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Cladistics is the hierarchical classification of species based on evolutionary ancestry. Cladistics is distinguished from other taxonomic classification systems because it focuses on evolution (rather than focusing on similarities between species), and because it places heavy emphasis on objective, quantitative analysis. Cladistics generates diagrams called cladograms that represent the evolutionary tree of life. DNA and RNA sequencing data are used in many important cladistic efforts. Computer programs are widely used in cladistics, due to the highly complex nature of cladogram-generation procedures. A major contributor to cladistics was the German entomologist Willi Hennig, who referred to it as phylogenetic systematics. The term phylogenetics is often used synonymously with cladistics. Cladistics originated in the field of biology but in recent years has found application in other disciplines. The word cladistics is derived from the ancient Greek κλάδος, klados, or "branch."
The starting point of cladistic analysis is a group of species and molecular, morphological, or other data characterizing those species. The end result is a tree-like relationship-diagram called a cladogram. The cladogram graphically represents a hypothetical evolutionary process. Cladograms are subject to revision as additional data becomes available.
Synonyms — The term evolutionary tree is often used synonymously with cladogram. The term phylogenetic tree is sometimes used synonymously with cladogram, but others treat phylogenetic tree as a broader term that includes trees generated with a non-evolutionary emphasis.
Subtrees are Clades — In a cladogram, all organisms lie at the leaves. The two taxa on either side of a split are called sister taxa or sister groups. Each subtree, whether it contains one item or a hundred thousand items, is called a clade.
2-Way versus 3-Way Forks — Many cladists require that all forks in a cladogram be 2-way forks. Some cladograms include 3-way or 4-way forks when the data is insufficient to resolve the forking to a higher level of detail, but nodes with more than two branches are discouraged by many cladists. See phylogenetic tree for more information about forking choices in trees.
Depth of a Cladogram — If a cladogram represents N species, the number of levels (the "depth") in the cladogram is on the order of log2(N). For example, if there are 32 species of deer, a cladogram representing deer will be around 5 levels deep (because 25=32). A cladogram representing the complete tree of life, with about 10 million species, would be about 23 levels deep. This formula gives a lower limit: in most cases the actual depth will be a larger value because the various branches of the cladogram will not be uniformly deep. Conversely, the depth may be shallower if forks larger than 2-way forks are permitted.
Number of Distinct Cladograms — For a given set of species, the number of distinct rooted cladograms that can be drawn (ignoring which cladogram best matches the species characteristics) is:
This exponential growth of the number of possible cladograms explains why manual creation of cladograms becomes very difficult when the number of species is large.
Extinct Species in Cladograms — Cladistics makes no distinction between extinct and non-extinct species, and it is appropriate to include extinct species in the group of organisms being analyzed. Cladograms that are based on DNA/RNA generally do not include extinct species because DNA/RNA samples from extinct species are rare. Cladograms based on morphology, especially morphological characteristics that are preserved in fossils, are more likely to include extinct species.
Time Scale of a Cladogram — A cladogram tree has an implicit time axis, with time running forward from the base of the tree to the leaves of the tree. If the approximate date (for example, expressed as millions of years ago) of all the evolutionary forks were known, those dates could be captured in the cladogram. Thus, the time axis of the cladogram could be assigned a time scale (e.g. 1 cm = 1 million years), and the forks of the tree could be graphically located along the time axis. Such cladograms are called scaled cladograms. Many cladograms are not scaled along the time axis, for a variety of reasons:
Cladistics compared with Linnaean taxonomy
Prior to the advent of cladistics, most taxonomists used Linnaean taxonomy to organize lifeforms. That traditional approach, still in use by some researchers (especially in works intended for a more general audience) uses several fixed levels of a hierarchy, such as Kingdom, Phylum, Class, Order, and Family. Cladistics does not use those terms, because one of the fundamental premises of cladistics is that the evolutionary tree is very deep and very complex, and it is not meaningful to use a fixed number of levels.
