The predictive power of genetic variation.

New analyses show that trait variability links evolution across vastly different timescales.

A timeline of bacterial and archaeal diversification in the ocean.

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 lineages that comprise the majority of prokaryotic biomass in the modern ocean.

Rules of teeth development align microevolution with macroevolution in extant and extinct primates.

Macroevolutionary biologists have classically rejected the notion that higher-level patterns of divergence arise through microevolutionary processes acting within populations. For morphology, this consensus partly derives from the inability of quantitative genetics models to correctly predict the behaviour of evolutionary processes at the scale of millions of years. Developmental studies (evo-devo) have been proposed to reconcile micro- and macroevolution. However, there has been little progress in establishing a formal framework to apply evo-devo models of phenotypic diversification. Here we reframe this issue by asking whether using evo-devo models to quantify biological variation can improve the explanatory power of comparative models, thus helping us bridge the gap between micro- and macroevolution. We test this prediction by evaluating the evolution of primate lower molars in a comprehensive dataset densely sampled across living and extinct taxa. Our results suggest that biologically informed morphospaces alongside quantitative genetics models allow a seamless transition between the micro- and macroscales, whereas biologically uninformed spaces do not. We show that the adaptive landscape for primate teeth is corridor like, with changes in morphology within the corridor being nearly neutral. Overall, our framework provides a basis for integrating evo-devo into the modern synthesis, allowing an operational way to evaluate the ultimate causes of macroevolution.

Conceptual and empirical bridges between micro- and macroevolution.

Explaining broad molecular, phenotypic and species biodiversity patterns necessitates a unifying framework spanning multiple evolutionary scales. Here we argue that although substantial effort has been made to reconcile microevolution and macroevolution, much work remains to identify the links between biological processes at play. We highlight four major questions of evolutionary biology whose solutions require conceptual bridges between micro and macroevolution. We review potential avenues for future research to establish how mechanisms at one scale (drift, mutation, migration, selection) translate to processes at the other scale (speciation, extinction, biogeographic dispersal) and vice versa. We propose ways in which current comparative methods to infer molecular evolution, phenotypic evolution and species diversification could be improved to specifically address these questions. We conclude that researchers are in a better position than ever before to build a synthesis to understand how microevolutionary dynamics unfold over millions of years.

A Cautionary Note on "A Cautionary Note on the Use of Ornstein Uhlenbeck Models in Macroevolutionary Studies".

Models based on the Ornstein-Uhlenbeck process have become standard for the comparative study of adaptation. Cooper et al. (2016) have cast doubt on this practice by claiming statistical problems with fitting Ornstein-Uhlenbeck models to comparative data. Specifically, they claim that statistical tests of Brownian motion may have too high Type I error rates and that such error rates are exacerbated by measurement error. In this note, we argue that these results have little relevance to the estimation of adaptation with Ornstein-Uhlenbeck models for three reasons. First, we point out that Cooper et al. (2016) did not consider the detection of distinct optima (e.g. for different environments), and therefore did not evaluate the standard test for adaptation. Second, we show that consideration of parameter estimates, and not just statistical significance, will usually lead to correct inferences about evolutionary dynamics. Third, we show that bias due to measurement error can be corrected for by standard methods. We conclude that Cooper et al. (2016) have not identified any statistical problems specific to Ornstein-Uhlenbeck models, and that their cautions against their use in comparative analyses are unfounded and misleading. [adaptation, Ornstein-Uhlenbeck model, phylogenetic comparative method.].

Cross-disciplinary information for understanding macroevolution.

Many different macroevolutionary models can produce the same observations. Despite efforts in building more complex and realistic models, it may still be difficult to distinguish the processes that have generated the biodiversity we observe. In this opinion we argue that we can make new progress by reaching out across disciplines, relying on independent data and theory to constrain macroevolutionary inference. Using mainly paleontological insights and data, we illustrate how we can eliminate less plausible or implausible models, and/or parts of parameter space, while applying comparative phylogenetic approaches. We emphasize that such cross-disciplinary insights and data can be drawn between many other disciplines relevant to macroevolution. We urge cross-disciplinary training, and collaboration using common-use databases as a platform for increasing our understanding.

