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Record W4385417534 · doi:10.1242/jeb.245019

In acidic water, crabs stink at smelling

2023· article· en· W4385417534 on OpenAlexaffabout
Angelina Dichiera

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSniffingSeawaterPredationFisheryBiologyEcologyZoology

Abstract

fetched live from OpenAlex

Human activities that burn fuel for energy are driving more carbon dioxide (CO2) into the atmosphere than the earth can handle. Consequently, the CO2 is being absorbed by the ocean's waters, making them more acidic and interfering with the behaviour and senses of many marine animals. Crabs have poor eyesight and rely primarily on their sense of smell to find food and avoid predators, and unfortunately acidic waters are wreaking havoc with this. Scientists are unsure exactly how higher acid levels in the ocean are changing crabs’ smelling skills, so Andrea Durant, Elissa Khodikian and Cosima Porteus of the University of Toronto Scarborough, Canada, set off to investigate the mechanics of a bad sniffer.Crabs sniff by flicking their antennae and flicking them faster when there's a good (or bad) smell around. To test how this sniffing behaviour changes, the scientists first put Dungeness crabs (Metacarcinus magister) into slightly acidic seawater for 10 days and then measured their response to either a ‘good’ or ‘bad’ smell at different strengths. Because some aquatic animals release ammonia-laden urine when they're startled, ammonia was chosen as the bad smell. But this ‘alarm cue’ didn't bother the crabs at all, whether they were in normal seawater or acidic seawater. Instead, they ‘sniffed’ the bad smell just as they would any other scent. For the good smell, the scientists used a scent called cadaverine, which is released when animals are decomposing and apparently smells delicious to hungry crabs. Normally, crabs would flick their antennae enthusiastically when they sniff this alluring aroma. Unfortunately, the crabs from acidic seawater had a hard time detecting this enticing smell. In fact, they needed a cue that was 10 times stronger than normal to set their antennae flicking, suggesting it was harder for the crabs to ‘sniff out’ their food.To understand why crabs in acidic waters have a poorer sense of smell, the scientists turned to the olfactory (smell-sensing) nerves. The team tested how sensitive these smell-sensing nerves of the crabs’ antennae were to cadaverine. As expected, the nerves of the crabs in acidic seawater reacted poorly to the feeding cue, barely responding to the delicious scent no matter how strong the dose of cadaverine was. The scientists then examined the structure of the nerves and assessed which proteins were present because the smell-sensing nerves in crabs rely on particular proteins to sense and process smells. Interestingly, the crabs in acidic seawater had smaller nerve cells and fewer receptors for scents. Unexpectedly, they also had more ammonia-transporting proteins, even though ammonia didn't affect their sniffing abilities or nerve function. What these ammonia transporters are doing in these nerves remains a mystery.This work by Durant and colleagues adds to the growing body of evidence that crabs are incredibly vulnerable to the increased acidity of the ocean resulting from climate change. Dungeness crabs in particular are one of the most economically important species in Northwest America and Canada and already experience periods of more acidic seawater, which will only intensify in the future. If only 10 days in acidic seawater makes it harder for crabs to smell food, well, that stinks.

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 score0.031
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)0.0020.001

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.

Opus teacher head0.025
GPT teacher head0.286
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 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
Published2023
Admission routes2
Has abstractyes

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