Indo-European phylogenetics with R

Abstract

The last twenty or so years have witnessed a dramatic increase in the use of computational methods for inferring linguistic phylogenies. Although the results of this research have been controversial, the methods themselves are an undeniable boon for historical and Indo-European linguistics, if for no other reason than that they allow the field to pursue questions that were previously intractable. After a review of the advantages and disadvantages of computational phylogenetic methods, I introduce the following methods of phylogenetic inference in R: maximum parsimony; distance-based methods (UPGMA and neighbor joining); and maximum likelihood estimation. I discuss the strengths and weaknesses of each of these methods and in addition explicate various measures associated with phylogenetic estimation, including homoplasy indices and bootstrapping. Phylogenetic inference is carried out on the Indo-European dataset compiled by Don Ringe and Ann Taylor, which includes phonological, morphological, and lexical characters.

Addressing Polymorphism in Linguistic Phylogenetics

“…There are many methods that have been developed for phylogeny estimation for languages, including distance-based methods, parsimony-and compatibility-based methods, and Bayesian estimation methods; see Nakhleh et al (2002Nakhleh et al ( , 2005b, Nichols & Warnow (2008), Goldstein (2020) for more information about these methods for use in linguistic phylogenetics.…”

“…2003; Nakhleh et al. 2005b; Goldstein 2020). Thus, developing methods with improved accuracy is important, and has the potential to advance discovery in many disciplines.…”

“…Linguistic phylogenies (i.e. evolutionary trees and/or phylogenetic networks) are estimated for different groups of languages using a variety of methods and based on a number of different types of linguistic features (Dunn et al 2005;Nichols & Warnow 2008;Calude & Verkerk 2016;Goldstein 2020Goldstein , 2022b. These phylogenies are then used in subsequent analyses, such as reconstruction of proto-languages and estimates of dates for diversification of languages, which can then be used to answer anthropological questions, such as the geographical and temporal origins of Indo-European (e.g.…”

“…whether the data are restricted to lexical characters only or also include other types of data), how the linguistic data are represented mathematically as character data for input to the method, etc. (Ringe et al 2002;Rexová et al 2003;Nakhleh et al 2005b;Goldstein 2020). Thus, developing methods with improved accuracy is important, and has the potential to advance discovery in many disciplines.…”

Using Historical Glottometry to Subgroup the Early Germanic Languages

“…If a historical linguist has posited branches on a tree on the basis of several shared innovations, but a later discovery of another potentially shared innovation contradicts the posited tree, then the newly discovered innovation is to be dismissed as a coincidental parallel innovation in order to satisfy the constraints of the model. Even complex computational cladistic models function under the expectation of such situations, so that maximum parsimony is sought in order to select the tree with the least homoplasy (that is, conflicting innovations) among several possible trees (see Goldstein 2020). However, the rigid constraint that a language must be faithful to one node on a tree or the other regarding all of its innovations is not accurate (Gray et al 2010) and does not hold for many language families through-out the world (Bossong 2009).…”