Statistical Physics of Ecological Systems
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CCS2018 Satellite. ReACT#3: Robustness, Adaptability and Critical Transitions in Living Systems #3.

After the success of the Satellite at ECCS 2014 and CCS 2016, we would like to propose a follow-up Satellite always focusing on Robustness, Adaptability and Critical Transitions in Living Systems

Living systems are characterized by the recurrent emergence of patterns: power-laws distributions, long-range correlations and structured self-organization in living matter are the norm, rather than the exception. All these features are also typical of thermo-dynamical systems poised near a critical point. The great lesson from physics is that criticality can emerge as a collective behaviour in a many-body system with simple (e.g. pairwise) interactions and its characteristics depend only on few details like the dimensionality or symmetries.

In a statistical-mechanics approach, it is fundamental to determine the order parameter, which characterizes the different system phases. This is a crucial step to obtain the key ingredients needed to formulate a modeling framework, so as to obtain a better understanding of the system's macroscopic behaviour. However, the understating of biological/social systems needs more than a mere generalization of the standard statistical mechanics approach.

One of the most striking feature of living systems is that they are structured as evolving systems were interactions can turn-on or off, as well as strengthening and weakening, reconfiguring the system connectivity. Thus, by rearranging both the structural and functional topology, living interacting systems may demonstrate unique evolvability, scalability and adaptability properties.

It is of crucial importance to make further steps in the understanding of the main properties that simultaneously confer to these systems high level of both adaptability and robustness. If we can “learn” from evolution, then we would be able to both better manage/supervise these systems and also design more optimal and sustainable new systems.

Relevance to the conference main tracks

1. Foundations of Complex Systems* (Complex networks, self-organization, nonlinear dynamics, statistical physics, mathematical modeling, simulation)

2. Biological Complexity* (Biological networks, system biology, evolution, natural science, medicine and physiology)
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Adaptive metabolic strategies explain diauxic shifts and promote species coexistence

Competitive systems are most commonly described mathematically using MacArthur's consumer-resource model, leading to the "competitive exclusion principle" which limits the number of coexisting competing species to the number of available resources. Nevertheless, several empirical evidences - in particular bacterial community cultures - show that this principle is violated in real ecosystems. Another experimental evidence that cannot be explained in this framework is the existence of diauxic (or polyauxic) shifts in microbial growth curves: bacteria consume resources sequentially, using first the one that ensures the highest growth rate and then, after a lag phase, they start growing slower using the second one. By introducing adaptive metabolic strategies whose dynamics tends to maximize species' relative fitness, we are able to explain both these empirical evidences, thus setting the paradigm for adaptive consumer-resource models.
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Universal migration predicts human movements under climate change

Universal migration predicts human movements under climate change | Statistical Physics of Ecological Systems | Scoop.it
Climate change is expected to displace millions of people through impacts like sea level rise, crop failures, and more frequent extreme weather. Yet scientists still cannot predict where these expected climate-induced migrants are likely to go in the coming decades. A new study, published today in Environmental Research Letters, seeks to address this nee
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Warnings and caveats in brain controllability 

Warnings and caveats in brain controllability  | Statistical Physics of Ecological Systems | Scoop.it
A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their “controllability”, drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain net- works provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework.
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Altruism in a volatile world

