++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + + POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY + + Bayesian inference using the structured coalescent + + + ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Compiled for a PARALLEL COMPUTER ARCHITECTURE One master and 10 compute nodes are available. PDF output enabled [Letter-size] Version 4.4.4(git:) [June-1-2019] Program started at Sun Oct 17 21:12:30 2021 Reading (1) Luzon ... Reading (2) N_Occidental ... Reading (3) Mindanao ... Options in use: --------------- Analysis strategy is BAYESIAN INFERENCE - Population size estimation: Theta [Exponential Distribution] - Geneflow estimation: Migration [Exponential Distribution] Proposal distribution: Parameter group Proposal type ----------------------- ------------------- Population size (Theta) Slice sampling Migration rate (M) Slice sampling Divergence Time (D) Slice sampling Divergence time spread (STD) Slice sampling Genealogy Metropolis-Hastings Prior distribution (Proposal-delta will be tuned to acceptance frequence 0.440000): Parameter group Prior type Minimum Mean(*) Maximum Delta Bins Updatefreq ------------------------- ------------ ---------- ---------- ---------- ---------- ------ ------- Population size (Theta_1) Exponential 0.000000 15.000000 30.000000 - 500 0.05556 Population size (Theta_2) Exponential 0.000000 15.000000 30.000000 - 500 0.05556 Population size (Theta_3) Exponential 0.000000 15.000000 30.000000 - 500 0.05556 Migration 2 to 1 (M) Exponential 0.000000 250.000000 500.000000 - 500 0.05556 Migration 3 to 1 (M) Exponential 0.000000 250.000000 500.000000 - 500 0.05556 Migration 1 to 2 (M) Exponential 0.000000 250.000000 500.000000 - 500 0.05556 Migration 3 to 2 (M) Exponential 0.000000 250.000000 500.000000 - 500 0.05556 Migration 1 to 3 (M) Exponential 0.000000 250.000000 500.000000 - 500 0.05556 Migration 2 to 3 (M) Exponential 0.000000 250.000000 500.000000 - 500 0.05556 Datatype: Microsatellite data [Brownian motion] Missing data is included Inheritance scalers in use for Thetas (specified scalars=10) All inheritance scalars are the same [1.0] [Each Theta uses the (true) inheritance scalar of the first locus as a reference] Pseudo-random number generator: Mersenne-Twister Random number seed (with internal timer) 2031700342 Start parameters: First genealogy was started using a random tree Start parameter values were generated Connection matrix: m = average (average over a group of Thetas or M, s = symmetric migration M, S = symmetric 4Nm, 0 = zero, and not estimated, * = migration free to vary, Thetas are on diagonal d = row population split off column population D = split and then migration 1 Luzon * * * 2 N_Occident * * * 3 Mindanao * * * Mutations rate among loci is varying with Rates per locus: 1.316, 1.053, 1.579, 1.053, 0.789, 1.316, 0.526, 0.789, 0.789, 0.789 [Estimated from the data using the Watterson estimator (ignoring migration)] Markov chain settings: Long chains (long-chains): 1 Steps sampled (long-inc*samples): 500000 Steps recorded (long-sample): 5000 Static heating scheme 4 chains with temperatures 1.00, 1.50, 3.00,100000.00 Swapping interval is 1 Burn-in per replicate (samples*inc): 100000 Print options: Data file: infile Haplotyping is turned on: NO Output file (ASCII text): outfile_Infile3modelPanmitic Output file (PDF): Infile3modelPanmitic.pdf Posterior distribution: bayesfile Print data: No Print genealogies: Yes, only the best Empirical Empirical Empirical Empirical Empirical Empirical Empirical Empirical Empirical Base Frequencies Base Frequencies ------------------------------------------------------------ ------------------------------------------------------------ Locus Sublocus Nucleotide Model parameters/ Base Frequencies Locus Sublocus Nucleotide Model parameters/ ------------------------------ ------------------------------------------------------------ A C G T(U) ------------------------------ Locus Sublocus Nucleotide Model parameters/ ---------------------------------------------------------------------- A C G T(U) ------------------------------ ---------------------------------------------------------------------- Locus Sublocus Region type Rate of change Probability Patch size Base Frequencies Base Frequencies A C G T(U) -------------------------------------------------------------------------- ------------------------------------------------------------ ------------------------------------------------------------ ---------------------------------------------------------------------- Locus Sublocus Region type Rate of change Probability Patch size Locus Sublocus Nucleotide Model parameters/ Locus Sublocus Region type Rate of change Probability Patch size 3 1 1 1.000 1.000 1.000 Locus Sublocus Nucleotide Model parameters/ Base Frequencies ------------------------------ Base Frequencies -------------------------------------------------------------------------- Base Frequencies -------------------------------------------------------------------------- ------------------------------ A C G T(U) Base Frequencies ------------------------------------------------------------ ------------------------------------------------------------ 7 1 1 1.000 1.000 1.000 10 1 1 1.000 1.000 1.000 A C G T(U) ------------------------------------------------------------ Locus Sublocus Nucleotide Model parameters/ ---------------------------------------------------------------------- Locus Sublocus Nucleotide Model parameters/ ------------------------------------------------------------ ---------------------------------------------------------------------- ------------------------------ Locus Sublocus Nucleotide Model parameters/ Locus Sublocus Region type Rate of change Probability Patch size Locus Sublocus Region type Rate of change Probability Patch size ------------------------------ Empirical A C G T(U) -------------------------------------------------------------------------- A C G T(U) ------------------------------ -------------------------------------------------------------------------- Locus Sublocus Nucleotide Model parameters/ ---------------------------------------------------------------------- ---------------------------------------------------------------------- A C G T(U) ------------------------------ Locus Sublocus Region type Rate of change Probability Patch size ---------------------------------------------------------------------- Locus Sublocus Region type Rate of change Probability Patch size -------------------------------------------------------------------------- A C G T(U) 2 1 1 1.000 1.000 1.000 6 1 1 1.000 1.000 1.000 Locus Sublocus Region type Rate of change Probability Patch size -------------------------------------------------------------------------- ---------------------------------------------------------------------- -------------------------------------------------------------------------- Locus Sublocus Region type Rate of change Probability Patch size -------------------------------------------------------------------------- 4 1 1 1.000 1.000 1.000 9 1 1 1.000 1.000 1.000 8 1 1 1.000 1.000 1.000 1 1 1 1.000 1.000 1.000 Base Frequencies ------------------------------------------------------------ Locus Sublocus Nucleotide Model parameters/ ------------------------------ A C G T(U) ---------------------------------------------------------------------- Locus Sublocus Region type Rate of change Probability Patch size -------------------------------------------------------------------------- 5 1 1 1.000 1.000 1.000 [ 6] 21:12:30 Burn-in of 100000 steps (Locus: 6/10, Replicate: 1/1) [ 10] 21:12:30 Burn-in of 100000 steps (Locus: 10/10, Replicate: 1/1) [ 9] 21:12:30 Burn-in of 100000 steps (Locus: 9/10, Replicate: 1/1) [ 8] 21:12:30 Burn-in of 100000 steps (Locus: 8/10, Replicate: 1/1) [ 7] 21:12:30 Burn-in of 100000 steps (Locus: 7/10, Replicate: 1/1) [ 1] 21:12:30 Burn-in of 100000 steps (Locus: 1/10, Replicate: 1/1) [ 2] 21:12:30 Burn-in of 100000 steps (Locus: 2/10, Replicate: 1/1) [ 3] 21:12:30 Burn-in of 100000 steps (Locus: 3/10, Replicate: 1/1) [ 5] 21:12:30 Burn-in of 100000 steps (Locus: 5/10, Replicate: 1/1) [ 4] 21:12:30 Burn-in of 100000 steps (Locus: 4/10, Replicate: 1/1)