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: DIFFERENT MODEL TYPES AND MODEL PARAMETERS APPLY
	TO EACH DATA SUBSET (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: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    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: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, 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: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    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
    with an invariant (invariable) site category, proportion estimated
      0.0355
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0355
      0.0334	0.2411
      0.2519	0.2411
      0.8203	0.2411
      2.8944	0.2411

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=852004

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 284.867 lnL
Optimizing branchlengths... improved 109.865 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13739.2722
optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates...
pass 1:+  197.592 (branch=   5.99 scale=  0.00 alpha= 39.14 freqs= 25.16 rel rates= 71.09 pinv=  0.00 subset rates= 56.21)
pass 2:+   93.534 (branch=  11.02 scale=  1.42 alpha=  7.20 freqs= 12.45 rel rates=  4.35 pinv=  0.79 subset rates= 56.30)
pass 3:+   52.621 (branch=   5.01 scale=  1.30 alpha=  7.59 freqs=  1.02 rel rates=  1.79 pinv=  0.01 subset rates= 35.90)
pass 4:+   16.507 (branch=   0.00 scale=  0.81 alpha=  0.95 freqs=  0.16 rel rates=  1.58 pinv=  0.01 subset rates= 13.00)
pass 5:+    8.128 (branch=   0.68 scale=  0.00 alpha=  0.01 freqs=  0.20 rel rates=  1.99 pinv=  0.01 subset rates=  5.23)
pass 6:+    0.727 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.15 rel rates=  0.01 pinv=  0.01 subset rates=  0.54)
pass 7:+    0.157 (branch=   0.00 scale=  0.00 alpha=  0.00 freqs=  0.13 rel rates=  0.01 pinv=  0.01 subset rates=  0.00)
lnL after optimization: -13370.0055
gen      current_lnL    precision  last_tree_imp  
0        -13370.0055      0.500           0 
100      -13318.7588      0.500          30 
200      -13317.8640      0.500          30 
300      -13317.3539      0.500          30 
400      -13317.1124      0.500          30 
500      -13316.9931      0.500          30 
600      -13316.7533      0.500          30 
Optimization precision reduced 
   Optimizing parameters...    improved    0.052 lnL
   Optimizing branchlengths... improved    0.000 lnL
700      -13316.5320      0.402          30 
800      -13316.4480      0.402          30 
900      -13316.4201      0.402          30 
1000     -13316.3574      0.402          30 
1100     -13316.2488      0.402          30 
Optimization precision reduced 
   Optimizing parameters...    improved    0.024 lnL
   Optimizing branchlengths... improved    0.000 lnL
1200     -13316.1758      0.304          30 
1300     -13316.1714      0.304          30 
1400     -13316.0938      0.304          30 
1500     -13316.0518      0.304          30 
1600     -13315.9958      0.304          30 
Optimization precision reduced 
   Optimizing parameters...    improved    0.013 lnL
   Optimizing branchlengths... improved    0.000 lnL
1700     -13315.9424      0.206          30 
1800     -13315.9278      0.206          30 
1900     -13315.8903      0.206          30 
2000     -13315.8642      0.206          30 
2100     -13315.8580      0.206          30 
Optimization precision reduced 
   Optimizing parameters...    improved    0.003 lnL
   Optimizing branchlengths... improved    0.000 lnL
2200     -13315.8540      0.108          30 
2300     -13315.8529      0.108          30 
2400     -13315.8461      0.108          30 
2500     -13315.8458      0.108          30 
2600     -13315.8453      0.108          30 
Optimization precision reduced 
   Optimizing parameters...    improved    0.001 lnL
   Optimizing branchlengths... improved    0.017 lnL
2700     -13315.8279      0.010          30 
2800     -13315.8275      0.010          30 
2900     -13315.8257      0.010          30 
3000     -13315.8242      0.010          30 
3100     -13315.8240      0.010          30 
3200     -13315.8237      0.010          30 
3300     -13315.8130      0.010          30 
3400     -13315.8130      0.010          30 
3500     -13315.8093      0.010          30 
3600     -13315.8087      0.010          30 
3700     -13315.8086      0.010          30 
3800     -13315.8078      0.010          30 
3900     -13315.8072      0.010          30 
4000     -13315.8072      0.010          30 
4100     -13315.8016      0.010          30 
4200     -13315.8007      0.010          30 
4300     -13315.7978      0.010          30 
4400     -13315.7973      0.010          30 
4500     -13315.7970      0.010          30 
4600     -13315.7967      0.010          30 
4700     -13315.7949      0.010          30 
4800     -13315.7948      0.010          30 
4900     -13315.7948      0.010          30 
5000     -13315.7919      0.010          30 
5100     -13315.7911      0.010          30 
5200     -13315.7911      0.010          30 
5300     -13315.7906      0.010          30 
5400     -13315.7906      0.010          30 
5500     -13315.7903      0.010          30 
5600     -13315.7899      0.010          30 
5700     -13315.7883      0.010          30 
5800     -13315.7880      0.010          30 
5900     -13315.7876      0.010          30 
6000     -13315.7873      0.010          30 
6100     -13315.7873      0.010          30 
6200     -13315.7870      0.010          30 
6300     -13315.7863      0.010          30 
6400     -13315.7851      0.010          30 
6500     -13315.7846      0.010          30 
6600     -13315.7846      0.010          30 
6700     -13315.7846      0.010          30 
6800     -13315.7846      0.010          30 
6900     -13315.7844      0.010          30 
7000     -13315.7838      0.010          30 
7100     -13315.7837      0.010          30 
7200     -13315.7837      0.010          30 
7300     -13315.7837      0.010          30 
7400     -13315.7837      0.010          30 
7500     -13315.7837      0.010          30 
7600     -13315.7837      0.010          30 
7700     -13315.7833      0.010          30 
Reached termination condition!
last topological improvement at gen 30
Improvement over last 500 gen = 0.00047
Current score = -13315.7833
Performing final optimizations...
pass 1 : -13315.7830   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 2 : -13315.7827   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 3 : -13315.7772   (branch= 0.0054  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 4 : -13315.7748   (branch= 0.0024  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 5 : -13315.7740   (branch= 0.0007  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 6 : -13315.7729   (branch= 0.0011  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 7 : -13315.7726   (branch= 0.0002  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 8 : -13315.7725   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13315.7723   (branch= 0.0001  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13315.7722   (branch= 0.0001  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 13: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 14: -13315.7721   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 15: -13315.7721   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 16: -13315.7721   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13315.7721
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: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    AC = 1.966, AG = 2.579, AT = 1.411, CG = 1.411, CT = 3.722, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3101 0.1767 0.2972 0.2160 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4097
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1890	0.2500
      0.7416	0.2500
      3.0513	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.380, AG = 7.085, AT = 1.609, CG = 7.085, CT = 4.380, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2692 0.1636 0.1605 0.4067 
  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
      0.6853	0.2500
      3.1512	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    AC = 1.000, AG = 4.936, AT = 3.391, CG = 0.457, CT = 4.936, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1565 0.3537 0.2877 0.2021 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      4.0142
    with an invariant (invariable) site category, proportion estimated
      0.0335
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0335
      0.4551	0.2416
      0.7757	0.2416
      1.0844	0.2416
      1.6848	0.2416

