Links between fluctuations in sockeye salmon abundance and riparian forest productivity identified by remote sensing
Bibliographic record
Abstract
Abstract Pacific salmon ( Oncorhynchus spp.) carcasses can fertilize riparian forests with marine‐derived nutrients when populations make their annual return to natal streams to spawn; however, the strength of this cross‐system linkage likely varies substantially among years due to the interannual fluctuations in abundance that characterize most salmon populations. Here, we used a 36‐yr time series (1984–2019) of satellite imagery and salmon abundance estimates to assess spatiotemporal associations between forest greenness (a measure of plant productivity) and adult sockeye salmon ( Oncorhynchus nerka ) abundance in the lower Adams River, British Columbia, Canada. The Adams River sockeye population displays a quadrennial pattern of abundance, with a dominant cohort that spawns every four years in numbers that are typically two to three orders of magnitude larger than non‐dominant cohorts. We found that variation in forest greenness was consistently explained best by models including dominant cohort year, whereas models lacking an index of salmon abundance were the lowest‐ranked. Greenness of riparian vegetation increased by an average of 0.015 NDVI units (approximately 1%) in the summer after a dominant cohort return, and this effect on greenness persisted into the subsequent fall (11–13 months after spawning). The positive association between quadrennial pulses of salmon and riparian greenness occurred in plots both within 30 m of the stream and 95–125 m away from the stream, indicating that the spatial extent of fertilization may occur well beyond areas directly adjacent to the riverbank. These results suggest that forests respond to cyclical variation in salmon abundance and that overwinter storage of marine‐derived nutrients within catchments allows plants to capitalize on these nutrients in the following growing season. Continued advances in remote sensing technology will enhance our understanding of cross‐system resource linkages and can inform the ecosystem‐based management of Pacific salmon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".