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Record W4388046855 · doi:10.1242/jeb.245032

Should I stay (in hot water) or should I go?

2023· article· en· W4388046855 on OpenAlexaff
Angelina Dichiera

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTroutHabitatTributaryFisheryPopulationFish migrationGlobal warmingFish <Actinopterygii>Climate changeWildlifeEnvironmental scienceGeographyEcologyBiology

Abstract

fetched live from OpenAlex

In an increasingly warm world, many scientists believe that animals only have a few options: they cope with their (hotter) environment or find a better habitat to live in. But often these choices aren't so simple. Many river habitats across the world are broken up by human-made dams, leaving some fish without the option to turn fin and escape warming waters. This is the case for some of the redband trout (Oncorhynchus mykiss newberrii) in the Klamath River Basin, USA. One population resides in Upper Klamath Lake, a shallow body of water prone to seasonal warming, but which also has cooler tributaries (∼11°C) for escape. Just south, the population of ‘Keno trout’ is trapped between two dams, a habitat which can reach above 25°C in the summertime. Because of their habitat differences, these neighbours might choose to use different coping mechanisms (stay or move away) to deal with warming. This led Nick Halhbeck and a team of scientists from Oregon State University, Oregon Department of Fish and Wildlife, the Wild Salmon Center and the University of California Santa Barbara, USA to predict that if there is a place to hide from the heat, fish will choose to change their behaviour and swim away; but if there's nowhere to go, fish are forced to change how their body works – at a greater cost.The team spent three summers researching the different populations’ behaviour, tracking where fish move when water warms. Unsurprisingly, Keno trout do not move much, but Upper Klamath Lake trout take full advantage of their escape routes, keeping cool during summer by moving to tributaries. To see if these two populations’ ability to handle intense exercise changed with warming, and figure out the maximum temperature they can handle, the team created a mini-lab in the field and tested the fish stream side. As predicted, Keno trout are better at maintaining their metabolism and recovering from exercise faster when it's hot, tolerating temperatures 2.4°C higher than Upper Klamath Lake trout and setting the record for the highest temperature that any rainbow trout studied could tolerate (at 31.3°C!). This means that these ‘trapped’ Keno trout are not only surviving but thriving in their hot habitat. But what is causing these differences between the two populations?The researchers examined genetic markers that might explain any differences in these populations’ heat tolerance, but genes don't seem to be the deciding factor for better (or worse) tolerance. But the team still expected that the process of adjusting their body to tolerate heat would be more taxing for Keno trout than the act of simply swimming away would be for Upper Klamath Lake trout. So, they took measurements of how much energy stores the fish had and how fast they're being used. Surprisingly, the data showed that moving to a new area was more energetically costly than tolerating the heat. Although all fish lost some of their energy stores over the summer due to the heat, the Upper Klamath Lake fish lost twice as much as the Keno trout. In fact, the Keno trout looked like they were recovering these stores by the end of the summer.Halhbeck and colleagues have shed new light onto how landscape determines how adaptable fish can be. With four of the six dams soon to be removed from the Klamath River Basin, what does this mean for fish that have adopted coping mechanisms based on this landscape for the last century? For now, Keno trout have a fin up on the Upper Klamath Lake trout, but we have much to learn about what choices these populations will make in a new barrier-free world.

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)0.002
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.033
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0330.023

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 head0.057
GPT teacher head0.325
Teacher spread0.268 · 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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