 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 chr22-3pop.vcf.gz                                                
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +                                                                +
 +   POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY  +
 +   Bayesian inference using the structured coalescent           +
 +                                                                +
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  Compiled for a PARALLEL COMPUTER ARCHITECTURE
  One master and 5 compute nodes are available.
  PDF output enabled [Letter-size]
  Version 6.0.1 [Mittag (merged with main Oct 11 2025)]   [October-11-2025]
  Program started at   Tue Jan  6 18:16:56 2026
         finished at Tue Jan  6 18:18:19 2026
                          


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)  Metropolis sampling
Migration rate      (M)  Metropolis sampling
Divergence Time (D)  Metropolis sampling
Divergence time spread (STD) Metropolis sampling
Genealogy                Metropolis-Hastings


Prior distribution (Proposal-delta will be tuned to acceptance frequency 0.440000):
Parameter group            Prior type   Minimum    Mean(*)    Maximum    Delta      Bins   Updatefreq
-------------------------  ------------ ---------- ---------- ---------- ---------- ------ -------
Population size (Theta_1)   Uniform        0.000000   0.050000   0.100000    0.010000   1500  0.05556
Population size (Theta_2)   Uniform        0.000000   0.050000   0.100000    0.010000   1500  0.05556
Population size (Theta_3)   Uniform        0.000000   0.050000   0.100000    0.010000   1500  0.05556
Migration 2 to 1   (M)      Uniform        0.000000  500.000000 1000.00000 100.000000   1500  0.05556
Migration 3 to 1   (M)      Uniform        0.000000  500.000000 1000.00000 100.000000   1500  0.05556
Migration 1 to 2   (M)      Uniform        0.000000  500.000000 1000.00000 100.000000   1500  0.05556
Migration 3 to 2   (M)      Uniform        0.000000  500.000000 1000.00000 100.000000   1500  0.05556
Migration 1 to 3   (M)      Uniform        0.000000  500.000000 1000.00000 100.000000   1500  0.05556
Migration 2 to 3   (M)      Uniform        0.000000  500.000000 1000.00000 100.000000   1500  0.05556
Datatype: DNA sequence data

Inheritance multipliers in use for Thetas (specified # 1)
All inheritance multipliers are the same [1.000000]

Pseudo-random number generator: Mersenne-Twister                                
Random number seed (with internal timer)           3686077306

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 Pop1           * * * 
   2 Pop2           * * * 
   3 Pop3           * * * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (long-inc*samples):              1000000
      Steps recorded (long-sample):                    10000
   Static heating scheme
      4 chains with  temperatures
       1.00, 1.50, 3.00,1000000.00
      Swapping interval is 1
   Burn-in per replicate (samples*inc):               100000

Print options:
   Data file:                                infile.explicit
   Parameter file:                     parmfile-xxxxxxxxx-5b
   Haplotyping is turned on:                              NO
   Output file (ASCII text):            outfile-xxxxxxxxx-5b
   Output file (PDF):               outfile-xxxxxxxxx-5b.pdf
   Print data:                                            No
   Print genealogies:                                     No



