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Record W4362592893 · doi:10.1242/jeb.245001

Brook trout can handle some heat…given enough time

2023· article· en· W4362592893 on OpenAlexaboutno aff
Jordan R. Glass

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
Fundersnot available
KeywordsTroutChristian ministryEcologyFish <Actinopterygii>Environmental scienceGeographyFisheryBiologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

On average, the world is getting warmer, and heatwaves are becoming more common and unpredictable – especially in areas closer to the Earth's poles. Warming near the poles may be bad news for animals, such as brook trout, living in these areas. Luckily, many animals have evolved ways to deal with heat, which could give them a buffer against severe weather. Some animals can adjust how their bodies work when the environment gets warmer, which can help them better handle brief periods of extreme heat. But these internal adjustments take time and may depend on what temperatures the animal has experienced recently. Predicting how animals will handle temperature changes is hard, because the ability to modify body function varies across the animal kingdom, and often we do not know how long it takes to make these adjustments. Erin Stewart and Graham Raby at Trent University, Canada, and Chris Wilson and Vince Frasca at the Ontario Ministry of Natural Resources and Forestry, Canada, wanted to know whether living in warmer conditions helps brook trout to better tolerate extreme temperatures and, if so, how long it takes for their tolerance to develop.To answer these questions, Stewart and colleagues used aquarium heaters and a slow, steady flow of water from a nearby creek to create three naturally fluctuating temperatures: unheated, warmed (+3°C) and warmer still (+6°C). All the water temperatures used in the experiment were within the 10–20°C range that brook trout typically survive best in. To find the hottest temperature that brook trout from each water temperature could stand before losing control of their body movements, Stewart slowly heated up an insulated tank of water containing a small number of trout until the fish reached the point where they began floating on their sides. After this, the trout were placed in a cool tank of water and monitored until they recovered. Almost all the trout survived this brush with extreme heat. To figure out whether the length of time a trout spent living in each creek water temperature (unheated or heated) influenced its heat tolerance, Stewart measured the heat tolerance of different fish from each group after 1, 4, 8, 16 and 30 days.The team found that living in warmer water improved the ability of the brook trout to handle hotter temperatures, but that it took time for the fish to adjust. Trout from the warmest treatment (+6°C) showed a marked increase in heat tolerance after only a single day of living in these warmer conditions. In contrast, trout from the warm group (+3°C) took about 8 days before they showed an improvement in survival over fish living in unheated creek water. Stewart and colleagues also found that the heat tolerance of both warmed groups continued to improve throughout the 30 days of living in the different creek water temperatures.These results suggest that brook trout can adjust to warming environments, within reasonable limits. More importantly, this study highlights an often-overlooked factor that applies to the study of heat tolerance for all animals, not just fish: how long these adjustments take. With climates changing faster than ever in history, many scientists are turning their attention to studying the heat sensitivity of animals. But, if we, as researchers and conservationists, want to use our finite time and resources wisely, we need to account for the fact that biological adjustments to warming temperatures take time.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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 designObservational
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 routes1
Has abstractyes

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