Biological benchmarks for the Canadian-origin Yukon River mainstem chinook salmon
Bibliographic record
Abstract
Recognizing the extended time period that Canadian-origin Yukon River Chinook Salmon have been managed under an interim escapement goal, the Yukon River Panel sought to explore the possibility of establishing a biologically based escapement goal for the Canadian-origin Chinook Salmon stock aggregate, excluding the Porcupine River drainage. As a first step in this process, the Joint Technical Committee undertook a quantitative review and analysis of available data to estimate Canadian-origin Chinook Salmon run size and productivity. Commonly used biological benchmarks, and uncertainties in them, were estimated from an integrated run reconstruction and spawner-recruitment model fitted to data spanning 1981- 2019 from various assessment projects that estimate in-river abundance, harvests, tributary escapements, stock-proportions, and age-composition. Equilibrium stock size (SEQ) was estimated to be 111,131 (81,595-252,704, posterior median and 95% CRI), the spawner abundance expected to maximize long-term sustainable yield (SMSY) was estimated to be 43,364 (29,764-97,664) and the spawner abundance expected to maximize recruitment (SMSR) was estimated to be 70,834 (40,638-192,642). SGen was not estimated for the stock aggregate as it is only relevant at the scale of populations. Inference about expected yield and recruitment across a range of future spawning escapements along with probability profiles were estimated to inform future management decisions (Figure 4). These analyses provide a quantitative foundation upon which to base the development of a Canadian stock aggregate escapement goal recommendation, but they do not prescribe one. Key considerations when developing an escapement goal include defining its objectives and decision context, identifying the magnitude of acceptable risk of not meeting stated objectives, and identifying key uncertainties and trade-offs to help ground the degree of precaution that should be taken when establishing an escapement goal in the face of imperfect information. Female Chinook Salmon age-of-maturity, and to a lesser extent the proportion of females in the spawning population, has declined over time (Figure 5). Since expected reproductive output per spawner is a function of these quantities, accounting for these characteristics was estimated to cause an average increase in the spawner abundance expected to maximize yield (SMSY) or recruitment (SMSR) by up to 14% and 22%, respectively, in recent years relative to baseline results. As an emerging area of research, these results represent an initial attempt to account for time-varying demographic changes for this stock aggregate and have been identified as an area of future work. Uncertainties in the estimated benchmarks arise, in part, from challenges in accurately estimating total run size, harvest, and escapement for the Canadian stock aggregate. These challenges include potential biases stemming from: bycatch in marine fisheries, hatchery contributions, contributions from other Canadian stocks (Porcupine River), uncertainty of U.S. and Canadian harvest, unaccounted en route mortality, and pre-spawn mortality (Table 2). Additional uncertainties include the structural form of the assumed spawner-recruitment relationship and the spatial scale at which processes such as density dependence occur which may influence the estimation of biological benchmarks. Key uncertainties that relate to the management relevance of the benchmarks included biological risks of aggregate escapement goals for individual component populations of the Canadian stock aggregate, climate driven change (e.g., temperature, freshet timing, sea ice cover, disease, regime shifts), and time-varying demography that may result in the past no longer being a good predictor of the future. This bilateral U.S. / Canada work provides a new foundation of information that is anticipated to pay dividends and contribute to a wide range of research and management applications that would not otherwise be possible. Key recommendations for future work include: consequences of changes in escapement quality, consideration of biological risks to individual populations, Management Strategy Evaluation to evaluate the ability of alternative management strategies to meet a broad range of objectives in the system, and research into drivers and magnitude of en route mortality and their consequences for management advice.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".