Running GARLI-PART Version 2.0.1008 (17 Mar 2011)
->Single processor version<-

##############################################################
 This is GARLI 2.0, the first "official" release including 
          partitioned models.  It is a merging of
   official release 1.0 and beta version GARLI-PART 0.97
  Briefly, it includes models for nucleotides, amino acids,
 codons, and morphology-like characters, any of which can be 
  mixed together and applied to different subsets of data.

    General program usage is extensively documented here:
            http://www.nescent.org/wg_garli/
      see this page for details on partitioned usage:
  http://www.nescent.org/wg_garli/Partition_testing_version
   and this page for details on Mkv mophology model usage:
    http://www.nescent.org/wg_garli/Mkv_morphology_model
         PLEASE LET ME KNOW OF ANY PROBLEMS AT:
                garli.support@gmail.com
##############################################################
This version has undergone much testing, but is still a BETA VERSION.
   - Please check results carefully! -
Compiled Mar 21 2011 13:13:18 using Intel icc compiler version 9.10
Using NCL version 2.1.10

#######################################################
Reading config file garli.conf
###################################################
READING OF DATA
Attempting to read data file in Nexus format (using NCL):
	zakonEtAl2006.11tax.nex ...
Reading DATA block... successful
Reading SETS block... successful

###################################################
PARTITIONING OF DATA AND MODELS

CHECK: ONE MODEL TYPE APPLIES TO ALL DATA SUBSETS,
	BUT WITH INDEPENDENT MODEL PARAMETERS (no linkage)

GARLI data subset 1
	CHARACTERS block #1 ("Untitled DATA Block 1")
	CHARPARTITION subset #1 ("1stpos")
	Data read as Nucleotide data,
	modeled as Nucleotide data
	Summary of data:
	  11 sequences.
	  441 constant characters.
	  171 parsimony-informative characters.
	  114 uninformative variable characters.
	  726 total characters.
	  238 unique patterns in compressed data matrix.
	Pattern processing required < 1 second

GARLI data subset 2
	CHARACTERS block #1 ("Untitled DATA Block 1")
	CHARPARTITION subset #2 ("2ndpos")
	Data read as Nucleotide data,
	modeled as Nucleotide data
	Summary of data:
	  11 sequences.
	  528 constant characters.
	  90 parsimony-informative characters.
	  108 uninformative variable characters.
	  726 total characters.
	  158 unique patterns in compressed data matrix.
	Pattern processing required < 1 second

GARLI data subset 3
	CHARACTERS block #1 ("Untitled DATA Block 1")
	CHARPARTITION subset #3 ("3rdpos")
	Data read as Nucleotide data,
	modeled as Nucleotide data
	Summary of data:
	  11 sequences.
	  103 constant characters.
	  507 parsimony-informative characters.
	  116 uninformative variable characters.
	  726 total characters.
	  549 unique patterns in compressed data matrix.
	Pattern processing required < 1 second


###################################################
NOTE: Unlike many programs, the amount of system memory that Garli will
use can be controlled by the user.
(This comes from the availablememory setting in the configuration file.
Availablememory should NOT be set to more than the actual amount of 
physical memory that your computer has installed)

For this dataset:
 Mem level		availablememory setting
  great			    >= 11 MB
  good			approx 10 MB to 9 MB
  low			approx 8 MB to 5 MB
  very low		approx 4 MB to 4 MB
the minimum required availablememory is 4 MB

You specified that Garli should use at most 512.0 MB of memory.

Garli will actually use approx. 16.1 MB of memory
**Your memory level is: great (you don't need to change anything)**

#######################################################
Found outgroup specification:  1

#######################################################
STARTING RUN

>>>Search rep 1 (of 5)<<<
MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3157 0.1746 0.3004 0.2093 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2703 0.1566 0.1628 0.4103 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1460 0.3609 0.2915 0.2015 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=406932

