Traits that can be used to determine evolutionary relationships between organisms are called

Abstract

Phylogenetic comparative methods (PCMs) are crucial to answering many questions regarding the evolution of animal behaviour. They allow researchers to assess how traits evolve over time, the order in which interrelated traits evolve, and the influence of an animal’s ecology on the evolution of a trait. The use of PCMs in the study of visual ecology, in particular, is aimed at understanding how signals evolve and diversify within groups of animals, potentially leading to speciation and thus greater biodiversity. Here I give an overview of PCMs in the context of animal behaviour research and discuss three recent studies on the evolution of visual signals employing a range of PCMs. These examples serve to highlight the breadth of tools available and their application.

Phylogenetic methods are instrumental to the identification and classification of bacteria. Here, we examine the utility and effectiveness of traditional alignment-based phylogenies for reconstructing the evolutionary relationships among bacterial species, but also consider the more recently developed whole-genome (as well as alignment-free) approaches. We addition, illustrate the power of phylogenetic approaches to identify coevolutionary interactions and cases of horizontal gene transfer (HGT) between bacteria, and between microbes and other organisms. Finally, we provide examples where phylogenetic techniques have been used in conjunction with proteomic and biochemical information to identify functional subgroups within large gene superfamilies. Ultimately, the versatility and utility of phylogenetic techniques have permitted the in-depth exploration of fundamental questions in evolutionary biology.

Abstract

“Phylogenetics” is the systematic study of reconstructing the past evolutionary history of extant species or taxa, based on present-day data, such as morphologies or molecular information (sequence data). This evolutionary history or phylogeny is ideally represented as a binary tree. In the method of “phylogenetic invariants,” a pivotal role is played by certain master functions (the so-called invariants, which give the method its name). The aim is to use the observed data as input for their evaluation, and dependent on the answers, to infer essential features of phylogenetic history without the need for parameter estimation. We compare and contrast “phylogenetic invariants” with “Markov invariants,” closely related quantities which we have recently introduced in phylogenetics.

Abstract

Phylogenetics is the study of the evolutionary relatedness among groups of organisms. Molecular phylogenetics uses sequence data to infer these relationships for both organisms and the genes they maintain. With the large amount of publicly available sequence data, phylogenetic inference has become increasingly important in all fields of biology. In the case of natural product research, phylogenetic relationships are proving to be highlyinformative in terms of delineating the architecture and function of the genes involved in secondary metabolite biosynthesis. Polyketide synthases and nonribosomal peptide synthetases provide model examples in which individual domain phylogenies display different predictive capacities, resolving features ranging from substrate specificity to structural motifs associated with the final metabolic product. This chapter provides examples in which phylogeny has proven effective in terms of predicting functional or structural aspects of secondary metabolism. The basics of how to build a reliable phylogenetic tree are explained along with information about programs and tools that can be used for this purpose. Furthermore, it introduces the Natural Product Domain Seeker, a recently developed Web tool that employs phylogenetic logic to classify ketosynthase and condensation domains based on established enzyme architecture and biochemical function.

Abstract

Phylogenetic networks are special types of labeled graphs that are used to display or model complex evolutionary relationships that are not well fit by a single tree. At the highest level, phylogenetic networks can be classified into data-display networks and evolutionary networks. Data-display networks extend undirected phylogenetic trees to allow for displaying and exploring conflicting evolutionary signals in the data that do not fit a single tree. Evolutionary networks extend rooted, directed phylogenetic trees to model the (potentially reticulate) evolutionary history of a set of taxa from their most recent common ancestor.

Phylogenetic Screens

Phylogenetic screens involve the pharmacological testing of related groups of organisms. Increased precision in elucidating phylogenies, largely due to rapid computer programs for cladistic analysis and the advent of molecular techniques for phylogenetic determination, facilitates the identification of relatives of any species showing pharmacological value. Phylogenetic screens, albeit in a crude sense, have long been utilized. For example, plants in the Apocynaceae, or milkweed family, have always merited special attention due to the family׳s abundance of alkaloid-producing species such as Catharanthus, which produces the antileukemia drug vincristine. Only recently have modern techniques encouraged investigators to study close relatives of a species for either (1) increased abundance of an important bioactive compound (such as species of Taxus for the presence of taxol) or (2) natural homologs of known pharmaceuticals (such as species of Catharanthus that may produce variant forms of vincristine or vinblastine).

RFLP in Phylogenetic Studies

Phylogenetics is the study of evolutionary relatedness among groups of organisms (e.g., species, populations). RFLP has been used for phylogenetic classification of different members of a species, for example, 18 species of Chinese Allium has been classified on the basis of PCR–RFLP. A dendrogram of a phylip tree is built based on PCR–RFLP data, which are informative of the genetic relatedness. RFLP assays have been used to determine the genetic relatedness of clinical isolates of Aspergillus fumigatus. PCR–RFLP has been used for species identification of ocular isolates of methicillin-resistant Staphylococci using mitochondrial gene regions.

Phylogenetic Basis of Species Demarcation

Two phylogenetic species concepts provide alternative approaches for objectively demarcating species. The cladistic species concept defines species as monophyletic groups of organisms that are distinguishable by a significant derived character shared only by members of the focus species. The insistence on monophyly is difficult for most biologists to accept because a species can cease to exist even if it endures no change; when a species spawns a new species, it becomes paraphyletic and loses its status as a species.

Another phylogenetic approach toward objectivity in species defines species as a phylogenetic group, which can be monophyletic or paraphyletic, that shares a particular taxonomic character. Neither of these phylogenetic concepts offers any guidance as to how phylogenetically large a species should be, or what counts as a set of diagnostic characters, as with the phenetic species concepts.

Phylogenetic Relationships among Fungal Partitiviruses

Phylogenetic analyses of the predicted amino acid sequences of fungal partitivirus RdRps and CPs (Figures 4(a) and 4(b), respectively) show that these viruses are closely related. Both analyses suggest that fungal partitiviruses form two clusters. The RdRp clusters have 100% bootstrap support (Figure 4(a)), whereas the CP clusters are also well supported (Figure 4(b)). These results are in agreement with recently reported phylogenetic analyses showing that the evolutionary rate of CPs is higher than that of the RdRps, and are in congruence with data suggesting a cross-serological relationship between the RdRps of AoV and PsV-S.

Abstract

‘Phylogenetics’ is the systematic study of reconstructing the past evolutionary history of extant species or taxa, based on present-day data, such as morphologies or molecular information (sequence data). This evolutionary history or phylogeny is ideally represented as a binary tree. In the method of ‘phylogenetic invariants’, a pivotal role is played by certain master functions (the so-called invariants, which give the method its name). The aim is to use the observed data as input for their evaluation, and dependent on the answers, to infer essential features of phylogenetic history without the need for parameter estimation.