Computational protocol: Comparative Analysis of Japanese Three-Spined Stickleback Clades Reveals the Pacific Ocean Lineage Has Adapted to Freshwater Environments while the Japan Sea Has Not

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Protocol publication

[…] Two phylogenetic trees were estimated from microsatellite data in anadromous and freshwater populations from the Japan Sea and Pacific Ocean lineages using both Nei's D and δμ2 , . Briefly, fish (n = 249) were genotyped using 10 microsatellite markers (Stn170, Stn233, Stn64, Stn159, Stn46, Stn90, Stn120, Stn278, Stn332 and Stn384) located on different three-spined stickleback linkage groups not linked to sex , . While coalescent methods for estimation of population history from microsatellite markers are available , they do not integrate phylogenies across multiple markers and are not suitable for large numbers of populations. We used two metrics of genetic distance in order to account for the shortcomings of each metric; Nei's D performs best when divergence time is relatively recent whereas δμ2 performs better when divergence is older , . Pairwise matrices of genetic distances and UPGMA trees were estimated and bootstrapped 200 times using Populations . Phylogenetic trees were then pruned using the R package ape so that only populations with ecological data remained (n = 19 for stable isotope data, n = 24 for gill rakers) .To test for lineage specific rates of diversification for gill raker number and niche use (i.e. δ13C and δ15N) we first used the method developed by O'Meara et al. (2006). This allows phenotypic traits to evolve along a phylogeny under Brownian motion (BM) and estimates the likelihood of two models; a single rate only and separate rates (σ2) for the Japan Sea (JS) and Pacific Ocean (PO) lineages. Lineage was mapped onto each tree and nested Brownian motion models were fitted using the brownie.lite function in phytools , .While widely applied, BM is a neutral model and may not be applicable when examining adaptive traits as it does not account for selection . The Ornstein-Uhlenbeck (OU) model is an extension of BM including the parameters è and α; the optimum trait mean and the strength of selection against deviations from the optimum , . Using Butler & King's (2004) method we tested OU models with single optimal trait value for both lineages (OU1), lineage specific optimal trait values (OU2) and lineage specific values with third optimal value for Pacific Ocean freshwater populations (OU3). If adaptive divergence has occurred between lineages, multiple optimum value models would be supported. As before, lineage was mapped onto the tree and nested OU models were fitted using the hansen function in the ouch R package .Phylogenetic model choice is not straightforward as uninformative data may result in false positives using information criteria . Through simulations under contrasting models, parametric bootstrapping produces likelihood ratios distributions which the observed data can be compared to, providing an estimate of power and a means to distinguish models . Using the pmc R package and custom functions, we performed parametric bootstrapping for the BM, OU and BM vs. OU tests based on 1000 simulated datasets. R scripts and datasets used to perform these analyses are available at the Dryad repository (doi:10.5061/dryad.s8f74).Our final strategy was to perform both BM and OU tests on all trait and tree combinations choosing either a single or multiple parameter model based on the bootstrapped distributions. We then used bootstrapping to test whether it was possible to distinguish between the best-supported BM and OU models. Support for either a multiple rate BM model or a three optimum OU model would indicate a difference in diversification and adaptive divergence between the two stickleback lineages. […]

Pipeline specifications

Software tools APE, Phytools
Application Phylogenetics
Organisms Gasterosteus aculeatus, Spinachia spinachia