Les hêtraies-sapinières-pessières de montagne paraissent particulièrement menacées par le réchauffement climatique. Pour appréhender la dynamique future de ces forêts et adapter la sylviculture à ces nouvelles conditions, il est important de mieux connaître les facteurs environnementaux impactant la démographie de ces espèces. Nous avons abordé cette problématique en combinant des données historiques de gestion, le modèle de dynamique forestière Samsara2 et une méthode de calibration basée sur le Calcul Bayésien Approché. Nous avons ainsi pu étudier conjointement les différents processus démographiques dans ces forêts. Nos analyses montrent que la démographie forestière peut varier fortement entre les parcelles et que le climat n'est pas toujours déterminant pour expliquer ces variations. Ainsi, malgré les changements climatiques attendus, la gestion irrégulière pratiquée actuellement devrait permettre de maintenir les services rendus par les peuplements mélangés situés en conditions mésiques. / The spruce-fir-beech mountain forests could be particularly threatened by the global warming. To better understand the future dynamics of these forests and adapt the silviculture to these new conditions, a better knowledge of the environmental factors affecting the species demograhics is needed. We studied this issue by combining a historical management data set, the forest dynamics model Samsara2 and a calibration method based on Approximate Bayesian Computation. We were able thus to study jointly the different demographic process in these forests. Our analysis show that the forest demographics can strongly vary between stands and that climate is not always determining to explain these variations. The unven-aged management currently applied seem adapted for the mixed stands located in mesic conditions, but the pure spruce forests and the low elevation stands could be highly impacted.

Les réseaux de gènes régulateurs représentent un ensemble de gènes qui interagissent, directement ou indirectement, les uns avec les autres ainsi qu'avec d'autres produits cellulaires. Comme ces interactions réglementent le taux de transcription des gènes et la production subséquente de protéines fonctionnelles, l'identification de ces réseaux peut conduire à une meilleure compréhension des systèmes biologiques complexes. Les technologies telles que les puces à ADN (microarrays) et le séquençage à ultra-haut débit (RNA sequencing) permettent une étude simultanée de l'expression des milliers de gènes chez un organisme, soit le transcriptome. En mesurant l'expression des gènes au cours du temps, il est possible d'inférer (soit "reverse-engineer") la structure des réseaux biologiques qui s'impliquent pendant un processus cellulaire particulier. Cependant, ces réseaux sont en général très compliqués et difficilement élucidés, surtout vu le grand nombre de gènes considérés et le peu de répliques biologiques disponibles dans la plupart des données expérimentales.<br /> <br /> Dans ce travail, nous proposons deux méthodes pour l'identification des réseaux de gènes régulateurs qui se servent des réseaux Bayésiens dynamiques et des modèles linéaires. Dans la première méthode, nous développons un algorithme dans un cadre bayésien pour les modèles linéaires espace-état (state-space model). Les hyperparamètres sont estimés avec une procédure bayésienne empirique et une adaptation de l'algorithme espérance-maximisation. Dans la deuxième approche, nous développons une extension d'une méthode de Approximate Bayesian Computation basé sur une procédure de Monte Carlo par chaînes de Markov pour l'inférence des réseaux biologiques. Cette méthode échantillonne des lois approximatives a posteriori des interactions gène-à-gène et fournit des informations sur l'identifiabilité et le robustesse des structures sous-réseaux. La performance des deux approches est étudié via un ensemble de simulations, et les deux sont appliqués aux données transcriptomiques.

