The importance and use of taxon sampling curves for comparative biodiversity research with forest arthropod assemblages
Bibliographic record
Abstract
Abstract For over three decades, the importance of taxon sampling curves for comparative biodiversity studies has been repeatedly stated. However, many entomologists (both within Canada and worldwide) continue to publish studies without standardizing their data to take sampling effort into account. We present a case study to illustrate the importance of such standardization, using the collection of spiders (Araneae) by pitfall traps as model data. Data were analyzed using rarefaction to represent one example of a taxon sampling curve, and by a variety of traditional diversity indices to describe alpha diversity. Raw species richness and single-index diversity measures (Shannon–Wiener, Simpson's, and Fisher's α) provided contradictory results. Rarefied species richness standardized to the number of individuals collected enabled more accurate comparisons of diversity and revealed when sampling was insufficient. Focusing on arthropods occurring in forested ecosystems, we also examined the use of taxon sampling curves in current literature by reviewing 133 published articles from 14 journals. Only 26% of the published articles in our review used a taxon sampling curve, and raw species richness and the Shannon–Wiener index of diversity were the most commonly used estimates. There is clearly a need to modify how alpha diversity is measured and compared for arthropod biodiversity studies. We recommend the abandonment of both raw species richness and single-index measures of diversity, and reiterate the need to use rarefaction or a related technique that allows for meaningful comparisons of species richness while taking into account sampling effort.
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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 | 0.192 | 0.375 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".