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 210.740 lnL
Optimizing branchlengths... improved 33.602 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13863.3954
optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates...
pass 1:+  205.400 (branch=   9.43 scale=  1.70 alpha=  5.05 freqs= 34.11 rel rates= 96.25 subset rates= 58.87)
pass 2:+  100.305 (branch=   8.26 scale=  1.50 alpha=  6.51 freqs= 16.99 rel rates=  8.80 subset rates= 58.25)
pass 3:+   45.314 (branch=   2.13 scale=  2.25 alpha=  3.89 freqs=  1.27 rel rates=  0.79 subset rates= 34.98)
pass 4:+   16.505 (branch=   0.00 scale=  0.00 alpha=  0.78 freqs=  0.61 rel rates=  2.25 subset rates= 12.86)
pass 5:+    4.860 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.14 rel rates=  0.01 subset rates=  4.70)
pass 6:+    0.817 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.12 rel rates=  0.69 subset rates=  0.00)
pass 7:+    0.114 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.09 rel rates=  0.01 subset rates=  0.00)
lnL after optimization: -13490.0811
gen      current_lnL    precision  last_tree_imp  
0        -13490.0811      0.500           0 
100      -13324.3912      0.500          55 
200      -13323.1357      0.500          55 
300      -13322.6035      0.500          55 
400      -13322.4782      0.500          55 
500      -13321.8755      0.500          55 
600      -13321.4740      0.500          55 
Optimization precision reduced 
   Optimizing parameters...    improved    1.779 lnL
   Optimizing branchlengths... improved    0.000 lnL
700      -13318.7011      0.402          55 
800      -13318.4686      0.402          55 
900      -13318.2340      0.402          55 
1000     -13318.0609      0.402          55 
1100     -13317.9954      0.402          55 
Optimization precision reduced 
   Optimizing parameters...    improved    0.036 lnL
   Optimizing branchlengths... improved    0.000 lnL
1200     -13317.8825      0.304          55 
1300     -13317.8431      0.304          55 
1400     -13317.7822      0.304          55 
1500     -13317.7491      0.304          55 
1600     -13317.7135      0.304          55 
Optimization precision reduced 
   Optimizing parameters...    improved    0.009 lnL
   Optimizing branchlengths... improved    0.000 lnL
1700     -13317.6211      0.206          55 
1800     -13317.5931      0.206          55 
1900     -13317.5875      0.206          55 
2000     -13317.5820      0.206          55 
2100     -13317.5775      0.206          55 
Optimization precision reduced 
   Optimizing parameters...    improved    0.004 lnL
   Optimizing branchlengths... improved    0.000 lnL
2200     -13317.5723      0.108          55 
2300     -13317.5679      0.108          55 
2400     -13317.5652      0.108          55 
2500     -13317.5605      0.108          55 
2600     -13317.5541      0.108          55 
Optimization precision reduced 
   Optimizing parameters...    improved    0.001 lnL
   Optimizing branchlengths... improved    0.000 lnL
2700     -13317.5471      0.010          55 
2800     -13317.5389      0.010          55 
2900     -13317.5358      0.010          55 
3000     -13317.5275      0.010          55 
3100     -13317.5268      0.010          55 
3200     -13317.5259      0.010          55 
3300     -13317.5245      0.010          55 
3400     -13317.5228      0.010          55 
3500     -13317.5221      0.010          55 
3600     -13317.5218      0.010          55 
3700     -13317.5218      0.010          55 
3800     -13317.5192      0.010          55 
3900     -13317.5162      0.010          55 
4000     -13317.5142      0.010          55 
4100     -13317.5139      0.010          55 
4200     -13317.5138      0.010          55 
4300     -13317.5138      0.010          55 
4400     -13317.5137      0.010          55 
4500     -13317.5132      0.010          55 
4600     -13317.5107      0.010          55 
4700     -13317.5107      0.010          55 
4800     -13317.5084      0.010          55 
4900     -13317.5074      0.010          55 
5000     -13317.5070      0.010          55 
5100     -13317.5059      0.010          55 
5200     -13317.5059      0.010          55 
5300     -13317.5043      0.010          55 
5400     -13317.5034      0.010          55 
5500     -13317.5034      0.010          55 
5600     -13317.5031      0.010          55 
5700     -13317.5028      0.010          55 
5800     -13317.5028      0.010          55 
5900     -13317.5028      0.010          55 
6000     -13317.5022      0.010          55 
6100     -13317.5022      0.010          55 
6200     -13317.5003      0.010          55 
6300     -13317.5002      0.010          55 
6400     -13317.5002      0.010          55 
6500     -13317.5001      0.010          55 
6600     -13317.4997      0.010          55 
6700     -13317.4997      0.010          55 
6800     -13317.4995      0.010          55 
6900     -13317.4995      0.010          55 
7000     -13317.4995      0.010          55 
7100     -13317.4985      0.010          55 
7200     -13317.4983      0.010          55 
7300     -13317.4965      0.010          55 
7400     -13317.4965      0.010          55 
7500     -13317.4965      0.010          55 
7600     -13317.4965      0.010          55 
7700     -13317.4964      0.010          55 
7800     -13317.4964      0.010          55 
Reached termination condition!
last topological improvement at gen 55
Improvement over last 500 gen = 0.00013
Current score = -13317.4964
Performing final optimizations...
pass 1 : -13317.4958   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0003  rel rates= 0.0003  subset rates= 0.0000)
pass 2 : -13317.4913   (branch= 0.0040  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0002  subset rates= 0.0000)
pass 3 : -13317.4845   (branch= 0.0050  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0015  subset rates= 0.0000)
pass 4 : -13317.4803   (branch= 0.0037  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 5 : -13317.4783   (branch= 0.0013  alpha= 0.0000  eq freqs= 0.0006  rel rates= 0.0001  subset rates= 0.0000)
pass 6 : -13317.4768   (branch= 0.0004  alpha= 0.0002  eq freqs= 0.0002  rel rates= 0.0007  subset rates= 0.0000)
pass 7 : -13317.4761   (branch= 0.0003  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0003  subset rates= 0.0000)
pass 8 : -13317.4759   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 9 : -13317.4757   (branch= 0.0001  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 13: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13317.4756
Time used so far = 0 hours, 1 minutes and 42 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.963, AG = 2.576, AT = 1.416, CG = 1.403, CT = 3.719, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3100 0.1768 0.2973 0.2159 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4095
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1888	0.2500
      0.7413	0.2500
      3.0518	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 4.355, AG = 7.089, AT = 1.612, CG = 7.106, CT = 4.408, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2694 0.1636 0.1605 0.4065 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3607
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1518	0.2500
      0.6850	0.2500
      3.1517	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.058, AG = 5.267, AT = 3.570, CG = 0.454, CT = 5.001, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1538 0.3560 0.2870 0.2032 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      2.9811
    Substitution rate categories under this model:
      rate	proportion
      0.3878	0.2500
      0.7305	0.2500
      1.0824	0.2500
      1.7993	0.2500

Subset rate multipliers:
    0.54  0.30  2.16
NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1)
    No branches were short enough to be collapsed.

