Phylogenetic Comparative Data Research Articles

First report of multidrug-resistant carbapenemase-producing Aeromonas caviae co-harboring mcr-3.43 and mcr-7.2.

Hospital sewage serves as a crucial reservoir for antibiotic resistance genes. As colistin and carbapenems are the last-resort antibiotics, the emergence of their resistance genes has become a significant concern in clinical settings. In this study, we found that two novel mcr alleles (mcr-3.43 and mcr-7.2) with two carbapenemase genes (blaNDM-1 and blaKPC-2) were encoded in a single Aeromonas caviae strain isolated from hospital sewage. Our phylogenetic analysis revealed that the mcr-3.43 gene clustered with mcr-3.17 (with 95.55% amino acid identity), while the mcr-7.2 gene clustered with mcr-7.1 (with 68.68% amino acid identity). BLAST search against GenBank showed that mcr-7.2 was exclusively detected in Aeromonas spp. Mobile genetic elements were not found in the genetic context of mcr-7.2, suggesting that the dissemination of mcr-7.2 in Aeromonas spp. may be dependent on vertical transfer or recombination. The blaNDM-1 was adjacent to a recombinase gene and flanked by two IS91 elements, indicating a potential mobilization mechanism mediated by recombination and/or ISs. The blaKPC-2 gene was located on an IncU plasmid and adjacent to an ISKpn6. In summary, our study provides evidence for Aeromonas spp. as one of the potential reservoirs of colistin and carbapenem resistance genes.IMPORTANCEThe study discovered two novel mcr genes (mcr-3.43 and mcr-7.2) and two carbapenemase genes (blaNDM-1 and blaKPC-2) in a single Aeromonas caviae strain retrieved from hospital sewage. Using phylogenetic analysis and comparative data evaluation, the study revealed the genetic relatedness and dissemination potential of the detected resistance genes. With the exclusive discovery that mcr-7.2 is only present in Aeromonas spp. and the lack of mobile genetic elements in its genetic context, there is a strong indication of limited dissemination. The identification of these four resistance genes in a single strain of Aeromonas provided valuable insights into their potential presence in this genus. This study revealed that hospital sewage functions as a significant reservoir for antibiotic resistance genes, including colistin and carbapenem resistance genes.

The effects of ecology and behavior on the evolution of coloration in Coraciiformes

Abstract What drives the evolution of plumage color in birds? Bird color is likely to be under both natural and sexual selection where natural selection may favor evolution toward crypsis or camouflage whereas sexual selection may favor evolution toward conspicuousness. The responses to selection are predicted to relate to species’ ecology, behavior, and life history. Key hypotheses have focused on habitat and light environment, breeding strategy, territoriality, and hunting behavior. We tested these potential causes of color variation in the Coraciiformes, a colorful clade of non-passerine birds, using phylogenetic comparative methods and data on chromatic and achromatic properties of plumage coloration measured from museum specimens. We found that correlates of color evolution in Coraciiformes vary across body regions and depend on the focal color property (chromatic or achromatic properties of plumage coloration). While the light environment showed widespread effects on coloration in multiple body regions for both color properties, selection pressures related to behavioral characteristics had more spatially localized effects (e.g. territoriality on achromatic properties of wing feathers and hunting strategy on chromatic properties of belly feathers). Our results reveal both general patterns that may hold across other bird clades and more nuanced effects of selection that are likely to be mediated through the visual ecology of the signaler and receiver and the behavioral characteristics of Coraciiform species.

Analyzing Disparity and Rates of Morphological Evolution with Model-Based Phylogenetic Comparative Methods.

