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Insights & Perspectives
Networks of lexical borrowing and
lateral gene transfer in language
and genome evolution
Johann-Mattis List
1)
*, Shijulal Nelson-Sathi
2)
, Hans Geisler
3)
and William Martin
2)
Like biological species, languages change over time. As noted by Darwin, there
are many parallels between language evolution and biological evolution.
Insights into these parallels have also undergone change in the past 150 years.
Just like genes, words change over time, and language evolution can be likened
to genome evolution accordingly, but what kind of evolution? There are
fundamental differences between eukaryotic and prokaryotic evolution. In the
former, natural variation entails the gradual accumulation of minor mutations in
alleles. In the latter, lateral gene transfer is an integral mechanism of natural
variation. The study of language evolution using biological methods has
attracted much interest of late, most approaches focusing on language tree
construction. These approaches may underestimate the important role that
borrowing plays in language evolution. Network approaches that were originally
designed to study lateral gene transfer may provide more realistic insights into
the complexities of language evolution.
Keywords:
.borrowing; language evolution; lateral transfer; network approaches;
prokaryotic evolution
:Additional supporting information may be found in the online version of this
article at the publisher’s web-site.
Introduction
For a long time, both biologists and
linguists have been using family trees to
model how species and languages
evolve. But in contrast to biology –
where the tree model is generally
accepted to be the most realistic way to
model how eukaryotic species (species
with nucleated cells, such as animals and
plants) evolve – linguists have always
treated language trees with a certain
suspicion. They have emphasized that –
given the important role that horizontal
transmission plays in language history –
such trees can only capture vertical
aspects of language evolution, while
horizontal aspects (which linguists
traditionally model as “waves” that
spread out in circles around a center
in geographic space) are ignored.
In the last decade, language trees
have experienced a strong revival, espe-
cially in the public notion of linguistics as
reflected in popular scientific literature
and in articles addressed to a not
exclusively linguistic readership [1]. Ear-
lier linguistic work on phylogenetic
reconstruction was, with a few excep-
tions [2–8], qualitative in its nature. But
starting about 10 years ago, computer
methods originally designed to infer trees
from molecular sequence data made their
way into the analysis of large linguistic
datasets, leading to a resurgence of
language trees [9–15]. If the reconstruc-
tion of trees had only played a minor role
in historical linguistics up to that point,
it has now become a specific field of
interest, and some scholars even go so
far as proclaiming tree construction
as a priority for historical linguistic
endeavor [16].
In traditional historical linguistics,
these new approaches are met with a
certain amount of reservation, since
their results are often not in concor-
dance with those achieved by tradition-
al methods [17–20]. One important
reason for such discrepancies is the
relatively large number of individual
DOI 10.1002/bies.201300096
1)
Research Center Deutscher Sprachatlas,
Philipps-University Marburg, Marburg, Germany
2)
Institute of Molecular Evolution, Heinrich-Heine
University D€
usseldorf, D€
usseldorf, Germany
3)
Institute of Romance Languages and Literature,
Heinrich-Heine University D€
usseldorf,
D€
usseldorf, Germany
*Corresponding author:
Johann-Mattis List
E-mail: mattis.list@uni-marburg.de
www.bioessays-journal.com 141Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc. This is an
open access article under the terms of the Creative Commons Attribution-NonCommercial License, which
permits use, distribution and reproduction in any medium, provided the original work is properly cited and is
not used for commercial purposes.
Think again
and methodological errors in linguistic
datasets [19]; this is reflected by numer-
ous cases of wrong translations, wrong
homology assessments (incorrect iden-
tification of cognate words), and
undetected cases of lateral transfer
(borrowing) [17, 18].
In this paper, we argue that the
problem of the new quantitative meth-
ods is that they focus too much on the
vertical aspects of language evolution,
thereby forcing the data into tree-
like structures. We show that network
approaches that were originally
designed to study reticulation and
lateral gene transfer in the evolution
of prokaryotic species (microbes with-
out cell nuclei, such as bacteria and
archaea) can cope with these problems,
hence providing a more realistic way to
model the complexities of language
history by combining both its tree-like
(vertical) and its wave-like (horizontal)
aspects.
