Estimating Diversity in Unsampled Habitats of a Biogeographical Province
Bibliographic record
Abstract
Abstract: Estimating the number of species in a biogeographical province can be problematic. A number of methods have been developed to overcome sample‐size limits within a single habitat. We evaluated six of these methods to see whether they could also compensate for incomplete habitat samples. We applied them to the butterfly species of the 110 ecoregions of Canada and the United States. Two of the methods use the frequency of species that occur in a few of the sampled ecoregions. These two methods did not work. The other four methods estimate the asymptote of the species‐accumulation curve ( the graph of “number of species in a set of samples” versus “number of species occurrences in those samples” ). The asymptote of this curve is the actual number of species in the system. Three of these extrapolation estimators produced good estimates of total diversity even when limited to 10% of the ecoregions. Good estimates depend on sampling ecoregions that are hyperdispersed in space. Clustered sampling designs ruin the usefulness of the three successful methods. To ascertain their generality, our results must be duplicated at other scales and for other taxa and in other provinces.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".