Is fisheries production within <scp>L</scp>arge <scp>M</scp>arine <scp>E</scp>cosystems determined by bottom‐up or top‐down forcing?
Bibliographic record
Abstract
Abstract Understanding the mechanisms driving fisheries production is essential if we are to accurately predict changes under climate change and exploit fish stocks in a sustainable manner. Traditionally, studies have sought to distinguish between the two most prominent drivers, ‘bottom‐up’ (resource driven) and ‘top‐down’ (consumer driven); however, this dichotomy is increasingly proving to be artificial as the relative importance of each mechanism has been shown to vary through space and time. Nevertheless, the reason why one predominates over another within a region remains largely unknown. To address this gap in understanding, we identified the dominant driver of commercial landings within 47 ecosystems, encompassing a wide range of biogeochemical conditions and fishing practices to elucidate general patterns. We show that bottom‐up and top‐down effects vary consistently with past fishing pressure and oceanographic conditions; bottom‐up control predominates within productive, overfished regions and top‐down in relatively unproductive and under‐exploited areas. We attribute these findings to differences in the species composition and oceanographic properties of regions, together with variation in fishing practices and (indicative) management effectiveness. Collectively, our analyses suggest that despite the complexity of ecological systems, it is possible to elucidate a number of generalities. Such knowledge could be used to increase the parsimony of ecosystem models and to move a step forward in predicting how the global ocean, particularly fisheries productivity, will respond to climate change.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".