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mvMORPH: An R package for fitting multivariate evolutionary models to morphometric data
Abstract
1. We present mvMORPH, a package of multivariate phylogenetic comparative methods for the R statistical environment. mvMORPH is freely available on the CRAN package repository (http://cran.r-project.org/web/packages/mvMORPH/).
2. mvMORPH allows fitting a range of multivariate evolutionary models under a maximum-likelihood criterion. Initially developed in the context of phylogenetic analysis of multiple morphometric traits, its use can be extended to any biological dataset with one or multiple covarying continuous traits. All the fitting models include the possibility to use SIMMAP-like mapping, which may be useful for fitting changes along lineages at a given point in time. All models provide diagnostic metrics for convergence and reliability of estimates, as well as the possibility to include trait measurement errors in model estimates.
3. New features provided by the mvMORPH package include the possibility of fitting models with changes in the mode of evolution along the phylogeny, which will be particularly meaningful in comparative analyses that include extinct taxa, e.g., when testing changes in evolutionary mode associated with global biotic/abiotic events.
4. We briefly describe the models already included in mvMORPH, and provide some demonstration of the use of the package with two simulated worked examples.
... These previous results suggest that the studied traits might have evolved mainly following a Brownian motion scenario, despite there seem to have been moments of deviation from the expectations under this model. In order to further elucidate the evolutionary processes driving the morphological diversification in the clade, we compared the fit of several evolutionary models to the PC and rECV data: single-rate Brownian motion (BM), Early Burst (EB) and single-selective regime Ornstein-Uhlenbeck (OU) processes, together with OU and BM multi-selective regime and multi-rate models based on Foley's 12 hypothesis about hominid diversification (Foley's OUM and BMM, respectively) 31,37 . These models allow to explicitly test for a random continuous-gradual (BM) versus a discontinuous radiation (OU) process in the clade based on previous hypotheses. ...
... (Fig. 3). All the models were fitted to the datasets employing the multivariate functions of mvMORPH package 37 . By means of Likelihood and Akaike Information Criteria (AIC and AICc), used to select the best evolutionary model, we concluded that Foley's successive radiations hypothesis following BMM and OUM models showed a better fit than the simplest model without shifts (Table 1) www.nature.com/scientificreports ...
... We finally tested the fit of the previously described evolutionary models to the adaptive landscape hypotheses obtained with SURFACE analyses, and compared these to the two previous hypotheses. All models were fitted to the datasets employing the multivariate functions of mvMORPH package 37 . ...
Over the last 150 years the diversity and phylogenetic relationships of the hominoids have been one of the main focuses in biological and anthropological research. Despite this, the study of factors involved in their evolutionary radiation and the origin of the hominin clade, a key subject for the further understanding of human evolution, remained mostly unexplored. Here we quantitatively approach these events using phylogenetic comparative methods and craniofacial morphometric data from extant and fossil hominoid species. Specifically, we explore alternative evolutionary models that allow us to gain new insights into this clade diversification process. Our results show a complex and variable scenario involving different evolutionary regimes through the hominid evolutionary radiation –modeled by Ornstein-Uhlenbeck multi-selective regime and Brownian motion multi-rate scenarios–. These different evolutionary regimes might relate to distinct ecological and cultural factors previously suggested to explain hominid evolution at different evolutionary scales along the last 10 million years.
... Bartoszek et al. (2012), Butler and King (2004) and Hansen (1997) showed the possibility of varying the deterministic optimum of OU processes. Beaulieu et al. (2012), Eastman et al. (2011), Clavel et al. (2015) and Manceau et al. (2016) went further to allow all parameters of the model to change at known ''shift'' points in the tree. The computationally harder task of inferring the branch and time of shift points has been addressed by Eastman et al. (2011), Ingram and Mahler (2013), Khabbazian et al. (2016), Gill et al. (2016), Caetano and Harmon (2017), and Bastide et al. (2018) with implementations in AUTEUR, SURFACE, l1ou, BEAST, ratematrix, and Phylogenet-icEM software packages, respectively. ...
... Such level of flexibility has proven necessary in previous works, e.g. Slater (2013) and Clavel et al. (2015). Slater (2013) used the geiger R-package to measure the statistical support for a shift from an OU to a BM process in the evolution of mammal body size occurring at the end of the Mesozoic. ...
... Later, though, in Slater (2014), he realized that the results of this study were compromised by an erroneous transformation of the branch lengths in the tree that causes inaccurate likelihood values for non-ultrametric trees. Clavel et al. (2015) implemented a non-pruning algorithm for multivariate likelihood calculation for shifts between BM, OU and the early burst (EB) model of adaptive radiation in their R-package mvMORPH. ...
Phylogenetic comparative methods (PCMs) have been used to study the evolution of quantitative traits in various groups of organisms, ranging from micro-organisms to animal and plant species. A common approach has been to assume a Gaussian phylogenetic model for the trait evolution along the tree, such as a branching Brownian motion (BM) or an Ornstein-Uhlenbeck (OU) process. Then, the parameters of the process have been inferred based on a given tree and trait data for the sampled species. At the heart of this inference lie multiple calculations of the model likelihood, that is, the probability density of the observed trait data, conditional on the model parameters and the tree. With the increasing availability of big phylogenetic trees, spanning hundreds to several thousand sampled species, this approach is facing a two-fold challenge. First, the assumption of a single Gaussian process governing the entire tree is not adequate in the presence of heterogeneous evolutionary forces acting in different parts of the tree. Second, big trees present a computational challenge, due to the time and memory complexity of the model likelihood calculation. Here, we explore a sub-family, denoted GLInv, of the Gaussian phylogenetic models, with the transition density exhibiting the properties that the expectation depends Linearly on the ancestral trait value and the variance is Invariant with respect to the ancestral value. We show that GLInv contains the vast majority of Gaussian models currently used in PCMs, while supporting an efficient (linear in the number of nodes) algorithm for the likelihood calculation. The algorithm supports scenarios with missing data, as well as different types of trees, including trees with polytomies and non-ultrametric trees. To account for the heterogeneity in the evolutionary forces, the algorithm supports models with "shifts" occurring at specific points in the tree. Such shifts can include changes in some or all parameters, as well as the type of the model, provided that the model remains within the GLInv family. This contrasts with most of the current implementations where, due to slow likelihood calculation, the shifts are restricted to specific parameters in a single type of model, such as the long-term selection optima of an OU process, assuming that all of its other parameters, such as evolutionary rate and selection strength, are global for the entire tree. We provide an implementation of this likelihood calculation algorithm in an accompanying R-package called PCMBase. The package has been designed as a generic library that can be integrated with existing or novel maximum likelihood or Bayesian inference tools.
... Gene expression evolution was modeled as a multivariate Brownian Motion (BM) process using the R package mvMORPH [37] in order to estimate coevolution of gene expression between pairs of proteins. This approach provides an estimate of the degree of correlation between two traits (in this case, our estimates of gene expression) across species that accounts for the phylogeny (see Methods for more details). ...
... However, simulations indicate more species might be needed to have sufficient statistical power (see Table 1), although this could vary depending on the tree and data in question. In theory, it is possible to expand this approach to test for gene expression coevolution in larger gene sets or correlate changes in gene expression with changes in other phenotypes, such as body size (see [37] for more details on using mvMORPH). With that in mind, recent work finds multivariate PCMs are in need of improvement, as parameter estimation accuracy decreases quickly as the number of traits (i.e. ...
... If a phylogenetic tree is not available for the species of interest, multiple sequence alignment tools and phylogenetic tree estimation tools have made building a reasonable phylogenetic tree efficient and easy, even for non-computational researchers. The phylogenetics community has made access to complex phylogenetic parameter estimation accessible via open-source, easy-to-use R packages, such as mvMORPH [37]. Although we strongly recommend the use of PCMs for interspecies data analysis, we emphasize that such approaches come with their own challenges and, in some cases, the PCM may not perform better than standard statistical approaches (see [59] for more details). ...
Background:
Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate.
Results:
We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric.
Conclusions:
Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.
... Under this process, the covariance between trait k at node i and trait l at node j (1 ≤ k, l ≤ p and 1 ≤ i, j ≤ m) is the product of (i) the covariance R kl between traits k and l and (ii) the shared evolutionary time t ij between species i and j, i.e. the time from the root to their most recent common ancestor (see Figure 1), plus the contribution of the root itself: Cov[Z i k ; Z j l ] = t ij R kl + Γ kl (see e.g. Clavel, Escarguel and Merceron, 2015). ...
... The OU process generalizes BM by adding a deterministic call-back term to a given value β that is interpreted as the optimal value of the trait of a species in a given environment: dZ t = −A(Z t − β)dt + R 1/2 dB t for all t ≥ 0. Matrix A is the "selection strength" that is constrained to have positive real parts of its eigenvalues and controls the dynamics of the pull toward the optimum. The covariance between two multivariate traits at two nodes can be explicitly formulated (Bartoszek et al., 2012;Clavel, Escarguel and Merceron, 2015), and, compared to the BM where the variance increases linearly in time, it is bounded by the stationary variance V of the process. ...
... Harmon (2019) for a recent and more comprehensive overview of these models. Clavel, Escarguel and Merceron (2015) provide a comprehensive maximum-likelihood framework to fit a wide range of models, using explicit estimators that can in some cases be computationally prohibitive. Pybus et al. (2012), Freckleton (2012), describe and implement likelihood computation algorithms that are linear in the number of observations in a framework similar to the one described here. ...
Phylogenetic comparative methods correct for shared evolutionary history among a set of non-independent organisms by modeling sample traits as arising from a diffusion process along on the branches of a possibly unknown history. To incorporate such uncertainty, we present a scalable Bayesian inference framework under a general Gaussian trait evolution model that exploits Hamiltonian Monte Carlo (HMC). HMC enables efficient sampling of the constrained model parameters and takes advantage of the tree structure for fast likelihood and gradient computations, yielding algorithmic complexity linear in the number of observations. This approach encompasses a wide family of stochastic processes, including the general Ornstein-Uhlenbeck (OU) process, with possible missing data and measurement errors. We implement inference tools for a biologically relevant subset of all these models into the BEAST phylogenetic software package and develop model comparison through marginal likelihood estimation. We apply our approach to study the morphological evolution in the superfamilly of Musteloidea (including weasels and allies) as well as the heritability of HIV virulence. This second problem furnishes a new measure of evolutionary heritability that demonstrates its utility through a targeted simulation study.