Understanding variation in rates of evolution and morphological disparity is a goal of macroevolutionary research. In a phylogenetic comparative methods framework, we present three explicit models for linking the rate of evolution of a trait to the state of another evolving trait. This allows testing hypotheses about causal influences on rates of phenotypic evolution with phylogenetic comparative data. We develop a statistical framework for fitting the models with generalized least-squares regression and use this to discuss issues and limitations in the study of rates of evolution more generally. We show that the power to detect effects on rates of evolution is low in that even strong causal effects are unlikely to explain more than a few percent of observed variance in disparity. We illustrate the models and issues by testing if rates of beak-shape evolution in birds are influenced by brain size, as may be predicted from a Baldwin effect in which presumptively more behaviorally flexible large-brained species generate more novel selection on themselves leading to higher rates of evolution. From an analysis of morphometric data for 645 species, we find evidence that both macro- and microevolution of the beak are faster in birds with larger brains, but with the caveat that there are no consistent effects of relative brain size.[Baldwin effect; beak shape; behavioral drive; bird; brain size; disparity; phylogenetic comparative method; rate of evolution.]

Memory in Trait Macroevolution.

The history of a trait within a lineage may influence its future evolutionary trajectory, but macroevolutionary theory of this process is not well developed. For example, consider the simplified binary trait of living in cave versus surface habitat. The longer a species has been cave dwelling, the more accumulated loss of vision, pigmentation, and defense may restrict future adaptation if the species encounters the surface environment. However, the Markov model of discrete trait evolution that is widely adopted in phylogenetics does not allow the rate of cave-to-surface transition to decrease with longer duration as a cave dweller. Here we describe three models of evolution that remove this memoryless constraint, using a renewal process to generalize beyond the typical Poisson process of discrete trait macroevolution. We then show how the two-state renewal process can be used for inference, and we investigate the potential of phylogenetic comparative data to reveal different influences of trait duration, or memory in trait evolution. We hope that such approaches may open new avenues for modeling trait evolution and for broad comparative tests of hypotheses that some traits become entrenched.

The evolution of parental care diversity in amphibians

Parental care is extremely diverse across species, ranging from simple behaviours to complex adaptations, varying in duration and in which sex cares. Surprisingly, we know little about how such diversity has evolved. Here, using phylogenetic comparative methods and data for over 1300 amphibian species, we show that egg attendance, arguably one of the simplest care behaviours, is gained and lost faster than any other care form, while complex adaptations, like brooding and viviparity, are lost at very low rates, if at all. Prolonged care from the egg to later developmental stages evolves from temporally limited care, but it is as easily lost as it is gained. Finally, biparental care is evolutionarily unstable regardless of whether the parents perform complementary or similar care duties. By considering the full spectrum of parental care adaptations, our study reveals a more complex and nuanced picture of how care evolves, is maintained, or is lost.

A General and Efficient Algorithm for the Likelihood of Diversification and Discrete-Trait Evolutionary Models.

As the size of phylogenetic trees and comparative data continue to grow and more complex models are developed to investigate the processes that gave rise to them, macroevolutionary analyses are becoming increasingly limited by computational requirements. Here, we introduce a novel algorithm, based on the "flow" of the differential equations that describe likelihoods along tree edges in backward time, to reduce redundancy in calculations and efficiently compute the likelihood of various macroevolutionary models. Our algorithm applies to several diversification models, including birth-death models and models that account for state- or time-dependent rates, as well as many commonly used models of discrete-trait evolution, and provides an alternative way to describe macroevolutionary model likelihoods. As a demonstration of our algorithm's utility, we implemented it for a popular class of state-dependent diversification models-BiSSE, MuSSE, and their extensions to hidden-states. Our implementation is available through the R package $\texttt{castor}$. We show that, for these models, our algorithm is one or more orders of magnitude faster than existing implementations when applied to large phylogenies. Our algorithm thus enables the fitting of state-dependent diversification models to modern massive phylogenies with millions of tips and may lead to potentially similar computational improvements for many other macroevolutionary models.

