Relationship between variations of sea bottom temperature and American lobster catch rate off Southwestern Nova Scotia during 2008–2023
Bibliographic record
Abstract
Abstract. American lobsters (Homarus americanus) are an iconic species and are the socioeconomic and cultural mainstay for many communities across Nova Scotia. Describing the changes in population biomass and providing annual stock assessment advice for this species are required for sustainable fisheries. In many areas the best information available for providing this advice comes from commercial fisheries data. Often there is an assumed relationship between fisheries performance (catch per unit effort; CPUE) and available biomass, however several studies indicate that this relationship can be affected by external factors such as sea bottom temperature. Including bottom temperature when developing a standardized CPUE index will potentially address these concerns, however it has proven difficult in the past due to the lack of readily available (near real time) and unbiased bottom temperature data at the spatial and temporal scales required. Here we explore a global ocean reanalysis product of the European Union Copernicus Marine Service with an application to the fishery catch data from Lobster Fishing Area 33 during 2008–2023. Comparison with observational data shows that this reanalysis product provides realistic variations of sea bottom temperatures in this region. Next, a hierarchical generalized linear modelling approach is applied to evaluate the relationship between within-season changes in lobster CPUE and sea bottom temperature. Positive relationships between the rates of change of two model parameters, during the first 60 days of the fishing season (from mid-November to mid-January), are found in the majority of the 10 subregions. A standardized CPUE index with influence of bottom temperature included, compared to the index without such influence, explains a high percentage of the deviance of CPUE data and hence is more consistent with available stock biomass. The outcomes of the model evaluation and relationship analysis encourage further applications of multi-decadal ocean reanalysis products to understand past changes, and the development of ocean forecasts for predicting future changes in marine ecosystems and fisheries, a product with broad-reaching social-economical value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".