... We use phylogenetic comparative methods to examine skeletal traits of extant mammals, including representatives of each of the six groups of extant gliders. We test for convergence using two methods: a pattern-based approach that examines whether gliders have evolved toward each other in phylo-morphospace (Stayton 2015a) and a process-based assessment of adaptive evolutionary change in gliders Clavel et al. 2015). Because extant gliders do not fully encompass the diversity of gliding mammal morphologies that have existed (e.g., Meng et al. 2017), we use fossil gliders to further test the convergence hypothesis, with our prediction being that fossil gliders are osteologically similar to extant gliders. ...
... To test for adaptive convergence among gliders , we fit 18 multivariate evolutionary models to PC1-PC5 scores (77% of trait variance) using functions within the mvMORPH R package (Clavel et al. 2015). Ideally, we would incorporate more (or all) variance by using additional PCs, but this would greatly increase computational time. ...
... Multiple-regime OU models (OUM) allow trait optima to vary among regimes, but σ 2 and α remain constant. We drop the root state influence (i.e., root = "stationary") because we are using an ultrametric tree without fossil evidence for the root state (Clavel et al. 2015). To calculate multivariate phylogenetic halflives (ln(2)/α), we first summed the σ 2 values of individual PCs and summed stationary variances of PCs, and then used those results and the equation for stationary variance (σ 2 /2α) to generate a multivariate α (Zelditch et al. 2020). ...
Ecology and biomechanics play central roles in the generation of phenotypic diversity. When unrelated taxa invade a similar ecological niche, biomechanical demands can drive convergent morphological transformations. Thus, identifying and examining convergence helps to elucidate the key catalysts of phenotypic change. Gliding mammals are often presented as a classic case of convergent evolution because they independently evolved in numerous clades, each possessing patagia ('wing' membranes) that generate lift during gliding. We use phylogenetic comparative methods to test whether the skeletal morphologies of the six clades of extant gliding mammals demonstrate convergence. Our results indicate that glider skeletons are convergent, with glider groups consistently evolving proportionally longer, more gracile limbs than arborealists, likely to increase patagial surface area. Nonetheless, we interpret gliders to represent incomplete convergence because (i) evolutionary model-fitting analyses do not indicate strong selective pressures for glider trait optima, (ii) the three marsupial glider groups diverge rather than converge, and (iii) the gliding groups remain separated in morphospace (rather than converging on a single morphotype), which is reflected by an unexpectedly high level of morphological disparity. That glider skeletons are morphologically diverse is further demonstrated by fossil gliders from the Mesozoic Era, which possess unique skeletal characteristics that are absent in extant gliders. Glider morphologies may be strongly influenced by factors such as body size and attachment location of patagia on the forelimb, which can vary among clades. Thus, convergence in gliders appears to be driven by a simple lengthening of the limbs, whereas additional skeletal traits reflect nuances of the gliding apparatus that are distinct among different evolutionary lineages. Our unexpected results add to growing evidence that incomplete convergence is prevalent in vertebrate clades, even among classic cases of convergence, and they highlight the importance of examining form-function relationships in light of phylogeny, biomechanics, and the fossil record. This article is protected by copyright. All rights reserved.
... where Y is an N × p matrix of multivariate phenotypic trait values, X is an N × k design matrix containing one or more independent (predictor) variables, B is a k × p matrix containing the model coefficients, and E is an N × p matrix of residuals (see Adams 2014b; Adams & Collyer 2015& Collyer , 2018bClavel et al. 2015). Unlike ordinary least squares models where the residual error (E) is assumed to be independent, the residuals of PGLS are not independent but instead contain the expected covariation between species as described by the phylogenetic covariance matrix under a specified model of evolutionary change [typically Brownian motion: Rohlf (2001)]. ...
... In this case, the residuals of the model are normally distributed but only under the specific model of evolutionary change (e.g., Brownian motion, OU, etc.). Viewed from this framework, different evolutionary models can be described by using different evolutionary covariance matrices embodied by E (see Adams & Collyer 2018a, Clavel et al. 2015. Thus, evolutionary model comparisons are accomplished by obtaining summary statistics [e.g., logL or Akaike information criterion (AIC)] describing the fit of the data to the phylogeny under differing models of trait evolution and selecting the preferred model based on these statistics (e.g., Butler & King 2004). ...
... Here the fit of the data to the phylogeny is obtained under a model containing a single rate of evolutionary change for all species and then under a second model where rates of evolution differ between two or more groups. For multivariate phenotypes, this is tantamount to fitting the data to the phylogeny using one or more evolutionary rate matrices, where the evolutionary rates (σ 2 ) for each phenotypic trait dimension are found along the diagonal of the p × p of R. One approach compares the fit of one or more evolutionary rate matrices to the phylogeny using likelihood ratio tests and AIC values (Clavel et al. 2015, Revell & Harmon 2008) (for a related Bayesian approach, see Caetano & Harmon 2019). Such likelihood methods can be appropriate when one is evaluating rates of phenotypic evolution from many taxa and just a few phenotypic trait variables (i.e., when N â p) (see Revell & Harmon 2008). ...
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.
Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 50 is November 4, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... We focused on two models, Brownian motion (BM) and Ornstein-Uhlenbeck (OU, refs. [62][63][64], both implemented in the R package mvMORPH (ref. 64, functions "mvBM" and "mvOU"). ...
... [62][63][64], both implemented in the R package mvMORPH (ref. 64, functions "mvBM" and "mvOU"). BM processes describe the accumulation of infinitesimal phenotypic change along the branches of a phylogenetic tree (with the amount of change controlled by the rate parameter σ); OU processes describe selection toward an optimal trait value (parameter θ, or two optima associated with terrestrial or amphibious lifestyles-hereafter called selective regimes) and add to the BM process an extra parameter (α) that describes the strength of selection toward the optimal trait value (62,63). ...
Olfaction and thermoregulation are key functions for mammals. The former is critical to feeding, mating, and predator avoidance behaviors, while the latter is essential for homeothermy. Aquatic and amphibious mammals face olfactory and thermoregulatory challenges not generally encountered by terrestrial species. In mammals, the nasal cavity houses a bony system supporting soft tissues and sensory organs implicated in either olfactory or thermoregulatory functions. It is hypothesized that to cope with aquatic environments, amphibious mammals have expanded their thermoregulatory capacity at the expense of their olfactory system. We investigated the evolutionary history of this potential trade-off using a comparative dataset of three-dimensional (3D) CT scans of 189 skulls, capturing 17 independent transitions from a strictly terrestrial to an amphibious lifestyle across small mammals (Afrosoricida, Eulipotyphla, and Rodentia). We identified rapid and repeated loss of olfactory capacities synchronously associated with gains in thermoregulatory capacity in amphibious taxa sampled from across mammalian phylogenetic diversity. Evolutionary models further reveal that these convergences result from faster rates of turbinal bone evolution and release of selective constraints on the thermoregulatory-olfaction trade-off in amphibious species. Lastly, we demonstrated that traits related to vital functions evolved faster to the optimum compared to traits that are not related to vital functions.
... Evolutionary modeling. Six different multivariate evolutionary models were fit to the complexity and organization data separately using the R package 'mvMORPH' 86 (Table 1). Shifts were specified on the phylogeny by creating a SIMMAP tree using 'paintSubTree' function in the package phytools 85 . ...
... Models were fit across the 60 sample trees and the median values taken. Fit of the models was first assessed using the Akaike Information Criterion, corrected for small sample sizes (AICc), from which relative Akaike weights of the various models could be compared 86 . Significance of the differences between optima values was assessed using jackknifed confidence intervals (see below) and Analysis of Variance with post-hoc Tukey HSD on the tip data (r functions: aov, TukeyHSD). ...
A fundamental concept in evolutionary biology is that life tends to become more complex through geologic time, but empirical examples of this phenomenon are controversial. One debate is whether increasing complexity is the result of random variations, or if there are evolutionary processes which actively drive its acquisition, and if these processes act uniformly across clades. The mammalian vertebral column provides an opportunity to test these hypotheses because it is composed of serially-repeating vertebrae for which complexity can be readily measured. Here we test seven competing hypotheses for the evolution of vertebral complexity by incorporating fossil data from the mammal stem lineage into evolutionary models. Based on these data, we reject Brownian motion (a random walk) and uniform increasing trends in favor of stepwise shifts for explaining increasing complexity. We hypothesize that increased aerobic capacity in non-mammalian cynodonts may have provided impetus for increasing vertebral complexity in mammals.
... A purely neutral model of evolution -Brownian Motion-(BM) and alternative, deterministic -Early Burst (EB) and Ornstein-Uhlenbeck (OU)-models explaining patterns of mandible shape were analyzed using the mvMORPH package v.1.1.0 for R (Clavel et al. 2015). OU processes with both a unique adaptive optimum (OU1) and with multiple optima were modeled (see Clavel et al. 2015). ...
... for R (Clavel et al. 2015). OU processes with both a unique adaptive optimum (OU1) and with multiple optima were modeled (see Clavel et al. 2015). Multiple-optima OU models were constructed by assuming ecological factors (OU Habit and OU Habitat; see above) and phylogeny (OU Clades; each family is considered to occupy a separate adaptive peak) as responsible for the structuring of the adaptive landscape of caviomorph mandible (Butler and King 2004). ...
Caviomorphs are a mainly South American rodent clade with high taxonomic and ecomorphological diversity. In this study, we combine geometric morphometric, functional, ancestral reconstruction, and macroevolutionary analyses to quantify the magnitude, direction, and rates of shape diversification of the caviomorph mandible, and to explore the morpho-functional implications and potential ecological catalysts of the observed shape changes. The mandible shape was significantly related to habits and size, and had a better fit with an evolutionary model where the main clades occupy distinct adaptive peaks. The morphological evolution of octodontoids is characterized by pulses of rate acceleration , but without reaching high disparity. Such pulses are mainly linked to the acquisition of fossorial specializations, including short and robust mandibles, and the increasement of forces at incisors. Conversely, derived cavioids show slower but continuous shape changes that allowed them to reach the most divergent, grazing morphologies in which slender mandibles with more marked antero-posterior movements for grinding action are favored. Interestingly, the major morphological changes occurred mainly during the early Oligocene and lower late Miocene, two time periods that involved global climatic events and strong changes in the vegetational structure of South America. The evolution of octodontoid and cavioid mandibles seems to be related to the occupation of subterranean and epigean niches, respectively , in the progressively expanded Cenozoic open landscapes of southern South America.
