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Web Topic 10.1
Estimating Evolutionary Trees


The evolutionary history of a group of species is called its phylogeny, and a phylogenetic tree is a graphical summary of this history. The tree describes the pattern and in some cases the timing of events that occurred as the group radiated into new species. The tree also documents which organisms are more closely related, and is an important tool for naming species and genera. Although we sometimes have a fossil record to examine certain morphological traits in some animal groups, this record is fragmentary, and we cannot obtain DNA sequence data except for very recently extinct and well-preserved specimens. We therefore must estimate the phylogenetic tree using extant species. This online unit first reviews the basic logic and methods for estimating trees. It then gives the justification and protocols for the two main uses of trees in animal communication studies: a) correcting for phylogenetic inflation of samples when correlating communication systems with ecological contexts and selection pressures; and b) mapping behavioral or communication signal traits on the phylogenetic tree to infer the sequence of trait evolution. This online unit draws extensively from Chapter 4 of Freeman and Herron’s textbook on evolutionary analysis (Freeman and Herron 2007) and from Hall’s useful manual (2004), and the interested reader should consult these texts for further details.

The logic of phylogenetic inference

The basic logic of inferring evolutionary relationships is that more closely related taxa should have more traits in common. We group species by their similarities and distinguish groups by their differences. In principle, all traits that have a genetic basis and that vary among taxa could be used to assess similarities and differences. Traditionally evolutionary biologists used morphological traits such as bone structures, bristles, and mouthparts, and mode of embryonic or larval development. In the last 25 years, genetic traits such as the presence of certain genes or alleles, the sequence of nucleotides in a particular gene, or the sequence of nucleic acids in a particular protein, have been used with increasing effectiveness. But some traits, loci, DNA/RNA sources, and proteins, are more informative than others.


Homologous characters are those that are similar due to descent from a common ancestor. Phylogenetic reconstruction must of course be based only on homologous traits. The subset of homologies that are most useful for estimating phylogenies are called synapomorphies, which are similar because they are modified from a common ancestor and different from the next more closely related taxon. Synapomorphies help us identify monophyletic groups, also called clades or lineages (Figure 1). Synapomorphies are also useful traits because they identify evolutionary branching points. When two populations of a species become isolated geographically and start to evolve independently, some of the homologous traits in each population will start to diverge due to mutation, selection, and drift. These changed traits are synomorphies that distinguish the two independent lineages. Furthermore, synapomorphies are nested. Over time, multiple branches occur. Each branching event adds one or more shared, derived traits, and the result is a hierarchy of synapomorphies. The clustering of synapomorphies graphed in this way is called a cladogram. A cladogram shows the pattern, or ordered sequence, of cumulative evolutionary change.

Figure 1: A phylogenetic tree with five species. The black lines are branches, which intersect and terminate at nodes. The nodes at the tip represent the five terminal extant species from which we have measured some character traits. The internal nodes (where two branches intersect) represent ancestral species whose traits we can only infer from existing taxa. Red circles enclose each clade or lineage, a monophyletic group of an ancestor plus its descendants. Synapomorphies are nested in a hierarchy to define different taxonomic levels (red arrows). Different synapomorphies define each clade. By convention, the specific character of a synapomorphy and the point where it arose in the evolutionary sequence is indicated on cladograms by a bar across the branches (yellow) and described by accompanying labels or keys. The different synapomorphies also identify the branching points of speciation events (blue arrows).


To accurately reconstruct a tree, researchers must identify characters that qualify as synapomorphies. This is not always easy to do, since traits that appear to be similar in two species or taxa may have evolved independently. In this case, the traits were not derived from a common ancestor, so they are neither homologous nor true synapomorphies. One reason for these superficial similarities is convergent evolution, which occurs when natural selection favors similar structures as solutions to an environmental factor. For example, both octopuses and ray-finned fishes possess a camera lens eye, because both depend on excellent vision to find food and detect predators. But the eye structure has evolved independently in the mollusk and the early vertebrate. Convergences can also occur for genetic traits, especially for DNA sequences, where there are only 4 possible nucleotide bases (A, G, T, C) and by chance they can be the same in short sequences for species that are completely unrelated. Mistaking a similar trait for a synapomorphy when it is in fact a convergence constitutes a serious problem for figuring out which species are most closely related.