A common mechanism drives the alignment between the micro- and macroevolution of primate molars.

A central challenge for biology is to reveal how different levels of biological variation interact and shape diversity. However, recent experimental studies have indicated that prevailing models of evolution cannot readily explain the link between micro- and macroevolution at deep timescales. Here, we suggest that this paradox could be the result of a common mechanism driving a correlated pattern of evolution. We examine the proportionality between genetic variance and patterns of trait evolution in a system whose developmental processes are well understood to gain insight into how such alignment between morphological divergence and genetic variation might be maintained over macroevolutionary time. Primate molars present a model system by which to link developmental processes to evolutionary dynamics because of the biased pattern of variation that results from the developmental architecture regulating their formation. We consider how this biased variation is expressed at the population level, and how it manifests through evolution across primates. There is a strong correspondence between the macroevolutionary rates of primate molar divergence and their genetic variation. This suggests a model of evolution in which selection is closely aligned with the direction of genetic variance, phenotypic variance, and the underlying developmental architecture of anatomical traits.

Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies.

Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent "parts", but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies-structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here, we assess whether evolutionary patterns can explain the proximity of ontology-annotated characters within an ontology. To do so, we measure phylogenetic information across characters and evaluate if it matches the hierarchical structure given by ontological knowledge-in much the same way as across-species diversity structure is given by phylogeny. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to data sets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially explained by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher-level subsets in the ontology. We further applied our approach to address particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that phylogenetic information does match ontology structure for some anatomical entities, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological data sets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may play a role in explaining it: phylogeny, development, or convergence. [Apidae; Bayesian phylogenetic information; Ostariophysi; Phenoscape; phylogenetic dissonance; semantic similarity.].

treedata.table: a wrapper for data.table that enables fast manipulation of large phylogenetic trees matched to data.

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/).

The allometry of locomotion.

Organismal locomotion mediates ecological interactions and shapes community dynamics. Locomotion is constrained by intrinsic and environmental factors and integrating these factors should clarify how locomotion affects ecology across scales. We extended general theory based on metabolic scaling and biomechanics to predict the scaling of five locomotor performance traits: routine speed, maximum speed, maximum acceleration, minimum powered turn radius, and angular speed. To test these predictions, we used phylogenetically informed analyses of a new database with 884 species and found support for our quantitative predictions. Larger organisms were faster but less maneuverable than smaller organisms. Routine and maximum speeds scaled with body mass to 0.20 and 0.17 powers, respectively, and plateaued at higher body masses, especially for maximum speed. Acceleration was unaffected by body mass. Minimum turn radius scaled to a 0.19 power, and the 95% CI included our theoretical prediction, as we predicted. Maximum angular speed scaled higher than predicted but in the same direction. We observed universal scaling among locomotor modes for routine and maximum speeds but the intercepts varied; flying organisms were faster than those that swam or ran. Acceleration was independent of size in flying and aquatic taxa but decreased with body mass in land animals, possibly due to the risk of injury large, terrestrial organisms face at high speeds and accelerations. Terrestrial mammals inhabiting structurally simple habitats tended to be faster than those in complex habitats. Despite effects of body size, locomotor mode, and habitat complexity, universal scaling of locomotory performance reveals the general ways organisms move across Earth's complex environments.

How should functional relationships be evaluated using phylogenetic comparative methods? A case study using metabolic rate and body temperature.

