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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".