Foray search: An effective systematic dispersal strategy in fragmented landscapes

In the absence of evidence to the contrary, population models generally assume that the dispersal trajectories of animals are random, but systematic dispersal could be more efficient at detecting new habitat and may therefore constitute a more realistic assumption. Here, we investigate, by means of simulations, the properties of a potentially widespread systematic dispersal strategy termed "foray search." Foray search was more efficient in detecting suitable habitat than was random dispersal in most landscapes and was less subject to energetic constraints. However, it also resulted in considerably shorter net dispersed distances and higher mortality per net dispersed distance than did random dispersal, and it would therefore be likely to lead to lower dispersal rates toward the margins of population networks. Consequently, the use of foray search by dispersers could crucially affect the extinction-colonization balance of metapopulations and the evolution of dispersal rates. We conclude that population models need to take the dispersal trajectories of individuals into account in order to make reliable predictions

Non-random dispersal in the butterfly Maniola jurtina: implications for metapopulation models

The dispersal patterns of animals are important in metapopulation ecology because they affect the dynamics and survival of populations. Theoretical models assume random dispersal but little is known in practice about the dispersal behaviour of individual animals or the strategy by which dispersers locate distant habitat patches. In the present study, we released individual meadow brown butterflies (Maniola jurtina) in a non-habitat and investigated their ability to return to a suitable habitat. The results provided three reasons for supposing that meadow brown butterflies do not seek habitat by means of random flight. First, when released within the range of their normal dispersal distances, the butterflies orientated towards suitable habitat at a higher rate than expected at random. Second, when released at larger distances from their habitat, they used a non-random, systematic, search strategy in which they flew in loops around the release point and returned periodically to it. Third, butterflies returned to a familiar habitat patch rather than a non-familiar one when given a choice. If dispersers actively orientate towards or search systematically for distant habitat, this may be problematic for existing metapopulation models, including models of the evolution of dispersal rates in metapopulations

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their ‘collective cognition’, or (ii) interaction with conspecifics can allow individuals to follow specific ‘leaders’, those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions – where individuals are able to consider the success of previous decision outcomes – the collective's aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated

Out of Sight but Not Out of Mind? Behavioral Coordination in Red-Tailed Sportive Lemurs (Lepilemur ruficaudatus)

Many animals are organized into social groups and have to synchronize their activities to maintain group cohesion. Although activity budgets, habitat constraints, and group properties may impact on behavioural synchrony, little is known regarding how members of a group reach a consensus on the timing of activities such as foraging bouts. Game theory predicts that pair partners should synchronize their activities when there is an advantage of foraging together. As a result of this synchronization, differences in the energetic reserves of the two players develop spontaneously and the individual with lower reserves emerges as a pacemaker of the synchrony. Here, we studied the behavioral synchrony of pair-living, nocturnal, red-tailed sportive lemurs (Lepilemur ruficaudatus). We observed 8 pairs continuously for ≥1 annual reproductive cycle in Kirindy Forest, Western Madagascar. During focal observations, one observer followed the female of a pair and, simultaneously, another observer followed the male. We recorded the location and behavioral state of the focal individual every 5 min via instantaneous sampling. Although behavioral synchrony of pair partners appeared to be due mainly to endogenous activity patterns, they actively synchronized when they were in visual contact (<10 m). Nevertheless, red-tailed sportive lemurs benefit from synchronizing their activity only for 15% of the time, when they are close together. The lack of an early warning system for predators and weak support for benefits via social information transfer in combination with energetic constraints may explain why red-tailed sportive lemurs do not spend more time together and thus reap the benefits of behavioral synchrony

Impact of medical specialists' locus of control on communication skills in oncological interviews

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Superfluid transport of information in turning flocks of starlings

Collective decision-making in biological systems requires all individuals in the group to go through a behavioural change of state. During this transition, the efficiency of information transport is a key factor to prevent cohesion loss and preserve robustness. The precise mechanism by which natural groups achieve such efficiency, though, is currently not fully understood. Here, we present an experimental study of starling flocks performing collective turns in the field. We find that the information to change direction propagates across the flock linearly in time with negligible attenuation, hence keeping group decoherence to a minimum. This result contrasts with current theories of collective motion, which predict a slower and dissipative transport of directional information. We propose a novel theory whose cornerstone is the existence of a conserved spin current generated by the gauge symmetry of the system. The theory turns out to be mathematically identical to that of superfluid transport in liquid helium and it explains the dissipationless propagating mode observed in turning flocks. Superfluidity also provides a quantitative expression for the speed of propagation of the information, according to which transport must be swifter the stronger the group's orientational order. This prediction is verified by the data. We argue that the link between strong order and efficient decision-making required by superfluidity may be the adaptive drive for the high degree of behavioural polarization observed in many living groups. The mathematical equivalence between superfluid liquids and turning flocks is a compelling demonstration of the far-reaching consequences of symmetry and conservation laws across different natural systems

A consistent approach for probabilistic residential flood loss modeling in Europe

In view of globally increasing flood losses, a significantly improved and more efficient flood risk management and adaptation policy are needed. One prerequisite is reliable risk assessments on the continental scale. Flood loss modeling and risk assessments for Europe are until now based on regional approaches using deterministic depth‐damage functions. Uncertainties associated with the risk estimation are hardly known. To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the private sector, BN‐FLEMOps. The model is applied on the mesoscale in the whole of Europe and can be adapted to regional situations. BN‐FLEMOps is validated in three case studies in Italy, Austria, and Germany. The officially reported loss figures of the past flood events are within the 95% quantile range of the probabilistic loss estimation, for all three case studies. In the Italian, Austrian, and German case studies, the median loss estimate shows an overestimation by 28% (2.1 million euro) and 305% (5.8 million euro) and an underestimation by 43% (104 million euro), respectively. In two of the three case studies, the performance of the model improved, when updated with empirical damage data from the area of interest. This approach represents a step forward in European wide flood risk modeling, since it delivers consistent flood loss estimates and inherently provides uncertainty information. Further validation and tests with respect to adapting the model to different European regions are recommended

Autocracy-Sustaining Versus Democratic Federalism:Explaining the Divergent Trajectories of Territorial Politics in Russia and Western Europe

This article provides a comparative assessment of territorial politics in Russia and Western Europe. The consolidation or deepening of regional autonomy in Western Europe contrasts with the transformation of Russia from a segmented and highly centrifugal state into a centralized authoritarian state in the course of just two decades. The consolidation of territorial politics in Western Europe is linked to the presence of endogenous safeguards that are built into their territorial constitutional designs and most importantly to the dynamics that emanate from multi-level party competition in the context of a liberal and multi-level democracy. In contrast, in Russia, neither endogenous safeguards nor multi-level party democracy play an important role in explaining the dynamics of Russian federalism, but who controls key state resources instead. We argue that under Putin power dependencies between the Russian center and the regions are strongest where regional democracy is at its weakest, thus producing ‘autocracy-sustaining’ instead of a democratic federation. By studying the relationship between federalism and democracy in cases where both concepts are mutually reinforcing (as in Western Europe) with the critical case of Russia where they are not, we question the widely held view that democracy is a necessary pre-condition for federalism.Peer reviewe