subscribe to our mailing list:
29+ Evidences for Macroevolution
Copyright © 1999-2004 by Douglas Theobald,
Introduction to Phylogenetics
from a common ancestor entails a process of branching and divergence of species,
in common with any genealogical process. Genealogies can be graphically
illustrated by tree-like diagrams, and this is why you will hear biologists
refer to the genealogy of species as the "tree of life". Diagrams such as these
are known as phylogenetic trees or phylogenies. The consensus model which
evolutionary biologists use to represent the well-supported branches of the
universal tree of life I will refer to as the "standard phylogenetic tree". Figure 1
shows a simplified example of some of the more familiar branches of the
universal phylogenetic tree. The macroevolutionary prediction of a unique,
historical universal phylogenetic tree is the most important, powerful, and
basic conclusion from the hypothesis of universal common descent. A thorough
grasp of this concept is necessary for understanding macroevolutionary
In the following section is a brief overview of phylogenetic trees and of how
biologists determine them. This overview becomes increasingly technical as it
proceeds. The material up until the maximum
parsimony heading is essential for understanding the rest of this FAQ. The
remaining phylogenetic discussion is given for completeness and to allow the
interested reader the opportunity to delve as far as is desired.
Figure 1. The Consensus Phylogenetic Tree of All
Phylogenetic trees represent evolutionary relationships
Figure 2: The parts of a phylogenetic tree. The taxa
in this tree are "human", "mouse", and "fly" (all of which have had their
full genomes sequenced). Several nodes are indicated, such as the "fly"
taxon node and an internal node that represents the common ancestor of
mice and humans. The root is indicated at left, representing the common
ancestor of all three taxa listed.
Phylogenetics is the scientific discipline concerned with describing and
reconstructing the patterns of genetic relationships among species and among
higher taxa. Phylogenetic trees are a convenient way of visually representing
the evolutionary history of life. These diagrams illustrate the inferred
relationships between organisms and the order of speciation events that led from
earlier common ancestors to their diversified descendants.
A phylogenetic tree has several parts, shown in Figure 2.
Nodes represent taxonomic units, such as an organism, a species, a
population, a common ancestor, or even an entire genus or other higher taxonomic
group. Branches connect nodes uniquely and represent genetic
relationships. The specific pattern of branching determines the tree's
topology. Scaled trees have branch lengths that are proportional
to some important biological property, such as the number of amino acid changes
between nodes on a protein phylogeny (see Figure 3).
Trees may also be rooted or unrooted. Rooted trees have a special
node, known as the root, that represents a common ancestor of all taxa
shown in the tree. Rooted trees are thus directional, since all taxa evolved
from the root. Unrooted trees illustrate relationships only, without reference
to common ancestors.
Figure 3: Various representations of a 5-taxa
phylogenetic tree. Each of these trees represents the same five modern
taxa: A, B, C, D, and E. The tree at upper left is rooted and scaled
according to evolutionary distance. The root is at left. Taxa C and E have
both undergone relatively large changes since divergence from the root, in
contrast to taxa B and D. The tree at lower left is rooted and unscaled.
Here the branch lengths are relative indicators of time since divergence.
The tree at right is scaled but unrooted. In this tree, while the root is
unkown, the relationships between taxa are identical to that shown in the
other two trees.
A common misconception is that some modern species are ancestral to other
modern species. However, all modern species are found at the tips of the tree's
branches, and one modern species is as "evolved" as any other. That is, although
mammals are thought to have evolved from something that resembled modern
reptiles, modern reptiles are just as "old" evolutionarily as modern mammals (Brooks
1991, p.68; Futuyma
Methods for determining phylogenetic trees:
Cladistics and numerical phylogenetics
Of all clean birds ye shall eat.
But these are they of which
ye shall not eat:
The eagle, and the ossifrage, and the ospray,
And the glede, and the kite, and the vulture after his kind,
every raven after his kind,
And the owl, and the night hawk, and the
cuckow, and the hawk after his kind,
The little owl, and the great
owl, and the swan,
And the pelican, and the gier eagle, and the
And the stork, and the heron after her kind, and the
and the bat.
