Variability, heritability and condition-dependence of the multidimensional male colour phenotype in a passerine bird

Which plumage patches provide information about condition and success in a female fairy-wren?

Recent evidence suggests that female ornaments can commonly act as signals. However, how signaling functions might be affected by the tendency for reduced ornament elaboration in relation to males is less well-understood. We address this in mutually ornamented purple-crowned fairy-wrens. We investigated putatively ornamental (tail, ear coverts, crown) and non-ornamental (throat, back) plumage patches in females and compared our findings to previous studies in males. Both sexes have brown backs, buff-white throats, and turquoise-blue tails (bluer in males), while ear coverts are rufous in females and black in males. Both sexes also have a seasonal crown (slate-gray in females, black-and-purple in males). Dominant (breeder) females expressed more complete and grayer (more ornamented) crowns, although variation in coloration should not be discriminable by individuals. Unexpectedly, subordinates showed more colorful (saturated) rufous ear coverts, which should be discriminable. Condition-dependence was only evident for crown completeness (% slate-gray cover). Females with more reddish-brown backs were more reproductively successful. Variation in plumage characteristics did not explain differential allocation by mates or chances of gaining dominance. Our outcomes were not entirely consistent with findings in males. The most notable disparity was for the crown, a signal used in male-male competition that in females seems to be expressed as an incomplete version of the male crown that is not associated with fitness benefits. Our study shows that in a species, multiple traits can vary in their information content and that female ornaments can sometimes be less informative than in males, even those that are produced seasonally.

Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses

Many important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K) to connect genotype to phenotype. Genomic relationship matrices share information among entries to estimate the observed entries’ genetic values and predict unobserved entries’ genetic values. One of the main parameters of such models is genomic variance (σg2), or the variance of a trait associated with a genome-wide sample of DNA polymorphisms, and genomic heritability (hg2); however, the seminal papers introducing different forms of K often do not discuss their effects on the model estimated variance components despite their importance in genetic research and breeding. Here, we discuss the effect of several standard methods for calculating the genomic relationship matrix on estimates of σg2 and hg2. With current approaches, we found that the genomic variance tends to be either overestimated or underestimated depending on the scaling and centering applied to the marker matrix (Z), the value of the average diagonal element of K, and the assortment of alleles and heterozygosity (H) in the observed population. Using the average semivariance, we propose a new matrix, KASV, that directly yields accurate estimates of σg2 and hg2 in the observed population and produces best linear unbiased predictors equivalent to routine methods in plants and animals.