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Microbial plankton play a central role in marine biogeochemical cycles, but the timing in which abundant lineages diversified into ocean environments remains unclear. Here, we reconstructed the timeline in which major clades of bacteria and archaea colonized the ocean using a high-resolution benchmarked phylogenetic tree that allows for simultaneous and direct comparison of the ages of multiple divergent lineages. Our findings show that the diversification of the most prevalent marine clades spans throughout a period of 2.2 Ga, with most clades colonizing the ocean during the last 800 million years. The oldest clades - SAR202, SAR324, Ca. Marinimicrobia, and Marine Group II - diversified around the time of the Great Oxidation Event, during which oxygen concentration increased but remained at microaerophilic levels throughout the Mid-Proterozoic, consistent with the prevalence of some clades within these groups in oxygen minimum zones today. We found the diversification of the prevalent heterotrophic marine clades SAR11, SAR116, SAR92, SAR86, and Roseobacter as well as the Marine Group I to occur near to the Neoproterozoic Oxygenation Event (0.8-0.4 Ga). The diversification of these clades is concomitant with an overall increase of oxygen and nutrients in the ocean at this time, as well as the diversification of eukaryotic algae, consistent with the previous hypothesis that the diversification of heterotrophic bacteria is linked to the emergence of large eukaryotic phytoplankton. The youngest clades correspond to the widespread phototrophic clades Prochlorococcus, Synechococcus, and Crocosphaera, whose diversification happened after the Phanerozoic Oxidation Event (0.45-0.4 Ga), in which oxygen concentrations had already reached their modern levels in the atmosphere and the ocean. Our work clarifies the timing at which abundant lineages of bacteria and archaea colonized the ocean, thereby providing key insights into the evolutionary history of li

Allometry—patterns of relative change in body parts—is a staple for examining how clades exhibit scaling patterns representative of evolutionary constraint on phenotype, or quantifying patterns of ontogenetic growth within a species. Reconstructing allometries from ontogenetic series is one of the few methods available to reconstruct growth in fossil specimens. However, many fossil specimens are deformed (twisted, flattened, and displaced bones) during fossilization, changing their original morphology in unpredictable and sometimes undecipherable ways. To mitigate against post burial changes, paleontologists typically remove clearly distorted measurements from analyses. However, this can potentially remove evidence of individual variation and limits the number of samples amenable to study, which can negatively impact allometric reconstructions. Ordinary least squares (OLS) regression and major axis regression are common methods for estimating allometry, but they assume constant levels of residual variation across specimens, which is unlikely to be true when including both distorted and undistorted specimens. Alternatively, a generalized linear mixed model (GLMM) can attribute additional variation in a model (e.g., fixed or random effects). We performed a simulation study based on an empirical analysis of the extinct cynodont, Exaeretodon argentinus, to test the efficacy of a GLMM on allometric data. We found that GLMMs estimate the allometry using a full dataset better than simply using only non-distorted data. We apply our approach on two empirical datasets, cranial measurements of actual specimens of E. argentinus (n = 16) and femoral measurements of the dinosaur Tawa hallae (n = 26). Taken together, our study suggests that a GLMM is better able to reconstruct patterns of allometry over an OLS in datasets comprised of extinct forms and should be standard protocol for anyone using distorted specimens.

The number of terminals in phylogenetic trees has significantly increased over the last decade. This trend reflects recent advances in next-generation sequencing, accessibility of public data repositories, and the increased use of phylogenies in many fields. Despite R being central to the analysis of phylogenetic data, manipulation of phylogenetic comparative datasets remains slow, complex, and poorly reproducible. Here, we describe the first R package extending the functionality and syntax of data.table to explicitly deal with phylogenetic comparative datasets. treedata.table significantly increases speed and reproducibility during the data manipulation steps involved in the phylogenetic comparative workflow in R. The latest release of treedata.table is currently available through CRAN (https://cran.r-project.org/web/packages/treedata.table/). Additional documentation can be accessed through rOpenSci (https://ropensci.github.io/treedata.table/).

