Éditeur(s) : Elsevier Science Bv
Résumé : Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling. (C) 2010 Elsevier B.V. All rights reserved.
Ecological Modelling (0304-3800) (Elsevier Science Bv), 2010-07 , Vol. 221 , N. 15 , P. 1757-1769

Droits : 2010 Elsevier B.V. All rights reserved.
http://archimer.ifremer.fr/doc/00009/11993/9342.pdf
DOI:10.1016/j.ecolmodel.2010.04.005
http://archimer.ifremer.fr/doc/00009/11993/

Éditeur(s) : HAL CCSD Wiley
Résumé : 1. Functional traits are posited to explain interspecific differences in performance, but these relationships are difficult to describe for long-lived organisms such as trees, which exhibit strong ontogenetic changes in demographic rates. Here, we use a size-dependent model of tree growth to test the extent to which of 17 functional traits related to leaf and stem economics, adult stature and seed size predict the ontogenetic trajectory of tree growth.[br/] 2. We used a Bayesian modelling framework to parameterize and contrast three size-dependent diameter growth models using 16 years of census data from 5524 individuals of 50 rain forest tree species: a size-dependent model, a size-dependent model with species-specific parameters and a size-dependent model based on functional traits.[br/] 3. Most species showed clear hump-shaped ontogenetic growth trajectories and, across species, maximum growth rate varied nearly tenfold, from 0.58 to 5.51 mm year(-1). Most species attained their maximum growth at 60% of their maximum size, whereas the magnitude of ontogenetic changes in growth rate varied widely among species.[br/] 4. The Trait-Model provided the best compromise between explained variance and model parsimony and needed considerably fewer parameters than the model with species terms.[br/] 5. Stem economics and adult stature largely explained interspecific differences in growth strategy. Maximum absolute diameter growth rates increased with increasing adult stature and leaf delta(13)C and decreased with increasing wood density. Species with light wood had the greatest potential to modulate their growth, resulting in hump-shaped ontogenetic growth curves. Seed size and leaf economics, generally thought to be of paramount importance for plant performance, had no significant relationships with the growth parameters.[br/] 6. Synthesis. Our modelling approach offers a promising way to link demographic parameters to their functional determinants and hence to predict growth trajectories in species-rich communities with little parameter inflation, bridging the gap between functional ecology and population demography.
ISSN: 0022-0477

hal-01032387
https://hal.archives-ouvertes.fr/hal-01032387
DOI : 10.1111/j.1365-2745.2011.01883.x

Éditeur(s) : HAL CCSD Elsevier Masson
Résumé :  
Modeling soil water availability for tropical trees is a prerequisite to predicting the future impact of climate change on tropical forests. In this paper we develop a discrete-time deterministic water balance model adapted to tropical rainforest climates, and we validate it on a large dataset that includes micrometeorological and soil parameters along a topographic gradient in a lowland forest of French Guiana. The model computes daily water fluxes (rainfall interception, drainage, tree transpiration and soil plus understorey evapotranspiration) and soil water content using three input variables: daily precipitation, potential evapotranspiration and solar radiation. A novel statistical approach is employed that uses Time Domain Reflectometer (TDR) soil moisture data to estimate water content at permanent wilting point and at field capacity, and root distribution. Inaccuracy of the TDR probes and other sources of uncertainty are taken into account by model calibration through a Bayesian framework. Model daily output includes relative extractable water, REW, i.e. the daily available water standardized by potential available water. The model succeeds in capturing temporal variations in REW regardless of topographic context. The low Root Mean Square Error of Predictions suggests that the model captures the most important drivers of soil water dynamics, i.e. water refilling and root water extraction. Our model thus provides a useful tool to explore the response of tropical forests to climate scenarios of changing rainfall regime and intensity.
ISSN: 0168-1923

hal-01032181
https://hal.archives-ouvertes.fr/hal-01032181
DOI : 10.1016/j.agrformet.2011.04.012

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : International audience
Variation of dead wood decay rates among tropical trees remains one source of uncertainty in global models of the carbon cycle. Taking advantage of a broad forest plot network surveyed for tree mortality over a 23-year period, we measured the remaining fraction of boles from 367 dead trees from 26 neotropical species widely varying in wood density (0.23–1.24 g cm-3) and tree circumference at death time (31.5–272.0 cm). We modeled decay rates within a Bayesian framework assuming a first order differential equation to model the decomposition process and tested for the effects of forest management (selective logging vs. unexploited), of mode of death (standing vs. downed) and of topographical levels (bottomlands vs. hillsides vs. hilltops) on wood decay rates. The general decay model predicts the observed remaining fraction of dead wood (R2 = 60%) with only two biological predictors: tree circumference at death time and wood specific density. Neither selective logging nor local topography had a differential effect on wood decay rates. Including the mode of death into the model revealed thatstanding dead trees decomposed faster than downed dead trees, but the gain of model accuracy remains rather marginal. Overall, these results suggest that the release of carbon from tropical dead trees to the atmosphere can be simply estimated using tree circumference at death time and wood density.
ISSN: 0029-8549

hal-01094187
https://hal-agroparistech.archives-ouvertes.fr/hal-01094187
DOI : 10.1007/s00442-010-1602-8