Vane macrostructure of primary feathers and its adaptations to flight in birds

The selection pressures that drive flight feather morphology are poorly understood. Using a phylogenetic comparative approach and data from 178 species of birds, we investigated whether both position along the wing length and flight feather length affected vane structure. We found that barb density was lower on distal primaries than on proximal primaries of the leading feather vane. In contrast, on the trailing vane only mid-point barb density differed and, here, it showed denser barbs on the distal primaries. This difference was greater at the feather base than at the tip. Barb angle was higher along the full length of the leading edge vane on the proximal primaries than on the distal primaries. Overall, barb density decreased from base to tip on both trailing and leading vanes on both the proximal and the distal primaries. In general, barb angle was less acute at the feather base than at the tip. Barbs were in general denser in continuous flapping fliers than in soarers and the angle of barbs on both the proximal and the distal primaries was affected by flight type. However, we did not identify consistent differences in the pattern of barb angle change among flight style groups. These findings add new perspectives to our understanding of the functional morphology of the flight feather vane, although we still have limited knowledge on how aerodynamic forces, particularly during take-off and landing, affect the morphology of the feather vane. (Less)

A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution.

Working with high-dimensional phylogenetic comparative data sets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits $p $ approaches the number of species $n $ and because some computational complications occur when $p $ exceeds $n$. Alternative phylogenetic comparative methods have recently been proposed to deal with the large $p $ small $n $ scenario but their use and performances are limited. Herein, we develop a penalized likelihood (PL) framework to deal with high-dimensional comparative data sets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian motion (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU), and Pagel's lambda models. We show using simulations that our PL approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when $p$ approaches $n$, and allows for their accurate estimation when $p$ equals or exceeds $n$. In addition, we show that PL models can be efficiently compared using generalized information criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic principal component analysis in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3D data set of brain shape in the New World monkeys. We find a clear support for an EB model suggesting an early diversification of brain morphology during the ecological radiation of the clade. PL offers an efficient way to deal with high-dimensional multivariate comparative data.

Evidence of a chimpanzee-sized ancestor of humans but a gibbon-sized ancestor of apes

Body mass directly affects how an animal relates to its environment and has a wide range of biological implications. However, little is known about the mass of the last common ancestor (LCA) of humans and chimpanzees, hominids (great apes and humans), or hominoids (all apes and humans), which is needed to evaluate numerous paleobiological hypotheses at and prior to the root of our lineage. Here we use phylogenetic comparative methods and data from primates including humans, fossil hominins, and a wide sample of fossil primates including Miocene apes from Africa, Europe, and Asia to test alternative hypotheses of body mass evolution. Our results suggest, contrary to previous suggestions, that the LCA of all hominoids lived in an environment that favored a gibbon-like size, but a series of selective regime shifts, possibly due to resource availability, led to a decrease and then increase in body mass in early hominins from a chimpanzee-sized LCA.

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.].

A Unifying Comparative Phylogenetic Framework Including Traits Coevolving Across Interacting Lineages.

Models of phenotypic evolution fit to phylogenetic comparative data are widely used to make inferences regarding the tempo and mode of trait evolution. A wide range of models is already available for this type of analysis, and the field is still under active development. One of the most needed development concerns models that better account for the effect of within- and between-clade interspecific interactions on trait evolution, which can result from processes as diverse as competition, predation, parasitism, or mutualism. Here, we begin by developing a very general comparative phylogenetic framework for (multi)-trait evolution that can be applied to both ultrametric and nonultrametric trees. This framework not only encapsulates many previous models of continuous univariate and multivariate phenotypic evolution, but also paves the way for the consideration of a much broader series of models in which lineages coevolve, meaning that trait changes in one lineage are influenced by the value of traits in other, interacting lineages. Next, we provide a standard way for deriving the probabilistic distribution of traits at tip branches under our framework. We show that a multivariate normal distribution remains the expected distribution for a broad class of models accounting for interspecific interactions. Our derivations allow us to fit various models efficiently, and in particular greatly reduce the computation time needed to fit the recently proposed phenotype matching model. Finally, we illustrate the utility of our framework by developing a toy model for mutualistic coevolution. Our framework should foster a new era in the study of coevolution from comparative data.

Inferring Bounded Evolution in Phenotypic Characters from Phylogenetic Comparative Data.

