Genetic polymorphism of <Emphasis Type="Italic">Microbotryum violaceum</Emphasis> s. l. Isolates collected from different plant species on the territory of Russia

The present-day studies in the field of systematics and phylogeny of microorganisms, fungi, in particular, are characterized by a wide use of new approaches and methods of molecular biology. The use of a diversity of genetic markers permits a distinct differentiation of closely related species into individual evolutionarily independent lines. It is shown in this work that all Microbotryum violaceum s. l. isolates studied by us are divided into five evolutionary groups according to the host plant.     

Bacterial evolution and the cost of antibiotic resistance.

Bacteria clearly benefit from the possession of an antibiotic resistance gene when the corresponding antibiotic is present. But do resistant bacteria suffer a cost of resistance (i.e., a reduction in fitness) when the antibiotic is absent? If so, then one strategy to control the spread of resistance would be to suspend the use of a particular antibiotic until resistant genotypes declined to low frequency. Numerous studies have indeed shown that resistant genotypes are less fit than their sensitive counterparts in the absence of antibiotic, indicating a cost of resistance. But there is an important caveat: these studies have put resistance genes into naive bacteria, which have no evolutionary history of association with the resistance genes. An important question, therefore, is whether bacteria can overcome the cost of resistance by evolving adaptations that counteract the harmful side-effects of resistance genes. In fact, several experiments (in vitro and in vivo) show that the cost of antibiotic resistance can be substantially diminished, even eliminated, by evolutionary changes in bacteria over rather short periods of time. As a consequence, it becomes increasingly difficult to eliminate resistant genotypes simply by suspending the use of antibiotics.     

Natural selection. VI. Partitioning the information in fitness and characters by path analysis

Three steps aid in the analysis of selection. First, describe phenotypes by their component causes. Components include genes, maternal effects, symbionts and any other predictors of phenotype that are of interest. Second, describe fitness by its component causes, such as an individual's phenotype, its neighbours’ phenotypes, resource availability and so on. Third, put the predictors of phenotype and fitness into an exact equation for evolutionary change, providing a complete expression of selection and other evolutionary processes. The complete expression separates the distinct causal roles of the various hypothesized components of phenotypes and fitness. Traditionally, those components are given by the covariance, variance and regression terms of evolutionary models. I show how to interpret those statistical expressions with respect to information theory. The resulting interpretation allows one to read the fundamental equations of selection and evolution as sentences that express how various causes lead to the accumulation of information by selection and the decay of information by other evolutionary processes. The interpretation in terms of information leads to a deeper understanding of selection and heritability, and a clearer sense of how to formulate causal hypotheses about evolutionary process. Kin selection appears as a particular type of causal analysis that partitions social effects into meaningful components.     

Evaluation measures of multiple sequence alignments.

Multiple sequence alignments (MSAs) are frequently used in the study of families of protein sequences or DNA/RNA sequences. They are a fundamental tool for the understanding of the structure, functionality and, ultimately, the evolution of proteins. A new algorithm, the Circular Sum (CS) method, is presented for formally evaluating the quality of an MSA. It is based on the use of a solution to the Traveling Salesman Problem, which identifies a circular tour through an evolutionary tree connecting the sequences in a protein family. With this approach, the calculation of an evolutionary tree and the errors that it would introduce can be avoided altogether. The algorithm gives an upper bound, the best score that can possibly be achieved by any MSA for a given set of protein sequences. Alternatively, if presented with a specific MSA, the algorithm provides a formal score for the MSA, which serves as an absolute measure of the quality of the MSA. The CS measure yields a direct connection between an MSA and the associated evolutionary tree. The measure can be used as a tool for evaluating different methods for producing MSAs. A brief example of the last application is provided. Because it weights all evolutionary events on a tree identically, but does not require the reconstruction of a tree, the CS algorithm has advantages over the frequently used sum-of-pairs measures for scoring MSAs, which weight some evolutionary events more strongly than others. Compared to other weighted sum-of-pairs measures, it has the advantage that no evolutionary tree must be constructed, because we can find a circular tour without knowing the tree.     

Phylogenomic networks

Phylogenomics is aimed at studying functional and evolutionary aspects of genome biology using phylogenetic analysis of whole genomes. Current approaches to genome phylogenies are commonly founded in terms of phylogenetic trees. However, several evolutionary processes are non tree-like in nature, including recombination and lateral gene transfer (LGT). Phylogenomic networks are a special type of phylogenetic network reconstructed from fully sequenced genomes. The network model, comprising genomes connected by pairwise evolutionary relations, enables the reconstruction of both vertical and LGT events. Modeling genome evolution in the form of a network enables the use of an extensive toolbox developed for network research. The structural properties of phylogenomic networks open up fundamentally new insights into genome evolution.     

