DOI: 10.5061/dryad.zcrjdfnjr
title: Data from: probing variation in reaction norms in wild populations: the importance of reliable environmental proxies

description: Many traits are phenotypically plastic, i.e., the same genotype expresses
 different phenotypes depending on the environment. Genotypes and
 individuals can vary in their response to the environment and this genetic
 (G×E) and individual (I×E) variation in reaction-norm slopes can have
 important ecological or evolutionary consequences. Studies on I×E/G×E
 often fail to show slope variation, potentially due to the choice of the
 environmental covariate. Identifying the genuine environmental driver of
 phenotypic plasticity (the cue) is practically impossible and hence only
 proxies can be used. If the proxy is too weakly correlated with the cue,
 this may lead researchers to conclude there is little or no (variation in)
 plasticity, and hence lead to downwardly biased estimates of the potential
 for plastic responses (or evolutionary change in the slope) in response to
 environmental change. Alternatively, the Environment-Specific Mean
 phenotype (ESM) across individuals—which captures all environmental
 effects on the phenotype—as covariate should be less prone to such bias.
 We showed by simulation—after verifying the concept analytically—that
 using weakly correlated proxies indeed biased estimates of slope variation
 vis-à-vis the true cue downward but that ESM as a covariate held up well,
 even when multiple sources of I×E or an interaction between environments
 (I×E×E) existed in the data. Analysis of two real datasets revealed that
 estimated I×E and G×E, respectively, were more sizeable and precise when
 using ESM as opposed to reasonably informative environmental proxies. We
 argue that the ESM approach should be adopted by biologists as a yardstick
 in the study of (variation in) plasticity in the wild and that it may
 serve as a useful starting point for the search of better environmental
 proxies and unravelling complex I×E or G×E patterns.
description: Data contain phenotypic breeding data and NAO data for the Common
 guillemot as described in Reed et al. (2006). These data were used as one
 of the two practical examples in the paper. See the readme file for
 details. For questions regarding these data, please contact Michael P.
 Harris (mph@ceh.ac.uk). For R
 scripts used to analyze the data, please see the supporting information to
 the main article associated with this dataset (Ramakers et al.
 2023). Ramakers, J.J.C., Reed, T.E., Harris, M.P.
 & Gienapp, P. (2023). Probing variation in reaction norms in wild
 populations: the importance of reliable environmental proxies. Oikos. doi:
 10.1111/oik.09592 Reed, T.E., Wanless,
 S., Harris, M.P., Frederiksen, M., Kruuk, L.E.B. & Cunningham,
 E.J.A. (2006) Responding to environmental change: plastic responses vary
 little in a synchronous breeder. <em>Proceedings of the Royal
 Society B: Biological Sciences,</em>
 <strong>273,</strong> 2713-2719.
description: The Guillemot data contain the following columns: ID: ID of the animal
 Count: number of times (years) an animal has been observed breeding YEAR:
 year of breeding Subcolony: cliff area in which the animal bred that year
 (SF = South Face C4 = Colony 4, WHI = Whiteface, HDE = Hide, DEN = Dense)
 Min.Age: minimum estimated age of the animal. LD: laying date (from
 January 1st). NAO: North-Atlantic Oscillation index. Note that Min.Age has
 some missing values that can be filled based on the year in which the
 animal was observed.