Bias and consequences in attempts to estimate historical salmon abundance
Bibliographic record
Abstract
Status of salmon populations thought to be at risk is frequently evaluated by comparing estimated abundances in recent decades with approximations of peak abundance in the era when fisheries began. However, spawner escapements of Pacific salmon were generally not surveyed until 1950 or later, so most estimates of salmon abundance before 1950 have been derived from harvest data alone. We investigated historical data on Oregon Coast coho (Oncorhynchus kisutch) to test the reliability of such estimates. In the “Recovery Plan for Oregon Coast Coho” (prepared by National Marine Fisheries Service (NMFS) in 2016), coho abundance was estimated back to 1892 by assuming that in-river landings before 1950 represented a constant 40% of the population. The first mark–recapture studies enabling estimation of harvest rates were completed in the early 1950s, shortly after regulatory action in 1947 sharply curtailed harvesting of coho. We used annual data on fishing licenses sold and length of fishing seasons before 1950 as indices of harvesting effort to reconstruct harvest rates before 1947 and determined in-river landings were near 80%, twice the level postulated by the Recovery Plan. Thus, spawner escapements before 1947 were roughly 20% rather than the 60% of adult recruitment assumed in the Recovery Plan. Actual spawner counts during the first decade of surveys (1945–1955) indicated that coho abundance was much lower and the population trend was opposite of that estimated in the Recovery Plan. However, when spawner abundance was estimated by assuming in-river harvest rates were 80% before 1947, predicted trends in spawner abundance and ocean harvest rates aligned with those from the earliest decade of studies, enabling direct estimates of spawner abundance and ocean harvest rates. Further, the revised simulations indicate that Oregon Coast coho abundance during 1892–1956 probably varied within a range similar to recent decades. We find that use of landings data to speculate about historical salmon abundance is unreliable and can lead to recovery goals that are unattainable, errant interpretation of population limiting factors, and high investment in well-intentioned but misaligned recovery actions. Correct diagnosis of population trends and their causes is best attained with contemporary data for which both precision and accuracy can be validated.
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 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 | 0.184 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".