A second problem is reversal, where a trait that has evolved in a lineage is subsequently lost in a terminal species. Reversals are also common in DNA data, for the same reason mentioned above concerning the likelihood of a mutation back to an earlier nucleotide base given that there are only 4 possibilities. Convergence and reversal are lumped under the term homoplasy. If similar traits are not due to homology then they are due to homoplasy. Homoplasy represents noise in the datasets used to reconstruct phylogenies.

The most effective way to distinguish homology from homoplasy is to use many different traits, or very long sequences, in reconstructing evolutionary relationships. For example, ray-finned fish and other vertebrates have a bony skeleton and a variety of other traits that distinguish them from octopuses and other mollusks. One would have to assume that all of these traits had also changed in concert with the eye to draw the conclusion that fish and mollusks were closely related. It requires many fewer changes to propose that camera lens eyes evolved twice in a convergent process. This type of argument illustrates the principle of parsimony. Parsimony is a general logical criterion whereby simpler explanations for a phenomenon are preferred over more complex ones. When applying parsimony to phylogenetic inference, we accept the tree that involves the fewest changes. While parsimony is logically appealing, sometimes it does not work. For example, trait loss (reversal to an ancestral condition of no trait) could be relatively common for extravagant sexually selected traits that evolved in an ancestor and proved to be very costly to bearers in some populations or species. Researchers therefore need to be very careful in selecting traits for phylogenetic reconstruction that are least subject to homoplasy and more reliable as sources of synapomorphies.


A tree is said to be rooted if there is a particular node, the root, from which all other nodes can be reached by moving forward. The root is the hypothetical common ancestor of all the taxa included in the analysis. An unrooted tree specifies only the relationships among the taxa, but it cannot tell us anything about the direction of evolution and the ordering of trait changes over time. Unrooted trees are illustrated with a radial format (Figure 2) because the ancestral node has not been defined. To root a tree, an outgroup species must be included among the set of species in the ingroup, the taxon of interest. An outgroup is a taxon that is more distantly related to each of the ingroup taxa than any of the ingroup taxa are to each other. Selection of a good outgroup taxon is not always easy. It must be sufficiently distantly related to the taxa being considered, but not so distant that it doesn’t share a common ancestor with the ingroup species, i.e., it is not homologous. In practice, researchers typically rely on external sources of information from (earlier) classical studies to identify one or more candidate outgroup species.

Figure 2: An unrooted tree of immunodeficiency viruses in different host species. This tree was created using genomic sequences and a neighbor-joining algorithm. Support at each internal node was assessed using 1000 bootstrap samplings. Branch lengths are based on the number of genetic changes. FIV = feline immunodeficiency virus (blue), SIM = simian immunodeficiency virus (shades of gray), and HIV = human immunodeficiency virus (shades of red). (After Yamamoto et al. 2006.)


Trees can be presented in different ways, and each has its advantages and disadvantages. A cladogram, which shows only the branching order of nodes, can be drawn in either slanted or rectangular style. Figure 3 illustrates this difference. Either can be used to show the tree topology, and the points at which key adaptations arise can be denoted in a similar way. If the number of species being examined is large, the rectangular style will be easier to display in a compact amount of space.

Figure 3: Slanted and rectangular style of trees. The same taxa and data are shown here using the two techniques: (A) slanted, and (B) rectangular. Both may be oriented horizontally or vertically. A polytomy is included in this example (with species D, E, and F) to show how unresolved nodes, or a multifurcation (as opposed to a bifurcation) is drawn in each case.

When illustrating some type of distance or time information is important, the rectangular style must be used. Such a graph is called a phylogram. When genetic sequence data has been used to create the tree, the branch lengths can be varied to show the number of sequence changes occurring on each branch. The number of changes is considered to be an indication of either evolutionary time or strength of selection (Figure 4a). If something is known about the rate of genetic change, then the x axis can be expressed as time. For example, a molecular clock can be estimated using mutation rates at a selectively neutral gene locus and calibrated to real time if some independent fossil evidence is available. This results in a phylogram where the node positions can indicate the time of divergence between two branches, or the time of speciation events (Figure 4b).

Figure 4: Phylograms with distance/time information. (A) A molecular phylogeny of Zenadia doves, using two other genera of doves as outgroups. Branch lengths reflect genetic distance—shorter branches indicate species or populations that had fewer changes and are considered more closely related (Johnson et al. 2001). (B) Estimated divergence times for the apes, based on a combination of data from dozens of proteins used as molecular clocks. The heavy bars show ±1 standard error around the time estimates and lighter bars show 95% confidence intervals (Stauffer et al. 2001).


