The functional role of producer diversity in ecosystems
Bibliographic record
Abstract
Over the past several decades, a rapidly expanding field of research known as biodiversity and ecosystem functioning has begun to quantify how the world's biological diversity can, as an independent variable, control ecological processes that are both essential for, and fundamental to, the functioning of ecosystems. Research in this area has often been justified on grounds that (1) loss of biological diversity ranks among the most pronounced changes to the global environment and that (2) reductions in diversity, and corresponding changes in species composition, could alter important services that ecosystems provide to humanity (e.g., food production, pest/disease control, water purification). Here we review over two decades of experiments that have examined how species richness of primary producers influences the suite of ecological processes that are controlled by plants and algae in terrestrial, marine, and freshwater ecosystems. Using formal meta-analyses, we assess the balance of evidence for eight fundamental questions and corresponding hypotheses about the functional role of producer diversity in ecosystems. These include questions about how primary producer diversity influences the efficiency of resource use and biomass production in ecosystems, how primary producer diversity influences the transfer and recycling of biomass to other trophic groups in a food web, and the number of species and spatial /temporal scales at which diversity effects are most apparent. After summarizing the balance of evidence and stating our own confidence in the conclusions, we outline several new questions that must now be addressed if this field is going to evolve into a predictive science that can help conserve and manage ecological processes in ecosystems.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".