Roles of spatial scale in quantifying stock–recruitment relationships for American lobsters in the inshore Gulf of Maine
Bibliographic record
Abstract
It is well known in ecological studies that the choice of spatial scale can influence the possibility of detecting ecological patterns and the type of patterns observed. However, this has rarely been evaluated for fish stock–recruitment (SR) dynamics. Inappropriate scales may result in failure to identify possible SR relationships, especially for species with complicated life history and stock structure and locally generated recruitment. Using American lobster (Homarus americanus) in the Gulf of Maine (GOM) as an example, we tested the hypotheses that the SR relationship is detectable only at certain spatial scales and the functional SR relationships vary with spatial scales. We estimated the SR relationship separately for American lobster in the eastern and western GOM, which have strongly differing oceanographic conditions that may result in different suitable spatial scale and SR dynamics for lobster. We analyzed data of 11 different spatial scales using a Bayesian method. The model fit and performances in the posterior predictive assessment for the SR models were convexly related to the spatial scales. The functional SR relationships differed for different spatial scale. The SR parameter estimates are negatively or concavely related to the spatial scale. The best model was found at medium spatial scale for both the eastern and western GOM, and the scale differed between the eastern and western GOM, suggesting that optimal spatial scale might be process-related. We demonstrated that the choice of spatial scale directly affected the possibility of identifying the SR relationship, the estimation of SR parameters, the type of SR relationships, and the predictive abilities of the SR models.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".