Explore the vast range of titles available.

Comparison is fundamental to evolutionary anthropology. When scientists study chimpanzee cognition, for example, they compare chimp performance on cognitive tasks to the performance of human children on the same tasks. And when new fossils are found, such as those of the tiny humans of Flores, scientists compare these remains to other fossils and contemporary humans. Comparison provides a way to draw general inferences about the evolution of traits and therefore has long been the cornerstone of efforts to understand biological and cultural diversity. Individual studies of fossilized remains, living species, or human populations are the essential units of analysis in a comparative study; bringing these elements into a broader comparative framework allows the puzzle pieces to fall into place, creating a means of testing adaptive hypotheses and generating new ones. With this book, Charles L. Nunn intends to ensure that evolutionary anthropologists and organismal biologists have the tools to realize the potential of comparative research. Nunn provides a wide-ranging investigation of the comparative foundations of evolutionary anthropology in past and present research, including studies of animal behavior, biodiversity, linguistic evolution, allometry, and cross-cultural variation. He also points the way to the future, exploring the new phylogeny-based comparative approaches and offering a how-to manual for scientists who wish to incorporate these new methods into their research.

This book presents a history of microbial evolutionary biology from the 19th century to the present. It follows the research of molecular evolutionists who explore the origins of the genetic system and the primary life forms: three domains and multiple kingdoms, created by mechanisms very unlike those considered by Darwin and his followers.

Functional traits mediate ecological responses of organisms to the environment, determining community structure. Community-weighted trait means (CWM) are often used to characterize communities by combining information on species traits and distribution. Relating CWM variation to environmental gradients allows for evaluating species sorting across the metacommunity, either based on correlation tests or ordinary least squares (OLS) models. Yet, it is not clear if phylogenetic signal in both traits and species distribution affect those analyses. On one hand, phylogenetic signal might indicate niche conservatism along clade evolution, reinforcing the environmental signal in trait assembly patterns. On the other hand, it might introduce phylogenetic autocorrelation to mean trait variation among communities. Under this latter scenario, phylogenetic signal might inflate type I error in analysis relating CWM variation to environmental gradients. We explore multiple ways phylogenetic history may influence analysis relating CWM to environmental gradients. We propose the concept of neutral trait diffusion, which predicts that for a functional trait x, CWM variation among local communities does not deviate from the expectation that x evolved according to a neutral evolutionary process. Based on this framework we introduce a graphical tool called neutral trait diffusion representation (NTDR) that allows for the evaluation of whether it is necessary to carry out phylogenetic correction in the trait prior to analyzing the association between CWM and environmental gradients. We illustrate the NTDR approach using simulated traits, phylogenies and metacommunities. We show that even under moderate phylogenetic signal in both the trait used to define CWM and species distribution across communities, OLS models relating CWM variation to environmental gradients lead to inflated type I error when testing the null hypothesis of no association between CWM and environmental gradient. To overcome this issue, we propose a phylogenetic correction for OLS models and evaluate its statistical performance (type I error and power). Phylogeny-corrected OLS models successfully control for type I error in analysis relating CWM variation to environmental gradients but may show decreased power. Combining the exploratory tool of NTDR and phylogenetic correction in traits, when necessary, guarantees more precise inferences about the environmental forces driving trait-mediated species sorting across metacommunities.

ModelTest-NG is a reimplementation from scratch of jModelTest and ProtTest, two popular tools for selecting the best-fit nucleotide and amino acid substitution models, respectively. ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate and introduces several new features, such as ascertainment bias correction, mixture, and free-rate models, or the automatic processing of single partitions. ModelTest-NG is available under a GNU GPL3 license at https://github.com/ddarriba/modeltest, last accessed September 2, 2019.

Should we build our own phylogenetic trees based on gene sequence data, or can we simply use available synthesis phylogenies? This is a fundamental question that any study involving a phylogenetic framework must face at the beginning of the project. Building a phylogeny from gene sequence data (purpose-built phylogeny) requires more effort, expertise, and cost than subsetting an already available phylogeny (synthesis-based phylogeny). However, we still lack a comparison of how these two approaches to building phylogenetic trees influence common community phylogenetic analyses such as comparing community phylogenetic diversity and estimating trait phylogenetic signal. Here, we generated three purpose-built phylogenies and their corresponding synthesis-based trees (two from Phylomatic and one from the Open Tree of Life, OTL). We simulated 1,000 communities and 12,000 continuous traits along each purpose-built phylogeny. We then compared the effects of different trees on estimates of phylogenetic diversity (alpha and beta) and phylogenetic signal (Pagel’s λ and Blomberg’s K). Synthesis-based phylogenies generally yielded higher estimates of phylogenetic diversity when compared to purpose-built phylogenies. However, resulting measures of phylogenetic diversity from both types of phylogenies were highly correlated (Spearman’s ρ > 0.8 in most cases). Mean pairwise distance (both alpha and beta) is the index that is most robust to the differences in tree construction that we tested. Measures of phylogenetic diversity based on the OTL showed the highest correlation with measures based on the purpose-built phylogenies. Trait phylogenetic signal estimated with synthesis-based phylogenies, especially from the OTL, was also highly correlated with estimates of Blomberg’s K or close to Pagel’s λ from purpose-built phylogenies when traits were simulated under Brownian motion. For commonly employed community phylogenetic analyses, our results justify taking advantage of recently developed and continuously improving synthesis trees, especially the Open Tree of Life.