Historical linguists were
always skeptical about
language trees
In 1853 the German linguist August
Schleicher (1821–1868) published two
articles [21, 22] (Fig. 1A and B) in which
he showed how branching trees can be
used to illustrate the historical develop-
ment of languages (Table 1A). It is
possible [23] that Schleicher himself
adopted the idea from a colleague,
the Czech linguist Frantis
ˇek Ladislav
C
ˇelakovsky
´(1759–1852), whose post-
humously published lectures contain
an early tree diagram of the Slavic
languages [24] (Fig. 1C). Schleicher was
very interested in biology, especially
botany, and in his work we find many
passages where he compares languages
with organisms, assuming that they
went through stages of birth, youth,
middle age, old age, and – finally –
death [25]. He emphasized that lan-
guage classification was quite similar
to biological classification of animals
or plants [25]. He also mentioned the
problem of distinguishing vertically
from horizontally transmitted traits,
drawing a parallel between “foreign
influence” due to language contact
in language history, and “crossbreed-
ing” in evolutionary biology [26]
(Table 1B).
In biology, the concept of evolution-
ary trees was not introduced until
Figure 1. Three early language trees in the
history of linguistics. A: August Schleicher’s
first tree of Germanic and Balto-Slavic
languages. B: Schleicher’s first tree of the
Indo-European language family. C: An early
tree of the Slavic languages by Frantis
ˇek
Ladislav C
ˇelakovsky
´.
J.-M. List et al. Insights & Perspectives .....
142 Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc.
Think again
Charles Darwin’s (1809–1882) mention-
ing of the “Great Tree of Life” in
1859 [27], but it soon became deeply
ingrained in thinking on the topic.
Notably, it was later reinforced by many
influential drawings from Ernst Haeckel
(1837–1919, see [28] for details), culmi-
nating in the inference of trees from
molecular sequences [29], and the
reconstruction of phylogenetic trees
for all organisms using ribosomal and
informational gene phylogenies [30].
In linguistics the popularity of
language trees began to fade soon after
it was first proposed [31]. In 1872
Johannes Schmidt (1843–1901) pointed
out that linguistic data contradicted
the idea of simple, tree-like differentia-
tion [32]. Instead of the family tree
theory he proposed the “wave theory”
(Wellentheorie in German), which
states that certain changes spread like
waves in concentric circles over neigh-
boring speech communities. And before
Schmidt, Hugo Schuchardt (1842–1927)
had criticized the idea of split and
independent differentiation [33], em-
phasizing that languages diverge grad-
ually while at the same time mutually
influencing each other (Table 1C). Even
today, historical linguists continue to
hold strong reservations about the tree
model. In text books on historical
linguistics, both the tree and the wave
theory are usually introduced as two
complementary models, each of which
only depicts one aspect of language
history [34, 35]. Thus, if linguists are
asked whether language evolves in a
tree-like manner, most linguists would
probably answer as Hoenigswald did in
1990: “Yes, of course it does, if we so
wish; but we had better be very
careful” [36].
Borrowing is a
constitutive part of
language history
If we take the most frequent 1,000 Latin
words and look at how they survived in
its daughter languages, we will find that
67% of all words were directly inherited
in at least one language, yet only 14%
were inherited in all Romance lan-
guages [37]. However, this drastic loss
of Latin words during Romance lan-
guage history is only part of the story:
Since Latin never ceased to serve as a
cultural adstrate language (a language
that co-exists in some form in parallel
with another language with which it is
in contact), with a particularly great
impact on written vernaculars, only
33% of all 1,000 words were completely
lost, and about 50% survive as borrow-
ings from the ancestor language in the
daughter languages [37]. Moreover,
lexical transfer during the history of
the Romance languages was not re-
stricted to the influence of Latin alone,
and contact among the Romance lan-
guages and other neighboring Indo-
European languages was very frequent
and vivid. According to a recent survey
of 2,137 common words in Roma-
nian [38], for example, 894 (41.8%)
were classified as loanwords from other
languages. The majority of these bor-
rowed words were transferred from
Slavic donor languages (about 14%).