Our understanding of phenotypic evolution over macroevolutionary timescales largely relies on the use of stochastic models for the evolution of continuous traits over phylogenies. The two most widely used models, Brownian motion and the Ornstein-Uhlenbeck (OU) process, differ in that the latter includes constraints on the variance that a trait can attain in a clade. The OU model explicitly models adaptive evolution toward a trait optimum and has thus been widely used to demonstrate the existence of stabilizing selection on a trait. Here we introduce a new model for the evolution of continuous characters on phylogenies: Brownian motion between two reflective bounds, or Bounded Brownian Motion (BBM). This process also models evolutionary constraints, but of a very different kind. We provide analytical expressions for the likelihood of BBM and present a method to calculate the likelihood numerically, as well as the associated R code. Numerical simulations show that BBM achieves good performance: parameter estimation is generally accurate but more importantly BBM can be very easily discriminated from both BM and OU. We then analyze climatic niche evolution in diprotodonts and find that BBM best fits this empirical data set, suggesting that the climatic niches of diprotodonts are bounded by the climate available in Australia and the neighboring islands but probably evolved with little additional constraints. We conclude that BBM is a valuable addition to the macroevolutionary toolbox, which should enable researchers to elucidate whether the phenotypic traits they study are evolving under hard constraints between bounds.

Have superfetation and matrotrophy facilitated the evolution of larger offspring in poeciliid fishes?

Superfetation is the ability of females to simultaneously carry multiple broods of embryos, with each brood at a different developmental stage. Matrotrophy is the post-fertilization maternal provisioning of nutrients to developing embryos throughout gestation. Several studies have demonstrated that, in viviparous fishes, superfetation and matrotrophy have evolved in a correlated way, such that species capable of bearing several simultaneous broods also exhibit advanced degrees of post-fertilization provisioning. The adaptive value of the concurrent presence of both reproductive modes may be associated with the production of larger newborns, which in turn may result in enhanced offspring fitness. In this study, we tested two hypotheses: (1) species with superfetation and moderate or extensive matrotrophy give birth to larger offspring compared to species without superfetation or matrotrophy; (2) species with higher degrees of superfetation and matrotrophy (i.e. more simultaneous broods and increased amounts of post-fertilization provisioning) give birth to larger offspring compared to species with relatively low degrees of superfetation and matrotrophy (i.e. fewer simultaneous broods and lesser amounts of post-fertilization provisioning). Using different phylogenetic comparative methods and data on 44 species of viviparous fishes of the family Poeciliidae, we found a lack of association between offspring size and the combination of superfetation and matrotrophy. Therefore, the concurrent presence of superfetation and moderate or extensive matrotrophy has not facilitated the evolution of larger offspring. In fact, these traits have evolved differently. Superfetation and matrotrophy have accumulated gradual changes that largely can be explained by Brownian motion, whereas offspring size has evolved fluidly, experiencing changes that likely resulted from selective responses to the local conditions.

Model Adequacy and the Macroevolution of Angiosperm Functional Traits.

Making meaningful inferences from phylogenetic comparative data requires a meaningful model of trait evolution. It is thus important to determine whether the model is appropriate for the data and the question being addressed. One way to assess this is to ask whether the model provides a good statistical explanation for the variation in the data. To date, researchers have focused primarily on the explanatory power of a model relative to alternative models. Methods have been developed to assess the adequacy, or absolute explanatory power, of phylogenetic trait models, but these have been restricted to specific models or questions. Here we present a general statistical framework for assessing the adequacy of phylogenetic trait models. We use our approach to evaluate the statistical performance of commonly used trait models on 337 comparative data sets covering three key angiosperm functional traits. In general, the models we tested often provided poor statistical explanations for the evolution of these traits. This was true for many different groups and at many different scales. Whether such statistical inadequacy will qualitatively alter inferences drawn from comparative data sets will depend on the context. Regardless, assessing model adequacy can provide interesting biological insights-how and why a model fails to describe variation in a data set give us clues about what evolutionary processes may have driven trait evolution across time.

