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Data and supplementary materials from: Large genetic divergence underpins cryptic local adaptation across ecological and evolutionary gradients

Cite this dataset

Sparks, Morgan (2022). Data and supplementary materials from: Large genetic divergence underpins cryptic local adaptation across ecological and evolutionary gradients [Dataset]. Dryad. https://doi.org/10.5061/dryad.sxksn036m

Abstract

Environmentally covarying local adaptation is a form of cryptic local adaptation in which the covariance of the genetic and environmental effects on a phenotype obscures the divergence between locally adapted genotypes. Here, we systematically document the magnitude and drivers of the genetic effect (VG) for two forms of environmentally covarying local adaptation: counter- and cogradient variation. Using a hierarchical Bayesian meta-analysis, we calculated the overall effect size of VG as 1.05 and 2.13 for populations exhibiting countergradient or cogradient variation, respectively. These results indicate that the genetic contribution to phenotypic variation represents a 1.05 to 2.13 standard deviation change in trait value between the most disparate populations depending on if populations are expressing counter- or cogradient variation. We also found that while there was substantial variance among abiotic and biotic covariates, the covariates with the largest mean effects were temperature (2.41) and gamete size (2.81). Our results demonstrate the pervasiveness and large genetic effects underlying environmentally covarying local adaptation in wild populations and highlights the importance of accounting for these effects in future studies.

Methods

To generate a full list of studies, we first analyzed studies indicated as showing counter- or cogradient variation in the qualitative analysis from Conover et al. [12]. We then searched the Web of Science topic field using the terms “counter*gradient variation” in May of 2018 and “co*gradient variation” (while excluding the previous search, as the wildcard renders them redundant) in June of 2019, resulting in 384 and 34 results, respectively. In August 2019, we also included studies citing the Conover et al. [12] review, as well as the earlier review article by Conover & Schultz [13]—682 studies total (some of which were redundant with studies in the prior search resulting in a total of 858 studies). These methods are discussed in more depth in the Supplementary Methods.

To be further included in our analysis, a study had to meet a set of qualifying criteria. Specifically, a study had to be designed to include some form of a common environment—this may have been in a common garden study or a reciprocal transplant using wild populations or populations recently brought into lab conditions. We did not include examples from domestic populations or lab induced selection. Additionally, studies must have included two or more common environments to determine the signal of local adaptation (countergradient, cogradient, or others such as GxE that were ultimately not included in this study), as well as two or more populations to compare the genotypic difference of the phenotypic response between populations. Finally, studies were determined to show counter- or cogradient based on plotted reaction norms to verify environmental and genetic effects covaried—i.e., populations in away environments minimized (countergradient) or maximized genetic variance (cogradient) (positive or negative covariance, Fig. 1c,d)—and genotypes maintained their rank order across environments (Fig. S1). Occasionally, studies investigated these distinctions across multiple species in the place of populations and such studies were discarded.

To calculate effect sizes, we took data from both tables and figures in manuscripts. For data in the form of figures, we used the software WebPlotDigitizer [39] to extract the relevant data. 

Usage notes

Any software able to open a Microsoft Excel file. 

.RDS files need to be opened with program R using the readRDS() function

Funding

National Science Foundation, Award: OCE-1924505

National Science Foundation, Award: DEB-1856710

Purdue University System, Award: Waser Fellowship