MétaCan
Menu
Back to cohort
Record W7133287815

Evaluation of potential rebuilding strategies for Inside Yelloweye Rockfish (Sebastes ruberrimus) in British Columbia

2022· other· en· W7133287815 on OpenAlexaboutno aff
Dana Haggarty, Quang C. Huynh, Robyn E. Forrest, Sean C. Anderson, Midoli J. Bresch, Elise A. Keppel

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGroundfishRockfishStock (firearms)Plan (archaeology)Stock assessmentFish stockManagement by objectives
DOInot available

Abstract

fetched live from OpenAlex

Under Canadian policy and legislation, fish stocks that have been assessed as being below the Limit Reference Point (LRP) require a rebuilding plan to grow the stock above the LRP. Re building plans should be based upon objectives characterised by (1) a target, (2) a desired time to reach the target, and (3) an acceptable probability of reaching the target. Rebuilding plans should also include planned management measures or management procedures (MPs), mile stone objectives, and should undergo regular evaluation. The inside stock of Yelloweye Rockfish (Sebastes ruberrimus, Inside Yelloweye Rockfish) is a data-limited stock, occurring in Groundfish Management Area 4B (Queen Charlotte Strait, Strait of Georgia, and Strait of Juan de Fuca) in British Columbia (BC). The stock was assessed as below the LRP in 2010, resulting in a published rebuilding plan. It is also listed under the Species at Risk Act (SARA) as a Species of Special Concern. The current MP for rebuilding is a fixed an nual total allowable catch (TAC) of 15 metric tonnes, which has not been re-evaluated since the last assessment. The purpose of this project is to provide scientific advice to support re-evaluation of the rebuild ing plan for Inside Yelloweye Rockfish. We apply a new management strategy evaluation (MSE) framework (the MP Framework), recently developed for BC groundfishes, to evaluate the perfor mance of alternative data-limited MPs, with respect to meeting rebuilding objectives. The MP Framework follows six best-practice steps for MSE: (1) defining the decision context, (2) setting objectives and performance metrics, (3) specifying operating models (OMs) to represent the un derlying system and calculate performance metrics, (4) selecting candidate MPs, (5) conducting closed-loop simulations to evaluate performance of the MPs, and (6) presenting results to facili tate evaluation of trade-offs. We followed this framework to evaluate the performance of 34 data-limited MPs with respect to meeting the principal objective, which is to rebuild the stock above the LRP over 1.5 generations with at least 95% [19 times out of 20] probability of success. We also evaluated performance of MPs with respect to two additional conservation metrics, four average-catch objectives, and one catch-variability objective. To account for uncertainty in underlying population dynamics and data sources, we developed six alternative OM scenarios, which differed with respect to specific model and data assumptions. These OM scenarios were divided into a “reference set” (four OMs) and a “robustness set” (two OMs). We conditioned all OMs on observed catch data, in dices of abundance, and available age composition data. We used closed-loop simulation to eval uate the performance of the MPs and screened out MPs that did not meet a basic set of criteria, resulting in five remaining candidate MPs: annual constant-catch MPs of 10 tonnes or 15 tonnes, and three MPs that adjust the TAC based on the relative slope of the inside hard-bottom longline (HBLL) survey index of abundance. All five final MPs met the principle objective with greater than 0.98 probability (49 times out of 50), across all four OM reference set scenarios. This was largely because none of the reference set OMs estimated the stock to be below the LRP in 2020. Within the two OM robustness set scenarios, the scenario that simulated higher variability in the future HBLL survey performed similarly to the reference set scenarios. However, under the scenario that assumed a lower rate of natural mortality for the stock (“Low M”), all MPs had lower probabilities of meeting the prin ciple objective, with the lowest probability achieved by the current MP (constant catch of 15 t). We present a number of visualizations to show trade-offs among conservation and catch objec tives for the different MPs across alternative OM scenarios. The visualizations present trade offs in tabular and graphical formats, intended to support the process of selecting the final MP Because all the MPs met the principle objective under the reference set scenarios, there were no strong trade-offs between conservation and catch objectives. Of the two OM robustness set sce narios, trade-offs were most apparent under the Low M scenario, where the probability of meet ing the principle objective decreased as the probability of achieving an average short-term catch of 10 t increased. We discuss major uncertainties, including uncertainty in natural mortality, selectivity, and his torical catches, noting that we attempted to account for these uncertainties by evaluating perfor mance of MPs across multiple OMs. We highlight issues regarding estimates of current stock status for Inside Yelloweye Rockfish, and the role of reference points in the MP Framework. We make recommendations for assessment frequency and suggest triggers for re-assessment. Per formance of MPs with respect to meeting two alternative assessment criteria for the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) are also evaluated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.250
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207