A cladogram is a diagram used in cladistics which shows relations among
organisms. A cladogram is not, however, an evolutionary tree because it does not
show how ancestors are related to descendants or how much they have
changed; many evolutionary trees can be inferred from a single cladogram. A
cladogram uses lines that branch off in different directions ending at a clade,
a groups of organisms with a last common ancestor. There are many shapes of
cladograms but they all have lines that branch off from other lines. The lines
can be traced back to where they branch off. These branching off points
represent a hypothetical ancestor which is inferred to exhibit the traits shared
among the terminal taxa above it. This hypothetical ancestor might then provide
clues about the order of evolution of various features, adaptation, and other
evolutionary narratives about ancestors. Although traditionally such cladograms
were generated largely on the basis of morphological characters, DNA and RNA
sequencing data and computational phylogenetics are now very commonly used
in the generation of cladograms, either on their own or in combination with
morphology. Generating a cladogram
= Molecular versus morphological data = The characteristics used to create a
cladogram can be roughly categorized as either morphological or molecular. Prior
to the advent of DNA sequencing, cladistic analysis primarily used
morphological data. Behavioral data may also be used.
As DNA sequencing has become cheaper and easier, molecular systematics has become
a more and more popular way to infer phylogenetic hypotheses. Using a
parsimony criterion is only one of several methods to infer a phylogeny
from molecular data; maximum likelihood and Bayesian inference, which
incorporate explicit models of sequence evolution, are non-Hennigian ways to
evaluate sequence data. Another powerful method of reconstructing phylogenies is
the use of genomic retrotransposon markers, which are thought to be less
prone to the problem of reversion that plagues sequence data. They are also
generally assumed to have a low incidence of homoplasies because it was
once thought that their integration into the genome was entirely random; this
seems at least sometimes not to be the case, however.
= Plesiomorphies and synapomorphies = Researchers must decide which character
states are "ancestral" and which are derived, because only synapomorphic
character states provide evidence of grouping. This determination is usually
done by comparison to the character states of one or more outgroups. States
shared between the outgroup and some members of the in-group are
symplesiomorphies; states that are present only in a subset of the in-group
are synapomorphies. Note that character states unique to a single terminal do
not provide evidence of grouping. The choice of an outgroup is a crucial step
in cladistic analysis because different outgroups can produce trees with
profoundly different topologies. = Homoplasies =
A homoplasy is a character state that is shared by two or more taxa due to some
cause other than common ancestry. The two main types of homoplasy are
convergence and reversion. Characters that are obviously homoplastic, such as
white fur in different lineages of Arctic mammals, should not be included
as a character in a phylogenetic analysis. as they do not contribute
anything to our understanding of relationships. However, homoplasy is
often not evident from inspection of the character itself, and is then detected
by its incongruence on a most-parsimonious cladogram. Note that
characters that are homoplastic may still contain phylogenetic signal.
A well-known example of homoplasy due to convergent evolution would be the
character, "presence of wings". Though the wings of birds, bats, and insects
serve the same function, each evolved independently, as can be seen by their
anatomy. If a bird, bat, and a winged insect were scored for the character,
"presence of wings", a homoplasy would be introduced into the dataset, and this
could potentially confound the analysis, possibly resulting in a false hypothesis
of relationships. Of course, the only reason a homoplasy is recognizable in
the first place is because there are other characters that imply a pattern of
relationships that reveal its homoplastic distribution..
= What is a cladogram and what is not a cladogram? =
As noted above, a cladogram is the diagrammatic result of a parsimony
analysis, which groups taxa on the basis of synapomorphies alone. There are many
other phylogenetic algorithms that treat data somewhat differently, and result in
phylogenetic trees that look like cladograms but are not cladograms. For
example, phenetic algorithms, such as UPGMA and Neighbor-Joining, group by
overall similarity, and treat both synapomorphies and symplesiomorphies as
evidence of grouping, The resulting diagrams are phenograms, not cladograms,
Similarly, the results of model-based methods that take into account both
branching order and "branch length," count both synapomorphies and
autapomorphies as evidence for or against grouping, The diagrams resulting
from those sorts of analysis are not cladograms, either.
