The carcass zone: salmon contribution to tree rings in old‑growth Sitka Spruce (<i>Picea sitchensis</i>) throughout coastal British Columbia
Bibliographic record
Abstract
The contribution of Pacific salmon to riparian forest biodiversity is widely recognized, yet the direct influence on coniferannual growth rings is less well-established. I examined broad spatial and temporal trends (1945–1999) in ring width, basal area increments (BAI), and nitrogen signatures in heartwood rings of 282 old-growth riparian Sitka Spruce (Picea sitchensis;average age ~300 years) from 79 watersheds in three regions of coastal British Columbia. Several large yearly fluctuations in salmon biomass entering streams were positively but weakly correlated with tree growth, lagged one to four years. General linear models indicate that tree age and salmon carcass proximity were the major growth predictors, while tree distance to stream and riparian slope were not significant. Average annual BAI (marginal means) in carcass zones were 80%, 150%, and 55% higher than adjacent control sites on the Mainland, Mid-coast Islands, and Haida Gwaii, respectively. Nitrogen isotope signatures (δ15N) in heartwood rings ranged from –8.6‰ to 8.0‰ and were about 3‰ higher in carcass trees than control trees. Total nitrogen (TN) ranged from 0.03% to 0.15% and was largely independent of salmon carcass occurrence. Bivariate plots (δ15N against TN) indicate a geographical clustering of elevated TN in Haida Gwaii watersheds, lower δ15N and TN in the Mid-coast Islands, and elevated δ15N and TN in watersheds with exceptionally high salmon carcass transfer and bear activity. These cumulative data robustly quantify accentuated conifer growth from salmon-derived nutrients in riparian zones that are largely independent of climatic influences and tree age.
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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 | 0.001 | 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".