Éditeur(s) : HAL CCSD
Résumé : International audience
In this work, we have led an analysis of the error of different global solar radiation prediction models according to the global solar radiation variability. Different predictions models where performed such as machine learning techniques (Neural Networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this study a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and tested with data from three 3 French islands: Corsica (42.15°N ; 9.08°E), Guadeloupe (16.25°N ; 61.58°W) and Reunion (21.15°S ; 55.5°E). Guadeloupe and Reunion are located in a subtropical climatic zone whereas Corsica is in a tempered climatic zone. Hence the global solar radiation variation differs significantly. The output error of the different models was quantified by the nRSME. In order to quantify the influence of the global solar radiation variability on the forecasting models error we performed a classification of typical days according to different typical days. Each class of typical day is defined by a variation of global solar radiation rate. For each class and for each location, the selected forecasting models where performed and the error was quantified. With this analysis a global solar radiation forecasting models can be selected according to the location, the global solar radiation fluctuations and hence the meteorological conditions. INTRODUCTION Large and frequent variations of solar radiation can be observed in tropical climates with amplitudes reaching 800 W/m² and occurring within a short time interval, from few seconds to few minutes, according to the geographical location. Such fluctuations can be due for example to the dynamic of clouds which can be very complex and depend on cloud type, size, speed and spatial distribution and, more generally, due to some specific local meteorological conditions. Thus, the solar energy forecasting, a process used to predict the amount of solar energy available in the current and near terms, might be a difficult task. Some of the best predictors found in literature are Autoregressive and moving average (ARMA) [5,7,8], Bayesian inferences [9,10], Markov chains [11], k-Nearest-Neighbors predictors [12] or artificial intelligence techniques as the Artificial Neural Network (ANN) [9-11]. Although these methodologies are potentially good in many areas, we observed in our previous studies on global radiation prediction [9,13,14] that the simple model based on the persistence of the clear sky index gives often very good results with acceptable errors [15] for short term forecasting time horizon (<= 1 hour). The goal of this paper is to determinate the influence of solar radiation variability regarding different classes of days on the expected error provided by different forecasting methods that the modeller can possibly implement. The paper is organized as follow: Section 2 describes the data we have used. Section 3 exposes the classification methodology and the results obtained for the three studied locations. In the two following sections, the forecasting methods are exposed and then 3 the errors on the forecasting results for each location and for each class are exposed.
The Third Southern African Solar Energy Conference (SASEC2015)
Kruger National Park, South Africa

Droits : info:eu-repo/semantics/OpenAccess
hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487/document
https://hal.archives-ouvertes.fr/hal-01099487/file/article%20SASEC.pdf

