Strong Top-Down Control in Southern California Kelp Forest Ecosystems
Bibliographic record
Abstract
Global-scale changes in anthropogenic nutrient input into marine ecosystems via terrestrial runoff, coupled with widespread predator removal via fishing, have created greater urgency for understanding the relative role of top-down versus bottom-up control of food web dynamics. Yet recent large-scale studies of community regulation in marine ecosystems have shown dramatically different results that leave this issue largely unresolved. We combined a multiyear, large-scale data set of species abundances for 46 species in kelp forests from the California Channel Islands with satellite-derived primary production and found that top-down control explains 7- to 10-fold more of the variance in abundance of bottom and mid-trophic levels than does bottom-up control. This top-down control was propagated via a variety of species-level direct and indirect responses to predator abundance. Management of top-down influences such as fishing may be more important in coastal marine ecosystems, particularly in kelp forest systems, than is commonly thought.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".