Wild salmon and the shifting baseline syndrome: application of archival and contemporary redd counts to estimate historical Chinook salmon (<i>Oncorhynchus tshawytscha</i>) production potential in the central Idaho wilderness
Bibliographic record
Abstract
The “shifting baseline syndrome” (SBS) is the paradigm whereby recent species abundances and environmental conditions are accepted as reflecting historical conditions. This leads to false impressions of the past, inaccurate baselines, and unrealistic recovery goals. Idaho biologists have counted Chinook salmon redds for >60 consecutive years, generating an invaluable database; however, inaccurate historical baselines compromise the utility of even such high-quality, long-term databases. To develop an accurate baseline, we integrated archival (1951–1964), maximum redd counts with contemporary (1995–2017), continuous counts and spawn timing datasets to estimate historical (1950s–1960s) wild Chinook salmon production potential. Current salmon populations average 3% of 1950s–1960s abundances, which may have been 30% of precommercial fishery (1880s) populations. Notably, the SBS has influenced contemporary managers as reflected in minimum viable abundance, sustainable escapement, and adequate seeding objectives equaling 10.4%, 17.9%, and 20.4%, respectively, of 1950s–1960s potential. Our approach provides a framework for utilizing archival and contemporary data to reconstruct historical baselines and repress SBS. Contrasting contemporary goals with maximum production potential provides new reference points and perspectives for managers to consider.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".