Inference of Adaptive Shifts for Multivariate Correlated Traits

Evolution of accessory bones in cetacean skull coupled with decreasing rate of ossification of cranial sutures

The skulls of cetaceans (whales, dolphins and porpoises) are distinguished by incomplete ossification of cranial sutures during their lifetime. We suggested suture ossification may correlate with the evolution of rarely reported accessory bones and examine the evolutionary patterns of cranial suture ossification and the presence of accessory elements in the skulls of 47 cetacean species and, for comparison, 15 terrestrial artiodactyls (even-hoofed mammals). A strong phylogenetic signal was found for both suture ossification rate and accessory bone presence. Cetaceans were shown to have a lower ossification rate than most terrestrial artiodactyls, except deer (Cervidae) and mouse deer (Tragulidae), which showed somewhat similar patterns. There were also several types of accessory bony elements, some of them first reported in the skulls of cetaceans and in one case, a muntjac deer. These elements (bones and clefts) evolved in the crania with the least number of ossified sutures. They can be identified as novel elements or, some of them, as plesiomorphies rarely seen in mammals but existing in reptiles and other ancestral groups. This leads to breaking a general trend of mammalian evolution: a reversal of Williston's law (reduction in bone number) and the development of new accessory cranial bones. Slowing cranial development rates explain these trends, and feeding mechanics may be a driver of decreasing suture ossification in cetaceans.

Diversification of social complexity following a major evolutionary transition in bees

How social complexity evolved remains a long-standing enigma. In most animal groups, social complexity is typically classified into a few discrete classes. This approach is oversimplified and constrains our inference of social evolution to a narrow trajectory consisting of transitions between classes. Such categorical classifications also limit quantitative studies on the molecular and environmental drivers of social complexity. The recent accumulation of relevant quantitative data has set the stage to overcome these limitations. Here, we propose a data-driven, high-dimensional approach for studying the full diversity of social phenotypes. We curated and analyzed a comprehensive dataset encompassing 17 social traits across 80 species and studied the evolution of social complexity in bees. We found that honey bees, stingless bees, and bumble bees underwent a major evolutionary transition ∼80 mya, inconsistent with the stepwise progression of the social ladder conceptual framework. This major evolutionary transition was followed by a phase of substantial phenotypic diversification of social complexity. Other bee lineages display a continuum of social complexity, ranging from solitary to simple societies, but do not reach the levels of social complexity seen in honey bees, stingless bees, and bumble bees. Bee evolution, therefore, provides a remarkable demonstration of a macroevolutionary process in which a major transition removed biological constraints and opened novel evolutionary opportunities, driving the exploration of the landscape of social phenotypes. Our approach can be extended to incorporate additional data types and readily applied to illuminate the evolution of social complexity in other animal groups.

Leveraging graphical model techniques to study evolution on phylogenetic networks

The evolution of molecular and phenotypic traits is commonly modelled using Markov processes along a phylogeny. This phylogeny can be a tree, or a network if it includes reticulations, representing events such as hybridization or admixture. Computing the likelihood of data observed at the leaves is costly as the size and complexity of the phylogeny grows. Efficient algorithms exist for trees, but cannot be applied to networks. We show that a vast array of models for trait evolution along phylogenetic networks can be reformulated as graphical models, for which efficient belief propagation algorithms exist. We provide a brief review of belief propagation on general graphical models, then focus on linear Gaussian models for continuous traits. We show how belief propagation techniques can be applied for exact or approximate (but more scalable) likelihood and gradient calculations, and prove novel results for efficient parameter inference of some models. We highlight the possible fruitful interactions between graphical models and phylogenetic methods. For example, approximate likelihood approaches have the potential to greatly reduce computational costs for phylogenies with reticulations.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.

The Carnivoran Adaptive Landscape Reveals Trade-offs among Functional Traits in the Skull, Appendicular, and Axial Skeleton

Analyses of form-function relationships are widely used to understand links between morphology, ecology, and adaptation across macroevolutionary scales. However, few have investigated functional trade-offs and covariance within and between the skull, limbs, and vertebral column simultaneously. In this study, we investigated the adaptive landscape of skeletal form and function in carnivorans to test how functional trade-offs among these skeletal regions contribute to ecological adaptations and the topology of the landscape. We found that morphological proxies of function derived from carnivoran skeletal regions exhibit trade-offs and covariation across their performance surfaces, particularly in the appendicular and axial skeletons. These functional trade-offs and covariation correspond as adaptations to different adaptive landscapes when optimized by various factors including phylogeny, dietary ecology, and, in particular, locomotor mode. Lastly, we found that the topologies of the optimized adaptive landscapes and underlying performance surfaces are largely characterized as a single gradual gradient rather than as rugged, multipeak landscapes with distinct zones. Our results suggest that carnivorans may already occupy a broad adaptive zone as part of a larger mammalian adaptive landscape that masks the form and function relationships of skeletal traits.

The evolution of gestation length in eutherian mammals

Eutherian mammals exhibit considerable variation in their gestation lengths, which has traditionally been linked to variation in other traits, including body mass and lifespan. To understand how gestation length variation, including its association with body mass and lifespan variation, changed over mammalian evolution, we conducted phylogeny-informed analyses of 845 representative extant species. We found that gestation length substantially differed in both whether and how strongly it was associated with body mass and lifespan across mammals. For example, gestation length was not associated with lifespan or body mass in Chiroptera and Cetacea but was strongly associated only with body mass in Carnivora. We also identified 52 evolutionary shifts in gestation length variation across the mammal phylogeny and 14 shifts when we jointly considered variation of all three traits; six shifts were shared. Notably, two of these shifts, both positive, occurred at the roots of Cetacea and Pinnipedia, respectively, coinciding with the transition of these clades to the marine environment, whereas a negative shift occurred at the root of Chiroptera, coinciding with the evolution of flight in this clade. These results suggest that the relationship between gestation length and the two other traits has varied substantially across mammalian phylogeny.