... Given this current limitation of multivariate comparative methods (Adams and Collyer 2018), we accounted for variable phylogenetic signal by fitting models across a range of branch length transformations (see above). We also fit and compared different models of trait evolution in a more flexible comparative framework (Beaulieu et al. 2012;Clavel et al. 2015) using three reduced datasets: (1) two linear traits obtained from 3D models, beak half-angle, and beak length, measured as the distance from the beak tip to the craniofacial hinge along the sagittal plane (see Fig. S3); (2) two functional traits, body mass, and predicted impact force (see below); and (3) the first three pPCA scores together accounting for 79% (skeletal beak) and 88% (rhamphotheca) of shape variation. We used OUwie version 1.50 (Beaulieu et al. 2012) and mvMORPH version 1.1.0 ...
... We used OUwie version 1.50 (Beaulieu et al. 2012) and mvMORPH version 1.1.0 (Clavel et al. 2015) to fit five distinct models of trait evolution: a single-rate BM model (BM1), a multiple-rate BM model (BMM), a single-regime OU model (OU1), a multiregime OU (OUM) model, and an EB model in which rates of evolution are allowed to slow down through time. This approach inputs a phylogenetic tree with a mapped evolutionary history for a trait (in this case, foraging behavior). ...
Innovations in foraging behavior can drive morphological diversity by opening up new ways of interacting with the environment, or limit diversity through functional constraints associated with different foraging behaviors. Several classic examples of adaptive radiations in birds show increased variation in ecologically relevant traits. However, these cases primarily focus on geographically narrow adaptive radiations, consider only morphological evolution without a biomechanical approach, or do not investigate tradeoffs with other non‐focal traits that might be affected by use of different foraging habitats. Here, we use X‐ray micro‐computed tomography (microCT), biomechanical modelling, and multivariate comparative methods to explore the interplay between foraging behavior and cranial morphology in kingfishers, a global radiation of birds with variable beaks and foraging behaviors, including the archetypal plunge‐dive into water. Our results quantify covariation between the shape of the outer keratin covering (rhamphotheca) and the inner skeletal core of the beak, as well as highlight distinct patterns of morphospace occupation for different foraging behaviors and considerable rate variation among these skull regions. We anticipate these findings will have implications for inferring beak shapes in fossil taxa and inform biomimetic design of novel impact‐reducing structures.
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... We ran models 1, 2 and 4 with the startingModel function and models 4-6 with the runSurface function of the R package SURFACE (Ingram & Mahler, 2013). Model 4 was run with the function mvEB function of the R package mvMORPH (Clavel et al., 2015). Model performance was assessed by its fit to the data using ΔAICc (the diference between the corrected Akaike Information Criterion values) among each AICc model against the lowest AICc of the best model, following the approach described by Ingram & Mahler (2013). ...
The coexistence of several anoles in the same place is attributed to differential partitioning of resources. Although several mainland and island communities show a similar structure, differences in life-history traits, absence of niche complementarity, higher food supply and higher numbers of predators in mainland environments support the idea that predation, rather than competition, is a more important structuring force in mainland than in island anole communities. To analyse the pattern of ecological structure in mainland anole communities, we studied communities in three tropical rain forests of northwestern South America to obtain data about the use of resources on three niche axes [spatial, thermal and morphological (as a proxy of diet)] for 17 species of anoles. We analysed the patterns of niche overlap for each axis and found that overlap on the dietary axis was less than the overlap on the other axes, indicating that species using similar spatial or thermal resources diverge strongly in their diet. In addition, we identified a niche complementarity among niche axes, suggesting that intraspecific competition is also an important process in those communities. Finally, this study revealed a similar ecological structure in different communities of mainland rainforest anoles, which share seven ecomorphs, suggesting ecological adaptation and convergence in mainland anoles.
... The second set of models can account for 'error' but does not adjust for covariates. In the context of these caveats, we fitted the evolutionary models using the mvMORPH package (Clavel et al., 2015). ...
Comparative analyses of locomotion in tetrapods reveal two patterns of stride cycle variability. Tachymetabolic tetrapods (birds and mammals) have lower inter-cycle variation in stride duration than bradymetabolic tetrapods (amphibians, lizards, turtles, and crocodilians). This pattern has been linked to the fact that birds and mammals share enlarged cerebella, relatively enlarged and heavily myelinated Ia afferents, and γ-motoneurons to their muscle spindles. Tachymetabolic tetrapod lineages also both possess an encapsulated Golgi tendon morphology, thought to provide more spatially precise information on muscle tension. The functional consequence of this derived Golgi tendon morphology has never been tested. We hypothesized that one advantage of precise information on muscle tension would be lower and more predictable limb bone stresses, achieved in tachymetabolic tetrapods by having less variable substrate reaction forces than bradymetabolic tetrapods. To test this hypothesis, we analyzed hindlimb substrate reaction forces during locomotion of 55 tetrapod species in a phylogenetic comparative framework. Variation in species-means of limb loading magnitude and timing confirm that, for most of the variables analyzed, variance in hindlimb loading and timing is significantly lower in species with encapsulated versus unencapsulated Golgi tendon organs. These findings suggest that maintaining predictable limb loading provides a selective advantage for birds and mammals by allowing for energy-savings during locomotion, lower limb bone safety factors, and quicker recovery from perturbations. The importance of variation in other biomechanical variables in explaining these patterns, such as posture, effective mechanical advantage, and center-of-mass mechanics, remains to be clarified.
... We applied a penalized likelihood approach to our high-dimensional phenotypic dataset of flower shapes of 19 Erica species to estimate the fit of three different evolutionary models; Brownian Motion (BM, random walk model), Ornstein-Uhlenbeck (OU, selective peak model) and Early Burst (EB, model of rapid morphological evolution followed by relative stasis) in order to better understand the process of floral-shape evolution in the clade . The analysis was carried out under the fit_t_pl function (RPANDA) (Morlon et al., 2016), and the best-fit of the abovementioned three models was assessed using the Generalized Information Criterion (GIC) with the GIC function (MVMORPH) (Clavel et al., 2015). Finally, we employed the parameters derived from the evolutionary model that best fitted www.newphytologist.com ...
Flowers have been hypothesized to contain either: modules of attraction and reproduction, functional modules (pollination‐effecting parts), or developmental modules (organ‐specific). Do pollination specialisation and syndromes influence floral modularity? In order to test these hypotheses and answer this question, we focussed on the genus Erica: we gathered 3D data from flowers of 19 species with diverse syndromes via Computed Tomography, and for the first time tested the abovementioned hypotheses via 3D geometric morphometrics. To provide an evolutionary framework for our results, we tested the evolutionary mode of floral shape, size, and integration under the syndromes regime, and ‐for the first time‐ reconstructed the high‐dimensional floral shape of their most recent common ancestor. We demonstrate that the modularity of the 3D shape of generalist flowers depends on development and that of specialists is linked to function: modules of pollen deposition and receipt in bird syndrome, and access‐restriction to the floral reward in long‐proboscid fly syndrome. Only size and shape principal component one showed multiple‐optima selection, suggesting that they were co‐opted by evolution to adapt flowers to novel pollinators. Whole floral shape followed an Ornstein‐Uhlenbeck (selection‐driven) evolutionary model, and differentiated relatively late. Flower shape modularity thus crucially depends on pollinator specialisation and syndrome.
... Ornstein-Uhlenbeck (OU) processes incorporate stabilising selection, constraining trait variance through time towards adaptive optima (Hansen 1997). All multivariate models were fitted using the mvMORPH R package (Clavel et al. 2015), and model comparison was based on the sample-size corrected Akaike Information Criterion (AICc). Lastly, we identified evolutionary shifts in adaptive peaks across the phylogeny for each dataset (PC1-3 scores) using a data-driven approach developed in the PhylogeneticEM R package (Bastide et al. 2018). ...
Bats use their forelimbs in different ways, flight being the most notable example of morphological adaptation. However, different behavioural specializations beyond flight have also been described in several bat lineages. Understanding the postcranial evolution during the locomotory and behavioural diversification of bats is fundamental to understanding bat evolution. We investigate whether different functional demands influenced the evolutionary trajectories of humeral cross-sectional shape and biomechanics. We found a strong ecological signal and no phylogenetic structuring in the morphological and biomechanical variation in humerus phenotypes. Decoupled modes of shape and biomechanical variation were consistently found, with size and diet explaining variation in shape and biomechanics respectively. We tested both hypothesis- and data-driven multivariate evolutionary models, revealing decoupled pathways of evolution across different sections of the humerus diaphysis. We found evidence for a complex evolutionary landscape where flight might have acted as an evolutionary constraint, while size- and diet-based ecological opportunities facilitated diversification. We also found shifts in adaptive regimes independent from the evolution of flight (i.e. terrestrial locomotion and upstand roosting). Our results suggest that complex and multiple evolutionary pathways interplay in the postcranium, leading to the independent evolution of different features and regions of skeletal elements optimised for different functional demands.
... An obvious extension of univariate stochastic processes is to re-cast them in a multivariate or multidimensional framework. There has been some research into multivariate phylogenetic comparative methods, including several software packages, largely based on BM, OU, and early-burst models (Adams, 2014a,b,c;Adams and Collyer, 2018;Bartoszek, 2011;Bartoszek et al., 2012;Caetano and Harmon, 2018;Clavel et al., 2018Clavel et al., , 2015Klingenberg, 2011;Klingenberg and Marugán-Lobón, 2013;Zheng et al., 2009). Certainly, multivariate diffusions are necessary to understand the correlation among characters . ...
Gaussian processes such as Brownian motion and the Ornstein-Uhlenbeck process have been popular models for the evolution of quantitative traits and are widely used in phylogenetic comparative methods. However, they have drawbacks which limit their utility. Here we describe new, non-Gaussian stochastic differential equation (diffusion) models of quantitative trait evolution. We present general methods for deriving new diffusion models, and develop new software for fitting non-Gaussian evolutionary models to trait data. The theory of stochastic processes provides a mathematical framework for understanding the properties of current and future phylogenetic comparative methods. Attention to the mathematical details of models of trait evolution and diversification may help avoid some pitfalls when using stochastic processes to model macroevolution.
... To determine whether patterns persisted when controlled for shared ancestry we conducted a phylogenetic MANCOVA (pMANCOVA) using mean PC values, the consensus population tree and the mvgls and manova.gls functions available in the developmental version of mvMORPH (Clavel et al., 2015(Clavel et al., ,2019. The same linear model used in the traditional two-way MANCOVA was fitted using generalized least squares under Pagel's lambda transformation and the Mahalanobis method which is an approximation of the leave one out cross-validation of the log-likelihood. ...
