Image Characterization and Classification by Physical Complexity

We present a method for estimating the complexity of an image based on Bennett's concept of logical depth. Bennett identified logical depth as the appropriate measure of organized complexity, and hence as being better suited to the evaluation of the complexity of objects in the physical world. Its use results in a different, and in some sense a finer characterization than is obtained through the application of the concept of Kolmogorov complexity alone. We use this measure to classify images by their information content. The method provides a means for classifying and evaluating the complexity of objects by way of their visual representations. To the authors' knowledge, the method and application inspired by the concept of logical depth presented herein are being proposed and implemented for the first time.Comment: 30 pages, 21 figure

How can model comparison help improving species distribution models?

Today, more than ever, robust projections of potential species range shifts are needed to anticipate and mitigate the impacts of climate change on biodiversity and ecosystem services. Such projections are so far provided almost exclusively by correlative species distribution models (correlative SDMs). However, concerns regarding the reliability of their predictive power are growing and several authors call for the development of process-based SDMs. Still, each of these methods presents strengths and weakness which have to be estimated if they are to be reliably used by decision makers. In this study we compare projections of three different SDMs (STASH, LPJ and PHENOFIT) that lie in the continuum between correlative models and process-based models for the current distribution of three major European tree species, Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L. We compare the consistency of the model simulations using an innovative comparison map profile method, integrating local and multi-scale comparisons. The three models simulate relatively accurately the current distribution of the three species. The process-based model performs almost as well as the correlative model, although parameters of the former are not fitted to the observed species distributions. According to our simulations, species range limits are triggered, at the European scale, by establishment and survival through processes primarily related to phenology and resistance to abiotic stress rather than to growth efficiency. The accuracy of projections of the hybrid and process-based model could however be improved by integrating a more realistic representation of the species resistance to water stress for instance, advocating for pursuing efforts to understand and formulate explicitly the impact of climatic conditions and variations on these processes

French Roadmap for complex Systems 2008-2009

This second issue of the French Complex Systems Roadmap is the outcome of the Entretiens de Cargese 2008, an interdisciplinary brainstorming session organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It capitalizes on the first roadmap and gathers contributions of more than 70 scientists from major French institutions. The aim of this roadmap is to foster the coordination of the complex systems community on focused topics and questions, as well as to present contributions and challenges in the complex systems sciences and complexity science to the public, political and industrial spheres

A formal model to integrate ecosystem components, processes, interactions and services

Les écosystèmes sont des objets encore mal compris et impossible à prédire. L’une des raisons de cette situation inconfortable, pour nous qui en dépendons, tient dans le fait que les écosystèmes ne sont ni tout à fait inertes, ni tout à fait vivants, mais bien une sorte de troisième état incompris. Une façon de s’attaquer au problème consiste à développer des modèles « intégrés » capables d’articuler entre elles les composantes physicochimiques et biologiques de l’écosystème. Souvent par le passé, de telles tentatives de modélisation intégrées se sont concentrées sur les bilans de matières ou d’énergie de l’écosystème (e.g. approche thermodynamique, (Odum, 1968)), ou sur les réseaux d’interaction des communautés d’espèces (approche biomathématique (Thébault et al., 2006), voire évolutive), mais rarement les deux simultanément. J’exposerai une autre piste de modélisation intégrée de l’écosystème, s’inspirant de l’approche DS² (pour Système Dynamique avec une Structure Dynamique, (Godin, 2000)), et des grammaires sur graphes. En plus d’offrir une formalisation appropriée à la modélisation d’écosystèmes, ces approches permettent de rendre compte de leurs nombreux changements (non stationnaires). Nous savons, en effet, que l’écosystème a de bonnes chances d’être hors-équilibre, et soumis à une certaine « évolution » (certainement très différente de celle de ses populations). De tels modèles ont déjà donné des résultats encourageants sur des paysages (Gaucherel et al., 2012) et je l’illustrerai sur des écosystèmes idéaux tels que des colonies d’insectes sociau

A Deep unity between scientific disciplines

Are scientific disciplines really different? This question often crystallizes into the old debate: Are Physics and Biology different? If Physics and Biology worked on highly different entities (objects), or if they had highly different methods, it would be straightforward to close the debate by a negative answer. However, if we cannot identify any differences, we should explore more deeply the status of the laws found in Physics and questioned in Biology. By slightly modifying the definition of what is a law, I argue here that both disciplines possess some laws exhibiting various “degrees of confirmation”. I finally propose explanations for why P and B give the illusion differing radically, although they both belong to the same continuum of a unified scientific domain

Le quotidien du chercheur, une chasse aux fantômes

Un ouvrage d’opinions sur la science et l’activité scientifique telle qu’elle est menée actuellement. Il nous livre une vision critique, mais légère, de l’étude de la nature qui nous entoure. Plusieurs disciplines appartenant au large domaine de l’environnement sont abordées. Il replace l’homme dans cette nature et des pensées liées à notre relation à la nature sont discutées. Enfin, il scrute plus finement cette interface particulière entre la nature et la culture qu’est le domaine de la recherche scientifique. Certains aspects du métier de chercheur, habituellement moins commentés dans la littérature, sont contés

Self-organisation of patchy landscapes: hidden optimisation of ecological processes

International audienceThis presentation aims at highlighting some newly found scaling properties in forested landscapes. A neutral landscape model has been developed to test the hypothesis of self-organized landscapes over multiple scales. Results showed that forested landscapes may often be optimized in this sense, with a model now able to efficiently mimic them

Self-organization of patchy landscapes : hidden optimization of ecological processes

International audienceOne of the main barriers in the understanding of landscape dynamics is the high spatial variability of the surface patterns (vegetation and land cover) to which ecological processes are intimately linked. The aim of this paper is to present some newly found scaling properties for forested landscapes. Furthermore, it is advocated that patchy landscapes can sometimes be self-organized by optimizing some effective functional. A neutral landscape model has been developed in order to test this self-organization hypothesis. This model was built on the basis of a simple function, called the “Hamiltonian” analogically to physical and biological systems. The Hamiltonian is then minimized to optimize the identified landscape interactions. Fully controlled data coming from five different hundred-year runs of a process-based model appeared to be self-similar over five magnitude orders, without being explicitly simulated. The neutral model is able to reproduce the studied observations and to easily model Optimal Patchy Landscapes. The limits to this parsimonious approach that requires only one parameter (the Hamiltonian slope in loglog plot) are also discussed. The links between the effective Hamiltonian and the ecological processes still need to be investigated. Finally, such landscape Hamiltonian function appears to be a fruitful theoretical framework to describe various landscapes and potentially opens the way to a more complete dynamic landscape theory

Analyses et co-analyses spatiales multi-échelles en écologie

National audienc