Spatial and length-based models for management of migratory transboundary species : application to Pacific hake (Merluccius productus)
Bibliographic record
Abstract
This dissertation develops new modeling tools to provide new scientific perspectives on migratory transboundary fish populations. I particularly focus on two main issues: (1) the interaction between age/size based migratory movement, spatial availability, and fisheries exploitation rates, and (2) time-varying fisheries selectivity caused by size based migration and cohort targeting. I use Pacific hake (Merluccius productus) as a case study. Pacific hake occurs off the Pacific coast of the U.S.A. and Canada and is characterized by ontogenetic migratory movement (older fish migrate further north), strong recruitment events, and time-varying selectivity due to targeting of strong cohorts. In this dissertation, I present two new modeling approaches, and explore the effects of spatial structure on management outcomes using a closed-loop evaluation. First, I use a Lagrangian approach to develop a migration model that describes the Pacific hake dynamics including seasonal migrations, fisheries dynamics, and cohort targeting. Second, I introduce a new stock assessment method that bypasses the requirement of estimating selectivity by using catch at length and growth parameters to produce estimates of exploitation rate at age. This method produces mixed results because of low precision in selectivity estimates. Third, I evaluate the impacts of harvest control rules on the outcomes experienced by Canada and the U.S.A while sharing the Pacific hake resource. I use the migration model described above in a closed-loop simulation to evaluate the long-term impact of 61 harvest control rules. The results indicate that there are differences in performance of harvest control rules between the two nations when maximizing potential long-term yield and log yields. This is a result of the reduced availability of the resource in Canadian waters as the overall harvest rate increases. Caps on allowable catch may help to avoid reduced availability issues. I believe that the results and conclusions presented in this dissertation can inform the future management and modeling of Pacific hake. In addition, the methods presented here could be used for management of other resources subject to time-varying selectivity and other transboundary stocks managed under agreements that do not consider spatial management explicitly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".