The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor, and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net.

Phylogenetic indexes summarize the evolutionary information within a given assemblage pool based on the topology and branch lengths of a hypothesized phylogenetic tree. However, different historical contingencies experienced by these assemblages can unevenly distribute evolutionary information through time and over the phylogeny. ‘treesliceR' is an R package containing tools to flexibly cut phylogenies at different depths, and also has built‐in functions to assess spatially explicit phylogenetic patterns over time. ‘treesliceR' can slice phylogenies in any temporal orientation (‘rootwardly' or ‘tipwardly'), using different criteria (million years or phylogenetic diversity). Moreover, ‘treesliceR' contains functions to assess the rates of accumulation of any phylogenetic information (e.g. α and β diversities) through time. These functions are unique to the package and provide outputs that are ready‐to‐use in graphing functions. We demonstrated the main uses of ‘treesliceR' by investigating areas of paleo‐endemism and neo‐endemism of Passeriformes in Australia. Finally, we mapped rates of accumulation of phylogenetic β‐diversity (Cpβrate) across Australia. ‘treesliceR' is an open‐source R package under continuous progress, designed to decompose temporally any phylogenetic information.

Phylogenomic subsampling is a procedure by which small sets of loci are selected from large genome-scale data sets and used for phylogenetic inference. This step is often motivated by either computational limitations associated with the use of complex inference methods or as a means of testing the robustness of phylogenetic results by discarding loci that are deemed potentially misleading. Although many alternative methods of phylogenomic subsampling have been proposed, little effort has gone into comparing their behavior across different data sets. Here, I calculate multiple gene properties for a range of phylogenomic data sets spanning animal, fungal, and plant clades, uncovering a remarkable predictability in their patterns of covariance. I also show how these patterns provide a means for ordering loci by both their rate of evolution and their relative phylogenetic usefulness. This method of retrieving phylogenetically useful loci is found to be among the top performing when compared with alternative subsampling protocols. Relatively common approaches such as minimizing potential sources of systematic bias or increasing the clock-likeness of the data are found to fare worse than selecting loci at random. Likewise, the general utility of rate-based subsampling is found to be limited: loci evolving at both low and high rates are among the least effective, and even those evolving at optimal rates can still widely differ in usefulness. This study shows that many common subsampling approaches introduce unintended effects in off-target gene properties and proposes an alternative multivariate method that simultaneously optimizes phylogenetic signal while controlling for known sources of bias.

Incongruence, or topological conflict, is prevalent in genome-scale data sets. Internode certainty (IC) and related measures were recently introduced to explicitly quantify the level of incongruence of a given internal branch among a set of phylogenetic trees and complement regular branch support measures (e.g., bootstrap, posterior probability) that instead assess the statistical confidence of inference. Since most phylogenomic studies contain data partitions (e.g., genes) with missing taxa and IC scores stem from the frequencies of bipartitions (or splits) on a set of trees, IC score calculation typically requires adjusting the frequencies of bipartitions from these partial gene trees. However, when the proportion of missing taxa is high, the scores yielded by current approaches that adjust bipartition frequencies in partial gene trees differ substantially from each other and tend to be overestimates. To overcome these issues, we developed three new IC measures based on the frequencies of quartets, which naturally apply to both complete and partial trees. Comparison of our new quartet-based measures to previous bipartition-based measures on simulated data shows that: (1) on complete data sets, both quartet-based and bipartition-based measures yield very similar IC scores; (2) IC scores of quartet-based measures on a given data set with and without missing taxa are more similar than the scores of bipartition-based measures; and (3) quartet-based measures are more robust to the absence of phylogenetic signal and errors in phylogenetic inference than bipartition-based measures. Additionally, the analysis of an empirical mammalian phylogenomic data set using our quartet-based measures reveals the presence of substantial levels of incongruence for numerous internal branches. An efficient open-source implementation of these quartet-based measures is freely available in the program QuartetScores (https://github.com/lutteropp/QuartetScores).

Understanding how both the mean (location) and variance (scale) of traits differ among species and lineages is fundamental to unveiling macroevolutionary patterns. Yet, traditional phylogenetic comparative methods primarily focus on modelling mean trait values, often overlooking variability and heteroscedasticity that can provide critical insights into evolutionary dynamics. Here, we introduce phylogenetic location–scale models (PLSMs), a novel framework that jointly analyses the evolution of trait means and variances. This dual approach captures heteroscedasticity and evolutionary changes in trait variability, allowing for the detection of clades with differing variances and revealing patterns of adaptation, diversification, and evolutionary constraints. Extending PLSMs to a multivariate context enables simultaneous analysis of multiple traits and their covariances, facilitating the testing of hypotheses about evolutionary trade‐offs, pleiotropy and phenotypic integration. By modelling covariances between phylogenetic effects in both the location and scale parts, we can discern whether changes in one trait's mean or variance are associated with changes in another's, thereby offering deeper insights into the mechanisms driving trait co‐evolution and co‐divergence or ‘contra‐divergence’. We also describe how an extended version of PLSMs incorporating within‐species variability can enhance our understanding of trait convergence and divergence arising from ecological and environmental factors. Our framework provides a powerful tool for exploring macroevolutionary patterns and can be used to reassess previously published comparative data, offering new insights into the mechanisms driving the diversity of life.