rarestR , a new R package designed for calculating abundance‐based α‐ and β‐diversity measures for inconsistent samples using rarefaction‐based metrics. The package also includes parametric extrapolation techniques to estimate the total expected number of species within a community, as well as the total number of species shared between two communities. Additionally, rarestR provides visualisation tools for curve‐fitting associated with these estimators. Main Conclusions Overall, the rarestR package is a valuable tool for comparing α‐ and β‐diversity values among incomplete samples, such as those involving highly mobile or species‐rich taxa. In addition, our species estimators offer a complementary approach to non‐parametric methods, including the Chao series of estimators.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.045 |
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".