Genome and life-history evolution link bird diversification to the end-Cretaceous mass extinction

Complex patterns of genome evolution associated with the end-Cretaceous [Cretaceous-Paleogene (K-Pg)] mass extinction limit our understanding of the early evolutionary history of modern birds. Here, we analyzed patterns of avian molecular evolution and identified distinct macroevolutionary regimes across exons, introns, untranslated regions, and mitochondrial genomes. Bird clades originating near the K-Pg boundary exhibited numerous shifts in the mode of molecular evolution, suggesting a burst of genomic heterogeneity at this point in Earth's history. These inferred shifts in substitution patterns were closely related to evolutionary shifts in developmental mode, adult body mass, and patterns of metabolic scaling. Our results suggest that the end-Cretaceous mass extinction triggered integrated patterns of evolution across avian genomes, physiology, and life history near the dawn of the modern bird radiation.

Challenges and advances in measuring phenotypic convergence

Tests of phenotypic convergence can provide evidence of adaptive evolution, and the popularity of such studies has grown in recent years due to the development of novel, quantitative methods for identifying and measuring convergence. These methods include the commonly applied C1-C4 measures of Stayton (2015a), which measure morphological distances between lineages, and Ornstein-Uhlenbeck (OU) model-fitting analyses, which test whether lineages converged on shared adaptive peaks. We test the performance of C-measures and other convergence measures under various evolutionary scenarios and reveal a critical issue with C-measures: they often misidentify divergent lineages as convergent. We address this issue by developing novel convergence measures-Ct1-Ct4-measures-that calculate distances between lineages at specific points in time, minimizing the possibility of misidentifying divergent taxa as convergent. Ct-measures are most appropriate when focal lineages are of the same or similar geologic ages (e.g., extant taxa), meaning that the lineages' evolutionary histories include considerable overlap in time. Beyond C-measures, we find that all convergence measures are influenced by the position of focal taxa in phenotypic space, with morphological outliers often statistically more likely to be measured as strongly convergent. Further, we mimic scenarios in which researchers assess convergence using OU models with a priori regime assignments (e.g., classifying taxa by ecological traits) and find that multiple-regime OU models with phenotypically divergent lineages assigned to a shared selective regime often outperform simpler models. This highlights that model support for these multiple-regime OU models should not be assumed to always reflect convergence among focal lineages of a shared regime. Our new Ct1-Ct4-measures provide researchers with an improved comparative tool, but we emphasize that all available convergence measures are imperfect, and researchers should recognize the limitations of these methods and use multiple lines of evidence to test convergence hypotheses.

Grossnickle D, Santana SE, Law CJ. Gliding toward an understanding of the origin of flight in bats

Bats are the only mammals capable of powered flight and have correspondingly specialized body plans, particularly in their limb morphology. The origin of bat flight is still not fully understood due to an uninformative fossil record but, from the perspective of a functional transition, it is widely hypothesized that bats evolved from gliding ancestors. Here, we test predictions of the gliding-to-flying hypothesis of the origin of bat flight by using phylogenetic comparative methods to model the evolution of forelimb and hindlimb traits on a dataset spanning four extinct bats and 231 extant mammals with diverse locomotor modes. Our results reveal that gliders exhibit adaptive trait optima (1) toward relatively elongate forelimbs that are intermediate between those of bats and non-gliding arborealists, and (2) toward relatively narrower but not longer hindlimbs that are intermediate between those of non-gliders and bats. We propose an adaptive landscape based on limb length and width optimal trends derived from our modeling analyses. Our results support a hypothetical evolutionary pathway wherein glider-like postcranial morphology precedes a bat-like morphology adapted to powered-flight, setting a foundation for future developmental, biomechanical, and evolutionary research to test this idea.

Robust Phylogenetic Regression

Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has shown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution-the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging-yet biologically important-settings.

The hierarchical radiation of phyllostomid bats as revealed by adaptive molar morphology

Adaptive radiations are bursts in biodiversity that generate new evolutionary lineages and phenotypes. However, because they typically occur over millions of years, it is unclear how their macroevolutionary dynamics vary through time and among groups of organisms. Phyllostomid bats radiated extensively for diverse diets-from insects to vertebrates, fruit, nectar, and blood-and we use their molars as a model system to examine the dynamics of adaptive radiations. Three-dimensional shape analyses of lower molars of Noctilionoidea (Phyllostomidae and close relatives) indicate that different diet groups exhibit distinct morphotypes. Comparative analyses further reveal that phyllostomids are a striking example of a hierarchical radiation; phyllostomids' initial, higher-level diversification involved an "early burst" in molar morphological disparity as lineages invaded new diet-affiliated adaptive zones, followed by subsequent lower-level diversifications within adaptive zones involving less dramatic morphological changes. We posit that strong selective pressures related to initial shifts to derived diets may have freed molars from morpho-functional constraints associated with the ancestral molar morphotype. Then, lineages with derived diets (frugivores and nectarivores) diversified within broad adaptive zones, likely reflecting finer-scale niche partitioning. Importantly, the observed early burst pattern is only evident when examining molar traits that are strongly linked to diet, highlighting the value of ecomorphological traits in comparative studies. Our results support the hypothesis that adaptive radiations are commonly hierarchical and involve different tempos and modes at different phylogenetic levels, with early bursts being more common at higher levels.

Analytical advances alleviate model misspecification in non-Brownian multivariate comparative methods

Adams and Collyer argue that contemporary multivariate (Gaussian) phylogenetic comparative methods are prone to favouring more complex models of evolution and sometimes rotation invariance can be an issue. Here we dissect the concept of rotation invariance and point out that, depending on the understanding, this can be an issue with any method that relies on numerical instead of analytical estimation approaches. We relate this to the ongoing discussion concerning phylogenetic principal component analysis. Contrary to what Adams and Collyer found, we do not observe a bias against the simpler Brownian motion process in simulations when we use the new, improved, likelihood evaluation algorithm employed by mvSLOUCH, which allows for studying much larger phylogenies and more complex model setups.

