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Master in Comunicazione delle Scienze

Master in Comunicazione delle Scienze | Statistical Physics of Ecological Systems | Scoop.it

Il master in Comunicazione delle Scienze dell’Università di Padova è un corso annuale interdisciplinare per la formazione professionale dei comunicatori al pubblico della scienza e della tecnologia con i vari media.

Le attività formative comprendono lezioni, laboratori, esercitazioni e seminari, tenuti nei giorni di venerdì e sabato, fra gennaio e novembre 2016, e un tirocinio professionalizzante di 200 ore presso aziende, enti e istituzioni con cui esistono convenzioni.

Docenti universitari, professionisti e specialisti nei vari campi della comunicazione, con notevoli esperienze operative, guideranno lo studio nelle seguenti aree tematiche:

• Comunicazione delle scienze e della tecnica e il loro linguaggio

• Temi attuali della ricerca scientifica e tecnologica

• Strumenti e metodi della comunicazione delle scienze e della tecnica

È possibile frequentare anche il corso singolo Social Network e Comunicazione Digitale. Giornalismo scritto, radiofonico, televisivo e on-line. Comunicazione istituzionale e d’impresa. Editoria tradizionale e multimediale

 

Per l’ammissione si richiede almeno una laurea triennale, o di vecchio ordinamento, in qualsiasi ambito disciplinare scientifico, tecnico o umanistico. Il superamento del corso dà diritto a 60 crediti. 

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Systematic review of current efforts to quantify the impacts of climate change on undernutrition

The World Health Organization and the Intergovernmental Panel for Climate Change propose undernutrition as the most significant impact of climate change on child health. The question then arises: Where does the empirical evidence to back this claim come from? Current evidence for the impacts of climate on childhood undernutrition draws on a limited number of heterogeneous studies with methodological limitations and is based predominantly on secondary data. Establishing and validating causal pathways among complex confounding factors remain the main challenge in quantifying the climate-attributable fraction of undernutrition. Systematically generating evidence from long-term, high-quality primary data on a range of factors (agricultural, environmental, socioeconomic, and health) at the household level is critical for designing adaptation strategies, particularly for subsistence farmers.

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Critical waves and the length problem of biology

This paper invokes physical principles to answer the question of why pure reaction–diffusion ideas do not produce a satisfactory explanation of biological growth and form. Two ideas have been missing. One is that oscillation is necessary to achieve the necessary design stability and plasticity. The other is that the system must be tuned to criticality to stabilize the propagation velocity, thus enabling clocks to function as meter sticks. The larger significance is twofold: First, a fundamental piece of the machinery of life is probably invisible to present-day biochemical methods because they are too slow. Second, the simplicity of growth and form identified a century ago by D'Arcy Thompson is probably a symptom of biological engineering strategies, not primitive law.

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Spectrum of controlling and observing complex networks : Nature Physics : Nature Publishing Group

The complex interactions inherent in real-world networks grant us precise system control via manipulation of a subset of nodes. It turns out that the extent to which we can exercise this control depends sensitively on the number of nodes perturbed.
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Recent studies have made important advances in identifying sensor or driver nodes, through which we can observe or control a complex system. But the observational uncertainty induced by measurement noise and the energy required for control continue to be significant challenges in practical applications. Here we show that the variability of control energy and observational uncertainty for different directions of the state space depend strongly on the number of driver nodes. In particular, we find that if all nodes are directly driven, control is energetically feasible, as the maximum energy increases sublinearly with the system size. If, however, we aim to control a system through a single node, control in some directions is energetically prohibitive, increasing exponentially with the system size. For the cases in between, the maximum energy decays exponentially when the number of driver nodes increases. We validate our findings in several model and real networks, arriving at a series of fundamental laws to describe the control energy that together deepen our understanding of complex systems.

