Future climate change-related decreases in food quality may affect juvenile Chinook salmon growth and survival
Bibliographic record
Abstract
Abstract The global temperature increase due to global change is predicted to be between 3.3 – 5.7°C by 2100 leading to changes at the base of the marine food web in species composition, abundance, and quality at the base of the marine food web leading to flow-on effects of higher trophic levels such as fish and humans. Changes in marine prey availability and nutritional quality can affect juvenile salmon conditions (i.e., growth, condition, and mortality) during the early marine phase. There is limited knowledge of the interplay between prey availability and prey quality and the importance of food quality under food-satiated conditions. Here, a three-phase feeding experiment measured the effects of nutritional quality (fatty acid composition and ratios) on juvenile Chinook salmon ( Oncorhynchus tshawytscha ) condition. Experimental diets represented the present three different climate scenarios with a present-day diet ( Euphausia pacifica ), a control diet (commercial aquaculture diet), and a predicted IPCC worst-case scenario diet with low essential fatty acid concentrations (IPCC SSP5-8.5). We tested how potential future low quality food affects growth rates, body condition, fatty acid composition and mortality rates in juvenile Chinook salmon compared to present-quality prey. Fatty acids were incorporated into the salmon muscle at varying rates but, on average, reflected dietary concentrations. High dietary concentrations of DHA, EPA and high DHA:EPA ratios resulted in increased fish growth and condition. In contrast, low concentrations of DHA and EPA and low DHA:EPA ratios in the diets were not compensated for by increased food quantity. This result highlights the importance of considering food quality when assessing fish response to changing ocean conditions. Highlights Climate change may decrease the quality of salmon prey through changes in the fatty acid composition. Low dietary essential fatty acid levels reduce growth and condition and increase mortality rates in juvenile Chinook salmon. Food quality changes within zooplankton species but also by changes between species. Results suggest potential cascading effects on higher trophic levels when zooplankton species composition shifts to lower quality species. Higher food intake cannot compensate for low food quality.
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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 | 0.000 |
| 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) | 0.001 | 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".