Evolutionary shift detection with ensemble variable selection

Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of phenotypes. The detection performances of different methods are influenced by many factors, including different numbers of shifts, shift sizes, where a shift occurs on a tree, and the types of phylogenetic structure. Furthermore, the model assumptions are oversimplified, so are likely to be violated in real data, which could cause the methods to fail. We perform simulations to assess the effect of these factors on the performance of shift detection methods. To make the comparisons more complete, we also propose an ensemble variable selection method (R package ELPASO) and compare it with existing methods (R packages [Formula: see text]1ou and PhylogeneticEM). The performances of methods are highly dependent on the selection criterion. [Formula: see text]1ou+pBIC is usually the most conservative method and it performs well when signal sizes are large. [Formula: see text]1ou+BIC is the least conservative method and it performs well when signal sizes are small. The ensemble method provides more balanced choices between those two methods. Moreover, the performances of all methods are heavily impacted by measurement error, tree reconstruction error and shifts in variance.

phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things)

Phylogenetic comparative methods comprise the general endeavor of using an estimated phylogenetic tree (or set of trees) to make secondary inferences: about trait evolution, diversification dynamics, biogeography, community ecology, and a wide range of other phenomena or processes. Over the past ten years or so, the phytools R package has grown to become an important research tool for phylogenetic comparative analysis. phytools is a diverse contributed R library now consisting of hundreds of different functions covering a variety of methods and purposes in phylogenetic biology. As of the time of writing, phytools included functionality for fitting models of trait evolution, for reconstructing ancestral states, for studying diversification on trees, and for visualizing phylogenies, comparative data, and fitted models, as well numerous other tasks related to phylogenetic biology. Here, I describe some significant features of and recent updates to phytools, while also illustrating several popular workflows of the phytools computational software.

Uncovering the mosaic evolution of the carnivoran skeletal system

The diversity of vertebrate skeletons is often attributed to adaptations to distinct ecological factors such as diet, locomotion, and sensory environment. Although the adaptive evolution of skull, appendicular skeleton, and vertebral column is well studied in vertebrates, comprehensive investigations of all skeletal components simultaneously are rarely performed. Consequently, we know little of how modes of evolution differ among skeletal components. Here, we tested if ecological and phylogenetic effects led to distinct modes of evolution among the cranial, appendicular and vertebral regions in extant carnivoran skeletons. Using multivariate evolutionary models, we found mosaic evolution in which only the mandible, hindlimb and posterior (i.e. last thoracic and lumbar) vertebrae showed evidence of adaptation towards ecological regimes whereas the remaining skeletal components reflect clade-specific evolutionary shifts. We hypothesize that the decoupled evolution of individual skeletal components may have led to the origination of distinct adaptive zones and morphologies among extant carnivoran families that reflect phylogenetic hierarchies. Overall, our work highlights the importance of examining multiple skeletal components simultaneously in ecomorphological analyses. Ongoing work integrating the fossil and palaeoenvironmental record will further clarify deep-time drivers that govern the carnivoran diversity we see today and reveal the complexity of evolutionary processes in multicomponent systems.

The Cauchy Process on Phylogenies: A Tractable Model for Pulsed Evolution

Phylogenetic comparative methods use random processes, such as the Brownian Motion, to model the evolution of continuous traits on phylogenetic trees. Growing evidence for non-gradual evolution motivated the development of complex models, often based on Lévy processes. However, their statistical inference is computationally intensive and currently relies on approximations, high-dimensional sampling, or numerical integration. We consider here the Cauchy Process (CP), a particular pure-jump Lévy process in which the trait increment along each branch follows a centered Cauchy distribution with a dispersion proportional to its length. In this work, we derive an exact algorithm to compute both the joint probability density of the tip trait values of a phylogeny under a CP and the ancestral trait values and branch increments posterior densities in quadratic time. A simulation study shows that the CP generates patterns in comparative data that are distinct from any Gaussian process, and that restricted maximum likelihood parameter estimates and root trait reconstruction are unbiased and accurate for trees with 200 tips or less. The CP has only two parameters but is rich enough to capture complex-pulsed evolution. It can reconstruct posterior ancestral trait distributions that are multimodal, reflecting the uncertainty associated with the inference of the evolutionary history of a trait from extant taxa only. Applied on empirical datasets taken from the Evolutionary Ecology and Virology literature, the CP suggests nuanced scenarios for the body size evolution of Greater Antilles Lizards and for the geographical spread of the West Nile Virus epidemics in North America, both consistent with previous studies using more complex models. The method is efficiently implemented in C with an R interface in package cauphy, which is open source and freely available online.

The evolution of gestation length in eutherian mammals

Gestation length, or the duration of pregnancy, is a critical component of mammalian reproductive biology1. Eutherian mammals exhibit striking variation in their gestation lengths2-5, which has traditionally been linked to and allometrically scales with variation in other life history traits, including body mass and lifespan5-8. How the phenotypic landscape of gestation length variation, including its associations with body mass and lifespan variation, changed over mammalian evolution remains unknown. Phylogeny-informed analyses of 845 representative extant eutherian mammals showed that gestation length variation substantially differed in both whether and how strongly it was associated with body mass and lifespan across mammalian clades. For example, gestation length variation in Chiroptera and Cetacea was not associated with lifespan or body mass but was strongly associated only with body mass in Carnivora. We also identified 52 adaptive shifts in gestation length variation across the mammal phylogeny and 14 adaptive shifts when considering all three life history traits; the placements of six adaptive shifts are common in the two analyses. Notably, two of these shifts occurred at the roots of Cetacea and Pinnipedia, respectively, coinciding with the transition of these clades to the marine environment. The varying dynamics of the phenotypic landscape of gestation length, coupled with the varying patterns of associations between gestation length and two other major life history traits, raise the hypothesis that evolutionary constraints on gestation length have varied substantially across mammalian phylogeny. This variation in constraints implies that the genetic architecture of gestation length differs between mammal clades.

The evolution of Ericaceae flowers and their pollination syndromes at a global scale

PREMISE

Floral evolution in large clades is difficult to study not only because of the number of species involved, but also because they often are geographically widespread and include a diversity of outcrossing pollination systems. The cosmopolitan blueberry family (Ericaceae) is one such example, most notably pollinated by bees and multiple clades of nectarivorous birds.