Genetic variation is a prerequisite for the viability and evolution of species. Information on population genetic patterns on spatial and temporal scales is therefore important for effective management and for protection of biodiversity. However, incorporation of genetics into management has been difficult, even though the need has been stressed for decades. In this thesis population genetic patterns in continuous environments are described, compared among species, and related to conservation management. The model systems are moose (Alces alces) in Sweden and multiple species in the Baltic Sea, with particular focus on the Northern pike (Esox lucius). The spatial scope of the studies is large, and much focus is dedicated towards comprehensive sampling over large geographic areas. The moose population in Sweden is shown to be divided into two major subpopulations, a northern and a southern one. Both subpopulations show genetic signals of major population bottlenecks, which coincide with known population reductions due to high hunting pressure (Paper I). The Northern pike in the Baltic Sea shows relatively weak, but temporally stable population genetic structure. An isolation by distance pattern suggests that gene flow primarily takes place among neighboring populations, either over shortest waterway distance or along the mainland coast, with island populations acting as stepping stones (Paper III). In a comparative study of seven Baltic Sea species no shared genetic patterns were found, either in terms of genetic divergence among or genetic diversity within geographic regions. These results complicate the incorporation of genetic data into management, because it suggests that no generalization can be made among species in the Baltic Sea, but that species-specific management is needed (Paper II). Over the last 50 years, 61 species in the Baltic Sea have been studied with respect to spatial genetic patterns. For over 20 of these species information of direct relevance for management is available. Relevant information is synthesized into management recommendations (Paper IV). This thesis provides vital information on spatial and temporal genetic structure for a number of ecologically and socio-economically important species. It shows that such information is important to consider species by species and that both local and metapopulation approaches are needed to effectively manage genetic diversity in e.g. moose and pike. Further, it identifies for which organisms in the Baltic Sea genetic information exists, how it can be used, and where important information is lacking. In order to successfully make use of genetic data in management, effective communication channels between academia and policy-makers are needed. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p>

More and more human genomic data has become available in recent years by the improvement of DNA sequencing technologies. These data provide abundant genetic variation information which is an important resource to help us to understand the evolutionary history of humans. In this thesis I evaluated the performance of the Approximate Bayesian Computation (ABC) approach for inferring demographic parameters for large-scale population genomic data. According to simulation results, I can conclude that the ABC approach will continue to be a useful tool for analysing realistic genome-wide population-genetic data in the post-genomic era. Secondly, I implemented the ABC approach to estimate the pre-historic events connected with the “Bantu-expansion”, the spread of peoples from West Africa. The analysis based on genetic data with a large number of loci support a rapid population growth in west Africans, which lead to their concomitant spread to southern and eastern Africa. Contrary to hypotheses based on language studies, I found that Bantu-speakers in south Africa likely migrated directly from west Africa, and not from east Africa. Thirdly, I evaluated Thomson's estimator of the time to most recent common ancestor (TMRCA). It is robust to different recombination rates and the least-biased compared to other commonly used approaches. I used the Thomson estimator to infer the genome-wide distribution of TMRCA for complete human genome sequence data in various populations from across the world and compare the result to simulated data. Finally, I investigated and analysed the effects of selection and demography on genetic polymorphism patterns. In particular, we could detect a clear signal in the distribution of TMRCA caused by selection for a constant-size population. However, if the population was growing, the signal of selection will be difficult to detect under some circumstances. I also discussed and gave a few suggestions that might lead to a more realistic path of successful identification of genes targeted by selection in large-scale genomic data.

Epidemiological models traditionally make the assumption that populations are homogeneous. By relaxing that assumption, models often become more complicated, but better representations of the real world. Here we describe new computational tools for studying heterogeneous populations, and we examine consequences of two particular types of heterogeneity: that people are not all equally likely to interact, and that people are not all equally likely to become infected if exposed to a pathogen. Contact network epidemiology provides a robust and flexible paradigm for thinking about heterogeneous populations. Despite extensive mathematical and algorithmic methods, however, we lack a programming framework for working with epidemiological contact networks and for the simulation of disease transmission through such networks. We present EpiFire, a C++ applications programming interface and graphical user interface, which includes a fast and efficient library for generating, analyzing and manipulating networks. EpiFire also provides a variety of traditional and network-based epidemic simulations. Heterogeneous population structure may cause multi-wave epidemics, but urban populations are generally assumed to be too well mixed to have such structure. Multi-wave epidemics are not predicted by simple models, and are particularly problematic for public health officials deploying limited resources. Using a unique empirical interaction network for 103,000 people in Montreal, Canada, we show that large, urban populations may feature sufficient community structure to drive multi-wave dynamics, and that highly connected individuals may play an important role in whether communities are synchronized. Finally, we show that heterogeneous immunity is an important determinant of influenza epidemic size. While many epidemic models assume a homogeneously susceptible population and describe dynamics for one season, the trans-seasonal dynamics of partially immunizing diseases likely play a critical role in determining both future epidemic size and pathogen evolution. We present a multi-season network model of a population exposed to a pathogen conferring partial cross-immunity that decays over time. We fit the model to 25 years of influenza-like illness epidemic data from France using a novel Bayesian technique. Using conservative priors, we estimate important epidemiological quantities that are consistent with empirical studies. / text