= Cladogram selection = There are several algorithms available
to identify the "best" cladogram. Most algorithms use a metric to measure how
consistent a candidate cladogram is with the data. Most cladogram algorithms use
the mathematical techniques of optimization and minimization.
In general, cladogram generation algorithms must be implemented as
computer programs, although some algorithms can be performed manually
when the data sets are trivial. Some algorithms are useful only when the
characteristic data are molecular; other algorithms are useful only when the
characteristic data are morphological. Other algorithms can be used when the
characteristic data includes both molecular and morphological data.
Algorithms for cladograms include least squares, neighbor-joining, parsimony,
maximum likelihood, and Bayesian inference.
Biologists sometimes use the term parsimony for a specific kind of
cladogram generation algorithm and sometimes as an umbrella term for all
cladogram algorithms. Algorithms that perform optimization
tasks can be sensitive to the order in which the input data is presented.
Inputting the data in various orders can cause the same algorithm to produce
different "best" cladograms. In these situations, the user should input the
data in various orders and compare the results.
Using different algorithms on a single data set can sometimes yield different
"best" cladograms, because each algorithm may have a unique definition
of what is "best". Because of the astronomical number of
possible cladograms, algorithms cannot guarantee that the solution is the
overall best solution. A nonoptimal cladogram will be selected if the
program settles on a local minimum rather than the desired global minimum.
To help solve this problem, many cladogram algorithms use a simulated
annealing approach to increase the likelihood that the selected cladogram
is the optimal one. The basal position is the direction of
the base of a rooted phylogenetic tree or cladogram. A basal clade is the
earliest clade to branch within a larger clade.
Cladogram statistics = Incongruence length difference test =
The incongruence length difference test is a measurement of how the combination
of different datasets contributes to a longer tree. It is measured by first
calculating the total tree length of each partition and summing them. Then
replicates are made by making randomly assembled partitions consisting of the
original partitions. The lengths are summed. A p value of 0.01 is obtained
for 100 replicates if 99 replicates have longer combined tree lengths.
= Measuring homoplasy = Some measures attempt to measure the
amount of homoplasy in a dataset with reference to a tree, though it is not
necessarily clear precisely what property these measures aim to quantify
Consistency index The consistency index measures the
consistency of a tree to a set of data – a measure of the minimum amount of
homoplasy implied by the tree. It is calculated by counting the minimum
number of changes in a dataset and dividing it by the actual number of
changes needed for the cladogram. A consistency index can also be calculated
for an individual character i, denoted ci.
Besides reflecting the amount of homoplasy, the metric also reflects the
number of taxa in the dataset, the number of characters in a dataset, the
degree to which each character carries phylogenetic information, and the
fashion in which additive characters are coded, rendering it unfit for purpose.
ci occupies a range from 1 to 12] in binary characters with an even state
distribution; its minimum value is larger when states are not evenly
spread. Retention index
The retention index was proposed as an improvement of the CI "for certain
applications" This metric also purports to measure of the amount of homoplasy,
but also measures how well synapomorphies explain the tree. It is
calculated taking the, and dividing by the.
The rescaled consistency index is obtained by multiplying the CI by the
RI; in effect this stretches the range of the CI such that its minimum
theoretically attainable value is rescaled to 0, with its maximum
remaining at 1. The homoplasy index is simply 1 − CI.
Homoplasy Excess Ratio This measures the amount of homoplasy
observed on a tree relative to the maximum amount of homoplasy that could
theoretically be present - 1 − /. A value of 1 indicates no homoplasy; 0
represents as much homoplasy as there would be in a fully random dataset, and
negative values indicate more homoplasy still. The HER is presented as the best
measure of homoplasy currently available.
See also Phylogenetics
Dendrogram Basal
References External links
Media related to Cladograms at Wikimedia Commons