This dissertation is primarily focused on the Buellia species with pluriseptate spores, often referred to the section or genus Diplotomma, and secondarily on the Physciaceae. An ultrastructural study at family level gives new information on spore characters. Variations in the composition and structure of the spore wall layers and septa are described. A phylogenetic analysis of the Physciaceae, based on morphological and chemical characters, is presented. In the resulting trees the species with pluriseptate spores appear in a poorly resolved clade together with other crustose species with 1-septate spores. A subsequent analysis, excluding foliose and fruticose species, shows that the species with pluriseptate spores do not form a monophyletic group, and that only a few are grouped together with the type species of Diplotomma. Due to low resolution, poor branch support, and uncertainty concerning the typification of Buellia all species treated are retained in Buellia. Altogether 35 species with pluriseptate spores are treated, including ten new to science, viz. B. aeruginosa, B. morsina, B. muriformis, B. oidaliella, B. pallido-marginata, B. romoletia, B. rubroreagens, B. terricola, B. tombadorensis, and B. tri-septata. Historical outlines are presented, and information on life strategy, morphology, chemistry, ecology, and distribution is given. All species are similar and macroscopically not easily distinguished, having crustose thalli and blackish apothecia, but there is a great variation in microscopic characters, secondary chemistry, substrate preferences, habitat ecology, and distribution range. Several names are typified and many synonyms are listed. References are given to all species referred to Diplotomma in literature.

Deciphering the rapid emergence of bilaterian animals around the time of the Cambrian Explosion and reconstructing the interrelationships of animal groups have long been two of the most elusive problems in Zoology. This thesis concerns the phylogenetic interrelationships within and among Acoela and Nemertodermatida, two groups of small worms that are believed to be basal bilaterians and which may provide important clues for understanding the early evolution of animals. In addition to trying to resolve the phylogenetic positions of these groups, major focus is put on inferring how ancestral animals might have looked, given the phylogenetic hypotheses put forward. The data used to infer phylogenies include nuclear ribosomal DNA, the mitochondrial COI gene and microRNAs. Based on phylogenetic analyses of a large number of 18S SSU ribosomal DNA sequences, it is proposed that Cnidaria is the sister taxon to Bilateria. Poor taxon sampling is suggested to be one of the reasons for why earlier assessments of the interrelationships among the most basal animal groups have yielded many conflicting results using the same gene. Analyses of new 18S SSU rDNA and 28S LSU rDNA sequences from six of the nine known species of nemertodermatids corroborate earlier indications that Acoela and Nemertodermatida are not sister taxa, as once thought. Being separate basal bilaterian animal groups, it is suggested that the last common ancestor of all bilaterians shared much of their comparatively simple morphology. Many methods are deployed to assess whether the phylogenetic results are mainly due to long-branch attraction, but no indication of this artifact is detected. The first comprehensive phylogenetic framework of Acoela is reconstructed from the 18S SSU, 28S LSU and COI genes, in combination with morphological data. The ancestral acoel worm is reconstructed using Bayesian methods and morphological observations in extant species. Two indeces, posterior similarity and reconstruction signal, are implemented to assess how similar different species are to the last common ancestor of all acoels and illustrate how clearly different characters or nodes are reconstructed. It is suggested that the ancestral acoel looked much like extant species of Diopisthoporus. The phylogenetic positions of Acoela and Nemertodermatida are assessed using new data on microRNAs in the acoel Hofstenia miamia and the nemertodermatid Meara stichopi. Acoela and Nemertodermatida are again found to be basal bilaterians, in congruence with earlier results. Using the work-flow and indeces developed earlier, it is concluded that the bilaterian ancestral microRNA repertoire can not yet be reconstructed with high confidence. All papers stress the importance of inclusive taxon sampling for making generalized inferences about ancestral features in animals.

