Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers

Marmosets, with their highly social nature and complex vocal communication system, are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalisations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalisations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalisations of up to 18 marmosets. We optimised the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex, and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21% – 94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalisations but also for analysing vocalisations and tracking vocal learning trajectories of other species

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%–94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species

A convergent interaction engine: vocal communication among marmoset monkeys

To understand the primate origins of the human interaction engine, it is worthwhile to focus not only on great apes but also on callitrichid monkeys (marmosets and tamarins). Like humans, but unlike great apes, callitrichids are cooperative breeders, and thus habitually engage in coordinated joint actions, for instance when an infant is handed over from one group member to another. We first explore the hypothesis that these habitual cooperative interactions, the marmoset interactional ethology, are supported by the same key elements as found in the human interaction engine: mutual gaze (during joint action), turn-taking, volubility, as well as group-wide prosociality and trust. Marmosets show clear evidence of these features. We next examine the prediction that, if such an interaction engine can indeed give rise to more flexible communication, callitrichids may also possess elaborate communicative skills. A review of marmoset vocal communication confirms unusual abilities in these small primates: high volubility and large vocal repertoires, vocal learning and babbling in immatures, and voluntary usage and control. We end by discussing how the adoption of cooperative breeding during human evolution may have catalysed language evolution by adding these convergent consequences to the great ape-like cognitive system of our hominin ancestors. This article is part of the theme issue ‘Revisiting the human ‘interaction engine’: comparative approaches to social action coordination’

Use of nanomaterials in the pretreatment of water samples for environmental analysis

The challenge of providing clean drinking water is of enormous relevance in today’s human civilization, being essential for human consumption, but also for agriculture, livestock and several industrial applications. In addition to remediation strategies, the accurate monitoring of pollutants in water sup-plies, which most of the times are present at low concentrations, is a critical challenge. The usual low concentration of target analytes, the presence of in-terferents and the incompatibility of the sample matrix with instrumental techniques and detectors are the main reasons that renders sample preparation a relevant part of environmental monitoring strategies. The discovery and ap-plication of new nanomaterials allowed improvements on the pretreatment of water samples, with benefits in terms of speed, reliability and sensitivity in analysis. In this chapter, the use of nanomaterials in solid-phase extraction (SPE) protocols for water samples pretreatment for environmental monitoring is addressed. The most used nanomaterials, including metallic nanoparticles, metal organic frameworks, molecularly imprinted polymers, carbon-based nanomaterials, silica-based nanoparticles and nanocomposites are described, and their applications and advantages overviewed. Main gaps are identified and new directions on the field are suggested.publishe

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%-94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species.ISSN:1742-5689ISSN:1742-566

Discussion of wildlife trade before and during the COVID-19 pandemic in professional opinion pieces and scientific articles

Wildlife trade is a multi-billion-dollar sector that impacts a wide range of species, and thus is of significant research and conservation interest. Wildlife trade has also become a prominent topic in the public-facing media, where coverage has intensified following the outbreak of the global COVID-19 pandemic due to the potential connection between wildlife trade and the origin of the SARS Cov2 virus. Given the importance of the media in shaping public understanding and discourse of complex topics such as wildlife trade, this could impact the implementation of and public support for policy decisions. In this study, we followed a standardised protocol to extract wildlife trade-related discussion from 285 professional opinion pieces (NGO reports or articles in conservation-themed forums) and 107 scientific articles published in two time periods: “pre-COVID” (June 1–December 31, 2019) and “during-COVID” (January 1–May 31, 2020). We compared opinion pieces and scientific articles across the two time periods and to each other to investigate potential differences in the presentation of wildlife trade and associated speakers. We found a shift in the way that wildlife trade was discussed in professional opinion pieces between the periods, in that the discussion became less specific in terms of defining the legality and purpose of trade, and the animal groups involved in the “during-COVID” period. The generalised framing of wildlife trade in our dataset also coincided with an increased discussion of highly generalised management strategies, such as blanket bans on wildlife trade. We also found that publications included more quotes from researchers in the “during-COVID” period. In both professional opinion pieces and scientific articles, we found that quotations or research were often from speakers whose affiliation region was different to the geographic range of the trade they were speaking about. This highlights the importance of incorporating local knowledge and considering the diversity of speakers and interviewees in both research and the public-facing media about the wildlife trade