At the core of evolutionary biology stands the study of divergence between populations and the formation of new species. This dissertation applies a diverse array of theoretical and statistical approaches to study how chromosomes evolve. In the first chapter, I build models that predict the amount of neutral genetic variation in chromosomal inversions involved in local adaptation, providing a foundation for future studies on the role of these rearrangements in population divergence. In the second chapter, I use a large dataset of the geographic variation in frequency of a chromosomal inversion to infer natural selection and non-random mating, revealing that this inversion could be implicated in strong reproductive isolation between subpopulations of a single species. In the third chapter, I use coalescent models for recombining sex chromosomes coupled with approximate Bayesian computation to estimate the recombination rate between X and Y chromosomes in European tree frogs. This novel approach allows me to infer a rate so low that would have been hard to detect with empirical methods. In the fourth chapter, I study the theoretical conditions that favor the evolution of a chromosome fusion that reduces recombination between locally adapted alleles. / text

Studies of demography and population structure give insight into important evolutionary processes such as speciation and diversification. In the present work I perform such studies in the genus Capsella, which has three species: C. grandiflora, an outcrossing diploid, C. rubella a selfing diploid, and C. bursa-pastoris a selfing tetraploid. These three species make a good model system for evolutionary studies because they encompass two major plant evolutionary processes: mating system shifts and polyploidization. To conduct my studies I have gathered a large number of samples across the distributions of each species and scored them both phenotypically and genotypically: more specifically we measured flowering time and collected DNA sequence data. In the tetraploid C. bursa-pastoris we applied an association mapping approach which takes population structure into account to search for genetic variation associated with variation in flowering time. Flowering time is an important and highly adaptive trait which is frequently subject to natural selection. We found evidence of association between flowering time and several single nucleotide polymorphisms (SNPs) within the flowering locus C (FLC) and cryptochrome 1 (CRY1). In the case of FLC these SNPs code for nonconsensus splice site variation in one of the two copies of the gene. The SNPs could potentially have functional consequences and our results imply that non-functionalization of duplicate genes could be an important source of phenotypic variation. Using a novel coalescent based approach, we investigated the polyploid origin of C. bursa-pastoris and find evidence supporting a recent autopolyploid origin of this species. In the two diploid species, I use sequence data to investigate population structure and demographic history and to assess the effects of selfing on C. rubella. Observed patterns of population structure and genetic diversity in C. rubella can be explained by a combination of both demographic history and mating system. Observed patterns in C. grandiflora suggest that the investigated populations do not deviate strongly from the SNM, which has rarely been found in modern demographic studies. Finally, we investigate the effect of sampling strategy on demographic inference. Extensive sampling both within and across our populations allow us to empirically test the effect of sampling strategy on demographic inference. We complement the empirical analysis with simulations and conclude that the effect of sampling strategy is in many cases weak compared with that of demographic events. Nevertheless, these effects are real and have the potential to lead to false inference and therefore sampling strategy should be carefully considered in demographic studies. / Felaktigt tryckt som Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 725

