The Phylogenetic Menu

Currently there are three methods in the inference of phylogenetic trees: Maximum Parsimony (MP), Maximun Likelihood (ML) and Bayesian analysis. Also, there is a great variety of programs that allow to execute these methods on the data. However, the doubt about which method is the best has always been present and even after many years of discussion it has not yet been possible to reach an agreement. Here I will show the menu available to a researcher who wants to do a phylogenetic analysis, showing the three main courses, with their advantages and disadvantages.The first is the most traditional, used for a long time, but has lost strength in recent years. 

Maximum Parsimony was proposed by Edwards and Cavalli-Sforza (1963) and is characterized because it does not use an explicit method of evolution, nor any other type of assumptions prior to analysis. Based on the knife of Ockham whose tree chosen is the tree that has lower cost, which is the tree that explains the data with the smaller amount of possible changes. There are several ways to do this method and they are based on different ways in which the transformations of the characters are possible, among these are: Fitch, Wagner, Camin-Sokal and the general matrix (Wiley, 2011). The main problems in using this analysis lie in the impossibility of obtaining the length value of the branches, since it does not take into account the amount of change because there is no explicit evolutionary model, in addition the parsimony has problems of inconsistency of the branches when the number of taxa is large and the exchange rates are high (Kim, 1996).

On the probabilistic and most recent side is Maximum Likelihood (ML), this method uses a previously established evolution model of the sequences, given the model this analysis finds the hypothesis that best explains the data -probability of the data given the model- (Salemi, 2009). Thanks to the model used, a new value can be estimated in the topology: the lengths of the branches, which are the amount of change accumulated since the time of the fork (Swoffod, 1996). However, this method is not absolved from uncertainties, it has been empirically demonstrated that this method can fail under certain conditions in the same way as MP (Farris, 1999).

The Bayesian method is also on the probabilistic side - weaknesses similar to those of ML -this method is the newest of the three and is the most widely used in recent times. The question is the alternative to Maximum likelihood: What is the probability of the model given the data? It is also based on previous knowledge known as priors, among which are: the evolutionary model of the sequence, heterogeneity and distribution of the data. The probabilistic space exploration is done with the help of the MCMC method where the node is chosen with greater posterior probability and the lengths of the branches based on the model are also taken into account (Holmes, 2003).

It seems to me that each one of the analysis can be useful depending on the data, the researcher must know very well his type of data and his research question, since these are the ones that determine which method to approach the problem. Although all have their shortcomings, the utility of the length of the branches at the time of dating or the philosophical support of MP are qualities that are difficult to reject.

Bibliography

• Farris, J. S. (1999). Likelihood and inconsistency. Cladistics, 15(2), 199-204.
• Holmes, S. (2003). Bootstrapping Phylogenetic Trees: Theory and Methods. Stat. Sci. 18, 241–255
• Kim, J. (1996). General inconsistency conditions for maximum parsimony: effects of branch lengths and increasing numbers of taxa. Systematic Biology, 45(3), 363-374.
• Salemi, M., Vandamme, A.-M., & Lemey, P. (2009). The phylogenetic handbook : a practical approach to phylogenetic analysis and hypothesis testing. Cambridge University Press.
• Swofford, D. L., Olsen, G. J., Waddell, P. J. & Hillis, D. M. (1996). Phylogenetic inference. Molecular Systematics 407–514.
• Wiley, E. O., & Lieberman, B. S. (2011). Phylogenetics: theory and practice of phylogenetic systematics. John Wiley & Sons.

Philosophy, Classification and Phylogeny

From the Greeks to the present day-passing by Darwin-the way in which we classify living beings has remained in constant discussion. Aristotle was one of the first to propose a character based method on a classification system. Theophrastus, Pedanius and Plinius continued to contribute in one way or another to the categorization of life (Manktelow, 2010). In the mid- 18th century, Carl Von Linné introduced the binary classification system, giving a more organized meaning to life, but Linné was only interested in descriptions of the species and was not in organizational levels superior to genres. Lammarck, de Jussieu and Adenson in different ways proposed the use of characters for a hierarchical organization of the species. Lamarck and Juisseu also believed that the similarities of the species corresponded to a continuity in the form. Then emerged the compared anatomy with Cuvier and Leclerc who went deeper into the importance of the characters (Stevens, 2002).