When incipient species meet in secondary contact, natural selection can rapidly reduce costly reproductive interactions by directly targeting reproductive traits. This process, called reproductive character displacement (RCD), leaves a characteristic pattern of geographic variation where divergence of traits between species is greater in sympatry than allopatry. However, because other forces can also cause similar patterns, care must be given in separating pattern from process. Here we show how the phylo-comparative method together with genomic data can be used to evaluate evolutionary processes at the population level in closely related species. Using this framework, we test the role of RCD in speciation of two cricket species endemic to Anatolian mountains by quantifying patterns of character displacement, rates of evolution and adaptive divergence. Our results show differing patterns of character displacement between species for reproductive vs. non-reproductive characters and strong patterns of asymmetric divergence. We demonstrate diversification results from rapid divergence of reproductive traits towards multiple optima under the dual influence of strong drift and selection. These results present the first solid evidence for RCD in Anatolian mountains, quantify the amount of drift and selection necessary for RCD to lead to speciation, and demonstrate the utility of phylo-comparative methods for quantifying evolutionary parameters at the population level.
... The PGLS is implemented in the R package "mvMORPH" and performed using the function mvgls(); significance of the model has been tested using the function manova.gls() by means of Wilks' lambda (69). In order to visualize the distribution of the shape variables on the phylogenetic tree, the first 3 PC scores were mapped on the phylogeny using the contMap() function from the R package "phytools" (70). ...
Most living birds exhibit cranial kinesis—movement between the rostrum and braincase—in which force is transferred through the palatal and jugal bars. The palate alone distinguishes the Paleognathae from the Neognathae, with cranial kinesis more developed in neognaths. Most previous palatal studies were based on 2D data and rarely incorporated data from stem birds despite great interest in their kinetic abilities. Here we reconstruct the vomer of the Early Cretaceous stem bird Sapeornis and the troodontid Sinovenator , taxa spanning the dinosaur–bird transition. A 3D shape analysis including these paravians and an extensive sampling of neornithines reveals their strong similarity to paleognaths and indicates that morphological differences in the vomer between paleognaths and neognaths are intimately related to their different kinetic abilities. These results suggest the skull of Mesozoic paravians lacked the kinetic abilities observed in neognaths, a conclusion also supported by our identification of an ectopterygoid in Sapeornis here. We conclude that cranial kinesis evolved relatively late, likely an innovation of the Neognathae, and is linked to the transformation of the vomer. This transformation increased palatal mobility, enabling the evolution of a diversity of kinetic mechanisms and ultimately contributing to the extraordinary evolutionary success of this clade.
... Implementation of pBIC functions in the backward-phase of SURFACE model fits, as well as the functions for fitting non-uniform trend-like models, were possible with scripts presented by Benson et al. [33]. Simulated data under BM (for assessing the possibility of spurious support to the SURFACE model) was obtained with package mvMORPH [135]. The additional clade-specific model-fitting analyses, using the OUwie algorithm, were implemented with the package OUwie [136]. ...
Background:
Little is known about the long-term patterns of body size evolution in Crocodylomorpha, the > 200-million-year-old group that includes living crocodylians and their extinct relatives. Extant crocodylians are mostly large-bodied (3-7 m) predators. However, extinct crocodylomorphs exhibit a wider range of phenotypes, and many of the earliest taxa were much smaller (< 1.2 m). This suggests a pattern of size increase through time that could be caused by multi-lineage evolutionary trends of size increase or by selective extinction of small-bodied species. Here, we characterise patterns of crocodylomorph body size evolution using a model fitting-approach (with cranial measurements serving as proxies). We also estimate body size disparity through time and quantitatively test hypotheses of biotic and abiotic factors as potential drivers of crocodylomorph body size evolution.
Results:
Crocodylomorphs reached an early peak in body size disparity during the Late Jurassic, and underwent an essentially continual decline since then. A multi-peak Ornstein-Uhlenbeck model outperforms all other evolutionary models fitted to our data (including both uniform and non-uniform), indicating that the macroevolutionary dynamics of crocodylomorph body size are better described within the concept of an adaptive landscape, with most body size variation emerging after shifts to new macroevolutionary regimes (analogous to adaptive zones). We did not find support for a consistent evolutionary trend towards larger sizes among lineages (i.e., Cope's rule), or strong correlations of body size with climate. Instead, the intermediate to large body sizes of some crocodylomorphs are better explained by group-specific adaptations. In particular, the evolution of a more aquatic lifestyle (especially marine) correlates with increases in average body size, though not without exceptions.
Conclusions:
Shifts between macroevolutionary regimes provide a better explanation of crocodylomorph body size evolution on large phylogenetic and temporal scales, suggesting a central role for lineage-specific adaptations rather than climatic forcing. Shifts leading to larger body sizes occurred in most aquatic and semi-aquatic groups. This, combined with extinctions of groups occupying smaller body size regimes (particularly during the Late Cretaceous and Cenozoic), gave rise to the upward-shifted body size distribution of extant crocodylomorphs compared to their smaller-bodied terrestrial ancestors.
... The idea of jointly fitting different types of Gaussian models dates back at least to the work of Slater(33), where he measured the statistical support for a shift from an OU to a BM process in the evolution of mammal body size occurring at the end of the Mesozoic (but see ref.34). Later, Clavel et al.(35) implemented a nonpruning algorithm for multivariate likelihood calculation for shifts between BM, OU, and the early burst (EB) model of adaptive radiation in their R-package mvMorph. These works assume a known point in time where a global shift occurs on all lineages of the tree. ...
Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein–Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are “intramodel,” meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters “global” for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain–body-mass allometry over the past 160 million y.
... We also evaluate performance evolution using more complex models in the r-package mvMORPH (Clavel et al. 2015). For this approach, we fit our performance data to several models consisting of different numbers of trait optima. ...
The relationship between form and function is thought to play an integral role in structuring broad-scale patterns of morphological evolution and resource utilization. In ecomorphological studies, mechanical performance is widely understood to constrain the evolution of form and function. However, the relationship between form, function and resource utilization is less clear. Additionally, seasonal fluctuations in resource availablity may further complicate patterns of resource use. How organisms cope with these complexities, and the effect of these factors on broadscale patterns of morphological evolution is also poorly understood. Here we use three-dimensional geometric morphometrics, biomechanics, stable isotope analysis, and gut-content analysis to study trophic evolution in a clade of riverine-adapted electric fishes from a region with high seasonal variability; the Amazon River. We find significant and phylogenetically structured relationships among measures of trophic ecology and skull shape. We also recover a significant relationship between the mechanical advantage of the mandible and trophic position, where species feeding at higher trophic levels have narrower jaws with lower mechanical advantages, and species feeding at lower trophic levels have deeper jaws with higher mechanical advantages. Our results indicate that selection is driving the evolution of mandible shape and performance towards specialization on different trophic ecologies.
... By treating the number of branchiostegals as a continuous trait, we first assessed the relative fit of models of trait evolution using the R packages mvMORPH v. 1.0.7 [43] and geiger v. 2.0.6 [44]. We fitted five models (Additional file 3: Figure S1): Brownian Motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), BM with a trend (called "drift" in geiger), and white noise. ...
Background:
The branchiostegal series consists of an alignment of bony elements in the posterior portion of the skull of osteichthyan vertebrates. We trace the evolution of the number of elements in a comprehensive survey that includes 440 extant and 66 extinct species. Using a newly updated actinopterygian tree in combination with phylogenetic comparative analyses, we test whether osteichthyan branchiostegals follow an evolutionary trend under 'Williston's law', which postulates that osteichthyan lineages experienced a reduction of bony elements over time.
Results:
We detected no overall macroevolutionary trend in branchiostegal numbers, providing no support for 'Williston's law'. This result is robust to the subsampling of palaeontological data, but the estimation of the model parameters is much more ambiguous.
Conclusions:
We find substantial evidence for a macroevolutionary dynamic favouring an 'early burst' of trait evolution over alternative models. Our study highlights the challenges of accurately reconstructing macroevolutionary dynamics even with large amounts of data about extant and extinct taxa.
... As a result, morphometrics and "ecomorphology" have long been powerful tools in organismal studies. The growing statistical toolkit for handling multi-variate shape data has made it easier for biologists to ask morphological questions than ever before (Adams and Ot arola-Castillo 2013;Clavel et al. 2015;Olsen and Westneat 2015;Schlager 2017). Likewise, there has been a proliferation of phylogenetic hypotheses for large numbers of organisms, and of phylogenetic comparative methods that allow us to study phenotypic change explicitly accounting for the history of evolutionary relationships among lineages (Revell, 2012;Magee et al. 2014;Pennell et al. 2014;Harmon 2019). ...
Ecomorphology is the study of relationships between organismal morphology and ecology. As such, it is the only way to determine if morphometric data can be used as an informative proxy for ecological variables of interest. To achieve this goal, ecomorphology often depends on, or directly tests, assumptions about the nature of the relationships among morphology, performance and ecology. We discuss three approaches to the study of ecomorphology: morphometry-driven, function-driven, and ecology-driven and study design choices inherent to each approach. We also identify ten assumptions that underlie ecomorphological research: four of these are central to all ecomorphological studies; the remaining six are variably applicable to some of the specific approaches described above. We discuss how these assumptions may impact ecomorphological studies and affect the interpretation of their findings. We also point out some limitations of ecomorphological studies, and highlight some ways by which we can strengthen, validate, or eliminate systematic assumptions.
... The combination of estimation, fitting, and testing allowed us to build confidence that the evolutionary patterns found were reliable if they converged on the same result. Evolutionary modeling was carried out in R using functions from the packages surface (59), mvMORPH (61), and geiger (62) for the estimation, fitting, and testing, respectively. ...
Lobe-fins transformed into limbs during the Devonian period, facilitating the water-to-land transition in tetra-pods. We traced the evolution of well-articulated skeletons across the fins-to-limbs transition, using a network-based approach to quantify and compare topological features of fins and limbs. We show that the topological arrangement of bones in pectoral and pelvic appendages evolved in parallel during the fins-to-limbs transition, occupying overlapping regions of the morphospace, following a directional trend, and decreasing their disparity over time. We identify the presence of digits as the morphological novelty triggering topological changes that discriminated limbs from fins. The origin of digits caused an evolutionary shift toward appendages that were less densely and heterogeneously connected, but more assortative and modular. Disparity likewise decreased for both appendages, more markedly until a time concomitant with the earliest-known tetrapod tracks. Last, we rejected the presence of a pectoral-pelvic similarity bottleneck at the origin of tetrapods.