Résumé : Cette thèse concerne la modélisation des dynamiques de maladies foliaires de cultures pérennes tropicales à différentes échelles spatiales. Cette approche de modélisation est appliquée à la cercosporiose noire du bananier. Il s’agit d’explorer et de déterminer les paramètres environnementaux et de résistance de l’hôte ayant une influence significative sur la dynamique spatiotemporelle de la maladie et d’apporter des éléments associés au contrôle de la cercosporiose noire.Deux modèles ont été développés dans le cadre de cette thèse. La dynamique épidémiologique au niveau de la plante est décrite par un modèle mécaniste décomposé en un modèle de croissance de la plante et un module épidémiologique décrivant le cycle épidémique du pathogène. L’architecture du bananier est prise en compte par le biais de compartiments foliaires positionnés dans l’espace. Le modèle a été validé par un jeu de données indépendant. Les expérimentations numériques et l’analyse de sensibilité du modèle réalisées par les méthodes de Morris et de e-FAST ont permis de mieux comprendre le fonctionnement épidémique de cette maladie et d’identifier les paramètres influant le plus la dynamique épidémique en particulier la vitesse d’extension des lésions, la durée d’incubation et l’efficacité d’infection .L’approche bayésienne a permis de prendre en compte l’information a priori disponible pour ces trois paramètres sur lesquels a porté l’inférence statistique. L’analyse de sensibilité du modèle a également permis d’identifier l’influence de deux paramètres liés à la croissance de la plante : le nombre de feuilles sur une plante et le rythme d’émission foliaire Un modèle de dynamique spatio-temporelle de la cercosporiose noire a été développé à l’échelle d’un territoire à partir d’enquêtes effectuées enMartinique pendant la période d’invasion de cette maladie de septembre 2010 à mai 2012. Les données récoltées étant censurées, l’inférence des paramètres du modèle a été réalisée dans un cadre bayésien en utilisant un algorithme d’augmentation de données. Le modèle et l’inférence développés permettent de reconstruire la dynamique spatio-temporelle de l’invasion et de prédire la fin d’invasion sur le territoire.Les deux modèles de dynamiques spatio-temporelles développés à deux échelles spatiales différentes ont permis d’acquérir des informations importantes pour construire des outils de conception de méthode de contrôle de la cercosporiose noire des bananiers.
This thesis concerns the modeling of the dynamics of foliar diseases of tropical tree crops at different spatial scales. This modeling approach is applied to black Sigatoka of banana. It is to explore and determine the environmental parameters and host resistance has a significant influence on the spatiotemporal dynamics of the disease and to provide elements associated with the control of Sigatoka noire.Deux models were developed as part of this thesis. The epidemiological dynamics at the plant is described by a mechanistic model decomposed into a growth model of the plant and an epidemiological unit describing the pathogen epidemic cycle. The architecture of the banana is taken into account through foliar compartments positioned in space. The model was validated by an independent data set. Numerical experiments and model sensitivity analysis performed by the methods of Morris and e-FAST enabled to better understand the functioning of this epidemic disease and identify the parameters affecting the most dynamic epidemic especially speed extension of the lesions, the incubation period and efficiency of Bayesian .L'approche infection allowed to take into account prior information available for these three parameters that were the statistical inference. The model sensitivity analysis also identified the influence of two parameters related to plant growth: the number of leaves on a plant and rate of leaf emission A dynamic model of space-time Black Sigatoka has been developed at the scale of a territory from surveys enMartinique during the period of invasion of the disease from September 2010 to May 2012. The data collected being censored, inference of model parameters has was performed in a Bayesian framework, using a data augmentation algorithm. The model developed and inference possible to reconstruct the spatiotemporal dynamics of the invasion and predict the end of the invasion territoire.Les two spatio-temporal dynamics models developed at two different spatial scales has been gained important information for build tools design method of control of black Sigatoka of banana.

http://www.theses.fr/2015AGUY0835/document

Éditeur(s) : HAL CCSD
Résumé : The purpose of this thesis is to improve the knowledge about and apply data mining techniques and some Bayesian model in the field of social and environmental health inequalities. On the neighborhood scale on the Paris, Marseille, Lyon and Lille metropolitan areas, the health event studied is infant mortality. We try to explain its risk with socio-economic data retrieved from the national census and environmental exposures such as air pollution, noise, proximity to traffic, green spaces and industries. The thesis is composed of two parts. The data mining part details the development of a procedure of creation of multidimensional socio-economic indices and of an R package that implements it, followed by the creation of a cumulative exposure index. In this part, data mining techniques are used to synthesize information and provide composite indicators amenable for direct usage by stakeholders or in the framework of epidemiological studies. The second part is about Bayesian models. It explains the "BYM" model. This model allows to take into account the spatial dimension of the data when estimating mortality risks. In both cases, the methods are exposed and several results of their usage in the above-mentioned context are presented. We also show the value of the socio-economic index procedure, as well as the existence of social inequalities of infant mortality in the studied metropolitan areas.
Cette thèse a pour but d'améliorer les connaissances concernant les techniques d'analyse de données et certains modèles bayésiens dans le domaine de l'étude des inégalités sociales et environnementales de santé. À l'échelle géographique de l'IRIS sur les agglomérations de Paris, Marseille, Lyon et Lille, l'événement sanitaire étudié est la mortalité infantile dont on cherchera à expliquer le risque avec des données socio-économiques issues du recensement et des expositions environnementales comme la pollution de l'air, les niveaux de bruit et la proximité aux industries polluantes, au trafic automobile ou aux espaces verts. Deux volets principaux composent cette thèse. Le volet analyse de données détaille la mise au point d'une procédure de création d'indices socio-économiques multidimensionnels et la conception d'un package R l'implémentant, puis la création d'un indice de multi-expositions environnementales. Pour cela, on utilise des techniques d'analyse de données pour synthétiser l'information et fournir des indicateurs composites utilisables directement par les décideurs publics ou dans le cadre d'études épidémiologiques. Le second volet concerne les modèles bayésiens et explique le modèle " BYM ". Celui-ci permet de prendre en compte les aspects spatiaux des données et est mis en œuvre pour estimer le risque de mortalité infantile. Dans les deux cas, les méthodes sont présentées et différents résultats de leur utilisation dans le contexte ci-dessus exposés. On montre notamment l'intérêt de la procédure de création d'indices socio-économiques et de multi-expositions, ainsi que l'existence d'inégalités sociales de mortalité infantile dans les agglomérations étudiées.
https://tel.archives-ouvertes.fr/tel-00943004

tel-00943004
https://tel.archives-ouvertes.fr/tel-00943004
https://tel.archives-ouvertes.fr/tel-00943004/document
https://tel.archives-ouvertes.fr/tel-00943004/file/Manuscrit_LallouA_final.pdf