The origin and evolution of protein superfamilies.

The organization of proteins into superfamilies based primarily on their sequences is introduced: examples are given of the methods used to cluster the related sequences and to elucidate the evolutionary history of the corresponding genes within each superfamily. Within the framework of this organization, the amount of sequence information currently and potentially available in all living forms can be discussed. The 116 superfamilies already sampled reflect possibly 10% of the total number. There are related proteins from many species in all of these superfamilies, suggesting that the origin of a new superfamily is rare indeed. The proteins so far sequenced are so rigorously conserved by the evolutionary process that we would expect to recognize as related descendants of any protein found in the ancestral vertebrate. The evolutionary history of the thyrotropin-gonadotropin beta chain superfamily is discussed in detail as an example. Some proteins are so constrained in structure that related forms can be recognized in prokaryotes and eukaryotes. Evolution in these superfamilies can be traced back close to the origin of life itself. From the evolutionary tree of the c-type cytochromes the identity of the prokaryote types involved in the symbiotic origin of mitochondria and chloroplasts begins to emerge.     

Evolutionary stasis, constraint and other terminology describing evolutionary patterns

Current use of terms to describe evolutionary patterns is vague and inconsistent. In this paper, logical definitions of terms that describe specific evolutionary patterns are proposed. Evolutionary inertia is defined in a manner analogous to inertia in physics. A character in a static state of evolutionary inertia represents evolutionary stasis while a character showing consistent directional evolutionary change represents evolutionary thrust. I argue that evolutionary stasis should serve as the null hypothesis in all character evolution studies. Deviations from this null model consistent with alternative hypotheses (e.g. random drift, adaptation) can then give us insight into evolutionary processes. Failure to reject a null hypothesis of evolutionary stasis should not be used as a serious explanation of data. The term evolutionary constraint is appropriate only when a selective advantage for a character state transition is established but this transition is prevented by specific, identified factors. One type of evolutionary constraint discussed is evolutionary momentum. A final pattern of evolutionary change discussed is closely related to evolutionary thrust and is referred to as evolutionary acceleration. I provide examples of how this set of definitions can improve our ability to communicate interpretations of evolutionary patterns.     

Microevolution and taxonomy of European reptiles with particular reference to the grass snake Natrix natrix and the wall lizards Podarcis sicula and P. melisellensis

The grass snake Natrix natrix and the wall lizards Podarcis sicula and P. melisellensis are used as examples to compare the procedure and achievements of the conventional approach to naming subspecies with the use of multivariate morphometries to investigate racial differentiation.
The conventional procedure, which has changed little over the last 50 years, fails to take into account the appropriate evolutionary facts or refer to any abstracted levels of divergence necessary for subspecific recognition. Consequently, the patterns of population differentiation are obscured by the recognition of a large number of rather meaningless subspecies. There is a tendency to section clines into artificial categories and arbitrarily delimit subspecies by physiographic features.
On the other hand, the use of multivariate morphometries reveals the patterns of population differentiation which can be related to geological events and patterns of differentiation in other species and species groups. The nature of 'hybrid' zones and population differentiation enables the relative importance of evolutionary forces such as gene flow, selection and genetic drift to be discussed and provides evidence concerning speciation mechanisms. These techniques also contribute to the discussion regarding the nature of species and provide abstracted and operational criteria for taxonomic decisions.
The difference between the results of multivariate analysis and the conventional approach cannot be explained solely on the basis of choice of characters. Some of the advantages and disadvantages of using multivariate morphometries, as opposed to other modern techniques, for investigating racial affinities are discussed.     

Evolution of complex life cycles in trophically transmitted helminths. I. Host incorporation and trophic ascent

Links between parasites and food webs are evolutionarily ancient but dynamic: life history theory provides insights into helminth complex life cycle origins. Most adult helminths benefit by sexual reproduction in vertebrates, often high up food chains, but direct infection is commonly constrained by a trophic vacuum between free‐living propagules and definitive hosts. Intermediate hosts fill this vacuum, facilitating transmission to definitive hosts. The central question concerns why sexual reproduction, and sometimes even larval growth, is suppressed in intermediate hosts, favouring growth arrest at larval maturity in intermediate hosts and reproductive suppression until transmission to definitive hosts? Increased longevity and higher growth in definitive hosts can generate selection for larger parasite body size and higher fecundity at sexual maturity. Life cycle length is increased by two evolutionary mechanisms, upward and downward incorporation, allowing simple (one‐host) cycles to become complex (multihost). In downward incorporation, an intermediate host is added below the definitive host: models suggest that downward incorporation probably evolves only after ecological or evolutionary perturbations create a trophic vacuum. In upward incorporation, a new definitive host is added above the original definitive host, which subsequently becomes an intermediate host, again maintained by the trophic vacuum: theory suggests that this is plausible even under constant ecological/evolutionary conditions. The final cycle is similar irrespective of its origin (upward or downward). Insights about host incorporation are best gained by linking comparative phylogenetic analyses (describing evolutionary history) with evolutionary models (examining selective forces). Ascent of host trophic levels and evolution of optimal host taxa ranges are discussed.     