<p>What makes our minds human? How did they evolve to be this way? This dissertation presents data from two complementary lines of research driven by these orienting questions. The first of these explores the `what' of human cognitive evolution through comparative studies with chimpanzees and bonobos. The general aim of these studies is to understand which aspects of cognition are unique to humans, and which are shared with our closest living relatives. Chapters 2-3 test the hypothesis that humans have unique cognitive skills for reasoning about the attention of other individuals (theory of mind), and unique motivation to use these skills in cooperative contexts with conspecifics. In Chapter 2 I show that understanding others' attention is unlikely to be the `small difference that makes the big difference', as some researchers have proposed. However, my data support the possibility that species differences in the ontogeny of these skills may have robust consequences for the adult cognitive phenotype. In Chapter 3 I show that (contrary to previous reports) nonhuman apes are also motivated to engage in some simple triadic social activities, which resemble those characteristic of human children. Again however, I identify important differences between humans and other apes in their spontaneous preferences for these types of activities, and their attitudes toward a partner when cooperative behaviors are interrupted. The second half of this dissertation (Chapters 4-5) explores the `why' and `how' of cognitive evolution. Chapter 4 outlines the kind of research questions and methods that comparative psychologists will need to embrace in order to use the comparative method to its full potential in the study of cognitive evolution. Chapter 5 provides a proof of principle for this approach using a dataset including 33 species tested on two cognitive tasks measuring inhibitory control. Here I show that cognitive skills for inhibitory control are closely related to phylogeny across species, and strongly predicted by absolute (but not relative) brain size. Further, I show that two of the other leading hypotheses put forth to explain primate intelligence, namely sociality and diet, do not predict cognitive skills on these tasks. These data illustrate the power of the comparative method for understanding cognitive evolution, and provide a starting point for future studies embracing this approach. Collectively, this research refines our understanding of how human cognition differs from that of other primates, and illustrates the utility of studying cognitive evolution from an explicitly phylogenetic comparative framework.</p> / Dissertation

Humans are generally having a strong, widespread, and negative impact on nature. Given the many ways we are impacting nature and the many ways nature is responding, it is useful to study responses in an integrative context. My thesis is focused largely (two out of the three data chapters) on butterfly species’ range shifts consistent with modern climate change in Canada. I employed a macroecological approach to my research, drawing on methods and findings from evolutionary biology, phylogenetics, conservation biology, and natural history. I answered three main research questions. First, is there a trade-off between population growth rate (rmax) and carrying capacity (K) at the mutation scale (Chapter 2)? I found rmax and K to not trade off, but in fact to positively co-vary at the mutation scale. This suggests trade-offs between these traits only emerge after selection removes mutants with low resource acquisition rates (i.e., unhealthy genotypes), revealing trade-offs between remaining genotypes with varied resource allocation strategies. Second, did butterfly species shift their northern range boundaries northward over the 1900s, consistent with climate warming (Chapter 3)? Leading a team of collaborators, we found that most butterfly species’ northern range boundaries did indeed shift northward over the 1900s. But range shift rates were slower than those documented in the literature for more recent time periods, likely reflecting the weaker warming experienced in the time period of my study. Third, were species’ rates of range shift related to their phylogeny (Chapter 3) or traits (Chapter 4)? I found no compelling relationships between rates of range shift and phylogeny or traits. If certain traits make some species more successful at northern boundary range expansion than others, their effect was not strong enough to emerge from the background noise inherent in the broad scale data set I used.

Understanding how protein sequence, structure and function coevolve is at the core of functional genome annotation and protein engineering. The fundamental problem is to determine whether sequence variation contributes to functional differences or if it is a consequence of evolutionary divergence that is unrelated to functional specificity. To address this problem, we cannot merely analyze sequence variation between homologous proteins that have different functions. For comparison, we need to understand the factors that determine sequence variation in proteins that have the same function, such as a set of orthologous enzymes. Here, we address this problem by analyzing the evolution of functionally important residues in the o-succinylbenzoate synthase (OSBS) family. The OSBS family consists of several hundred enzymes that catalyze a step in menaquinone (Vit. K2) synthesis. Based on phylogeny, the OSBS family can be divided into eight major subfamilies. We assayed wild-type OSBS enzyme activities. The results show that the enzymes from γ-Proteobacteria subfamily 1 and Bacteroidetes have relatively low values, the enzyme from Cyanobacteria subfamily 1 is intermediate, and the values for the proteins from the Actinobacteria and Firmicutes subfamilies are relatively high. We are using computational and experimental methods to identify functionally important amino acids in each subfamily. Our data suggest that each subfamily has a different set of functionally important residues, even though the enzymes catalyze the same reaction. These differences may have accumulated because different mutations were required in each subfamily to compensate for deleterious mutations or to adapt to changing environments. We assessed the roles of these amino acids in enzyme structure and function. Our method achieved 70% successful rate to identify positions that play important roles in one family but not another. The residues P119 and A329 play important role in D. psychrophila but not in T.fusca OSBS. We also observed two class switch mutations in T.fusca, P11 and P22. The mutations at these two position have a similar kinetic parameters as wild-type D. psychrophila OSBS.