Éditeur(s) : Ieee-inst Electrical Electronics Engineers Inc
Résumé : The synthetic aperture radar (SAR) Doppler centroid has been used to estimate the scatter line-of-sight radar velocity. In weak to moderate ocean surface current environment, the SAR Doppler centroid is dominated by the directionality and strength of wave-induced ocean surface displacements. In this paper, we show how this sea state signature can be used to improve surface wind retrieval from SAR. Doppler shifts of C-band radar return signals from the ocean are thoroughly investigated by colocating wind measurements from the ASCAT scatterometer with Doppler centroid anomalies retrieved from Envisat ASAR. An empirical geophysical model function (CDOP) is derived, predicting Doppler shifts at both VV and HH polarization as function of wind speed, radar incidence angle, and wind direction with respect to radar look direction. This function is used into a Bayesian inversion scheme in combination with wind from a priori forecast model and the normalized radar cross section (NRCS). The benefit of Doppler for SAR wind retrieval is shown in complex meteorological situations such as atmospheric fronts or low pressure systems. Using in situ information, validation reveals that this method helps to improve the wind direction retrieval. Uncertainty of the calibration of Doppler shift from Envisat ASAR hampers the inversion scheme in cases where NRCS and model wind are accurate and in close agreement. The method is however very promising with respect of future SAR missions, in particular Sentinel-1, where the Doppler centroid anomaly will be more robustly retrieved.
Ieee Transactions On Geoscience And Remote Sensing (0196-2892) (Ieee-inst Electrical Electronics Engineers Inc), 2012-07 , Vol. 50 , N. 7 , P. 2901-2909

Droits : 2012 IEEE
http://archimer.ifremer.fr/doc/00088/19896/17554.pdf
DOI:10.1109/TGRS.2011.2174998
http://archimer.ifremer.fr/doc/00088/19896/

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling. This approach has been very successful in ecology and agronomy. We analyze the development of this approach applied to a few examples of natural resources management.
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand succès depuis une quinzaine d'années grâce au développement des méthodes de Monte Carlo et aux performances toujours croissantes des moyens de calcul. Au travers d'outils comme les méthodes de Monte Carlo par chaîne de Markov et les méthodes de Monte Carlo séquentielles, l'inférence bayésienne se combine efficacement à la modélisation markovienne. Cette approche est également très répendue dans le domaine de l'écologie et l'agronomie. Nous faisons le point sur les développements de cette approche appliquée à quelques exemples de gestion de ressources naturelles.
ARIMA

inria-00506383
https://hal.inria.fr/inria-00506383
https://hal.inria.fr/inria-00506383/document

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
The origin of plate tectonics is one of the most fundamental issues in earth and planetary sciences. Laboratory experiments indicate that the viscosity of silicate rocks is so strongly temperature-dependent that the entire surface of the Earth should be one immobile rigid plate. The rheology of oceanic lithosphere is, however, still poorly understood, and there exist few constraints on the temperature dependency of viscosity on the field scale. Here we report a new kind of observational constraint based on the geoid along oceanic fracture zones. We identify a large number of conspicuous small-scale geoid anomalies, which cannot be explained by the standard evolution model of oceanic lithosphere, and estimate their source density perturbations using a new Bayesian inversion method. Our results suggest that they are caused most likely by small-scale convection involving temperature perturbations of ∼300 K±100 K∼300 K±100 K. Such thermal contrast requires the activation energy of mantle viscosity to be as low as View the MathML source100±50 kJmol−1 in case of diffusion creep, and View the MathML source225±112 kJmol−1 in case of dislocation creep, substantially reducing the thickness of the stiffest part of oceanic lithosphere. Oceanic lithosphere may thus be broken and bent much more easily than previously thought, facilitating the operation of plate tectonics.
ISSN: 0012-821X

hal-01467827
https://hal.archives-ouvertes.fr/hal-01467827
DOI : 10.1016/j.epsl.2016.05.027

Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
1. Estimating forest above-ground biomass (AGB), or carbon (AGC), in tropical forests has become a majorconcern for scientists and stakeholders. However, AGB assessment procedures are not fully standardized andeven more importantly the uncertainty associated with AGB estimates is seldom assessed.2. Here, we present an R package designed to compute both AGB/AGC estimate and its associated uncertaintyfrom forest plot datasets, using a Bayesian inference procedure. The package builds upon previous work onpantropical and regional biomass allometric equations and published datasets by default but it can alsointegrate unpublished or complementary datasets in many steps.3. BIOMASS performs a number of standard tasks on input forest tree inventories: i) tree speciesidentification, if available, is automatically corrected; ii) wood density is estimated from tree species identity;iii) if height data are available, a local height-diameter allometry may be built; else height is inferred frompantropical or regional models; iv) finally, AGB/AGC are estimated by propagating the errors associated withall the calculation steps up to the final estimate. R code is given in the paper and in the appendix forillustration purpose.4. The BIOMASS package should contribute to improved standards for AGB calculation for tropical foreststands, and will encourage users to report the uncertainties associated with stand-level AGB/AGC estimatesin future studies.
ISSN: 2041-210X

hal-01558180
https://hal.archives-ouvertes.fr/hal-01558180
DOI : 10.1111/2041-210X.12753