>>>Completed Search rep 1 (of 5)<<<

>>>Search rep 2 (of 5)<<<
MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3157 0.1746 0.3004 0.2093 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2703 0.1566 0.1628 0.4103 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1460 0.3609 0.2915 0.2015 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=459000111

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 201.185 lnL
Optimizing branchlengths... improved 31.443 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13690.6959
optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates...
pass 1:+  202.627 (branch=   5.99 scale=  1.70 alpha= 13.57 freqs= 30.18 rel rates= 91.73 subset rates= 59.46)
pass 2:+   98.686 (branch=   9.86 scale=  1.14 alpha=  6.13 freqs= 15.70 rel rates=  7.50 subset rates= 58.36)
pass 3:+   43.190 (branch=   4.46 scale=  1.45 alpha=  2.52 freqs=  0.86 rel rates=  0.01 subset rates= 33.89)
pass 4:+   15.348 (branch=   0.47 scale=  0.00 alpha=  0.73 freqs=  0.17 rel rates=  1.82 subset rates= 12.15)
pass 5:+    5.652 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.18 rel rates=  0.68 subset rates=  4.78)
pass 6:+    0.170 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.15 rel rates=  0.01 subset rates=  0.00)
lnL after optimization: -13325.0231
gen      current_lnL    precision  last_tree_imp  
0        -13325.0231      0.500           0 
100      -13322.0616      0.500           0 
200      -13320.9899      0.500           0 
300      -13320.2533      0.500           0 
400      -13319.6815      0.500           0 
500      -13319.4627      0.500           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.075 lnL
   Optimizing branchlengths... improved    0.000 lnL
600      -13319.2426      0.402           0 
700      -13318.8351      0.402           0 
800      -13318.6816      0.402           0 
900      -13318.6055      0.402           0 
1000     -13318.4483      0.402           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.022 lnL
   Optimizing branchlengths... improved    0.000 lnL
1100     -13318.3744      0.304           0 
1200     -13318.0244      0.304           0 
1300     -13317.9528      0.304           0 
1400     -13317.9440      0.304           0 
1500     -13317.8239      0.304           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.017 lnL
   Optimizing branchlengths... improved    0.000 lnL
1600     -13317.7723      0.206           0 
1700     -13317.7206      0.206           0 
1800     -13317.7058      0.206           0 
1900     -13317.6828      0.206           0 
2000     -13317.6463      0.206           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.007 lnL
   Optimizing branchlengths... improved    0.000 lnL
2100     -13317.6293      0.108           0 
2200     -13317.6100      0.108           0 
2300     -13317.6100      0.108           0 
2400     -13317.5512      0.108           0 
2500     -13317.5457      0.108           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.012 lnL
   Optimizing branchlengths... improved    0.017 lnL
2600     -13317.5109      0.010           0 
2700     -13317.5104      0.010           0 
2800     -13317.5057      0.010           0 
2900     -13317.5023      0.010           0 
3000     -13317.4979      0.010           0 
3100     -13317.4977      0.010           0 
3200     -13317.4901      0.010           0 
3300     -13317.4901      0.010           0 
3400     -13317.4901      0.010           0 
3500     -13317.4896      0.010           0 
3600     -13317.4894      0.010           0 
3700     -13317.4894      0.010           0 
3800     -13317.4894      0.010           0 
3900     -13317.4883      0.010           0 
4000     -13317.4877      0.010           0 
4100     -13317.4871      0.010           0 
4200     -13317.4871      0.010           0 
4300     -13317.4870      0.010           0 
4400     -13317.4870      0.010           0 
4500     -13317.4870      0.010           0 
4600     -13317.4869      0.010           0 
4700     -13317.4869      0.010           0 
4800     -13317.4864      0.010           0 
4900     -13317.4859      0.010           0 
5000     -13317.4855      0.010           0 
5100     -13317.4848      0.010           0 
5200     -13317.4848      0.010           0 
5300     -13317.4839      0.010           0 
5400     -13317.4839      0.010           0 
5500     -13317.4839      0.010           0 
5600     -13317.4839      0.010           0 
5700     -13317.4839      0.010           0 
5800     -13317.4832      0.010           0 
5900     -13317.4832      0.010           0 
6000     -13317.4830      0.010           0 
6100     -13317.4829      0.010           0 
6200     -13317.4824      0.010           0 
6300     -13317.4823      0.010           0 
6400     -13317.4816      0.010           0 
6500     -13317.4813      0.010           0 
6600     -13317.4812      0.010           0 
6700     -13317.4812      0.010           0 
6800     -13317.4811      0.010           0 
6900     -13317.4811      0.010           0 
7000     -13317.4811      0.010           0 
7100     -13317.4810      0.010           0 
7200     -13317.4805      0.010           0 
7300     -13317.4805      0.010           0 
7400     -13317.4804      0.010           0 
7500     -13317.4804      0.010           0 
7600     -13317.4803      0.010           0 
Reached termination condition!
last topological improvement at gen 0
Improvement over last 500 gen = 0.00072
Current score = -13317.4803
Performing final optimizations...
pass 1 : -13317.4799   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 2 : -13317.4797   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 3 : -13317.4795   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 4 : -13317.4794   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 5 : -13317.4771   (branch= 0.0021  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 6 : -13317.4766   (branch= 0.0004  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 7 : -13317.4763   (branch= 0.0003  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 8 : -13317.4760   (branch= 0.0003  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13317.4758   (branch= 0.0002  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 10: -13317.4757   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13317.4757   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 13: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 14: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13317.4756
Time used so far = 0 hours, 3 minutes and 23 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.961, AG = 2.575, AT = 1.415, CG = 1.402, CT = 3.716, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3100 0.1768 0.2973 0.2159 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4094
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1888	0.2500
      0.7413	0.2500
      3.0518	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 4.345, AG = 7.074, AT = 1.608, CG = 7.087, CT = 4.397, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2695 0.1636 0.1605 0.4065 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3606
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1518	0.2500
      0.6849	0.2500
      3.1518	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.058, AG = 5.267, AT = 3.569, CG = 0.454, CT = 5.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1538 0.3560 0.2870 0.2032 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      2.9825
    Substitution rate categories under this model:
      rate	proportion
      0.3879	0.2500
      0.7306	0.2500
      1.0824	0.2500
      1.7991	0.2500

