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Human visitors are associated with the unintended dispersal of weeds, seeds and pathogens across ecological communities. With the increasing popularity of nature‐based tourism, access to protected areas has increased, in turn increasing the risks of unintended dispersal of exotic species to these areas.Here, we assess the potential contribution of both international and domestic visitors travelling within New Zealand to the spread of exotic species. To get an overview of the visitors' travelling patterns across the country, we constructed visitation networks at two spatial scales—a regional scale (which is a coarse scale) and a local territorial scale (which is a finer scale).We then used a Mixed Membership Stochastic Block Model to identify characteristic groups of visitors and places based on the similarities of the visitors' travelling patterns across the country. Overall, we found that there are 10 characteristic groups of visitors travelling to 12 characteristic groups of places at the regional scale and 6 characteristic groups of visitors travelling to 6 characteristic groups of places at the territorial scale.The resulting characteristic travelling patterns of the visitors across New Zealand further allowed us to estimate the different visitor groups' likelihood to travel to protected areas. Overall, we found that some visitor groups are much more likely than others to travel to protected areas of high protection status, at both spatial scales.Synthesis and applications. Our results highlight the importance of accounting for human behaviour—that is, understanding how visitors travel to places—when assessing human‐mediated dispersal. More specifically, we illustrate how to assess the relative contribution of a potential vector dispersing exotic species based on their travelling patterns—especially in cases where the target exotic species are not yet identified or when there is limited information regarding the dispersal routes of exotic species and their potential vectors. As a result, our work offers a holistic perspective on human‐mediated dispersal of exotic species. Moreover, it provides a potential baseline against which both field biologists and practitioners can identify areas that would benefit from further investigation to better understand invasion processes in their focal systems. [ABSTRACT FROM AUTHOR]

With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. [ABSTRACT FROM AUTHOR]

In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical patterns, characterized by exponentially decaying log-variations of the weight of social ties and of individuals’ social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years. [ABSTRACT FROM AUTHOR]

Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to evaluate their merit and, thus, to help establish which mechanisms are most plausible. Here, we propose models and develop rigorous inference approaches to predict strategic decisions in dyadic social dilemmas. In particular, we use bipartite stochastic block models that incorporate information about the dilemmas faced by individuals. We show, combining these models with empirical data on strategic decisions in dyadic social dilemmas, that individual strategic decisions are to a large extent predictable, despite not being “rational.” The analysis of these models also allows us to conclude that: (i) individuals do not perceive games according their game-theoretical structure; (ii) individuals make decisions using combinations of multiple simple strategies, which our approach reveals naturally. [ABSTRACT FROM AUTHOR]