Bottom‐up effects of species diversity on the functioning and stability of food webs
Bibliographic record
Abstract
1. The importance of species diversity for the stability of populations, communities and ecosystem functions is a central question in ecology. 2. Biodiversity experiments have shown that diversity can impact both the average and variability of stocks and rates at these levels of ecological organization in single trophic-level ecosystems. Whether these impacts hold in food webs and across trophic levels is still unclear. 3. We asked whether resource species diversity, community composition and consumer feeding selectivity in planktonic food webs impact the stability of resource or consumer populations, community biomass and ecosystem functions. We also tested the relative importance of resource diversity and community composition. 4. We found that resource diversity negatively affected resource population stability, but had no effect on consumer population stability, regardless of the consumer's feeding selectivity. Resource diversity had positive effects on most ecosystem functions and their stability, including primary production, resource biomass and particulate carbon, nitrogen and phosphorus concentrations. 5. Community composition, however, generally explained more variance in population, community and ecosystem properties than species diversity per se. This result points to the importance of the outcomes of particular species interactions and individual species' effect traits in determining food web properties and stability. 6. Among the stabilizing mechanisms tested, an increase in the average resource community biomass with increasing resource diversity had the greatest positive impact on stability. 7. Our results indicate that resource diversity and composition are generally important for the functioning and stability of whole food webs, but do not have straightforward impacts on consumer populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".