Phylogenetic comparative methods are often used to test functional relationships between traits. However, million-year macroevolutionary observational datasets cannot definitively prove causal links between traits-correlation does not equal causation and experimental manipulation over such timescales is impossible. Although this caveat is widely understood, it is less appreciated that different phylogenetic approaches imply different causal assumptions about the functional relationships of traits. To make meaningful inferences, it is critical that our statistical methods make biologically reasonable assumptions. Here we illustrate the importance of causal reasoning in comparative biology by examining a recent study by Avaria-Llautureo et al (2019). that tested for the evolutionary coupling of metabolic rate and body temperature across endotherms and found that these traits were unlinked through evolutionary time and that body temperatures were, on average, higher in the early Cenozoic than they are today. We argue that the causal assumptions embedded into their models made it impossible for them to test the relevant functional and evolutionary hypotheses. We reanalyze their data using more biologically appropriate models and find support for the exact opposite conclusions, corroborating previous evidence from physiology and paleontology. We highlight the vital need for causal thinking, even when experiments are impossible.

Scaling between macro- to microscale climatic data reveals strong phylogenetic inertia in niche evolution in plethodontid salamanders.

Macroclimatic niches are indirect and potentially inadequate predictors of the realized environmental conditions that many species experience. Consequently, analyses of niche evolution based on macroclimatic data alone may incompletely represent the evolutionary dynamics of species niches. Yet, understanding how an organisms' climatic (Grinnellian) niche responds to changing macroclimatic conditions is of vital importance for predicting their potential response to global change. In this study, we integrate microclimatic and macroclimatic data across 26 species of plethodontid salamanders to portray the relationship between microclimatic niche evolution in response to changing macroclimate. We demonstrate stronger phylogenetic signal in microclimatic niche variables than at the macroclimatic scale. Even so, we find that the microclimatic niche tracks climatic changes at the macroscale, but with a phylogenetic lag at million-year timescales. We hypothesize that behavioral tracking of the microclimatic niche over space and phenology generates the lag: salamanders preferentially select microclimates similar to their ancestral conditions rather than adapting with changes in physiology. We demonstrate that macroclimatic variables are weak predictors of niche evolution and that incorporating spatial scale into analyses of niche evolution is critical for predicting responses to climate change.

Rethinking phylogenetic comparative methods.

As a result of the process of descent with modification, closely related species tend to be similar to one another in a myriad different ways. In statistical terms, this means that traits measured on one species will not be independent of traits measured on others. Since their introduction in the 1980s, phylogenetic comparative methods (PCMs) have been framed as a solution to this problem. In this article, we argue that this way of thinking about PCMs is deeply misleading. Not only has this sowed widespread confusion in the literature about what PCMs are doing but has led us to develop methods that are susceptible to the very thing we sought to build defenses against-unreplicated evolutionary events. Through three Case Studies, we demonstrate that the susceptibility to singular events is indeed a recurring problem in comparative biology that links several seemingly unrelated controversies. In each Case Study, we propose a potential solution to the problem. While the details of our proposed solutions differ, they share a common theme: unifying hypothesis testing with data-driven approaches (which we term "phylogenetic natural history") to disentangle the impact of singular evolutionary events from that of the factors we are investigating. More broadly, we argue that our field has, at times, been sloppy when weighing evidence in support of causal hypotheses. We suggest that one way to refine our inferences is to re-imagine phylogenies as probabilistic graphical models; adopting this way of thinking will help clarify precisely what we are testing and what evidence supports our claims.

Functional biogeography of angiosperms: life at the extremes.

Nonlinear relationships between species and their environments are believed common in ecology and evolution, including during angiosperms' rise to dominance. Early angiosperms are thought of as woody evergreens restricted to warm, wet habitats. They have since expanded into numerous cold and dry places. This expansion may have included transitions across important environmental thresholds. To understand linear and nonlinear relationships between angiosperm structure and biogeographic distributions, we integrated large datasets of growth habits, conduit sizes, leaf phenologies, evolutionary histories, and environmental limits. We consider current-day patterns and develop a new evolutionary model to investigate processes that created them. The macroecological pattern was clear: herbs had lower minimum temperature and precipitation limits. In woody species, conduit sizes were smaller in evergreens and related to species' minimum temperatures. Across evolutionary timescales, our new modeling approach found conduit sizes in deciduous species decreased linearly with minimum temperature limits. By contrast, evergreen species had a sigmoidal relationship with minimum temperature limits and an inflection overlapping freezing. These results suggest freezing represented an important threshold for evergreen but not deciduous woody angiosperms. Global success of angiosperms appears tied to a small set of alternative solutions when faced with a novel environmental threshold.

