version 3.5c

              DOLLOP  -- Dollo and Polymorphism Parsimony Program


(c) Copyright  1986-1993  by  Joseph  Felsenstein  and  by  the  University  of
Washington.  Written by Joseph Felsenstein.  Permission is granted to copy this
document provided that no fee is charged for it and that this copyright  notice
is not removed.

     This program carries out the Dollo  and  polymorphism  parsimony  methods.
The  Dollo  parsimony  method was first suggested in print in verbal form by Le
Quesne (1974) and was first well-specified by Farris  (1977).   The  method  is
named  after  Louis  Dollo  since  he  was  one  of the first to assert that in
evolution it is harder to  gain  a  complex  feature  than  to  lose  it.   The
algorithm  explains  the  presence of the state 1 by allowing up to one forward
change 0-->1 and as many reversions 1-->0  as  are  necessary  to  explain  the
pattern  of  states seen.  The program attempts to minimize the number of 1-->0
reversions necessary.

     The assumptions of this method are in effect:

     1. We know which state is the ancestral one (state 0).

     2. The characters are evolving independently.

     3. Different lineages evolve independently.

     4. The  probability  of  a  forward  change  (0-->1)  is  small  over  the
evolutionary times involved.

     5. The probability of a reversion (1-->0) is also  small,  but  still  far
larger  than  the  probability of a forward change, so that many reversions are
easier to envisage than even one extra forward change.

     6. Retention  of  polymorphism  for  both  states  (0  and  1)  is  highly
improbable.

     7. The lengths of the segments of the true tree are not  so  unequal  that
two changes in a long segment are as probable as one in a short segment.

     One problem can arise when using additive binary recoding to  represent  a
multistate  character  as  a series of two-state characters.  Unlike the Camin-
Sokal, Wagner, and Polymorphism  methods,  the  Dollo  method  can  reconstruct
ancestral states which do not exist.  An example is given in my 1979 paper.  It
will be necessary to check the output to make sure that this has not occurred.

     The polymorphism parsimony method was first used by me,  and  the  results
published  (without  a clear specification of the method) by Inger (1967).  The
method was independently published by Farris (1978a) and  by  me  (1979).   The
method  assumes  that  we can explain the pattern of states by no more than one
origination (0-->1) of state 1, followed by retention of polymorphism along  as
many  segments  of the tree as are necessary, followed by loss of state 0 or of
state 1 where necessary.  The program tries to minimize  the  total  number  of
polymorphic  characters,  where  each  polymorphism  is  counted  once for each
segment of the tree in which it is retained.

     The assumptions of the polymorphism parsimony method are in effect:

     1. The ancestral state (state 0) is known in each character.



     2. The characters are evolving independently of each other.

     3. Different lineages are evolving independently.

     4. Forward change (0-->1) is highly improbable over  the  length  of  time
involved in the evolution of the group.

     5. Retention of polymorphism is also improbable,  but  far  more  probable
that  forward change, so that we can more easily envisage much polymorhism than
even one additional forward change.

     6. Once state 1 is reached, reoccurrence of state 0  is  very  improbable,
much less probable than multiple retentions of polymorphism.

     7. The lengths of segments in the true tree are not so unequal that we can
more  easily  envisage  retention events occurring in both of two long segments
than one retention in a short segment.

     That these are the assumptions of parsimony methods has been documented in
a  series of papers of mine: (1973a, 1978b, 1979, 1981b, 1983b, 1988b).  For an
opposing  view  arguing  that  the  parsimony  methods  make   no   substantive
assumptions  such  as  these, see the papers by Farris (1983) and Sober (1983a,
1983b), but also read the exchange between Felsenstein and Sober (1986).

     The input format is the standard one, with "?", "P", "B"  states  allowed.
The options are selected using a menu:


Dollo and polymorphism parsimony algorithm, version 3.5c

Settings for this run:
  U                 Search for best tree?  Yes
  P                     Parsimony method?  Dollo
  J     Randomize input order of species?  No. Use input order
  T              Use Threshold parsimony?  No, use ordinary parsimony
  A   Use ancestral states in input file?  No
  M           Analyze multiple data sets?  No
  0   Terminal type (IBM PC, VT52, ANSI)?  ANSI
  1    Print out the data at start of run  No
  2  Print indications of progress of run  Yes
  3                        Print out tree  Yes
  4     Print out steps in each character  No
  5     Print states at all nodes of tree  No
  6       Write out trees onto tree file?  Yes

Are these settings correct? (type Y or the letter for one to change)

     The options U, J, T, A, and M are the usual User Tree,  Jumble,  Ancestral
States,   and  Multiple  Data  Sets  options,  described  either  in  the  main
documentation file or in the Discrete Characters Programs  documentation  file.
The  A  (Ancestral  States) option allows implementation of the unordered Dollo
parsimony and unordered polymorphism parsimony methods which I  have  described
elsewhere (1984b).  When the A option is used the ancestor is not to be counted
as one of the species.  The O (outgroup) option is not available since the tree
produced is already rooted.  Since the Dollo and polymorphism methods produce a
rooted tree, the user-defined trees required by the U option have two-way forks
at each level.

     The  P  (Parsimony  Method)  option  is  the  one  that  toggles   between
polymorphism  parsimony  and  Dollo  parsimony.   The program defaults to Dollo
parsimony.


