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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.005 | 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".