... We contrasted the adaptive landscape model generated by l1ou with other potential drivers of skull shape evolution in bats, including models of rate heterogeneity (i.e., an early burst predicted under an adaptive radiation) and models reflecting ecological or performance traits thought to constrain bat skull evolution (e.g., dietary ecology and echolocation). Fully multivariate evolutionary model fitting was implemented using mvMORPH 72 . While l1ou ignores trait covariance while estimating adaptive shift positions, mvMORPH includes all trait covariances in calculating model support. ...
Morphological diversity may arise rapidly as a result of adaptation to novel ecological opportunities, but early bursts of trait evolution are rarely observed. Rather, models of discrete shifts between adaptive zones may better explain macroevolutionary dynamics across radiations. To investigate which of these processes underlie exceptional levels of morphological diversity during ecological diversification, we use modern phylogenetic tools and 3D geometric morphometric datasets to examine adaptive zone shifts in bat skull shape. Here we report that, while disparity was established early, bat skull evolution is best described by multiple adaptive zone shifts. Shifts are partially decoupled between the cranium and mandible, with cranial evolution more strongly driven by echolocation than diet. Phyllostomidae, a trophic adaptive radiation, exhibits more adaptive zone shifts than all other families combined. This pattern was potentially driven by ecological opportunity and facilitated by a shift to intermediate cranial shapes compared to oral-emitters and other nasal emitters.
... Fortunately, the development of phylogenetic comparative methods has enabled assessment of the correlations between ecological and morphological characters among much larger samples of species (Felice et al., 2019;Navalón et al., 2019;Ricklefs, 2005). These methods now facilitate testing for the presence of morphological optima among different ecological strategies that can be considered consistent with alternate selection pressures through time (Beaulieu & O'Meara, 2016;Butler & King, 2004;Clavel, Escarguel, & Merceron, 2015;Lapiedra, Sol, Carranza, & Beaulieu, 2013;Mahler, Ingram, Revell, & Losos, 2013). Assuming ecological selection is a significant influence upon morphological evolution, statistical differences in the distribution of trait values should be expected among sets of species that differ in their ecological strategies (e.g. ...
Strong relationships between morphological and ecological characters are commonly predicted to reflect the association between form and function, with this hypothesis being well supported in restricted taxonomic and geographic contexts. Conversely, among broader sets of species, ecological variables have been shown to have limited power to explain morphological variation. To understand these apparent discrepancies, for a large and globally distributed passerine radiation we test (i) whether the character states of four ecological variables (foraging mode, diet, strata and habitat) have different morphological optima, (ii) whether ecological variables explain substantial variance in morphology, and (iii) whether ecological character states can be accurately predicted from morphology. We collected ten linear morphological measurements for 782 species of corvoid passerines, and assessed (i) the fit of models of continuous trait evolution with different morphological optima for each ecological character state, (ii) variation in morphological traits among ecological character states using phylogenetically corrected regressions, and (iii) the accuracy of morphological traits in predicting species-level membership of ecological character states using linear discriminant analysis (LDA). Models of morphological evolution with different ecological optima were well supported across numerous morphological axes, corresponding with significant differences in trait distributions among ecological character states. LDA also showed that membership of the ecological categories can be predicted with relatively high accuracy by morphology. In contrast to these findings, ecological variables explain limited amounts of variation in morphological traits. For a global radiation of passerine birds, we confirm that the generation of morphological variation is generally consistent with ecological selection pressures, but that ecological characters are of limited utility in explaining morphological differences among species. Although selection towards different optima means that membership of ecological character states tend to be well predicted by morphology, the overall morphospace of individual ecological character states tend to be broad, implying that morphology can evolve in multiple ways in response to similar selection pressures. Extensive variation in morphological adaptations among similar ecological strategies is likely to be a widespread phenomenon across the tree of life.
... All evolutionary modeling and ancestral state estimation analyses use the first three principal components (PCs) derived from the PCA. The PC scores were used instead of the original data to avoid analytical problems surrounding correlations among variables (Clavel et al., 2015) and to maximize statistical power (Boettiger et al., 2012). Ardipithecus ramidus was added to a molecular phylogenetic tree from 10 k trees (Arnold et al., 2010) as a stem hominin (Strait and Grine, 2004;White et al., 2009;Dembo et al., 2015) with a branch length of 1.4 million years in accordance with first and last appearance data for the genus (5. ...
The ancestral condition from which humans evolved is critical for understanding the adaptive origin of bipedal locomotion. The 4.4 million-year-old hominin partial skeleton attributed to Ardipithecus ramidus preserves a foot that purportedly shares morphometric affinities with monkeys, but this interpretation remains controversial. Here I show that the foot of Ar. ramidus is most similar to living chimpanzee and gorilla species among a large sample of anthropoid primates. The foot morphology of Ar. ramidus suggests that the evolutionary precursor of hominin bipedalism was African ape-like terrestrial quadrupedalism and climbing. The elongation of the midfoot and phalangeal reduction in Ar. ramidus relative to the African apes is consistent with hypotheses of increased propulsive capabilities associated with an early form of bipedalism. This study provides evidence that the modern human foot was derived from an ancestral form adapted to terrestrial plantigrade quadrupedalism.
... The PCA approach implies the same preprocessing approach and the creation of seven principal components (based on the elbow method) using a generalized least-squares approach. 31 The ICA approach also implies the same preprocessing treatment and the creation of seven components using a nonlinear method. 32 For this preliminary analysis, binary classification models were trained 33 using built-in NEGATIVE and POSITIVE controls to explore the options of the original feature space or dimensionality reduction in combination with three classification algorithms. ...
There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data. This improves the selection and labeling of data for creating training sets before the application of machine learning. We demonstrate this using a high-content genome-wide small interfering RNA screen. We perform an unsupervised exploratory data analysis to facilitate the identification of four robust phenotypes, which we subsequently use as a training set for building a high-quality random forest machine learning model to differentiate four phenotypes with an accuracy of 91.1% and a kappa of 0.85. Our approach enhanced our ability to extract new knowledge from the screen when compared with the use of unsupervised methods alone.
... In addition, multiple evolutionary optima (OUM models) may be incorporated into OU models if selection is hypothesized to act differently in two or more groups (Butler & King, 2004); groups were the same as those examined in PGLS analyses (Borago-type species vs. other types) and the same phylogenetic tree was used. We used the package mvmorph (Clavel, Escarguel, & Merceron, 2015) to evaluate these models in R and selected among alternative hypotheses using corrected Akaike information criterion values (hereafter AICc) and, for nested models, the likelihood ratio test implemented in mvmorph. ...
While the presence of secondary compounds in floral nectar has received considerable attention, much less is known about the ecological significance and evolutionary origin of secondary ‘toxic’ compounds in pollen. It is unclear whether the presence of these compounds in pollen is non‐adaptive and due to physiological ‘spillover’ from other floral tissues, or whether these compounds serve an adaptive function related to plant–pollinator interactions, such as protection of pollen against pollen thieves.
Combining an experimental approach with phylogenetic comparative methods, and using western Palaearctic Boraginaceae as a model system, we investigate how pollen secondary metabolites influence, and are influenced by, relationships with bees, the main functional group of pollen‐foraging pollinators.
We found a significant relationship between the levels of secondary compounds in the corollas and those in the pollen in the investigated species of Boraginaceae, suggesting that baseline levels of pollen secondary compounds may partly be due to spillover from floral tissues. At realistic levels, pollen secondary compounds showed significant detrimental effects on bee pre‐imaginal development, in agreement with previous egg‐transfer experiments showing that in some cases Boraginaceae pollen did not support pre‐imaginal development in bees not specialized on these plants.
We also show that phylogenetically independent Boraginaceae taxa rewarding pollinators with pollen in addition to nectar exhibit significantly lower levels of toxic compounds in the pollen than taxa where the main reward is postulated to be nectar. Lastly, in contrast to our predictions, there was no positive association between toxin levels in the pollen of a given plant taxon and the number of bee species specialized on this taxon.
We integrate all these findings and formulate an evolutionary scenario to account for the presence of toxic compounds in the pollen of Boraginaceae. We suggest that baseline levels of toxic compounds may be found in pollen due to spillover from other floral tissues and not primarily because of bee–flower interactions. Since pollen toxins can have detrimental effects on bees, we propose that selection acts to lower pollen toxin levels in plants where pollen, in addition to nectar, serves as a reward to bees.
... JIVE is available in the bite (Bayesian Integrative models of 10 Trait Evolution) R package, as well as in BEAST 2. he two implementations ofer the same 11 continuous trait evolutionary models, but difer in their use and types of analyses. he R 12 implementation allows for faster analyses by taking the phylogeny as data, while providing 13 graphical and statistical functions as part of tools for model comparison, result parsing and 14 summary, and ploting. In the BEAST 2 implementation, the species tree is a parameter, and ...
1. Evolutionary forces affect the distribution of phenotypes both within and among species. Yet, at the macro‐evolutionary scale, the evolution of intraspecific variance is rarely considered. Here, we present an R and a BEAST 2 implementation that extends the JIVE (Joint inter‐ and Intraspecific Variance Evolution) model aimed at the analysis of continuous trait evolution at both inter‐ and intraspecific level. 2. Using a hierarchical Bayesian approach, we implemented a range of models for continuous trait evolution that operate independently on species means and variances along a phylogeny. The package uses Markov chain Monte Carlo for the inference of parameters and the evaluation of model fit. JIVE is available in the bite (Bayesian Integrative models of Trait Evolution) R package, as well as in BEAST 2. The two implementations offer the same continuous trait evolutionary models, but differ in their use and types of analyses. The R implementation allows for faster analyses by taking the phylogeny as data, while providing graphical and statistical functions as part of tools for model comparison, result parsing and summary, and plotting. In the BEAST 2 implementation, the species tree is a parameter, and both its topology and divergence times are jointly estimated with trait model parameters. 3. The bite package and the BEAST 2 implementation introduce new frameworks within comparative phylogenetics that explicitly model intraspecific variance. These tools allow users to tackle long‐standing questions in evolutionary biology, such as the identification of key evolutionary processes determining niche conservatism, niche partitioning, and life‐history strategies.