METHODS

We combined data on floral traits, pollination ecology, and geography with a comprehensive phylogeny to examine the structuring of floral diversity across pollination systems and continents. We focused on ornithophilous systems to test the hypothesis that some Old World Ericaceae were pollinated by now-extinct hummingbirds.

RESULTS

Despite some support for floral differentiation at a continental scale, we found a large amount of variability within and among landmasses, due to both phylogenetic conservatism and parallel evolution. We found support for floral differentiation in anther and corolla traits across pollination systems, including among different ornithophilous systems. Corolla traits show inconclusive evidence that some Old World Ericaceae were pollinated by hummingbirds, while anther traits show stronger evidence. Some major shifts in floral traits are associated with changes in pollination system, but shifts within bee systems are likely also important.

CONCLUSIONS

Studying the floral evolution of large, morphologically diverse, and widespread clades is feasible. We demonstrate that continent-specific radiations have led to widespread parallel evolution of floral morphology. We show that traits outside of the perianth may hold important clues to the ecological history of lineages.

Biogeography and the evolution of acoustic communication in the polyploid North American grey treefrog complex

After polyploid species are formed, interactions between diploid and polyploid lineages may generate additional diversity in novel cytotypes and phenotypes. In anurans, mate choice by acoustic communication is the primary method by which individuals identify their own species and assess suitable mates. As such, the evolution of acoustic signals is an important mechanism for contributing to reproductive isolation and diversification in this group. Here, we estimate the biogeographical history of the North American grey treefrog complex, consisting of the diploid Hyla chrysoscelis and the tetraploid Hyla versicolor, focusing specifically on the geographical origin of whole genome duplication and the expansion of lineages out of glacial refugia. We then test for lineage-specific differences in mating signals by applying comparative methods to a large acoustic data set collected over 52 years that includes >1500 individual frogs. Along with describing the overall biogeographical history and call diversity, we found evidence that the geographical origin of H. versicolor and the formation of the midwestern polyploid lineage are both associated with glacial limits, and that the southwestern polyploid lineage is associated with a shift in acoustic phenotype relative to the diploid lineage with which they share a mitochondrial lineage. In H. chrysoscelis, we see that acoustic signals are largely split by Eastern and Western lineages, but that northward expansion along either side of the Appalachian Mountains is associated with further acoustic diversification. Overall, results of this study provide substantial clarity on the evolution of grey treefrogs as it relates to their biogeography and acoustic communication.

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.].

Bridging Performance and Adaptive Landscapes to Understand Long-Term Functional Evolution

AbstractUnderstanding functional adaptation demands an integrative framework that captures the complex interactions between form, function, ecology, and evolutionary processes. In this review, we discuss how to integrate the following two distinct approaches to better understand functional evolution: (1) the adaptive landscape approach (ALA), aimed at finding adaptive peaks for different ecologies, and (2) the performance landscape approach (PLA), aimed at finding performance peaks for different ecologies. We focus on the Ornstein-Uhlenbeck process as the evolutionary model for the ALA and on biomechanical modeling to estimate performance for the PLA. Whereas both the ALA and the PLA have each given insight into functional adaptation, separately they cannot address how much performance contributes to fitness or whether evolutionary constraints have played a role in form-function evolution. We show that merging these approaches leads to a deeper understanding of these issues. By comparing the locations of performance and adaptive peaks, we can infer how much performance contributes to fitness in species' current environments. By testing for the relevance of history on phenotypic variation, we can infer the influence of past selection and constraints on functional adaptation. We apply this merged framework in a case study of turtle shell evolution and explain how to interpret different possible outcomes. Even though such outcomes can be quite complex, they represent the multifaceted relations among function, fitness, and constraints.

Evolution of cortical geometry and its link to function, behaviour and ecology

Studies in comparative neuroanatomy and of the fossil record demonstrate the influence of socio-ecological niches on the morphology of the cerebral cortex, but have led to oftentimes conflicting theories about its evolution. Here, we study the relationship between the shape of the cerebral cortex and the topography of its function. We establish a joint geometric representation of the cerebral cortices of ninety species of extant Euarchontoglires, including commonly used experimental model organisms. We show that variability in surface geometry relates to species' ecology and behaviour, independent of overall brain size. Notably, ancestral shape reconstruction of the cortical surface and its change during evolution enables us to trace the evolutionary history of localised cortical expansions, modal segregation of brain function, and their association to behaviour and cognition. We find that individual cortical regions follow different sequences of area increase during evolutionary adaptations to dynamic socio-ecological niches. Anatomical correlates of this sequence of events are still observable in extant species, and relate to their current behaviour and ecology. We decompose the deep evolutionary history of the shape of the human cortical surface into spatially and temporally conscribed components with highly interpretable functional associations, highlighting the importance of considering the evolutionary history of cortical regions when studying their anatomy and function.

Decoupled evolution of the cranium and mandible in carnivoran mammals

The relationship between skull morphology and diet is a prime example of adaptive evolution. In mammals, the skull consists of the cranium and the mandible. Although the mandible is expected to evolve more directly in response to dietary changes, dietary regimes may have less influence on the cranium because additional sensory and brain-protection functions may impose constraints on its morphological evolution. Here, we tested this hypothesis by comparing the evolutionary patterns of cranium and mandible shape and size across 100+ species of carnivoran mammals with distinct feeding ecologies. Our results show decoupled modes of evolution in cranial and mandibular shape; cranial shape follows clade-based evolutionary shifts, whereas mandibular shape evolution is linked to broad dietary regimes. These results are consistent with previous hypotheses regarding hierarchical morphological evolution in carnivorans and greater evolutionary lability of the mandible with respect to diet. Furthermore, in hypercarnivores, the evolution of both cranial and mandibular size is associated with relative prey size. This demonstrates that dietary diversity can be loosely structured by craniomandibular size within some guilds. Our results suggest that mammal skull morphological evolution is shaped by mechanisms beyond dietary adaptation alone.