Deuteronomy 14:11-18, KJV
If modern species have descended from ancestral ones in this tree-like,
branching manner, it should be possible to infer the true historical tree that
traces their paths of descent. Phylogenies have been inferred by biologists ever
since Darwin first proposed that life was united by common descent over 140
years ago. Rigorous algorithmic methodologies for inferring phylogenetic trees
have been in use for over the past 50 years.
In 1950, taxonomist Willi Hennig proposed a
method for determining phylogenetic trees based on morphology by classifying
organisms according to their shared derived characters, which are called
1966). This method, now called cladistics, does not assume
genealogical relatedness a priori, since it can be used to classify
anything in principle, even things like books, cars, or chairs that are
obviously not genealogically related in a biological sense (Kitching
et al. 1998, Ch. 1, p. 26; ). Using firm evolutionary arguments, however,
Hennig justified this method as the most appropriate classification technique
for estimating evolutionary relationships generated by lineal descent. In fact,
Hennig's cladistic method is nothing more than a formalization of the methods
systematic biologists had been using intuitively ever since Linnaeus penned
Systema Naturae. Biologists today construct their phylogenetic trees
based on Hennig's method, and because of cladistics these phylogenetic trees are
reproducible and independently testable (Brooks
1991, Ch. 2; Kitching
et al. 1998).
Cladistic methods are often contrasted with "phenetic" methods. Phenetic
methods cluster and classify species based upon the number of identical
characters that they share, that is, based upon overall similarity. Such methods
can run into trouble with organisms like dolphins and tuna, which have many
superficial similarities. These organisms, however, are not closely related and
should not be classified together if one expects classification to reflect
In contrast, cladistic-based phylogenies group taxa into nested hierarchies,
and they are determined using only shared derived
characters of organisms, not shared primitive
1991, pp. 35-36; Kitching
et al. 1998, Ch. 1; Maddison
and Maddison 1992, p. 49). In technical phylogenetic jargon, primitive
characters are called plesiomorphies, and derived characters are called
apomorphies. In cladistics, related species are grouped together because
they share derived characters (i.e., apomorphies) that originated in a common
ancestor of the group, but were not present in other, earlier ancestors of the
group. These shared, derived features are called synapomorphies.
Primitive and derived are therefore relative terms, depending upon the specific
group being considered. For example, backbones are primitive characters of
vertebrates, while hair is a derived character particular to mammalian
vertebrates. However, when considering mammals only, hair is primitive, whereas
an opposable thumb is derived.
In real-life phylogenetic analyses, shared derived characters may be in
conflict with other derived characters. Thus, objective methods are required for
resolving this character conflict (Kitching
et al. 1998, Ch. 1; Maddison
and Maddison 1992, p. 49). For instance, wings are a derived character of
birds and of bats. Based upon this character alone, the cladistic method would
group bats and birds together, which is how the author of Deuteronomy grouped
them in the Biblical quote above. However, other shared derived characters
indicate that bats should be grouped with wingless mammals, and that birds
should be grouped with wingless dinosaurs.
In the past 40 years, several algorithmic methods have been devised to
resolve such instances of character conflict and to infer correct phylogenetic
2004, Ch. 10). The following sections outline some of the most successful of
these methods. Each method attempts to infer a phylogeny from existing data, and
each has its respective strengths and weaknesses. Years of empirical testing and
simulation have shown that, in general, these different algorithms, each with
very different underlying assumptions, converge on trees that are highly similar
when judged statistically (Li 1997,
Chs 5 and 6; Nei and
Kumar 2000, Chs 6, 7, and 8).
One of the oldest, most basic, and most frequently used methods for character
resolution is the maximum parsimony (MP) criterion (Edwards
and Cavalli-Sforza 1963; Kitching
et al. 1998). The parsimony criterion mandates that the best tree describing
the data is the tree that minimizes the amount of character conflict. For
example, consider a dataset containing 10 shared derived characters that group
bats with apes (rather than with birds), and with one character that groups bats
with birds (rather than apes). According to the parsimony criterion, the tree
giving the first grouping should be preferred.
Currently, parsimony is the method of choice for reconstructing morphological
et al. 1998). It is very fast computationally, and it can be robust to
differences in evolutionary rate among characters. However, maximum parsimony
consistently finds the correct phylogeny only when we expect character conflict
to be low or evolution to proceed parsimoniously (Felsenstein
2004, Ch. 9; Kitching
et al. 1998, p. 17). If rates of evolution are slow and branches are short,
character conflict will be low and parsimony will work well (Felsenstein
2004, Ch. 9; Felsenstein
1981a; Li 1997, p.