Evolutionary Branching in a Finite Population: Deterministic Branching vs. Stochastic Branching

Adaptive dynamics formalism demonstrates that, in a constant environment, a continuous trait may first converge to a singular point followed by spontaneous transition from a unimodal trait distribution into a bimodal one, which is called “evolutionary branching.” Most previous analyses of evolutionary branching have been conducted in an infinitely large population. Here, we study the effect of stochasticity caused by the finiteness of the population size on evolutionary branching. By analyzing the dynamics of trait variance, we obtain the condition for evolutionary branching as the one under which trait variance explodes. Genetic drift reduces the trait variance and causes stochastic fluctuation. In a very small population, evolutionary branching does not occur. In larger populations, evolutionary branching may occur, but it occurs in two different manners: in deterministic branching, branching occurs quickly when the population reaches the singular point, while in stochastic branching, the population stays at singularity for a period before branching out. The conditions for these cases and the mean branching-out times are calculated in terms of population size, mutational effects, and selection intensity and are confirmed by direct computer simulations of the individual-based model.     

Experiences in Teaching Scientific Writing in the U.S.A.

The origin and intended meaning of the phrase ‘survival of the fittest’ are discussed. The development of the concept of ‘fitness’ in a neo-Darwinian sense is traced, and the use of the term in other contexts is outlined. The treatment of ‘fitness’ in various popular biology texts is considered, and some suggestions about the use of the term in schools are made.

The relationship of interpretations of ‘fitness’ to a broader understanding of evolutionary mechanisms is stressed throughout.     

Phenotypic plasticity and selection in Drosophila life history evolution. 2. Diet,mates and the cost of reproduction

While it is commonplace for biologists to use the response to environmental manipulation as a guide to evolutionary responses to selection, the relationship between phenotypic plasticity and genetic change is not generally well-established. The life-histories of laboratory Drosophila populations are among the few experimental systems which simultaneously afford information on phenotypic plasticity and evolutionary trajectories. We employed a combination of two replicated selectively differentiated stocks (postponed aging stocks and their controls; 10 populations in total) and two different environmental manipulations (nutrition and mating) to explore the empirical relationship between phenotypic plasticity and evolutionary trajectories. While there are a number of parallels between the results obtained using these two approaches, there are important differences. In particular, as the detail of the biological characterization of either type of response increases, so their disparities multiply. Nonetheless, the combination of environmental manipulation with evolutionary divergence provides valuable information about the biological connections between life-history, caloric reserves, and reproductive physiology in Drososphila.     

The stability of the G‐matrix: The role of spatial heterogeneity

The temporal stability of the genetic variance‐covariance matrix ( G ) has been discussed for a long time in the evolutionary literature. A common assumption in all studies, including empirical ones, is that spatial heterogeneity is minor such that the population can be represented by a single mean and variance. We use the well‐established allocation‐acquisition model to analyze the effect of relaxing of this assumption, simulating a case where the population is divided into patches with a variance in quality between patches. This variance can in turn differ between years. We found that changes in spatial variance in patch quality over years can make the G ‐matrix vary substantially over years and that the estimated genetic correlations, evolvability, and response to selection are different dependent on whether spatial heterogeneity is taken into account or not. This will have profound implications for our ability to predict evolutionary change and understanding of the evolutionary process.     

Integrating GIS-based environmental data into evolutionary biology

Many evolutionary processes are influenced by environmental variation over space and time, including genetic divergence among populations, speciation and evolutionary change in morphology, physiology and behaviour. Yet, evolutionary biologists have generally not taken advantage of the extensive environmental data available from geographic information systems (GIS). For example, studies of phylogeography, speciation and character evolution often ignore or use only crude proxies for environmental variation (e.g. latitude and distance between populations). Here, we describe how the integration of GIS-based environmental data, along with new spatial tools, can transform evolutionary studies and reveal new insights into the ecological causes of evolutionary patterns.     