Defining plant ecological specialists and generalists: Building a framework for identification and classification

Specialization is a widespread but highly ambiguous and context-dependent ecological concept. This quality makes comparisons across related studies difficult and makes associated terms such as "specialist" and "generalist" scientifically obscure. Here, we present a metric-based framework to quantify specialization in 141 Quercus species using functional traits, biogeography, and species interactions. Rankings of specialization based on five metrics were used to answer questions about how specialization is used colloquially (i.e., individual species assessment by experts) and influenced by phylogenetics (Ancestral Character State Reconstruction, Automatic Shift Detection), biogeography (patterns of clustering by region and with climate), and species threat level (IUCN Red List). Metric-based ranking can be representative of specialization in a consistent and practical manner, correlating with IUCN Red List data, and the mean scores of individual expert assessments. Specialization is shown to be highly correlated with precipitation seasonality and only moderately influenced by evolutionary history. Data-deficient species were more likely to be highly specialized, and higher specialization was positively correlated with greater IUCN threat level. Frameworks for characterizing specialization and generalization can be done using metric ranking and can turn concepts that are often unclear into a definitive system. Metric-based rankings of specialization can also be used to reveal interesting insights about a clade's evolutionary history and geographic distribution when paired with the related phylogenetic and geographic data. Metric-based rankings can be applied to other systems and be a valuable tool for identifying species at risk and in need of conservation.

Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis

Biological phenotypes are products of complex evolutionary processes in which selective forces influence multiple biological trait measurements in unknown ways. Phylogenetic comparative methods seek to disentangle these relationships across the evolutionary history of a group of organisms. Unfortunately, most existing methods fail to accommodate high-dimensional data with dozens or even thousands of observations per taxon. Phylogenetic factor analysis offers a solution to the challenge of dimensionality. However, scientists seeking to employ this modeling framework confront numerous modeling and implementation decisions, the details of which pose computational and replicability challenges.We develop new inference techniques that increase both the computational efficiency and modeling flexibility of phylogenetic factor analysis. To facilitate adoption of these new methods, we present a practical analysis plan that guides researchers through the web of complex modeling decisions. We codify this analysis plan in an automated pipeline that distills the potentially overwhelming array of decisions into a small handful of (typically binary) choices.We demonstrate the utility of these methods and analysis plan in four real-world problems of varying scales. Specifically, we study floral phenotype and pollination in columbines, domestication in industrial yeast, life history in mammals, and brain morphology in New World monkeys.General and impactful community employment of these methods requires a data scientific analysis plan that balances flexibility, speed and ease of use, while minimizing model and algorithm tuning. Even in the presence of non-trivial phylogenetic model constraints, we show that one may analytically address latent factor uncertainty in a way that (a) aids model flexibility, (b) accelerates computation (by as much as 500-fold) and (c) decreases required tuning. These efforts coalesce to create an accessible Bayesian approach to high-dimensional phylogenetic comparative methods on large trees.

Data integration in Bayesian phylogenetics

Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.

Topographically distinct adaptive landscapes for teeth, skeletons, and size explain the adaptive radiation of Carnivora (Mammalia)

Models of adaptive radiation were originally developed to explain the early, rapid appearance of distinct modes of life within diversifying clades. Phylogenetic tests of this hypothesis have yielded limited support for temporally declining rates of phenotypic evolution across diverse clades, but the concept of an adaptive landscape that links form to fitness, while also crucial to these models, has received more limited attention. Using methods that assess the temporal accumulation of morphological variation and estimate the topography of the underlying adaptive landscape, I found evidence of an early partitioning of mandibulo-dental morphological variation in Carnivora (Mammalia) that occurs on an adaptive landscape with multiple peaks, consistent with classic ideas about adaptive radiation. Although strong support for this mode of adaptive radiation is present in traits related to diet, its signal is not present in body mass data or for traits related to locomotor behavior and substrate use. These findings suggest that adaptive radiations may occur along some axes of ecomorphological variation without leaving a signal in others and that their dynamics are more complex than simple univariate tests might suggest.

Patterns of Phenotypic Evolution Associated with Marine/Freshwater Transitions in Fishes

Evolutionary transitions between marine and freshwater ecosystems have occurred repeatedly throughout the phylogenetic history of fishes. The theory of ecological opportunity predicts that lineages that colonize species-poor regions will have greater potential for phenotypic diversification than lineages invading species-rich regions. Thus, transitions between marine and freshwaters may promote phenotypic diversification in trans-marine/freshwater fish clades. We used phylogenetic comparative methods to analyze body size data in nine major fish clades that have crossed the marine/freshwater boundary. We explored how habitat transitions, ecological opportunity, and community interactions influenced patterns of phenotypic diversity. Our analyses indicated that transitions between marine and freshwater habitats did not drive body size evolution, and there are few differences in body size between marine and freshwater lineages. We found that body size disparity in freshwater lineages is not correlated with the number of independent transitions to freshwaters. We found a positive correlation between body size disparity and overall species richness of a given area, and a negative correlation between body size disparity and diversity of closely related species. Our results indicate that the diversity of incumbent freshwater species does not restrict phenotypic diversification, but the diversity of closely related taxa can limit body size diversification. Ecological opportunity arising from colonization of novel habitats does not seem to have a major effect in the trajectory of body size evolution in trans-marine/freshwater clades. Moreover, competition with closely related taxa in freshwaters has a greater effect than competition with distantly related incumbent species.

Fast Likelihood Calculations for Automatic Identification of Macroevolutionary Rate Heterogeneity in Continuous and Discrete Traits

Understanding phenotypic disparity across the tree of life requires identifying where and when evolutionary rates change on phylogeny. A primary methodological challenge in macroevolution is therefore to develop methods for accurate inference of among-lineage variation in rates of phenotypic evolution. Here, we describe a method for inferring among-lineage evolutionary rate heterogeneity in both continuous and discrete traits. The method assumes that the present-day distribution of a trait is shaped by a variable-rate process arising from a mixture of constant-rate processes and uses a single-pass tree traversal algorithm to estimate branch-specific evolutionary rates. By employing dynamic programming optimization techniques and approximate maximum likelihood estimators where appropriate, our method permits rapid exploration of the tempo and mode of phenotypic evolution. Simulations indicate that the method reconstructs rates of trait evolution with high accuracy. Application of the method to datasets on squamate reptile reproduction and turtle body size recovers patterns of rate heterogeneity identified by previous studies but with computational costs reduced by many orders of magnitude. Our results expand the set of tools available for detecting macroevolutionary rate heterogeneity and point to the utility of fast, approximate methods for studying large scale biodiversity dynamics.