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.

Robust Regression and Posterior Predictive Simulation Increase Power to Detect Early Bursts of Trait Evolution

A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated--a so-called "Early Burst." Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst--the rate at which phenotypic evolution declines--is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.

INTEGRATING FOSSILS WITH MOLECULAR PHYLOGENIES IMPROVES INFERENCE OF TRAIT EVOLUTION

Comparative biologists often attempt to draw inferences about tempo and mode in evolution by comparing the fit of evolutionary models to phylogenetic comparative data consisting of a molecular phylogeny with branch lengths and trait measurements from extant taxa. These kinds of approaches ignore historical evidence for evolutionary pattern and process contained in the fossil record. In this article, we show through simulation that incorporation of fossil information dramatically improves our ability to distinguish among models of quantitative trait evolution using comparative data. We further suggest a novel Bayesian approach that allows fossil information to be integrated even when explicit phylogenetic hypotheses are lacking for extinct representatives of extant clades. By applying this approach to a comparative dataset comprising body sizes for caniform carnivorans, we show that incorporation of fossil information not only improves ancestral state estimates relative to those derived from extant taxa alone, but also results in preference of a model of evolution with trend toward large body size over alternative models such as Brownian motion or Ornstein-Uhlenbeck processes. Our approach highlights the importance of considering fossil information when making macroevolutionary inference, and provides a way to integrate the kind of sparse fossil information that is available to most evolutionary biologists.

FITTING MODELS OF CONTINUOUS TRAIT EVOLUTION TO INCOMPLETELY SAMPLED COMPARATIVE DATA USING APPROXIMATE BAYESIAN COMPUTATION

In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic data. Here, we develop and implement a new, tree-based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood-dependent approaches are lacking.

The evolution of chloroplast genome structure in ferns

The plastid genome (plastome) is a rich source of phylogenetic and other comparative data in plants. Most land plants possess a plastome of similar structure. However, in a major group of plants, the ferns, a unique plastome structure has evolved. The gene order in ferns has been explained by a series of genomic inversions relative to the plastome organization of seed plants. Here, we examine for the first time the structure of the plastome across fern phylogeny. We used a PCR-based strategy to map and partially sequence plastomes. We found that a pair of partially overlapping inversions in the region of the inverted repeat occurred in the common ancestor of most ferns. However, the ancestral (seed plant) structure is still found in early diverging branches leading to the osmundoid and filmy fern lineages. We found that a second pair of overlapping inversions occurred on a branch leading to the core leptosporangiates. We also found that the unique placement of the gene matK in ferns (lacking a flanking intron) is not a result of a large-scale inversion, as previously thought. This is because the intron loss maps to an earlier point on the phylogeny than the nearby inversion. We speculate on why inversions may occur in pairs and what this may mean for the dynamics of plastome evolution.

EARLY BURSTS OF BODY SIZE AND SHAPE EVOLUTION ARE RARE IN COMPARATIVE DATA

George Gaylord Simpson famously postulated that much of life's diversity originated as adaptive radiations-more or less simultaneous divergences of numerous lines from a single ancestral adaptive type. However, identifying adaptive radiations has proven difficult due to a lack of broad-scale comparative datasets. Here, we use phylogenetic comparative data on body size and shape in a diversity of animal clades to test a key model of adaptive radiation, in which initially rapid morphological evolution is followed by relative stasis. We compared the fit of this model to both single selective peak and random walk models. We found little support for the early-burst model of adaptive radiation, whereas both other models, particularly that of selective peaks, were commonly supported. In addition, we found that the net rate of morphological evolution varied inversely with clade age. The youngest clades appear to evolve most rapidly because long-term change typically does not attain the amount of divergence predicted from rates measured over short time scales. Across our entire analysis, the dominant pattern was one of constraints shaping evolution continually through time rather than rapid evolution followed by stasis. We suggest that the classical model of adaptive radiation, where morphological evolution is initially rapid and slows through time, may be rare in comparative data.