Biodiversity mediates top–down control in eelgrass ecosystems: a global comparative‐experimental approach
Bibliographic record
Abstract
Nutrient pollution and reduced grazing each can stimulate algal blooms as shown by numerous experiments. But because experiments rarely incorporate natural variation in environmental factors and biodiversity, conditions determining the relative strength of bottom-up and top-down forcing remain unresolved. We factorially added nutrients and reduced grazing at 15 sites across the range of the marine foundation species eelgrass (Zostera marina) to quantify how top-down and bottom-up control interact with natural gradients in biodiversity and environmental forcing. Experiments confirmed modest top-down control of algae, whereas fertilisation had no general effect. Unexpectedly, grazer and algal biomass were better predicted by cross-site variation in grazer and eelgrass diversity than by global environmental gradients. Moreover, these large-scale patterns corresponded strikingly with prior small-scale experiments. Our results link global and local evidence that biodiversity and top-down control strongly influence functioning of threatened seagrass ecosystems, and suggest that biodiversity is comparably important to global change stressors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".