Interactions between small pelagic fish and young cod across the North Atlantic
Bibliographic record
Abstract
Species interactions that play out over large spatial scales are difficult to observe, particularly in the oceans. The current lack of empirical evidence for biologically meaningful interaction parameters likely delays the application of holistic management procedures. Here we estimate interactions during the early life history of fish across regions. We present separate and hierarchical Bayesian models that estimate the direction and strength of interactions between Atlantic cod and dominant pelagic fishes across much of their range in the North Atlantic. We test the hypothesis that small pelagic fish may reduce survival of cod at early life stages, and thereby contribute to the delayed recovery of depleted cod populations. Significant regional variation exists between cod recruitment and Atlantic herring abundance with eight of 14 regions displaying a negative relationship, four regions displaying no relationship, and a positive relationship observed in two regions. In contrast, most regions where Atlantic mackerel co-occurs showed no relationship with cod recruitment, with the possible exception of Gulf of St. Lawrence and Celtic Sea regions. Regions with sprat or capelin as dominant pelagics also displayed weak or no relationship, although the probability of a negative interaction with sprat increased when time series autocorrelation was accounted for. Overall, the interaction between herring and young cod was found to be negative with 94% probability, while the probability of negative interactions with mackerel was only 68%. Our findings suggest that the strength of predation or competition effects on young cod varies among small pelagic species but appears consistently for Atlantic herring; this effect may need to be considered in recovery trajectories for depleted cod populations. The methods introduced here are applicable in the investigation of species interactions from time series data collected across different study systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".