Only a small number of words were
borrowed from Latin (about 3%).
On the “borrowability scale” [39],
which ranks the ease with which
different elements of language are
assimilated by recipient languages,
borrowing of words ranks highest.
Lexical borrowing can affect only small
parts of the vocabulary of a given
language (such as specific terms for
religious concepts, cultural items, or
artifacts), or result in a situation where
large parts of the language’s original
lexicon are replaced. This can even
result in complete relexification, as in
Creole languages. In the World Loan-
word Database [40] the frequency of
direct borrowing events documented for
41 languages varies greatly, ranging
from 1% for Mandarin Chinese to 62%
for Selice Romani, with an average
of 25% and a standard deviation of
13% [41].
Borrowing cannot be
ignored in quantitative
approaches
With few exceptions [42–44], the major-
ity of the new biological methods for
tree construction makes use of lexical
language data. This is due to the fact
that it is much easier to compile lexical
datasets for large numbers of lan-
guages: in many cases – especially for
less-well studied language families –
wordlists are the only things available
for study. However, analysis of lexical
items also reflects the basic practice of
the traditional method for linguistic
reconstruction, which starts with the
comparison of words and mor-
phemes [35, 45, 46]. Similarly to earlier
quantitative approaches in historical
linguistics [8], the biological methods
require that borrowings be filtered out
of the data before the analysis is
applied. Since reliable automatic meth-
ods are lacking, cognate and borrowing
Table 1. Early quotes on language history from August Schleicher and Hugo Schuchardt
(A) August Schleicher [26]
We know both the Old Latin and the Romance languages which
demonstrably descended from the former via differentiation
and – you would call it crossbreeding – foreign influence
Wir kennen sowohl das Altlateinische, als auch die durch
Differenzierung und durch fremden Einfluss – Ihr w €
urdet sagen
durch Kreuzung – nachweislich aus ihm hervorgegangenen
romanischen Sprachen
(B) August Schleicher [22]
These assumptions which logically follow from the previous
research can be best illustrated with the help of a branching
tree
Diese Annahmen, logisch folgend aus den Ergebnissen der
bisherigen Forschung, lassen sich am besten unter dem Bilde
eines sich ver€
astelnden Baumes anschaulich machen
(C) Hugo Schuchardt [32]
We connect the branches and twigs of the family tree with
countless horizontal lines and it ceases to be a tree
Wir verbinden die
€
Aste und Zweige des Stammbaums durch
zahllose horizontale Linien, und er h€
ort auf ein Stammbaum zu
sein
.....Insights & Perspectives J.-M. List et al.
143
Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc.
Think again
assignments are usually carried out
manually. In order to make this pains-
taking process easier, scholars revived
an old idea proposed in the 1950s [4, 5,
47], and restrict the lexical comparison
to words that belong to the realm of the
so-called “basic vocabulary” [12]. Basic
vocabulary is merely a technical term
that refers to a list of about 100–200
basic concepts (such as “hand”, “foot”,
“stone”) that are translated into the
languages under investigation. These
lists are usually called Swadesh lists,in
acknowledgement of Morris Swadesh
(1909–1967), who popularized their use
in linguistics. The basic assumption
regarding Swadesh lists is that (a) every
language has words that express the
concepts, (b) the words evolve slowly
(enabling us to recognize similarities
across languages), and (c) the words are
rather resistant to borrowing [16]. Un-
fortunately, the last assumption, in
particular, is highly problematic. Al-
though the use of Swadesh lists may
decrease the number of borrowings to a
certain degree, it cannot exclude all of
them. In a recent survey of 1,504
common words in English, for example,
616 (41%) were judged to be loan-
words [48], yet in the traditional English
Swadesh list there are still 32 borrow-
ings out of 200 (16.5%), mostly from Old
Norse and Old French [18]; and in a
recent revision of the Albanian Swadesh
list, 34 out of 107 words (31.8%) were
identified as possible borrowings [49].
