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Record W2088562598 · doi:10.1242/jeb.077800

GROW FAST, DIE YOUNG

2013· article· en· W2088562598 on OpenAlexaff
Constance M. O’Connor

Bibliographic record

VenueJournal of Experimental Biology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLife history theoryJuvenileEcologyGasterosteusLife historyBiologyReproductionPremiseReproductive successFish <Actinopterygii>DemographyFisherySociologyPopulation

Abstract

fetched live from OpenAlex

Anyone who has seen a middle-aged rock star understands that certain lifestyles take more of a toll than others. Live fast, die young! This rock star mantra is also the premise of life-history theory, one of the central tenants of ecological research. Life-history traits are all of those characteristics related to an individual's lifestyle, such as growth rate, reproduction and lifespan. Life-history theory states that not all life-history traits can be maximized simultaneously, and individuals will need to make trade-offs among competing functions.Theoretically, one of the key life-history trade-offs may be between growth rate and total lifespan. However, it is difficult to manipulate growth without manipulating other confounding factors, like nutrient availability. Who-Seung Lee, Pat Monaghan and Neil Metcalfe from the University of Glasgow addressed this long-standing question by taking advantage of the biology of juvenile three-spined sticklebacks (Gasterosteus aculeatus). Like all fish, sticklebacks are cold-blooded, and their metabolism and hence growth can be sped up or slowed down by changing water temperature. Furthermore, sticklebacks reproduce in the spring, and as reproductive success is related to body size, they are motivated to attain a large size prior to their first spring.To examine the effect of growth rate on total lifespan, the scientists used water temperature and photoperiod to manipulate both how much the fish would need to grow and how fast they would have to grow prior to their first spring. The researchers predicted that higher growth rates would come at a cost to overall lifespan. Using juvenile fish captured in November, they experimentally manipulated the period available for growth; half the fish were kept under a normal photoperiod, while the other half were exposed to a delayed photoperiod, so that the fish perceived that they had an extra month before spring. Fish from both groups were then subjected to a ‘cold snap’ (6°C) or a ‘warm spell’ (14°C), or kept at a constant 10°C for 4 weeks. While all fish were fed the same diet, their metabolism, and therefore growth rate, was influenced by temperature. Thus, fish in the cold snap group grew more slowly than those in the other groups. After 4 weeks, all the fish were returned to 10°C for the rest of their lives.When the fish were returned to 10°C, they faced a resource allocation decision. The fish stunted by the cold snap were much smaller than their counterparts, and to attain reproductive size they would need to grow quickly by directing all of their resources towards growth. Conversely, fish exposed to a warm spell were already larger and could afford to grow at a more leisurely pace. These warm spell fish could use some resources to fuel other processes that are important for overall lifespan, such as the immune system. There was even less pressure on fish that had been subjected to a delayed photoperiod – they had an extra month for growth. But did different growth rates influence overall lifespan?Excitingly, the results matched the researchers' predictions. All of the fish had similar final sizes, but fish exposed to the cold snap directed more resources towards growth and grew more quickly once returned to 10°C, and consequently had the shortest lives. By replicating the experiment with fish captured in January, with mere weeks before spring, the researchers found that lifespans were shortened still further by the shortened growth time frame. This paper elegantly provides the first experimental evidence that growth rates are directly linked to total lifespan, and this indeed represents a key life-history trade-off. There is now empirical evidence that if you grow fast, you die young.

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)0.002
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 score0.071
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0710.048

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 head0.016
GPT teacher head0.282
Teacher spread0.265 · 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
Published2013
Admission routes1
Has abstractyes

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