Understanding Chinook Salmon-Ecosystem Interactions at the Limit of Their Inland Range, as Told by Trees and Teslin Tlingit Knowledge Holders
Bibliographic record
Abstract
Pacific salmon (Oncorhynchus spp.) function as major sources of sustenance and nutrients in moving from marine environments inland. This has been demonstrated in coastal systems by positive relationships between Pacific salmon abundance and riparian tree growth and δ15N, likely mediated by predators and scavengers fertilizing the soil through consuming and transporting salmon carcasses. This thesis investigated whether these relationships occur at the limit of Pacific salmon distribution on the Teslin Tlingit Council (TTC) Traditional Territory in Southern Yukon, other ecosystem roles of salmon and population declines in the area, and the interactions of these processes. Tree growth chronologies were created at five riparian sites for 40-50 trees (N = 220) and related to salmon escapement or abundance data from the Yukon and Teslin rivers. Site growth chronologies were significantly and positively related to salmon escapement at three of four salmon-bearing sites and not at the negative (salmon-free) control site. Mean annual growth was higher at all salmon-bearing sites with significant salmon-growth relationships than at the negative control site. Salmon were estimated to increase tree growth by 17-39%. Mean δ15N was significantly higher at salmon-bearing sites compared to the negative control. Interviews were conducted with three Teslin Tlingit knowledge holders to study salmon-ecosystem interactions on the Traditional Territory. Interviews revealed measures of a healthy salmon run and large population declines that have negatively impacted local ecosystems (namely bears) and human wellbeing. Western scientific methods and Indigenous Knowledge included in this study suggest salmon population declines in the area have likely altered their role as nutrient sources. This study demonstrates the ubiquity of salmon as ecological and cultural keystone species, and the importance of considering multiple ways of knowing to improve research in complex ecological (and social-ecological) systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".