Mathematical modeling has been instrumental to the development of natural sciences over the last half-century. Through iterated interactions between modeling and real-world exper- imentation, these models have furthered our understanding of the processes in biology and chemistry that they seek to represent. In certain application domains, such as the field of car- diac biology, communities of modelers with common interests have emerged, leading to the development of many models that attempt to explain the same or similar phenomena. As these communities have developed, however, reporting standards for modeling studies have been in- consistent, often focusing on the final parameterized result, and obscuring the assumptions and data used during their creation. These practices make it difficult for researchers to adapt exist- ing models to new systems or newly available data, and also to assess the identifiability of said models - the degree to which their optimal parameters are constrained by data - which is a key step in building trust that their formulation captures truth about the system of study. In this thesis, we develop tools that allow modelers working with biological or chemical time series data to assess identifiability in an automated fashion, and embed these tools within a novel online community resource that enforces reproducible standards of reporting and facilitates exchange of models and data. We begin by exploring the application of Bayesian and approximate Bayesian inference methods, which parameterize models while simultaneously assessing uncertainty about these estimates, to assess the identifiability of models of the cardiac action potential. We then demon- strate how the side-by-side application of these Bayesian and approximate Bayesian methods can be used to assess the information content of experiments where system observability is limited to "summary statistics" - low-dimensional representations of full time-series data. We next investigate how a posteriori methods of identifiability assessment, such as the above inference techniques, compare against a priori methods based on model structure. We compare these two approaches over a range of biologically relevant experimental conditions, and high- light the cases under which each strategy is preferable. We also explore the concept of optimal experimental design, in which measurements are chosen in order to maximize model identifia- bility, and compare the feasibility of established a priori approaches against a novel a posteriori approach. Finally, we propose a framework for representing and executing modeling experiments in a reproducible manner, and use this as the foundation for a prototype "Modeling Web Lab" where researchers may upload specifications for and share the results of the types of inference explored in this thesis. We demonstrate the Modeling Web Lab's utility across multiple mod- eling domains by re-creating the results of a contemporary modeling study of the hERG ion channel model, as well as the results of an original study of electrochemical redox reactions. We hope that this works serves to highlight the importance of both reproducible standards of model reporting, as well as identifiability assessment, which are inherently linked by the desire to foster trust in community-developed models in disciplines across the natural sciences.

Lecanosticta acicola is a heterothallic ascomycete that causes brown spot needle blight (BSNB) on native and non-native Pinus spp. in many regions of the world. The aim of this thesis was to elucidate the origin of L. acicola populations in Europe and consider the reproductive mode of the pathogen in affected areas. In order to study the population genetics of L. acicola, eleven polymorphic microsatellite markers were developed. In addition, mating type markers that amplify both mating type idiomorphs (MAT1-1 and MAT1-2) were designed and the protocols for their applications were optimised. Collections of diseased material were obtained from 17 host species in Asia, Europe and America. In total, 201 isolates from diseased pine needles were obtained. All isolates were screened with the microsatellite markers and the mating type idiomorph determined with the mating type markers. For 87 individuals, part of the Translation Elongation Factor 1-alfa gene was sequenced. The isolates from Central America were unique, highly diverse and most likely represent a new cryptic species. The isolates from East Asia formed a discrete group. Two distinct populations were identified in both North America and Europe. Approximate Bayesian Computation analyses strongly suggest independent introductions of two populations from North America into Europe. Microsatellite data and mating type distributions showed the presence of sexual reproduction in North America and in Europe. Results from this thesis have showed that European populations of L. acicola originate from North America. This is the first study of L. acicola populations on a global scale.