These researchers prior to the theory of natural selection, had in common that they conceived the species as immutable categories, that is, could not emerge new species and these are simply transformed into other “Scala naturae”. With the advent of Darwin and Wallace's theory of natural selection, species began to be understood as groups of organisms that changed their shape by various factors, the idea of the common ancestor gained strength and the change of organisms over time began to be represented with phylogenies (Ruse, 2009), taking the classification from an evolutionary perspective. The use of phylogenies represented in trees made it necessary to establish techniques to infer these trees. So, the definitions of homology and homoplasy became important when choosing the characters, but outside the already famous discussion between the three schools, the philosophical background of the methods used in cladistics has also been generating great controversy from the beginning.

Willi Hennig proposed the maximum parsimony method based on Ockham's knife and Poper's epistemology. Popper believed to have solved the problem of inductivism in science, the problem is about making a posteriori conclusions based on repetitive prior knowledge, since these are merely "historical memories" and have no predictive power-they do not assure what will happen in the future -. For Popper the only important thing is the data at the moment and if a hypothesis can be falsifiable, then it is a valid hypothesis (Reippel, 2003). The falsifiability implies that a theory is capable of being rejected by empirical and verifiable evidence, the rest would be pseudoscience (Popper, 1981). After Hennig, various methods of inference (contrary to popperian ideology) have emerged, that use either likelihood (Maximum Likelihood) or later probability (Bayesian Analysis). These two methods considered deductive by those who defend parsimony -as they use other information besides the data- are considered contradictory to Popper's epistemological theory, however, some authors claim that likelihood can be considered popperian (Helfenbein, 2005).

In my opinion, any type of statement that is made in science must be supported by empirical and explicit evidence, in addition to being subject to falsification, either by new information available or by a new interpretation of the data. It is a good way to avoid falling into dogmatism and to advance in the creation of knowledge.

Bibliography

• Helfenbein, K. G., & DeSalle, R. (2005). Falsifications and corroborations: Karl Popper’s influence on systematics. Molecular Phylogenetics and Evolution, 35(1), 271-280.
• Manktelow, M. (2010). History of taxonomy. Lecture from Dept. of Systematic Biology, Uppsala University.
• Popper, K. (1981). Science, pseudo-science, and falsifiability. Scientific thinking, 92-99.
• Rieppel, O. (2003). Popper and systematics. Systematic Biology, 52(2), 259-271.
• Stevens, P. F. (2003). History of Taxonomy. eLS.
• Ruse, M. (2009). Darwin y la filosofía. teorema, 28(2).

Phylogenetic analyzes.

Phylogenetic analyzes.

Phylogenetic analysis is the means to estimate evolutionary relationships, such relationships are usually represented as a diagram known as a phylogenetic tree. Different methodologies have been proposed or exist with which phylogenetic analyses are carried out, each with advantages and disadvantages. But the important question is how to make a phylogenetic analysis well?. A large amount of data available for phylogenetic analyses is expanding at an exponential rate. As data sets have become larger, it has become increasingly critical to understand the advantages and disadvantages of using various methods of phylogenetic inference (Rosenberg & Kumar, 2001).

The most used methods we can find are Parsimony, Distance, Maximum Likelihood and Bayesian Inference. Generally, in the construction by the parsimony method look for the representation (cladogram) that has the least number of genetic changes with respect to the sequence of a common ancestor. Evaluate all the possible trees that represent evolution and look for the most optimal. Distance-based methods first calculate the total distance between all pairs of taxa, taking into account the differences in their sequences, and then calculate a tree based on those distances (Sourdis & Nei, 1987). Maximum Likelihood (ML) and Bayesian Inference (IB) are statistical methods, where a priori knowledge about the data is taken into account. The ML method estimates the probability of how well the character matrix is explained by phylogenetic trees (Felsenstein, 2004), while IB estimates the probability of how well phylogenetic trees are explained by the data (Huelsenbeck et al., 2001, Brooks et al., 2007). But these widely used methods have also been criticized; Maximum Parsimony is affected by the attraction of long branches (Felsenstein, 1978), which causes trees to reflect spurious phylogenetic relationships when the number of homoplastic characters overwhelms the homologous characters (Bergsten, 2005). Maximum Likelihood and IB is affected by the "repulsion" of sibling groups when they are located on long branches of trees (Siddall, 1998).

Personally I consider IB as the best method for phylogenetic reconstructions since it presents consistency regardless of the zone or branch length (Steel, 2013), besides other advantages such as computational efficiency (Ronquist & Huelsenbeck, 2009), integration of complex evolutionary models, incorporation of priors, in addition to an easy interpretation of the posterior probability (Yang, 2006).

