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Record W2109252671 · doi:10.1073/pnas.0701638104

Mechanisms for climate-induced mortality of fish populations in whole-lake experiments

2007· article· en· W2109252671 on OpenAlexaff
Peter A. Biro, John R. Post, David J. Booth

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPredationBiologyClimate changeRainbow troutEcologyTroutPredatorFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

The effects of climate change on plant and animal populations are widespread and documented for many species in many areas of the world. However, projections of climate impacts will require a better mechanistic understanding of ecological and behavioral responses to climate change and climate variation. For vertebrate animals, there is an absence of whole-system manipulative experiments that express natural variation in predator and prey behaviors. Here we investigate the effect of elevated water temperature on the physiology, behavior, growth, and survival of fish populations in a multiple whole-lake experiment, by using 17 lake-years of data collected over 2 years with differing average temperatures. We found that elevated temperatures in excess of the optimum reduced the scope for growth through reduced maximum consumption and increased metabolism in young rainbow trout, Oncorhynchus mykiss. Increased metabolism at high temperatures resulted in increased feeding activity (consumption) by individuals to compensate and maintain growth rates similar to that observed at cooler (optimum) temperatures. However, greater feeding activity rates resulted in greater vulnerability to predators that reduced survival to only half that of the cooler year. Our work therefore identifies temperature-dependent physiology and compensatory feeding behavior as proximate mechanisms for substantial climate-induced mortality in fish populations at the scale of entire populations and waterbodies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.108
GPT teacher head0.363
Teacher spread0.255 · 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.

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

Citations127
Published2007
Admission routes1
Has abstractyes

Explore more

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