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: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    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: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, 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: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    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
    with an invariant (invariable) site category, proportion estimated
      0.0355
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0355
      0.0334	0.2411
      0.2519	0.2411
      0.8203	0.2411
      2.8944	0.2411

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=1163395386

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 251.052 lnL
Optimizing branchlengths... improved 42.822 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13708.2945
optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates...
pass 1:+  195.142 (branch=   3.84 scale=  0.00 alpha= 39.93 freqs= 28.21 rel rates= 67.92 pinv=  0.00 subset rates= 55.24)
pass 2:+   90.129 (branch=   9.79 scale=  1.73 alpha=  6.49 freqs= 11.51 rel rates=  4.22 pinv=  1.00 subset rates= 55.39)
pass 3:+   50.181 (branch=   8.72 scale=  0.00 alpha=  4.95 freqs=  1.58 rel rates=  1.37 pinv=  0.01 subset rates= 33.53)
pass 4:+   18.019 (branch=   1.73 scale=  0.71 alpha=  0.96 freqs=  0.15 rel rates=  2.28 pinv=  0.01 subset rates= 12.18)
pass 5:+    5.247 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.14 rel rates=  0.54 pinv=  0.01 subset rates=  4.54)
pass 6:+    0.677 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.64 rel rates=  0.01 pinv=  0.01 subset rates=  0.00)
pass 7:+    0.077 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.04 rel rates=  0.01 pinv=  0.01 subset rates=  0.00)
lnL after optimization: -13348.8218
gen      current_lnL    precision  last_tree_imp  
0        -13348.8218      0.500           0 
100      -13319.1023      0.500           3 
200      -13318.5364      0.500           3 
300      -13317.6152      0.500           3 
400      -13317.5790      0.500           3 
500      -13317.4175      0.500           3 
600      -13317.3894      0.500           3 
Optimization precision reduced 
   Optimizing parameters...    improved    0.627 lnL
   Optimizing branchlengths... improved    0.000 lnL
700      -13316.5392      0.402           3 
800      -13316.3891      0.402           3 
900      -13316.3181      0.402           3 
1000     -13316.1841      0.402           3 
1100     -13316.1042      0.402           3 
Optimization precision reduced 
   Optimizing parameters...    improved    0.014 lnL
   Optimizing branchlengths... improved    0.000 lnL
1200     -13316.0886      0.304           3 
1300     -13316.0664      0.304           3 
1400     -13316.0481      0.304           3 
1500     -13316.0285      0.304           3 
1600     -13315.9524      0.304           3 
Optimization precision reduced 
   Optimizing parameters...    improved    0.004 lnL
   Optimizing branchlengths... improved    0.000 lnL
1700     -13315.9479      0.206           3 
1800     -13315.9284      0.206           3 
1900     -13315.8732      0.206           3 
2000     -13315.8515      0.206           3 
2100     -13315.8477      0.206           3 
Optimization precision reduced 
   Optimizing parameters...    improved    0.002 lnL
   Optimizing branchlengths... improved    0.000 lnL
2200     -13315.8461      0.108           3 
2300     -13315.8265      0.108           3 
2400     -13315.8118      0.108           3 
2500     -13315.8107      0.108           3 
2600     -13315.8043      0.108           3 
Optimization precision reduced 
   Optimizing parameters...    improved    0.001 lnL
   Optimizing branchlengths... improved    0.000 lnL
2700     -13315.8013      0.010           3 
2800     -13315.8000      0.010           3 
2900     -13315.7998      0.010           3 
3000     -13315.7995      0.010           3 
3100     -13315.7995      0.010           3 
3200     -13315.7993      0.010           3 
3300     -13315.7975      0.010           3 
3400     -13315.7975      0.010           3 
3500     -13315.7973      0.010           3 
3600     -13315.7973      0.010           3 
3700     -13315.7972      0.010           3 
3800     -13315.7972      0.010           3 
3900     -13315.7972      0.010           3 
4000     -13315.7906      0.010           3 
4100     -13315.7905      0.010           3 
4200     -13315.7903      0.010           3 
4300     -13315.7901      0.010           3 
4400     -13315.7901      0.010           3 
4500     -13315.7901      0.010           3 
4600     -13315.7901      0.010           3 
4700     -13315.7901      0.010           3 
4800     -13315.7901      0.010           3 
4900     -13315.7900      0.010           3 
5000     -13315.7900      0.010           3 
5100     -13315.7899      0.010           3 
5200     -13315.7899      0.010           3 
5300     -13315.7899      0.010           3 
5400     -13315.7899      0.010           3 
5500     -13315.7899      0.010           3 
5600     -13315.7899      0.010           3 
5700     -13315.7899      0.010           3 
5800     -13315.7889      0.010           3 
5900     -13315.7889      0.010           3 
6000     -13315.7889      0.010           3 
6100     -13315.7889      0.010           3 
6200     -13315.7889      0.010           3 
6300     -13315.7884      0.010           3 
6400     -13315.7884      0.010           3 
6500     -13315.7884      0.010           3 
6600     -13315.7873      0.010           3 
6700     -13315.7873      0.010           3 
6800     -13315.7873      0.010           3 
6900     -13315.7870      0.010           3 
7000     -13315.7870      0.010           3 
7100     -13315.7870      0.010           3 
7200     -13315.7869      0.010           3 
7300     -13315.7866      0.010           3 
7400     -13315.7836      0.010           3 
7500     -13315.7836      0.010           3 
7600     -13315.7836      0.010           3 
7700     -13315.7836      0.010           3 
7800     -13315.7836      0.010           3 
7900     -13315.7836      0.010           3 
Reached termination condition!
last topological improvement at gen 3
Improvement over last 500 gen = 0.00001
Current score = -13315.7836
Performing final optimizations...
pass 1 : -13315.7832   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 2 : -13315.7829   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 3 : -13315.7783   (branch= 0.0043  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 4 : -13315.7757   (branch= 0.0025  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 5 : -13315.7732   (branch= 0.0024  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 6 : -13315.7728   (branch= 0.0003  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 7 : -13315.7726   (branch= 0.0002  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 8 : -13315.7726   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13315.7725   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13315.7725   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13315.7724   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0001)
Looking for minimum length branches...
Final score = -13315.7723
Time used so far = 0 hours, 3 minutes and 27 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    AC = 1.962, AG = 2.575, AT = 1.408, CG = 1.408, CT = 3.715, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3102 0.1767 0.2971 0.2160 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4096
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1889	0.2500
      0.7415	0.2500
      3.0515	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.400, AG = 7.116, AT = 1.617, CG = 7.116, CT = 4.400, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2692 0.1636 0.1605 0.4067 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3611
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1521	0.2500
      0.6854	0.2500
      3.1509	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    AC = 1.000, AG = 4.938, AT = 3.393, CG = 0.457, CT = 4.938, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1565 0.3537 0.2877 0.2021 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      4.0160
    with an invariant (invariable) site category, proportion estimated
      0.0335
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0335
      0.4552	0.2416
      0.7758	0.2416
      1.0844	0.2416
      1.6846	0.2416

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: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    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: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, 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: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    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
    with an invariant (invariable) site category, proportion estimated
      0.0355
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0355
      0.0334	0.2411
      0.2519	0.2411
      0.8203	0.2411
      2.8944	0.2411

