We claim that making sense of the typological diversity of languages demands a historical/evolutionary approach. We are pleased that the target paper (Dunn et al. 2011a) has served to bring discussion of this claim into prominence, and are grateful that leading typologists have taken the time to respond (commentaries denoted by boldface). It is unfortunate though that a number of the commentaries in this special issue show significant misunderstandings of our paper.
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In the following section we try to explain the basic underlying reasoning, turning in the remaining sections to some of these recurrent points of contention. In the final section, we collect some responses to points made in individual commentaries that did not fit neatly into the body of our response.

The Levenshtein distance is a simple distance metric derived from the number of edit operations needed to transform one string into another. This metric has received recent attention as a means of automatically classifying languages into genealogical subgroups. In this paper I test the performance of the Levenshtein distance for classifying languages by subsampling three language subsets from a large database of Austronesian languages. Comparing the classification proposed by the Levenshtein distance to that of the comparative method shows that the Levenshtein classification is correct only 40% of time. Standardising the orthography increases the performance, but only to a maximum of 65% accuracy within language subgroups. The accuracy of the Levenshtein classification decreases rapidly with phylogenetic distance, failing to discriminate homology and chance similarity across distantly related languages. This poor performance suggests the need for more linguistically nuanced methods for automated language classification tasks.

There is disagreement about whether human political evolution has proceeded through a sequence of incremental increases in complexity, or whether larger, non-sequential increases have occurred. The extent to which societies have decreased in complexity is also unclear. These debates have continued largely in the absence of rigorous, quantitative tests. We evaluated six competing models of political evolution in Austronesian-speaking societies using phylogenetic methods. Here we show that in the best-fitting model political complexity rises and falls in a sequence of small steps. This is closely followed by another model in which increases are sequential but decreases can be either sequential or in bigger drops.

The results indicate that large, non-sequential jumps in political complexity have not occurred during the evolutionary history of these societies. This suggests that, despite the numerous contingent pathways of human history, there are regularities in cultural evolution that can be detected using computational phylogenetic methods.

In this paper we outline two debates about the nature of human cultural history. The first focuses on the extent to which human history is treelike (its shape), and the second on the unity of that history (its fabric).

Proponents of cultural phylogenetics are often accused of assuming that human history has been both highly tree-like and consists of tightly linked lineages. Critics have pointed out obvious exceptions to these assumptions.

Instead of a priori dichotomous disputes about the validity of cultural phylogenetics phylogenies, we suggest that the debate is better conceptualized as involving positions along continuous dimensions. The challenge for empirical research is therefore to determine where particular aspects of culture lie on these dimensions.

We discuss the ability of current computational methods derived from evolutionary biology to address these questions. These methods are then used to compare the extent to which lexical evolution is treelike in different parts of the world and to evaluate the coherence of cultural and linguistic lineages.

Phylogenetic comparative methods (PCMs) provide a potentially powerful toolkit for testing hypotheses about cultural evolution. Here we build on previous simulation work by Nunn et al. (2006) to assess the effect horizontal transmission between cultures has on the ability of both phylogenetic and non-phylogenetic methods to make inferences about trait evolution.

We found that the mode of horizontal transmission of traits has important consequences for both methods. Where traits were horizontally transmitted separately PCMs accurately reported when trait evolution was not correlated even at the highest levels of horizontal transmission.

In contrast, linear regression analyses often incorrectly concluded that traits were correlated. Where simulated trait evolution was not correlated and traits were horizontally transmitted as a pair both methods showed increased levels of positive correlation with increasing horizontal transmission.

Where simulated trait evolution was correlated increasing rates of separate horizontal transmission led to decreasing levels of correlation for both methods, but that increasing rates of paired horizontal transmission did not. Furthermore, the PCM was also able to make accurate inferences about the ancestral state of traits.

These results suggest that under certain conditions PCMs can be robust to the effects of horizontal transmission. We discuss ways that future work can investigate the mode and tempo of horizontal transmission of cultural traits.

