לוח שנה משולב

Understanding how social influence shapes biological processes is a central challenge in con-temporary science, essential for achieving progress in a variety of fields ranging from the or-ganization and evolution of coordinated collective action among cells, or animals, to the dy-namics of information exchange in human societies. Using an integrated experimental and theoretical approach I will address how, and why, animals exhibit highly-coordinated collective behavior. I will demonstrate new imaging technology that allows us to reconstruct (automatcally) the dynamic, time-varying networks that correspond to the visual cues employed by organisms when making movement decisions [1]. Sensory networks are shown to provide a much more accurate representation of how social influence propagates in groups, and their analysis allows us to identify, for any instant in time, the most socially-influential individuals within groups, and to predict the magnitude of complex behavioral cascades before they actually occur [2]. I will also investigate the coupling between spatial and information dynamics in groups and reveal that emergent problem solving is the predominant mechanism by which mobile groups sense, and respond to complex environmental gradients [3]. Evolutionary modeling demonstrates such ‘physical computation’ readily evolves within populations of selfish organisms, and allowing individuals to compute collectively the spatial distribution of rsources and to allocate themselves effectively among distinct, and distant, resource patches,
Without requiring information about the number, location or size of patches [4].
Finally I will reveal the critical role uninformed, or unbiased, individuals play in effecting fast and democratic consensus decision-making in collectives [5-7], and will test these predictions with experiments involving schooling fish [6] and wild baboons [8].

1) Strandburg-Peshkin, A., Twomey, C.R., Bode, N.W., Kao, A.B., Katz, Y., Ioannou, C.C., Rosenthal, S.B., Torney, C.J., Wu, H., Levin, S.A. & Couzin, I.D. (2013) Visual sensory networks and effective information transfer in animal groups, Current Biology 23(17), R709-711.
2) Rosenthal, S.B., Twomey, C.R., Hartnett, A.T., Wu, H.S. & Couzin, I.D. (2015) Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion, PNAS 112(15), 4690-4695.
3) Berdahl, A., Torney, C.J., Ioannou, C.C., Faria, J. & Couzin, I.D. (2013) Emergent sensing of complex environments by mobile animal groups, Science 339(6119) 574-576.
4) Hein, A. M., Rosenthal, S.B., Hagstron, G.I., Berdahl, A., Torney, C.J. & Couzin, I.D. (2015) The evolution of distribued sensing and collective computation in animal populations, eLife, in press.
5) Couzin, I.D., Krause, J., Franks, N.R. & Levin, S.A. (2005) Effective leadership and decision making in animal groups on the move. Nature 433, 513-516.
6) Couzin, I.D., Ioannou, C.C., Demirel, G., Gross, T., Torney, C.J., Hartnett, A., Conradt, L., Levin, S.A. & Leonard, N.E. (2011) Uninformed individuals promote democratic consensus in animal groups. Science 334(6062) 1578-1580.
7) Hartnett, A.T., Schertzer, E., Levin, S.A. & Couzin, I.D. (2015) The role of heterogeneous preference and local nonlinearity in consensus decision-making, Physical Review Letters.
8) Strandburg-Peshkin, A., Farine, D.R., Couzin, I.D. & Crofoot, M.C. (2015) Shared decision-making drives collective movement in wild baboons. Science 348(6241), 1358-1361.

Dry air intrusions (DAIs) are large-scale descending airstreams. A DAI is typically referred to as a coherent airstream in the cold sector of an extratropical cyclone. Emerging evidence suggests that DAIs are linked to severe surface wind gusts. However, there is yet no strict Lagrangian definition of DAIs, and so their climatological frequency, physical characteristics as well as their seasonal and spatial distributions are unknown. Furthermore, it is unclear how many of the DAIs occur together with a cyclone, and the dynamical interaction between DAIs and strong surface winds is not fully understood.
Here, we suggest a Lagrangian definition for DAI air parcels, namely a minimum pressure increase along a trajectory of 400 hPa in 48 hours. Based on this criterion, the open questions are addressed by: (i) a novel global Lagrangian climatology for the ECMWF ERA-Interim reanalysis dataset for the years 1979-2014; (ii) examples for the interaction between DAIs and strong surface winds, shown with composite analysis and with a case study of a high-impact cyclone, using a mesoscale regional model simulation.
We find that DAIs occur predominantly in winter, with higher occurrence frequency in the northern hemisphere. DAIs coherently descend from the upper troposphere (its stratospheric origin is small), to the mid- and low levels, where they mix with their environment and diverge. Different physical characteristics typify DAIs in the different regions and seasons, and when occurring together with a cyclone. Finally, we demonstrate the different mechanisms by which DAIs can destabilize the boundary layer and facilitate the formation of strong surface winds.