The steps for a "good" phylogenetic analysis would be the following: Obtain the data (molecular or morphological), alignment, select the best substitution model (for nucleotide sequences, these range from the simple JC69 to the GTR complex, and for data morphological the Mk model (Nascimento et al., 2017), selection of priors (the prior should summarize the biologist's best knowledge about the model or parameters before the data are analyzed). The correlation must be ignored and independent priorities assigned for the parameters (Nascimento et al., 2017)), phylogenetic reconstruction by IB (The tree space is explored by visiting many combinations of parameter values in different trees by routes modeled by Monte Carlo Markov Chains (MCMC). Starting from a T1 tree in space, each new random address is regulated by the decision rules of the algorithm. In each step (generation) the value of some of the parameters is modified, and the posterior probability of the T2 tree is calculated under that new combination of parameters. The Markov Chain "visits" another combination of probable values of the parameters associated with another tree to evaluate if the posterior probability value is slightly lower, equal or higher with respect to the combination of parameters of the previous tree (De Luna et al., 2005). If you explore trees of many "generations" and repeat several chains, the analysis eventually stabilizes. Ideally, the MCMC would be long enough to obtain a reliable estimate of the subsequent distribution, but not as long as to waste computational resources. After reaching a point of equilibrium, MCMC visits trees frequently in proportion to their posterior probability (Huelsenbeck et al., 2002), visualization and understanding of the results.

Bibliography.

Bergsten J. 2005. A review of long-branch attraction. Cladistics, 21, 163–193.

Brooks D.R., J. Bilewitch, C. Condy, et al. 2007. Quantitative Phylogenetic Analysis in the 21st Century. Revista Mexicana de Biodiversidad, 78, 225–252.

De Luna, Efrain, Guerrero, José A., & Chew-Taracena, Tania. (2005). Sistemática biológica: avances y direcciones en la teoría y los métodos de la reconstrucción filogenética. Hidrobiológica, 15(3), 351-370.

Felsenstein J. 1978. Cases in which parsimony and compatibility methods will be positively misleading. Systematic Zoology, 27, 401–410.

Felsenstein J. 2004. Inferring phylogenies. Sinauer Associates, Sunderland, Massachusetts.

Huelsenbeck J. P., Ronquist F., Rasmus N., Bollback J. P. 2001. Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology. Science. 294 (5550) : 2310-2314

Huelsenbeck  J. P., Larget B., Miller  & Ronquist. 2002. Potential applications and pitfalls of Bayesian inference of phylogeny. Systematic Biology 51 (5): 673-688. 

Sourdis, J & NEI, M. Relative Efficiencies of the Maximum Parsimony and Distance-Matrix Methods in Obtaining the Correct Phylogenetic Tree. Center for Demographic and Population Genetics, The University of Texas Health Science Center at Houston. , 1987; 14 páginas.

Nascimento, F; Reis, M;Yang, Z. 2017.A biologist’s guide to Bayesian phylogenetic analysis. Nature Ecology & Evolution.DOI: 10.1038/s41559-017-0280-x

Siddall M.E. 1998. Success of Parsimony in the Four-Taxon Case: Long-Branch Repulsion by Likelihood in the Farris Zone. Cladistics, 14, 209–220.

Steel, M. (2013). Consistency of Bayesian inference of resolved phylogenetic trees. Journal of theoretical biology 336: 246–49.

Ronquist F., van der Mark P., Huelsenbeck J. P. 2009. Bayesian phylogenetic analysis using MrBayes. In: Lemey P., Salemi M., and Anne-Mieke V. (eds.) The Phylogenetic Handbook: a Practical Approach to Phylogenetic Analysis and Hypothesis Testing. Cambridge University Press. 219-236.

Rosenberg, S & Kumar, S. 2001. Traditional Phylogenetic Reconstruction Methods Reconstruct Shallow and Deep Evolutionary Relationships Equally Well, Molecular Biology and Evolution, Volume 18, Issue 9, 1 September 2001, Pages 1823–1827.

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press, Oxford, England

How to make a good phylogenetic analysis?

Definitions such as homology, synapomorphy, phylogenetic relationships, homoplasy, have always been the focus of almost all discussions about phylogeny. But it is worth mentioning and discussing the eternal discussion of which method is the best, and why. These last heated discussions always end in divided opinions and even conflicts among researchers. This is why it is important to define the three most used methods; Parsimony, Maximum Likelihood and Bayesian Inference.