When many traits are used in phylogenetic reconstruction, and the number of species being compared is moderate to large, computer software must be used to construct the tree. PAUP, PHYLIP, MrBayes, BEAST, RAXML, and PHYLLAB are six commonly used packages that build trees using several of the methods mentioned below, and others are more specific to certain methods (for details see Hall 2004, and web resources at end of this unit). The four main methods are neighbor joining, maximum parsimony, maximum likelihood and Bayesian Markov chain Monte Carlo methods. Neighbor joining is an algorithmic method that computes a single best tree, whereas the others are tree-searching methods. In the latter case, many possible trees are identified, and then one must decide which tree is best. Alternative methods involve computing a probability of likelihood that alternative trees are supposed by the data.

Neighbor joining: This is a distance method that converts discrete character data, such as the presence or absence of a morphological trait or the identity of a nucleotide at a homologous location in a gene, into a distance value (or in other words, the inverse of a similarity value). For example, two species are separated by a genetic distance of 10% if an average of 10 nucleotides have change per 100 bases. Many discrete characters can be combined into an overall average distance value. A matrix of pairwise distance estimates is computed between all pairs of taxa, and a clustering algorithm is used to group the most similar taxa (smallest genetic distance) together. A single tree is constructed.

Maximum parsimony: Parsimony can be employed with multiple traits, by summing all of the changes across all of the characters and selecting the tree with the lowest sum, but this strategy may not provide the most reasonable answer. This method typically finds several to many trees with the same, or only slightly different, numbers of changes. With several more or less equally parsimonious trees, one must then use other methods to select among them, or combine them.

Maximum likelihood: The essence of the maximum likelihood method with genetic sequence data is to determine how likely one would obtain a particular set of DNA sequences, given a mathematical formula that describes the probability that different types of nucleotide substitution will occur, and given a particular phylogenetic tree with known branch lengths. A computer can evaluate this question for all possible tree patterns, and the one with the highest likelihood is chosen. RAXML is the best software program to use for this method.

Bayesian analysis: Bayesian approaches are similar to maximum likelihood, except one asks what the probability is of a particular tree being correct, given the data and a model of how the traits in question change over time. Bayesian methods usually produce a set of trees of approximately equal likelihoods. The results are easy to interpret because the frequency of a given clade in that set of trees is identical to the probability of that clade, so no bootstrapping (see below) is required to assess the confidence in the structure of the tree. BEAST is the best software program to use for this method.

Which method is best? Although it seems very efficient to use an algorithmic method that finds the single best tree, one can be misled by not considering other possible trees. The “correct” tree doesn’t exist because we are trying to deduce the order in which existing taxa diverged from a hypothetical common ancestor and the amount of change along the branches between the diverging events. We can’t be sure that a tree topology accurately reflects the historical branching order. The tree-comparing methods are becoming increasingly more popular because they provide a statistical test for estimating how much better one tree is over another. If several very good trees have been obtained with slight differences, one can combine them into a single consensus tree.

Having obtained what we believe is the best tree, we can then evaluate how well supported a particular branch of the tree is using a technique called bootstrapping. This question is analogous to asking about the reliability of measuring height in a group of people. After measuring some people and computing the average of the sample, one evaluates whether the sample average is an accurate representation of the true average for the entire population by computing a measure of the variance around the mean. If the variance is high, one becomes less confident that the sample average is reliable. In bootstrap analysis with trees, one compares trees with and without a particular branch. A computer creates new datasets from the existing one by repeated sampling. With many repeats, one can then compute how many times a particular branch was estimated from the resampled data. If this number is very high, 90–100%, one can be very confident that the branch is accurate. If the number is around 50%, then it is best to conclude that support for the branch is low, and collapse that portion of the tree into a polytomy, or a point of uncertainty, in the published tree (see Figure 3).

Using phylogenies to answer questions

Once a well-supported phylogeny has been obtained for a taxon of interest, the phylogeny can be used to answer a variety of questions. These questions fall into two broad categories: those in which the phylogeny is used to control for taxonomic relatedness so that relationships among traits and ecological variables can be analyzed across species, and those in which the phylogeny is used to generate and test evolutionary hypotheses. We take up these two categories in turn.


