BA3‐SNPs: Contemporary migration reconfigured in BayesAss for next‐generation sequence data

Abstract

Quantifying “demographic independence” is a vital step in establishing potential conservation units for a species in that it effectively distinguishes migration from within‐population reproduction. This is an important aspect because it allows for an accurate estimate of recruitment. For example, populations may be designated as 'management units' (=MUs) if indeed population growth results from local demography rather than immigration. Of additional interest is the calculation of immigrant ancestry and ascertainment of the temporal context over which immigration occurred. This is because MUs depend largely upon local (self‐sustaining) birth and death rates, and the quantification of ancestry is necessary to validate demographic independence. Dispersal rate is also of immediate interest to conservation biologists, and can be assessed by quantifying genetic divergence among populations. The capacity with which to gauge these benchmarks has now been extended herein to genome‐wide molecular data, in an attempt to adjust an analytical tool that was until now intractable for the next generation sequencing data. In this study, a popular legacy program for migrant detection (i.e. BayesAss3) has been modified to accept SNP (single nucleotide polymorphism) data. We validated BA3‐SNPs using empirical data to demonstrate its suitability for both high‐performance and desktop computing environments. We also facilitate high analytical throughput by presenting a binary search algorithm that automates MCMC (Markov chain Monte Carlo) parameter tuning. Our BA3‐SNPs‐autotune program required five or fewer rounds of optimization for 99% of input files, with acceptable mixing parameters derived in 100% of our test cases. Runtime for BA3‐SNPs is a function of the number of loci analysed. Benchmarking yielded an average runtime <32 hr (10 million MCMC generations) for datasets containing thousands of SNPs. The BA3 algorithm remains a viable option for analysing modern SNP datasets. Source code (C++ and Python) is released publicly under the GNU General Public License v3.0, and is available for download (Linux and Mac OSX) from the following URL: .

Mixed support for gene flow as a constraint to local adaptation and contributor to the limited geographic range of an endemic salamander

“…BayesAss estimates the posterior probability distribution of the proportion of migrants between all pairwise populations. Thus, this method allowed us to identify recent asymmetric migration by determining the average proportion of migrants (m P ) that are assigned to populations that are closer to the periphery of the distribution (Mussmann et al., 2019; Wilson & Rannala, 2003). For each lineage, we ran MCMC chains 10 identical times for 10 × 10 5 generations (2 × 10 6 burnin) with a sampling frequency of 2 × 10 3 and chose the run with the lowest Bayesian deviance for further analyses to overcome poor MCMC sampling (see Faubet et al., 2007).…”

Gene flow in the Antarctic bivalve Aequiyoldia eightsii (Jay, 1839) suggests a role for the Antarctic Peninsula Coastal Current in larval dispersal

“…Given that A. eightsii has a pelagic life stage, and its dispersal is likely to be influenced by ocean currents, rates of migration were tested using divM igrate -online [ 33 ] and B ayes A ss 3-SNPS [ 34 ]. The divM igrate -online method is based on defining a hypothetical pool of migrants for a given pair of populations and estimating an appropriate measure of genetic differentiation between each of the two populations and the hypothetical pool.…”

Genomic population structure of sympatric sexual and asexual populations in a parasitic wasp, Meteorus pulchricornis (Hymenoptera: Braconidae), inferred from six hundred single‐nucleotide polymorphism loci

“…bayesass3‐snps (Mussmann et al, 2019; Wilson & Rannala, 2003) was used to estimate recent migration rates among populations through MCMC steps. ba3‐autotune was used to select the change size of the parameter value (mixing parameter) at each iteration of the MCMC in bayesass3‐snps as follows: mixing parameter for migration rates (m) 0.15625, mixing parameter for allele frequencies (a) 0.325, mixing parameter for inbreeding coefficients (f) 0.100.…”