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=1320446270

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 182.213 lnL
Optimizing branchlengths... improved 23.611 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13653.6032
optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates...
pass 1:+  178.674 (branch=  11.12 scale=  0.00 alpha= 11.90 freqs= 26.62 rel rates= 64.15 pinv=  0.37 subset rates= 64.52)
pass 2:+   86.362 (branch=   4.68 scale=  1.93 alpha=  5.43 freqs= 12.73 rel rates=  3.97 pinv=  0.01 subset rates= 57.60)
pass 3:+   44.763 (branch=   3.77 scale=  0.92 alpha=  3.02 freqs=  0.93 rel rates=  2.17 pinv=  0.01 subset rates= 33.93)
pass 4:+   17.413 (branch=   1.07 scale=  0.00 alpha=  0.66 freqs=  0.18 rel rates=  3.09 pinv=  0.01 subset rates= 12.39)
pass 5:+    5.472 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.17 rel rates=  0.63 pinv=  0.01 subset rates=  4.66)
pass 6:+    0.158 (branch=   0.00 scale=  0.00 alpha=  0.00 freqs=  0.13 rel rates=  0.01 pinv=  0.01 subset rates=  0.00)
lnL after optimization: -13320.7617
gen      current_lnL    precision  last_tree_imp  
0        -13320.7617      0.500           0 
100      -13318.9005      0.500           0 
200      -13318.2255      0.500           0 
300      -13318.0181      0.500           0 
400      -13317.3604      0.500           0 
500      -13316.9407      0.500           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.061 lnL
   Optimizing branchlengths... improved    0.000 lnL
600      -13316.6639      0.402           0 
700      -13316.5914      0.402           0 
800      -13316.5551      0.402           0 
900      -13316.4227      0.402           0 
1000     -13316.4019      0.402           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.025 lnL
   Optimizing branchlengths... improved    0.000 lnL
1100     -13316.3551      0.304           0 
1200     -13316.3373      0.304           0 
1300     -13316.2828      0.304           0 
1400     -13316.2796      0.304           0 
1500     -13316.2045      0.304           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.014 lnL
   Optimizing branchlengths... improved    0.000 lnL
1600     -13316.1466      0.206           0 
1700     -13316.1059      0.206           0 
1800     -13316.0999      0.206           0 
1900     -13316.0997      0.206           0 
2000     -13316.0620      0.206           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.121 lnL
   Optimizing branchlengths... improved    0.000 lnL
2100     -13315.9412      0.108           0 
2200     -13315.9384      0.108           0 
2300     -13315.9384      0.108           0 
2400     -13315.9007      0.108           0 
2500     -13315.8810      0.108           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.034 lnL
   Optimizing branchlengths... improved    0.000 lnL
2600     -13315.8424      0.010           0 
2700     -13315.8416      0.010           0 
2800     -13315.8416      0.010           0 
2900     -13315.8294      0.010           0 
3000     -13315.8285      0.010           0 
3100     -13315.8282      0.010           0 
3200     -13315.8272      0.010           0 
3300     -13315.8240      0.010           0 
3400     -13315.8209      0.010           0 
3500     -13315.8195      0.010           0 
3600     -13315.8173      0.010           0 
3700     -13315.8173      0.010           0 
3800     -13315.8158      0.010           0 
3900     -13315.8157      0.010           0 
4000     -13315.8122      0.010           0 
4100     -13315.8112      0.010           0 
4200     -13315.8112      0.010           0 
4300     -13315.8112      0.010           0 
4400     -13315.8112      0.010           0 
4500     -13315.8112      0.010           0 
4600     -13315.8083      0.010           0 
4700     -13315.8075      0.010           0 
4800     -13315.8070      0.010           0 
4900     -13315.8070      0.010           0 
5000     -13315.8070      0.010           0 
5100     -13315.8070      0.010           0 
5200     -13315.8067      0.010           0 
5300     -13315.8067      0.010           0 
5400     -13315.8059      0.010           0 
5500     -13315.8058      0.010           0 
5600     -13315.8040      0.010           0 
5700     -13315.8026      0.010           0 
5800     -13315.8026      0.010           0 
5900     -13315.8026      0.010           0 
6000     -13315.8024      0.010           0 
6100     -13315.8015      0.010           0 
6200     -13315.8010      0.010           0 
6300     -13315.8010      0.010           0 
6400     -13315.7996      0.010           0 
6500     -13315.7993      0.010           0 
6600     -13315.7993      0.010           0 
6700     -13315.7964      0.010           0 
6800     -13315.7926      0.010           0 
6900     -13315.7926      0.010           0 
7000     -13315.7925      0.010           0 
7100     -13315.7925      0.010           0 
7200     -13315.7925      0.010           0 
7300     -13315.7925      0.010           0 
7400     -13315.7925      0.010           0 
7500     -13315.7924      0.010           0 
7600     -13315.7922      0.010           0 
Reached termination condition!
last topological improvement at gen 0
Improvement over last 500 gen = 0.00030
Current score = -13315.7922
Performing final optimizations...
pass 1 : -13315.7848   (branch= 0.0069  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0003  rel rates= 0.0002  subset rates= 0.0000)
pass 2 : -13315.7809   (branch= 0.0032  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0004)
pass 3 : -13315.7772   (branch= 0.0034  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 4 : -13315.7770   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 5 : -13315.7750   (branch= 0.0017  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 6 : -13315.7737   (branch= 0.0009  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0002)
pass 7 : -13315.7730   (branch= 0.0006  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 8 : -13315.7727   (branch= 0.0001  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0000)
pass 9 : -13315.7724   (branch= 0.0002  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13315.7724   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 12: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 13: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 14: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 15: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 16: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13315.7722
Time used so far = 0 hours, 5 minutes and 27 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    AC = 1.964, AG = 2.577, AT = 1.410, CG = 1.410, CT = 3.720, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3101 0.1767 0.2972 0.2160 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4098
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1890	0.2500
      0.7416	0.2500
      3.0512	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.382, AG = 7.087, AT = 1.610, CG = 7.087, CT = 4.382, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2692 0.1636 0.1605 0.4067 
  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
      0.6853	0.2500
      3.1512	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    AC = 1.000, AG = 4.938, AT = 3.393, CG = 0.457, CT = 4.938, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1565 0.3537 0.2877 0.2021 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      4.0078
    with an invariant (invariable) site category, proportion estimated
      0.0334
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0334
      0.4548	0.2416
      0.7755	0.2416
      1.0844	0.2416
      1.6853	0.2416