Bayesian estimates
==================

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00000  0.00060  0.00210  0.00367  0.01647  0.00317  0.00467
    1  Theta_2         0.00000  0.00007  0.00217  0.00453  0.02813  0.00443  0.00915
    1  Theta_3         0.00000  0.00053  0.00157  0.00247  0.00453  0.00197  0.00119
    1  M_2->1          80.6667 674.0000 705.6667 788.0000 993.3333 541.0000 535.5885
    1  M_3->1         212.0000 694.6667 965.6667 989.3333 999.3333 704.3333 661.7971
    1  M_1->2         153.3333 742.6667 879.0000 980.6667 986.6667 531.0000 522.7802
    1  M_3->2         213.3333 754.6667 958.3333 991.3333 999.3333 750.3333 697.0024
    1  M_1->3          23.3333  46.0000 136.3333 267.3333 856.6667 477.0000 485.1229
    1  M_2->3         167.3333 580.0000 687.0000 921.3333 990.6667 543.0000 533.5855
    2  Theta_1         0.00000  0.00053  0.00150  0.00240  0.00460  0.00197  0.00111
    2  Theta_2         0.00000  0.00000  0.00223  0.01693  0.08307  0.01697  0.02755
    2  Theta_3         0.00000  0.00047  0.00150  0.00233  0.00467  0.00197  0.00110
    2  M_2->1           2.0000  18.6667 130.3333 404.0000 838.0000 390.3333 422.8848
    2  M_3->1          16.0000 103.3333 273.6667 310.0000 942.0000 467.6667 476.9603
    2  M_1->2         150.0000 690.0000 956.3333 987.3333 999.3333 663.6667 626.9172
    2  M_3->2         275.3333 779.3333 965.0000 994.0000 999.3333 786.3333 732.1876
    2  M_1->3          87.3333 712.0000 777.0000 906.0000 993.3333 561.6667 548.0932
    2  M_2->3           8.0000  17.3333 213.0000 322.0000 858.6667 427.6667 450.4809
    3  Theta_1         0.00000  0.00013  0.00210  0.00447  0.04207  0.00430  0.00850
    3  Theta_2         0.00000  0.00033  0.00150  0.00260  0.02040  0.00230  0.00335
    3  Theta_3         0.00000  0.00033  0.00150  0.00253  0.01013  0.00223  0.00183
    3  M_2->1         124.6667 654.0000 966.3333 990.0000 999.3333 663.6667 618.3941
    3  M_3->1         114.6667 641.3333 959.0000 988.6667 999.3333 652.3333 610.0028
    3  M_1->2         12.00000 20.66667 89.66667 403.33333 855.33333 445.66667 464.90571
    3  M_3->2          70.6667 637.3333 858.3333 985.3333 993.3333 576.3333 554.3606
    3  M_1->3          14.0000 109.3333 206.3333 390.6667 911.3333 459.0000 472.8831
    3  M_2->3          77.3333 580.6667 965.6667 990.0000 996.6667 587.0000 561.1153
    4  Theta_1         0.00000  0.00053  0.00150  0.00247  0.00513  0.00203  0.00122
    4  Theta_2         0.00000  0.00040  0.00130  0.00213  0.00393  0.00177  0.00068
    4  Theta_3         0.00000  0.00000  0.00223  0.01920  0.08360  0.01923  0.02864
    4  M_2->1         110.6667 605.3333 942.3333 982.0000 998.6667 607.6667 580.0331
    4  M_3->1           6.6667  18.0000 102.3333 396.0000 902.6667 424.3333 446.8767
    4  M_1->2          30.6667 147.3333 244.3333 416.6667 960.6667 485.6667 491.8272
    4  M_3->2          2.00000 10.66667 41.00000 403.33333 884.00000 395.00000 427.07812
    4  M_1->3         219.3333 718.6667 965.0000 992.0000 999.3333 726.3333 683.1677
    4  M_2->3         252.6667 761.3333 965.6667 994.0000 999.3333 768.3333 714.8884
    5  Theta_1         0.00000  0.00000  0.00210  0.00573  0.06180  0.00577  0.01501
    5  Theta_2         0.00060  0.00100  0.00510  0.03920  0.08787  0.03883  0.04256
    5  Theta_3         0.00000  0.00040  0.00137  0.00213  0.00393  0.00177  0.00076
    5  M_2->1          26.0000  92.6667 154.3333 441.3333 952.6667 465.0000 478.1388
    5  M_3->1         166.0000 726.0000 964.3333 993.3333 999.3333 733.0000 679.3465
    5  M_1->2          99.3333 648.0000 825.0000 983.3333 998.0000 597.0000 574.1834
    5  M_3->2         490.6667 842.0000 965.6667 992.0000 999.3333 850.3333 814.7696
    5  M_1->3          0.00000 11.33333 98.33333 355.33333 875.33333 367.66667 407.07062
    5  M_2->3          0.00000  9.33333 93.00000 324.00000 866.66667 315.66667 363.28609
  All  Theta_1         0.00000  0.00053  0.00143  0.00227  0.00373  0.00177  0.00146
  All  Theta_2         0.00000  0.00067  0.00157  0.00240  0.00380  0.00183  0.00156
  All  Theta_3         0.00000  0.00033  0.00117  0.00193  0.00347  0.00157  0.00117
  All  M_2->1         166.0000 623.3333 767.0000 835.3333 991.3333 617.6667 596.4079
  All  M_3->1         394.6667 820.0000 963.0000 986.0000 999.3333 796.3333 757.6675
  All  M_1->2         196.0000 725.3333 960.3333 980.0000 996.6667 688.3333 647.1469
  All  M_3->2         695.3333 898.6667 955.6667 983.3333 999.3333 910.3333 884.0589
  All  M_1->3         162.6667 916.0000 955.6667 973.3333 990.6667 613.6667 595.8769
  All  M_2->3         167.3333 658.0000 843.0000 980.0000 997.3333 659.0000 619.9965
-----------------------------------------------------------------------------------



Log-Probability of the data given the model (marginal likelihood = log(P(D|thisModel))
--------------------------------------------------------------------
[Use this value for Bayes factor calculations:
BF = Exp[log(P(D|thisModel) - log(P(D|otherModel)]
shows the support for thisModel]



