Limited mass-independent individual variation in resting metabolic rate in a wild population of snow voles (Chionomys nivalis).
adaptation
constraints
measurement error
metabolism
mixed model
repeatability
rodent
Journal
Journal of evolutionary biology
ISSN: 1420-9101
Titre abrégé: J Evol Biol
Pays: Switzerland
ID NLM: 8809954
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
27
08
2019
revised:
15
01
2020
accepted:
19
01
2020
pubmed:
28
1
2020
medline:
3
7
2021
entrez:
28
1
2020
Statut:
ppublish
Résumé
Resting metabolic rate (RMR) is a potentially important axis of physiological adaptation to the thermal environment. However, our understanding of the causes and consequences of individual variation in RMR in the wild is hampered by a lack of data, as well as analytical challenges. RMR measurements in the wild are generally characterized by large measurement errors and a strong dependency on mass. The latter is problematic when assessing the ability of RMR to evolve independently of mass. Mixed models provide a powerful and flexible tool to tackle these challenges, but they have rarely been used to estimate repeatability of mass-independent RMR from field data. We used respirometry to obtain repeated measurements of RMR in a long-term study population of snow voles (Chionomys nivalis) inhabiting an environment subject to large circadian and seasonal fluctuations in temperature. Using both uni- and bivariate mixed models, we quantify individual repeatability in RMR and decompose repeatability into mass-dependent and mass-independent components, while accounting for measurement error. RMR varies among individuals, that is, is repeatable (R = .46) and strongly co-varies with BM. Indeed, much of the repeatability of RMR is attributable to individual variation in BM, and the repeatability of mass-independent RMR is reduced by 41% to R = .27. These empirical results suggest that the evolutionary potential of RMR independent of mass may be severely constrained. This study illustrates how to leverage bivariate mixed models to model field data for metabolic traits, correct for measurement error and decompose the relative importance of mass-dependent and mass-independent physiological variation.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
608-618Informations de copyright
© 2020 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2020 European Society For Evolutionary Biology.
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