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: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    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: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, 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: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    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
    with an invariant (invariable) site category, proportion estimated
      0.0355
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0355
      0.0334	0.2411
      0.2519	0.2411
      0.8203	0.2411
      2.8944	0.2411

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=490393635

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 254.487 lnL
Optimizing branchlengths... improved 50.375 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13796.1049
optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates...
pass 1:+  200.360 (branch=  15.47 scale=  0.00 alpha= 26.67 freqs= 29.27 rel rates= 69.20 pinv=  0.96 subset rates= 58.78)
pass 2:+   84.224 (branch=   7.28 scale=  1.63 alpha=  5.32 freqs= 13.47 rel rates=  3.51 pinv=  1.20 subset rates= 51.81)
pass 3:+   47.400 (branch=   4.57 scale=  1.26 alpha=  5.73 freqs=  1.41 rel rates=  2.51 pinv=  0.01 subset rates= 31.92)
pass 4:+   16.242 (branch=   1.20 scale=  0.50 alpha=  0.72 freqs=  0.16 rel rates=  1.54 pinv=  0.00 subset rates= 12.11)
pass 5:+    5.048 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.13 rel rates=  0.01 pinv=  0.00 subset rates=  4.90)
pass 6:+    1.167 (branch=   0.00 scale=  0.00 alpha=  0.00 freqs=  0.11 rel rates=  0.58 pinv=  0.00 subset rates=  0.47)
pass 7:+    0.096 (branch=   0.00 scale=  0.00 alpha=  0.00 freqs=  0.09 rel rates=  0.01 pinv=  0.00 subset rates=  0.00)
lnL after optimization: -13441.5669
gen      current_lnL    precision  last_tree_imp  
0        -13441.5669      0.500           0 
100      -13319.1751      0.500          27 
200      -13318.6657      0.500          27 
300      -13318.5942      0.500          27 
400      -13318.5663      0.500          27 
500      -13318.4781      0.500          27 
600      -13318.2512      0.500          27 
Optimization precision reduced 
   Optimizing parameters...    improved    0.082 lnL
   Optimizing branchlengths... improved    0.000 lnL
700      -13317.5316      0.402          27 
800      -13317.2393      0.402          27 
900      -13316.9226      0.402          27 
1000     -13316.6411      0.402          27 
1100     -13316.3656      0.402          27 
Optimization precision reduced 
   Optimizing parameters...    improved    0.045 lnL
   Optimizing branchlengths... improved    0.000 lnL
1200     -13316.2876      0.304          27 
1300     -13316.2215      0.304          27 
1400     -13316.1849      0.304          27 
1500     -13316.1600      0.304          27 
1600     -13316.1287      0.304          27 
Optimization precision reduced 
   Optimizing parameters...    improved    0.027 lnL
   Optimizing branchlengths... improved    0.000 lnL
1700     -13315.9846      0.206          27 
1800     -13315.9797      0.206          27 
1900     -13315.9603      0.206          27 
2000     -13315.9509      0.206          27 
2100     -13315.9348      0.206          27 
Optimization precision reduced 
   Optimizing parameters...    improved    0.009 lnL
   Optimizing branchlengths... improved    0.000 lnL
2200     -13315.9119      0.108          27 
2300     -13315.9083      0.108          27 
2400     -13315.9054      0.108          27 
2500     -13315.8826      0.108          27 
2600     -13315.8815      0.108          27 
Optimization precision reduced 
   Optimizing parameters...    improved    0.014 lnL
   Optimizing branchlengths... improved    0.021 lnL
2700     -13315.8419      0.010          27 
2800     -13315.8414      0.010          27 
2900     -13315.8414      0.010          27 
3000     -13315.8345      0.010          27 
3100     -13315.8324      0.010          27 
3200     -13315.8270      0.010          27 
3300     -13315.8117      0.010          27 
3400     -13315.8091      0.010          27 
3500     -13315.8058      0.010          27 
3600     -13315.8048      0.010          27 
3700     -13315.8048      0.010          27 
3800     -13315.8017      0.010          27 
3900     -13315.8016      0.010          27 
4000     -13315.8016      0.010          27 
