Environmental variability alters the relationship between richness and variability of community abundances in aquatic rock pool microcosms
Bibliographic record
Abstract
The effect of species richness on the temporal variability of communities and populations continues to inspire investigations and debates; however, few empirical studies have addressed the crucial question of how the relationship between richness and variability changes along a gradient of environmental variability. We determined the relationship between species richness (S) and variability (coefficient of variation, CV) for both community and population abundances of aquatic invertebrates inhabiting 49 tropical coastal rock pools that differ in environmental variability. When all pools are considered, results support the hypothesis that variability in community abundance decreases with increases in species richness. In contrast, abundances of individual populations in more speciose communities vary no more than in species-poor communities. Richness-community variability relationships were detected in rock pools with low environmental variability (as measured by a multivariate index of environmental variability) and in rock pools with low variability in specific physicochemical variables, i.e., temperature, salinity, dissolved oxygen, and pH. The presence of richness-variability relationships in the less environmentally variable rock pools and not in the more variable rock pools suggests that environmental variability may play an important role in modulating richness-variability relationships.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".