Manual detection of borrowings can
range between trivial and impossible,
depending on the case in point. Some
borrowing processes are very transpar-
ent. Neither a linguist nor a German
speaker has problems in identifying the
word Job “job” as a recent borrowing
from English, since the initial sound of
the word is not yet “integrated” into the
German sound system. But the situation
is not always that simple. Thus, while
no German native speaker would hesi-
tate to assume that Fett “grease” is a
“normal” German word, the word has in
fact been borrowed from Low German
dialects [50], as can be proven from its
irregular correspondence with English
fat: If the words were truly cognate, we
would expect the German word to end
with an [s] (spelled as ßin German)
instead of a [t], as in German heiß “hot,”
which is truly cognate with English
hot [50]. Identifying borrowings with
help of these techniques requires expert
knowledge of the languages under
investigation, and the deeper one goes
back in time, the harder it becomes even
for the experts, since the available
phonological information may be lost.
Recent tests on simulated data have
shown how crucial it is to screen
the linguistic data carefully before
applying quantitative analyses [51].
How difficult it is to prepare the data
and to filter out all borrowings correctly
is reflected by the fact that the most
frequently used datasets, the Compara-
tive Indo-European Database ([52],
http://www.wordgumbo.com/ie/cmp/),
and the Austronesian Basic Vocabulary
Database ([53], http://language.psy.
auckland.ac.nz/austronesian/), contain
many undetected borrowings and vari-
ous levels of erroneous cognate judg-
ments [17–19, 49]. But “scrubbing” the
data of false cognate assignments does
not seem to be feasible for large data-
sets. Quantitative studies that are based
on the Indo-European Lexical Cognacy
Database (IELex, http://ielex.mpi.nl/),
whose goal was to significantly enhance
the notoriously flawed database com-
posed by [52], still yield subgroupings
that contradict traditional genetic clas-
sification (compare, for example, the
strange grouping of Polish in [13]
and [54]). One reason for these problems
is that the database still contains many
undetected borrowings and other
errors. The other reason is that the
exclusion of borrowings necessarily
yields a loss of information that can
have large impacts on the results [49]. It
seems that the a priori exclusion of
suspected borrowings from the data is
not enough, especially in cases where
the history of a language family is not
yet well understood. Instead of making
tree reconstruction the key objective of
historical linguistics, we need quantita-
tive methods that can deal with borrow-
ings and – ideally – handle both vertical
and lateral transmission.
Language history bears a
close resemblance to
prokaryote evolution
If historical linguists want to profit from
biological expertise in large-scale anal-
yses of big datasets, they need to make
up their mind regarding the methods
they need in linguistics, and the meth-
ods that biology can provide. That
evolutionary biology has developed
some sophisticated tools to reconstruct
phylogenetic trees, and that these tools
can be easily applied to linguistic
datasets, has been demonstrated fre-
quently during the last decade. Yet is
this really all that biology has to offer?
In several fundamental aspects, the
genomes of eukaryotic species – such as
animals and plants – and prokaryotic
species – such as bacteria and archaea –
evolve in very different ways, and lateral
gene transfer is generally at the root
of those differences. Gene families are
one example. Gene families are sets of
homologous (cognate) genes that were
formed by duplication of an ancestral
gene, quite similar to the reflexes of
the root of a word in the same or
different language. In eukaryotes, gene
families arise through duplication: a
resident gene duplicates, perhaps sev-
eral times, and the resulting gene family
consists of members that are closely
related at the outset and undergo
divergence and functional specializa-
tion [55]. In prokaryotes, gene families
arise via the acquisition of related
sequences through lateral gene
transfer, not through duplication [56].
As another example, in eukaryotes,
meiosis ensures that only members of
the same species exchange genes, and
recombination is reciprocal. In prokar-
yotes, there are well-studied mechanisms
that mediate gene transfer, both within
and across species boundaries [57].