Altruism in a volatile world | Statistical Physics of Ecological Systems | Scoop.it
The evolution of altruism—costly self-sacrifice in the service of others—has puzzled biologists1 since The Origin of Species. For half a century, attempts to understand altruism have developed around the concept that altruists may help relatives to have extra offspring in order to spread shared genes2. This theory—known as inclusive fitness—is founded on a simple inequality termed Hamilton’s rule2. However, explanations of altruism have typically not considered the stochasticity of natural environments, which will not necessarily favour genotypes that produce the greatest average reproductive success3,4. Moreover, empirical data across many taxa reveal associations between altruism and environmental stochasticity5,6,7,8, a pattern not predicted by standard interpretations of Hamilton’s rule. Here we derive Hamilton’s rule with explicit stochasticity, leading to new predictions about the evolution of altruism. We show that altruists can increase the long-term success of their genotype by reducing the temporal variability in the number of offspring produced by their relatives. Consequently, costly altruism can evolve even if it has a net negative effect on the average reproductive success of related recipients. The selective pressure on volatility-suppressing altruism is proportional to the coefficient of variation in population fitness, and is therefore diminished by its own success. Our results formalize the hitherto elusive link between bet-hedging and altruism4,9,10,11, and reveal missing fitness effects in the evolution of animal societies.
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Natural selection and the predictability of evolution in Timema stick insects

Evolution results from expected effects, such as selection driving alleles toward fixation, and stochastic effects, such as unusual environmental variation and genetic drift. To determine the potential to predict evolutionary change, Nosil et al. examined three naturally occurring morphs of stick insects (see the Perspective by Reznik and Travis). They wanted to determine which selective parameters could be used to foresee changes, despite varying environmental conditions. One morph fit a model of negative frequency-dependent selection, likely owing to predation, but changes in other morph frequencies remained unpredictable. Thus, for specific cases, we can forecast short-term changes within populations, but evolution is more difficult to predict when it involves a balance between multiple selective factors and uncertainty in environmental conditions.
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Fluctuating interaction network and time-varying stability of a natural fish community

Fluctuating interaction network and time-varying stability of a natural fish community | Statistical Physics of Ecological Systems | Scoop.it
Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time1,2,3. Although this theory has experimental support2,4,5, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time)6 and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series6,7,8,9 and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.
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EcoNet - NetSci 2018 Satellite. Submit your abstract!

EcoNet - NetSci 2018 Satellite. Submit your abstract! | Statistical Physics of Ecological Systems | Scoop.it

EcoNet 2018: Ecological networks: spandrels, selection, and assembly. Satellite @NetSCI2018


The effect of ecological network structure on the dynamics, stability, and, ultimately, diversity of ecological communities has been the center of an ongoing debate in the past 45 years. For example, there is evidence that a nested network structure promotes species coexistence by minimizing competition but some studies have failed to recover that relationship. While the implications of network structure for community/population dynamics are well studied, we know very little regarding the effect of dynamics on the structure of networks. In particular, what is the relative contribution of species pruning resulting from community assembly dynamics vs. that of contingent processes (e.g., mutation, migration, etc) to structure? In the first scenario, ecological interactions are shaped by selection and their structure is optimized. In the second case, new evidence suggests that the non-random features of ecological networks may be spandrels -- null consequences of nontrivial constraints and the contingencies of the assembly process. The goal of this satellite is to explore how these two alternative processes contribute to the structure of ecological interactions and to explore whether, and how, they can be disentangled. This debate is crucial for our understanding of community assembly in particular and for understanding processes of network optimization in nature in general.


Submit your abstract: https://easychair.org/cfp/econet2018

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Workshop "Stochastic models in ecology and evolutionary biology", 5-7th April 2018, Venice. Registration Open!

Workshop "Stochastic models in ecology and evolutionary biology", 5-7th April 2018, Venice. Registration Open! | Statistical Physics of Ecological Systems | Scoop.it
Living systems are characterized by the emergence of recurrent dynamical patterns at all scales of magnitude. Self-organized behaviors are observed both in large communities of microscopic components - like neural oscillations and gene network activity - as well as on larger levels - as predator-prey equilibria to name a few. Such regularities are deemed to be universal in the sense they are due to common mechanisms, independent of the details of the system. This belief justifies investigation through quantitative models able to grasp key features while disregarding inessential complications. The attempt of modeling such complex systems leads naturally to consider large families of microscopic identical units. Complexity and self-organization then arise on a macroscopic scale from the dynamics of these minimal components that evolve coupled by interaction terms. Within this scenario, probability theory and statistical mechanics come into play very soon. Aim of the workshop is to bring together scientists with different background - biology, physics and mathematics - interested in stochastic models in ecology and evolutionary biology, to discuss issues and exchange ideas. A partial list of topics includes: stochastic population dynamics, branching processes, interacting particle systems and statistical mechanics models in ecology, robustness and adaptability of ecosystems, resilience and criticality of ecological systems, models and prediction of biodiversity, molecular evolution, and neuroscience.
The style of the workshop will be rather informal. The idea is to have the opportunity to freely share ideas and discuss. Talks will be organised in different thematic sessions, and we will have both colloquia and more technical presentations.
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Assistant Professor in Data Science | Central European University

