You don’t belong here: explaining the excess of rare species in terms of habitat, space and time
Bibliographic record
Abstract
Ecological communities are composed of a few common and several rare species. Many studies have evaluated the shape of abundance distribution curves, but few studies have assessed the causes of rarity. Using a dataset of stream macroinvertebrates, we investigated whether the excess of rare species in three focal communities of stones in riffles were common 1) in other habitats at the same stream site and period of sampling (environment), 2) in other stream sites in the same habitat and period of sampling (space), and 3) in other years in the same stream site and habitat (time). We observed that around 28% of the rare species were common in other habitats (environment), stream sites (space) or years (time). Among the three factors, rarity was mostly explained by habitat type, whereas a significant portion of the rare species in riffles were common in pools, submerged roots of terrestrial plants or in partially submerged moss patches. This result suggests that the presence in non‐optimum habitat is a strong determinant of the rarity observed in natural communities and most rare species are due to sampling artifacts or accidentally sampled transient species.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".