Subset rate multipliers:
    0.54  0.30  2.16
NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1)
    No branches were short enough to be collapsed.

>>>Completed Search rep 2 (of 5)<<<

>>>Search rep 3 (of 5)<<<
MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3157 0.1746 0.3004 0.2093 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2703 0.1566 0.1628 0.4103 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1460 0.3609 0.2915 0.2015 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=401425806

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 179.383 lnL
Optimizing branchlengths... improved 52.565 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13715.9016
optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates...
pass 1:+  198.274 (branch=   5.93 scale=  1.29 alpha=  6.51 freqs= 33.68 rel rates= 89.23 subset rates= 61.63)
pass 2:+   98.585 (branch=   9.39 scale=  0.81 alpha=  6.30 freqs= 15.61 rel rates=  7.49 subset rates= 58.98)
pass 3:+   43.758 (branch=   2.89 scale=  1.86 alpha=  3.08 freqs=  1.01 rel rates=  0.58 subset rates= 34.34)
pass 4:+   15.850 (branch=   0.00 scale=  0.00 alpha=  0.80 freqs=  0.69 rel rates=  1.97 subset rates= 12.39)
pass 5:+    5.880 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.13 rel rates=  0.85 subset rates=  4.89)
pass 6:+    0.109 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.09 rel rates=  0.01 subset rates=  0.00)
lnL after optimization: -13353.4450
gen      current_lnL    precision  last_tree_imp  
0        -13353.4450      0.500           0 
100      -13322.7799      0.500          10 
200      -13321.2908      0.500          10 
300      -13320.4759      0.500          10 
400      -13319.9059      0.500          10 
500      -13319.7203      0.500          10 
600      -13319.0563      0.500          10 
Optimization precision reduced 
   Optimizing parameters...    improved    0.032 lnL
   Optimizing branchlengths... improved    0.000 lnL
700      -13318.2812      0.402          10 
800      -13318.0210      0.402          10 
900      -13317.9775      0.402          10 
1000     -13317.8428      0.402          10 
1100     -13317.7889      0.402          10 
Optimization precision reduced 
   Optimizing parameters...    improved    0.013 lnL
   Optimizing branchlengths... improved    0.000 lnL
1200     -13317.6984      0.304          10 
1300     -13317.6918      0.304          10 
1400     -13317.6882      0.304          10 
1500     -13317.6602      0.304          10 
1600     -13317.6430      0.304          10 
Optimization precision reduced 
   Optimizing parameters...    improved    0.006 lnL
   Optimizing branchlengths... improved    0.000 lnL
1700     -13317.6200      0.206          10 
1800     -13317.6164      0.206          10 
1900     -13317.6138      0.206          10 
2000     -13317.5718      0.206          10 
2100     -13317.5611      0.206          10 
Optimization precision reduced 
   Optimizing parameters...    improved    0.003 lnL
   Optimizing branchlengths... improved    0.000 lnL
2200     -13317.5572      0.108          10 
2300     -13317.5302      0.108          10 
2400     -13317.5302      0.108          10 
2500     -13317.5288      0.108          10 
2600     -13317.5158      0.108          10 
Optimization precision reduced 
   Optimizing parameters...    improved    0.001 lnL
   Optimizing branchlengths... improved    0.000 lnL
2700     -13317.5085      0.010          10 
2800     -13317.5085      0.010          10 
2900     -13317.5075      0.010          10 
3000     -13317.5069      0.010          10 
3100     -13317.5067      0.010          10 
3200     -13317.5067      0.010          10 
3300     -13317.5067      0.010          10 
3400     -13317.5067      0.010          10 
3500     -13317.5067      0.010          10 
3600     -13317.5056      0.010          10 
3700     -13317.5039      0.010          10 
3800     -13317.5011      0.010          10 
3900     -13317.5008      0.010          10 
4000     -13317.5008      0.010          10 
4100     -13317.5008      0.010          10 
4200     -13317.5008      0.010          10 
4300     -13317.5008      0.010          10 
4400     -13317.5007      0.010          10 
4500     -13317.5007      0.010          10 
4600     -13317.5006      0.010          10 
4700     -13317.5001      0.010          10 
4800     -13317.5001      0.010          10 
4900     -13317.5001      0.010          10 
5000     -13317.5000      0.010          10 
5100     -13317.5000      0.010          10 
5200     -13317.4968      0.010          10 
5300     -13317.4968      0.010          10 
5400     -13317.4933      0.010          10 
5500     -13317.4933      0.010          10 
5600     -13317.4933      0.010          10 
5700     -13317.4933      0.010          10 
5800     -13317.4933      0.010          10 
5900     -13317.4933      0.010          10 
6000     -13317.4933      0.010          10 
6100     -13317.4933      0.010          10 
6200     -13317.4933      0.010          10 
6300     -13317.4933      0.010          10 
6400     -13317.4932      0.010          10 
6500     -13317.4924      0.010          10 
6600     -13317.4909      0.010          10 
6700     -13317.4909      0.010          10 
6800     -13317.4907      0.010          10 
6900     -13317.4907      0.010          10 
7000     -13317.4905      0.010          10 
7100     -13317.4905      0.010          10 
7200     -13317.4902      0.010          10 
7300     -13317.4902      0.010          10 
7400     -13317.4902      0.010          10 
7500     -13317.4902      0.010          10 
7600     -13317.4874      0.010          10 
7700     -13317.4873      0.010          10 
7800     -13317.4873      0.010          10 
7900     -13317.4873      0.010          10 
8000     -13317.4873      0.010          10 
8100     -13317.4873      0.010          10 
Reached termination condition!
last topological improvement at gen 10
Improvement over last 500 gen = 0.00010
Current score = -13317.4873
Performing final optimizations...
pass 1 : -13317.4869   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0002  subset rates= 0.0000)
pass 2 : -13317.4838   (branch= 0.0028  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0002  subset rates= 0.0000)
pass 3 : -13317.4806   (branch= 0.0031  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 4 : -13317.4784   (branch= 0.0019  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 5 : -13317.4773   (branch= 0.0010  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 6 : -13317.4765   (branch= 0.0005  alpha= 0.0000  eq freqs= 0.0003  rel rates= 0.0001  subset rates= 0.0000)
pass 7 : -13317.4759   (branch= 0.0005  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 8 : -13317.4757   (branch= 0.0001  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13317.4755   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13317.4755   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13317.4755
Time used so far = 0 hours, 5 minutes and 12 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.965, AG = 2.579, AT = 1.417, CG = 1.404, CT = 3.722, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3100 0.1768 0.2973 0.2159 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4095
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1888	0.2500
      0.7413	0.2500
      3.0518	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 4.351, AG = 7.084, AT = 1.610, CG = 7.097, CT = 4.404, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2694 0.1636 0.1605 0.4065 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3607
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1518	0.2500
      0.6850	0.2500
      3.1517	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.057, AG = 5.261, AT = 3.564, CG = 0.454, CT = 4.995, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1538 0.3560 0.2870 0.2032 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      2.9824
    Substitution rate categories under this model:
      rate	proportion
      0.3879	0.2500
      0.7306	0.2500
      1.0824	0.2500
      1.7991	0.2500