A General Model for Estimating Macroevolutionary Landscapes.

The evolution of quantitative characters over long timescales is often studied using stochastic diffusion models. The current toolbox available to students of macroevolution is however limited to two main models: Brownian motion and the Ornstein-Uhlenbeck process, plus some of their extensions. Here, we present a very general model for inferring the dynamics of quantitative characters evolving under both random diffusion and deterministic forces of any possible shape and strength, which can accommodate interesting evolutionary scenarios like directional trends, disruptive selection, or macroevolutionary landscapes with multiple peaks. This model is based on a general partial differential equation widely used in statistical mechanics: the Fokker-Planck equation, also known in population genetics as the Kolmogorov forward equation. We thus call the model FPK, for Fokker-Planck-Kolmogorov. We first explain how this model can be used to describe macroevolutionary landscapes over which quantitative traits evolve and, more importantly, we detail how it can be fitted to empirical data. Using simulations, we show that the model has good behavior both in terms of discrimination from alternative models and in terms of parameter inference. We provide R code to fit the model to empirical data using either maximum-likelihood or Bayesian estimation, and illustrate the use of this code with two empirical examples of body mass evolution in mammals. FPK should greatly expand the set of macroevolutionary scenarios that can be studied since it opens the way to estimating macroevolutionary landscapes of any conceivable shape. [Adaptation; bounds; diffusion; FPK model; macroevolution; maximum-likelihood estimation; MCMC methods; phylogenetic comparative data; selection.].

The Evolution of Energetic Scaling across the Vertebrate Tree of Life.

Metabolism is the link between ecology and physiology-it dictates the flow of energy through individuals and across trophic levels. Much of the predictive power of metabolic theories of ecology derives from the scaling relationship between organismal size and metabolic rate. There is growing evidence that this scaling relationship is not universal, but we have little knowledge of how it has evolved over macroevolutionary time. Here we develop a novel phylogenetic comparative method to investigate how often and in which clades the macroevolutionary dynamics of the metabolic scaling have changed. We find strong evidence that the metabolic scaling relationship has shifted multiple times across the vertebrate phylogeny. However, shifts are rare and otherwise strongly constrained. Importantly, both the estimated slope and intercept values vary widely across regimes, with slopes that spanned across theoretically predicted values such as 2/3 or 3/4. We further tested whether traits such as ecto-/endothermy, genome size, and quadratic curvature with body mass (i.e., energetic constraints at extreme body sizes) could explain the observed pattern of shifts. Though these factors help explain some of the variation in scaling parameters, much of the remaining variation remains elusive. Our results lay the groundwork for further exploration of the evolutionary and ecological drivers of major transitions in metabolic strategy and for harnessing this information to improve macroecological predictions.

Tempo and mode of performance evolution across multiple independent origins of adhesive toe pads in lizards.

Understanding macroevolutionary dynamics of trait evolution is an important endeavor in evolutionary biology. Ecological opportunity can liberate a trait as it diversifies through trait space, while genetic and selective constraints can limit diversification. While many studies have examined the dynamics of morphological traits, diverse morphological traits may yield the same or similar performance and as performance is often more proximately the target of selection, examining only morphology may give an incomplete understanding of evolutionary dynamics. Here, we ask whether convergent evolution of pad-bearing lizards has followed similar evolutionary dynamics, or whether independent origins are accompanied by unique constraints and selective pressures over macroevolutionary time. We hypothesized that geckos and anoles each have unique evolutionary tempos and modes. Using performance data from 59 species, we modified Brownian motion (BM) and Ornstein-Uhlenbeck (OU) models to account for repeated origins estimated using Bayesian ancestral state reconstructions. We discovered that adhesive performance in geckos evolved in a fashion consistent with Brownian motion with a trend, whereas anoles evolved in bounded performance space consistent with more constrained evolution (an Ornstein-Uhlenbeck model). Our results suggest that convergent phenotypes can have quite distinctive evolutionary patterns, likely as a result of idiosyncratic constraints or ecological opportunities.

