Supertrees
can
be
used
to
combine
partially
overalapping
trees
and
generate
more
inclusive
phylogenies.
It
has
been
proposed
that
Maximum
Likelihood
(ML)
supertrees
method
(SM)
could
be
developed
using
an
exponential
probability
distribution
to
model
errors
in
the
input
trees
(given
a
proposed
supertree).
When
the
tree-‐to-‐tree
distances
used
in
the
ML
computation
are
symmetric
differences,
the
ML
SM
has
been
shown
to
be
equivalent
to
a
Majority-‐Rule
consensus
SM,
and
hence,
exactly
as
the
latter,
it
has
the
desirable
property
of
being
a
median
tree
(with
reference
to
the
set
of
input
trees).
The
ability
to
estimate
the
likelihood
of
supertrees,
allows
implementing
Bayesian
(MCMC)
approaches,
which
have
the
advantage
to
allow
the
support
for
the
clades
in
a
supertree
to
be
properly
estimated.
I
present
here
the
L.U.St
software
package;
it
contains
the
first
implementation
of
a
ML
SM
and
allows
for
the
first
time
statistical
tests
on
supertrees.
I
also
characterized
the
first
implementation
of
the
Bayesian
(MCMC)
SM.
Both
the
ML
and
the
Bayesian
(MCMC)
SMs
have
been
tested
for
and
found
to
be
immune
to
biases.
The
Bayesian
(MCMC)
SM
is
applied
to
the
reanalyses
of
a
variety
of
datasets
(i.e.
the
datasets
for
the
Metazoa
and
the
Carnivora),
and
I
have
also
recovered
the
first
Bayesian
supertree-‐based
phylogeny
of
the
Eubacteria
and
the
Archaebacteria.
These
new
SMs
are
discussed,
with
reference
to
other,
well-‐
known
SMs
like
Matrix
Representation
with
Parsimony.
Both
the
ML
and
Bayesian
SM
offer
multiple
attractive
advantages
over
current
alternatives
