DOI: 10.5061/dryad.6qd88
title: Data from: Are molecular markers useful predictors of adaptive potential?

description: Estimates of molecular genetic variation are often used as a cheap and
 simple surrogate for a population's adaptive potential, yet empirical
 evidence suggests they are unlikely to be a valid proxy. However, this
 evidence is based on molecular genetic variation poorly predicting
 estimates of adaptive potential rather than how well it predicts true
 values. As a consequence, the relationship has been systematically
 underestimated and the precision with which it could be measured severely
 overstated. By collating a large database, and using suitable statistical
 methods, we obtain a 95% upper bound of 0.26 for the proportion of
 variance in quantitative genetic variation explained by molecular
 diversity. The relationship is probably too weak to be useful, but this
 conclusion must be taken as provisional: less noisy estimates of
 quantitative genetic variation are required. In contrast, and perhaps
 surprisingly, current sampling strategies appear sufficient for
 characterising a population's molecular genetic variation at
 comparable markers.
description: LociLoci contains
 information from both single locus and multiple loci estimates with the
 following fields: # Study: Reference # Population: Population description
 given in the source paper # Species: Species # Year: Year measurements
 made # Season: Season measurements made # Age.Class: Adults or chicks #
 Sample.size: Number of individuals the estimate is based on #
 Type.of.loci: Molecular marker used # Total.loci: Number of loci the
 estimate is based on # Number of sites: Only relevant for nucleotide
 diversity estimates # Locus: Name of the locus # Mean.diversity: Average
 diversity for either single or multilocus estimates # SE.Mean.Diversity:
 Any standard errors reported # SD.Mean.Diversity: Any standard deviations
 reported # Diversity.Measure: Heterozygosity (Microsatellite, Allozyme,
 AFLP) or pi (Nucleotide diversities) # Type.of.chromosome: Autosome or sex
 # Type.of.site: For nucleotide diversities, i.e. silent, intronic,
 non-coding, synonymous, mixed sites # Sampling strategy: Estimate from
 single or multiple populationsQuanQuan contains the
 heritability and coefficients of additive genetic variance estimates with
 the following fields: # Study: Reference # Population: Population
 description given in the source paper # Type.of.relative: The type of
 relatives used in the estimation. AM = animal model, C = clonal, FS =
 full-sib, HS = half-sib, MPO = mid-parent-offspring, SPO =
 single-parent-offspring # Male.Female: Was a maternal or paternal parent
 used in SPO or forming a HS family # Lab.Field: Lab = laboratory
 estimates, Field = wild estimates, Field/Lab = parental measurements made
 in the wild and F1 estimates made in a laboratory # Species: Species #
 Trait: The trait measured (this is copied and pasted from the original
 study removing case and white space is necessary) # Group: Information on
 the experimental groups used. E.g. experimentally different temperatures #
 Trait.From.Hansen.et.al: Is the initial information for an estimate
 sourced from Hansen et al. (2011)? Additional information has been added
 here. # Trait.Type: Classification of the trait into B = behaviour, LH =
 life-history, M = morphological, P = physiological # Sample.size: Number
 of individuals used in the estimation # Trait.Mean: Average value for the
 trait # SD.Trait.Mean: Any standard deviations reported #
 Precision.Trait.Mean: Any precision reported #
 Precision.Measure.Trait.Mean: SE = standard error, CI - Confidence
 intervals, Range # Heritability: Heritability estimate reported #
 SD.Heritability: Any standard deviations reported #
 Precision.Heritability: Any precision reported #
 Precision.Measure.Heritability: SE = standard error, CI - Confidence
 intervals # Var.H2: Variation in heritability estimate # Cva: Coefficient
 of additive genetic variance # SE.ia: Standard error reported for
 CVaScriptR script to process data
 and run models.QuantitativeVsMolecular.R