In studies using the comparative method, the investigator compares the quantitative value of two (or more) traits across a range of species, or the association of a trait with some ecological variable, looking for significant correlations. The study may address whether ambient selective pressures play an important role in observed patterns of trait diversity, or whether the relationship with ecological factors meets some theoretical expectation. A bit of history may be useful here.

The field of animal behavior in the 1950s was large focused on relating the diversity of animal behaviors and signals to their phylogeny at the ultimate level and to their inherited physiological mechanisms at the proximate level. Behavioral traits were even proposed as useful criteria for determining or at least confirming, phylogenetic trees. The assumption that behavior was as tightly linked to phylogeny as morphology was shattered by a number of pioneering comparative studies in the 1960s. These included correlations between avian mating systems and habitats by Crook (1964), Brown (1964), Verner and Willson (1966), and Lack (1968); similar correlative studies were published on primates (Crook and Gartlan 1966; Crook and Aldrich-Blake 1968) and antelopes (Jarman 1974). Statistical tests were undertaken to identify which correlations were significant and which not. The resulting significant correlations found among so many taxa triggered efforts to derive evolutionary models that could explain the correlations: Orians' (1969) model for the evolution of polygyny is one important example. Overviews by Emlen and Oring (1977) and Clutton-Brock and Harvey (1977) then sought to integrate process and correlations into general theories of social and mating system evolution in animals. By the late 1970s, it was largely accepted that ecology was usually a better predictor of social, mating, and communication systems than was phylogeny. However, concern arose when Felsenstein (1985) pointed out that such comparative studies among a set of species contained an inherent statistical problem: because species are part of a hierarchically structured phylogeny and related to each other to varying degrees, they are not truly statistically independent samples for correlational studies. Even if the phylogenetic effects are subtle, drawing conclusions from correlations between behavior and habitat for 50 rodents, one ungulate, two primates, and a whale might be a risky business. At best, phylogenetic relatedness adds noise to such comparisons, and at worst, sample inflation by one group of related species might lead to spurious conclusions. Clearly, the comparative method required careful corrections for phylogenetic relationships among those species sampled.

Several techniques have been proposed for how best to go about doing this (Harvey and Pagel 1991; Harvey and Purvis 1991; Garland et al. 1992; Martins and Hansen 1997; Butler and King 2004; Garland et al. 2005) but they boil down to three basic methods: independent contrasts, Monte Carlo computer simulations, and generalized least squares models. Independent contrasts, proposed by Felsenstein, involves taking the difference between the trait values for two adjacent species or groups of species on the tree; each difference is independent of other differences elsewhere on the tree, and therefore can be used as a statistically independent point for correlational analyses (Felsenstein 1985; Garland et al. 1992). Monte Carlo simulation, by randomly moving the trait values around on a given tree, provides an empirical null distribution and defines a critical 5% acceptance value against which the observed correlation can be compared (Martins and Garland 1991; Garland et al. 1993). Phylogenetic generalized least squares regression (PGLS) is currently the favored method. The investigator develops a model of the rate of evolutionary change, such as the basic random Brownian model or the Ornstein-Ulenbeck model that assumes selective optima, and uses this expectation along with an independently derived phylogeny (with known branch lengths) to figure out how much of the variance and covariance among traits can be expected just from phylogenetic history. The residuals are then used in a regression analysis between the traits of interest. This method allows for the exploration of multivariate models. Both MATLAB and R are good software platforms with a variety of existing routines for exploring alternative models (Lavin et al. 2008; PHYSIG, see web resources at end of this unit).

A good communication-related example of the use of PGLS to examine the possible environmental selective pressures affecting signaling traits is the comparative study of song complexity in mockingbirds and their relatives (Mimidae). Ten quantitative measurements were made on song clips of 29 species and reduced with factor analysis to three orthogonal song characters: heterospecific mimicry, short-term note diversity, and song complexity. From the known geographic ranges of each species, the variance and predictability of rainfall and temperature were obtained; habitat type and the migration behavior of each species were also considered. The environmental and song variables were analyzed with multiple regression models while controlling for phylogenetic relatedness. All three of the song variables, but especially mimicry, were significantly associated with more variable and unpredictable climates. Mimicry was especially strongly associated with facultative migration. Mimicry and short-term diversity had strong phylogenetic components. Possible functional explanations for these relationships included stronger selection in more variable and unpredictable climates leading to more elaborated signals of quality, or to signals of intelligence in the mate attraction context (Botero et al. 2009).

