Social-network dynamics have profound consequences for biological processes such as information flow, but are notoriously difficult to measure in the wild. We used novel transceiver technology to chart association patterns across 19 days in a wild population of the New Caledonian crow—a tool-using species that may socially learn, and culturally accumulate, tool-related information. To examine the causes and consequences of changing network topology, we manipulated the environmental availability of the crows' preferred tool-extracted prey, and simulated, in silico, the diffusion of information across field-recorded time-ordered networks. Here we show that network structure responds quickly to environmental change and that novel information can potentially spread rapidly within multi-family communities, especially when tool-use opportunities are plentiful. At the same time, we report surprisingly limited social contact between neighbouring crow communities. Such scale dependence in information-flow dynamics is likely to influence the evolution and maintenance of material cultures.

The role of relatedness in structuring animal societies has attracted considerable interest. Whilst a significant number of studies have documented kin recognition in shoaling fish under laboratory conditions, there is little evidence that relatedness plays a significant role in structuring social interactions in wild populations that are characterised by fission-fusion dynamics. Previous work has tended to compare relatedness within and among entire shoals. Such an approach however, does not have the ability to detect social sub-structuring within groups, which appears to be a major factor driving the social organisation of fission-fusion animal societies. Here, we use social network analysis combined with DNA microsatellite genotyping to examine the role of relatedness in structuring social relationships in a wild population of guppies (Poecilia reticulata). Consistent with previous findings, female-female dyads formed the strongest social relationships, which were stable over time. Interestingly, we also observed significant co-occurrence of male-male interactions, which is in contrast to previous work. Although we observed social sub-structuring in the population, we found no evidence for relatedness playing a significant role in underpinning this structure. Indeed, only seven first-degree relative dyads were identified among the 180 fish genotyped, indicating that the majority of individuals do not have a first-degree relative in the population. The high genetic diversity observed in this population is indicative of a large effective population size typical of lowland guppy populations. We discuss our findings in the context of the evolution of social organisation and the mechanisms and constraints that may drive the observed patterns in wild populations.

Background: With increasing interest in animal social networks, field biologists have started exploring the use of advanced tracking technologies for mapping social encounters in free-ranging subjects. Proximity logging, which involves the use of animal-borne tags with the capacity for two-way communication, has attracted particular attention in recent years. While the basic rationale of proximity logging is straightforward, systems generate very large datasets which pose considerable challenges in terms of processing and visualisation. Technical aspects of data handling are crucial for the success of proximity-logging studies, yet are only rarely reported in full detail. Here, we describe the procedures we employed for mining the data generated by a recent deployment of a novel proximitylogging system, "Encounternet", to study social-network dynamics in tool-using New Caledonian crows.Results: Our field deployment of an Encounternet system produced some 240,000 encounter logs for 33 crows over a 19-day study period. Using this dataset, we illustrate a range of procedures, including: examination of tag reciprocity (i.e. whether both tags participating in an encounter detected the encounter and, if so, whether their records differed); filtering of data according to a predetermined signal-strength criterion (to enable analyses that focus on encounters within a particular distance range); amalgamation of temporally clustered encounter logs (to remove data artefacts and to enable robust analysis of biological patterns); and visualisation of dynamic network data as timeline plots (which can be used, among other things, to visualise the simulated diffusion of information). Conclusions:Researchers wishing to study animal social networks with proximity-logging systems should be aware of the complexities involved. Successful data analysis requires not only a sound understanding of hardware and software operation, but also bioinformatics expertise. Our paper aims to facilitate future projects by explaining in detail some of the subtleties that are easily overlooked in first-pass analyses, but are key for reaching valid biological conclusions. We hope that this work will prove useful to other researchers, especially when read in conjunction with three recently published companion papers that report aspects of system calibration and key results.

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Combining items of clothing into an outfit is a major task in fashion retail. Recommending sets of items that are compatible with a particular seed item is useful for providing users with guidance and inspiration, but is currently a manual process that requires expert stylists and is therefore not scalable or easy to personalise. We use a multilayer neural network fed by visual and textual features to learn embeddings of items in a latent style space such that compatible items of different types are embedded close to one another. We train our model using the ASOS outfits dataset, which consists of a large number of outfits created by professional stylists and which we release to the research community. Our model shows strong performance in an offline outfit compatibility prediction task. We use our model to generate outfits and for the first time in this field perform an AB test, comparing our generated outfits to those produced by a baseline model which matches appropriate product types but uses no information on style. Users approved of outfits generated by our model 21% and 34% more frequently than those generated by the baseline model for womenswear and menswear respectively.