Currently there are few articles in which the effectiveness of the three most commonly used methods is compared (Merl et al, 2005), however, that does not prevent this conflict from arising in the conversations, in which different opinions are always based on the philosophy of different methods, even defend them according to the time in which each has been proposed and the time it has been used.

However, from the results which of the three results better? According to Merl et al, (2005) there is very little consistency in the results of the three methods. However, according to the results obtained in their analysis the methods of Maximum Likelihood and Bayesian Inference do not present many differences, nevertheless, their analysis is based on comparing not only the methods, but the software in which they do the runs, so their results may be biased more toward the software than the method.

Because the parsimony method does not allow to see lengths of branches since it provides ultrametric trees, that is, all the branches have the same length (Kim, 1996), it is not useful in cases that want to take into account quantity of change or time. Likewise, it should be noted that despite having equal branch lengths, this is not a method that is exempt from the long-path attractors in this case (Swofford, 2001). And since the length of the branches is the same, it is even more difficult to determine which groups are affected by this phenomenon. However, it is important to highlight the ease of use of the method and its speed.

In the case of Maximum Likelihood and Bayesian Inference, Berli (2006) proposes that the Bayesian inference method should be preferred over Maximum Likelihood because in its population size analyzes, the Maximum Likelihood method has difficulties in recovering the expected values when the data set is very variable, however, Bayesian inference with the same data set is closer to the "true" values for all scenarios (PP> 95%). However, it should be noted that Berli (2006) suggests that other methods should be taken into account despite having obtained better results with Bayesian Inference.

However, Lemmon et al, (2009) compare the two methods with respect to ambiguous or missing data proposing that both methods are affected in the same way with respect to this type of data. Smith et al, (1987) propose according to their results that Bayesian should be used on Maximum Likelihood because this method is less sensitive to the modification of its parameters, nevertheless it highlights the importance of having well-defined priors as this could affect the analysis not only in its "veracity" but in the time spent computationally.

REFERENCES:

- Berli, P. (2006). Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics Vol. 22 no. 3, pages 341–345

- Kim , J. 1996. General inconsistency conditions for maximum parsimony: Effects of branch lengths and increasing numbers of taxa. Syst. Biol. 45:363±374.

- Lemmon et al, (2009). The Effect of Ambiguous Data on Phylogenetic Estimates Obtained by Maximum Likelihood and Bayesian Inference. Syst. Biol. 58(1):130–145.

- Merl et al, (2005). Comparison of Bayesian, maximum likelihood and parsimony methods for detecting positive selection. University of California Santa Cruz.

- Smith et al, (1987) A Comparison of Maximum Likelihood and Bayesian Estimators for the Three-parameter Weibull Distribution. Appl. Statist. 36, No. 3, pp. 358-369.

- Swofford et al, (2001). Bias in Phylogenetic Estimation and Its Relevance to the Choice between Parsimony and Likelihood Methods. Syst. Biol. 50(4):525-539.

Particularities and conflicts of phylogenetic analysis

Systematics has been a discipline that over the years has undergone changes both in its philosophy and in its concepts and methods. In particular, I believe it should be started by explaining what the objectives of this discipline have been over the years. The main ones are usually to describe the kinship relations between the different groups, understand the processes or causal factors of these relationships, and even to propose hypotheses of historical relationships and the evolution of the different groups (Gould, 1977).

But how to classify the different groups? According to Farris (1983) are classified from genealogical relationships, which are evidenced from synapomorphies, the latter being the one that provides evidence of these evolutionary relationships. Patterson (1982) proposes that the term synapomorphy is synonymous with homology, however, this has been much debated. Hennig (1966) proposes that both the term synapomorphy and symplesiomorphy are types of homologies. Likewise, Ritcher (2016) proposes that the two terms should not be considered as synonyms because although they both respond to the same phenomenon, they consider it from different perspectives. That is, homology does not imply a direction in the transformation, contrary to synapomorphy, which implies a character "derived" from that symplesiomorphy or "primitive" character.

Due to all this, it is important to try to define the concept of homology, which according to Farris et al, (1970) and De Pinna (1991) is the one that pass the first homology test proposed by Patterson (1988) which corresponds to similarity , which could lead to think that a character is homologous if and only if it is that which makes a group monophilic, which according to Donoghue (1994) that is not true, because this character must be considered as a synapomorphy to be considered later as homology. If this initial character does not behave like synapomorphy, it becomes a homoplasic character. This is why De Pinna (1991) proposes primary homology as a conjecture of homology and secondary homology as supported by phylogenetic hypotheses.