Furthermore, if we sequence 61
human genomes, we will find – to all
intents and purposes – the same
collection of about 30,000 genes in
each individual, with allelic variants at
many loci, and the 46 chromosomes will
almost always be colinear: the genes
appearing at similar positions. If we
sequence 61 genomes of Escherichia
coli, a bacterium usually found in the
intestines of warm-blooded species, we
will find about 4,500 genes in each
individual genome, but only about
1,000 genes that are present in all
genomes. Summing up the different
genes we find in all individuals, there
are about 18,000 different genes dis-
tributed among them, and this count
will further increase if we add more
individual genomes to this calculation,
J.-M. List et al. Insights & Perspectives .....
144 Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc.
Think again
hence yielding an ever growing pange-
nome of Escherichia coli [58]. These
examples underscore fundamental dif-
ferences in the nature of the processes of
evolutionary divergence in prokaryotic
and eukaryotic populations: Eukaryotic
populations generate tree-like struc-
tures of divergence over time [59], while
genome evolution in prokaryotes gen-
erates both tree-like and net-like com-
ponents of relatedness over time [60].
Recalling the scores on shared
inherited words and borrowings we
reported for the Romance languages
earlier, it seems obvious that language
history shows a much closer resem-
blance to prokaryotic evolution than to
eukaryotic evolution. Thus, if one says
that language history and genome
evolution have a lot in common, it
seems much more appropriate to em-
phasize that language evolution may
resemble prokaryotic evolution much
more than it resembles eukaryotic
evolution. We do not claim to make a
binary distinction here: As the amount
of contact-induced change differs from
language to language, so do the under-
lying evolutionary processes, and it is
rather a continuum between strictly tree-
like and strictly network-like evolution
that we are dealing with. Nevertheless, if
we want to employ quantitative methods
from biology to supplement our research
in historical linguistics, it could be much
more fruitful to get away from focusing
exclusively on those methods that yield
simple family trees, and instead look for
methods that were designed to handle
lateral transfer.
Network approaches offer
new possibilities for
quantitative analyses in
language evolution
Despite the dissatisfaction of many
historical linguists with both the tree
and the wave model, there are – to our
knowledge – only a few attempts to
combine both approaches within a new
framework [35, 61, 62]; furthermore,
unfortunately most of these proposals
remain a mere visualization of the
scholars’ intuitions regarding the data,
from which no further insights can be
drawn. If one wants to include both the
vertical and the horizontal aspects, it
seems natural to turn to networks as a
format to represent language history.
In evolutionary biology, different
network approaches have been devel-
oped in order to study reticulation in
biological datasets (see the overviews
in [63] and [64]). Among the most
popular of these methods are those that
produce unrooted networks (splits
graphs) such as split decomposition [65]
or NeighborNet [66]. These methods
enjoy some popularity in recent quanti-
tative studies in historical linguistics,
and have been applied to quite a few
different datasets [67–71]. In contrast to
the popular quantitative methods for
tree construction, such as Neighbor-
Joining [72], or Bayesian inference [73],
they are unbiased with respect to “tree-
likeness”, and provide a direct visuali-
zation of the degree of conflict in a given
dataset [74]. They have proven to be a
very useful tool for data exploration,
and have even been used to measure
reticulation directly from lexical dis-
tance matrices across the world’s lan-
guage families [75]. The drawback of
these methods is that they are distance-
based, hence aggregating lexical infor-
mation on the taxonomic level. The
information on shared cognates in the
underlying datasets is converted to
distance scores, and the result is an
unrooted network that only indicates
whether there are conflicting signals in
the data, but does not directly point to
the cognate sets that are responsible for
these conflicts.
A more realistic modeling of lan-
guage history could be achieved by
methods that automatically infer hid-
den borrowings in the data. While quite
common in evolutionary biology [76,
77], these methods are still in their
infancy in historical linguistics. Two
early approaches [70, 78] are distance-
based, and therefore do not allow the
direct identification of the characters
that conflict in the reference trees. The
first character-based approach to this
problem [79] uses maximum parsimony
to determine the characters that conflict
with an inferred family tree. Unfortu-
nately, the method has only been
tested on a very small dataset, and no
further applications are known to us.
An alternative proposal expands the
notion of perfect phylogenetic trees [10]
to the notion of perfect phylogenetic
networks [80]. The method yields direct
statements as to which characters have
been inferred as being borrowed in a
given dataset. Unfortunately, the algo-
rithm is very time-consuming, and it is
thus not feasible to apply it to larger
datasets [81].