Vacancy summary: 
The Center for Network Science of the Central European University (CEU) invites applications for a full-time Assistant Professor position in the area of Data Science. We expect applications from scholars with a PhD in Computer Science or Data Science, who have an excellent publication record with applications of data science methods to large datasets, addressing problems related to computational social science. The candidate should have strong motivation for interdisciplinary research (especially with the social sciences) and be interested in participating in projects with several departments at CEU. Experience in data science areas, such as: data mining, machine learning, natural languages processing, or visualization is a must and should be emphasized in the application. Capability of high quality teaching is assumed. The Center for Network Science was established as an interdisciplinary unit, integrating natural science and social science approaches.
Position for:  Faculty
Unit:  Center for Network Science (CNS)
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Visiting Professor Call,  University of Padova

Call "Visiting Scientists" - Deadline: January 9, 2018
The University of Padova provides funding for a total of Euro 260.000,00 (50% on the budget of the International Relations Office and 50% on the budget of on the budget of the Departments involved and/or third parties) for granting scholarships to scholars with proven scientific expertise, related to foreign universities/research centers/entities.

Visiting Scientists will work for one or three months in a Department / Center of the University of Padova, that will ensure the availability of the rooms, libraries, equipment and facilities needed by the Visiting Scientist to carry out his/her activities.

The scholarship for one month stay will be of Euro 3.225,80 - gross percipient, the scholarship for three month stay will be of euro 6.912,44 - gross percipient.

Applications must be submitted by January 9, 2018,  by lecturers of the University of Padova through an online form.

The stay can begin after the publication of the results of the call and must end by December 31, 2018.

Visiting Scientist 2018
Call for applications (unipd professors only).

IF YOU ARE INTERESTED PLEASE WRITE TO Liph.unipd@gmail.com before December 18, 2017 

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Una foresta in un computer

Una foresta in un computer | Statistical Physics of Ecological Systems | Scoop.it
Misurare la biodiversità a grandi scale con un approccio fisico-statistico, in questo consiste il metodo sviluppato da Anna Tovo, Marco Favretti e Marco Formentin del dipartimento di Matematica “Tullio Levi-Civita”, Samir Suweis e Amos Maritan del dipartimento di Fisica e Astronomia “Galileo Galilei” dell’Università di Padova insieme a Sandro Azaele dell’università di Leeds e Jayanth Banavar dell’università dell’Oregon.
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Network Theory Faculty position at UC Davis

The University of California, Davis invites applications for a faculty position in network theory, which is an interdisciplinary academic field using graph theory, statistical mechanics, and applied mathematics to elucidate the mathematical foundations underlying the organization and function of networks across a wide range of applied disciplines. The appointment will be at the Assistant or Associate Professor level, with opportunity for a more senior level appointment for an exceptional candidate, with an intended start date of Fall 2018. The appointment will reside in the Department of Computer Science or the Department of Physics, with the ideal candidate qualified to hold a joint appointment based in both departments. To ensure full consideration, applications must be submitted by December 15th, 2017.