4100     -13315.8011      0.010          27 
4200     -13315.8011      0.010          27 
4300     -13315.8011      0.010          27 
4400     -13315.8011      0.010          27 
4500     -13315.7962      0.010          27 
4600     -13315.7962      0.010          27 
4700     -13315.7962      0.010          27 
4800     -13315.7953      0.010          27 
4900     -13315.7953      0.010          27 
5000     -13315.7953      0.010          27 
5100     -13315.7953      0.010          27 
5200     -13315.7953      0.010          27 
5300     -13315.7936      0.010          27 
5400     -13315.7935      0.010          27 
5500     -13315.7933      0.010          27 
5600     -13315.7931      0.010          27 
5700     -13315.7928      0.010          27 
5800     -13315.7914      0.010          27 
5900     -13315.7914      0.010          27 
6000     -13315.7914      0.010          27 
6100     -13315.7912      0.010          27 
6200     -13315.7910      0.010          27 
6300     -13315.7896      0.010          27 
6400     -13315.7876      0.010          27 
6500     -13315.7875      0.010          27 
6600     -13315.7868      0.010          27 
6700     -13315.7867      0.010          27 
6800     -13315.7867      0.010          27 
6900     -13315.7867      0.010          27 
7000     -13315.7866      0.010          27 
7100     -13315.7865      0.010          27 
7200     -13315.7865      0.010          27 
7300     -13315.7865      0.010          27 
7400     -13315.7865      0.010          27 
7500     -13315.7865      0.010          27 
7600     -13315.7859      0.010          27 
7700     -13315.7848      0.010          27 
7800     -13315.7848      0.010          27 
7900     -13315.7842      0.010          27 
8000     -13315.7842      0.010          27 
8100     -13315.7842      0.010          27 
8200     -13315.7842      0.010          27 
Reached termination condition!
last topological improvement at gen 27
Improvement over last 500 gen = 0.00063
Current score = -13315.7842
Performing final optimizations...
pass 1 : -13315.7835   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0003  rel rates= 0.0002  subset rates= 0.0002)
pass 2 : -13315.7829   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0003  rel rates= 0.0001  subset rates= 0.0001)
pass 3 : -13315.7779   (branch= 0.0035  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0015  rel rates= 0.0000  subset rates= 0.0000)
pass 4 : -13315.7760   (branch= 0.0014  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0001  subset rates= 0.0000)
pass 5 : -13315.7734   (branch= 0.0023  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0000  subset rates= 0.0000)
pass 6 : -13315.7730   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0003  rel rates= 0.0000  subset rates= 0.0000)
pass 7 : -13315.7727   (branch= 0.0002  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0000  subset rates= 0.0000)
pass 8 : -13315.7724   (branch= 0.0003  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 9 : -13315.7722   (branch= 0.0001  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 10: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 11: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13315.7722
Time used so far = 0 hours, 7 minutes and 46 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    AC = 1.965, AG = 2.577, AT = 1.410, CG = 1.410, CT = 3.720, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3102 0.1767 0.2972 0.2160 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4097
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1889	0.2500
      0.7415	0.2500
      3.0514	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.384, AG = 7.090, AT = 1.611, CG = 7.090, CT = 4.384, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2692 0.1636 0.1605 0.4067 
  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: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    AC = 1.000, AG = 4.939, AT = 3.394, CG = 0.457, CT = 4.939, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1565 0.3537 0.2877 0.2021 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      4.0115
    with an invariant (invariable) site category, proportion estimated
      0.0335
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0335
      0.4550	0.2416
      0.7756	0.2416
      1.0844	0.2416
      1.6850	0.2416

