The mangrove forest is an ecosystem distributed worldwide along tropical andsubtropical coastlines. The environmental conditions are known to affect species distributionpatterns, and to understand them is one of the main goals of biogeography. The association ofenvironmental factors (e.g. temperature and precipitation) and species distribution patterns haschallenged scientists since long. Species are distributed in a geographical space, however, whyspecies are present in a certain location but absent in another is a fundamental question inbiogeography. This PhD thesis aims at better understanding the most intriguing issues relatedto the role of environmental factors associated with mangrove distribution range limits andspecies richness from regional (Brazilian mangroves) to global scales.For the Brazilian mangroves, two scales were used to comprehend the spatial ecological nicheof mangrove tree species. First, the entire Brazilian mangrove-lined coast was considered, andseveral environmental variables were used to explain what is driving mangrove tree speciesdistribution. Second, a detailed analysis of daily Sea Surface Temperature (SST) data for theBrazilian mangrove forest at the southernmost mangrove range limit was done, and comparedwith a site beyond the range limit. The chilling events of SST data, also considering oceancurrents, show that Laguncularia racemosa is probably bounded by the southernmost Brazilianmangrove limit because of a dispersal constraint rather than by the frequency of chillingevents. However, the abundance of L. racemosa in Laguna at the Southern mangrove rangelimit, compared with only a few individuals of A. schaueriana, is probably associated with thechilling events of SST constituting a constraint to the latter species. This proves that mangroverange limits are not explained by a single causeThis PhD thesis also has the objective to evaluate the influence of upwelling intensity onthe distribution worldwide, which is its approach at a global scale. The sea surface temperature(SST) had been identified as one of the key drivers of global mangrove distribution, given thecoincidence of isotherms with mangrove global limits. However, other factors play a role in thevariability of SST. Cold waters from deep ocean layers rise to the surface (upwelling systems),which reduces SST values and can trigger aridity. Although previous research has confirmedthat mangrove distribution is driven by a variety of factors, this PhD thesis additionallydemonstrates a significant influence of upwelling intensity, and hence provides elements for abetter understanding of the factors driving mangrove expansion/retraction at a global scale. Inaddition, this work emphasizes climate and oceanographic processes influencing mangroverange limits. While contributing to a better understanding of some of the most intriguingquestions on the macroecology of mangrove, this PhD thesis also raises new questions thatshould be studied by future research. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

What explains the geographic distribution of pathogens? Better understanding and characterising disease patterns will help scientists to identify areas likely to host future epidemics and epizootics and to prioritise surveillance and intervention. However, the use of disease surveillance data to assess the risk of transmission and generate risk maps raises conceptual and methodological issues. Indeed, pathogens and more particularly viruses aren't ”species” like others that live in the open environment and must be studied with methods and concepts of their own. Avian influenza (AI), a disease caused by a virus infecting bird populations, has been selected to study these issues. AI has a major economic impact on the poultry industry in many countries, raises concerns of livelihood in low and middle-income countries, and represents a major concern for human health. The aim of this PhD thesis was to improve the knowledge on the spatial epidemiology of AI in different settings and conditions (i). For this, recent epizootics caused by the subtypes A (H5N1) and A (H7N9) were selected as case studies. First, highly pathogenic subtypes of the A (H5N1) virus have been studied in poultry farms (ducks and chickens) at different spatial scales: at the continental scale and the regional scale in the Mekong (Cambodia, Laos, Vietnam, Thailand) and the Nile Delta in Egypt. All these cases occurred between 2003, the date on which the virus starts to spread outside China, and 2015; the HPAI A (H5N1) subtypes are still reported today in many countries. Human infections caused by the A (H7N9) virus in China from March 2013 to 2017 were also studied. Studied different AI subtypes at different spatial scales within different host species also allowed to develop a conceptual model of AI transmission and to discuss the issue of the transferability of results in epidemiology (ii). Lastly, this PhD thesis leads to a discussion about the transfer of methods and concepts from ecology to spatial epidemiology, with a particular emphasis on their possible limitations (iii). / Doctorat en Sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

La compréhension des processus écologiques qui sous-tendent l’assemblage des communautés végétales et la coexistence des espèces est un objectif central en écologie. Ces processus sont potentiellement nombreux et de natures contrastées. Ainsi, la composition d’une communauté de plantes dépend de processus déterministes liés aux conditions environnementales abiotiques (climat, conditions physiques et chimiques du sol, lumière) et d’interactions biotiques complexes, positives (facilitation, symbioses) comme négatives (compétition, prédation, pathogènes). En outre, les communautés sont influencées par des processus stochastiques (capacité de dispersion limitée, dérive écologique). Si les mécanismes à l’origine de ces processus sont très différents, ils ont néanmoins en commun la génération de motifs (patterns) spatiaux de distribution d’espèces dans les communautés. L’analyse de la structure spatiale des communautés permet ainsi une étude indirecte des processus régissant les communautés. La nature complexe de ces patterns spatiaux a mené au développement de nombreuses méthodes statistiques de détection et de description de patterns. Les méthodes basées sur des vecteurs propres spatiaux sont parmi les plus puissantes et précises pour détecter des patterns complexes et multi-échelles. Ces vecteurs propres, utilisés comme prédicteurs spatiaux, peuvent être combinés à un ensemble de variables environnementales dans un cadre de partition de variation. Celui-ci permet, en théorie, de démêler les effets uniques et l’effet conjoint des variables environnementales et spatiales sur la variation de composition d’une communauté. Il mène ainsi à une quantification de l’action des processus déterministes et des processus stochastiques sur l’assemblage de la communauté. Néanmoins, je montre dans cette thèse qu’un certain flou méthodologique concernant deux étapes déterminantes des analyses basées sur les vecteurs propres spatiaux a mené une proportion élevée d’études à utiliser ces méthodes de manière sous-optimale, voire fortement biaisée. Ceci compromet la fiabilité des patterns spatiaux détectés et des processus écologiques inférés. Une autre limitation de ce cadre d’analyse concerne la fraction de la partition de variation décrivant l’effet environnemental spatialement structurés qu’aucune méthode ne permet de tester.Cette thèse présente des solutions non biaisées, puissantes et précises à ces différentes limitations méthodologiques et permet d’élargir le cadre de l’inférence de processus écologique à partir de patterns spatiaux de communautés. Les différentes étapes d’amélioration de ces méthodes ont également été illustrées dans la thèse au travers de trois cas d’études fournis par deux communautés d’arbres tropicale et tempérée et une communauté de champignons symbiotiques des arbres. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished