Computational protocol: Intraspecific evolutionary relationships among peregrine falcons in western North American high latitudes

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Protocol publication

[…] Details of analyses to assess levels of population and regional genetic structure are provided in . Significance of spatial variation in microsatellite allele frequencies between populations was assessed using F-statistics (FST and RST) and their analogs ([,], respectively), using several computer programs routinely used to analyze genetic data [–]. Significance of FST and RST values were based on random permutation tests (n = 1,000); Critical α-values were adjusted using Bonferroni corrections. Because populations are represented by unequal sample sizes [], we also estimated population differentiation based on the distribution of microsatellite alleles across populations, using Fisher’s combined probability test [,]; significance was judged based on random permutations tests (n = 1,000) across loci between populations and adjusting critical α-values using Bonferroni tests. We used the likelihood ratio test criterion in MODELTEST 3.06 [] to determine the evolutionary distance model that best fit the mtDNA control region sequence data and applied those weighted distances to calculate population pairwise FST (ϕST; []) and test for significance []. Population differentiation estimated from the distributions of mtDNA haplotypes were based on the log-likelihood (G) test [] with significance based on random permutation tests (n = 1,000). Microsatellite data were also analyzed using Bayesian cluster analyses [,] to detect the occurrence of population structure without a priori knowledge of putative populations.We used hierarchical analyses of molecular variance (AMOVA, []) to test for significant geographic partitioning of a priori hypothesized regional units (subspecies), using ARLEQUIN 3.1 []. Calculations for mtDNA sequence data incorporated a distance matrix based on the model of evolution determined by MODELTEST that best fit the data, and were also conducted without applying a distance matrix. In addition to the a priori groups of testable subspecies hypotheses applicable to peregrine falcons of North America, we experimented with various a posteriori groups in AMOVA analyses for both marker types that tested relationships of certain populations to named subspecies (e.g., the placement of SCCOA and SJI relative to anatum and pealei). We assumed that the best geographic subdivisions were significantly different from random distributions and had maximum among group variance (ΦCT values). We also examined regional interrelationships by constructing and visualizing a network based on Cavalli-Sforza and Edwards [] genetic distances (CSE) among microsatellite loci and neighbor-joining methods [–]. Two analyses were conducted: 1) populations were analyzed separately, and 2) populations were pooled into assigned subspecies, except for F. p. pealei, which was partitioned into the Aleutian group and the eastern group, based on results of population differentiation analyses. […]

Pipeline specifications

Software tools ModelTest-NG, Arlequin
Applications Phylogenetics, Population genetic analysis
Organisms Falco peregrinus