Digest: Data tests and testable definitions of evolutionary phenomena

How can we best test evolutionary phenomena that have historically muddled definitions? How do we ascertain whether data are appropriate or sufficient to explore our hypotheses? Moen et al. demonstrate a framework for accomplishing these goals that can be applied to most studies that use trait and tree data. Simultaneously, they provide a generalized approach for testing for adaptive radiation, a phenomenon with potential to help us understand drivers of evolution.

Adaptive shifts underlie the divergence in wing morphology in bombycoid moths

The evolution of flapping flight is linked to the prolific success of insects. Across Insecta, wing morphology diversified, strongly impacting aerodynamic performance. In the presence of ecological opportunity, discrete adaptive shifts and early bursts are two processes hypothesized to give rise to exceptional morphological diversification. Here, we use the sister-families Sphingidae and Saturniidae to answer how the evolution of aerodynamically important traits is linked to clade divergence and through what process(es) these traits evolve. Many agile Sphingidae evolved hover feeding behaviours, while adult Saturniidae lack functional mouth parts and rely on a fixed energy budget as adults. We find that Sphingidae underwent an adaptive shift in wing morphology coincident with life history and behaviour divergence, evolving small high aspect ratio wings advantageous for power reduction that can be moved at high frequencies, beneficial for flight control. By contrast, Saturniidae, which do not feed as adults, evolved large wings and morphology which surprisingly does not reduce aerodynamic power, but could contribute to their erratic flight behaviour, aiding in predator avoidance. We suggest that after the evolution of flapping flight, diversification of wing morphology can be potentiated by adaptative shifts, shaping the diversity of wing morphology across insects.

Gene expression evolution in pattern-triggered immunity within Arabidopsis thaliana and across Brassicaceae species

Plants recognize surrounding microbes by sensing microbe-associated molecular patterns (MAMPs) to activate pattern-triggered immunity (PTI). Despite their significance for microbial control, the evolution of PTI responses remains largely uncharacterized. Here, by employing comparative transcriptomics of six Arabidopsis thaliana accessions and three additional Brassicaceae species to investigate PTI responses, we identified a set of genes that commonly respond to the MAMP flg22 and genes that exhibit species-specific expression signatures. Variation in flg22-triggered transcriptome responses across Brassicaceae species was incongruent with their phylogeny, while expression changes were strongly conserved within A. thaliana. We found the enrichment of WRKY transcription factor binding sites in the 5'-regulatory regions of conserved and species-specific responsive genes, linking the emergence of WRKY-binding sites with the evolution of gene expression patterns during PTI. Our findings advance our understanding of the evolution of the transcriptome during biotic stress.

Assessing the fit of the multi-species network coalescent to multi-locus data

MOTIVATION

With growing genome-wide molecular datasets from next-generation sequencing, phylogenetic networks can be estimated using a variety of approaches. These phylogenetic networks include events like hybridization, gene flow or horizontal gene transfer explicitly. However, the most accurate network inference methods are computationally heavy. Methods that scale to larger datasets do not calculate a full likelihood, such that traditional likelihood-based tools for model selection are not applicable to decide how many past hybridization events best fit the data. We propose here a goodness-of-fit test to quantify the fit between data observed from genome-wide multi-locus data, and patterns expected under the multi-species coalescent model on a candidate phylogenetic network.

RESULTS

We identified weaknesses in the previously proposed TICR test, and proposed corrections. The performance of our new test was validated by simulations on real-world phylogenetic networks. Our test provides one of the first rigorous tools for model selection, to select the adequate network complexity for the data at hand. The test can also work for identifying poorly inferred areas on a network.

AVAILABILITY AND IMPLEMENTATION

Software for the goodness-of-fit test is available as a Julia package at https://github.com/cecileane/QuartetNetworkGoodnessFit.jl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sensory adaptations reshaped intrinsic factors underlying morphological diversification in bats

Background

Morphological evolution may be impacted by both intrinsic (developmental, constructional, physiological) and extrinsic (ecological opportunity and release) factors, but can intrinsic factors be altered by adaptive evolution and, if so, do they constrain or facilitate the subsequent diversification of biological form? Bats underwent deep adaptive divergences in skull shape as they evolved different sensory modes; here we investigate the potential impact of this process on two intrinsic factors that underlie morphological variation across organisms, allometry, and modularity.

Results

We use comparative phylogenetic and morphometric approaches to examine patterns of evolutionary allometry and modularity across a 3D geometric morphometric dataset spanning all major bat clades. We show that allometric relationships diverge between echolocators and visually oriented non-echolocators and that the evolution of nasal echolocation reshaped the modularity of the bat cranium.

Conclusions

Shifts in allometry and modularity may have significant consequences on the diversification of anatomical structures, as observed in the bat skull.

Exploring adaptive landscapes across deep time: A case study using echinoid body size

Adaptive landscapes are a common way of conceptualizing the phenotypic evolution of lineages across deep time. Although multiple approaches exist to implement this concept into operational models of trait evolution, inferring adaptive landscapes from comparative datasets remains challenging. Here, I explore the macroevolutionary dynamics of echinoid body size using data from over 5000 specimens and a phylogenetic framework incorporating a dense fossil sampling and spanning approximately 270 million years. Furthermore, I implement a novel approach of exploring alternative parameterizations of adaptive landscapes that succeeds in finding simpler, yet better-fitting models. Echinoid body size has been constrained to evolve within a single adaptive optimum for much of the clade's history. However, most of the morphological disparity of echinoids was generated by multiple regime shifts that drove the repeated evolution of miniaturized and gigantic forms. Events of body size innovation occurred predominantly in the Late Cretaceous and were followed by a drastic slowdown following the Cretaceous-Paleogene mass extinction. The discovery of these patterns is contingent upon directly sampling fossil taxa. The macroevolution of echinoid body size is therefore characterized by a late increase in disparity (likely linked to an expansion of ecospace), generated by active processes driving lineages toward extreme morphologies.

