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/

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 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 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 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 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 IJCA
Résumé : International audience
International Journal of Computer Applications

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

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

Éditeur(s) : HAL CCSD Wiley
Résumé :
How Quaternary climatic and geological disturbances influenced the composition of Neotropical forests is hotly debated. Rain fall and temperature changes during and/or immediately after the Last Glacial Maximum (LGM) are thought to have strongly affected the geographical distribution and local abundance of tree species. The paucity of the fossil records in Neotropical forests prevents a direct reconstruction of such processes. To describe community-level historical trends in forest composition, we turned therefore to inferential methods based on the reconstruction of past demographic changes. In particular, we modelled the history of rainforests in the Eastern Guiana Shield over a time scale of several thousand generations, through the application of Approximate Bayesian Computation (ABC) and Maximum Likelihood (ML) methods to diversity data at nuclear and chloroplast loci in eight species or subspecies of rainforest trees. Depending on the species and on the method applied, we detected population contraction, expansion or stability, with a general trend in favour of stability or expansion, with changes presumably having occurred during or after the LGM. These findings suggest that Guiana Shield rain forests have globally persisted, while expanding, through the Quaternary, but that different species have experienced different demographic events, with a trend towards the increase in frequency of light-demanding, disturbance-associated species.
ISSN: 0962-1083

hal-01512150
https://hal.archives-ouvertes.fr/hal-01512150
https://hal.archives-ouvertes.fr/hal-01512150/document
https://hal.archives-ouvertes.fr/hal-01512150/file/Barthe_et_al-_%7B0B6A8C5E-DDFF-4C4E-822C-4E7BC22A6C9B%7D.pdf
DOI : 10.1111/mec.13949

Éditeur(s) : HAL CCSD Wiley
Résumé : [b]Aim[/b][br/] Many tropical tree species have poorly delimited taxonomic boundaries and contain undescribed or cryptic species. We examined the genetic structure of a species complex in the tree genus Carapa in the Neotropics in order to evaluate age, geographic patterns of diversity and evolutionary relationships, and to quantify levels of introgression among currently recognized species.[br/] [br/] [b]Location[/b][br/] Lowland moist forests in the Guiana Shield, the Central and Western Amazon Basin, Chocó and Central America.[br/] [br/] [b]Methods[/b][br/] Genetic structure was analysed using seven nuclear simple sequence repeats (nuSSR), five chloroplast SSRs (cpSSR), and two chloroplast DNA (cpDNA) intergenic sequences (trnH-psbA and trnC-ycf6). Bayesian clustering analysis of the SSR data was used to infer population genetic structure and to assign 324 samples to their most likely genetic cluster. Bayesian coalescence analyses were performed on the two cpDNA markers to estimate evolutionary relationships and divergence times.[br/] [br/] [b]Results[/b][br/] Two genetic clusters (nu_guianensis and nu_surinamensis) were detected, which correspond to the Neotropical species C. guianensis (sensu latu) and C. surinamensis. Fourteen cpDNA haplotypes clustered into six haplogroups distributed between the two nuclear genetic clusters. Divergence between the haplogroups was initiated in the Miocene, with some haplotype structure evolving as recently as the Pleistocene. The absence of complete lineage sorting between the nuclear and chloroplast genomes and the presence of hybrid individuals suggest that interspecific reproductive barriers are incomplete. NuSSR diversity was highest in C. guianensis and, within C. guianensis, cpDNA diversity was highest in the Central and Western Amazon Basin. Regional genetic differentiation was strong but did not conform to an isolation-by-distance process or exhibit a phylogeographical signal.[br/] [br/] [b]Main conclusions[/b][br/] The biogeographical history of Neotropical Carapa appears to have been influenced by events that took place during the Neogene. Our results point to an Amazonian centre of origin and diversification of Neotropical Carapa, with subsequent migration to the Pacific coast of South America and Central America. Gene flow apparently occurs among species, and introgression events are supported by inconsistencies between chloroplast and nuclear lineage sorting. The absence of phylogeographical structure may be a result of the ineffectiveness of geographical barriers among populations and of reproductive isolation mechanisms among incipient and cryptic species in this species complex.
ISSN: 0305-0270

hal-01032395
https://hal.archives-ouvertes.fr/hal-01032395
DOI : 10.1111/j.1365-2699.2011.02678.x

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 INRIA
Résumé : International audience
Markov chain Monte Carlo (MCMC) methods together with hidden Markov models are extensively used in the Bayesian inference for many scientific fields like environment and ecology. Through simulated examples we show that the speed of convergence of these methods can be very low. In order to improve the convergence properties, we propose a method to make parallel chains interact. We apply this method to a biomass evolution model for fisheries.
Les méthodes de Monte Carlo par chaînes de Markov (MCMC) couplées à des modèles de Markov cachés sont utilisées dans de nombreux domaines, notamment en environnement et en écologie. Sur des exemples simples, nous montrons que la vitesse de convergence de ces méthodes peut être très faible. Nous proposons de mettre en interaction plusieurs algorithmes MCMC pour accélérer cette convergence. Nous appliquons ces méthodes à un modèle d'évolution de la biomasse d'une pêcherie.
ISSN: 1638-5713

inria-00506386
https://hal.inria.fr/inria-00506386
https://hal.inria.fr/inria-00506386/document
https://hal.inria.fr/inria-00506386/file/campillo2008d.pdf