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Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

Relation between stability and resilience determines the performance of early warning signals under different environmental drivers | Statistical Physics of Ecological Systems | Scoop.it
Alternative stable states and critical transitions are widespread in nature and can have profound consequences for conservation, climate changes, and human health. Our current toolbox of early warning signals before critical transitions has seen both successes and failures. Understanding the limitations of these indicators is crucial for application in real-world scenarios. In this study, we explored the population dynamics of laboratory yeast under different forms of environmental deterioration. We found that the performance of early warning signals under different environmental drivers is determined by the underlying relation between stability and resilience. This work presents a framework to evaluate the utility of early warning signals, and it sets a foundation for further studies on how dynamical systems respond to environmental changes.
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Predicting global community properties from uncertain estimates of interaction strengths

Predicting global community properties from uncertain estimates of interaction strengths | Statistical Physics of Ecological Systems | Scoop.it

The community matrix measures the direct effect of species on each other in an ecological community. It can be used to determine whether a system is stable (returns to equilibrium after small perturbations of the population abundances), reactive (perturbations are initially amplified before damping out), and to determine the response of any individual species to perturbations of environmental parameters. However, several studies show that small errors in estimating the entries of the community matrix translate into large errors in predicting individual species responses. Here, we ask whether there are properties of complex communities one can still predict using only a crude, order-of-magnitude estimate of the community matrix entries. Using empirical data, randomly generated community matrices, and those generated by the Allometric Trophic Network model, we show that the stability and reactivity properties of systems can be predicted with good accuracy. We also provide theoretical insight into when and why our crude approximations are expected to yield an accurate description of communities. Our results indicate that even rough estimates of interaction strengths can be useful for assessing global properties of large systems.

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What Can Interaction Webs Tell Us About Species Roles?

What Can Interaction Webs Tell Us About Species Roles? | Statistical Physics of Ecological Systems | Scoop.it
Author Summary Ecological interactions are highly diverse even when considering a single species: the species might feed on a first, disperse the seeds of a second, and pollinate a third. Here we extend the group model, a method for identifying broad patterns of interaction across a food web, to networks which contain multiple types of interactions. Using this new method, we ask whether the traditional approach of building a network for each type of interaction (food webs for consumption, pol
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2nd Conference on Physics of Biological and Complex Systems

2nd Conference on Physics of Biological and Complex Systems | Statistical Physics of Ecological Systems | Scoop.it

*2nd Third Infinity conference, 14th - 16th of October 2015, Goettingen, Germany - Call for Abstracts - deadline 14th August 2015*

A conference organized by the PhD students of the International Max Planck Research School for Physics of Biological and Complex Systems.
This conference aims to bring together young researchers and leading scientists working on complex systems from the three fundamental perspectives: theory, experiments and simulations.

http://www.thirdinfinity.mpg.de/ 


Via Complexity Digest
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Effect of Localization on the Stability of Mutualistic Ecological Networks

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
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The relationships between the core-periphery architecture of the species interaction network and the mechanisms ensuring the stability in mutualistic ecological communities are still unclear. In particular, most studies have focused their attention on asymptotic resilience or persistence, neglecting how perturbations propagate through the system. Here we develop a theoretical framework to evaluate the relationship between architecture of the interaction networks and the impact of perturbations by studying localization, a measure describing the ability of the perturbation to propagate through the network. We show that mutualistic ecological communities are localized, and localization reduces perturbation propagation and attenuates its impact on species abundance. Localization depends on the topology of the interaction networks, and it positively correlates with the variance of the weighted degree distribution, a signature of the network topological hetereogenity. Our results provide a different perspective on the interplay between the architecture of interaction networks in mutualistic communities and their stability.

 
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Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree–herbivore networks.