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: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    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: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.000, AG = 4.000, AT = 1.000, CG = 4.000, 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: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    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
    with an invariant (invariable) site category, proportion estimated
      0.0355
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0355
      0.0334	0.2411
      0.2519	0.2411
      0.8203	0.2411
      2.8944	0.2411

Subset rate multipliers:
    1.00  1.00  1.00
Starting with seed=1922346580

creating likelihood stepwise addition starting tree...
number of taxa added:
 4  5  6 
Optimizing parameters... improved 198.984 lnL
Optimizing branchlengths... improved 64.290 lnL
 7  8  9  10  11 
Initial ln Likelihood: -13643.0835
optimizing: starting branch lengths, alpha shape, prop. invar, rel rates, eq freqs, subset rates...
pass 1:+  166.300 (branch=   6.38 scale=  1.32 alpha=  5.62 freqs= 26.48 rel rates= 65.82 pinv=  0.81 subset rates= 59.87)
pass 2:+   86.601 (branch=   7.41 scale=  1.89 alpha=  5.35 freqs= 12.24 rel rates=  3.04 pinv=  0.66 subset rates= 56.01)
pass 3:+   47.155 (branch=   5.78 scale=  0.66 alpha=  3.94 freqs=  0.98 rel rates=  2.37 pinv=  0.01 subset rates= 33.42)
pass 4:+   17.442 (branch=   0.49 scale=  0.57 alpha=  0.74 freqs=  0.18 rel rates=  3.03 pinv=  0.02 subset rates= 12.41)
pass 5:+    5.438 (branch=   0.00 scale=  0.00 alpha=  0.01 freqs=  0.17 rel rates=  0.56 pinv=  0.02 subset rates=  4.69)
pass 6:+    0.155 (branch=   0.00 scale=  0.00 alpha=  0.00 freqs=  0.13 rel rates=  0.01 pinv=  0.01 subset rates=  0.00)
lnL after optimization: -13319.9928
gen      current_lnL    precision  last_tree_imp  
0        -13319.9928      0.500           0 
100      -13318.8931      0.500           0 
200      -13318.1402      0.500           0 
300      -13317.7489      0.500           0 
400      -13317.4083      0.500           0 
500      -13316.8158      0.500           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.067 lnL
   Optimizing branchlengths... improved    0.000 lnL
600      -13316.5461      0.402           0 
700      -13316.4355      0.402           0 
800      -13316.3446      0.402           0 
900      -13316.3035      0.402           0 
1000     -13316.2369      0.402           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.035 lnL
   Optimizing branchlengths... improved    0.000 lnL
1100     -13316.1538      0.304           0 
1200     -13316.0789      0.304           0 
1300     -13316.0516      0.304           0 
1400     -13316.0267      0.304           0 
1500     -13316.0232      0.304           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.020 lnL
   Optimizing branchlengths... improved    0.000 lnL
1600     -13315.9865      0.206           0 
1700     -13315.9675      0.206           0 
1800     -13315.9224      0.206           0 
1900     -13315.9215      0.206           0 
2000     -13315.9196      0.206           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.006 lnL
   Optimizing branchlengths... improved    0.000 lnL
2100     -13315.9095      0.108           0 
2200     -13315.9037      0.108           0 
2300     -13315.8985      0.108           0 
2400     -13315.8890      0.108           0 
2500     -13315.8796      0.108           0 
Optimization precision reduced 
   Optimizing parameters...    improved    0.013 lnL
   Optimizing branchlengths... improved    0.011 lnL
2600     -13315.8524      0.010           0 
2700     -13315.8419      0.010           0 
2800     -13315.8399      0.010           0 
2900     -13315.8380      0.010           0 
3000     -13315.8310      0.010           0 
3100     -13315.8292      0.010           0 
3200     -13315.8292      0.010           0 
3300     -13315.8290      0.010           0 
3400     -13315.8288      0.010           0 
3500     -13315.8260      0.010           0 
3600     -13315.8255      0.010           0 
3700     -13315.8253      0.010           0 
3800     -13315.8209      0.010           0 
3900     -13315.8209      0.010           0 