     The T (Threshold) option  has  already  been  described  in  the  Discrete
Characters  programs documentation file.  Setting T at or below 1.0 but above 0
causes  the  criterion  to  become  compatibility  rather   than   polymorphism
parsimony,  although there is no advantage to using this program instead of MIX
to do a compatibility method.  Setting the threshold value higher brings  about
an  intermediate  between  the  Dollo or polymorphism parsimony methods and the
compatibility method, so that there is some rationale for  doing  that.   Since
the  Dollo  and  polymorphism  methods produces a rooted tree, the user-defined
trees required by the U option have two-way forks at each level.

     Using a threshold value of 1.0 or lower, but above 0,  one  can  obtain  a
rooted  (or,  if  the  A option is used with ancestral states of "?", unrooted)
compatibility criterion, but there is no particular  advantage  to  using  this
program  for  that  instead  of  MIX.   Higher  threshold  values are of course
meaningful and provide intermediates between Dollo and compatibility methods.

     In the input file the W (Weights) option is available, as usual.   It  and
the  A  (Ancestral states) option also require the option to be declared on the
first line of the input file and other information to be present in  the  input
file.   If  the Ancestral States information in present in the input file the A
option must be chosen from the menu.  The X (Mixed parsimony methods) option is
not available in this program.  The F (Factors) option is also not available in
this program, as it would have no effect on the result even if that information
were provided in the input file.

     Output is standard: a list of equally parsimonious trees, and, if the user
selects  menu  option  4, a table of the numbers of reversions or retentions of
polymorphism necessary in each character.  If any of the ancestral  states  has
been specified to be unknown, a table of reconstructed ancestral states is also
provided.  When reconstructing the placement of forward changes and  reversions
under  the  Dollo method, keep in mind that each polymorphic state in the input
data will require one  "last  minute"  reversion.   This  is  included  in  the
tabulated counts.  Thus if we have both states 0 and 1 at a tip of the tree the
program will assume that the lineage had state 1 up to  the  last  minute,  and
then state 0 arose in that population by reversion, without loss of state 1.

     If the user selects menu option 5, a table is printed out after each tree,
showing  for  each  branch whether there are known to be changes in the branch,
and what the states are inferred to have been at the top end of the branch.  If
the  inferred  state  is  a  "?"  there  may  be  multiple equally-parsimonious
assignments of states; the user must work these out for themselves by hand.

     If the A option is used, then the program will infer,  for  any  character
whose  ancestral state is unknown ("?") whether the ancestral state 0 or 1 will
give the best tree.  If these are tied, then it may not  be  possible  for  the
program to infer the state in the internal nodes, and these will all be printed
as ".".  If this has happened and you want to know more about the states at the
internal  nodes,  you  will find helpful to use DOLMOVE to display the tree and
examine its interior states, as the algorithm in DOLMOVE shows all that can  be
known  in  this case about the interior states, including where there is and is
not amibiguity.  The algorithm in DOLLOP gives up  more  easily  on  displaying
these states.

     If the U (User Tree) option is used and more than one  tree  is  supplied,
the program also performs a statistical test of each of these trees against the
best tree.  This test, which  is  a  version  of  the  test  proposed  by  Alan
Templeton  (1983)  and  evaluated  in a test case by me (1985a).  It is closely
parallel to a test using log likelihood differences  invented  by  Kishino  and
Hasegawa  (1989),  and  uses  the mean and variance of step differences between
trees, taken across characters.   If  the  mean  is  more  than  1.96  standard
deviations  different then the trees are declared significantly different.  The


program prints out a table of the steps for each tree, the differences of  each
from  the  highest one, the variance of that quantity as determined by the step
differences at individual sites, and a conclusion as to whether that tree is or
is  not  significantly  worse  than the best one. It is important to understand
that  the  test  assumes  that  all  the   binary   characters   are   evolving
independently,  which  is  unlikely to be true for many suites of morphological
characters.

     The constants at the beginning of the program include maxchr, the  maximum
number  of characters allowed, "nmlngth", the number of characters in a species
name, and "maxtrees", the maximum number of trees which the program will  store
for output.

     The algorithm is a fairly simple adaptation of the one used in the program
SOKAL,  which  was formerly in this package and has been superseded by MIX.  It
requires two passes through each tree to count the numbers of reversions.

------------------------------TEST DATA SET-----------------------------

     5    6
Alpha     110110
Beta      110000
Gamma     100110
Delta     001001
Epsilon   001110

---------- TEST SET OUTPUT (with all numerical options on) -------------

Dollo and polymorphism parsimony algorithm, version 3.5c

Dollo parsimony method


Name         Characters
----         ----------

Alpha        11011 0
Beta         11000 0
Gamma        10011 0
Delta        00100 1
Epsilon      00111 0



One most parsimonious tree found:




  +-----------Delta
--3
  !  +--------Epsilon
  +--4
     !  +-----Gamma
     +--2
        !  +--Beta
        +--1
           +--Alpha


requires a total of      3.000


 reversions in each character:
         0   1   2   3   4   5   6   7   8   9
     *-----------------------------------------
    0!       0   0   1   1   1   0

From    To     Any Steps?    State at upper node
                             ( . means same as in the node below it on tree)

root      3         yes    ..1.. .
  3    Delta        yes    ..... 1
  3       4         yes    ...11 .
  4    Epsilon      no     ..... .
  4       2         yes    1.0.. .
  2    Gamma        no     ..... .
  2       1         yes    .1... .
  1    Beta         yes    ...00 .
  1    Alpha        no     ..... .