Nestedness and modularity are measures of ecological networks whose causative effects are little understood. We analyzed antagonistic plant–herbivore bipartite networks using common gardens in two contrasting environments comprised of aspen trees with differing evolutionary histories of defence against herbivores. These networks were tightly connected owing to a high level of specialization of arthropod herbivores that spend a large proportion of the life cycle on aspen. The gardens were separated by ten degrees of latitude with resultant differences in abiotic conditions. We evaluated network metrics and reported similar connectance between gardens but greater numbers of links per species in the northern common garden. Interaction matrices revealed clear nestedness, indicating subsetting of the bipartite interactions into specialist divisions, in both the environmental and evolutionary aspen groups, although nestedness values were only significant in the northern garden. Variation in plant vulnerability, measured as the frequency of herbivore specialization in the aspen population, was significantly partitioned by environment (common garden) but not by evolutionary origin of the aspens. Significant values of modularity were observed in all network matrices. Trait-matching indicated that growth traits, leaf morphology, and phenolic metabolites affected modular structure in both the garden and evolutionary groups, whereas extra-floral nectaries had little influence. Further examination of module configuration revealed that plant vulnerability explained considerable variance in web structure. The contrasting conditions between the two gardens resulted in bottom-up effects of the environment, which most strongly influenced the overall network architecture, however, the aspen groups with dissimilar evolutionary history also showed contrasting degrees of nestedness and modularity. Our research therefore shows that, while evolution does affect the structure of aspen–herbivore bipartite networks, the role of environmental variations is a dominant constraint.

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Diversity improves performance in excitable networks

As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. Nonetheless, the behavior of the whole network can outperform all subgroups. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities.
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Statistical physics of neural systems with non-additive dendritic coupling

How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such non-additive dendritic processing on single neuron responses and the performance of associative memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.
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Diversity waves in collapse-driven population dynamics

Populations of species in ecosystems are often constrained by availability of resources within their environment. In effect this means that a growth of one population needs to be balanced by comparable reduction in populations of others. In neutral models of biodiversity all populations are assumed to change incrementally due to stochastic births and deaths of individuals. Here we propose and model another redistribution mechanism driven by abrupt and severe collapses of the entire population of a single species freeing up resources for the remaining ones. This mechanism may be relevant e.g. for communities of bacteria, with strain-specific collapses caused e.g. by invading bacteriophages, or for other ecosystems where infectious diseases play an important role. The emergent dynamics of our system is cyclic ``diversity waves'' triggered by collapses of globally dominating populations. The population diversity peaks at the beginning of each wave and exponentially decreases afterwards. Species abundances are characterized by a bimodal time-aggregated distribution with the lower peak formed by populations of recently collapsed or newly introduced species, while the upper peak - species that has not yet collapsed in the current wave. In most waves both upper and lower peaks are composed of several smaller peaks. This self-organized hierarchical peak structure has a long-term memory transmitted across several waves. It gives rise to a scale-free tail of the time-aggregated population distribution with a universal exponent of 1.7. We show that diversity wave dynamics is robust with respect to variations in the rules of our model such as diffusion between multiple environments, species-specific growth and extinction rates, and bet-hedging strategies.

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Toward major evolutionary transitions theory 2.0

The impressive body of work on the major evolutionary transitions in the last 20 y calls for a reconstruction of the theory although a 2D account (evolution of informational systems and transitions in individuality) remains. Significant advances include the concept of fraternal and egalitarian transitions (lower-level units like and unlike, respectively). Multilevel selection, first without, then with, the collectives in focus is an important explanatory mechanism. Transitions are decomposed into phases of origin, maintenance, and transformation (i.e., further evolution) of the higher level units, which helps reduce the number of transitions in the revised list by two so that it is less top-heavy. After the transition, units show strong cooperation and very limited realized conflict. The origins of cells, the emergence of the genetic code and translation, the evolution of the eukaryotic cell, multicellularity, and the origin of human groups with language are reconsidered in some detail in the light of new data and considerations. Arguments are given why sex is not in the revised list as a separate transition. Some of the transitions can be recursive (e.g., plastids, multicellularity) or limited (transitions that share the usual features of major transitions without a massive phylogenetic impact, such as the micro- and macronuclei in ciliates). During transitions, new units of reproduction emerge, and establishment of such units requires high fidelity of reproduction (as opposed to mere replication).

