The Formation of Collective Silk Balls in the Spider Mite Tetranychus urticae Koch

Tetranychus urticae is a phytophagous mite that forms colonies of several thousand individuals. These mites construct a common web to protect the colony. When plants become overcrowded and food resources become scarce, individuals gather at the plant apex to form a ball composed of mites and their silk threads. This ball is a structure facilitating group dispersal by wind or animal transport. Until now, no quantitative study had been done on this collective form of migration. This is the first attempt to understand the mechanisms that underlie the emergence and growth of the ball. We studied this collective behaviour under laboratory conditions on standardized infested plants. Our results show that the collective displacement and the formation of balls result from a recruitment process: by depositing silk threads on their way up to the plant apex, mites favour and amplify the recruitment toward the balls. A critical threshold (quorum response) in the cumulative flow of mites must be reached to observe the emergence of a ball. At the beginning of the balls formation, mites form an aggregate. After 24 hours, the aggregated mites are trapped inside the silk balls by the complex network of silk threads and finally die, except for recently arrived individuals. The balls are mainly composed of immature stages. Our study reconstructs the key events that lead to the formation of silk balls. They suggest that the interplay between mites' density, plant morphology and plant density lead to different modes of dispersions (individual or collective) and under what conditions populations might adopt a collective strategy rather than one that is individually oriented. Moreover, our results lead to discuss two aspects of the cooperation and altruism: the importance of Allee effects during colonization of new plants and the importance of the size of a founding group

Computational history : from big data to big simulations

The first section of this chapter gives an overview on how big data and their mathematical calculation enter in the historical discourse. It introduces the two main issues that prevent ‘big’ results from emerging so far. Firstly, the input is problematic because historical records cannot be easily and comprehensively decomposed into unambiguous fields, except for the population and taxation ones, which are rare and scattered throughout space and time till the nineteenth century. Secondly, even if we run machine-learning tools on properly structured data, big results cannot emerge until we built formal models, with explanatory and predictive powers. The second section of the chapter presents a complex network, data-driven approach to mining historical sources and supporting the perennial historical chase for truth. In the time-integrated network obtained by overlaying all records from the historians’ databases, the nodes are actors, while the links are actions. The third section explains how this tool allows historians to deal with historical data issues (e.g., source criticism, facts validation, trade-conflict-diplomacy relationships, etc.), and take advantage of automatic extraction of key narratives to formulate and test their hypotheses on the courses of history in other actions or in additional data sets. The conclusions describe the vision of how this narrative-driven analysis of historical big data can lead to the development of multiscale agent-based models and simulations to generate ensembles of counterfactual histories that would deepen our understanding of why our actual history developed the way it did and how to treasure these human experiences.Accepted versio