Exploring the proportion of rarity in tropical insects: evaluating hypotheses and variables
Bibliographic record
Abstract
Theoretical models suggest that rare species in a community should be few, but empirical evidence indicates the opposite in insect communities. We reviewed 1170 articles for suitable datasets and selected 100 publications with 140 datasets for our rarity analysis. The objective was to estimate the rarity percentage among insects and whether this value is related to positional, methodological, environmental, or variables intrinsic to the communities. Information was found for eight insect orders, of which Hymenoptera and Coleoptera were the most studied. The authors of 70% of the articles did not discuss hypotheses explaining the observed percentage of rare species. In the remaining, the most discussed hypotheses were undersampling (10%), distribution range (15%), study group phenology (4%), and diffusive rarity (1%). Only two studies tested hypotheses of rarity. In 66 datasets, the proportion of rare species was between 11% and 30%; in 70 datasets, this proportion was higher. The greatest effects on species rarity were sample coverage, abundance, and richness. The study of rarity remains a critical problem in community ecology; this study shows that the issues are not solely based on methodological limitations like undersampling but need deeper conceptual and empirical approaches.
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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 | 0.149 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".