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Complex dynamics of synergistic coinfections on realistically clustered networks

Concurrent infection with multiple pathogens is an important factor for human disease. For example, rates of Streptococcus pneumoniae carriage (a leading cause of pneumonia) in children under five years can exceed 80%, and coinfection with other respiratory infections (e.g., influenza) can increase mortality drastically; despite this, examination of interacting coinfections on realistic human contact structures remains an understudied problem in epidemiology and network science. Here we show that clustering of contacts, which usually hinders disease spread, can speed up spread of both diseases by keeping synergistic infections together and that a microscopic change in transmission rates can cause a macroscopic change in expected epidemic size, such that clustered networks can sustain diseases that would otherwise die in random networks.
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Adaptation to sensory input tunes visual cortex to criticality

Adaptation to sensory input tunes visual cortex to criticality | Statistical Physics of Ecological Systems | Scoop.it
Sensory nervous systems adapt to their environment[mdash]a mechanism thought to ensure network dynamics remain critical. Visual cortex experiments show that adaptation maintains criticality even as sensory input drives the system away from this regime.
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A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing1, 2, 3. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex4, 5, 6, 7, 8, 9, 10, but the impact of sensory input on these dynamics is largely unknown. Thus, the foundations of this hypothesis—the self-organization process and how it manifests during strong sensory input—remain unstudied experimentally. Here we show in visual cortex and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality4, 11. Our findings establish sensory adaptation as a self-organizing mechanism that maintains criticality in visual cortex during sensory information processing.

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Analysis of a non-autonomous mutualism model driven by Levy jumps

This article is concerned with a mutualism ecological model with Levy noise. The local existence and uniqueness of a positive solution are obtained with positive initial value, and the asymptotic behavior to the problem is studied. Moreover, we show that the solution is stochastically bounded and stochastic permanence. The sufficient conditions for the system to be extinct are given and the condition for the system to be persistent are also established.

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Manage water in a green way

Reliance on “hard,” human-engineered structures—“gray” infrastructure—has been the conventional way to manage water needs for economic development. But building dams, piping water, and constructing protective barriers is capital intensive and may address only a few water problems (1). Gray infrastructure often damages or eliminates biophysical processes necessary to sustain people, ecosystems and habitats, and livelihoods. Consequently, there is renewed focus on “green” infrastructure, which can be more flexible and cost effective for providing benefits besides water provision. Supplementing or integrating gray infrastructure with biophysical systems is critical to meeting current and future water needs. Gray and green infrastructures combined are synergistic and can have superior results to one or the other.

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Predicting the stability of large structured food webs

The stability of ecological systems has been a long-standing focus of ecology. Recently, tools from random matrix theory have identified the main drivers of stability in ecological communities whose network structure is random. However, empirical food webs differ greatly from random graphs. For example, their degree distribution is broader, they contain few trophic cycles, and they are almost interval. Here we derive an approximation for the stability of food webs whose structure is generated by the cascade model, in which ‘larger’ species consume ‘smaller’ ones. We predict the stability of these food webs with great accuracy, and our approximation also works well for food webs whose structure is determined empirically or by the niche model. We find that intervality and broad degree distributions tend to stabilize food webs, and that average interaction strength has little influence on stability, compared with the effect of variance and correlation.

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Human domination of the biosphere: Rapid discharge of the earth-space battery foretells the future of humankind

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Earth is a chemical battery where, over evolutionary time with a trickle-charge of photosynthesis using solar energy, billions of tons of living biomass were stored in forests and other ecosystems and in vast reserves of fossil fuels. In just the last few hundred years, humans extracted exploitable energy from these living and fossilized biomass fuels to build the modern industrial-technological-informational economy, to grow our population to more than 7 billion, and to transform the biogeochemical cycles and biodiversity of the earth. This rapid discharge of the earth’s store of organic energy fuels the human domination of the biosphere, including conversion of natural habitats to agricultural fields and the resulting loss of native species, emission of carbon dioxide and the resulting climate and sea level change, and use of supplemental nuclear, hydro, wind, and solar energy sources. The laws of thermodynamics governing the trickle-charge and rapid discharge of the earth’s battery are universal and absolute; the earth is only temporarily poised a quantifiable distance from the thermodynamic equilibrium of outer space. Although this distance from equilibrium is comprised of all energy types, most critical for humans is the store of living biomass. With the rapid depletion of this chemical energy, the earth is shifting back toward the inhospitable equilibrium of outer space with fundamental ramifications for the biosphere and humanity. Because there is no substitute or replacement energy for living biomass, the remaining distance from equilibrium that will be required to support human life is unknown

