Increasing temperatures accentuate negative fitness consequences of a marine parasite
Bibliographic record
Abstract
Infectious diseases are key drivers of wildlife populations and agriculture production, but whether and how climate change will influence disease impacts remains controversial. One of the critical knowledge gaps that prevents resolution of this controversy is a lack of high-quality experimental data, especially in marine systems of significant ecological and economic consequence. Here, we performed a manipulative experiment in which we tested the temperature-dependent effects on Atlantic salmon (Salmo salar) of sea lice (Lepeophtheirus salmonis)-a parasite that can depress the productivity of wild-salmon populations and the profits of the salmon-farming industry. We explored sea-louse impacts on their hosts across a range of temperatures (10, 13, 16, 19, and 22 °C) and infestation levels (zero, 'low' (mean abundance ± SE = 1.6 ± 0.1 lice per fish), and 'high' infestation (6.8 ± 0.4 lice per fish)). We found that the effects of sea lice on the growth rate, condition, and survival of juvenile Atlantic salmon all worsen with increasing temperature. Our results provide a rare empirical example of how climate change may influence the impacts of marine disease in a key social-ecological system. These findings underscore the importance of considering climate-driven changes to disease impacts in wildlife conservation and agriculture.
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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) | 0.002 | 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".