Multi‐century reconstructions of Pacific salmon abundance from climate‐sensitive tree rings in west central British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT Short records of naturally fluctuating Pacific salmon (Oncorhynchus spp.) populations elicit challenges for the long‐term management of North American fisheries. A dendrochronologic approach was used to reconstruct long proxy histories of regional Pacific salmon populations returning to streams along west central British Columbia coastlines. Tree rings from five tree species collected at 18 sites were found to be sensitive to interannual fluctuations in large‐scale ocean and atmospheric circulation patterns. Using a regional network of climate‐sensitive ring‐width and ring‐density tree‐ring chronologies in linear regression models, multi‐century long‐term abundance records for populations of sockeye, chinook, chum and pink salmon were generated. Models explained between 27% and 39% of the variability in escapement records and were more proficient at capturing trends than annual magnitudes. The reconstructions vary in length, dating from 1400, 1536 and 1638 ad and extending to 2009 ad. Notable fluctuations in salmon abundance over the past six centuries are described, with significant population collapses shown to occur during the early 1400s, the late 1500s, the mid‐1600s, the early 1700s, the early 1800s and parts of the 1900s. These models are the first to utilize climate‐sensitive tree‐ring records to reconstruct regional abundance histories and thus contribute potentially valuable information for salmon fisheries managers. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".