rarestR: An R package using rarefaction metrics to estimate α-diversity (species richness) and β-diversity (species shared) for incomplete samples
Bibliographic record
Abstract
Abstract Species abundance data is commonly used to study biodiversity patterns. In this context, estimating α- and β-diversity based on incomplete samples can lead to ‘undersampling biases’. It is therefore essential to employ methods that enable accurate comparisons of α- and β-diversity across varying sample sizes. This involves relying on biodiversity measures that are focused on accurately estimating the total number of species within a community, as well as the total number of species shared by two communities. Rarefaction offers such a method, where α-diversity is estimated for standardized sample sizes. Rarefaction methods can also be used as a basis for β-diversity calculations for standardized sample sizes. In this application note, we introduce a new R package, rarestR , designed to estimate abundance-based α- and β-diversity measures for inconsistent samples using rarefaction metrics. Additionally, the package offers parametric extrapolations to estimate the total expected number of species within a single community and the total expected number of species shared between two communities. Furthermore, it provides visualization for the curve fitting associated with these estimators. Overall, the rarestR package is useful in estimating α- and β-diversity values for incomplete samples, for example in studies involving highly mobile or species-rich taxa. These species estimators offer a complementary approach to non-parametric methods, such as 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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 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 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.033 |
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".