Ancestral genome sizes
reveal the minimum
amount of lateral transfer
in microbial evolution
A more recent method for lateral gene
transfer detection in prokaryotic genomes
is the so-called minimal lateral network
approach (MLN, [82]). This method
applies the technique of gain-loss map-
ping [83–85] to presence-absence pat-
ternsofgenefamiliesinordertoinfer
patterns that are suggestive of lateral
transfer. Gain-loss mapping starts from a
given reference tree that should reflect the
vertical component of evolution as closely
as possible. With help of the reference
tree, specific gain-loss scenarios for all
gene families in the dataset are inferred. A
gain-loss scenario provides an explana-
tion of how a given character could have
evolved along the reference tree when
character evolution is modeled as a
simple process of gain and loss events.
In order to confirm the assumption that a
given character evolves in an exclusively
vertical manner, the inferred gain-loss
scenario should contain only one gain
event. If more than one gain event is
inferred, the character is judged to be
suggestive of lateral transfer (see Fig. 2 for
an example applied to linguistic data).
The crucial point of the MLN method
is to select the best gain-loss scenarios
out of the multitude of possible ones.
The key argument in biology is the
notion of ancestral genome size distri-
butions [84]: If, for example, all gene
families are assumed to originate only
once along the reference tree, this may
result in ancestral genomes that contain
much more genes than are observed
in the contemporary genomes. If, on
the other hand, one assumes that all
gene families are explained by lateral
gene transfer only, then the vertical
component of genome evolution dis-
appears, and ancestral genome sizes
become too tiny to support life. Between
those extremes there are amounts of
vertical and lateral inheritance that will
.....Insights & Perspectives J.-M. List et al.
145
Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc.
Think again
Figure 2. Illustration of the MLN method. A: Two cognate sets for “to count” in three Germanic and three Romance languages. The
English word is a known borrowing from Old French. The original reflex of Proto-Germanic tal- is still preserved in English “to tell,”
but its original meaning has shifted under the influence of the borrowing from Old French, and it is thus not listed in this sample. B: The
loss-only scenario assumes that the cognate set with reflexes of Latin originated in the root and was then lost independently in both
German and Danish. C: The two-gain scenario infers two separate origins of the cognate sets. The pattern is thus suggestive of lateral
transfer, and one lateral transfer event is inferred. This is marked by the link drawn between the two nodes where the characters first
originate. D: Combination of scenarios for both cognate sets based on the loss-only scenario in B. Note that this scenario forces us to
assume that the ancestor of the Germanic languages had two words expressing the concept “to count.” Whilethisisnotimprobableper
se, cases of inferred overwhelming amounts of synonymy are suspicious in language history. E: Combination of scenarios for both
cognate sets based on the two-gain scenario in C. This scenario is preferred by the MLN method, since the number of synonyms in the
ancestral languages is in balance with the modern languages. Note that the inference does not tell us which language is the real donor
(which is Old French). According to our model, it could be any of the three Romance languages. For this reason, the edge is drawn
between the ancestor off all languages.
J.-M. List et al. Insights & Perspectives .....
146 Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc.
Think again
bring the distribution of inferred ances-
tral genome sizes into agreement with
the attested distribution of contempo-
rary genome sizes. Those distributions
can be tested statistically, and the gain-
loss scenarios with the amount of lateral
gene transfer that best fits the data can
be determined. Having selected the best
scenarios, a rooted phylogenetic net-
work can be reconstructed. Here, multi-
ple origins of the same gene family on
different branches of the reference tree
are connected by lateral links; edges
connecting the same two nodes for
different gene families are joined to
form weighted edges [82].