Phylogenetic corrections are now expected of any multispecies comparative study. In a recent review of 194 comparative studies that also reported the raw (uncorrected) result, the phylogenetic correction changed the conclusion in only a few cases (Garamszegi and Møller 2010). Moreover, the direction of the change after correction either improved or diminished the correlations in roughly equal numbers of cases. This review article also points out that most studies neglect the effects of within species variation. Heterogeneous sampling within species can potentially be as important as phylogenetic effects in comparative analyses. Some additional comparative analyses of communication-related traits include the following: Aparicio et al. 2003; Hagman and Forsman 2003; Kraaijeveld 2003; Galeotti et al. 2003; Bleiweiss 2004; Stuart-Fox and Ord 2004; Emlen et al. 2005; Olson and Owens 2005; Seddon 2005; Soler et al. 2005; Richards 2006; Berg et al. 2006; Bokony et al. 2007; Del Castillo and Gwynne 2007; Mank 2007; Doucet et al. 2007.


Several kinds of questions are driven by explicit knowledge of the phylogenetic tree. Evolutionary biologists of course want to know which species form meaningful clades so they can name them correctly. This entails determining whether taxa classified and named using classical morphological characters still hold up once genetically-based trees have been made (de Queiroz and Cantino 2001). Why certain species are found in certain parts of the world and how geographic distributions have changed through time can be examined with phylogenetic analyses (Pagel 1999; Raxworthy et al. 2002). Co-speciation between parasites and their hosts is another interesting question, in which independent trees are built for both groups, and one asks whether they speciated together (Clark et al. 2000). Behavioral biologists would like to know whether and which behavioral traits are useful taxonomic characters (de Queiroz and Wimberger 1993; Slikas 1998; Blomberg et al. 2003). Animal communication researchers often seek the ancestral form of signaling traits and the progression of signal elaboration across species (Phelps and Ryan 2000). Finally, we need very accurate trees to ask whether a signal trait or a receiver trait evolved first.

For questions that involve mapping traits onto a tree and assessing their evolutionary pattern across clades, one needs a quantitative measure of the degree of homology (or homoplasy). Several such measures have been developed. A simple and very useful measure, especially for discrete traits, is the rescaled consistency index, which quantifies the minimum amount of change a character may show on any tree divided by the observed number of changes, scaled by the maximum possible amount of homoplasy (Kluge and Farris 1969; Farris 1989a, b). The index has a value of 0 when the character is completely homoplasious, and 1 when the character is perfectly homologous. Another type of measure is called the test for serial independence (Abouheif 1999). It measures the degree of non-randomness in a sequence of trait values for the terminal taxa in a tree. The variation in the differences between successive taxa is divided by the sum of squares of the observations. Completely random sequences have a value of 2 (i.e., homoplasious), a value less than 2 indicates homology, and a value greater than 2 indicates non-random alternation. This test is independent of branch lengths and any type of model of evolutionary change, which has been considered a problem. Other randomization tests have been devised that allow such refinements. Here, the trait values for the terminal taxa are permuted many times to obtain a random expectation, and observed values are compared. Variants of these models can take into account different branch lengths on the tree, and different evolutionary models for the tempo of evolutionary change, either accelerating or decelerating (Blomberg et al. 2003). Another strategy is to compute a measure of similarity between all pairs of terminal taxa using one or more traits, and then correlate these values with the genetic distances between the same pairs of taxa (Slabbekoorn et al. 1999). This technique has the usual problem with spurious and confounding correlations. For example, a correlation between genetic distance and a display trait character could be a spurious result of a true correlation between the display trait and habitat features, because habitat is correlated with genetic distance (Rheindt et al. 2004). Regardless of which method is used, when there is a significant amount of homology for a trait mapped onto a tree, we conclude that there is a strong phylogenetic signal (not to be confused with a communication signal).