Allometry—patterns of relative change in body parts—is a staple for examining how clades exhibit scaling patterns representative of evolutionary constraint on phenotype, or quantifying patterns of ontogenetic growth within a species. Reconstructing allometries from ontogenetic series is one of the few methods available to reconstruct growth in fossil specimens. However, many fossil specimens are deformed (twisted, flattened, and displaced bones) during fossilization, changing their original morphology in unpredictable and sometimes undecipherable ways. To mitigate against post burial changes, paleontologists typically remove clearly distorted measurements from analyses. However, this can potentially remove evidence of individual variation and limits the number of samples amenable to study, which can negatively impact allometric reconstructions. Ordinary least squares (OLS) regression and major axis regression are common methods for estimating allometry, but they assume constant levels of residual variation across specimens, which is unlikely to be true when including both distorted and undistorted specimens. Alternatively, a generalized linear mixed model (GLMM) can attribute additional variation in a model (e.g., fixed or random effects). We performed a simulation study based on an empirical analysis of the extinct cynodont, Exaeretodon argentinus, to test the efficacy of a GLMM on allometric data. We found that GLMMs estimate the allometry using a full dataset better than simply using only non-distorted data. We apply our approach on two empirical datasets, cranial measurements of actual specimens of E. argentinus (n = 16) and femoral measurements of the dinosaur Tawa hallae (n = 26). Taken together, our study suggests that a GLMM is better able to reconstruct patterns of allometry over an OLS in datasets comprised of extinct forms and should be standard protocol for anyone using distorted specimens.

Blasi et al. (Research article, 15 March 2019) argue in support of Hockett’s hypothesis – that languages of hunter-gatherers are less likely to develop labiodental sounds than those of agricultural societies due to the former’s heavywear diet that favors edge-to-edge bite, thereby decreasing likelihood of labiodental articulation. We reanalyze the data in Blasi et al. and find little to no support in favor of the Hockett’s hypothesis. The negative association between labiodentals and hunter-gatherers instead appears to be an artifact of labiodental decline with increasing distance from Africa, which is a general trend of language phonemes.

Blasi et al. (Research article, 15 March 2019) argue in support of Hockett’s hypothesis – that languages of hunter-gatherers are less likely to develop labiodental sounds than those of agricultural societies due to the former’s heavywear diet that favors edge-to-edge bite, thereby decreasing likelihood of labiodental articulation. We reanalyze the data in Blasi et al. and find little to no support in favor of the Hockett’s hypothesis. The negative association between labiodentals and hunter-gatherers instead appears to be an artifact of labiodental decline with increasing distance from Africa, which is a general trend of language phonemes.

Ancestral character state reconstruction has been long used to gain insight into the evolution of individual traits in organisms. However, organismal anatomies (= entire phenotypes) are not merely ensembles of individual traits, rather they are complex systems where traits interact with each other due to anatomical dependencies (when one trait depends on the presence of another trait) and developmental constraints. Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of entire organismal anatomies or organismal body regions. Herein, we present a new approach named PARAMO (Phylogenetic Ancestral Reconstruction of Anatomy by Mapping Ontologies, Tarasov and Uyeda 2019) that takes into account anatomical dependencies and uses stochastic maps (i.e., phylogenetic trees with an instance of mapped evolutionary history of characters, Huelsenbeck et al. 2003) along with anatomy ontologies to reconstruct organismal anatomies. Our approach treats the entire phenotype or its component body regions as single complex characters and allows exploring and comparing phenotypic evolution at different levels of anatomical hierarchy. These complex characters are constructed by ontology-informed amalgamation of elementary characters (i.e., those coded in character matrix) using stochastic maps. In our approach, characters are linked with the terms from an anatomy ontology, which allows viewing them not just as an ensemble of character state tokens but as entities that have their own biological meaning provided by the ontology. This ontologyinformed framework provides new opportunities for tracking phenotypic radiations and anatomical evolution of organisms, which we explore using a large dataset for the insect order Hymenoptera (sawflies, wasps, ants and bees).

