Species richness–variability relationships in multi‐trophic aquatic microcosms
Bibliographic record
Abstract
While species loss may affect the temporal variability of populations and communities differently in multi‐ versus single‐trophic level communities, the nature of these differences are poorly understood. Here, we report on an experiment where we manipulated species richness of multi‐trophic rock pool invertebrate communities to determine the effects of species richness, S, on the temporal variability of communities, populations, and individual species. As in single‐trophic level studies, temporal variability in community abundance decreased with increasing species richness. However, in contrast to most studies in single‐trophic level systems, temporal variability of populations also decreased as species richness increased. Furthermore, the variability of the constituent populations strongly correlated with variability of community abundance suggesting that, in rock pools, S affects community variability through its stabilizing effect on component populations. Our results suggest that species loss may affect population and community variability differently in multi‐trophic versus single trophic level communities. If this is so, then the mechanisms proposed to underlie the effects of S on community variability in single‐trophic communities may have to be supplemented by those that describe contributions to population stability in order to fully describe the patterns observed in multi‐trophic communities.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".