128). If character conflict is moderate or high in reality, then it is very
unlikely that the true tree will have the least amount of character conflict.
When rates of evolution are high, or when some branches are very long, or when
the number of possible character states is limited, character conflict can be
common. This is often true for nucleotide sequences, which have only four
possible character states (A, C, T, or G). In cases such as these, other
phylogenetic methods can be more accurate than parsimony.
Another commonly used phylogenetic criterion is maximum likelihood
(ML), an effective and robust statistical technique now used in all scientific
and Cavalli-Sforza 1964; Felsenstein
1912). Many well-known statistical estimators are actually maximum likelihood
estimators. For example, the common sample average as an estimate of the mean of
a Gaussian distribution and the least-squares fit of a line to a set of points
are both maximum likelihood estimators. Using ML, one can infer rates of
evolution directly from the data and determine the tree that best describes that
data given those inferred rates. In other words, ML finds the tree and
evolutionary parameters that maximize the probability of the observed data.
Unlike parsimony, ML finds trees with the expected amount of character conflict
given the evolutionary rates inferred from the data, even if those rates are
high. ML is a computationally intensive method that can be very time-consuming.
Due to their computational speed, distance matrix methods are some of the
most popular for inferring phylogenies (Nei and
Kumar 2000, Ch. 6). All distance methods transform character data into a
matrix of pairwise distances, one distance for each possible pairing of the taxa
under study. Distance matrix methods are not cladistic, since the information
about derived and primitive characters has been lost during this transformation.
Distance methods approach phylogenetic inference strictly as a statistical
problem, and they are used almost exclusively with molecular data. Although they
are not cladistic, distance methods can be thought of as approximations to
cladistic methods, and several of the methods are guaranteed mathematically to
converge on the correct tree as more data is included.
The most simple distance metric is merely the number of character differences
between two taxa, such as the number of nucleotide differences between two DNA
sequences. Many other ways of calculating molecular sequence distances exist,
and most attempt to correct for the possibility of multiple changes at a single
site during evolution. Methods for calculating distances between sequences are
usually named for their originators, such as Kimura's two-parameter (K2P),
Jukes-Cantor (JC), Tamura-Nei (TN), Hasegawa, Kishino, and Yano (HKY), and
Felsenstein 1984 (F84). Other important distance metrics are General Time
Reversible (GTR) and LogDet (Felsenstein
2004, pp. Chs 11 and 13; Nei and
Kumar 2000, Chs 2 and 3; Li 1997,
Chs 3 and 4).
Once a distance matrix for the taxa being considered is in hand, there are
several distance-based criteria and algorithms that may be used to estimate the
phylogenetic tree from the data (Felsenstein
2004, Ch. 11; Li 1997,
Ch. 5). The minimum evolution (ME) criterion finds the tree in which the
sum of all the branch lengths is the smallest. Weighted and unweighted least
squares criteria calculate the discrepancy between the observed pairwise
distances and the pairwise distances calculated from the branch lengths of the
inferred tree. Least squares then finds the tree that minimizes the square of
that discrepancy. Least squares methods are some of the most statistically
justified and will converge on the correct tree as more data are included in the
analysis (given a mathematically proper distance metric). The
neighbor-joining (NJ) algorithm is extremely fast and is an approximation
of the least squares and minimum evolution methods. If the distance matrix is an
exact description of the true tree, then neighbor-joining is guaranteed to
reconstruct the correct tree. The UPGMA clustering algorithm (a confusing
acronym) is also extremely fast, but it is based upon the unlikely assumption
that evolutionary rates are equal in all lineages. UPGMA is rarely used today
except as an instructional tool.
Statistical Support for Phylogenies
A phylogeny is a best approximation of the correct, historical tree using a
given phylogenetic method. Some phylogenetic analyses are strongly supported by
the data, some are weakly supported, and different parts of a tree may have more
support than others. When comparing two independently determined phylogenies,
one must take into account the statistical support assigned to each branch of
the phylogenies. As with all scientific analyses, the details of a phylogenetic
tree may change as new information and data are incorporated (Maddison
and Maddison 1992, pp. 112-123; Li 1997,
pp. 36-146; Felsenstein
1998, p. 99; Hillis
and Bull 1993; Huelsenbeck
et al. 2001; Swofford
et al. 1996, pp. 504-509).