Ancestral character state reconstruction has been long used to gain insight into the evolution of individual traits in organisms. However, organismal anatomies (= entire phenotypes) are not merely ensembles of individual traits, rather they are complex systems where traits interact with each other due to anatomical dependencies (when one trait depends on the presence of another trait) and developmental constraints. Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of entire organismal anatomies or organismal body regions. Herein, we present a new approach named PARAMO (Phylogenetic Ancestral Reconstruction of Anatomy by Mapping Ontologies, Tarasov and Uyeda 2019) that takes into account anatomical dependencies and uses stochastic maps (i.e., phylogenetic trees with an instance of mapped evolutionary history of characters, Huelsenbeck et al. 2003) along with anatomy ontologies to reconstruct organismal anatomies. Our approach treats the entire phenotype or its component body regions as single complex characters and allows exploring and comparing phenotypic evolution at different levels of anatomical hierarchy. These complex characters are constructed by ontology-informed amalgamation of elementary characters (i.e., those coded in character matrix) using stochastic maps. In our approach, characters are linked with the terms from an anatomy ontology, which allows viewing them not just as an ensemble of character state tokens but as entities that have their own biological meaning provided by the ontology. This ontologyinformed framework provides new opportunities for tracking phenotypic radiations and anatomical evolution of organisms, which we explore using a large dataset for the insect order Hymenoptera (sawflies, wasps, ants and bees).

The study of how the observable features of organisms, i.e., their phenotypes, result from the complex interplay between genetics, development, and the environment, is central to much research in biology. The varied language used in the description of phenotypes, however, impedes the large scale and interdisciplinary analysis of phenotypes by computational methods. The Phenoscape project (www.phenoscape.org) has developed semantic annotation tools and a gene–phenotype knowledgebase, the Phenoscape KB, that uses machine reasoning to connect evolutionary phenotypes from the comparative literature to mutant phenotypes from model organisms. The semantically annotated data enables the linking of novel species phenotypes with candidate genes that may underlie them. Semantic annotation of evolutionary phenotypes further enables previously difficult or novel analyses of comparative anatomy and evolution. These include generating large, synthetic character matrices of presence/absence phenotypes based on inference, and searching for taxa and genes with similar variation profiles using semantic similarity. Phenoscape is further extending these tools to enable users to automatically generate synthetic supermatrices for diverse character types, and use the domain knowledge encoded in ontologies for evolutionary trait analysis. Curating the annotated phenotypes necessary for this research requires significant human curator effort, although semi-automated natural language processing tools promise to expedite the curation of free text. As semantic tools and methods are developed for the biodiversity sciences, new insights from the increasingly connected stores of interoperable phenotypic and genetic data are anticipated.

The study of how the observable features of organisms, i.e., their phenotypes, result from the complex interplay between genetics, development, and the environment, is central to much research in biology. The varied language used in the description of phenotypes, however, impedes the large scale and interdisciplinary analysis of phenotypes by computational methods. The Phenoscape project (www.phenoscape.org) has developed semantic annotation tools and a gene–phenotype knowledgebase, the Phenoscape KB, that uses machine reasoning to connect evolutionary phenotypes from the comparative literature to mutant phenotypes from model organisms. The semantically annotated data enables the linking of novel species phenotypes with candidate genes that may underlie them. Semantic annotation of evolutionary phenotypes further enables previously difficult or novel analyses of comparative anatomy and evolution. These include generating large, synthetic character matrices of presence/absence phenotypes based on inference, and searching for taxa and genes with similar variation profiles using semantic similarity. Phenoscape is further extending these tools to enable users to automatically generate synthetic supermatrices for diverse character types, and use the domain knowledge encoded in ontologies for evolutionary trait analysis. Curating the annotated phenotypes necessary for this research requires significant human curator effort, although semi-automated natural language processing tools promise to expedite the curation of free text. As semantic tools and methods are developed for the biodiversity sciences, new insights from the increasingly connected stores of interoperable phenotypic and genetic data are anticipated.