Are fish immunocompetent enough to face climate change?
Bibliographic record
Abstract
Ongoing climate change has already been associated with increased disease outbreaks in wild and farmed fish. Here, we evaluate the current knowledge of climate change-related ecoimmunology in teleosts with a focus on temperature, hypoxia, salinity and acidification before exploring interactive effects of multiple stressors. Our literature review reveals that acute and chronic changes in temperature and dissolved oxygen can compromise fish immunity which can lead to increased disease susceptibility. Moreover, temperature and hypoxia have already been shown to enhance the infectivity of certain pathogens/parasites and to accelerate disease progression. Too few studies exist that have focussed on acidification, but direct immune effects seem to be limited while salinity studies have led to contrasting results. Likewise, multi-stressor experiments essential for unravelling the interactions of simultaneously changing environmental factors are still scarce. This ultimately impedes our ability to estimate to what extent climate change will hamper fish immunity. Our review about epigenetic regulation mechanisms highlights the acclimation potential of the fish immune response to changing environments. However, due to the limited number of epigenetic studies, overarching conclusions cannot be drawn. Finally, we provide an outlook on how to better estimate the effects of realistic climate change scenarios in future immune studies in fish.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".