Via Complexity Digest
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The Global Food‐Energy‐Water Nexus - D'Odorico - - Reviews of Geophysics - Wiley Online Library

Water availability is a major factor constraining humanity's ability to meet the future food and energy needs of a growing and increasingly affluent human population. Water plays an important role in the production of energy, including renewable energy sources and the extraction of unconventional fossil fuels that are expected to become important players in future energy security. The emergent competition for water between the food and energy systems is increasingly recognized in the concept of the “food‐energy‐water nexus.” The nexus between food and water is made even more complex by the globalization of agriculture and rapid growth in food trade, which results in a massive virtual transfer of water among regions and plays an important role in the food and water security of some regions. This review explores multiple components of the food‐energy‐water nexus and highlights possible approaches that could be used to meet food and energy security with the limited renewable water resources of the planet. Despite clear tensions inherent in meeting the growing and changing demand for food and energy in the 21st century, the inherent linkages among food, water, and energy systems can offer an opportunity for synergistic strategies aimed at resilient food, water, and energy security, such as the circular economy.
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The future of hyperdiverse tropical ecosystems

The future of hyperdiverse tropical ecosystems | Statistical Physics of Ecological Systems | Scoop.it
The immense biodiversity of tropical ecosystems is threatened by multiple interacting local and global stressors that can only be addressed by the concerted efforts of grassroots organizations, researchers, national governments and the international community.
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High parasite diversity accelerates host adaptation and diversification

Virtually all organisms are parasitized by multiple species, but our current understanding of host-parasite interactions is based on pairwise species interactions. Betts et al. address this by using the bacterium Pseudomonas aeruginosa and five different phage virus parasites. Increasing parasite diversity accelerated the rate of host evolution, driving both faster genomic evolution within populations and greater divergence between populations. Thus, different parasite loads prompt different evolutionary dynamics and profoundly shape host evolution by different mechanisms.

Science , this issue p. [907][1]


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Post Doc OPENING @LIPh_Lab, University of Padova.

Post-Doc Opening!! 


 The LIPh Group of the University of Padua invites applications for postdoctoral positions. Appointment duration 24 months. 


 Starting date (indicative) April 2018 / December 2018 


Salary Depending on candidate expertise. Competitive for Italian living standards. 


Profile We seek for candidates with an established expertise and proven portfolio of research within one or more of the following research fields: 


- Statistical Physics modelling of Ecological systems 

- Non Equilibrium Statistical Mechanics 

- Neural Networks, Data mining and Advanced Statistics 

- Biological inspired Complex Systems and Networks 


Formal requirements 

The candidate must possess a Ph.D. or a post-doc in a subject area relevant to the profile. An excellent level of both written and spoken English is mandatory. 


Specific requirements The candidate must prove ability in effective and persuasive communication, ability to supervise the work of others to focus team efforts, ability to work in a multidisciplinary team and to be able to communicate complex information to individuals from a range of disciplines.


Research Environment The LIPH group is active on a broad spectrum of subjects and it is a vibrant, interdisciplinary research team that benefits greatly from collaboration all around the world. 


University of Padova is one of the most prestigious and big University of Italy. Padova, is a small and beautiful town, shaped around the University. Low living costs, great food, and you can bike everywhere!


The candidate will work in strict collaboration with Samir Suweis and Amos Maritan, who lead the LIPh Lab. 


How to apply Interested applicants are requested to submit a CV, publication list, a brief cover letter (max 1 pages) and at least two letters of recommendation. 


All material should be sent to liph(dot)unipd(at)gmail(dot)com The deadline for applications is May 3. Later applications will also be considered until the positions are filled. 


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Global patterns of tropical forest fragmentation

