Jean-Jacques Boreux

Inverse vegetation modeling by Monte Carlo sampling to reconstruct palaeoclimates under changed precipitation seasonality and CO2 conditions: application to glacial climate in Mediterranean region

Abstract Atmospheric CO 2 concentration has greatly fluctuated during the Quaternary. These variations have influenced the vegetation changes. The assumption that the relationship vegetation–climate sensu stricto was constant through time should be reconsidered taking into account the impact of the atmospheric CO 2 content on the plants. Here we propose to use a process-based vegetation model (BIOME3) in an inverse mode to reconstruct from pollen data the most probable climate under precipitation seasonality change and under lowered CO 2 concentration in the biosphere. Appropriate tools to match the model outputs with the pollen data are developed to generate a probability distribution associated with the reconstruction (Monte Carlo sampling and neural network techniques). The method is validated with modern pollen samples from Greece and Italy: it proves to be able to reconstruct modern climate with a more or less large error bar from pollen data. The error bar depends in fact on the tolerance of the vegetation to the corresponding climatic variable. The application to six pollen assemblages from Greece and Italy, representing the last glacial maximum (LGM: 18 000 14 C-year B.P.), is done into three experiments: (1) modern CO 2 concentration; (2) LGM CO 2 concentration; (3) LGM CO 2 concentration and high winter precipitation. The latter experiment is motivated by evidence of high lake-levels in Greece during the LGM which has been attributed to winter rainfall. These experiments show that winter was ca. 15–20°C colder than the present, in agreement with previous climate reconstruction. The apparent discrepancy between the high lake-levels and the steppe vegetation during the LGM, can be explained by an increase of the winter precipitation (which leads to high lake level) while the summer season is mild and dry (which affects the vegetation). The summer temperature has three stable states (−16°C, −10°C, −2°C), but the warmest one is the most probable if we take into account the lowered CO 2 and the high lake-levels.

Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and Particle Filtering algorithms

Finely tuned process-based tree-growth models are of considerable help in understanding the variations of biomass increments measured in the dendrochronological series. Using site and species parameters, as well as daily climate variables, the MAIDEN model computes the water balance at ecosystem level and the daily increment of carbon storage in the stem through photosynthesis processes to reproduce the structure of the tree-ring series. In this paper, we use three techniques to calibrate this model with Pinus halepensis data sampled in the Mediterranean part of France: a standard optimization (PEST), Monte Carlo Markov Chains (MCMC) and Particle Filtering (PF). Contrary to PEST, which tries to find an optimum fit (giving the lowest error between observations and simulations), the principle of MCMC and PF is to walk, from a priori distributions, in the parameter space according to particular statistical rules to compute each parameter distribution. The PEST and MCMC calibrations of our dendrochronological series lead to rather similar adjustments between simulations and observations. PF and MCMC calibrations give different parameter distributions, showing how complementary are these methods, with a better fit for MCMC. Yet, independent validations over 11 independent meteorological years show a higher efficiency of the recent PF method over the others.

Age model estimation in paleoclimatic research: fuzzy regression and radiocarbon uncertainties

Abstract A fuzzy linear regression-based method to relate the depth and age of sediment layers is described. Because algae, moss or local plants respond to climatic change, the age and depth of sediment layers are interrelated variables but the link between them is often imprecise. In most cases, due to the limited number of layers in a core (i.e., small number of data points), estimation of the slope and uncertainties in classical regression analysis does not take into account the uncertainties in radiocarbon dating. Here, fuzzy linear regression, which may be applied even to a very small data set, is utilized. The method, illustrated through a practical example, with eight data points, appears to be a promising tool in stratigraphic studies to link sediment age to layer depth and takes into account uncertainties from radiocarbon dating.

A joint individual-based model coupling growth and mortality reveals that tree vigor is a key component of tropical forest dynamics

Tree vigor is often used as a covariate when tree mortality is predicted from tree growth in tropical forest dynamic models, but it is rarely explicitly accounted for in a coherent modeling framework. We quantify tree vigor at the individual tree level, based on the difference between expected and observed growth. The available methods to join nonlinear tree growth and mortality processes are not commonly used by forest ecologists so that we develop an inference methodology based on an MCMC approach, allowing us to sample the parameters of the growth and mortality model according to their posterior distribution using the joint model likelihood. We apply our framework to a set of data on the 20-year dynamics of a forest in Paracou, French Guiana, taking advantage of functional trait-based growth and mortality models already developed independently. Our results showed that growth and mortality are intimately linked and that the vigor estimator is an essential predictor of mortality, highlighting that trees growing more than expected have a far lower probability of dying. Our joint model methodology is sufficiently generic to be used to join two longitudinal and punctual linked processes and thus may be applied to a wide range of growth and mortality models. In the context of global changes, such joint models are urgently needed in tropical forests to analyze, and then predict, the effects of the ongoing changes on the tree dynamics in hyperdiverse tropical forests.

Modelling the propagation pathway of street-traffic noise: practical comparison of German guidelines and real-world measurements

Abstract In Germany, several guidelines were developed to model the noise propagation pathway. The study compares the guidelines DIN 18005, RLS-90 and VDI 2714, all of which provide some kind of sound object line source suited to model street traffic noise. Differences between those guidelines are explained, and their effect on practical calculations is shown in a real-world situation, National Road N4, in Arlon, Belgium. Calculated results are compared to measurements made on critical points along the road. The paper emphasises the understanding of the inner workings of models. In order to avoid differences in calculated results due to software design methods applied, all calculations are made using one single commercially available simulation program. Additionally, this allows for a test of guideline sensitivity to changes in input parameters.

