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Record W2598574831 · doi:10.1242/jeb.147322

Too much hibernation isn't always good for you

2017· article· en· W2598574831 on OpenAlexaboutno aff
Julia Nowack

Bibliographic record

VenueJournal of Experimental Biology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTorporHibernation (computing)BiologyPredationEcologyZoologyThermoregulation

Abstract

fetched live from OpenAlex

Many animals undergo phases of torpor by reducing body temperature and metabolic rate, which ultimately lowers their energy demands during harsh periods. Torpor use and especially hibernation, during which animals remain torpid in protected burrows or nests for months, are generally viewed as very advantageous survival mechanisms. Hibernating animals are less often subject to predation than non-hibernators and can withstand long periods when food is scarce. But surprisingly, individuals often vary in their use of torpor, leading to the question why do some individuals use less torpor than others if torpor is such a beneficial strategy?Melanie Dammhahn, a researcher from the University of Potsdam in Germany, and colleagues from the Université du Québec à Montréal and McGill University in Canada analysed torpor patterns of free-ranging eastern chipmunks (Tamias striatus) to find out why some individuals forgo torpor when it has clear survival benefits. Eastern chipmunks are relatively small seasonal hibernators that regularly interrupt torpor bouts during winter to feed on food reserves stored in their burrows. The researchers collected skin temperatures of 55 chipmunks over five winters, analysed the amount of time that each chipmunk spent torpid and linked this to the animals’ survival and birth rates.Dammhahn and colleagues found that, as expected, the use of torpor varied between individuals. Even more interesting, chipmunks maintained a stable torpor pattern over the whole hibernation season; animals that used less torpor at the beginning of the hibernation season also used less torpor later in winter. Linking the chipmunks’ skin temperature measurements with their survival rates, the scientists found that chipmunks using less torpor in autumn had higher death rates in years when food was plentiful, but not when food was scarce. Additionally, they found that the chipmunks that used more torpor early on and survived until the breeding season produced fewer offspring in spring.While a lower birth rate in animals that use more torpor suggests that increased torpor use could be related to a slower pace of life – as animals increase their survival chances via torpor and spread their reproductive output over more years – the other results are not as easy to explain. Variation in torpor use might reflect individual personalities or could just be caused by differences in food availability in the animals’ habitats.Extended torpor use may also be beneficial as individuals entering torpor early in autumn can spend more time underground and have a lower risk of predation, whereas animals that remain active have a higher risk of falling prey to a predator. In a year when food is abundant, an animal retreating underground early will probably have enough food stored to survive the entire winter season. However, in years when food is scarce, an animal that reduces its use of torpor – remaining active for longer in autumn – would probably have the advantage of establishing a larger food cache, thereby increasing its survival chances.While it remains a matter of speculation whether the variation in torpor use is part of a pace-of-life syndrome or is simply caused by differences in food availability around burrows, one thing is clear: variation in torpor patterns within a population guarantees that some individuals will survive the winter regardless of the environmental conditions.

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 score0.012
Threshold uncertainty score0.039

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 studies0.0030.004
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0120.007

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 head0.045
GPT teacher head0.309
Teacher spread0.264 · 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
Published2017
Admission routes1
Has abstractyes

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