How minimal lateral
networks can be applied
to linguistic data
Technically, the application of the MLN
approach to language data can be
carried out in a rather straightforward
way, by investigating presence-absence
patterns of cognate sets instead of
presence-absence patterns in gene fam-
ilies. Theoretically, however, the appli-
cation of the approach requires some
caveats: while genomes are physical
entities whose size can be directly
determined, the linguistic data consist
of samples based on meaning lists. We
can restate the genome size criterion for
scenario selection in such a way that we
prefer those scenarios in which the
number of words used to express
specific meanings does not differ much
between ancestral and contemporary
languages. However, we need to keep in
mind that new words can also shift into
the meaning slots from outside the
sample. Although parallel semantic
shift involving cognate words in differ-
ent branches of a language family is
surely much rarer than borrowing, this
has to be considered when applying the
method to linguistic data.
The MLN approach was first applied
to the well-known Comparative Indo-
European Database [52], and revealed a
rather high degree of non-tree-like
signal: 61% of all 2,346 cognate sets
in the data were found to be suggestive
of borrowing [86]. Since the study
employed a very simple top-down algo-
rithm for gain-loss mapping [84], the
inferred amount of cognate sets contra-
dicting the reference tree is surely too
high. In order to test whether more
refined techniques of gain-loss mapping
can yield more realistic results, we
applied a refined variant of the MLN
approach to a subset of 40 Indo-
European languages taken from the
IELex (dump from May 2013 kindly
provided by M. Dunn). The modified
MLN approach is implemented as part
of a freely available Python library for
quantitative tasks in historical linguis-
tics [87]. It employs weighted parsimony
for the task of gain-loss mapping [83]
and also allows for a certain proportion
of parallel evolution. A Python script
along with the data to run all analyses
can be downloaded from: https://gist.
github.com/LinguList/7475830. The ad-
vantage of the IELex is that known
borrowings are not only marked as
such, but that they are also assigned
to the cognate sets to which they would
belong, if they were not borrowings.
Thus, English mountain is clustered
with the reflexes of Vulgar Latin
montanea (derived from Latin mo¯ns)
in the Romance languages, such as,
among others, French montagne,Italian
montagna, and Spanish montan
˜a.This
gives us the possibility to test the
usefulness of the refined MLN approach.
We corrected some obvious errors in the
data, especially in some of the Slavic
languages (the whole dataset is provided
in Supplementary Material I). Excluding
1,864 words that could not be shown to
be cognate to any other word in the data,
this yielded a total of 1,190 cognate sets.
As a reference tree, we chose the one
provided by Ethnologue [88]. The choice
of this tree is for practical reasons,
since it was proposed independently of
quantitative methods, and reflects an
openly available “quasi-standard”. This
does not mean that we are unaware of
the many problems that this tree con-
tains, especially in the classification of
the subgroups.
Figure 3 shows the rooted phylo-
genetic network that the refined MLN
Figure 3. Minimal Lateral Network of 40
Indo-European languages. The size of the
nodes reflects the number of cognate sets in
each language as inferred by the MLN
approach. The links reflect the minimal
amount of lateral transfer events that is
needed to bring the distributions of synonyms
in the contemporary languages (leaves of the
tree) and the ancestral languages (internal
nodes of the tree) as closely together as
possible.
.....Insights & Perspectives J.-M. List et al.
147
Bioessays 36: 141–150, ß2013 The Authors. BioEssays Published by WILEY Periodicals, Inc.
Think again
approach reconstructed from the data.
As can be seen, the method nicely
recovers some well-known cases of
contact relations among the languages
in the sample. English, for example
shows two heavily weighted edges, one
with the ancestor of the Scandinavian
languages, and one with the ancestor of
the Romance languages, nicely reflect-
ing two of its major donors: Scandina-
vian words made their way into the
English lexicon as a result of Danish and
northern Scandinavian invasions start-
ing in the 8th–9th century [89], and Old
Norman (a northern French dialect)
came to England as a result of the
Norman conquest in 1066. Old Norman
even developed into a distinct variety
called Anglo-Norman which was spoken
in England by the higher social strata
from 12th to 15th century. The ensuing
intensive language contact results in a
boom of “French” loans, which eventu-
ally became a formative element of the
English lexicon [89]. Albanian shows
also strong connections with the ances-
tor of the Romance languages, reflecting
the large number of Latin loanwords in
the language [49].