4000     -13315.8207      0.010           0 
4100     -13315.8207      0.010           0 
4200     -13315.8176      0.010           0 
4300     -13315.8130      0.010           0 
4400     -13315.8127      0.010           0 
4500     -13315.8122      0.010           0 
4600     -13315.8122      0.010           0 
4700     -13315.8089      0.010           0 
4800     -13315.8080      0.010           0 
4900     -13315.8080      0.010           0 
5000     -13315.8080      0.010           0 
5100     -13315.8080      0.010           0 
5200     -13315.8080      0.010           0 
5300     -13315.8080      0.010           0 
5400     -13315.8076      0.010           0 
5500     -13315.8076      0.010           0 
5600     -13315.8071      0.010           0 
5700     -13315.8071      0.010           0 
5800     -13315.8071      0.010           0 
5900     -13315.8071      0.010           0 
6000     -13315.8066      0.010           0 
6100     -13315.8063      0.010           0 
6200     -13315.8063      0.010           0 
6300     -13315.8049      0.010           0 
6400     -13315.8048      0.010           0 
6500     -13315.8048      0.010           0 
6600     -13315.8046      0.010           0 
6700     -13315.8023      0.010           0 
6800     -13315.8017      0.010           0 
6900     -13315.8017      0.010           0 
7000     -13315.8016      0.010           0 
7100     -13315.7993      0.010           0 
7200     -13315.7991      0.010           0 
7300     -13315.7991      0.010           0 
7400     -13315.7941      0.010           0 
7500     -13315.7939      0.010           0 
7600     -13315.7927      0.010           0 
7700     -13315.7923      0.010           0 
7800     -13315.7923      0.010           0 
7900     -13315.7921      0.010           0 
8000     -13315.7921      0.010           0 
8100     -13315.7921      0.010           0 
Reached termination condition!
last topological improvement at gen 0
Improvement over last 500 gen = 0.00061
Current score = -13315.7921
Performing final optimizations...
pass 1 : -13315.7914   (branch= 0.0000  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0003  rel rates= 0.0004  subset rates= 0.0000)
pass 2 : -13315.7882   (branch= 0.0000  alpha= 0.0026  pinv= 0.0000  eq freqs= 0.0003  rel rates= 0.0002  subset rates= 0.0000)
pass 3 : -13315.7851   (branch= 0.0013  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0017  rel rates= 0.0002  subset rates= 0.0000)
pass 4 : -13315.7799   (branch= 0.0041  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0002  rel rates= 0.0008  subset rates= 0.0000)
pass 5 : -13315.7758   (branch= 0.0020  alpha= 0.0004  pinv= 0.0000  eq freqs= 0.0010  rel rates= 0.0006  subset rates= 0.0001)
pass 6 : -13315.7743   (branch= 0.0009  alpha= 0.0000  pinv= 0.0002  eq freqs= 0.0001  rel rates= 0.0001  subset rates= 0.0001)
pass 7 : -13315.7735   (branch= 0.0003  alpha= 0.0001  pinv= 0.0000  eq freqs= 0.0001  rel rates= 0.0002  subset rates= 0.0000)
pass 8 : -13315.7733   (branch= 0.0001  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0002  subset rates= 0.0000)
pass 9 : -13315.7727   (branch= 0.0002  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0003  subset rates= 0.0000)
pass 10: -13315.7726   (branch= 0.0001  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 11: -13315.7725   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0001  subset rates= 0.0000)
pass 12: -13315.7724   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 13: -13315.7724   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 14: -13315.7724   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 15: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 16: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 17: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 18: -13315.7723   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 19: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
pass 20: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
 optimization up to ...
pass 21: -13315.7722   (branch= 0.0000  alpha= 0.0000  pinv= 0.0000  eq freqs= 0.0000  rel rates= 0.0000  subset rates= 0.0000)
Looking for minimum length branches...
Final score = -13315.7722
Time used = 0 hours, 9 minutes and 50 seconds