Éditeur(s) : HAL CCSD
Résumé : 27 pages
Cascade Stochastic Differential Equation (SDE), a continuous time model for energy dissipation in turbulence, is a generalization of the Yaglom discrete cascade model. We extend this SDE to a model in random environment by assuming that its two parameters are switched by a continuous time Markov chain whose states represent the states of the environment. Moreover, a Dirichlet process is placed as a prior on the space of sample paths of this chain. We propose a Bayesian estimation method of this model which is tested both on simulated data and on real data of wind speed measured at the entrance of the mangrove ecosystem in Guadeloupe.
https://hal.archives-ouvertes.fr/hal-00286131

hal-00286131
https://hal.archives-ouvertes.fr/hal-00286131
https://hal.archives-ouvertes.fr/hal-00286131/document
https://hal.archives-ouvertes.fr/hal-00286131/file/Srik11_PhysicaDDspace.pdf

Éditeur(s) : HAL CCSD
Résumé : Point processes are often used as spatial or spatio-temporal distribution models of occurrences. In this PhD dissertation, we focus first on Cox processes driven by a hidden process associated with a Dirichlet process. This model corresponds to hidden occurrences influencing the stochastic intensity of observed occurrences. We generalize the notion of "Shot noise Cox process" introduced by M{o}ller and develop its bayesian analysis by a Gibbs sampler combined with a Metropolis-Hastings algorithm. We show that our MCMC method is a reversible jump one. The model takes into account a random number of hidden contributions producing effects on the observed point process intensity. Therefore the parameter space has a variable dimension. We focus the statistical inference on the estimation of the hidden contribution expected value, the hidden contribution expected number, the spatial influence and correlation parameters. The contribution equality test and contribution independence test are proposed. Applications in epidemiology and ecology are shown from extit{Rubus fruticosa} data, extit{Ibicella lutea} data and death number data in counties of Georgia, USA. Two situations are considered with respect to available data~: firstly, the spatial positions of occurrences are observed between several pairs of consecutive dates; secondly, counts are carried out over a fixed time interval in several spatial sampling units. Secondly, we focus on point processes with memory introduced by Kagan, Ogata and Vere-Jones, pioneers in statistical seismology. In fact, spatio-temporal point processes play an important role in the studies of earthquake catalogs since they consist of seismic events with their dates and spatial locations. We studied an ETAS (Epidemic Type Aftershock Sequence) model with time independent background intensity and several triggering functions taking into account previous events. We illustrate our approach with a seismicity study of the Lesser Antilles arc. A comparaison study of Gamma, Weibull, Log-Normal and modified Omori law triggering function models is also carried out. We show that the modified Omori law does not fit the Lesser Antilles seismic data and the best adjusted triggering function is the Weibull model. Consequently, the waiting time between aftershocks is weaker in the Lesser Antilles zone compared to the one in regions with seismicity described by the modified Omori law. In other words, aftershock aggregativity is higher in the Lesser Antilles region. The stochastic background intensity following a Dirichlet process centered on a spatial log-normal process is discussed.
Les processus ponctuels sont souvent utilisés comme modèles de répartitions spatiales ou spatio-temporelles d'occurrences. Dans cette thèse, nous nous intéressons tout d'abord à des processus de Cox dirigés par un processus caché associé à un processus de Dirichlet. Ce modèle correspond à des occurrences cachées influençant l'intensité stochastique des occurrences observées. Nous généralisons la notion de " Shot noise Cox process " introduite par Moller et développons le traitement bayésien par un échantillonneur de Gibbs combiné à un algorithme de Metropolis-Hastings. Nous montrons que cette méthode MCMC est à sauts réversibles. Le modèle prend en compte, en effet, un nombre aléatoire de contributions cachées influençant l'intensité du processus ponctuel observé donc a un espace paramétrique de dimension variable. Nous focalisons l'inférence statistique sur l'estimation de la valeur espérée de chaque contribution cachée, le nombre espéré de contributions cachées, le degré d'influence spatiale de ces contributions et leur degré de corrélation. Le test d'égalité des contributions et celui de leur indépendance sont ainsi développés. L'utilité en épidémiologie et en écologie est alors démontrée à partir de données de Rubus fruticosa, Ibicella lutea et de mortalité dans les cantons de Georgia, USA. En termes de données observées, deux situations sont considérées: premièrement, les positions spatiales des occurrences sont observées entre plusieurs paires de dates consécutives; deuxièmement, des comptages sont effectués, au cours d'une période fixée, dans des unités d'échantillonnage spatiales. D'autre part, nous nous intéressons aux processus ponctuels à mémoire introduits par Kagan, Ogata et Vere-Jones, précurseurs de la statistique sismologique. En effet, les processus ponctuels spatio-temporels ont une place importante dans l'étude des catalogues sismiques puisque ces derniers sont généralement constitués d'événements sismiques datés et géo-référencés. Nous avons étudié un modèle ETAS (Epidemic Type Aftershock Sequence) avec une intensité d'arrière-plan indépendante du temps et plusieurs fonctions déclenchantes permettant d'intégrer les événements antérieurs récents. Cette approche est utilisée pour étudier la sismicité de l'arc des Petites Antilles. Une étude comparative des modèles Gamma, Weibull, Log-Normal et loi d'Omori modifiée pour les fonctions déclenchantes est menée. Nous montrons que la loi d'Omori modifiée ne s'ajuste pas aux données sismiques des Petites Antilles et la fonction déclenchante la plus adaptée est le modèle de Weibull. Cela implique que le temps d'attente entre répliques dans la zone des Petites Antilles est plus faible que celui des régions à sismicité décrite par la loi d'Omori modifiée. Autrement dit, l'agrégation des répliques après un événement majeur est plus prononcée dans la zone des Petites Antilles. La possibilité d'inclure une intensité d'arrière-plan suivant un processus de Dirichlet centré sur un processus spatial log-gaussien est discutée.
https://tel.archives-ouvertes.fr/tel-00841146

