Relationships of sea ice extent and bottom water temperature with abundance of snow crab (Chionoecetes opilio) on the Newfoundland - Labrador Shelf
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.It is widely believed that recent increases in crustacean resources, including snow crab, in Atlantic Canada are due to reduced predation (top-down effects). We advance an alternative hypothesis that snow crab production and early survival are regulated primarily by effects of ocean climate variation (bottom up effects) during early life history. We address this hypothesis by using time series analysis to establish relationships between recruitment indices and ocean climate indices at various time lags. We selected two indices of ocean climate variation, one to represent effects on epipelagic larval stages (ice coverage) and another to represent effects on early benthic stages (bottom temperature). Using catch per unit effort (CPUE) from the commercial snow crab fishery as our index of snow crab abundance we applied an autoregressive, integrated moving average (ARIMA) procedure with environmental input (transfer function) to explore relationships between biological production or early survival and changes in ocean climate. The procedure facilitated cross-correlation analysis between CPUE and each environmental index (corrected for autocorrelation) for each of four fishery areas. The approach was extended for our longest time series using ice coverage to illustrate how ocean climate variables can be incorporated into predictive models. We also compared our CPUE index with an index of predation - survey biomass of Atlantic cod (Gadus morhua). Our results showed consistent correlations for each of the ocean climate indices and across all areas. CPUE was positively correlated with ice cover for all three areas that are affected by ice, at lags of 8-10 years, approximating age at recruitment. These correlations were significant for the two longest time series, while there was no relationship for a fourth, most-southern area, that is unaffected by ice cover. Consistent negative relationships were found between CPUE and bottom temperature for all four areas, at lags generally one year shorter than the corresponding CPUE-ice relationships, although none were significant. Crabcod relationships were not significant, the cod index being unidirectional. We conclude that abundance is regulated primarily by effects of ocean climate variation on early life history stages. Cold conditions in early life favor production or survival at either or both pelagic and early benthic stages but functional relationships remain unknown
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".