Global patterns of tropical forest fragmentation | Statistical Physics of Ecological Systems | Scoop.it
Remote sensing enables the quantification of tropical deforestation with high spatial resolution1,2. This in-depth mapping has led to substantial advances in the analysis of continent-wide fragmentation of tropical forests1,2,3,4. Here we identified approximately 130 million forest fragments in three continents that show surprisingly similar power-law size and perimeter distributions as well as fractal dimensions. Power-law distributions5,6,7 have been observed in many natural phenomena8,9 such as wildfires, landslides and earthquakes. The principles of percolation theory7,10,11 provide one explanation for the observed patterns, and suggest that forest fragmentation is close to the critical point of percolation; simulation modelling also supports this hypothesis. The observed patterns emerge not only from random deforestation, which can be described by percolation theory10,11, but also from a wide range of deforestation and forest-recovery regimes. Our models predict that additional forest loss will result in a large increase in the total number of forest fragments—at maximum by a factor of 33 over 50 years—as well as a decrease in their size, and that these consequences could be partly mitigated by reforestation and forest protection.
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Physics of data - a.y. 2018/19 | University of Padova -> Apply for fellowships!

The onset of big data has led to an explosion of datasets with a far more complex structure and beyond standard physics. In this program, we will provide new theoretical and computational tools to tackle this challenge within the physicist mindset
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Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models

High-quality climate predictions are crucial for understanding the impacts of different greenhouse gas emission scenarios and for mitigating and adapting to the resulting climatic changes. Bonan and Doney review advances in Earth system models that include the terrestrial and marine biosphere. Such models capture interactions between physical and biological aspects of the Earth system. This provides insight into climate impacts of societal importance, such as altered crop yields, wildfire risk, and water availability. Further research is needed to better understand model uncertainties, some of which may be unavoidable, and to better translate observations into abstract model representations.
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Special Issue: Scales and Complexity in Ecological Communities: Models, Methods, and Predictions. Complexity Journal (IF 4.6)

Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life. Complexity Get Call For Paper". Submit your paper to the special Issue!

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The Emergence of Consensus: A Primer

The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and scattered widely across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges 'spontaneously' in absence of centralised institutions and covers topic that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
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Ivon Prefontaine, PhD's curator insight, December 14, 2017 4:59 PM
There is a link to a PDF article.

The article explores how consensus arises when institutions are not involved.
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Visiting Scientist. Call @UNIPD

Visiting Scientist. Call @UNIPD | Statistical Physics of Ecological Systems | Scoop.it
Call "Visiting Scientists" - Deadline: January 9, 2018
The University of Padova provides funding for a total of Euro 260.000,00 (50% on the budget of the International Relations Office and 50% on the budget of on the budget of the Departments involved and/or third parties) for granting scholarships to scholars with proven scientific expertise, related to foreign universities/research centers/entities.
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The fundamental advantages of temporal networks

The fundamental advantages of temporal networks | Statistical Physics of Ecological Systems | Scoop.it
Historically, network science focused on static networks, in which nodes are connected by permanent links. However, in networked systems ranging from protein-protein interactions to social networks, links change. Although it might seem that permanent links would make it easier to control a system, Li et al. demonstrate that temporality has advantages in real and simulated networks. Temporal networks can be controlled more efficiently and require less energy than their static counterparts.
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The conference on Analysis and Modeling of Complex Oscillatory Systems (AMCOS)

The conference on Analysis and Modeling of Complex Oscillatory Systems (AMCOS) | Statistical Physics of Ecological Systems | Scoop.it

Analysis and Modeling of Complex Oscillatory Systems (AMCOS)

March 19 – 23, 2018: PRBB, Barcelona, Spain.

The conference on Analysis and Modeling of Complex Oscillatory Systems (AMCOS) aims to bring together theoretical and experimental researchers working on the state of the art in the field of complex oscillatory systems.

AMCOS will also provide an ideal atmosphere for young researchers, enhancing scientific exchange and stimulating fruitful discussions across different fields.

The main topics of the conference comprise both (a) the modeling of complex systems and the emergence of collective behavior, as well as (b) the analysis of complex data sets in order to infer the underlying structure and functionality of networks. Furthermore, particular focus will be also put on oscillatory phenomena in neuroscience.

We gratefully acknowledge support from the European Union through the Marie Curie Initial Training Network COSMOS (Complex Oscillatory Systems: Modeling and Analysis).

The AMCOS conference will be held at PRBB Parc de Recerca Biomèdica de Barcelona.

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