Bootstrapping is the most popular statistical method for assessing the
reliability of the branches in a phylogenetic tree (Felsenstein
1985). Bootstrapping is a statistical technique for empirically estimating
the variability of a parameter (Efron
and Gong 1983). In a bootstrap analysis, a fictional dataset is created by
randomly sampling data from the real dataset until a new dataset is created of
the same size. This process is done repeatedly (hundreds or thousands of times),
and the parameter of interest is estimated from each fictional dataset. The
variability of these bootstrapped estimations is itself an estimate of the
variability of the parameter of interest.
In phylogenetics, a new phylogeny is inferred from each bootstrapped dataset
1985). These bootstrapped phylogenies will likely have different topologies.
From these different bootstrapped trees, the variability in the inferred tree
can be estimated. The parts of the bootstrapped trees that are in common are
ascribed a high confidence, while the parts that vary extensively are assigned a
low confidence. Trees constructed from random data do not result in high
confidence trees or branches when bootstrapped. Thus, bootstrapping provides one
way to test whether a phylogenetic tree is genuine.
Does Phylogenetic Inference Find Correct Trees?
In order to establish their validity in reliably determining phylogenies,
phylogenetic methods have been empirically tested in cases where the true
phylogeny is known with certainty, since the true phylogeny was directly
Bacteriophage T7 was propagated and split sequentially in the presence of a
mutagen, where each lineage was tracked. Out of 135,135 possible phylogenetic
trees, the true tree was correctly determined by phylogenetic methods in a
blind analysis. Five different phylogenetic methods were used independently,
and each one chose the correct tree (Hillis
et al.1992 ).
In another study, 24 strains of mice were used in which the genealogical
relationships were known. Cladistic analysis reproduced almost perfectly the
known phylogeny of the 24 strains (Atchely
and Fitch 1991).
et al. used phylogenetic analysis to retrospectively predict the
correct evolutionary tree of human Influenza A virus 83% of the time for the
flu seasons spanning 1983 to 1994.
In 1998, researchers used 111 modern HIV-1 (AIDS virus) sequences in a
phylogenetic analysis to predict the nucleotide sequence of the viral ancestor
of which they were all descendants. The predicted ancestor sequence closely
matched, with high statistical probability, an actual ancestral HIV sequence
found in an HIV-1 seropositive African plasma sample collected and archived in
the Belgian Congo in 1959 (Zhu
et al.1998 ).
In the past decade, phylogenetic analyses have played a significant role in
successful convictions in several criminal court cases (Albert
et al. 1994; Arnold
et al. 1995; Birch
et al. 2000; Blanchard
et al. 1998; Goujon
et al. 2000; Holmes
et al. 1993; Machuca
et al. 2001; Ou et
al. 1992; Veenstra
et al. 1995; Vogel
et al. 1997), and phylogenetic reconstructions have now been
admitted as expert legal testimony in the United States (97-KK- 2220 State
of Louisiana v. Richard J. Schmidt [PDF]). The
legal test in the U. S. for admissibility of expert testimony is the
Daubert guidelines (U. S. Supreme Court Case Daubert
v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579, 587-89, 113 S. Ct.
2786, 2794, 125 L. Ed. 2d 469, 1993). The Daubert guidelines state that a
trial court should consider five factors in determining "whether the
testimony's underlying reasoning or methodology is scientifically valid": (1)
whether the theory or technique in question can be and has been tested; (2)
whether it has been subjected to peer review and publication; (3) its known or
potential error rate; (4) the existence and maintenance of standards
controlling its operation; and (5) whether it has attracted widespread
acceptance within the relevant scientific community (quoted nearly verbatim).
Phylogenetic analysis has officially met these legal requirements.
Caveats with Phylogenetic Inference
As with any investigational scientific method, certain conditions must hold
in order for the results to be reliable. A common premise of all molecular
phylogenetic methods is that genes are transmitted via vertical, lineal
inheritance, i.e. from ancestor to descendant. If this premise is violated, gene
trees will never recapitulate an organismic phylogeny. This assumption is
violated in instances of horizontal transfer, e.g. in transformation of a
bacterium by a DNA plasmid, or in retroviral insertion into a host's genome.