Subset rate multipliers:
    0.54  0.30  2.16
NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1)
    No branches were short enough to be collapsed.

>>>Completed Search rep 3 (of 5)<<<

>>>Search rep 4 (of 5)<<<
MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3157 0.1746 0.3004 0.2093 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2703 0.1566 0.1628 0.4103 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1460 0.3609 0.2915 0.2015 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=110869126

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 222.535 lnL
Optimizing branchlengths... improved 12.552 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13686.2656
optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates...
pass 1:+  197.479 (branch=   6.03 scale=  1.63 alpha=  8.07 freqs= 28.85 rel rates= 93.04 subset rates= 59.85)
pass 2:+   96.629 (branch=  12.32 scale=  0.50 alpha=  5.59 freqs= 14.32 rel rates=  6.45 subset rates= 57.45)
pass 3:+   46.043 (branch=   7.56 scale=  0.62 alpha=  2.44 freqs=  1.83 rel rates=  0.01 subset rates= 33.58)
pass 4:+   17.375 (branch=   1.70 scale=  0.00 alpha=  0.69 freqs=  0.15 rel rates=  2.55 subset rates= 12.29)
pass 5:+    5.749 (branch=   0.00 scale=  0.00 alpha=  0.29 freqs=  0.69 rel rates=  0.01 subset rates=  4.75)
pass 6:+    1.159 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.10 rel rates=  0.47 subset rates=  0.59)
pass 7:+    0.086 (branch=   0.00 scale=  0.00 alpha=  0.00 freqs=  0.07 rel rates=  0.01 subset rates=  0.00)
lnL after optimization: -13321.7448
gen      current_lnL    precision  last_tree_imp  
0        -13321.7448      0.500           0 
100      -13320.2658      0.500           0 
200      -13320.0646      0.500           0 
300      -13319.3706      0.500           0 
400      -13318.6406      0.500           0 
500      -13318.2430      0.500           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.042 lnL
   Optimizing branchlengths... improved    0.000 lnL
600      -13318.0693      0.402           0 
700      -13317.8631      0.402           0 
800      -13317.7904      0.402           0 
900      -13317.7352      0.402           0 
1000     -13317.6734      0.402           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.014 lnL
   Optimizing branchlengths... improved    0.000 lnL
1100     -13317.6275      0.304           0 
1200     -13317.6191      0.304           0 
1300     -13317.6089      0.304           0 
1400     -13317.5867      0.304           0 
1500     -13317.5818      0.304           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.006 lnL
   Optimizing branchlengths... improved    0.000 lnL
1600     -13317.5683      0.206           0 
1700     -13317.5612      0.206           0 
1800     -13317.5607      0.206           0 
1900     -13317.5592      0.206           0 
2000     -13317.5559      0.206           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.004 lnL
   Optimizing branchlengths... improved    0.000 lnL
2100     -13317.5491      0.108           0 
2200     -13317.5483      0.108           0 
2300     -13317.5483      0.108           0 
2400     -13317.5478      0.108           0 
2500     -13317.5468      0.108           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.001 lnL
   Optimizing branchlengths... improved    0.023 lnL
2600     -13317.5204      0.010           0 
2700     -13317.5187      0.010           0 
2800     -13317.5187      0.010           0 
2900     -13317.5180      0.010           0 
3000     -13317.5113      0.010           0 
3100     -13317.5058      0.010           0 
3200     -13317.5058      0.010           0 
3300     -13317.5044      0.010           0 
3400     -13317.5042      0.010           0 
3500     -13317.5042      0.010           0 
3600     -13317.5042      0.010           0 
3700     -13317.5041      0.010           0 
3800     -13317.4994      0.010           0 
3900     -13317.4994      0.010           0 
4000     -13317.4991      0.010           0 
4100     -13317.4991      0.010           0 
4200     -13317.4982      0.010           0 
4300     -13317.4979      0.010           0 
4400     -13317.4979      0.010           0 
4500     -13317.4964      0.010           0 
4600     -13317.4964      0.010           0 
4700     -13317.4962      0.010           0 
4800     -13317.4959      0.010           0 
4900     -13317.4954      0.010           0 
5000     -13317.4953      0.010           0 
5100     -13317.4949      0.010           0 
5200     -13317.4932      0.010           0 
5300     -13317.4931      0.010           0 
5400     -13317.4931      0.010           0 
5500     -13317.4931      0.010           0 
5600     -13317.4930      0.010           0 
5700     -13317.4928      0.010           0 
5800     -13317.4926      0.010           0 
5900     -13317.4926      0.010           0 
6000     -13317.4926      0.010           0 
6100     -13317.4926      0.010           0 
6200     -13317.4923      0.010           0 
6300     -13317.4923      0.010           0 
6400     -13317.4920      0.010           0 
6500     -13317.4920      0.010           0 
6600     -13317.4920      0.010           0 
6700     -13317.4920      0.010           0 
6800     -13317.4920      0.010           0 
6900     -13317.4920      0.010           0 
7000     -13317.4920      0.010           0 
7100     -13317.4920      0.010           0 
7200     -13317.4920      0.010           0 
7300     -13317.4920      0.010           0 
7400     -13317.4920      0.010           0 
7500     -13317.4920      0.010           0 
7600     -13317.4919      0.010           0 
Reached termination condition!
last topological improvement at gen 0
Improvement over last 500 gen = 0.00011
Current score = -13317.4919
Performing final optimizations...
pass 1 : -13317.4915   (branch= 0.0000  alpha= 0.0001  eq freqs= 0.0003  rel rates= 0.0001  subset rates= 0.0000)
pass 2 : -13317.4913   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 3 : -13317.4809   (branch= 0.0101  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0000  subset rates= 0.0000)
pass 4 : -13317.4791   (branch= 0.0016  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 5 : -13317.4775   (branch= 0.0014  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 6 : -13317.4765   (branch= 0.0009  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 7 : -13317.4759   (branch= 0.0003  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 8 : -13317.4758   (branch= 0.0001  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13317.4757   (branch= 0.0001  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13317.4757   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13317.4756
Time used so far = 0 hours, 6 minutes and 51 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.965, AG = 2.580, AT = 1.418, CG = 1.405, CT = 3.723, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3100 0.1768 0.2973 0.2159 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4094
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1887	0.2500
      0.7412	0.2500
      3.0520	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 4.350, AG = 7.083, AT = 1.610, CG = 7.097, CT = 4.403, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2695 0.1636 0.1605 0.4065 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3609
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1520	0.2500
      0.6852	0.2500
      3.1513	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.058, AG = 5.268, AT = 3.569, CG = 0.454, CT = 5.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1538 0.3560 0.2870 0.2032 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      2.9819
    Substitution rate categories under this model:
      rate	proportion
      0.3879	0.2500
      0.7305	0.2500
      1.0824	0.2500
      1.7992	0.2500

Subset rate multipliers:
    0.54  0.30  2.16
NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1)
    No branches were short enough to be collapsed.

