Influence of spawning salmon on tree-ring width, isotopic nitrogen, and total nitrogen in old-growth Sitka spruce from coastal British Columbia
Bibliographic record
Abstract
Coastal watersheds of the North Pacific benefit immensely from bear-mediated uploading of salmon nutrients, which increases aquatic and terrestrial productivity. To quantify the influence of spawning salmon on tree-ring signatures, we analyzed 543 rings from the heartwood of 13 old-growth Sitka spruce (Picea sitchensis (Bong.) Carrière) trees from five geographically separated watersheds in coastal British Columbia. In comparison with adjacent control trees, those receiving salmon nutrients (salmon trees) have rings that are, on average, 1.5 mm wider, 4.5‰ more enriched in isotopic nitrogen, and 0.021% more elevated in total nitrogen (P < 0.001, Mann–Whitney–Wilcoxon test). In this study, salmon nutrients enhance average stem growth by 19%. Furthermore, salmon trees show that increases in tree-ring width and nitrogen values lag sporadic, high salmon runs by 0 to 5 years. Using differences between control and salmon trees from the same site, our results collectively indicate that tree-ring width, isotopic nitrogen, and total nitrogen are valid, complementary tools for investigating historic, annual fluctuations in salmon abundance in coastal watersheds. We recommend their use in future, tree ring based reconstructions of past nutrient cycling over decadal to centennial time scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".