MODEL REPORT - Parameter values are FINAL
Model 1
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 2 3 4 ) 
    AC = 1.962, AG = 2.575, AT = 1.409, CG = 1.409, CT = 3.717, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.3102 0.1767 0.2971 0.2160 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.4097
    Substitution rate categories under this model:
      rate	proportion
      0.0181	0.2500
      0.1890	0.2500
      0.7416	0.2500
      3.0513	0.2500

Model 2
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 1 0 3 ) 
    AC = 4.381, AG = 7.086, AT = 1.609, CG = 7.086, CT = 4.381, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.2692 0.1636 0.1605 0.4067 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      0.3612
    Substitution rate categories under this model:
      rate	proportion
      0.0115	0.2500
      0.1522	0.2500
      0.6856	0.2500
      3.1507	0.2500

Model 3
  Number of states = 4 (nucleotide data)
  Nucleotide Relative Rate Matrix: 
    User specified matrix type: ( 0 1 2 3 1 0 ) 
    AC = 1.000, AG = 4.938, AT = 3.393, CG = 0.457, CT = 4.938, GT = 1.000
  Equilibrium State Frequencies: estimated
    (ACGT) 0.1565 0.3537 0.2877 0.2021 
  Rate Heterogeneity Model:
    4 discrete gamma distributed rate categories, alpha param estimated
      4.0171
    with an invariant (invariable) site category, proportion estimated
      0.0335
    Substitution rate categories under this model:
      rate	proportion
      0.0000	0.0335
      0.4553	0.2416
      0.7758	0.2416
      1.0844	0.2416
      1.6845	0.2416

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 : -13315.7721 (best)
Replicate 2 : -13315.7723        (same topology as 1)
Replicate 3 : -13315.7722        (same topology as 1)
Replicate 4 : -13315.7722        (same topology as 1)
Replicate 5 : -13315.7722        (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:   1.966  2.579  1.411  1.411  3.722      1 0.310 0.177 0.297 0.216 0.410
rep 2:   1.962  2.575  1.408  1.408  3.715      1 0.310 0.177 0.297 0.216 0.410
rep 3:   1.964  2.577   1.41   1.41   3.72      1 0.310 0.177 0.297 0.216 0.410
rep 4:   1.965  2.577   1.41   1.41   3.72      1 0.310 0.177 0.297 0.216 0.410
rep 5:   1.962  2.575  1.409  1.409  3.717      1 0.310 0.177 0.297 0.216 0.410

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:    4.38  7.085  1.609  7.085   4.38      1 0.269 0.164 0.160 0.407 0.361
rep 2:     4.4  7.116  1.617  7.116    4.4      1 0.269 0.164 0.161 0.407 0.361
rep 3:   4.382  7.087   1.61  7.087  4.382      1 0.269 0.164 0.160 0.407 0.361
rep 4:   4.384   7.09  1.611   7.09  4.384      1 0.269 0.164 0.160 0.407 0.361
rep 5:   4.381  7.086  1.609  7.086  4.381      1 0.269 0.164 0.161 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  pinv
rep 1:       1  4.936  3.391 0.4568  4.936      1 0.157 0.354 0.288 0.202 4.014 0.034
rep 2:       1  4.938  3.393 0.4572  4.938      1 0.157 0.354 0.288 0.202 4.016 0.034
rep 3:       1  4.938  3.393 0.4569  4.938      1 0.157 0.354 0.288 0.202 4.008 0.033
rep 4:       1  4.939  3.394 0.4572  4.939      1 0.157 0.354 0.288 0.202 4.011 0.033
rep 5:       1  4.938  3.393 0.4572  4.938      1 0.157 0.354 0.288 0.202 4.017 0.034

Treelengths and subset rate multipliers:
          TL   R(1)  R(2)  R(3)
rep 1:  1.693 0.541 0.300 2.159
rep 2:  1.693 0.541 0.300 2.158
rep 3:  1.693 0.541 0.300 2.159
rep 4:  1.693 0.541 0.300 2.159
rep 5:  1.693 0.541 0.301 2.158

Saving final trees from all search reps to 3diffModels.byCodonPos.best.all.tre

Saving final tree from best search rep (#1) to 3diffModels.byCodonPos.best.tre
#######################################################