... In particular, the traditional approach consists in performing a principal component analysis (PCA) and retaining only axes with non-zero eigenvalues, using the scores on these axes as dependent variables in the linear model. The second approach (Clavel et al. 2019), implemented in the R package mvMORPH (Clavel et al. 2015), uses a penalized likelihood framework to circumvent the problem of singularity of covariance matrices. This approach is more powerful than both MANOVA on PCA scores and distance-based approaches (Clavel and Morlon in press). ...
Chromosomal evolution is widely considered an important driver of speciation because it can promote the establishment of reproductive barriers. Karyotypic reorganization is also expected to affect the mean phenotype, as well as its development and patterns of phenotypic integration, through processes such as variation in genetic linkage between QTL regions or between regulatory regions and their targets. Here we explore the relationship between chromosomal evolution and phenotypic integration by analysing a well-known house mouse parapatric contact zone between a highly derived Robertsonian race (2n = 22) and populations with standard karyotype (2n = 40). Populations with hybrid karyotypes are scattered throughout the hybrid zone connecting the two parental races. Using mandible shape data and geometric morphometrics, we test the hypothesis that patterns of integration progressively diverge from the “normal” integration pattern observed in the standard race as they accumulate Robertsonian fusions. We find that the main pattern of integration observed between the posterior and anterior part of the mandible can be largely attributed to allometry. We find no support for a gradual increase in divergence from normal patterns of integration as fusions accumulate. Surprisingly, however, we find that the derived Robertsonian race (2n = 22) has a distinct allometric trajectory compared to the standard race. Our results suggest that either individual fusions disproportionately affect patterns of integration or that there are mechanisms which “purge” extreme variants in hybrids (e.g., reduced fitness of hybrid shape).
... We also used maximum likelihood to evaluate a series of models (see MODELS, below), as implemented in the R package mvMORPH (Clavel et al. 2015). Models fit to shape require reducing the dimensionality of the data because the number of parameters for complex (multivariate) models can exceed the number of species. ...
A classic hypothesis posits that lineages exhibiting long‐term stasis are broadly adapted generalists that remain well‐adapted despite environmental change. However, lacking constraints that steepen adaptive peaks and stabilize the optimum, generalists’ phenotypes might drift around a broad adaptive plateau. We propose that stasis would be likely for morphological specialists that behave as ecological generalists much of the time because specialists’ functional constraints stabilize the optimum, but those with a broad niche can, like generalists, persist despite environmental change. Tree squirrels (Callosciurinae and Sciurini) exemplify ecologically versatile specialists, being extreme in adaptations for forceful biting that expand rather than limit niche breadth. Here, we examine the structure of disparity and the evolutionary dynamics of their trophic morphology (mandible size and shape) to determine if they exhibit stasis. In both lineages, a few dietary specialists disproportionately account for disparity; excluding them, we find compelling evidence for stasis of jaw shape but not size. The primary optima of these lineages diverge little, if at all over approximately 30 million years. Once their trophic apparatus was assembled, their morphological specialization steepened the slopes of their adaptive peak and constrained the position of the optima without limiting niche breadth.
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... If λ > 0.5 and we could reject λ = 0 (P < 0.05) for a given variable, we considered there to be significant phylogenetic signal. We also calculated the phylogenetic half-life (t 1/2 ) of a single optimum OU model using the package mvMORPH (Clavel et al. 2015), which provides an alternative measure of overall phylogenetic signal that can be interpreted as the amount of time required for a lineage to get halfway to its phenotypic optimum (Hansen et al. 2008;Münkemüller et al. 2015). A short t 1/2 (relative to the length of the tree) means the phylogenetic signal degrades at a rapid pace (e.g., if the half-life is near the age of the youngest split in the tree). ...
Macroclimatic niches are indirect and potentially inadequate predictors of the realized environmental conditions that many species experience. Consequently, analyses of niche evolution based on macroclimatic data alone may incompletely represent the evolutionary dynamics of species niches. Yet, understanding how an organisms’ climatic (Grinnellian) niche responds to changing macroclimatic conditions is of vital importance for predicting their potential response to global change. In this study, we integrate microclimatic and macroclimatic data across 26 species of plethodontid salamanders to portray the relationship between microclimatic niche evolution in response to changing macroclimate. We demonstrate stronger phylogenetic signal in microclimatic niche variables than at the macroclimatic scale. Even so, we find that the microclimatic niche tracks climatic changes at the macroscale, but with a phylogenetic lag at million‐year timescales. We hypothesize that behavioral tracking of the microclimatic niche over space and phenology generates the lag: salamanders preferentially select microclimates similar to their ancestral conditions rather than adapting with changes in physiology. We demonstrate that macroclimatic variables are weak predictors of niche evolution and that incorporating spatial scale into analyses of niche evolution is critical for predicting responses to climate change. This article is protected by copyright. All rights reserved
... The models were fitted to individual or collective PPCA axes using the R packages GEIGER, OUWIE (univariate; Beaulieu & O'Meara, 2015;Harmon et al., 2008), and MVMORPH (multivariate; Clavel, Escarguel, & Merceron, 2015). The relative fit of models was assessed using a model-averaging approach where we calculated the Akaike weights (AIC W ) for each model (i.e. the relative likelihood of each model) by means of the second-order Akaike information criterion using reduced sample-size corrections (AIC C ) (Burnham & Anderson, 2002). ...
• While fish reproduction has played a critical role in development of life‐history theory, the collective effects of a marine‐to‐freshwater invasion on a clade's reproductive ecology have rarely been explored in a phylogenetic context. We analysed and compared a range of quantitative and qualitative components of reproductive ecology in the Australasian terapontid fishes, a family distributed widely across marine, estuarine and freshwater habitats in the Indo‐Pacific region. We specifically tested hypotheses that life‐history strategies such as larger egg sizes and reduced fecundities are a key characteristic of freshwater species in comparison with their close marine relatives, and also fit a range of currently available evolutionary models describing the processes of ecomorphological and macrohabitat‐related diversification.
• Using recently developed phylogenetic comparative methods, differences in several quantitative reproductive traits were evident between marine and freshwater species, with reductions in average fecundity and increases in average egg size specifically characterising freshwater species. Evolutionary modelling of major trait axes, as well as specific traits across the family, highlighted significant increases in rates of evolutionary diversification across both freshwater lineages and within freshwater subclades. Modelling also supported the evolution of distinctive morpho‐ecotype optima between marine and freshwater species over simpler models of random‐walk evolution or single morphological optima.
• Review of life‐history behaviour identified environmental stimuli related to photoperiod, temperature, and lunar‐tidal cycles (and possibly combinations thereof) as playing an important role in stimulating spawning behaviour in most marine–euryhaline species. While some of these variables (temperature and photoperiod) continue to play an important role in some freshwater species, flow regime, particularly streamflow increases, appear more important in stimulating spawning responses, underlining the role of flow regime emerging as a master variable shaping evolutionary trajectories in freshwater clades.
• In this review and meta‐analysis, we document that adaptation to an entirely freshwater existence has catalysed significant, and in several cases, relatively rapid adaptive evolution to very different life‐history strategies within freshwater species. The invasion of freshwaters has had profound impacts on the trajectory of terapontid life‐history evolution, driving significant changes in a range of traits relating to fecundity, egg size, spawning stimuli, and spawning substratum. Collective results suggest a distinct adaptive landscape difference between marine and freshwaters. Terapontids can provide a useful model for assessing the consistency of these outcomes with other freshwater‐invading groups.
... In addition, we evaluated the fit of 24 alternative models (all listed in Table 5) based on the states of a discrete character (Table 4) implemented in the mvMORPH package in R [116] using a maximum likelihood inference. We used the 'fitDiscrete' function in 'geiger' v.1.3-1 ...
Background:
Unlike most mammals, toothed whale (Odontoceti) skulls lack symmetry in the nasal and facial (nasofacial) region. This asymmetry is hypothesised to relate to echolocation, which may have evolved in the earliest diverging odontocetes. Early cetaceans (whales, dolphins, and porpoises) such as archaeocetes, namely the protocetids and basilosaurids, have asymmetric rostra, but it is unclear when nasofacial asymmetry evolved during the transition from archaeocetes to modern whales. We used three-dimensional geometric morphometrics and phylogenetic comparative methods to reconstruct the evolution of asymmetry in the skulls of 162 living and extinct cetaceans over 50 million years.
Results:
In archaeocetes, we found asymmetry is prevalent in the rostrum and also in the squamosal, jugal, and orbit, possibly reflecting preservational deformation. Asymmetry in odontocetes is predominant in the nasofacial region. Mysticetes (baleen whales) show symmetry similar to terrestrial artiodactyls such as bovines. The first significant shift in asymmetry occurred in the stem odontocete family Xenorophidae during the Early Oligocene. Further increases in asymmetry occur in the physeteroids in the Late Oligocene, Squalodelphinidae and Platanistidae in the Late Oligocene/Early Miocene, and in the Monodontidae in the Late Miocene/Early Pliocene. Additional episodes of rapid change in odontocete skull asymmetry were found in the Mid-Late Oligocene, a period of rapid evolution and diversification. No high-probability increases or jumps in asymmetry were found in mysticetes or archaeocetes. Unexpectedly, no increases in asymmetry were recovered within the highly asymmetric ziphiids, which may result from the extreme, asymmetric shape of premaxillary crests in these taxa not being captured by landmarks alone.
Conclusions:
Early ancestors of living whales had little cranial asymmetry and likely were not able to echolocate. Archaeocetes display high levels of asymmetry in the rostrum, potentially related to directional hearing, which is lost in early neocetes-the taxon including the most recent common ancestor of living cetaceans. Nasofacial asymmetry becomes a significant feature of Odontoceti skulls in the Early Oligocene, reaching its highest levels in extant taxa. Separate evolutionary regimes are reconstructed for odontocetes living in acoustically complex environments, suggesting that these niches impose strong selective pressure on echolocation ability and thus increased cranial asymmetry.
... These included commonly employed evolutionary models: Brownian Motion (BM), single-peak Ornstein Uhlenbeck (OU), and Early Burst (EB), and a multi-optima OU model which does not require a priori designation of adaptive regimes (l1ou). Because of the high dimensionality of our data, we fit these models using the R package mvMORPH (Clavel et al. 2015), which allows a multivariate implementation of those commonly used evolutionary models. From these model fits we extracted the appropriate parameters (BM-σ; OU-σ, α; EB-σ, β; l1ou-σ, α per regime, number and placement of regimes), and simulated data under each model using empirical parameter estimates. ...
