Application of Conservation Genomics to Investigate the Role of Pathogens on the Migration of Sea‐Run Brown Trout (<i>Salmo trutta</i>)
Bibliographic record
Abstract
Pathogens play a key role in individual function and the dynamics of wild populations, but the link between pathogens and individual performance has rarely been investigated in the wild. Migrating salmonids offer an ideal study system to investigate how infection with pathogens affects performance given that climate change and fish farming portend increasing prevalence of pathogens in wild populations. To test for effects of pathogen burden on the performance of a migrating salmonid, we paired data from individual brown trout tagged with acoustic accelerometer transmitters and gill biopsies to investigate how pathogen infection affected whole animal activity during the spawning migration. Generalised additive models fitted to the acceleration data revealed individual and temporal variation in acceleration as expected, but also provided a significant effect of relative infection burden on acceleration. However, when linking this pathogen-specific effect to a relevant bioenergetic change, it was evident that the effect had little impact on the exercise-related oxygen consumption at the individual level, especially in cases where fish were not exerting high exercise activity. The results are a powerful example of how pairing non-lethal biopsies with individual tracking technologies can be used to assess how pathogens impact fish in situ.
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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".