Below are a four interesting examples of studies in which communication signals have been mapped onto trees to evaluate the pattern of evolution of the traits. The first example (Figure 5) examined song characteristics of oropendolas, neotropical songbirds with black and yellow plumage in the Icterid family. The well-supported tree was derived from DNA sequence data, and branch lengths reflect molecular changes. Genera include Psarocolius, Gymnostinops, and Ocyalus. Of 32 acoustic features examined, the 22 shown here revealed unambiguous evolutionary changes (i.e., significant homology). Many of these traits evolved once and were subsequently retained in the clade, The two lower taxa have complex song repertoires, while the remaining species all have one song type (11) and perform the bow display (9). The trill (6) evolved once and now defines a subclade comprising P. atrovirens and the three P. angustifrons subspecies. The harsh broadband crash note (7) showed a reversal, arising in the upper clade and then was lost in two of the P. a. atrovirens subspecies. Multiple evolutionary changes occurred in the branch for G. montezuma, a highly polygynous species whose song is long and intense with large frequency shifts and note overlap. The variable characters based on frequency properties of songs (26, 28–31) were uniquely derived in terminal taxa; this implies rapid evolutionary change of these features that are the most vulnerable to degradation during transmission in different habitats.

(1) Introductory rattle (11) Song versatility (23) Rattle rate
(3) Broadband rattle (12) Intersong interval (24) Trill note rate
(4) Rattle-whistle (15) Song duration (25) Highest peak frequency
(5) Click (16) Note percentage (28) Frequency range
(6) Trill (18) Note overlap (29) Frequency shift rate
(7) Crash (20–21) Pause duration (30) Max frequency shift
(9) Bow (22) Pause rate (31) Frequency slope

Figure 5: Phylogenetic analysis of the evolution of song features in oropendolas. Song traits shown enumerated in the table are mapped onto the phylogeny, with green indicating traits that were gained and red indicating traits that were lost. Spectrograms of male songs are shown on the right. (After Price and Lanyon 2002.)

Another interesting phylogenetic analysis examined the acoustic structure of the male call in two related groups of Australian psyllids (Hemiptera: Psylloidae, family Triozidae), plant-sucking insects related to whiteflies and aphids (Figure 6). One group, contained in the genus Schedotrioza, feeds on Eucalyptus; species are allopatric by microhabitat segregation because each feeds on a different host plant species. The other group is more heterogeneous morphologically and feeds on various host plants in the family Casuarinaceae; species in this group all feed on the same set of host plant species, often on the same individual plant, and are therefore usually sympatric with each other. The sounds are produced by stridulation using forewing vibrations, which are primarily transmitted through the plant substrate. In many species, females answer male calls with their own unique stridulatory sounds, and courting pairs engage in antiphonal duetting. The phylogeny shows the Schedotrioza to be a monophyletic and more recently derived genus, with a distinctive male call structure defining the clade. Furthermore, the correlation between genetic distance and acoustic distance was positive within the group, as it was when all species were combined, indicating a strong phylogenetic signal in the call structure. However, within the Casuarinaceae-feeding species, the genetic–acoustic distance correlation was negative, which may reflect increased selection for acoustic divergence in closely related, sympatric taxa. The phylogenetic tree indicates that this is probably not a monophyletic group (Percy et al. 2006).

Figure 6: Phylogenetic analysis of acoustic stridulatory signals in Australian psyllids. (A) Single tree obtained from maximum parsimony analysis of mitochondrial DNA sequences. Host plant groups are indicated by color-coded branches of the psyllid tree and host names are given after the psyllid name. Male call shown on right. (B) Acoustic distance between all pairs of the 11 species studied acoustically is positively correlated with genetic distance for all species combined (black line). Within Schedotrioza (red) the correlation is positive, but is negative within the Casuarinaceae-feeding group (blue). Purple circles denote comparisons between pairs of species from different groups. (From Percy et al. 2006.)

The third study examined plumage color patterns in the New World orioles (genus Icterus). Each species exhibits a unique pattern of black, orange or yellow, and white color patches, yet they vary within similar themes. There are two common themes found in several sets of species, the Baltimore type with black heads and white edging on most wing coverts, and the Altamira type with carotenoid-colored heads and white patches on the outer primaries. This study attempted to determine whether these plumage types reflected common ancestry (synapomorphy) or independently derived convergence or reversal (homoplasy). The molecular phylogeny was based on DNA sequence data from two mitochondrial genes, and revealed three clades within the genus. Forty-four feather patch areas were scored as being white, black, or carotenoid. Each one was separately mapped onto the phylogeny and various measures of homology (consistency index CI and percent phylogenetic signal strength) were computed for each. Figure 7 shows the mapping of one patch—the coloration of the lesser wing coverts—which had one of the lowest CI values and appeared repeatedly in all three of the main clades, yet there was still a significant phylogenetic signal in this character. The table summarizes these two measures for all 44 patches. Most patch areas showed repeated reversals and convergences. The Baltimore plumage type included species from clades C and A, and the Altamira type included species from all three clades. This finding indicates that distantly related species frequently converge on a similar overall pattern. Despite the generally high level of homoplasy for individual patches, there were few uniquely derived character states. The researchers concluded that there was a surprising amount of plumage lability at one level, but a strong degree of plumage conservatism at another level (Omland and Lanyon 2000).