Given the above, what happens when the character is the absence of it? According to Hennig (1966) it is erroneous to consider the absence of a character as a homology, because the term homology applies only to structures that are present, for which Richter (2016) suggests the term "synapomorphy" which can also apply to those characters where a state represents the absence of the structure or organ.

All these differences of opinion and others that are still to be mentioned, have long been debated, however, it is important to consider each and every one of them in order to define a good phylogenetic analysis according to the researcher.

References:

- De Pinna, M.C.C., 1991. Concepts and tests of homology in the cladistic paradigm. Cladistics 7, 367–394.

- Donoghue, M. (1994) Complexity and homology in plants. Department of Organismic and Evolutionary Biology. Harvard University, Cambridge.

- Farris, J.S., Kluge, A.G., Eckhardt, M.J., 1970. A numerical approach to phylogenetic systematics. Syst. Zool. 19, 172–191.

- Farris, J. (1983) The Logical Basis of Phylogenetic Analysis. Disponible en http://www.reocities.com/Hollywood/makeup/5023/Farris1983.pdf.

- Gould, Stephen J, (1977). Ontogeny and Phylogeny. Cambridge: Harvard University Press. https://books.google.com.co/books (Accessed 10 Dec 2017).

- Hennig, W., 1966. Phylogenetic Systematics. University of Illinois Press, Urbana.

- Patterson, C. (1988) Homology in Classical and Molecular Biology. Mol. Biol. Evol. 5(6):603-625.

- Ritcher, S. (2016) Homology and synapomorphy-symplesiomorphy—neither synonymous nor equivalent but different perspectives on the same phenomenon. Cladistics 0, 1–5

- Sereno, P. (2005) The Logical Basis of Phylogenetic Taxonomy. Syst. Biol. 54(4):595–619

Philosophy in phylogenetic analysis

Philosophy in phylogenetic analysis

To speak of philosophy in phylogenetic analysis it is important to know several positions on the subject, some authors favor methods with good philosophical support (coherence with the theory of epistemology), others leave these in the background and are more interested by the performance of the methods (simulations of real well-supported phylogenies to then evaluate the methods that best "reconstruct", based on probability).

Siddall & Kluge (1997) argued that parsimony fits the theory of epistemology developed by Karl Popper, while probability does not. On the other hand, they presented parsimony better than probability based on Popper's concept of corroboration. For Popper, testing a theory means trying to refute it by means of a counterexample. If it is not possible to refute it, this theory is corroborated, and it can be accepted provisionally, but not verified; that is, no theory is absolutely true, but not refuted.

The principle of parsimony, known as the Ockham knife proposed by the Franciscan William Ockham. This principle establishes that entities should not be multiplied beyond necessity, which is often interpreted as implying that when alternative hypotheses explain data equally well, the simplest is preferred⁴. This principle principle is not the same as the parsimony method used for phylogenetic reconstruction, which classifies phylogenetic trees in a variable way according to the minimum number of character transformations among the taxa. The method of parsimony is a set of methods, this method is adjusted to the principle of parsimony in which the transformations of a hypothetical nature is not multiplied beyond the need (dependent on the costs assigned to different kinds of transformations).⁴

On the other hand probabilistic models have the advantage of developing stochasticity (Process whose behavior is non-deterministic) in their methods. The maximum likelihood analyzes how much the data fits to a given hypothesis; where a tree is considered a hypothesis when choosing the highest probability for that event; In the Bayesian analysis it is quantified that both a previous test (Prior) is adjusted to a later one.

De Queiroz & Poe 2001 say that contrary to the views of the authors who have criticized the likelihood approach to phylogenetic inference as incompatible with Karl Popper's degree of corroboration, an examination of Popper's own writings reveals that the concept general likelihood is the basis of its degree of corroboration. Consequently, it is not surprising that phylogenetic inference probability methods are fully compatible with Popper's corroboration and that methods of cladistic parsimony are compatible with corroboration only if they are interpreted as incorporating probabilistic assumptions. But if parsimony methods are interpreted as incorporating probabilistic assumptions, these assumptions are models that can be used in the context of probability, and the non-probabilistic implementations of those methods are simply approximations for their probabilistic counterparts. Interpreted in this way, there is no conflict between parsimony and maximum likelihood, because the general statistical perspective of maximum likelihood and popperian corroboration summarizes all the individual methods and models that can be applied within the context of that perspective, including those that of cladistic parsimony. ^{2, 5, 6}

Other authors such as Brooks et. al., 2007 speak of a new strategy in which neither Popper's philosophy nor statistical coherence can give priority to a method of quantitative phylogenetic analysis; It is common for an author to present parsimony, maximum likelihood and Bayesian inference and then have a preference for some of them. ¹

Personally I agree with what De Queiroz & Poe 2001 proposes, since they are based on methods with good philosophical support, and corroborations, not on finding the absolute truth. Phylogenetic analyzes should be based on corroborations and not absolute truths; Independent of the method, each one presents a hypothesis that can be corroborated.