Locus          TI(1a)       BTI(1b)         HS(2)
-------------------------------------------------
      1      -1525.79      -1447.74      -1428.63
      2      -1499.39      -1420.04      -1404.00
      3      -1480.76      -1414.87      -1404.81
      4      -1493.34      -1415.90      -1400.57
      5      -1508.95      -1436.58      -1421.27
---------------------------------------------------------------
  All        -7507.08      -7133.97      -7099.51
[Scaling factor = 1.150568]


(1a) TI: Thermodynamic integration: log(Prob(D|Model)): Good approximation with many temperatures
(1b) BTI: Bezier-approximated Thermodynamic integration: when using few temperatures USE THIS!
(2)  HS: Harmonic mean approximation: Overestimates the marginal likelihood, poor variance



MCMC run characteristics
========================




Acceptance ratios for all parameters and the genealogies
---------------------------------------------------------------------

Parameter           Accepted changes               Ratio
Theta_1                 137543/278267            0.49428
Theta_2                 167609/277385            0.60425
Theta_3                 169273/277254            0.61053
M_2->1                  209934/278057            0.75500
M_3->1                  205090/277713            0.73850
M_1->2                  210630/277549            0.75889
M_3->2                  191506/277547            0.68999
M_1->3                  207866/277789            0.74829
M_2->3                  202123/277247            0.72904
Genealogies            1361722/2501192           0.54443



Autocorrelation for all parameters and the genealogies
-------------------------------------------------------------------

Parameter           Autocorrelation           Effective Sample size
Theta_1                   0.910                  2430.524
Theta_2                   0.782                  7191.699
Theta_3                   0.876                  3383.573
M_2->1                    0.384                 22310.578
M_3->1                    0.383                 22394.099
M_1->2                    0.391                 21939.036
M_3->2                    0.373                 23028.965
M_1->3                    0.379                 22615.403
M_2->3                    0.403                 21350.161
Genealogies               0.537                 15105.332
(*) averaged over loci.



Temperatures during the run using the standard heating scheme
===========================================================================

Chain Temperature               log(marginal likelihood)  log(mL_steppingstone)
    1    1.00000          -1411.85502  -1055.24673
    2    1.00000          -1416.42914  -693.58761
    3    1.00000          -1441.15648  -298.67775
    4    1.00000          -1882.85764   -21.32239

POTENTIAL PROBLEMS
------------------------------------------------------------------------------------------
This section reports potential problems with your run, but such reporting is often not 
very accurate. Whith many parameters in a multilocus analysis, it is very common that 
some parameters for some loci will not be very informative, triggering suggestions (for 
example to increase the prior range) that are not sensible. This suggestion tool will 
improve with time, therefore do not blindly follow its suggestions. If some parameters 
are flagged, inspect the tables carefully and judge wether an action is required. For 
example, if you run a Bayesian inference with sequence data, for macroscopic species 
there is rarely the need to increase the prior for Theta beyond 0.1; but if you use 
microsatellites it is rather common that your prior distribution for Theta should have a 
range from 0.0 to 100 or more. With many populations (>3) it is also very common that 
some migration routes are estimated poorly because the data contains little or no 
information for that route. Increasing the range will not help in such situations, 
reducing number of parameters may help in such situations.
------------------------------------------------------------------------------------------
Param 5 (Locus 1): Upper prior boundary seems too low! 
Param 6 (Locus 1): Upper prior boundary seems too low! 
Param 7 (Locus 1): Upper prior boundary seems too low! 
Param 9 (Locus 1): Upper prior boundary seems too low! 
Param 6 (Locus 2): Upper prior boundary seems too low! 
Param 7 (Locus 2): Upper prior boundary seems too low! 
Param 8 (Locus 2): Upper prior boundary seems too low! 
Param 4 (Locus 3): Upper prior boundary seems too low! 
Param 5 (Locus 3): Upper prior boundary seems too low! 
Param 7 (Locus 3): Upper prior boundary seems too low! 
Param 9 (Locus 3): Upper prior boundary seems too low! 
Param 4 (Locus 4): Upper prior boundary seems too low! 
Param 8 (Locus 4): Upper prior boundary seems too low! 
Param 9 (Locus 4): Upper prior boundary seems too low! 
Param 5 (Locus 5): Upper prior boundary seems too low! 
Param 6 (Locus 5): Upper prior boundary seems too low! 
Param 7 (Locus 5): Upper prior boundary seems too low! 
Param 5 (all loci): Upper prior boundary seems too low! 
Param 6 (all loci): Upper prior boundary seems too low! 
Param 7 (all loci): Upper prior boundary seems too low! 
Param 8 (all loci): Upper prior boundary seems too low! 
Param 9 (all loci): Upper prior boundary seems too low! 
------------------------------------------------------------------------------------------