Sovint, la sistemàtica, basada principalment en caràcters morfològics, no es correspon amb els processos evolutius relacionats amb l'aparició dels grups d'organismes. En l'actualitat, la utilització de les dades moleculars es fa indispensable per a una revisió i millora de la classificació biològica de diversos organismes, com els peixos Acanthopterygii. A la sèrie Mugilomorpha la incongruència entre la taxonomia i la filogènia sorgeix de l'elevada semblança morfològica trobada per part dels seus membres. Pel que fa referència a la sèrie Atherinomorpha, la problemàtica principal resideix en determinar la seva proximitat evolutiva respecte a la sèrie anterior i en establir les relacions filogenètiques dins de la mateixa. Per tant, s'hi ha volgut estimar tant la divergència genètica dins de cada sèrie com inferir les relacions filogenètiques entre ambdues mitjançant la seqüenciació directa del DNA de les regions mitocondrials corresponents al tRNA-Phe, 12S rRNA, COI, cytb, tRNA-Thr, tRNA-Pro i regió control. / Often, systematics, based mainly on morphologic characters, does not correspond with the evolutionary processes related to the emergence of the groups of organisms. Nowadays, the utilization of molecular data turns indispensable to a revise and improve the biological classification of several organisms, such as Acanthopterygii fishes. In series Mugilomorpha, the incongruence between taxonomy and phylogeny arises from the high morphological similarity found between its members. Concerning series Atherinomorpha, the main problem lies in determining its evolutionary proximity in relation to series Mugilimorpha and in establishing the phylogenetical relationships inside itself. Therefore, both genetic divergence within each series and phylogenetical relationships between them have been wanted to estimate. For this reason, the direct sequencing of the mitochondrial regions corresponding to tRNA-Phe, 12S rRNA, COI, cytb, tRNA-Thr, tRNA-Pro and control region was achieved.

Phylogenetic analysis, or the inference of evolutionary history is done routinely by biologists and is one of the most important problems in systematic biology. In this thesis, we study two computational problems in the area. First, we study the evolutionary tree reconstruction problem under the character compatibility (CC) paradigm and give a polynomial time approximation scheme (PTAS) for a variation of the formulation called fractional character compatibility (FCC), which has been proven to be NP-hard. We also present a very simple algorithm called the Ordinal Split Method (OSM) to generate bipartitions given sequence data, which can be served as a front-end to the PTAS. The performance of the OSM and the validity of the FCC formulation are studied through simulation experiments. The second part of this thesis presents an efficient algorithm to compare evolutionary trees using the quartet metric. Different evolutionary hypothesis arises when different data sets are used or when different tree inference methods are applied to the same data set. Tree comparisons are routinely done by biologists to evaluate the quality of their tree inference experiments. The quartet metric has many desirable properties but its use has been hindered by its relatively heavy computational requirements. We address this problem by giving the first O(n^2) time algorithm to compute the quartet distance between two evolutionary trees.