Complex interactions underlie the correlated evolution of floral traits and their association with pollinators in a clade with diverse pollination systems

Natural selection by pollinators is an important factor in the morphological diversity and adaptive radiation of flowering plants. Selection by similar pollinators in unrelated plants leads to convergence in floral morphology, or "floral syndromes." Previous investigations into floral syndromes have mostly studied relatively small and/or simple systems, emphasizing vertebrate pollination. Despite the importance of multiple floral traits in plant-pollinator interactions, these studies have examined few quantitative traits, so their co-variation and phenotypic integration have been underexplored. To gain better insights into pollinator-trait dynamics, we investigate the model system of the phlox family (Polemoniaceae), a clade of ∼400 species pollinated by a diversity of vectors. Using a comprehensive phylogeny and large dataset of traits and observations of pollinators, we reconstruct ancestral pollination system, accounting for the temporal history of pollinators. We conduct phylogenetically controlled analyses of trait co-variation and association with pollinators, integrating many analyses over phylogenetic uncertainty. Pollinator shifts are more heterogeneous than previously hypothesized. The evolution of floral traits is partially constrained by phylogenetic history and trait co-variation, but traits are convergent and differences are associated with different pollinators. Trait shifts are usually gradual, rather than rapid, suggesting complex genetic and ecological interactions of flowers at macroevolutionary scales.

Natural selection in the evolution of SARS-CoV-2 in bats created a generalist virus and highly capable human pathogen

Virus host shifts are generally associated with novel adaptations to exploit the cells of the new host species optimally. Surprisingly, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has apparently required little to no significant adaptation to humans since the start of the Coronavirus Disease 2019 (COVID-19) pandemic and to October 2020. Here we assess the types of natural selection taking place in Sarbecoviruses in horseshoe bats versus the early SARS-CoV-2 evolution in humans. While there is moderate evidence of diversifying positive selection in SARS-CoV-2 in humans, it is limited to the early phase of the pandemic, and purifying selection is much weaker in SARS-CoV-2 than in related bat Sarbecoviruses. In contrast, our analysis detects evidence for significant positive episodic diversifying selection acting at the base of the bat virus lineage SARS-CoV-2 emerged from, accompanied by an adaptive depletion in CpG composition presumed to be linked to the action of antiviral mechanisms in these ancestral bat hosts. The closest bat virus to SARS-CoV-2, RmYN02 (sharing an ancestor about 1976), is a recombinant with a structure that includes differential CpG content in Spike; clear evidence of coinfection and evolution in bats without involvement of other species. While an undiscovered "facilitating" intermediate species cannot be discounted, collectively, our results support the progenitor of SARS-CoV-2 being capable of efficient human-human transmission as a consequence of its adaptive evolutionary history in bats, not humans, which created a relatively generalist virus.

Relaxed Random Walks at Scale

Relaxed random walk (RRW) models of trait evolution introduce branch-specific rate multipliers to modulate the variance of a standard Brownian diffusion process along a phylogeny and more accurately model overdispersed biological data. Increased taxonomic sampling challenges inference under RRWs as the number of unknown parameters grows with the number of taxa. To solve this problem, we present a scalable method to efficiently fit RRWs and infer this branch-specific variation in a Bayesian framework. We develop a Hamiltonian Monte Carlo (HMC) sampler to approximate the high-dimensional, correlated posterior that exploits a closed-form evaluation of the gradient of the trait data log-likelihood with respect to all branch-rate multipliers simultaneously. Our gradient calculation achieves computational complexity that scales only linearly with the number of taxa under study. We compare the efficiency of our HMC sampler to the previously standard univariable Metropolis-Hastings approach while studying the spatial emergence of the West Nile virus in North America in the early 2000s. Our method achieves at least a 6-fold speed increase over the univariable approach. Additionally, we demonstrate the scalability of our method by applying the RRW to study the correlation between five mammalian life history traits in a phylogenetic tree with $3650$ tips.[Bayesian inference; BEAST; Hamiltonian Monte Carlo; life history; phylodynamics, relaxed random walk.].

Phylogenetic Trees and Networks Can Serve as Powerful and Complementary Approaches for Analysis of Genomic Data

Genomic data have had a profound impact on nearly every biological discipline. In systematics and phylogenetics, the thousands of loci that are now being sequenced can be analyzed under the multispecies coalescent model (MSC) to explicitly account for gene tree discordance due to incomplete lineage sorting (ILS). However, the MSC assumes no gene flow post divergence, calling for additional methods that can accommodate this limitation. Explicit phylogenetic network methods have emerged, which can simultaneously account for ILS and gene flow by representing evolutionary history as a directed acyclic graph. In this point of view, we highlight some of the strengths and limitations of phylogenetic networks and argue that tree-based inference should not be blindly abandoned in favor of networks simply because they represent more parameter rich models. Attention should be given to model selection of reticulation complexity, and the most robust conclusions regarding evolutionary history are likely obtained when combining tree- and network-based inference.

Climate and phylogenetic history structure morphological and architectural trait variation among fine-root orders

Fine roots mediate below-ground resource acquisition, yet understanding of how fine-root functional traits vary along environmental gradients, within branching orders and across phylogenetic scales remains limited. Morphological and architectural fine-root traits were measured on individual root orders of 20 oak species (genus Quercus) from divergent climates of origin that were harvested after three growing seasons in a glasshouse. These were then compared with similar measurements obtained from a phylogenetically diverse dataset of woody species from the Fine-Root Ecology Database (FRED). For the oaks, only precipitation seasonality and growing season moisture availability were correlated to aspects of root diameter and branching. Strong correlations among root diameters and architecture of different branch orders were common, while correlations between diameter and length were weakly negative. By contrast, the FRED dataset showed strong positive correlations between diameter and length and fewer correlations between root diameter and architectural traits. Our findings suggest that seasonal patterns of water availability are more important drivers of root adaptation in oaks than annual averages in precipitation and temperature. Furthermore, contrasting patterns of trait relationships between the oak and FRED datasets suggest that branching patterns are differentially constrained at narrow vs broad phylogenetic scales.

Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or non-heritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale.

Natural selection in the evolution of SARS-CoV-2 in bats, not humans, created a highly capable human pathogen

RNA viruses are proficient at switching host species, and evolving adaptations to exploit the new host's cells efficiently. Surprisingly, SARS-CoV-2 has apparently required no significant adaptation to humans since the start of the COVID-19 pandemic, with no observed selective sweeps since genome sampling began. Here we assess the types of natural selection taking place in Sarbecoviruses in horseshoe bats versus SARS-CoV-2 evolution in humans. While there is moderate evidence of diversifying positive selection in SARS-CoV-2 in humans, it is limited to the early phase of the pandemic, and purifying selection is much weaker in SARS-CoV-2 than in related bat Sarbecoviruses . In contrast, our analysis detects significant positive episodic diversifying selection acting on the bat virus lineage SARS-CoV-2 emerged from, accompanied by an adaptive depletion in CpG composition presumed to be linked to the action of antiviral mechanisms in ancestral hosts. The closest bat virus to SARS-CoV-2, RmYN02 (sharing an ancestor ∼1976), is a recombinant with a structure that includes differential CpG content in Spike; clear evidence of coinfection and evolution in bats without involvement of other species. Collectively our results demonstrate the progenitor of SARS-CoV-2 was capable of near immediate human-human transmission as a consequence of its adaptive evolutionary history in bats, not humans.

3D Digitization in Functional Morphology: Where is the Point of Diminishing Returns?

Modern computational and imaging methods are revolutionizing the fields of comparative morphology, biomechanics, and ecomorphology. In particular, imaging tools such as X-ray micro computed tomography (µCT) and diffusible iodine-based contrast enhanced CT allow observing and measuring small and/or otherwise inaccessible anatomical structures, and creating highly accurate three-dimensional (3D) renditions that can be used in biomechanical modeling and tests of functional or evolutionary hypotheses. But, do the larger datasets generated through 3D digitization always confer greater power to uncover functional or evolutionary patterns, when compared with more traditional methodologies? And, if so, why? Here, we contrast the advantages and challenges of using data generated via (3D) CT methods versus more traditional (2D) approaches in the study of skull macroevolution and feeding functional morphology in bats. First, we test for the effect of dimensionality and landmark number on inferences of adaptive shifts during cranial evolution by contrasting results from 3D versus 2D geometric morphometric datasets of bat crania. We find sharp differences between results generated from the 3D versus some of the 2D datasets (xy, yz, ventral, and frontal), which appear to be primarily driven by the loss of critical dimensions of morphological variation rather than number of landmarks. Second, we examine differences in accuracy and precision among 2D and 3D predictive models of bite force by comparing three skull lever models that differ in the sources of skull and muscle anatomical data. We find that a 3D model that relies on skull µCT scans and muscle data partly derived from diceCT is slightly more accurate than models based on skull photographs or skull µCT and muscle data fully derived from dissections. However, the benefit of using the diceCT-informed model is modest given the effort it currently takes to virtually dissect muscles from CT scans. By contrasting traditional and modern tools, we illustrate when and why 3D datasets may be preferable over 2D data, and vice versa, and how different methodologies can complement each other in comparative analyses of morphological function and evolution.

Adaptation, phylogeny, and covariance in milk macronutrient composition

Background

Milk is a complicated chemical mixture often studied through macronutrient concentrations of fat, protein, and sugar. There is a long-standing natural history tradition describing interspecific diversity in these concentrations. However, recent work has shown little influence of ecological or life history variables on them, aside from maternal diet effects, along with a strong phylogenetic signal.

Methods

I used multivariate phylogenetic comparative methods to revisit the ecological and life history correlates of milk macronutrient composition and elaborate on the nature of the phylogenetic signal using the phylogenetic mixed model. I also identified clades with distinctive milks through nonparametric tests (KSI) and PhylogeneticEM evolutionary modeling.

Results

In addition to the previously reported diet effects, I found increasingly aquatic mammals have milk that this is lower in sugar and higher in fat. Phylogenteic heritabilities for each concentration were high and phylogenetic correlations were moderate to strong indicating coevolution among the concentrations. Primates and pinnipeds had the most outstanding milks according to KSI and PhylogeneticEM, with perissodactyls and marsupials as other noteworthy clades with distinct selection regimes.

Discussion

Mammalian milks are diverse but often characteristic of certain higher taxa. This complicates identifying the ecological and life history correlates of milk composition using common phylogenetic comparative methods because those traits are also conservative and clade-specific. Novel methods, careful assessment of data quality and hypotheses, and a “phylogenetic natural history” perspective provide alternatives to these traditional tools.

Phylogenetic Comparative Methods and the Evolution of Multivariate Phenotypes

Evolutionary biology is multivariate, and advances in phylogenetic comparative methods for multivariate phenotypes have surged to accommodate this fact. Evolutionary trends in multivariate phenotypes are derived from distances and directions between species in a multivariate phenotype space. For these patterns to be interpretable, phenotypes should be characterized by traits in commensurate units and scale. Visualizing such trends, as is achieved with phylomorphospaces, should continue to play a prominent role in macroevolutionary analyses. Evaluating phylogenetic generalized least squares (PGLS) models (e.g., phylogenetic analysis of variance and regression) is valuable, but using parametric procedures is limited to only a few phenotypic variables. In contrast, nonparametric, permutation-based PGLS methods provide a flexible alternative and are thus preferred for high-dimensional multivariate phenotypes. Permutation-based methods for evaluating covariation within multivariate phenotypes are also well established and can test evolutionary trends in phenotypic integration. However, comparing evolutionary rates and modes in multivariate phenotypes remains an important area of future development.