Linnaean taxonomy insists that groups reflect phylogenies, but in contrast to cladistics allows both monophyletic and paraphyletic groups as taxa. Since the early 20th century, Linnaean taxonomists have generally attempted to make genus- and lower-level taxa monophyletic.
Cladistics originated in the work of Willi Hennig, and since that time, there has been a spirited debate about the relative merits of cladistics versus Linnaean classification. Some of the debates that the cladists engaged in had been running since the 19th century, but they entered these debates with a new fervor, as can be learned from the Foreword to Hennig (1979) in which Rosen, Nelson, and Patterson wrote the following:
Encumbered with vague and slippery ideas about adaptation, fitness, biological species and natural selection, neo-Darwinism (summed up in the "evolutionary" systematics of Mayr and Simpson) not only lacked a definable investigatory method, but came to depend, both for evolutionary interpretation and classification, on consensus or authority. (Foreword, page ix)
Proponents of cladistics enumerate key distinctions between cladistics and Linnaean taxonomy as follows:
Proponents of Linnaean taxonomy contend that it has some advantages over cladistics, such as:
Cladistics compared to phenetics
For some decades in the mid-late 20th century, a commonly used methodology was phenetics ("numerical taxonomy"). This can be seen as a precedessor to some methods of today's cladistics (namely distance matrix methods like neighbor-joining), but made no attempt to resolve phylogeny, only similarities. Considered cutting-edge at its time as they were among the first bioinformatics applications, phenetic methods are today superseded by cladistic analyses due to their inability of phenetics to provide an evolutionary hypothesis, except by chance.
Monophyletic groups encouraged
Many cladists discourage the use of paraphyletic groups because they detract from cladisitcs' emphasis on clades (monophyletic groups). In contrast, proponents of the use of paraphyletic groups argue that any dividing line in a cladogram creates both a monophyletic section above and a paraphyletic section below. They also contend that paraphyletic taxa are necessary for classifying earlier sections of the tree – for instance, the early vertebrates that would someday evolve into the family Hominidae cannot be placed in any other monophyletic family. They also argue that paraphyletic taxa provide information about significant changes in organisms' morphology, ecology, or life history – in short, that both paraphyletic groups and clades are valuable notions with separate purposes.
Simplified step by step procedure
A simplified procedure for generating a cladogram is:
Step 1: Gather and organize data
A cladistic analysis begins with the following data:
For example, if analyzing 20 species of birds, the data might be:
Molecular versus morphological data
The characteristics used to create a cladogram can be roughly categorized as either morphological (synapsid skull, warm-blooded, notochord, unicellular, etc.) or molecular (DNA, RNA, or other genetic information). Prior to the advent of DNA sequencing, all cladistic analysis used morphological data.
As DNA sequencing has become cheaper and easier, molecular systematics has become a more and more popular way to reconstruct phylogenies. 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.
Ideally, morphological, molecular, and possibly other phylogenies should be combined into an analysis of total evidence: All have different intrinsic sources of error. For example, character convergence (homoplasy) is much more common in morphological data than in molecular sequence data, but character reversions that cannot be noticed as such are more common in the latter (see long branch attraction). Morphological homoplasies can usually be recognized as such if character states are defined with enough attention to detail.
Plesiomorphies and synapomorphies
The researcher decides which character states were present before the last common ancestor of the species group (plesiomorphies) and which were present in the last common ancestor (synapomorphies) by considering one or more outgroups. This makes the choice of an outgroup an important task, since this choice can profoundly change the topology of a tree. Note that only synapomorphies are of use in characterising clades.
A homoplasy is a character that is shared by multiple species due to some cause other than common ancestry. Typically, homoplasies occur due to convergent evolution. Use of homoplasies when building a cladogram is sometimes unavoidable but is to be avoided when possible.
A well-known example of homoplasy due to convergent evolution would be a 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 confounds the analysis, possibly resulting in a false evolutionary scenario.