Evolutionary Medicine: A Key to Introducing Evolution

Science teachers can use examples and concepts from evolutionary medicine to teach the three concepts central to evolution: common descent, the processes or mechanisms of evolution, and the patterns produced by descent with modification. To integrate medicine into common ancestry, consider how the evolutionary past of our (or any) species affects disease susceptibility. That humans are bipedal has produced substantial changes in our musculoskeletal system, as well as causing problems for childbirth. Mechanisms such as natural selection are well exemplified in evolutionary medicine, as both disease-causing organism and their targets adapt to one another. Teachers often use examples such as antibiotic resistance to teach natural selection: it takes little alteration of the lesson plan to make explicit that evolution is key to understanding the principles involved. Finally, the pattern of evolution can be illustrated through evolutionary medicine because organisms sharing closer ancestry also share greater susceptibility to the same disease-causing organisms. Teaching evolution using examples from evolutionary medicine can make evolution more interesting and relevant to students, and quite probably, more acceptable as a valid science.     

SIMULATION‐BASED LIKELIHOOD APPROACH FOR EVOLUTIONARY MODELS OF PHENOTYPIC TRAITS ON PHYLOGENY

Phylogenetic comparative methods (PCMs) have been used to test evolutionary hypotheses at phenotypic levels. The evolutionary modes commonly included in PCMs are Brownian motion (genetic drift) and the Ornstein–Uhlenbeck process (stabilizing selection), whose likelihood functions are mathematically tractable. More complicated models of evolutionary modes, such as branch‐specific directional selection, have not been used because calculations of likelihood and parameter estimates in the maximum‐likelihood framework are not straightforward. To solve this problem, we introduced a population genetics framework into a PCM, and here, we present a flexible and comprehensive framework for estimating evolutionary parameters through simulation‐based likelihood computations. The method does not require analytic likelihood computations, and evolutionary models can be used as long as simulation is possible. Our approach has many advantages: it incorporates different evolutionary modes for phenotypes into phylogeny, it takes intraspecific variation into account, it evaluates full likelihood instead of using summary statistics, and it can be used to estimate ancestral traits. We present a successful application of the method to the evolution of brain size in primates. Our method can be easily implemented in more computationally effective frameworks such as approximate Bayesian computation (ABC), which will enhance the use of computationally intensive methods in the study of phenotypic evolution.     

Natural selection. IV. The Price equation

The Price equation partitions total evolutionary change into two components. The first component provides an abstract expression of natural selection. The second component subsumes all other evolutionary processes, including changes during transmission. The natural selection component is often used in applications. Those applications attract widespread interest for their simplicity of expression and ease of interpretation. Those same applications attract widespread criticism by dropping the second component of evolutionary change and by leaving unspecified the detailed assumptions needed for a complete study of dynamics. Controversies over approximation and dynamics have nothing to do with the Price equation itself, which is simply a mathematical equivalence relation for total evolutionary change expressed in an alternative form. Disagreements about approach have to do with the tension between the relative valuation of abstract versus concrete analyses. The Price equation's greatest value has been on the abstract side, particularly the invariance relations that illuminate the understanding of natural selection. Those abstract insights lay the foundation for applications in terms of kin selection, information theory interpretations of natural selection and partitions of causes by path analysis. I discuss recent critiques of the Price equation by Nowak and van Veelen.     

The phylogenetic relationships of the extant pelicans inferred from DNA sequence data

The pelicans are a charismatic group of large water birds, whose evolutionary relationships have been long debated. Here we use DNA sequence data from both mitochondrial and nuclear genes to derive a robust phylogeny of all the extant species. Our data rejects the widespread notion that pelicans can be divided into white- and brown-plumaged groups. Instead, we find that, in contrast to all previous evolutionary hypotheses, the species fall into three well-supported clades: an Old World clade of the Dalmatian, Spot-billed, Pink-backed and Australian Pelicans, a New World clade of the American White, Brown and Peruvian Pelicans, and monospecific clade consisting solely of the Great White Pelican, weakly grouped with the Old World clade. We discuss possible evolutionary scenarios giving rise to this diversity.     

Evolutionary tendencies in some annual species of Brotnus (Bromus L. sect. Genea Dum. (Poaceae))

A multidisciplinary investigation of the eight annual taxa of Bromus sect. Genea throughout their total ranges has brought a fresh insight into their evolutionary pathways. These species, mainly in southwest Asia and the Mediterranean, are still actively in the process of speciation and their study illuminates evolutionary processes in annual grasses as a whole. Following a discussion of Genea features, a list of ancestral and derived characters in the group is presented. The recent emergence of a multiplicity of niches is probably the reason for the rapid emergence of many new kinds of plants in sect. Genea.