Ecological opportunities can be provided to organisms that cross stringent biogeographic barriers towards environments with new ecological niches. Wallace's and Lyddeker's lines are arguably the most famous biogeographic barriers, separating the Asian and Australo-Papuan biotas. One of the most ecomorphologically diverse groups of reptiles, the pythons, is distributed across these lines, and are remarkably more diverse in phenotype and ecology east of Wallace's line in Australo-Papua. We used an anchored hybrid enrichment approach, with near complete taxon sampling, to extract mitochondrial genomes and 376 nuclear loci to resolve and date their phylogenetic history. Biogeographic reconstruction demonstrates that they originated in Asia around 38-45 Ma and then invaded Australo-Papua around 23 Ma. Australo-Papuan pythons display a sizeable expansion in morphological space, with shifts towards numerous new adaptive optima in head and body shape, coupled with the evolution of new micro-habitat preferences. We provide an updated taxonomy of pythons and our study also demonstrates how ecological opportunity following colonization of novel environments can promote morphological diversification in a formerly ecomorphologically conservative group.
... No matter which disparity indices have been calculated, the research question must be framed in an appropriate statistical context. The multidimensional statistical toolkit for ecology and evolution has greatly expanded in recent years [53,54], but some of these advances have yet to be implemented in disparity analyses. Instead, hypothesis testing has been mostly confined to a small set of well-established methods. ...
Analyses of morphological disparity have been used to characterize and investigate the evolution of variation in the anatomy, function and ecology of organisms since the 1980s. While a diversity of methods have been employed, it is unclear whether they provide equivalent insights. Here, we review the most commonly used approaches for characterizing and analysing morphological disparity, all of which have associated limitations that, if ignored, can lead to misinterpretation. We propose best practice guidelines for disparity analyses, while noting that there can be no 'one-size-fits-all' approach. The available tools should always be used in the context of a specific biological question that will determine data and method selection at every stage of the analysis.
... Finally, we investigated the tempo and mode of phenotypic evolution in pikas to assess the patterns of cranial diversification through time. We first tested the fit of a set of multivariate evolutionary models using the mvMORPH package (Clavel, Escarguel, & Merceron, 2015). Models include single-rate and multi-rate Brownian motion (BM1 and BMM, respectively), early burst (EB), single-selective hypotheses of adaptive radiation with multiple regimes driven by elevation and lifestyles. ...
1.Life in extreme environments is possible through multilevel adaptations to physical and biotic stresses. At high elevations, species face numerous challenges, besides low oxygen levels, but previous studies have focused on genetic and physiological adaptations to chronic hypoxia while overlooking other key strategies for thriving in alpine landscapes.
2. Here, we investigate resource‐use trait adaptations to extreme elevations using pikas as a model, lagomorphs distributed up to 6200 metres and reaching maximum diversity on the Qinghai‐Tibet plateau, the highest plateau on Earth. Specifically, we assess cranial evolution in pikas using geometric morphometric and phylogenetic comparative techniques to determine whether adaptations among high‐elevation biota shown at the molecular and physiological levels also occur in resource‐use traits. We further explore the roles of two contrasting lifestyles (burrowing and rocky‐dwelling) in cranial evolution.
3. We found that alpine species exhibit striking phenotypic specialization to distinct microhabitats. Contrary to physiological and genetic adaptive convergence, we show that the cranium has undergone adaptive divergence likely reflecting past resource competition in highlands and long‐term association with alpine landscapes. Our analyses also reveal that the evolution of burrowing lifestyle allows high‐elevation pikas to explore novel niches and boosts their phenotypic diversification. In addition to cold and hypoxia tolerance, high cranial specialization, the appearance of burrowing habits, and strong niche separation explain how pikas overcome alpine stresses and flourish on the highest plateau on Earth. By contrast, when moving to spatially complex and heterogeneous vegetation zonation, pikas exhibit generalist skull forms able to exploit diverse habitats. These findings mirror previously reported intraspecific patterns in mammals, suggesting a general morphological response in resource‐exploiting traits to cope with distinct selection pressures across elevation zones. Phenotypic diversity is further constrained by rocky habitats, resulting in convergent skulls.
4. Our study highlights that adaptations to extreme environments occur at multiple levels of organization, but can lead to distinct evolutionary paths depending on which selective forces they respond to. The evolution of burrowing behaviour represents a landmark in the evolutionary history of pikas. We further show that rocky habitats impose strong ecological pressures, leading to convergent responses in resource‐use traits, which is rarely documented in mammals.
... Alternative, more complex models with many shifts in trait evolutionary rates (e.g. Venditti et al., 2011;Mitchell & Rabosky, 2017) or considering many traits in one model (Clavel et al., 2015) exist, but these are more appropriate to fit to clades with many more species (e.g. several thousands of species of birds; Cooney et al., 2017;Chira et al., 2018). ...
Although the links between species richness and diversification rates with clade age have been studied extensively, few studies have investigated the relationship between the rates of trait evolution and clade age. The rate of morphological trait evolution has repeatedly been shown to vary through time, as expected, for example, for adaptive radiations, but the strength and sources of this variation are not well understood. We compare the relationship between the rates of trait evolution and clade age across eight monophyletic clades of passerine birds by investigating ecomorphological traits, i.e. morphological traits that influence the ecology of the species directly. We study the ecomorphological divergence pattern using analyses of the disparity through time and determine the best-fitting model of evolution for each trait in each clade. We find no support for a consistent dependence of evolutionary rates on clade age across wing, tail, tarsus and beak shape in our eight clades and also show that early burst models of trait evolution are rarely the best-fitting models within these clades. These results suggest that key innovations or adaptive radiations might be less common evolutionary patterns and processes than generally thought or might depend on the taxonomic level investigated.
... Furthermore, they are limited to the location of peaks and are unable to identify valleys which may constrain evolution. Other limitations of the OU models include the unrealistic modeling of the OU 'regime' as an infinite basin of attraction with a Gaussian distribution, and the fact that they are often limited to univariate data, although some generalizations exist Clavel et al. 2015;Khabbazian et al. 2016 We can narrow these gaps in our understanding of the proximate source of trait space occupation by quantifying the relationship between morphology and performance by creating a performance landscape. While adaptive landscapes illustrate how population fitness varies across trait space, performance landscapes illustrate how the performance of individuals (at specific tasks) varies across trait space. ...
The complex interplay between form and function forms the basis for generating and maintaining organismal diversity. Fishes that rely on suction-feeding for prey capture exhibit remarkable phenotypic and trophic diversity. Yet the relationships between fish phenotypes and feeding performance on different prey types are unclear, partly because the morphological, biomechanical, and hydrodynamic mechanisms that underlie suction-feeding are complex. Here we demonstrate a general framework to investigate the mapping of multiple phenotypic traits to performance by mapping kinematic variables to suction-feeding capacity. Using a mechanistic model of suction-feeding that is based on core physical principles, we predict prey capture performance across a broad range of phenotypic trait values, for three general prey types: mollusk-like prey, copepod-like prey, and fish-like prey. Mollusk-like prey attach to surfaces, copepod-like prey attempt to escape upon detecting the hydrodynamic disturbance produced by the predator, and fish-like prey attempt to escape when the predator comes within a threshold distance. This approach allowed us to evaluate suction-feeding performance for any combination of six key kinematic traits, irrespective of whether these trait combinations were observed in an extant species, and to generate a multivariate mapping of phenotype to performance. We used gradient ascent methods to explore the complex topography of the performance landscape for each prey type, and find evidence for multiple peaks. Characterization of phenotypes associated with performance peaks indicates that the optimal kinematic parameter range for suction-feeding on copepod-like and fish-like prey is distinct and narrower from the optimal range for feeding on mollusk-like prey, suggesting different functional constraints for the three prey types. These performance landscapes can be used to generate hypotheses regarding the distribution of extant species in trait space and their evolutionary trajectories towards adaptive peaks on macroevolutionary fitness landscapes.
Phylogenetic comparative analyses use trees of evolutionary relationships between species to understand their evolution and ecology. A phylogenetic tree of n taxa can be algebraically transformed into an n by n squared symmetric phylogenetic covariance matrix C where each element [Formula: see text] in C represents the affinity between extant species i and extant species j. This matrix C is used internally in several comparative methods: for example, it is often inverted to compute the likelihood of the data under a model. However, if the matrix is ill-conditioned (ie, if [Formula: see text], defined by the ratio of the maximum eigenvalue of C to the minimum eigenvalue of C, is too high), this inversion may not be stable, and thus neither will be the calculation of the likelihood or parameter estimates that are based on optimizing the likelihood. We investigate this potential issue and propose several methods to attempt to remedy this issue.
Environmental changes can lead to evolutionary shifts in phenotypic traits, which in turn facilitate the exploitation of novel adaptive landscapes and lineage diversification. The global cooling, increased aridity and expansion of open grasslands during the past 50 Myr are prime examples of new adaptive landscapes that spurred lineage and ecomorphological diversity of several mammalian lineages such as rodents and large herbivorous megafauna. However, whether these environmental changes facilitated evolutionary shifts in small- to mid-sized predator morphology is unknown. Here, I used a complete cranial and body morphological dataset to examine the timing of evolutionary shifts in cranial shape, body size and body shape within extant mustelids (martens, otters, polecats and weasels) during the climatic and environmental changes of the Cenozoic. I found that evolutionary shifts in all three traits occurred within extant mustelid subclades just after the onset of the Mid-Miocene Climate Transition. These mustelid subclades first shifted towards more elongate body plans followed by concurrent shifts towards smaller body sizes and more robust crania. I hypothesize that these cranial and body morphological shifts enabled mustelids to exploit novel adaptive zones associated with the climatic and environmental changes of the Mid to Late Miocene, which facilitated significant increases in clade carrying capacity.
Homoplasy is a strong indicator of a phenotypic trait's adaptive significance when it can be linked to a similar function. We assessed homoplasy in functionally relevant scapular and femoral traits of Marmotini and Xerini, two sciuromorph rodent clades that independently acquired a fossorial lifestyle from an arboreal ancestor. We studied 125 species in the scapular dataset and 123 species in the femoral dataset. Pairwise evolutionary model comparison was used to evaluate whether homoplasy of trait optima is more likely than other plausible scenarios. The most likely trend of trait evolution among all traits was assessed via likelihood scoring of all considered models. The homoplasy hypothesis could never be confirmed as the single most likely model. Regarding likelihood scoring, scapular traits most frequently did not differ among Marmotini, Xerini, and arboreal species. For the majority of femoral traits, results indicate that Marmotini, but not Xerini, evolved away from the ancestral arboreal condition. We conclude on the basis of the scapular results that the forelimbs of fossorial and arboreal sciuromorphs share mostly similar functional demands, whereas the results on the femur indicate that the hind limb morphology is less constrained, perhaps depending on the specific fossorial habitat. This study is concerned with the scapular and femoral trait evolution in sciuromorph rodents with an emphasis on trait homoplasy between two fossorial lineages, Marmotini and Xerini. Evolutionary model comparison does not suggest homoplasy to be a likely scenario. Instead, it appears that the scapula is more conserved, reflecting the ancestral arboreal condition, whereas the femoral morphology shifted away from this condition in Marmotini, but not in Xerini.