There are approximately 7000 languages spoken in the world today. This diversity reflects the legacy of thousands of years of cultural evolution. How far back we can trace this history depends largely on the rate at which the different components of language evolve. Rates of lexical evolution are widely thought to impose an upper limit of 6-10 thousand years on reliably identifying language relationships. In contrast, it has been argued that certain structural elements of language are much more stable. Just as biologists use highly conserved genes to uncover the deepest branches in the tree of life, highly stable linguistic features hold the promise of identifying deep relationships between the world’s languages.
Here we present the first global network of languages based on this typological information. We evaluate the relative evolutionary rates of both typological and lexical features in the Austronesian and Indo-European language families. The first indications are that typological features evolve at similar rates to basic vocabulary but their evolution is substantially less treelike. Our results suggest that, whilst rates of vocabulary change are correlated between the two language families, the rates of evolution of typological features and structural sub-types show no consistent relationship across families.

You can download a preprint of this paper here.

A network showing the relationships between 99 languages across the world. This network is built from structural information about language properties (phonology, morphology, grammar and lexicon). The length of the branches is proportional to the amount of divergence between the languages, and the box-like structures on the network show conflicting signal due to borrowing between languages and chance similarity.

We recently used computational phylogenetic methods on lexical data to test between two scenarios for the peopling of the Pacific. Our analyses of lexical data supported a pulse-pause scenario of Pacific settlement in which the Austronesian speakers originated in Taiwan around 5,200 years ago and rapidly spread through the Pacific in a series of expansion pulses and settlement pauses. We claimed that there was high congruence between traditional language subgroups and those observed in the language phylogenies, and that the estimated age of the Austronesian expansion at 5,200 years ago was consistent with the archaeological evidence. However, the congruence between the language phylogenies and the evidence from historical linguistics was not quantitatively assessed using tree comparison metrics. The robustness of the divergence time estimates to different calibration points was also not investigated exhaustively. Here we address these limitations by using a systematic tree comparison metric to calculate the similarity between the Bayesian phylogenetic trees and the subgroups proposed by historical linguistics, and by re-estimating the age of the Austronesian expansion using only the most robust calibrations. The results show that the Austronesian language phylogenies are highly congruent with the traditional subgroupings, and the date estimates are robust even when calculated using a restricted set of historical calibrations.

Many papers about language subgrouping make the argument that if a word form is present in two language subgroups, then it probably reflects their common proto-language. This fairly reasonable assumption has taken center-stage in the reconstruction of Proto-Central-Pacific and Proto-Oceanic, and subsequent inferences about these respective societies. In this talk I will introduce a method that calculates the actual probability that a given form reflects the proto-language. This method builds on the linguistic comparative method, and provides a powerful tool for testing and confirming reconstructions in a probabilistic framework.

“I believe we were all glad to leave New Zealand. It is not a pleasant place. Amongst the natives there is absent that charming simplicity which is found in Tahiti; and the greater part of the English are the very refuse of society. Neither is the country itself attractive. (Darwin 1860, p. 430)”

Around 1000 years earlier another set of travellers arrived in New Zealand – the ancestors of the Maori. Unlike Darwin, the Maori liked New Zealand and decided to stay.

Historical linguistics has never been particularly intimate with computers. The first wave of computational historical linguistics—lexicostatistics—was developed in the 1950s and quickly applied to language groups around the world from Indo-European to Austronesian. However, critics were quick to point out the problems caused by assuming a single constant rate of lexical replacement and repeatedly noted the erroneous results that this produced. As a consequence of these critiques lexicostatistics has been widely rejected by mainstream historical linguists. The last few years have seen a second wave of computational approaches entering historical linguistics: phylogenetic methods. These techniques, drawn from evolutionary biology, have been used to investigate some provocative and controversial claims about human prehistory. Given the combination of strong claims, new techniques, and the high-profile reporting of results, it is not surprising that these studies are often controversial. Sadly many of these criticisms are mired in misunderstanding.

Computational phylogenetic methods are not just lexicostatistics redux, but a powerful supplement to the comparative method used in historical linguistics. Here we will focus on one of the great battlegrounds between lexicostatistics and the traditional comparative method: the Austronesian language family. First, we will describe how Bayesian phylogenetic methods work, and then give a step-by-step explanation of an analysis of a large lexical dataset for 400 Austronesian languages.