¹ Brooks, D; Bilewitch, J; Condy, C; Evans, D; Folinsbee, K; Fröbisch, J; Halas, D; Hill, S; McLennan, D; Mattern, M; Tsuji, L; Ward, L; Wahlberg, N; Zamparo, D & David Zanatta, D. 2007. Quantitative Phylogenetic Analysis in the 21 st Century.Análisis Filogenéticos Cuantitativos en el siglo XXI.Revista Mexicana de Biodiversidad 78: 225- 252, 2007

² De Queiroz,K & Poe, S. 2001 .Philosophy and Phylogenetic Inference: A Comparison of Likelihood and Parsimony Methods in the Context of Karl Popper’s Writings on Corroboration .Systematic Biology. 50(3):305–321, 2001

³ Siddall , M & Kluge, A . 1997. Probabilism and phylogenetic inference. Cladistics 13:313–336.

⁴ Sober , E. 1994. From a biological point of view. Cambridge Univ. Press, Cambridge, England.

⁵ Popper K. R. 1968. The logic of scientifi c discovery. Harper and Row, New York. 544 p.

⁶ Popper K. R. 1997. The demarcation between science and metaphysics. In The philosophy of Rudolph Carnap, P. A. Schilpp (ed.). Open Court, La Salle. p. 183-22

Philosophy in homology

According to Popper, the decision to give as true a hypothesis is only possible in the scenario where any other hypothesis refutes this truth; for Popper the way of approaching science is through the deductive method, where the hypothesis can be corroborated or distorted through observations (Helfenbein & Desalle, 2005)

In this context, the scientific community has criticized but also has applied the premise that a character used in comparative biology must comply with the Paterson test consisting of three tests; the first of similarity, the one of conjunction and finally the one of congruence (Patterson, 1988); to be denominated homology and in this way to be analyzed to find the relations of parentage between all the existing species.

According to Pinna (1991), "a homology in her basic form is understood as the equivalence of the parts", that is, the relation derived from the parts, which are homologous (Williams & Ebach, 2008)

Following the idea of corroboration proposed by Popper (1983), one can find the logical probability that a hypothesis (cladogram or diagram of ramifications that summarizes the general knowledge about the types and relationships of organisms (Platnick & Nelson, 1978)) is supported by evidence (homology) given background knowledge or background (Figure 1).

Figure 1. Equation of corroboration (Popper, 1983).

The above expression corresponds to Popper's corroboration definition, where p = probability, h = hypothesis, e = evidence, b = background, hb refers to the conjunction between h y b so p (e, hb) is the probability of the evidence given the hypothesis and the background; that is, of the conditional probability form P (A | B). Thus, the value of C is positive when the evidence supports the hypothesis, it is negative when the evidence does not contribute to the hypothesis and C = 0 when the evidence does not guide the hypothesis (Queiroz & Poe, 2001).

Is very important the clarity about the definition of homology because as a scientific community agreed to analyze the evolutionary tracks with the same lens; and we can share the Williams & Ebach (2008) statement that homology must be considered the unit of classification.

Bibliography

Helfenbein, K. G., & Desalle, R. (2005). Falsifications and corroborations : Karl Popper’s influence on systematics, 35, 271–280.

Patterson, C. (1988). Homology in Classical and Molecular Biology, 5(6), 603–625.

Pinna, M. G. G. De. (1991). Concepts and tests of homology in the cladistic paradigm, 367–394.

Platnick, N. I., & Nelson, G. (1978). A method of analysis for historical biogeography.

Queiroz, K., & Poe, S. (2001). Philosophy and Phylogenetic Inference : A Comparison of Likelihood and Parsimony Methods in the Context of Karl Popper ’ s, 50(3), 305–321.

Williams, D., & Ebach, M. (2008). Foundations of Systematics and Biogeography.