Comparative analyses of surplus production dynamics of functional feeding groups across 12 northern hemisphere marine ecosystems
Bibliographic record
Abstract
Progress on ecosystem approaches to fisheries management requires comparative studies with standardized methods that incorporate readily available data.This precludes complex ecosystem models in favor of simpler models such as surplus production models.Surplus production models for individual species can provide estimates of common biological reference points such as maximum sustainable yield (MSY) and the corresponding stock biomass level (B MSY ).For ecosystem approaches, summing multiple single-species surplus production models to estimate the ecosystem MSY ignores potential biological and fishery interactions among species.Improved estimates of ecosystem-level MSY can be obtained by aggregating species, thereby accounting for known interactions among species.Here, we fit surplus production models to 3 different types of aggregations for 12 northern hemisphere marine ecosystems.Aggregations were based on habitat (benthic/pelagic), foraging guild (planktivore/zoopivore/benthivore/piscivore) and size class (small/medium/large).The objectives of this work were to explore, compare and contrast model outputs across the various types of aggregations and among ecosystems.We found that regardless of the type of aggregation, aggregate production never exceeded 6 t km -2 and was generally less than 3 t km -2 .Patterns of production varied among ecosystems with no particular pattern with respect to ocean basin, latitude or component species.Aggregated surplus production models can provide biological reference points that are familiar to fishery managers and can be used to set overall removals with respect to aggregate group as long as less productive stocks are protected.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".