Ponzi, EricaKeller, Lukas F.Bonnet, TimothéeMuff, Stefanie2025-03-302025-03-30researchoutputwizard:u9511635xPUB1903Scopus:85052846139WOS:WOS:000448834600002https://dspace-test.anu.edu.au/handle/1885/733747241Quantitative genetic analyses require extensive measurements of phenotypic traits, a task that is often not trivial, especially in wild populations. On top of instrumental measurement error, some traits may undergo transient (i.e., nonpersistent) fluctuations that are biologically irrelevant for selection processes. These two sources of variability, which we denote here as measurement error in a broad sense, are possible causes for bias in the estimation of quantitative genetic parameters. We illustrate how in a continuous trait transient effects with a classical measurement error structure may bias estimates of heritability, selection gradients, and the predicted response to selection. We propose strategies to obtain unbiased estimates with the help of repeated measurements taken at an appropriate temporal scale. However, the fact that in quantitative genetic analyses repeated measurements are also used to isolate permanent environmental instead of transient effects requires that the information content of repeated measurements is carefully assessed. To this end, we propose to distinguish “short-term” from “long-term” repeats, where the former capture transient variability and the latter help isolate permanent effects. We show how the inclusion of the corresponding variance components in quantitative genetic models yields unbiased estimates of all quantities of interest, and we illustrate the application of the method to data from a Swiss snow vole population.This work would not have been possible without generous funding from the Faculty of Science and the Forschungskredit of the University of Zurich, grant no. FK-16-097. The snow vole monitoring was supported by the Claraz-Donation, and by the Swiss National Science Foundation project grants 31003A 141110 and 31003A 159462/1 to E. Postma. We thank E. Postma, G. Camenisch, and P. Wandeler for data collection and conceptual input. The thoughtful inputs of two anonymous reviewers and the associate editor, M. Morrissey, greatly improved the manuscript. The authors declare they have no competing interests.13EnglishPublisher Copyright: © 2018 The Author(s). Evolution © 2018 The Society for the Study of Evolution.Animal modelBreeder's equationRobertson–Price identityerror variancepermanent environmental effectsquantitative genetics201810.1111/evo.13573http://www.scopus.com/inward/record.url?scp=85052846139&partnerID=8YFLogxK