>>>Completed Search rep 4 (of 5)<<<

>>>Search rep 5 (of 5)<<<
MODEL REPORT - Parameters are at their INITIAL values (not yet optimized)
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3157 0.1746 0.3004 0.2093 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2703 0.1566 0.1628 0.4103 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.000, AG = 4.000, AT = 1.000, CG = 1.000, CT = 4.000, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1460 0.3609 0.2915 0.2015 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.5000
    Substitution rate categories under this model:
      rate	proportion
      0.0334	0.2500
      0.2519	0.2500
      0.8203	0.2500
      2.8944	0.2500

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=1432730019

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 204.175 lnL
Optimizing branchlengths... improved 54.430 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13850.6663
optimizing: starting branch lengths, alpha shape, rel rates, eq freqs, subset rates...
pass 1:+  206.901 (branch=  10.49 scale=  0.59 alpha=  2.66 freqs= 34.28 rel rates= 99.72 subset rates= 59.16)
pass 2:+   96.794 (branch=   7.46 scale=  2.85 alpha=  6.26 freqs= 16.52 rel rates=  6.62 subset rates= 57.09)
pass 3:+   45.055 (branch=   5.78 scale=  0.00 alpha=  3.33 freqs=  1.52 rel rates=  1.31 subset rates= 33.11)
pass 4:+   20.882 (branch=   3.40 scale=  0.00 alpha=  0.78 freqs=  0.71 rel rates=  3.02 subset rates= 12.98)
pass 5:+    6.430 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.07 rel rates=  1.39 subset rates=  4.96)
pass 6:+    0.079 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.06 rel rates=  0.01 subset rates=  0.00)
lnL after optimization: -13474.5256
gen      current_lnL    precision  last_tree_imp  
0        -13474.5256      0.500           0 
100      -13324.0391      0.500          40 
200      -13323.4406      0.500          40 
300      -13322.7068      0.500          40 
400      -13322.3876      0.500          40 
500      -13321.4452      0.500          40 
600      -13320.9924      0.500          40 
Optimization precision reduced 
   Optimizing parameters...    improved    0.541 lnL
   Optimizing branchlengths... improved    0.000 lnL
700      -13319.6463      0.402          40 
800      -13319.1584      0.402          40 
900      -13318.8903      0.402          40 
1000     -13318.5134      0.402          40 
1100     -13318.2613      0.402          40 
Optimization precision reduced 
   Optimizing parameters...    improved    0.027 lnL
   Optimizing branchlengths... improved    0.000 lnL
1200     -13318.1072      0.304          40 
1300     -13318.0828      0.304          40 
1400     -13317.9779      0.304          40 
1500     -13317.9633      0.304          40 
1600     -13317.9586      0.304          40 
Optimization precision reduced 
   Optimizing parameters...    improved    0.015 lnL
   Optimizing branchlengths... improved    0.000 lnL
1700     -13317.8625      0.206          40 
1800     -13317.7611      0.206          40 
1900     -13317.6621      0.206          40 
2000     -13317.6395      0.206          40 
2100     -13317.6333      0.206          40 
Optimization precision reduced 
   Optimizing parameters...    improved    0.005 lnL
   Optimizing branchlengths... improved    0.000 lnL
2200     -13317.6222      0.108          40 
2300     -13317.6178      0.108          40 
2400     -13317.6061      0.108          40 
2500     -13317.6061      0.108          40 
2600     -13317.6048      0.108          40 
Optimization precision reduced 
   Optimizing parameters...    improved    0.001 lnL
   Optimizing branchlengths... improved    0.049 lnL
2700     -13317.5462      0.010          40 
2800     -13317.5293      0.010          40 
2900     -13317.5292      0.010          40 
3000     -13317.5266      0.010          40 
3100     -13317.5249      0.010          40 
3200     -13317.5230      0.010          40 
3300     -13317.5205      0.010          40 
3400     -13317.5202      0.010          40 
3500     -13317.5202      0.010          40 
3600     -13317.5202      0.010          40 
3700     -13317.5200      0.010          40 
3800     -13317.5094      0.010          40 
3900     -13317.4994      0.010          40 
4000     -13317.4985      0.010          40 
