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Record W4401370336 · doi:10.1242/jeb.246614

Some baby fish like it hot, but not too hot

2024· article· en· W4401370336 on OpenAlexaboutno aff
Angelina Dichiera

Bibliographic record

VenueJournal of Experimental Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
Fundersnot available
KeywordsFish <Actinopterygii>Environmental scienceFisheryBiology

Abstract

fetched live from OpenAlex

In a warming world, some fish are primed and ready to face the heat, but others are not. Who is going to be a ‘winner’ in warm waters is influenced by many factors, including where the fish live and what temperatures they encounter there. Atlantic killifish (Fundulus heteroclitus) live in salt marshes all along the Atlantic coast of North America. With this great span of habitat, killifish in the south are living at much higher temperatures than their northern counterparts – up to 10°C warmer. Interestingly, both southern and northern killifish can handle changing temperature, but most of what we know is focused on adults, and scientists suspect that young fish may be more vulnerable to temperatures than older fish. To understand the vulnerability of the young fish, Tessa Blanchard and a team of researchers from the University of British Columbia, Canada, set off to investigate how young killifish from the north and south cope with warming as they grow.Blanchard and the team first went to the field to find adults from both populations, collecting southern killifish in Georgia, USA, and northern killifish in New Hampshire, USA. These fish then took a cross-country trip back to the lab, where the team mixed and matched the adults to create four different types of offspring: embryos from northern parents, embryos from southern parents, as well as two types of ‘hybrid’ embryos. These hybrids were fish with southern mothers and northern fathers or northern mothers and southern fathers, to help the team understand which parent they get their heat-handling skills from.Afterwards, the researchers placed the resulting eggs in water at one of eight different temperatures that they might encounter in their habitats across the Atlantic coast – from 15°C to 36°C – and raised them until they hatched. The team monitored how many eggs survived until hatching at each temperature and how fast they developed. Like the adults, southern offspring did best at warmer temperatures – surviving best at 31°C – whereas the ideal temperature for northern killifish offspring was nearly 6°C cooler. Similarly, southern fish grew fastest at warm temperatures close to 32°C, as did the hybrids with mothers from the south. This suggests that southern mothers are better at passing their fondness for warm temperatures onto their offspring. Although the northern offspring favoured cooler temperatures, more of them survived across all temperatures and hatched faster than the other killifish offspring. And though northern killifish hatched at a smaller size, this is not necessarily bad. Because the temperature changes so drastically with the seasons up north, adult northern killifish have a much shorter time for breeding than southern killifish, so having fast-growing young is an excellent strategy for success. Clearly, northern and southern populations of killifish both have their own approaches to deal with temperature changes, and they get some of that from their parents.However, Blanchard and the team found that regardless of which population they came from, all young fish coped with a smaller range of temperatures and were slightly more sensitive to warming than adults. In fact, southern offspring couldn't handle cool temperatures below 24°C, and no offspring could handle waters above 33°C. In contrast, the adults could tolerate waters as hot as 38°C. Because most killifish offspring are born in the spring and summer, it's more important that they can deal with hot water as they may not encounter cool temperatures as often. With summer heatwaves becoming a frequent occurrence and young fish being more vulnerable to temperatures than older fish, there could be serious consequences for who is going to be a ‘winner’ in warm waters, even if some like it warmer than others.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0740.029

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 head0.027
GPT teacher head0.280
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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