In this study, the novel idea of in-vivo directed evolution was applied in order to achieve variants of the enzyme galactose oxidase with increased activity. This procedure was done under starvation conditions in Escherichia coli BL21 Star (DE3). Previous studies have been carried out in order to improve the activity of this enzyme using directed evolution methods. In this study, the same idea was used in-vivo, during stationary phase adaptive mutations inside the host organism, hence called in-vivo directed evolution. This method gave variants with improved enzyme activity as compared with the wild-type enzyme, and some variants showed activities that were even higher than the variants of previous directed evolution studies, hence making this method a promising approach for the random mutagenesis of genes of interest. The above mentioned mutations are carried out by a special group of polymerases, the error-prone polymerases. Phylogenetic analysis of these error-prone polymerases was also carried out in order to investigate the relationship between the number of error-prone polymerases and the level of complexity of organisms, and both the number of error-prone polymerases and the ratio of error-prone polymerases to total DNA polymerases of six organisms were studied. It was found that as the organism gets more complex, the number of error-prone polymerases and their ratio to the total polymerases increase.

Protein-protein interactions are important for the prediction of protein functions since two interacting proteins usually have similar functions in a cell. Available protein interaction networks are incomplete / but, they can be used to predict new interactions in a supervised learning framework. However, in the case that the known protein network includes large number of protein pairs, the training time of the machine learning algorithm becomes quite long. In this thesis work, our aim is to predict protein-protein interactions with a known portion of the interaction network. We used Support Vector Machines (SVM) as the machine learning algoritm and used the already known protein pairs in the network. We chose to use phylogenetic profiles of proteins to form the feature vectors required for the learner since the similarity of two proteins in evolution gives a reasonable rating about whether the two proteins interact or not. For large data sets, the training time of SVM becomes quite long, therefore we reduced the data size in a sensible way while we keep approximately the same prediction accuracy. We applied a number of clustering techniques to extract the most representative data and features in a two categorical framework. Knowing that the training data set is a two dimensional matrix, we applied data reduction methods in both dimensions, i.e., both in data size and in feature vector size. We observed that the data clustered by the k-means clustering technique gave superior results in prediction accuracies compared to another data clustering algorithm which was also developed for reducing data size for SVM training. Still the true positive and false positive rates (TPR-FPR) of the training data sets constructed by the two clustering methods did not give satisfying results about which method outperforms the other. On the other hand, we applied feature selection methods on the feature vectors of training data by selecting the most representative features in biological and in statistical meaning. We used phylogenetic tree of organisms to identify the organisms which are evolutionarily significant. Additionally we applied Fisher&sbquo / &Auml / &ocirc / s test method to select the features which are most representative statistically. The accuracy and TPR-FPR values obtained by feature selection methods could not provide to make a certain decision on the performance comparisons. However it can be mentioned that phylogenetic tree method resulted in acceptable prediction values when compared to Fisher&sbquo / &Auml / &ocirc / s test.

Prediction of protein-protein interactions is an important part in understanding the biological processes in a living cell. There are completely sequenced organisms that do not yet have experimentally verified protein-protein interaction networks. For such organisms, we can not generally use a supervised method, where a portion of the protein-protein interaction network is used as training set. Furthermore, for newly-sequenced organisms, many other data sources, such as gene expression data and gene ontology annotations, that are used to identify protein-protein interaction networks may not be available. In this thesis work, our aim is to identify and cluster likely protein-protein interaction pairs using only sequence of proteins and evolutionary information. We use a protein&rsquo / s phylogenetic profile because the co-evolutionary pressure hypothesis suggests that proteins with similar phylogenetic profiles are likely to interact. We also divide phylogenetic profile into smaller profiles based on the evolutionary lines. These divided profiles are then used to score the similarity between all possible protein pairs. Since not all profile groups have the same number of elements, it is a difficult task to assess the similarity between such pairs. We show that many commonly used measures do not work well and that the end result greatly depends on the type of the similarity measure used. We also introduce a novel similarity measure. The resulting dense putative interaction network contains many false-positive interactions, therefore we apply the Markov Clustering algorithm to cluster the protein-protein interaction network and filter out the weaker edges. The end result is a set of clusters where proteins within the clusters are likely to be functionally linked and to interact. While this method does not perform as well as supervised methods, it has the advantage of not requiring a training set and being able to work only using sequence data and evolutionary information. So it can be used as a first step in identifying protein-protein interactions in newly-sequenced organisms.