Droits : info:eu-repo/semantics/OpenAccess
NNT : 2012AGUY0555
tel-00841146
https://tel.archives-ouvertes.fr/tel-00841146
https://tel.archives-ouvertes.fr/tel-00841146/document
https://tel.archives-ouvertes.fr/tel-00841146/file/these_VALMY.pdf

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Ctenophores are a phylum of non-bilaterian marine (mostly planktonic) animals, characterised by several unique synapomorphies (e.g., comb rows, apical organ). Relationships between and within the nine recognised ctenophore orders are far from understood, notably due to a paucity of phylogenetically informative anatomical characters. Previous attempts to address ctenophore phylogeny using molecular data (18S rRNA) led to poorly resolved trees but demonstrated the paraphyly of the order Cydippida. Here we compiled an updated 18S rRNA data set, notably including a few newly sequenced species representing previously unsampled families (Lampeidae, Euryhamphaeidae), and we constructed an additional more rapidly evolving ITS1 + 5.8S rRNA + ITS2 alignment. These data sets were analysed separately and in combination under a probabilistic framework, using different methods (maximum likelihood, Bayesian inference) and models (e.g., doublet model to accommodate secondary structure; data partitioning). An important lesson from our exploration of these datasets is that the fast-evolving internal transcribed spacer (ITS) regions are useful markers for reconstructing high-level relationships within ctenophores. Our results confirm the paraphyly of the order Cydippida (and thus a ``cydippid-like'' ctenophore common ancestor) and suggest that the family Mertensiidae could be the sister group of all other ctenophores. The family Lampeidae (also part of the former ``Cydippida'') is probably the sister group of the order Platyctenida (benthic ctenophores). The order Beroida might not be monophyletic, due to the position of Beroe abyssicola outside of a clade grouping the other Beroe species and members of the ``Cydippida'' family Haeckeliidae. Many relationships (e.g. between Pleurobrachiidae, Beroida, Cestida, Lobata, Thalassocalycida) remain unresolved. Future progress in understanding ctenophore phylogeny will come from the use of additional rapidly evolving markers and improvement of taxonomic sampling.
ISSN: 0944-2006

hal-01544475
https://hal.archives-ouvertes.fr/hal-01544475
DOI : 10.1016/j.zool.2014.06.004

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
It has been suggested that prenatal exposure to insecticides adversely affects fetal growth, but the overall results have been inconsistent, partly because of the different exposure sources and exposure assessments used. In the French PELAGIE (Perturbateurs Endocriniens: Étude Longitudinale sur les Anomalies de la Grossesse, l'Infertilité et l'Enfance) mother and child cohort (2002-2006), the authors investigated the association between fetal growth and insecticide exposure (n = 1,213) using an integrated Bayesian latent variable model to include multiple exposure sources: agricultural activities, nonorganic diet, household insecticide use on plants, and household insecticide use against insects. They used a questionnaire to collect information on household use and organic diet, and a national agricultural census provided data on agricultural activities in the women's municipalities of residence. A 0.10-cm decrease in head circumference at birth (95% credibility interval: -0.22, 0.01) was associated with fetal insecticide exposure from agricultural activities in the municipality of residence. Decreases in average birth weight (-27 g; 95% credibility interval: -59, 6) and head circumference (-0.12 cm; 95% credibility interval: -0.26, 0.01) were associated with household insecticide use to treat plants. The present results suggest an inverse association between fetal growth and prenatal insecticide exposure from nearby agricultural activity or household use. Bayesian modeling via latent variables is a natural framework for including multiple sources of exposure to environmental pollutants.
ISSN: 0002-9262

