Abstract: TBA

Abstract: Considerable evidence suggests that there are generic signals that indicate whether a system is approaching a tipping point to a new state. These indicators, such as an increased auto- and cross-correlation, increased variance and increased skewness, can be derived from time series analysis of the systems state before the tipping point. An important question that is not addressed by these indicators is what the system will look like after it passed its tipping point. In this study, we propose a new method using principal component analysis, to predict the future ?post-tipping point? state of the system. We formulate a method that works on systems in which the new system state is separated from the current system state by an unstable equilibrium. This is a situation that can be observed in various ecological systems. We derived the method analytically and illustrated it with an example based on data generated using an ODE-model. For our model, the method correctly predicts which variables increase and decrease in value after the shift. We could show that it was robust for some difficult cases, such as differences in noise between variables and having variables in the system that are not part of the shift. We believe that this method is generally useful for a variety of complex systems that contain such a tipping point and is especially valuable if the knowledge of the future state can help deciding on prevention measures.

Abstract: Theoretical studies have indicated that nestedness of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we constructed a large dataset of ecological networks, including food-web, pollinator, and seed-dispersal networks, and used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks [1]. We found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. According to a theory, these results are an indication that mutualistic networks form in such a way as to enhance ecosystem stability against environmental changes or perturbations. Unlike in mutualistic networks, however, our results suggest that food-web stability decreases in response to environmental changes. Our findings enhance our understanding of the effects of environmental change on ecological communities.[1] Takemoto K, Kajihara K (2016) Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks. PLoS ONE 11(6): e0157929. doi:10.1371/journal.pone.0157929

Abstract: As is observed very often in physics, the wide variety of natural phenomena at very large or very small scales follows some simple rules that can be understood and described via formulas and a rigorous mathematical formulation.In the last 60 years ecologists have been collecting census data from a wide variety of different ecosystems, and despite the striking diversity of shapes and forms, they highlighted how deep commonalities emerge over wide scales of space.Our research focuses on the spatial distribution of plant species in an environment, and involves analytical calculations of global patterns that can be measured. Its aim is to derive from a single theory a set of predictions, and possibly anticipate new and unexpected empirical discoveries (e.g. stochastic pattern formation, quasi-cycles,...).In this talk I will give a brief overview of an approach based on a Stochastic partial differential equation. We calculated an approximate analytical solution, and, comparing it to simulated data, we checked whether it is a good estimate of the true solution. We also showed how this approach can be used to tackle real ecological problems involving species distribution and biodiversity preservation.

Abstract: TBA

Abstract: The brain has been shaped by evolution as a sophisticated information processing system capable to adapt behavioural outputs to the ever-changing ?real world? inputs in an efficient and robust manner. A key ingredient for such degree of adaptability and robustness is the peculiar brain organization, with neurons that are structurally and functionally connected through adaptive synapses to form a complex evolving architecture. The emergence of criticality in brain circuits has been proposed as an important signature of brain computation. However, assessing critical behaviour of neuronal circuits is posing severe experimental challenges. We will present most advanced neurotechnologies enabling the measurement of neuronal networks and brain circuits and discuss their advantages and limitations to investigate the emergence of patterns resulting from collective activity of neuronal populations.?

Abstract: In order to study the robustness of tissue architecture to variation in the forces that contribute to tissue shape, we rely on the 2D cell-based numerical model of epithelium formation by Farhadifar and collaborators (1), but dynamics and boundary conditions where modified, as explained in Merzouki et al (2). We aim to understand whether perturbations in mechanical properties of cells will affect the size and regularity of tissues. For example, does variation among cells in the two key parameters normalized cell contractility (?) and membrane line tension (?) affect tissue architecture? This architecture can be quantified by the distribution of shapes and areas of cells.To find out, we allow these values to vary among cells during tissue growth. Specifically, we assign values drawn from a bivariate normal distribution with a given mean (?,?) and standard deviation (?(?),?(?)) to these parameters for every newly created cell in a growing tissue. In the absence of perturbations, different values of ? and ? lead to tissue structures that fall into a small number of classes. The most prominent distinction is that between stable tissue, where most cells have a preferred shape (e.g., hexagonal), and where deviations from the preferred shape distribution lead to unfavorable energy, and an unstable tissue, where deviations from a preferred shape do not have a strong energy cost. Our preliminary observations show that within these parameter regimes, tissues react very differently to perturbations. Below we show exemplary observations for two pairs of mean values of ? and ?. In the first (?=0.04 and ?=0), cells in the tissue adopt a stable hexagonal shape in the absence of perturbations. For the other pair (?=0.12 and ?=?0.8), the tissue is unstable and cells adopt a greater variety of shapes. We vary the extent of perturbation by varying the standard deviation (?(?),?(?)) 0.05 and 0.1, and also perform control simulations without perturbations, in which the standard deviation is zero.Surprisingly, we find that the distribution of cell shapes in unstable tissues is more robust to perturbation than that of stable tissues. In addition, the perturbations affect stable and unstable tissues differently in systematic ways. Specifically, perturbation of a stable tissue creates fewer 6-sided cells and more 4- and 7-sided cells when compared with the control. We define the mean shape of cells as the average number of edges among all the cells of the tissue, and observe that this mean shape is systematically reduced in response to perturbations. In contrast, for unstable tissues, perturbations cause fewer 4-sided, 6-sided and 8-sided cells, but more 5-sided and 7-sided cells. Overall, however, these changes compensate for one another, such that the mean shape of cells does not change greatly.Currently, we are studying possible explanations for this pattern, which suggests that the soft energy constraints of unstable tissues may convey an advantage in allowing cells to conserve their shape distribution to a greater extent. We are also studying potential molecular factors that could explain this pattern. In our next steps, we will explore the robustness of other phenotypes, such as cell area and cell regularity.References1 - Reza Farhadifar, Jens Christian R?per, Benoit Aigouy, Suzanne Eaton, and Frank J?licher. The influence of cell mechanics, cell-cell interactions, and proliferation on epithelial packing. Current biology: CB, 17 (24): 2095?104, (2007).2 - Aziza Merzouki, Orestis Malaspinas and Bastien Chopard. The properties of a cell-based numerical model of epithelium under stretching constraints. Soft Matter, in press, (2016).