For the data matrices, we estimated 100.000 trees at random from all possible tree topologies (replacements allowed).
As surrogates for the genetic covariance matrices, we estimated the pooled phenotypic within-population covariance matrix and the phenotypic between-population covariance matrix of the T. moorii populations (Cheverud, 1988; Roff, 1997).
After deriving this matrix, we estimate innovation linkages in East Asian countries and draw implications for growth strategies in the East Asian region and in the USA.
Then, using the elements, (r^{(l)}_{boldsymbol {theta }}[m-n]), extracted from the l th column of the covariance matrix, we estimate (as described later) from (14) the K+1 unknown filter coefficients.
For the IBD-segment-based relatedness matrix, we estimate f by detecting segments of homozygosity by descent (HBD) using Beagle (Browning and Browning 2010) and, for each individual, dividing the sum of HBD lengths by the length of the genome.
For the Open system matrix, we estimated a common survival for this early survival stage (coded as size state '0') across locations that provided a population growth rate of one.
G -matrices G-matrices We estimated the matrix of additive genetic variances and covariances (the G-matrix) for each Longshanks line separately using linear mixed effects models, also known as "animal models" [ 68].
Based on the binary data matrix, we estimated the total number of polymorphic loci and percentage polymorphic loci per primer combination.
We assumed that the matrix we estimated between generations 1 and 4 included both genetic and non-genetic effects (i.e. phenotypic plasticity, parental and grand-parental effects), and thus represented the P-matrix.