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PNAS: The butterfly plant arms-race escalated by gene and genome duplications (2015)

PNAS: The butterfly plant arms-race escalated by gene and genome duplications (2015) | Statistical Physics of Ecological Systems | Scoop.it

Coevolutionary interactions are thought to have spurred the evolution of key innovations and driven the diversification of much of life on Earth. However, the genetic and evolutionary basis of the innovations that facilitate such interactions remains poorly understood. We examined the coevolutionary interactions between plants (Brassicales) and butterflies (Pieridae), and uncovered evidence for an escalating evolutionary arms-race. Although gradual changes in trait complexity appear to have been facilitated by allelic turnover, key innovations are associated with gene and genome duplications. Furthermore, we show that the origins of both chemical defenses and of molecular counter adaptations were associated with shifts in diversification rates during the arms-race. These findings provide an important connection between the origins of biodiversity, coevolution, and the role of gene and genome duplications as a substrate for novel traits.


See also blog post https://decodingscience.missouri.edu/2015/06/22/scientists-uncover-how-caterpillars-created-condiments/


Via Kamoun Lab @ TSL
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Marcelo Errera's curator insight, July 30, 9:23 AM

That's an interesting study. How diversity was (and still is) built over time is one of the greatest challenge in evolutionary biology.

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Stochastic game dynamics under demographic fluctuations

This contribution breaks with the tradition to restrict stochastic evolutionary game dynamics to populations of constant size and introduces a theoretical framework to investigate relevant and natural changes arising in populations that vary in size according to fitness—a feature common to many real biological systems. Explicitly including ecological variation can result in significant effects on the stochastic evolutionary trajectories while providing a transparent link to the established, deterministic Lotka–Volterra systems.
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Multiway spectral community detection in networks

One of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity. Of the many maximization techniques that have been used in this context, some of the most conceptually attractive are the spectral methods, which are based on the eigenvectors of the modularity matrix. Spectral algorithms have, however, been limited by and large to the division of networks into only two or three communities, with divisions into more than three being achieved by repeated two-way division. Here we present a spectral algorithm that can directly divide a network into any number of communities. The algorithm makes use of a mapping from modularity maximization to a vector partitioning problem, combined with a fast heuristic for vector partitioning. We compare the performance of this spectral algorithm with previous approaches and find it to give superior results, particularly in cases where community sizes are unbalanced. We also give demonstrative applications of the algorithm to two real-world networks and find that it produces results in good agreement with expectations for the networks studied.
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The geometry of coexistence in large ecosystems - does network architecture has a role in sustaining biodiversity?

The role of interactions in shaping the interplay between the stability of an ecosystem and its biodiversity is still not well understood. We introduce a geometrical approach, that lends itself to both analytic and numerical analyses, for studying the domain of interaction parameters that results in stable coexistence. We find the remarkable result that just a few attributes of the interactions are responsible for stable coexistence in large random ecosystems. We analyze more than 100 empirical networks and find that their architecture generally has a limited effect on in sustaining biodiversity.

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Metacommunities in Dynamic Landscapes

Predictions from theory, field data, and experiments have shown that high landscape connectivity promotes higher species richness than low connectivity. However, examples demonstrating high diversity in low connected landscapes also exist. Here we describe the many factors that drive landscape connectivity at different spatiotemporal scales by varying the amplitude and frequency of changes in the dispersal radius of spatial networks. We found that the fluctuations of landscape connectivity support metacommunities with higher species richness than static landscapes. Our results also show a dispersal radius threshold below which species richness drops dramatically in static landscapes. Such a threshold is not observed in dynamic landscapes for a broad range of amplitude and frequency values determining landscape connectivity. We conclude that merging amplitude and frequency as drivers of landscape connectivity together with patch dynamics into metacommunity theory can provide new testable predictions about species diversity in rapidly changing landscapes.

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