Of the 105 cognate sets in the data
that contain known hidden borrowings,
the method identifies 76 correctly (see
the specific results in Supplementary
Material I). In total, the method iden-
tifies 369 out of 1,190 cognate sets (31%)
that do not correspond to the reference
tree. If the number of known borrowings
reflected the true amount of borrowings
in the data, and the reference tree
displayed the true vertical history of
the languages, this would mean that the
method largely overstates the amount of
lateral transfer. However, given the
uncertainty regarding the subgrouping
of the Indo-European languages that is
also reflected in the reference tree, and
the uncertainty of the cognate judg-
ments in the data, we are confident that
the results provide a good starting point
for further research that may reveal
further hidden borrowings and errone-
ous cognate judgments.
This can be exemplified by an
inspection of the specific results that
the method yields for English: Of the 32
borrowings into English [18], eight are
singletons and five have reflexes in
almost all Germanic languages in the
sample and can thus technically not be
identified by the MLN approach. Of
the remaining 19 words, 17 (89%) are
correctly identified. 17 further words are
found to be not compatible with the
reference tree, but three of these words
are known borrowings in other lan-
guages. Of the remaining 14 words, four
words (belly,narrow,dull,smoke), are
obviously erroneously coded, since they
are linked with words outside the
Germanic branch, although their deeper
etymology or the etymology of their
presumed cognates is unclear; and four
words (at,leaf,small,know) seem to be
real cases of parallel semantic develop-
ment (be it retention or innovation) with
other languages (see Supplementary
Material II). The remaining six words
(back,few,many,snake,tree,with) are
exclusively shared with the Scandina-
vian languages inside the Germanic
branch. Whether this pattern results
from innovations on the West Germanic
mainland, by which the reflexes of the
words in Frisian, German, and Dutch
were replaced, or from hitherto unno-
ticed Scandinavian influence requires
further investigation. A full list of all
words with further comments is sup-
plied in Supplementary Material II.
The modified MLN approach is
surely not perfect. It heavily relies on
the underlying data, and especially the
selection of the reference tree can have a
strong influence on the results. Further-
more, it can only recover those cases of
borrowing that occur inside a given
language family. External influences
cannot be recovered. Further research
is required in order to assess to which
degree it overestimates borrowing rates
because of its incapacity of handling
independent parallel developments.
However, it is a first step en route to
more realistic quantitative models of
language evolution, and could prove
useful for scholars working on quanti-
tative applications in historical linguis-
tics, since it not only tests the tree-
likeness of datasets but also provides
direct hints as to the characters that
cause reticulation. It can help us to
improve the quality of our datasets by
identifying possible hidden borrowings
and erroneous cognate assignments.
Conclusion and outlook
Different metaphors and models have,
over the past century or two, been
developed to describe the evolution of
languages, but realistic quantitative
models that can explain horizontal
evolutionary processes in addition to
genealogical relationships were lacking.
Since similar evolutionary processes
shaped both genomes and languages
into contemporary forms, it is possible
to apply methods that are developed to
study genome evolution to study lan-
guage evolution. Since lateral transfer
in language evolution constitutes a real
form of natural variation, phylogenetic
network approaches provide a better
means to model language evolution
than strictly bifurcating phylogenetic
trees. We strongly support the recent
attempts to strengthen the quantitative
basis of historical linguistics by building
large databases and adapting computa-
tional methods from biology. Great work
has been done in the past 10 years, and
we know that errors are unavoidable
when building large databases that
accumulate historical linguistic knowl-
edge. However, since errors are not only
unavoidable, but – in the case of
undetected borrowings – also reflect
one vivid aspect of language history, we
think it is time to rethink claims about
the major processes underlying lan-
guage evolution. Applying network
approaches in historical linguistics
can provide new insights into both the
vertical and the lateral components of
language history, and help to bring
traditional and more quantitative re-
search closer together.
Acknowledgements
This research was supported by the ERC
grants 240816 and F020515005. We
thank Søren Wichmann and two anon-
ymous reviewers for constructive com-
ments. We also thank Michael Dunn for
providing us with the Indo-European
data which we used for the analyses
presented in this study.
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