Após a chegada dos conquistadores europeus, as populações nativas americanas foram dizimadas por diversas razões, como guerras e doenças, o que possivelmente levou diversas linhagens genéticas autóctones à extinção. Entretanto, durante essa invasão, houve miscigenação entre os colonizadores e os povos nativos e muitos estudos genéticos têm mostrado uma importante contribuição matrilinear nativa americana na formação da população colonial. Portanto, se muitos indivíduos na atual população urbana brasileira carregam linhagens nativas americanas no seu DNA mitocondrial (mtDNA), muito da diversidade genética nativa perdida durante o período colonial pode ter se mantido, por miscigenação, nas populações urbanas. Assim, essas populações representam, efetivamente, um importante reservatório genético de linhagens nativas americanas no Brasil e em outros países americanos, constituindo o reflexo mais fiel da diversidade genética pré-colombiana em populações nativas. Baseado nisso, este estudo teve como objetivos 1) comparar os padrões de diversidade genética de linhagens nativas americanas do mtDNA em populações nativas do Sul do Brasil e da população urbana (miscigenada) adjacente; e 2) comparar, através de Computação Bayesiana Aproximada (ABC), a história demográfica de ambas populações para chegar a uma estimativa do nível de redução do tamanho efetivo populacional (Ne) das populações indígenas aqui tratadas. Foram utilizados dados já publicados da região hipervariável (HVS-I) do mtDNA de linhagens nativas de 396 indivíduos Nativos Americanos (NAT) pertencentes aos grupos Guarani, Caingangue e Charrua e de 309 indivíduos de populações miscigenadas urbanas (URB) do Sul do Brasil e do Uruguai As análises de variabilidade e estrutura genética, bem como testes de neutralidade, foram feitos no programa Arlequin 3.5 e a rede de haplótipos mitocondriais foi estimada através do método Median-Joining utilizando o programa Network 5.0. Estimativas temporais do tamanho populacional efetivo foram feitas através de Skyline Plot Bayesiano utilizando o pacote de programas do BEAST 1.8.4. Por fim, o programa DIYABC 2.1 foi utilizado para testar cenários evolutivos e para estimar o Ne dos nativos americanos pré- (Nanc) e pós-contato (Nnat), para assim, se estimar o impacto da redução de variação genética causada pela colonização europeia. Os resultados deste estudo indicam que URB é a melhor preditora da diversidade nativa ancestral, possuindo uma diversidade substancialmente maior que NAT, pelo menos na região Sul do Brasil e no Uruguai (H = 0,96 vs. 0,85, Nhap = 131 vs. 27, respectivamente). Ademais, a composição de haplogrupos é bastante diferente entre as populações, sugerindo que a população nativa tenha tido eventos de gargalo afetando os haplogrupos B2 e C1 e super-representando o haplogrupo A2. Em relação à demografia histórica, observou-se que URB mantém sinais de expansão remetendo à entrada na América, contrastando com NAT em que esses sinais estão erodidos, apenas retendo sinais de contração populacional recente. De acordo com as estimativas aqui geradas, o declínio populacional em NAT foi de cerca de 300 vezes (84 – 555). Em outras palavras, a população efetiva nativa amricana nessa região corresponderia a apenas 0,33% (0,18% – 1,19%) da população ancestral– 99,8%, corroborando os achados de outros estudos genéticos e também com os registros históricos. / After the arrival of the European conquerors, the Native American populations were decimated due to multiple reasons, such as wars and diseases, which possibly led many autochtonous genetic lineages to extinction. However, during the European invasion of the Americas, colonizers and indigenous people admixed, and many genetic studies have shown an important Native American matrilineal contribution to the formation of the Colonial population. Therefore, if many individuals in the current urban population harbor Native American lineages in their mitochondrial DNA (mtDNA), much of Native American genetic diversity that have been lost during the Colonial Era may have been mantained by admixture in urban populations. In this case, these populations effectively represent an important reservoir of Native lineages in Brazil and other American countries, constituting the most accurate portrait of pre-Columbian genetic diverstity of Native populations. Based on this, the aims of the presente study were 1) to compare the patterns of genetic diversity of Native American mtDNA lineages in Native populations from Southern Brazil and the surrounding admixed urban populations; and 2) to compare, using Approximate Bayesian Computation (ABC), the demographic history of both groups to estimate the level of reduction in the effective population size (Ne) for the indigenous groups present here. We used mtDNA hypervariable segment (HVS-I) data of indigenous origin already published from 396 Native American individuals (NAT) belonging to the Guarani, Kaingang, and Charrua groups, and 309 individuals from Southern Brazilian and Uruguayan admixed urban populations (URB) The analyzes of variability and genetic structure, as well as the neutrality tests were accomplished using Arlequin 3.5, and the mitochondrial haplotype network estimated through the Median-Joining method available in Network 5.0. Time estimates for effective population size were performed using Bayesian Skyline Plot available in the BEAST 1.8.4 package. Finally, the DIYABC 2.1 software was used to test evolutionary scenarios and to estimate the pre (Nanc) and post-contact (Nnat) Native American Ne, and estimate the impact of the colonization process on the Native American genetic variability. The results indicate that URB is the best predictor of ancestral Native diversity, having substancially greater genetic diversity than NAT, at least in the Southern Brazilian and Uruguayan regions (H = 0.96 vs. 0.85, Nhap = 11 vs. 27, respectively). Moreover, the haplogroup compositions are very distinct between these groups, suggesting that the Native population passed through bottleneck events affecting the haplogroups B2 and C1, and overrepresenting the haplogroup A2. In relation to demographic history, we observed that URB retains signals of population expansion back to the entry in the Americas. In contrast, these signals are eroded in NAT, which maintains only signals of recent population contraction. According to our estimates, the population decline in NAT was around 300x (84 – 555x). In other words, the effective Native American population in this region would correspond to only 0.33% (0.18% – 1.19%) of the ancestral population, corroborating the findings of other genetic studies and historical records.