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Record W4407342088 · doi:10.1242/jeb.249930

Feeling the burn: young fish struggle in acidic water

2025· article· en· W4407342088 on OpenAlexaffabout
Alexandra N. Schoen

Bibliographic record

VenueJournal of Experimental Biology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsFish <Actinopterygii>FeelingPsychologyFisheryBiologySocial psychology

Abstract

fetched live from OpenAlex

Climate change has many effects on aquatic environments, including changes in temperature and chemicals in the water. One chemical, carbon dioxide (CO2), is discussed a lot among scientists because it has a big impact on how acidic the water is (pH, or the number of hydrogen molecules in the water). More CO2 in water makes the water more acidic, which can cause problems for aquatic animals and plants. Many adult animals can handle changes in water acidity, but earlier stages of life (e.g. eggs, larvae) in animals such as fish might not tolerate even small increases in acidity. This is because fish eggs and larvae do not have the fully developed body parts that are resistant to changes in acidity and may not be able to escape higher acidity areas as well as adults can. Knowing this, Grace Wallace, Rosemary Minns and Caleb Hasler of the University of Winnipeg, Canada, were particularly interested in how small increases in acidity might affect the eggs and larvae of Japanese medaka (Oryzias latipes), a fast-growing freshwater fish that is often raised and studied in laboratories.To study this, Wallace's team bred medaka and raised their offspring for 9 days. At 3 days old, the medaka inside the eggs had developed beating hearts, so Wallace decided to place the eggs into acidic water for 24 h at this time, to see whether it changed their heartbeats. Different groups of eggs were placed into five different levels of acidic water from normal (pH 7.1) to more acidic (pH 6.4, 6.1 and 5.8), to the most acidic (pH 5.7). The researchers found that the fish's heartbeats slowed more every time the water became more acidic. Wallace and her colleagues suggested that the more acidic water (and higher CO2) might have affected the communication systems in the heart, causing the heart muscle to beat slower. Wallace also recorded how often the medaka moved inside the eggs: while they moved more when the water was slightly acidic (pH 6.4), movement did not increase further when the water became even more acidic (pH 6.1, 5.8 and 5.7).After the acidic water treatment, the scientists tracked how many eggs survived for 4 days. Like the heartbeat results, Wallace found that eggs survived better in less acidic water, possibly because the fish's hearts beat better in less acidic water. When the medaka hatched, Wallace placed a group of 9 day old larvae in the same levels of acidic water for 24 h. This time, the researchers were interested in the swimming behaviour of the larvae, as lower pH has been shown to change different behaviours of larval fish of other species. Wallace placed the larvae into a circular tank and recorded how much time each fish spent in the centre or close to the walls of the tank over the course of 10 min. She also measured how far and fast the larvae swam during this time.Interestingly, there was no change in how far or fast the larvae swam. However, in some of the acidic waters (pH 6.4 and 5.8), larvae spent more time in the inside zone of the circular tank. Wallace found this strange because their behaviour did not change like their heartbeat across acidic water levels, showing that some body systems in medaka may respond more to acidity than others.Although Wallace noted that medaka raised in the laboratory might act differently than in the wild, more acidic water still caused the heartbeats of these fish to slow down. However, their swimming behaviour might be more resilient to increases in acidity. This suggests that medaka, and potentially other freshwater fish, may not survive if climate change causes aquatic environments to become more acidic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.267
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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