During the early evolution of life, before the advent of multicellular
organisms, horizontal transfer was likely very frequent (as it is today in the
observed evolution of bacteria and other unicellular organisms). Thus, it is
questionable whether molecular methods are applicable, even in principle, to
resolving the phylogeny of the early evolution of life near the most recent
common ancestor of all living organisms (Doolittle
The list below gives some of the more important caveats that scientists must
keep in mind when interpreting the results of a phylogenetic analysis (Swofford
1996, pp. 493-509). In general, the contribution of each of these concerns
will be "averaged out" by including more independent characters in the
phylogenetic analysis, such as more genes and longer sequences.
Correlated characters: each character used in the analysis optimally
should be genetically independent. Characters that are strongly functionally
correlated are better thought of as a single character. There are statistical
tests that can help control for unrecognized character correlation, such as
the block bootstrap and jackknife.
True structural convergence: structures that have undergone
convergent evolution can artificially result in incorrect tree topologies.
Including more characters in the analysis also aids in overcoming convergent
Character reversals: characters that revert to an ancestral state
pose a challenge similar to convergence. Because DNA and RNA only have four
different character states, they are especially prone to reversals during
Lost characters: lineages that have lost characters (such as whales
and their hindlimbs) can also pose cladistic problems. Often, if a cladistic
analysis indicates strongly that a certain character has been lost during
evolution, it is best to omit this character in higher resolution analyses of
Missing characters: incomplete fossils are problematic, since they
may lack important characters. Better fossils are the answer.
Intractable number of possible phylogenetic trees: for computational
reasons, this is one of the most important phylogenetic challenges to
overcome. The goal of a phylogenetic reconstruction is to determine the best
tree that the data supports. For an analysis of only five species, there are
15 possible trees. For an analysis of 50 species, there are over
1074 possible trees that must be searched—which is computationally
impossible. This problem is not as bad as it first sounds, since narrowing
down the number of reasonable trees can be trivial in many cases (for
instance, using the branch and bound algorithm). Several methods have been
developed to work around this issue successfully, and ultimately more powerful
computers are better.
Maximum Likelihood assumptions: the maximum likelihood
method makes explicit assumptions about the pattern of nucleotide
substitutions based upon a given model of nucleotide evolution. These
assumptions are based upon a solid statistical foundation; however, the
validity of the models must be considered when evaluating the results.
Long branch attraction: lineages that diverged relatively long ago
will tend to "cluster" together in a phylogenetic reconstruction under the
appropriate conditions. The mathematical reasons are somewhat complicated, but
using more slowly evolving genes (or regions of genes) helps overcome the
Rate variation between lineages: rates of nucleotide substitution
may differ between lineages; this can contribute to long branch attraction and
result in incorrect tree topologies. However, maximum likelihood and least
squares methods are particularly useful here.
Rate variation within a single gene: rates of nucleotide
substitution can vary along the length of a single gene—this also exacerbates
long branch attraction.
Gene trees are not equivalent to species trees: from simple
Mendelian genetics we know that genes segregate individually, and that
throughout time individual genes do not necessarily follow organismic
and Wollenberg 1997; Fitch
2001; Wu 1991).
An obvious example is the fact that while you may have brown eyes, your child
may have the genes for blue eyes—but that does not mean your child is not your
descendent, or that your brown-eyed children are more closely related to you
than your blue-eyed children. Including multiple genes in the analysis is a
solution to this conundrum. Based upon simple genetic calculations, an
analysis of more than five genes is usually necessary to accurately
reconstruct a species phylogeny (Wu 1991).
For more information on cladistics, you can consult one of several excellent
online cladistic resources, such as the SASB Introduction to
Phylogenetics, UC Berkeley's Integrative Biology Phylogenetics
Lab, or Diana Lipscomb's stellar Basics of
Cladistic Analysis, downloadable in Adobe Acrobat PDF
format. A good, concise description for the layperson can be found at the Journal of Avocational
Paleontology. Finally, you can read Charles Darwin's explanation in The
Origin of Species of the "Tree of
Life", where the concept of a phylogenetic tree was first introduced.