4100     -13317.4984      0.010          40 
4200     -13317.4984      0.010          40 
4300     -13317.4980      0.010          40 
4400     -13317.4947      0.010          40 
4500     -13317.4942      0.010          40 
4600     -13317.4942      0.010          40 
4700     -13317.4927      0.010          40 
4800     -13317.4927      0.010          40 
4900     -13317.4921      0.010          40 
5000     -13317.4912      0.010          40 
5100     -13317.4907      0.010          40 
5200     -13317.4906      0.010          40 
5300     -13317.4906      0.010          40 
5400     -13317.4906      0.010          40 
5500     -13317.4903      0.010          40 
5600     -13317.4896      0.010          40 
5700     -13317.4895      0.010          40 
5800     -13317.4894      0.010          40 
5900     -13317.4894      0.010          40 
6000     -13317.4881      0.010          40 
6100     -13317.4880      0.010          40 
6200     -13317.4876      0.010          40 
6300     -13317.4875      0.010          40 
6400     -13317.4875      0.010          40 
6500     -13317.4874      0.010          40 
6600     -13317.4874      0.010          40 
6700     -13317.4873      0.010          40 
6800     -13317.4873      0.010          40 
6900     -13317.4873      0.010          40 
7000     -13317.4872      0.010          40 
7100     -13317.4872      0.010          40 
7200     -13317.4871      0.010          40 
7300     -13317.4868      0.010          40 
7400     -13317.4862      0.010          40 
7500     -13317.4860      0.010          40 
7600     -13317.4857      0.010          40 
7700     -13317.4857      0.010          40 
7800     -13317.4857      0.010          40 
7900     -13317.4826      0.010          40 
8000     -13317.4826      0.010          40 
8100     -13317.4825      0.010          40 
8200     -13317.4821      0.010          40 
8300     -13317.4821      0.010          40 
8400     -13317.4819      0.010          40 
Reached termination condition!
last topological improvement at gen 40
Improvement over last 500 gen = 0.00076
Current score = -13317.4819
Performing final optimizations...
pass 1 : -13317.4814   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0003  rel rates= 0.0001  subset rates= 0.0000)
pass 2 : -13317.4810   (branch= 0.0000  alpha= 0.0001  eq freqs= 0.0002  rel rates= 0.0002  subset rates= 0.0000)
pass 3 : -13317.4807   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 4 : -13317.4805   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 5 : -13317.4768   (branch= 0.0031  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0004  subset rates= 0.0000)
pass 6 : -13317.4763   (branch= 0.0004  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 7 : -13317.4759   (branch= 0.0002  alpha= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 8 : -13317.4758   (branch= 0.0001  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13317.4756   (branch= 0.0002  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 13: -13317.4756   (branch= 0.0000  alpha= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13317.4756
Time used = 0 hours, 8 minutes and 41 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.963, AG = 2.577, AT = 1.416, CG = 1.403, CT = 3.719, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3100 0.1768 0.2973 0.2159 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4095
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1888	0.2500
      0.7413	0.2500
      3.0518	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 4.354, AG = 7.088, AT = 1.611, CG = 7.102, CT = 4.407, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2694 0.1636 0.1605 0.4065 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3607
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1518	0.2500
      0.6850	0.2500
      3.1517	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix:     6 rates 
    AC = 1.057, AG = 5.260, AT = 3.564, CG = 0.454, CT = 4.994, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1538 0.3560 0.2870 0.2032 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      2.9829
    Substitution rate categories under this model:
      rate	proportion
      0.3879	0.2500
      0.7306	0.2500
      1.0824	0.2500
      1.7991	0.2500

