Biodiversity‐ecosystem function research: Insights gained from streams
Bibliographic record
Abstract
Abstract In this review, we address the extent to which stream ecology has contributed to biodiversity‐ecosystem function (BEF) theory and empirical testing. BEF research first targeted the implications of the ongoing loss of biodiversity for ecosystems and humans. Terrestrial ecology has played a leading role in this field, whereas the contribution of riverine science to the debate has been more limited. Nevertheless, a considerable merit of stream ecology has been to consider a wide range of ecological groups (riparian litter producers, aquatic micro‐fungi, macroinvertebrates, and fishes). Through a meta‐analysis of these unique data, we show that the relative importance of species numberversusassemblage composition increases as we go towards higher trophic levels. Whether this pattern is general or specific to stream ecosystems needs to be evaluated through cross‐ecosystem comparisons looking more closely at mechanistic processes. It is evident from stream studies that trophic and non‐trophic (e.g. facilitation) interactions govern the functional consequences of biodiversity. These studies also indicate that richness‐function relationships are altered by a multitude of factors, such as evenness, non‐taxonomic diversity (genetic/phenotypic diversity), species extinction order, the environmental context, as well as experimental setups. This review highlights the relevance of stream ecology to BEF research. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".