Albert, J., Wahlberg, J., Leitner, T., Escanilla,
D. and Uhlen, M. (1994) "Analysis of a rape case by direct sequencing of the
human immunodeficiency virus type 1 pol and gag genes." J Virol 68: 5918-24. [PubMed]
Arnold, C., Balfe, P. and Clewley, J. P. (1995)
"Sequence distances between env genes of HIV-1 from individuals infected from
the same source: implications for the investigation of possible transmission
events." Virology 211: 198-203. [PubMed]
Atchely, W. R., and Fitch, W. M. (1991) "Gene
trees and the origins of inbred strains of mice." Science 254: 554-558. [PubMed]
Avise, J. C., and Wollenberg, K. (1997)
"Phylogenetics and the origin of species." PNAS 94: 7748-7755. http://www.pnas.org/cgi/content/full/94/15/7748
Birch, C. J., McCaw, R. F., Bulach, D. M., Revill,
P. A., Carter, J. T., Tomnay, J., Hatch, B., Middleton, T. V., Chibo, D.,
Catton, M. G., Pankhurst, J. L., Breschkin, A. M., Locarnini, S. A. and Bowden,
D. S. (2000) "Molecular analysis of human immunodeficiency virus strains
associated with a case of criminal transmission of the virus." J Infect Dis 182:
Blanchard, A., Ferris, S., Chamaret, S.,
Guetard, D. and Montagnier, L. (1998) "Molecular evidence for nosocomial
transmission of human immunodeficiency virus from a surgeon to one of his
patients." J Virol 72: 4537-40. http://jvi.asm.org/cgi/content/full/72/5/4537?view=full&pmid=9557756
Brooks, D. R., and McLennan, D. A. (1991) Phylogeny,
ecology, and behavior. Chicago: University of Chicago Press.
Bush, R. M., C. A. Bender, et al. (1999)
"Predicting the evolution of human influenza A." Science 286: 1921-1925. [PubMed]
Doolittle, W. F. (1999) "Phylogenetic Classification
and the Universal Tree." Science 284: 2124. [PubMed]
Doolittle, W. F. (2000) "The nature of the universal
ancestor and the evolution of the proteome." Current Opinion in Structural
Biology 10: 355-358. [PubMed]
Edwards, A. W. F. and Cavalli-Sforza, L.
L. (1963) "The reconstruction of evolution." Annals of Human Genetics 27:
Efron, B. (1979) "Bootstrap methods: Another look at the
jackknife." Annals of Statistics 7: 1-26.
Efron, B. and Gong, G. (1983) "A leisurely look at
the bootstrap, the jackknife, and cross validation." American Statistician 37:
Edwards, A. W. F. and Cavalli-Sforza, L.
L. (1964) "Reconstruction of phylogenetic trees." in Phenetic and
Phylogenetic Classification. ed. Heywood, V. H. and McNeill. London:
Systematics Assoc. Pub No. 6.
Felsenstein, J. (1981) "A likelihood approach to
character weighting and what it tells us about parsimony and compatibility."
Biol J Linn Soc Lond 16: 183-196.
Felsenstein, J. (1981) "Evolutionary trees from
DNA sequences: A maximum likelihood approach." J Mol Evol 17: 368-376. [PubMed]
Felsenstein, J. (1985) "Confidence limits on
phylogenies: an approach using the bootstrap." Evolution 39: 783-791.
Felsenstein, J. (2004) Inferring
Phylogenies. Sunderland, MA: Sinauer Associates.
Fisher, R. A. (1912) "On an absolute criterion for
fitting frequency curves." Messenger of Mathematics 41: 155-160.
Fitch, W. M. (1970) "Distinguishing homologous from
analogous proteins." Syst. Zool. 28: 132-163.
Futuyma, D. (1998) Evolutionary Biology. Third
edition. Sunderland, MA: Sinauer Associates.
Goujon, C. P., Schneider, V. M., Grofti, J.,
Montigny, J., Jeantils, V., Astagneau, P., Rozenbaum, W., Lot, F.,
Frocrain-Herchkovitch, C., Delphin, N., Le Gal, F., Nicolas, J. C.,
Milinkovitch, M. C. and Deny, P. (2000) "Phylogenetic analyses indicate an
atypical nurse-to-patient transmission of human immunodeficiency virus type 1."
