Updated Reference Points and Harvest Options for the Giant Red Sea Cucumber (Apostichopus californicus) Fishery in British Columbia using data from Experimental Fishing Areas
Bibliographic record
Abstract
The Giant Red Sea Cucumber, Apostichopus californicus, is the subject of a lucrative commercial dive fishery in British Columbia (BC), Canada. Despite considerable research, the life history of this species is poorly understood and many biological parameters cannot be estimated, preventing the use of typical fisheries models. As a result, Four Experimental Fishing Areas (EFAs) were established in BC in 1998 to study the effects of harvest on sea cucumber densities. After 10 years, EFA data were analyzed, a latent productivity model was developed, and recommendations were made regarding harvest rates and provisional reference points (Hand et al. 2009). The EFAs continued until 2015, generating another 8 years of data. This document updates harvest advice based on the original latent productivity model (with some updates) and the full time series of EFA data. Maximum sustainable harvests are presented for various harvest strategies, combining harvest intervals of 1 to 5 years with different minimum equilibrium stock level thresholds (minimum observed, 0.50 B0, 0.60 B0, and 0.80 B0) and estimates of either current or virgin biomass. Recommendations include implementing harvests that do not exceed the range of the lower 0.01 quantile for equilibrium stock outcomes above 0.60 B0. For example, for annual harvest rates, the range not to be exceeded is 2.0 to 8.0% of estimated pre-harvest biomass, whereas for triennial harvest it is 5.7 to 18.8% of estimated pre harvest biomass, with the caveat that the upper ranges may only be appropriate for highly productive areas. Furthermore, the adoption of empirical reference points is recommended: a conservative Limit Reference Point of 0.029 sea cucumbers m-2 on sea cucumber habitat, and an Upper Stock Reference Point of 0.038 sea cucumbers m-2 on sea cucumber habitat.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".