Figure 7: Plylogeny of color patches among orioles. (A) Tree reconstructed from molecular sequence data, with color states of the lesser wing coverts indicated. (B) Table showing consistency index (CI) and percent phylogenetic signal for all color patches. (After Omland and Lanyon 2000.)

The final example of a phylogenetic approach to signal evolution deals with the waveform of electric pulses in African Mormyroid elephantfishes (Sullivan et al. 2000). The electric discharge is used for both electrolocation and for communicating with conspecifics. The pulses are discharged continuously and have a consistent species-specific shape. In this phylogenetic example, the morphology of the electric signal-producing organ has also been determined for each species and the innervation pattern is associated with the waveform shape, so the precise evolutionary pattern of changes in the signal generation mechanism can also be deduced. The tree shown in Figure 8 comprises 41 species found in west-central Africa. The sister group to the Mormyrid elephantfishes is the Gymnarchids, which has a primitive electric organ.

The electric organ in this taxon consists of disk-like cells called electrocytes arranged in a series of columns in the narrow region of the tail. The flat surface of the disks is oriented perpendicular to the skin surface. A nerve stalk innervates each disk, and disks in a column are simultaneously stimulated. Electrocytes can be classified into six types based on the presence or absence of penetrating stalks and the side of innervation. The type S cell is stalkless (innervated by a simple branching nerve as shown in the figure) and produces a monophasic pulse. This type is found in the Gymnarchus outgroup. There are two major categories of stalked electrocytes. In the first, stalks arising from the posterior face of the electrocyte disk are non-penetrating and receive innervation on the posterior side (type NPp). All fish with this type produce simple biphasic electric organ discharge waveforms. In the second category, the stalk penetrates through the electrocyte. In the simplest of these, the stalk penetrates through the electrocyte to the opposite side, so that it receives innervation from the anterior side (type Pa). Others have stalks arising from the anterior face that penetrate through to receive innervation from the posterior face (type Pp). These two types produce biphasic pulses. In Brienomyrus, the stalk penetrates through the electrocyte a second time to receive innervation on the face from which it originated (called a doubly penetrating stalk with posterior innervation, type DPp). This arrangement produces a triphasic pulse. Species in one genus, Pollimyrus, have both doubly penetrating and non-penetrating stalks (type DPNP). The figure shows the point at which each of these electrocyte innervation strategies is believed to have arisen. What is especially interesting in this phylogenetic analysis is that is it very easy for reversals to occur, i.e., for a multiple penetrating stalk to revert to a simpler form with fewer penetrations, because these represent more primitive developmental stages.

Figure 8: Phylogeny of electric organ evolution in Mormyroid elephantfishes. The robust phylogenic tree is based on genetic sequences from 4 gene loci. The six electric organ morphological types and typical waveform EOD that they might generate are illustrated on the left, and arrows point to the hypothesized position in the tree where they first arose. Numbers refer to hypothesized character state changes. Note the frequent occurrence of reversals, from the typical Pa organ type shown in red to the simpler, ancestral NPp type shown in black. (From Sullivan et al. 2000. Reproduced with permission from the Journal of Experimental Biology. Colored figure courtesy of Carl Hopkins.)

Web resources

PAUP: http://paup.sc.fsu.edu/

PHYLIP: http://www.phylip.com/

MrBayes: http://mrbayes.csit.fsu.edu/

BEAST: http://beast.bio.ed.ac.uk/

RAXML: http://www.embl.de/~seqanal/courses/commonCourseContent/usingRaxml.html and

R packages: http://www.r-phylo.org/wiki/Main_Page

PHYSIG: http://www.biology.ucr.edu/people/faculty/Garland/PHYSIG.html

MESQUITE: https://mesquiteproject.wikispaces.com/

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