Analysis of cyclic population dynamics of snow crab in the southern Gulf of St. Lawrence using a stage-structured discrete-time population model
Bibliographic record
Abstract
The snow crab fishery is one of Canada’s most profitable fisheries and is a significant economic driver in coastal communities in Atlantic Canada and Quebec. Snow crab is generally considered to be a stenothermic species and warming due to climate change is a concern. Population models are valuable tools for understanding and predicting how biological populations change over time and how they respond to environmental and anthropic pressures. However, due to the species’ complex life cycle, developing an adequate population dynamics model is challenging. To address this, we develop a discrete-time population model incorporating three developmental stages for each sex : immature, adolescent and adult for the male; immature, prepubescent and adult for the female. The model is parameterized to study the snow crab population in the southern Gulf of St. Lawrence and includes density-dependent processes (i.e. intercohort cannibalism), while groundfish predation is excluded due to its presumed minimal impact in this region. Results show that cannibalism can regulate snow crab population dynamics by generating natural cycles or stabilizing abundance, depending on fertility levels. These findings highlight the importance of including such biological interactions in stock assessment models to better capture long-term population variability. We also examine how variability in recruitment, natural mortality, and fishing mortality can contribute to cyclic dynamics. Bifurcation analysis and periodograms are used to further characterize the population’s behaviour across a range of scenarios. • Population model for snow crab in the southern Gulf of St. Lawrence (sGSL). • Model leads to a good representation of trawl survey data in the sGSL. • Better understanding of main driver of cyclic population dynamics.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".