J Virol 74: 2525-32. http://jvi.asm.org/cgi/content/full/74/6/2525?view=full&pmid=10684266
Hennig, W. (1966) Phylogenetic Systematics.
(English Translation). Urbana: University of Illinios Press.
Hillis, D. M., and Bull, J. J. (1993) "An empirical
test of bootstrapping as a method for assessing confidence on phylogenetic
analysis." Syst. Biol. 42: 182-192.
Hillis, D. M., J. J. Bull, et al. (1992)
"Experimental phylogenetics: Generation of a known phylogeny." Science 255:
Holmes, E. C., Zhang, L. Q., Simmonds, P., Rogers,
A. S. and Brown, A. J. (1993) "Molecular investigation of human immunodeficiency
virus (HIV) infection in a patient of an HIV-infected surgeon." J Infect Dis
167: 1411-4. [PubMed]
Hudson, R. R. (1992) "Gene trees, species trees and the
segregation of ancestral alleles." Genetics 131: 509-513. [PubMed]
Huelsenbeck, J. P., Ronquist, F., Nielsen, R.,
and Bollback, J. P. (2001) "Bayesian inference of phylogeny and its impact on
evolutionary biology." Science 294: 2310-2314. [PubMed]
Kitching, I. J., Forey, P. L., Humphries, C. J.,
and Williams, D. M. (1998) Cladistics: The Theory and Practice of Parsimony
Analysis. Second Edition. The Systematics Association Publication No. 11.
Oxford: Oxford University Press.
Li, W.-H. (1997) Molecular Evolution. Sunderland, MA:
Machuca, R., Jorgensen, L. B., Theilade, P. and
Nielsen, C. (2001) "Molecular investigation of transmission of human
immunodeficiency virus type 1 in a criminal case." Clin Diagn Lab Immunol 8:
Maddison, W. P., and Maddison, D. R. (1992)
MacClade. Sunderland, MA: Sinauer Associates.
Nei, M. and Kumar, S. (2000) Molecular Evolution
and Phylogenetics. New York, NY: Oxford University Press.
Nichols, R. (2001) "Gene trees and species trees are
not the same." Trends Ecol Evol. 16: 358-364. [PubMed]
Ou, C. Y., Ciesielski, C. A., Myers, G., Bandea, C. I.,
Luo, C. C., Korber, B. T., Mullins, J. I., Schochetman, G., Berkelman, R. L.,
Economou, A. N. and et al. (1992) "Molecular epidemiology of HIV
transmission in a dental practice." Science 256: 1165-71. [PubMed]
Swofford, D. L., Olsen, G. J., Waddell, P. J.,
and Hillis, D. M. (1996) "Phylogenetic inference." In Molecular
Systematics, pp 407-514. Hillis, D. M., Moritiz, C. and Mable, B. K. eds.,
Sunderland, Massachusetts: Sinauer.
Veenstra, J., Schuurman, R., Cornelissen, M.,
van't Wout, A. B., Boucher, C. A., Schuitemaker, H., Goudsmit, J. and Coutinho,
R. A. (1995) "Transmission of zidovudine-resistant human immunodeficiency virus
type 1 variants following deliberate injection of blood from a patient with
AIDS: characteristics and natural history of the virus." Clin Infect Dis 21:
Vogel, G. (1997) "Phylogenetic analysis: getting its day
in court." Science 275: 1559-60. [PubMed]
Woese, C. (1998) "The universal ancestor." PNAS 95:
Wu, C. I. (1991) "Inferences of species phylogeny in
relation to segregation of ancient polymorphisms." Genetics 127: 429-435. [PubMed]
Yirrell, D. L., Robertson, P., Goldberg, D. J.,
McMenamin, J., Cameron, S. and Leigh Brown, A. J. (1997) "Molecular
investigation into outbreak of HIV in a Scottish prison." Bmj 314: 1446-50. http://bmj.com/cgi/content/full/314/7092/1446?view=full&pmid=9167560
Zhu, T., B. Korber, et al. (1998) "An African
HIV-1 sequence from 1959 and implications for the origin of the epidemic."
Nature 391: 594-597. [PubMed]