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Record W4392391003 · doi:10.1098/rsbl.2023.0346

Are fish immunocompetent enough to face climate change?

2024· review· en· W4392391003 on OpenAlexaff
Andrea Franke, Anne Beemelmanns, Joanna J. Miest

Bibliographic record

VenueBiology Letters · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversité Laval
FundersVolkswagen FoundationCanterbury Christ Church University
KeywordsBiologyClimate changeStressorEcologyHypoxia (environmental)Immune systemDiseaseImmunityFish <Actinopterygii>ImmunologyFisheryNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.020

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.

Opus teacher head0.053
GPT teacher head0.314
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations42
Published2024
Admission routes1
Has abstractyes

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