Join forces or cheat: evolutionary analysis of a consumer-resource system

International audienceIn this contribution we consider a seasonal consumer-resource system and focus on the evolution of consumer behavior. It is assumed that consumer and resource individuals live and interact during seasons of fixed lengths separated by winter periods. All individuals die at the end of the season and the size of the next generation is determined by the the consumer-resource interaction which took place during the season. Resource individuals are assumed to reproduce at a constant rate, while consumers have to trade-off between foraging for resources, which increases their reproductive abilities, or reproducing. Firstly, we assume that consumers cooperate in such a way that they maximize each consumer's individual fitness. Secondly, we consider the case where such a population is challenged by selfish mutants who do not cooperate. Finally we study the system dynamics over many seasons and show that mutants eventually replace the original cooperating population, but are finally as vulnerable as the initial cooperating consumers

Coevolution in a One Predator–Two Prey System

Background: Our understanding of coevolution in a predator–prey system is based mostly on pair-wise interactions. Methodology and Principal Findings: Here I analyze a one-predator–two-prey system in which the predator’s attack ability and the defense abilities of the prey all evolve. The coevolutionary consequences can differ dramatically depending on the initial trait value and the timing of the alternative prey’s invasion into the original system. If the invading prey species has relatively low defense ability when it invades, its defense is likely to evolve to a lower level, stabilizing the population dynamics. In contrast, if when it invades its defense ability is close to that of the resident prey, its defense can evolve to a higher level and that of the resident prey may suddenly cease to evolve, destabilizing the population dynamics. Destabilization due to invasion is likely when the invading prey is adaptively superior (evolution of its defense is less constrained and fast), and it can also occur in a broad condition even when the invading prey is adaptively inferior. In addition, invasion into a resident system far from equilibrium characterized by population oscillations is likely to cause further destabilization

A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Red Queen Coevolution on Fitness Landscapes

Species do not merely evolve, they also coevolve with other organisms. Coevolution is a major force driving interacting species to continuously evolve ex- ploring their fitness landscapes. Coevolution involves the coupling of species fit- ness landscapes, linking species genetic changes with their inter-specific ecological interactions. Here we first introduce the Red Queen hypothesis of evolution com- menting on some theoretical aspects and empirical evidences. As an introduction to the fitness landscape concept, we review key issues on evolution on simple and rugged fitness landscapes. Then we present key modeling examples of coevolution on different fitness landscapes at different scales, from RNA viruses to complex ecosystems and macroevolution.Comment: 40 pages, 12 figures. To appear in "Recent Advances in the Theory and Application of Fitness Landscapes" (H. Richter and A. Engelbrecht, eds.). Springer Series in Emergence, Complexity, and Computation, 201

Stabilizing switching automata for discrete-time switched linear systems

We develop an algorithm for the automatic generation of a switching automaton that stabilizes a given discrete-time switched linear system. The algorithm iteratively checks the stability of the system constrained by the current automaton and modifies the automaton's graph, until a stabilizing solution, or the empty graph, is reached. Stability is checked by means of a recently developed direct algorithm, that in case of instability provides a graph's cycle with unstable corresponding matrix product. Our heuristic to modify the switching graph limits the number of consecutive repetitions of the unstable periodic path. It is based on an ergodic result that tightly lower bounds the system's Constrained Joint Spectral Radius - the largest long-term average growth rate of the system's state - by looking at the matrix products along cycles of the switching graph. By only limiting the execution of unstable cycles, termination is not yet proved, though it is granted by a second, more constraining heuristic, that simply cuts the cycle if a prescribed length is exceeded. Our procedure can start either from the graph allowing arbitrary switching or from an application-specific graph under which the system is unstable

Valutazione della dose al paziente in radiologia interventistica

none7L. D’Ercole; L. Mantovani; Ottolenghi Andrea; F. Lisciandro; L. Andreucci; P. Quaretti; F. Zappoli Thyrion.L., D’Ercole; L., Mantovani; Ottolenghi, ANDREA DAVIDE; F., Lisciandro; L., Andreucci; P., Quaretti; F., Zappoli Thyrio