Homoplasies can often be avoided outright in morphological datasets by defining characters more precisely and increasing their number. In the example above, utilizing "wings supported by bony endoskeleton" and "wings supported by chitinous exoskeleton" as characters would avoid the homoplasy. When analyzing "supertrees" (datasets incorporating as many taxa of a suspected clade as possible), it may become unavoidable to introduce character definitions that are imprecise, as otherwise the characters might not apply at all to a large number of taxa. The "wings" example would be hardly useful if attempting a phylogeny of all Metazoa as most of these don't have wings at all. Cautious choice and definition of characters thus is another important element in cladistic analyses. With a faulty outgroup or character set, no method of evaluation is likely to produce a phylogeny representing the evolutionary reality.
Step 2: Consider possible cladograms
When there are just a few species being organized, it is possible to do this step manually, but most cases require a computer program. There are scores of computer programs available to support cladistics. See phylogenetic tree for more information about tree-generating computer programs.
Because the total number of possible cladograms grows exponentially with the number of species, it is impractical for a computer program to evaluate every individual cladogram. A typical cladistic program begins by using heuristic techniques to identify a small number of candidate cladograms. Many cladistic programs then continue the search with the following repetitive steps:
Computer programs that generate cladograms use algorithms that are very computationally intensive, because the cladogram algorithm is NP-hard.
Step 3: Select the best cladogram
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 (for example, just a few species and a couple of characteristics).
Some algorithms are useful only when the characteristic data is molecular (DNA, RNA) data. Other algorithms are useful only when the characteristic data is morphological data. 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 (such as building cladograms) can be sensitive to the order in which the input data (the list of species and their characteristics) 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 non-optimal 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.
How complex is the Tree of Life?
One of the arguments in favor of cladistics is that it supports arbitrarily complex, arbitrarily deep trees. Especially when extinct species are considered (both known and unknown), the complexity and depth of the tree can be very large. Every single speciation event, including all the species that are now extinct, represents an additional fork on the hypothetical, complete cladogram representing the full tree of life. Fractals can be used to represent this notion of increasing detail: as a viewpoint zooms into the tree of life, the complexity remains virtually constant. This great complexity of the tree, and the uncertainty associated with the complexity, is one of the reasons that cladists cite for the attractiveness of cladistics over traditional taxonomy.
Proponents of non-cladistic approaches to taxonomy point to puncuated equilibrium to bolster the case that the tree-of-life has a finite depth and finite complexity. If the number of species currently alive is finite, and the number of extinct species that we will ever know about is finite, then the depth and complexity of the tree of life is bounded, and there is no need to handle arbitrarily deep trees.
Phylocode approach to naming species
A formal code of phylogenetic nomenclature, the PhyloCode, is currently under development for cladistic taxonomy. It is intended for use by both those who would like to abandon Linnaean taxonomy and those who would like to use taxa and clades side by side. In several instances (see for example Hesperornithes) it has been employed to clarify uncertainties in Linnaean systematics so that in combination they yield a taxonomy that is unambiguously placing the group in the evolutionary tree in a way that is consistent with current knowledge.
Origin of the term "cladistics"
Hennig's major book, even the 1979 version, does not contain the term cladistics in the index. He referred to his own approach as phylogenetic systematics, implied by the book's title. A review paper by Dupuis observes that the term clade was introduced in 1958 by Julian Huxley, cladistic by Cain and Harrison in 1960, and cladist (for an adherent of Hennig's school) by Mayr in 1965.
Three definitions of clade
There are three ways to define a clade for use in a cladistic taxonomy.
Applying Cladistics to other disciplines
The processes used to generate cladograms are not limited to the field of biology. The generic nature of cladistics means that cladistics can be used to organize groups of items in many different realms. The only requirement is that the items have chararacteristics that can be identified and measured.
For example, one could take a group of 200 spoken languages, measure various characteristics of each language (vocabulary, phonemes, rhythms, accents, dynamics, etc) and then apply a cladogram algorithm to the data. The result will be a tree that may shed light on how, and in what order, the languages came into existence.
Thus, cladistic methods have recently been usefully applied to non-biological systems, including determining language families in historical linguistics, culture, history, and filiation of manuscripts in textual criticism.
|This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Cladistics". A list of authors is available in Wikipedia.|