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Record W2971443555 · doi:10.1242/jeb.193011

Andean birds with slow metabolism live longer

2019· article· en· W2971443555 on OpenAlexaffabout
William Joyce

Bibliographic record

VenueJournal of Experimental Biology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBiologyBasal metabolic rateEcologyAltitude (triangle)Energy metabolismMetabolic rateEffects of high altitude on humansZoology

Abstract

fetched live from OpenAlex

Metabolism – the chemical process that generates energy for cells to function – is so fundamental to existence that it has been dubbed ‘the fire of life’. However, the debate over how the rate of metabolism can influence an animal's survival has burned on for decades. The ‘pace of life’ theory predicts that species with high metabolic rates accrue more damage and succumb to death earlier than animals with slower metabolism. Whilst this makes sense in theory – a gently burning flame lasts longer than a firework – experimentally proving it has been a tricky business.To shed light on how metabolic rate correlates with survival in wild tropical birds, a team led by Micah Scholer from the University of British Columbia, Canada, studied a diverse cohort of 37 species in the Peruvian Andes, from the humid foothills up to 3000 m altitude. The group caught birds in mist-nets and released them, tagged but unharmed, at different sites over several years to assess annual survival. They then combined this dataset with previously acquired measurements of basal metabolic rate of the same species from the same field sites – achieved by measuring oxygen consumption in resting animals – in a sophisticated mathematical model to dissect the interplay between metabolism, survival and habitat.Survival varied greatly between different species; in some cases, annual survival exceeded 70%, whereas in the most vulnerable species, less than half this proportion survived each year. The team further revealed that, although metabolic rate was similar between birds inhabiting different altitudes, montane species exhibited lower survival than those from the foothills. Avian life at high altitude is something of a double-edged sword; the harsher, drier environment provides challenges, yet this also discourages predators. It appears here that the costs of living at altitude outweighed the benefits of lower predation, although the research team was unable to exclude the possibility that the high-altitude birds were emigrating from the study site instead of perishing.The standout result was that, irrespective of altitude, the species with the lowest metabolic rate – or the slowest pace of life – indeed exhibited the highest rate of survival. Whether annual survival per se equates to evolutionary success is doubtful, because even the species with the lowest survival rate are, by definition, successful enough to prosper today. Apparently, they are able to reproduce effectively in their short but busy lifetime, which may be fuelled by their high metabolic rate. The fact that species with both short and long lifespans persist suggests that neither strategy is optimal.By revealing a well-defined link between metabolism and survival, this study not only represents a great advance for environmental physiology but also helps pave a path for future work. For example, researchers could measure metabolic rates in animals’ natural environments, which is tricky but technically feasible, to allow us to describe how metabolism changes seasonally in the different habitats. Ultimately, this will help paint an ever-clearer picture of how and why survival and metabolism vary between different species.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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
Published2019
Admission routes2
Has abstractyes

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