Subset rate multipliers:
    0.54  0.30  2.16
NOTE: Collapsing of minimum length branches was requested (collapsebranches = 1)
    No branches were short enough to be collapsed.

>>>Completed Search rep 5 (of 5)<<<

#######################################################

Completed 5 replicate search(es) (of 5).

NOTE: Unless the following output indicates that search replicates found the
	same topology, you should assume that they found different topologies.
Results:
Replicate 1 : -13317.4756       
Replicate 2 : -13317.4756        (same topology as 1)
Replicate 3 : -13317.4755 (best) (same topology as 1)
Replicate 4 : -13317.4756        (same topology as 1)
Replicate 5 : -13317.4756        (same topology as 1)

Parameter estimates across search replicates:

Partition model subset 1:
         r(AC)  r(AG)  r(AT)  r(CG)  r(CT)  r(GT) pi(A) pi(C) pi(G) pi(T) alpha
rep 1:   1.963  2.576  1.416  1.403  3.719      1 0.310 0.177 0.297 0.216 0.409
rep 2:   1.961  2.575  1.415  1.402  3.716      1 0.310 0.177 0.297 0.216 0.409
rep 3:   1.965  2.579  1.417  1.404  3.722      1 0.310 0.177 0.297 0.216 0.409
rep 4:   1.965   2.58  1.418  1.405  3.723      1 0.310 0.177 0.297 0.216 0.409
rep 5:   1.963  2.577  1.416  1.403  3.719      1 0.310 0.177 0.297 0.216 0.409

Partition model subset 2:
         r(AC)  r(AG)  r(AT)  r(CG)  r(CT)  r(GT) pi(A) pi(C) pi(G) pi(T) alpha
rep 1:   4.355  7.089  1.612  7.106  4.408      1 0.269 0.164 0.160 0.407 0.361
rep 2:   4.345  7.074  1.608  7.087  4.397      1 0.269 0.164 0.160 0.407 0.361
rep 3:   4.351  7.084   1.61  7.097  4.404      1 0.269 0.164 0.160 0.407 0.361
rep 4:    4.35  7.083   1.61  7.097  4.403      1 0.269 0.164 0.160 0.406 0.361
rep 5:   4.354  7.088  1.611  7.102  4.407      1 0.269 0.164 0.160 0.407 0.361

Partition model subset 3:
         r(AC)  r(AG)  r(AT)  r(CG)  r(CT)  r(GT) pi(A) pi(C) pi(G) pi(T) alpha
rep 1:   1.058  5.267   3.57 0.4542  5.001      1 0.154 0.356 0.287 0.203 2.981
rep 2:   1.058  5.267  3.569 0.4543      5      1 0.154 0.356 0.287 0.203 2.982
rep 3:   1.057  5.261  3.564 0.4538  4.995      1 0.154 0.356 0.287 0.203 2.982
rep 4:   1.058  5.268  3.569 0.4544      5      1 0.154 0.356 0.287 0.203 2.982
rep 5:   1.057   5.26  3.564 0.4538  4.994      1 0.154 0.356 0.287 0.203 2.983

Treelengths and subset rate multipliers:
          TL   R(1)  R(2)  R(3)
rep 1:  1.704 0.538 0.298 2.164
rep 2:  1.704 0.538 0.298 2.164
rep 3:  1.704 0.538 0.298 2.164
rep 4:  1.704 0.537 0.299 2.164
rep 5:  1.704 0.538 0.298 2.164

Saving final trees from all search reps to GTRG.byCodonPos.best.all.tre

Saving final tree from best search rep (#3) to GTRG.byCodonPos.best.tre
#######################################################