hal-00875979
https://hal.archives-ouvertes.fr/hal-00875979
DOI : 10.1093/aje/kwr422
PUBMED : 22534205

Éditeur(s) : HAL CCSD
Résumé : This paper presents the experimental results obtained for an automatic detection of ships in High Resolution optical satellite images. Our images are panchromatic SPOT 5 images, whose resolution is 5m per pixel. Our detection method is part of an industrial project of maritime surveillance. It is based on the Bayesian decision theory and does not need any preprocessing.The detection is composed of three stages. The first one is a pre-detection of targets that gives us candidates. The second one is a precise segmentation of each candidate. The third one is a classification of candidates into three classes : real small targets, real big targets and false alarms. The two first stages are based on the bayesian theory, using a very simple image model that leads to very fast algorithms. For now, the classification is based on multivariate decision tree. Finally, the overall results of the method are given for a set of images, as close as possible to the operational conditions and show the performances of the proposed method (detection of almost all the ships, false alarms due to crests of waves and an acceptable computing time).
Journée Nationale de Photogrammétrie et Télédétection

hal-00601854
https://hal.archives-ouvertes.fr/hal-00601854

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : Tree mortality in tropical forests is a complex ecological process for which modelling approaches need to be improved to better understand, and then predict, the evolution of tree mortality in response to global change. The mortality model introduced here computes an individual probability of dying for each tree in a community. The mortality model uses the ontogenetic stage of the tree because youngest and oldest trees are more likely to die. Functional traits are integrated as proxies of the ecological strategies of the trees to permit generalization among all species in the community. Data used to parametrize the model were collected at Paracou study site, a tropical rain forest in French Guiana, where 20,408 trees have been censused for 18 years. A Bayesian framework was used to select useful covariates and to estimate the model parameters. This framework was developed to deal with sources of uncertainty, including the complexity of the mortality process itself and the field data, especially historical data for which taxonomic determinations were uncertain. Uncertainty about the functional traits was also considered, to maximize the information they contain. Four functional traits were strong predictors of tree mortality: wood density, maximum height, laminar toughness and stem and branch orientation, which together distinguished the light-demanding, fast-growing trees from slow-growing trees with lower mortality rates. Our modelling approach formalizes a complex ecological problem and offers a relevant mathematical framework for tropical ecologists to process similar uncertain data at the community level.
ISSN: 1932-6203

hal-01190561
https://hal.archives-ouvertes.fr/hal-01190561
PRODINRA : 208582
PUBMED : 23675500
DOI : 10.1371/journal.pone.0063678

Éditeur(s) : HAL CCSD
Résumé : il s'agit d'un type de produit dont les métadonnées ne correspondent pas aux métadonnées attendues dans les autres types de produit : ACTIVITY_REPORT
Understanding genomic evolution in wild populations is of particular importance because evolution is a central process in our understanding of populations’ history and their future response to global changes. In addition to fundamental knowledge, the study of populations’ evolution has wide applications such as conservation/restoration genetics or epidemiology. Indeed, genetic diversity is the ‘fuel’ for populations’ evolution and adaptation to new conditions, and estimating the extent and the geographic structure of genetic diversity, as well as identifying the evolutionary forces behind (demography, gene flow, and adaptation) are thus major challenges in the current context of global changes. [br/]A particular attention is currently devoted to the process of genetic divergence in wild populations, with emphases on gene flow, local adaptation, and their interactions. Indeed, adaptation in wild populations is supported in many species by many kinds of experiments based on both quantitative phenotypic traits and molecular data. However, the processes governing populations’ evolution are poorly understood in the tropical rainforest of Amazonia, and the relative influence of neutral and adaptive processes are continuously discussed. [br/]I am a young scientist interested in the process of genomic divergence in wild (tree) populations. I aimed at understanding how evolution structures the genetic diversity and governs molecular divergence in wild populations, with particular emphases on gene flow and local adaptation. During five years as a young scientist, I worked on answering the following questions, focusing on two tree species occupying vulnerable areas: mainly [i]Eperua falcata[/i] in Amazonia and, to a lesser extent, [i]Abies alba[/i] in the Mediterranean area.[br/][u]Scientific questions:[/u][br/]- Which evolutionary drivers are responsible of the structure of genetic diversity in wild populations occupying wide areas? This question was addressed at different geographical scales: from large (regional) to very local (microgeographic) scales. [br/]- What is the relative influence of neutral (gene flow, inbreeding) and adaptive drivers in the process of genetic divergence?[br/]- Is microgeographic adaptation possible in spite of extensive gene flow between populations occupying the extreme boundaries of an ecological gradient? [br/]- What is the extent of natural selection in the (whole) genome of wild populations? [br/][br/]My research work is at the interface between three promising disciplines: Evolutionary genomics (i.e. the study of populations evolution and the evolutionary drivers behind), Bioinformatics (i.e. the treatment of next-generation data), and Bayesian modelling (i.e. the empirical calibration of complex mechanistic model). I intend to take advantage of the recent advances in sequencing and informatic technologies to bring up to date the field of ecological genetics and evolutionary biology, by developing modern research strategies to study the process of genomic divergence in wild populations.
https://hal.archives-ouvertes.fr/hal-01204218

