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The brown marmorated stink bug (Halyomorpha halys) is one of the main insect pest species causing economic damage to several agricultural commodities worldwide and one of the worst threats to tree fruit crops in northern Italy, especially in the Emilia-Romagna region. Previous efforts in implementing H. halys surveillance at the regional level were mainly focused on studying the H. halys phenology, but they were not designed to provide a public service. In this paper, we propose a data-driven approach to support the application of Integrated Pest Management strategies against H. halys. The proposal is based on the experience of a three-year project in which a network of monitoring traps has been deployed throughout the whole Emilia-Romagna region and a data platform has been implemented to enable the real-time tracking of H. halys occurrence and distribution, integrating these information with multiple data sources, and analytical capabilities through a public website. Besides the real-time pest surveillance, the data platform allowed us to increase our understanding about H. halys seasonal invasion dynamics and the main factors contributing to its spread. The results will help individual growers in protecting their crops and the whole region in promoting more efficient usage of insecticides and more sustainable and healthy agricultural productions.

The brown marmorated stink bug Halyomorpha halys is an invasive agricultural pest with a worldwide distribution. Classical biological control has been identified as the most promising method to reduce the populations of H. halys. Adventive populations of two candidates for releases, Trissolcus japonicus and Trissolcus mitsukurii, have recently been detected in Europe. To assess their distribution and abundance, a large-scale survey was performed. From May to September 2019, a wide area covering northern Italy and parts of Switzerland was surveyed, highlighting the expanding distribution of both Tr. japonicus and Tr. mitsukurii. Within four years after their first detection in Europe, both species have rapidly spread into all types of habitats where H. halys is present, showing a wide distribution and continuous expansion. Both exotic Trissolcus showed high levels of parasitism rate towards H. halys, while parasitization of non-target species was a rare event. The generalist Anastatus bifasciatus was the predominant native parasitoid of H. halys, while the emergence of native scelionids from H. halys eggs was rarely observed. The presence of the hyperparasitoid Acroclisoides sinicus was also recorded. This study provided fundamental data that supported the development of the first inoculative release program of Tr. japonicus in Europe.

Abstract Networks to distribute goods, from raw materials to food and medicines, are the backbone of a functioning economy. They are shaped by several supply relations connecting manufacturers, distributors, and final buyers worldwide. We present a network-based model to describe the mechanisms underlying the emergence and growth of distribution networks. In our model, firms consider two practices when establishing new supply relations: centralization, the tendency to choose highly connected partners, and multi-sourcing, the preference for multiple suppliers. Centralization enhances network efficiency by leveraging short distribution paths; multi-sourcing fosters resilience by providing multiple distribution paths connecting final buyers to the manufacturer. We validate the proposed model using data on drug shipments in the US. Drawing on these data, we reconstruct 22 nationwide pharmaceutical distribution networks. We demonstrate that the proposed model successfully replicates several structural features of the empirical networks, including their out-degree and path length distributions as well as their resilience and efficiency properties. These findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures.

Abstract Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in five communities. We then show that these relations correspond to the ones reported by the individuals themselves. Additionally, using inferred relations, we study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. Finally, we study group cohesion in the analyzed communities by evaluating triad statistics in the reconstructed signed network.

Abstract High skill labour is an important factor underpinning the competitive advantage of modern economies. Therefore, attracting and retaining scientists has become a major concern for migration policy. In this work, we study the migration of scientists on a global scale, by combining two large data sets covering the publications of 3.5 million scientists over 60 years. We analyse their geographical distances moved for a new affiliation and their age when moving, this way reconstructing their geographical “career paths”. These paths are used to derive the world network of scientists’ mobility between cities and to analyse its topological properties. We further develop and calibrate an agent-based model, such that it reproduces the empirical findings both at the level of scientists and of the global network. Our model takes into account that the academic hiring process is largely demand-driven and demonstrates that the probability of scientists to relocate decreases both with age and with distance. Our results allow interpreting the model assumptions as micro-based decision rules that can explain the observed mobility patterns of scientists.

Abstract The mobility of scientists between different universities and countries is important to foster knowledge exchange. At the same time, the potential mobility is restricted by geographic and institutional constraints, which leads to temporal correlations in the career trajectories of scientists. To quantify this effect, we extract 3.5 million career trajectories of scientists from two large scale bibliographic data sets and analyze them applying a novel method of higher-order networks. We study the effect of temporal correlations at three different levels of aggregation, universities, cities and countries. We find strong evidence for such correlations for the top 100 universities, i.e. scientists move likely between specific institutions. These correlations also exist at the level of countries, but cannot be found for cities. Our results allow to draw conclusions about the institutional path dependence of scientific careers and the efficiency of mobility programs.

Abstract We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22,000 R&D alliances between 14,500 firms, and science, specifically 300,000 co-authorship relations between 95,000 scientists. Considering the different domains of the data sets, we address two questions: (a) to what extent do the collaboration networks reconstructed from the data share common structural features, and (b) can their structure be reproduced by the same agent-based model. In our data-driven modeling approach we use aggregated network data to calibrate the probabilities at which agents establish collaborations with either newcomers or established agents. The model is then validated by its ability to reproduce network features not used for calibration, including distributions of degrees, path lengths, local clustering coefficients and sizes of disconnected components. Emphasis is put on comparing domains, but also sub-domains (economic sectors, scientific specializations). Interpreting the link probabilities as strategies for link formation, we find that in R&D collaborations newcomers prefer links with established agents, while in co-authorship relations newcomers prefer links with other newcomers. Our results shed new light on the long-standing question about the role of endogenous and exogenous factors (i.e., different information available to the initiator of a collaboration) in network formation.

We quantify a social organization’s potentiality, that is, its ability to attain different configurations. The organization is represented as a network in which nodes correspond to individuals and (multi-)edges to their multiple interactions. Attainable configurations are treated as realizations from a network ensemble. To have the ability to encode interaction preferences, we choose the generalized hypergeometric ensemble of random graphs, which is described by a closed-form probability distribution. From this distribution we calculate Shannon entropy as a measure of potentiality. This allows us to compare different organizations as well as different stages in the development of a given organization. The feasibility of the approach is demonstrated using data from three empirical and two synthetic systems.