Éditeur(s) : HAL CCSD Springer Verlag/EDP Sciences
Résumé : International audience
Key message: Genetic diversity appears to be unaffected by disturbance in a stand of the light-demanding Neotropical treeV. michelii. Although spatial genetic structure is modified in post-disturbance cohorts, mixing of seeds from different mother trees in canopy gaps appears to efficiently maintain genetic admixture. Context: The interplay between genetic and demographic processes has major consequences on population viability. Population size affects demographic trends, while genetic diversity insures viability by reducing risks of inbreeding depression and by maintaining adaptive potential. Yet, the consequences of increases in census size (as opposed to effective size) on genetic diversity of forest populations are poorly known. Aims: We have studied the structure of genetic diversity in populations of saplings of the light-responsive tree, Virola michelii (Myristicaceae, the nutmeg family), in two plots having undergone different levels of canopy-gap opening disturbance. This allowed us to test the “intermediate disturbance” hypothesis, which generally applies to species diversity, at the intra-specific scale. Methods: Levels and distribution of genetic diversity were compared between plots and between life stages. Sapling parentage was analysed to infer each adult tree’s contribution to regeneration. Results: Genetic diversity was higher, and spatial genetic structure was stronger in the post-disturbance than in the control seedling population. Parentage analysis suggested that a limited number of parents contributed to most of the regeneration, but that efficient mixing of their progeny may have enhanced the diversity of saplings occupying canopy gaps. Conclusion: A mixture of demo-genetic processes may contribute to maintain genetic diversity in spite of, or possibly due to, ecosystem disturbance in V. michelii.
ISSN: 1286-4560

hal-01502942
https://hal-agroparistech.archives-ouvertes.fr/hal-01502942
https://hal-agroparistech.archives-ouvertes.fr/hal-01502942/document
https://hal-agroparistech.archives-ouvertes.fr/hal-01502942/file/art_10.1007_s13595-015-0508-3-1_%7B869D58F4-272C-491F-A903-DAD6CDBDC042%7D.pdf
DOI : 10.1007/s13595-015-0508-3

Résumé : Les processus ponctuels sont souvent utilisés comme modèles de répartitions spatiales ou spatio-temporelles d'occurrences Dans cette thèse, nous nous intéressons 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 et développons le traitement bayésien. Nous focalisons l'inférence statistique sur l'estimation de la valeur espérée de chaque contribution cachée, leur nombre espéré, degré d'influence spatiale et degré de corrélation L'utilité en épidémiologie et en écologie est démontrée à partir de données de Rubus fruticosa, lbicella lutea et de mortalité dans les cantons de Georgie, USA. En termes de données observées, deux situations sont considérées: d'abord, les positions spatiales des occurrences sont observées entre plusieurs paires de dates consécutives; puis, des comptages sont effectués dans des unités d'échantillonnage spatiales. D'autre part, nous nous intéressons aux processus ponctuels à mémoire introduits par Kagan, Ogata et Vere-Jones. En effet, les processus ponctuels ont une place importante dans l'étude des catalogues sismiques. Nous avons étudié un modèle Epidemie 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 des Petites Antilles. Une étude comparative des modèles Gamma, Weibull, Log-Normal et loi d'Omori modifiée pour les fonctions déclenchantes est menée.
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 and develop its bayesian analysis. 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. Applications in epidemiology and ecology are shown from Rubus fruticosa data, 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 intr oduced by Kagan, Ogata and Vere-Jones. 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 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

http://www.theses.fr/2012AGUY0555/document

Éditeur(s) : HAL CCSD Wiley
Résumé : [b]Aim[/b][br/] [br/] The phylogeographical history of Neotropical species can be difficult to reconstruct because of superimposed Neogene and Quaternary histories, and because of taxonomic uncertainty. We analysed range-wide genetic diversity in a widespread pioneer tree species, Jacaranda copaia (Aubl.) D. Don, to characterize phylogeographical structure, date the evolutionary relationships among lineages, and evaluate the role of dispersal and vicariance in establishing the present geographical range.[br/] [br/] [br/] [b]Location[/b][br/] [br/] Guiana Shield; central, southern and western Amazon Basin; Chocó region; Central America.[br/] [br/] [br/] [b]Methods[/b][br/] [br/] We analysed nine nuclear simple sequence repeat loci (nuSSR), eight chloroplast SSRs (cpSSR), and two cpDNA intergenic sequences in 341 adult trees. Genetic differentiation at nuSSRs was inferred using Bayesian clustering. Dating of chloroplast lineage divergence was obtained using a range of published mutation rates and Bayesian coalescence analyses. Population divergence dating was performed using an isolation-with-migration model for eight loci (one cp sequence and seven nuSSRs).[br/] [br/] [br/] [b]Results[/b][br/] [br/] Nuclear SSR variation identified three geographically overlapping clusters (nu-1, nu-2, nu-3). Twelve cpDNA haplotypes were clustered into two haplogroups (cp-1, cp-2) with the central Amazon harbouring the highest diversity. Molecular dating analysis suggests that cpDNA haplotype diversification started around the end of the Pliocene (2.61 Ma on average), whereas population divergence was more recent and occurred during the mid-Quaternary (point estimates between 357 and 436 ka).[br/] [br/] [br/] [b]Main conclusions[/b][br/] [br/] The genetic variation of J. copaia in the Neotropics was shaped mainly by Pleistocene events. Chloroplast diversity did not display the expected cis/trans Andean disjunction, indicating recent dispersal. Nuclear variation revealed that separate regions share a recent history, with a centre of diversity in the central Amazon Basin. The geographical pattern of diversity is congruent with the distribution of the two subspecies, J. copaia copaia and J. copaia spectabilis, and evidence of nuSSR admixture between the two taxa supports their classification as subspecies.
ISSN: 0305-0270

hal-01032410
https://hal.archives-ouvertes.fr/hal-01032410
DOI : 10.1111/j.1365-2699.2012.02797.x