A fuzzy approach to the definition of standardized visibility in fog

Abstract The problem of defining the physical concept of standardized visibility is addressed by means of a fuzzy logic approach. Assuming that several hypotheses hold, it is possible to evaluate a standardized visibility V k (or meteorological visual range) as the ratio of a psychometric coefficient k and the extinction coefficient σ of the aerosol. The main disadvantage of this relationship due to Koschmieder [7] stems from the fact that the meteorological visual range can be very different from visibility estimated by a human observer with normal sight. This is particularly true during fog occurrences. A fuzzy approach is used to evaluate a relationship between the fog extinction coefficient supplied by a transmissometer and the corresponding visibility evaluated by an expert. Calculations are performed according to two different approaches. First, a so-called fuzzy hyperbolic model based on a distance defined by Bardossy [2] is presented, yielding a crisp (nonfuzzy) value of the coefficient k . Next, we use the multiplication of two fuzzy numbers, taken as triangular, to build an average fuzzy number k that accounts for the different values found in the literature. The defuzzification of k confirms the result obtained by the first approach.

Les avantages de la modélisation hiérarchique: application à la capture-marquage-recapture des saumons

Voici un modele bayesien hierarchique (MBH) pour l’analyse des donnees de capture-marquage-recapture de saumons. Ce chapitre se presente comme une suite au chapitre 8 et s’appuie sur l’etude (Rivot et Prevost, 2002). Chaque annee i, ces deux chercheurs de l’INRA de Rennes veulent estimer le nombre inconnu V i de saumons qui remontent la riviere Oir pour frayer ainsi que la probabilite de capture θi du piege utilise pour effectuer ces mesures. Ils disposent d’une serie d’observations allant de 1984 a 2000 collectees sur le terrain par les techniciens de la station experimentale du Moulin des Princes, Nicolas Jeannot et Francois Burban, aides de Jean-Yves Moelo. Pour analyser de telles donnees, on peut vouloir, en premier lieu, faire l’hypothese d’independance complete entre les annees, c’est-a-dire imaginer que les resultats des experiences de capture-marquage-recapture d’une annee ne nous amenent aucune information quant aux resultats possibles des autres annees. A l’oppose, on peut etre tente d’ignorer la variabilite entre chaque annee en regroupant en un meme echantillon les donnees de toutes les annees comme si elles provenaient du meme modele d’observation. Le modele hierarchique realise un compromis astucieux entre ces points de vue extremes. Il suppose que les annees ne sont ni completement identiques ni completement independantes et considere que les θi et les ν i sont issus d’une meme distribution de probabilite dont les parametres sont inconnus.

Le modèle linéaire généralisé

Une tâche recurrente de l’activite scientifique est d’expliquer le comportement d’une variable endogene ou reponse a partir de variables exogenes ou stimuli. Le modele de regression lineaire est certainement le plus utilise, pas toujours a bon escient. Le modele lineaire generalise, moins gourmand en hypotheses que le modele lineaire, postule que la reponse est un membre de la famille exponentielle des distributions statistiques a deux parametres. Le modele de regression logistique est certainement le plus connu. Nous l’appliquerons a une enquete menee aupres de 68 prothesistes dentaires appartenant a 10 entreprises differentes du Grand-Duche de Luxembourg (Marion, 2007). Nous terminerons avec le modele de Finney qui exploite la regression logistique pour evaluer les performances de melange d’insecticides (Finney, 1971).

Le cardinal sort du rang: la cible est une variable latente

Sous ce titre quelque peu humoristique, nous nous interessons a un probleme generique qui a de nombreuses applications pratiques: connaissant le rang d’un element d’un ensemble fini ordonne E, on se propose d’inferer le nombre d’elements de E. En fait, c’est la generalisation de ce probleme a une collection d’ensembles finis ordonnes qui nous interesse. Sous le paradigme bayesien, le recours aux variables latentes (voir chap 3, p. 3.2.3) permet d’introduire un second niveau de variation entre les ensembles, ce qui confere au modele une structure hierarchique. Mais un modele n’est utile que s’il est calculable et c’est pourquoi les modeles hierarchiques et les methodes de Monte-Carlo sont indissociables.

Pratique du calcul des lois a posteriori

Qui dit modeles realistes, dit aussi difficultes calculatoires. Le but de ce quatrieme chapitre est de donner un apercu des principales familles de methodes d’approximation des distributions a posteriori Dans le cas tres particulier ou le prior est non informatif et que la taille de l’echantillon est grande, la densite a posteriori peut etre approchee par une loi normale multidimensionnelle. Cette approximation asymptotique repose sur les proprietes des estimateurs du maximum de vraisemblance (section 4.2). A l’ere des ordinateurs personnels puissants, cette approximation — fondee sur des hypotheses assez restrictives — est avantageusement abandonnee au profit des methodes numeriques stochastiques. Ce sont d’abord les methodes de Monte-Carlo par chaines de Markov (MCMC). Ces techniques de simulation avec dependance sont presentees en section 4.3, notamment l’algorithme general de Metropolis-Hasting s et lechantillonnage de Gibbs. Ces deux algorithmes sont d’ailleurs implantes dans le logiciel WinBUGS. Les techniques classiques de simulation avec independance ou methodes de Monte-Carlo (MC), issues de lechantillonnage pondere, avec ou sans re-echantillonnage, ont eu plus recemment des developpements importants sous le nom generique de methodes des particules (section 4.4).