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Record W4247905118 · doi:10.1242/jeb.135855

Striped catfish lose the plot in low CO2 water

2016· article· en· W4247905118 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Experimental Biology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsCatfishPlot (graphics)Environmental scienceFisheryBiologyFish <Actinopterygii>MathematicsStatistics

Abstract

fetched live from OpenAlex

As the politicians keep on squabbling over the best ways to alleviate climate change, and CO2 emissions continue rising, much of the gas ends up in our oceans and rivers. At first glance, it didn't seem as if this gradual acidification was going to pose a problem for the planet's fishy residents. Matthew Regan from the University of British Columbia, Canada, says, ‘They are well prepared physiologically and biochemically to tolerate even the most depressing of future CO2 projections’. However, more recently it has become apparent that fish that have been exposed to future levels of CO2 experience behavioural problems: in addition to becoming hyperactive and bolder, they also suffer visual disturbance and anxiety, and are attracted to predators. The associated mild chemical imbalances in the fish's bodies affect their inhibitions. Following on from this discovery, Sjannie Lefevre and Göran Nilsson from the University of Oslo, Norway, wondered how fish that already reside in water with high CO2 would cope if the situation was reversed and they were transferred into normal (low CO2) water?Teaching at a graduate course on air-breathing fish in 2014 organised by Mark Bayley at Can Tho University, Vietnam, Lefevre and Nilsson had the ideal opportunity to address the conundrum. The Mekong Delta is home to the striped catfish (Pangasianodon hypophthalmus), which thrives in high-CO2 water. So, with a flourishing aquaculture industry on hand to supply the fish and a team of enthusiastic students available to run the experiments, they were in the perfect place to test the fish's reactions to low-CO2 water.Collecting fish provided by Do Thi Thanh Huong and Nguyen Thanh Phuong from a nearby farm, Regan and his fellow students Andy Turko, Joe Heras and Mads Kuhlmann Andersen transferred some of the animals into low-CO2 water, while the rest remained in high-CO2. Then, with the help of Colin Brauner and Tobias Wang, they began testing the fish's reactions to a range of situations, from the arrival of an unfamiliar object (a brick) in their surroundings to how much time they spent schooling with their own kind, to find out how the water conditions had affected their behaviour.Not surprisingly, striped catfish that resided in their habitual high-CO2 conditions showed all of the usual reactions to unfamiliar situations, avoiding the frightening brick and remaining out of reach of a threatening predator. However, the fish that had been held in freshwater began behaving strangely. Not only were they unalarmed by the presence of the predator but also they were unfazed by the arrival of the brick. In addition, they were less attracted to a school of their own species and were much more active. However, when the team gave the low-CO2 catfish a dose of a drug that counteracts the effects of the GABA neurotransmitter – the neurotransmitter that malfunctions and triggers bold behaviour when fish that normally reside in low CO2 are exposed to high CO2 – the emboldened catfish lost their courage and began behaving normally.So, the GABA neurotransmitter had lost its inhibitory effects in the catfish that had been transferred to low-CO2 water because of the subtle chemical changes in their brains caused by the alteration in their surrounding water. And Regan suggests that other species may be able to adapt their brains to the brave new world that striped catfish already survive in, provided they can keep pace with change.

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 categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score0.999

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)0.0020.000

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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