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Geographic variation in the use of torpor and roosting behaviour of female western long‐eared bats

2007· article· en· W2053214748 on OpenAlexaffabout
Donald I. Solick, Robert M. R. Barclay

Bibliographic record

VenueJournal of Zoology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTorporEcologyBiologyMicroclimateHomeothermyRange (aeronautics)ForagingThermoregulationZoology

Abstract

fetched live from OpenAlex

Abstract The costs and benefits of torpor may vary across the range of widespread heterothermic species, resulting in different thermoregulatory patterns and other behaviours for individuals inhabiting different environments. We compared torpor use and roosting behaviour for rock‐roosting populations of western long‐eared bats Myotis evotis living in the mountains and prairies of Alberta, Canada. We monitored body temperatures and located roosts for pregnant and lactating females using radio‐telemetry. We also took roost measurements, and assessed roost microclimate using temperature dataloggers. Females in the mountains entered torpor less frequently and spent less time in torpor than bats in the prairies, supporting our hypothesis that reproductive time constraints in the mountains outweigh the benefits of torpor. Alternatively, increased torpor use in the prairies may be attributed to a greater need for water conservation. Females in the mountains minimized thermoregulatory costs and maintained homeothermy by selecting exposed roosts with warm microclimates during pregnancy, and then switching to clustering with other individuals during lactation when conditions were cooler. In the prairies, females roosted alone in cooler, less exposed roost types, which facilitate the use of torpor. Our results illustrate the importance of considering geographic variation in behaviour across the range of a 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 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)0.001
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 score0.012
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.054
GPT teacher head0.250
Teacher spread0.196 · 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

Citations27
Published2007
Admission routes2
Has abstractyes

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