Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, “Analysing vocal sequences in animals”. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.

Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.

Few mammalian species produce vocalizations that are as richly structured as bird songs, and this greatly restricts the capacity for information transfer. Syntactically complex mammalian vocalizations have been previously studied only in primates, cetaceans and bats. We provide evidence of complex syntactic vocalizations in a small social mammal: the rock hyrax (Procavia capensis: Hyracoidea). We adopted three algorithms, commonly used in genetic sequence analysis and information theory, to examine the order of syllables in hyrax calls. Syntactic dialects exist, and the syntax of hyrax calls is significantly different between different regions in Israel. Call syntax difference is positively correlated to geographical distance over short distances. No correlation is found over long distances, which may reflect limited dispersal movement. These findings indicate that rich syntactic structure is more common in the vocalizations of mammalian taxa than previously thought and suggest the possibility of vocal production learning in the hyrax.

Bottlenose dolphins (Tursiops truncatus) produce many vocalisations, including whistles that are unique to the individual producing them. Such “signature whistles” play a role in individual recognition and maintaining group integrity. Previous work has shown that humans can successfully group the spectrographic representations of signature whistles according to the individual dolphins that produced them. However, attempts at using mathematical algorithms to perform a similar task have been less successful. A greater understanding of the encoding of identity information in signature whistles is important for assessing similarity of whistles and thus social influences on the development of these learned calls. We re-examined 400 signature whistles from 20 individual dolphins used in a previous study, and tested the performance of new mathematical algorithms. We compared the measure used in the original study (correlation matrix of evenly sampled frequency measurements) to one used in several previous studies (similarity matrix of time-warped whistles), and to a new algorithm based on the Parsons code, used in music retrieval databases. The Parsons code records the direction of frequency change at each time step, and is effective at capturing human perception of music. We analysed similarity matrices from each of these three techniques, as well as a random control, by unsupervised clustering using three separate techniques: k-means clustering, hierarchical clustering, and an adaptive resonance theory neural network. For each of the three clustering techniques, a seven-level Parsons algorithm provided better clustering than the correlation and dynamic time warping algorithms, and was closer to the near-perfect visual categorisations of human judges. Thus, the Parsons code captures much of the individual identity information present in signature whistles, and may prove useful in studies requiring quantification of whistle similarity.

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n-gram models, repeat distribution, and edit distance), and data generated by different stochastic 30 processes (entropy rate and n-grams). However, the string edit (Levenshtein) distance performed 31 consistently and significantly better than all other tested metrics (including entropy, Markov 32 chains, n-grams, mutual information) for all empirical datasets, despite being less commonly used 33 in the field of animal acoustic communication.

Wolves, coyotes, and other canids are members of a diverse genus of top predators of considerable conservation and management interest. Canid howls are long-range communication signals, used both for territorial defence and group cohesion. Previous studies have shown that howls can encode individual and group identity. However, no comprehensive study has investigated the nature of variation in canid howls across the wide range of species. We analysed a database of over 2000 howls recorded from 13 different canid species and subspecies. We applied a quantitative similarity measure to compare the modulation pattern in howls from different populations, and then applied an unsupervised clustering algorithm to group the howls into natural units of distinct howl types. We found that different species and subspecies showed markedly different use of howl types, indicating that howl modulation is not arbitrary, but can be used to distinguish one population from another. We give an example of the conservation importance of these findings by comparing the howls of the critically endangered red wolves to those of sympatric coyotes Canis latrans, with whom red wolves may hybridise, potentially compromising reintroduced red wolf populations. We believe that quantitative cross-species comparisons such as these can provide important understanding of the nature and use of communication in socially cooperative species, as well as support conservation and management of wolf populations.

The study of animal behavior in the wild requires the ability to locate and observe animals with the minimum disturbance to their natural behavior. This can be challenging for animals that avoid humans, are difficult to detect, or range widely between sightings. Global Positioning System (GPS) collars provide one solution but limited battery life, and the disturbance to the animal caused by capture and collaring can make this impractical in many applications. Wild wolves Canis lupus are an example of a species that is difficult to study in the wild, yet are of considerable conservation and management importance. This manuscript presents a system for accurately locating wolves using differences in the time of arrival of howl vocalizations at multiple recorders (multilateration), synchronized via GPS. This system has been deployed in Yellowstone National Park for two years and has recorded over 1200 instances of howling behavior. As most instances of howling occur at night, or when human observers are not physically present, the system provides location information that would otherwise be unavailable to researchers. The location of a vocalizing animal can, under some circumstances, be determined to within an error of approximately 20 m and at ranges up to 7 km. V