A Comprehensive Study of Cyanobacterial Morphological and Ecological Evolutionary Dynamics through Deep Geologic Time.

Cyanobacteria have exerted a profound influence on the progressive oxygenation of Earth. As a complementary approach to examining the geologic record-phylogenomic and trait evolutionary analyses of extant species can lead to new insights. We constructed new phylogenomic trees and analyzed phenotypic trait data using novel phylogenetic comparative methods. We elucidated the dynamics of trait evolution in Cyanobacteria over billion-year timescales, and provide evidence that major geologic events in early Earth's history have shaped-and been shaped by-evolution in Cyanobacteria. We identify a robust core cyanobacterial phylogeny and a smaller set of taxa that exhibit long-branch attraction artifacts. We estimated the age of nodes and reconstruct the ancestral character states of 43 phenotypic characters. We find high levels of phylogenetic signal for nearly all traits, indicating the phylogeny carries substantial predictive power. The earliest cyanobacterial lineages likely lived in freshwater habitats, had small cell diameters, were benthic or sessile, and possibly epilithic/endolithic with a sheath. We jointly analyzed a subset of 25 binary traits to determine whether rates of trait evolution have shifted over time in conjunction with major geologic events. Phylogenetic comparative analysis reveal an overriding signal of decreasing rates of trait evolution through time. Furthermore, the data suggest two major rate shifts in trait evolution associated with bursts of evolutionary innovation. The first rate shift occurs in the aftermath of the Great Oxidation Event and "Snowball Earth" glaciations and is associated with decrease in the evolutionary rates around 1.8-1.6 Ga. This rate shift seems to indicate the end of a major diversification of cyanobacterial phenotypes-particularly related to traits associated with filamentous morphology, heterocysts and motility in freshwater ecosystems. Another burst appears around the time of the Neoproterozoic Oxidation Event in the Neoproterozoic, and is associated with the acquisition of traits involved in planktonic growth in marine habitats. Our results demonstrate how uniting genomic and phenotypic datasets in extant bacterial species can shed light on billion-year old events in Earth's history.

Comparative Analysis of Principal Components Can be Misleading.

Most existing methods for modeling trait evolution are univariate, although researchers are often interested in investigating evolutionary patterns and processes across multiple traits. Principal components analysis (PCA) is commonly used to reduce the dimensionality of multivariate data so that univariate trait models can be fit to individual principal components. The problem with using standard PCA on phylogenetically structured data has been previously pointed out yet it continues to be widely used in the literature. Here we demonstrate precisely how using standard PCA can mislead inferences: The first few principal components of traits evolved under constant-rate multivariate Brownian motion will appear to have evolved via an "early burst" process. A phylogenetic PCA (pPCA) has been proprosed to alleviate these issues. However, when the true model of trait evolution deviates from the model assumed in the calculation of the pPCA axes, we find that the use of pPCA suffers from similar artifacts as standard PCA. We show that data sets with high effective dimensionality are particularly likely to lead to erroneous inferences. Ultimately, all of the problems we report stem from the same underlying issue--by considering only the first few principal components as univariate traits, we are effectively examining a biased sample of a multivariate pattern. These results highlight the need for truly multivariate phylogenetic comparative methods. As these methods are still being developed, we discuss potential alternative strategies for using and interpreting models fit to univariate axes of multivariate data.

A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data.

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here, we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data-such as microevolutionary estimates of selection parameters and paleontological timeseries-allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.