hal-01204218
https://hal.archives-ouvertes.fr/hal-01204218
PRODINRA : 286326

Résumé : En 2013, Le Groupe d'experts Intergouvernemental sur l'Evolution du Climat (GIEC) publie son cinquième rapport concernant les changements climatiques. Il y est souligné que le réchauffement climatique est sans équivoque, et que de nouvelles émissions de gaz à effet de serre impliqueront une poursuite du réchauffement et des changements affectant toutes les composantes du système climatique. En région tropicale, une hausse de la température, ainsi qu'une intensification des événements de sécheresse et de pluviométrie extrêmes sont à prévoir. C'est dans ce contexte que s'inscrit ce travail, dont le but est d'étudier la réponse de la forêt tropicale à ces changements climatiques prédits en Guyane Française. Pour ce faire, j'ai utilisé les données du dispositif de suivi forestier de Paracou pour construire un modèle de dynamique individuel basé sur les traits fonctionnels des arbres. Un modèle de mortalité a d'abord été réalisé puis couplé à un modèle de croissance.Le modèle couplé ainsi construit permet de modéliser la croissance et la mortalité des arbres sur un pas de temps de 2 ans tout en tenant compte de leur ontogénie et de leurs traits fonctionnels. Ce modèle a d'abord été appliqué aux essences commerciales de Guyane Française en forêt naturelle et exploitée en y ajoutant un indice de stress hydrique. Ceci permet de montrer que le stress hydrique fait baisser la croissance et augmenter la mortalité, tandis que l'exploitation a l'effet inverse. Malgré le signal commun, différentes réponses sont observées selon les espèces. Le modèle a ensuite été appliqué à la communauté en forêt naturelle pour identifier les drivers climatiques et les processus impactés. Il ressort que la croissance est impactée par le stress hydrique et la température, et que la mortalité est impactée par le stress hydrique et la pluviométrie totale.Ces résultats ont enfin permis de construire un modèle complet de dynamique forestière climat dépendant, et de simuler l'évolution d'une communauté pendant un siècle selon différents scénarios correspondant aux prédictions du GIEC. Les simulations mettent en évidence une très forte diminution de la croissance, ainsi qu'une plus faible diminution de la mortalité. Ceci entraine une diminution notable de la surface terrière, du diamètre quadratique et de la biomasse fraiche. Une analyse de sensibilité montre que ces changements sont principalement dus à l'augmentation sévère des températures prédites pour le siècle à venir. Des pistes de réflexion sur les enjeux de modélisation et les échelles considérées sont proposées en discussion de ce travail.
In 2013 the intergovernmental panel on climate change (ipcc) publishes its fifth report. This report underlines that an increase of temperature and a strengthening of drought and extreme rainfall are expected in tropical regions. This work was made in this context of climate changes, and aimed to study the response of the rainforest to predicted climate changes. To do this, i used the data from the study site of Paracou French Guiana to build an individual based dynamics model based on the functional traits of trees. This model was first applied to species with a commercial interest in French Guiana, in natural and logged forest and adding a water stress index as predictor. Water stress decreases growth and increases mortality, while logging had the opposite effect. The model was then applied to the community in natural forest for identifying potential climate drivers and impacted processes. Growth is impacted by the water stress and temperature and mortality is imp acted by the water stress and the total rainfall. These results allowed us to build a climate dependent model of forest dynamics and to run simulations of the evolution of a community under different scenarios for the next century. Simulations showed a decrease of growth and a small decrease of mortality. This resulted in a substantial decrease of basal area, squared diameter and fresh biomass.

http://www.theses.fr/2014AGUY0802/document

Éditeur(s) : HAL CCSD IJCA
Résumé : International audience
International Journal of Computer Applications

hal-01010081
https://hal.archives-ouvertes.fr/hal-01010081
DOI : 10.5120/16691-6825