Incorporating extinct taxa in phylogenetic comparative methods is rapidly becoming invaluable in studies of character evolution. An increasing number of studies have evaluated the effects of extinct taxa, and different numbers of extinct taxa, on model selection and parameter estimation. Body mass is a well-studied phenotype, but individual mass estimates may vary dramatically depending on the particular measurement used. Here, we perform an analysis of body mass evolution in a large clade of principally arboreal birds, incorporating 76 extinct species. We evaluate how different methods for estimating body mass of extinct taxa, and different phylogenetic hypotheses, affect our understanding of the rate and pattern of body mass evolution. Our results show that model selection can vary dramatically depending on the phenotypic and phylogenetic hypothesis used in the reconstruction. Even small changes in phenotype estimates can lead to different model selection and, as a result, affect the inferred evolutionary history. The best-fit models support an increase in the rate of evolution following the K–Pg boundary, with variation accumulating linearly through the Cenozoic. These results provide additional insight into the application of comparative models of evolution, as well as the evolutionary history of one of the most spectacular vertebrate radiations.
Tetrapod limbs have been used as a model system to investigate how selective pressures and constraints shape morphological evolution. Anurans have had many independent transitions to various microhabitats, allowing us to dissect how these factors influence limb morphology. Furthermore, anurans provide a unique system to test the generality of developmental constraints proposed in mammals, namely that later‐developing limb bones are under less constraint and show more variation. We used micro‐computed tomography scans of 236 species from 52 of 55 families, geometric morphometrics, and modern phylogenetic comparative methods to examine how limb bones are related to microhabitat, phylogeny, allometry, and developmental timing. Although there was significant phylogenetic signal, anuran limb shape showed a relationship with microhabitat and to a lesser extent, body size. We found that distal bones had higher evolutionary rates than proximal bones, providing evidence that developmental constraints are reduced in later‐developing bones. Distal bones also showed increased selection related to allometry and microhabitat, providing an additional explanation for higher evolutionary rates. By looking at the evolution of limb shape across a diverse clade, we demonstrated that multiple factors have shaped anuran limbs and that greater evolutionary lability in later‐developing limb bones is likely a general trend among tetrapods.
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The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no comprehensive non-parametric framework for characterising and comparing patterns of phenotypic evolution. Here we build on a recently introduced approach for using the phylogenetic spectral density profile to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for non-parametric analysis of phylogenetic trait data. We show how to construct the spectral density profile of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.
Change in species' climatic niches is a key mechanism influencing species distribution patterns. The question of which factors impact niche change remains a highly debated topic in evolutionary biology. Previous studies have proposed that rates of climatic niche change might be correlated with climatic oscillations at high latitude or adaptation to new environmental conditions. Yet, very few studies have asked if those factors are also predominant in aquatic environments. Here, we reconstruct the climatic niche changes of fish species on a new phylogeny encompassing 12,616 species. We first confirm that the rate of niche change is faster at high latitude and show that this association is steeper for freshwater than for marine species. We also show that freshwater species have slower rates of niche change than marine species. These results may be explained by the fact that freshwater species have larger climatic niche breadth and thermal safety margin than marine species at high latitude. Overall, our study sheds a new light on the environmental conditions and species features impacting rates of climatic niche change in aquatic habitats.
Homoplasy is a strong indicator of a phenotypic trait's adaptive significance when it can be linked to a similar function. We assessed homoplasy in functionally relevant scapular and femoral traits of Marmotini and Xerini, two sciuromorph rodent clades that independently acquired a fossorial lifestyle from an arboreal ancestor. We studied 125 species in the scapular dataset and 123 species in the femoral dataset. Pairwise evolutionary model comparison was used to evaluate whether homoplasy of trait optima is more likely than other plausible scenarios. The most likely trend of trait evolution among all traits was assessed via likelihood scoring of all considered models. The homoplasy hypothesis could never be confirmed as the single most likely model. Regarding likelihood scoring, scapular traits most frequently did not differ among Marmotini, Xerini, and arboreal species. For the majority of femoral traits, results indicate that Marmotini, but not Xerini, evolved away from the ancestral arboreal condition. We conclude on the basis of the scapular results that the forelimbs of fossorial and arboreal sciuromorphs share mostly similar functional demands, whereas the results on the femur indicate that the hind limb morphology is less constrained, perhaps depending on the specific fossorial habitat.
Model-based approaches are increasingly popular in ecological studies. A good example of this trend is the use of joint species distribution models to ask questions about ecological communities. However, most current applications of model-based methods do not include phylogenies despite the well-known importance of phylogenetic relationships in shaping species distributions and community composition. In part, this is due to lack of accessible tools allowing ecologists to fit phylogenetic species distribution models easily.
To fill this gap, the R package phyr (pronounced fire) implements a suite of metrics, comparative methods and mixed models that use phylogenies to understand and predict community composition and other ecological and evolutionary phenomena. The phyr workhorse functions are implemented in C++ making all calculations and model estimations fast.
phyr can fit a variety of models such as phylogenetic joint-species distribution models, spatiotemporal-phylogenetic autocorrelation models, and phylogenetic trait-based bipartite network models. phyr also estimates phylogenetically independent trait correlations with measurement error to test for adaptive syndromes and performs fast calculations of common alpha and beta phylogenetic diversity metrics. All phyr methods are united under Brownian motion or Ornstein-Uhlenbeck models of evolution and phylogenetic terms are modelled as phylogenetic covariance matrices.
The functions and model formula syntax we propose in phyr serves as a simple and unified framework that ignites the use of phylogenies to address a variety of ecological questions.
Understanding what shapes species phenotypes over macroevolutionary timescales from comparative data often requires studying the relationship between phenotypes and putative explanatory factors or testing for differences in phenotypes across species groups. In phyllostomid bats for example, is mandible morphology associated to diet preferences? Performing such analyses depends upon reliable phylogenetic regression techniques and associated tests (e.g. phylogenetic Generalized Least Squares, pGLS and phylogenetic analyses of variance and covariance, pANOVA, pANCOVA). While these tools are well established for univariate data, their multivariate counterparts are lagging behind. This is particularly true for high dimensional phenotypic data, such as morphometric data. Here we implement much-needed likelihood-based multivariate pGLS, pMANOVA and pMANCOVA, and use a recently developed penalized likelihood framework to extend their application to the difficult case when the number of traits p approaches or exceeds the number of species n. We then focus on the pMANOVA and use intensive simulations to assess the performance of the approach as p increases, under various levels of phylogenetic signal and correlations between the traits, phylogenetic structure in the predictors, and under various types of phenotypic differences across species groups. We show that our approach outperforms available alternatives under all circumstances, with greater power to detect phenotypic differences across species group when they exist, and a lower risk of improperly detecting nonexistent differences. Finally, we provide an empirical illustration of our pMANOVA on a geometric-morphometric dataset describing mandible morphology in phyllostomid bats along with data on their diet preferences. Overall our results show significant differences between ecological groups. Our approach, implemented in the R package mvMORPH and illustrated in a tutorial for end-users, provides efficient multivariate phylogenetic regression tools for understanding what shapes phenotypic differences across species.
Background and aims
The drivers of variations in leaf nutrient concentrations in cave-dwelling plants remain poorly understood. We aimed to explore the effects of light, soil chemistry and phylogeny on leaf nutrient concentrations in cave-dwelling plants.
Methods
We quantified light availability and sampled top-soils and leaves of the co-existing herbs and ferns in three caves. We used the traditional and phylogenetic comparative methods to determine the effects of light, soil chemistry and phylogeny on leaf nutrient concentrations and the cross-species correlations between leaf nutrients.
Results
Leaf nutrient concentrations differed little among caves due to the non-significant relationships of leaf nutrient concentrations with light availability and soil nutrient concentrations across caves. The phylogenetic signals in leaf nutrient concentrations were significant for Ca, Mg and N but non-significant for the remaining nutrients. The evolutionary rates of leaf nutrient concentrations tended to increase with decreasing phylogenetic signals and were faster in herbs than ferns. These contrasting degrees of phylogenetic conservatism in leaf nutrient concentrations were best generated by Ornstein-Uhlenbeck models, i.e., stabilizing selection towards an optimum across species for P, K, S, Fe, Mn and Zn or higher optimal concentrations in herbs than ferns for Ca, Mg and N. Strong cross-species correlations between leaf nutrient concentrations such as Ca vs Mg and N vs P were found.
Conclusions
Leaf nutrient concentrations in cave-dwelling plants showed convergent adaptations to cave environments and presented contrasting degrees of phylogenetic conservatism to produce leaf nutritional diversity for the co-existing herbs and ferns in caves.
Comparative studies tend to differ from optimality and functionality studies in how they treat adaptation. While the comparative approach focuses on the origin and change of traits, optimality studies assume that adaptations are maintained at an optimum by stabilizing selection. This paper presents a model of adaptive evolution on a macroevolutionary time scale that includes the maintenance of traits at adaptive optima by stabilizing selection as the dominant evolutionary force. Interspecific variation is treated as variation in the position of adaptive optima. The model illustrates how phylogenetic constraints not only lead to correlations between phylogenetically related species, but also to imperfect adaptations. From this model, a statistical comparative method is derived that can be used to estimate the effect of a selective factor on adaptive optima in a way that would be consistent with an optimality study of adaptation to this factor. The method is illustrated with an analysis of dental evolution in fossil horses. The use of comparative methods to study evolutionary trends is also discussed.
An adaptive landscape concept outlined by G.G. Simpson constitutes the major conceptual bridge between the fields of micro- and macroevolutionary study. Despite some important theoretical extensions since 1944, this conceptual bridge has been ignored in many empirical studies. In this article, we review the status of theoretical work and emphasize the importance of models for peak movement. Although much theoretical work has been devoted to evolution on stationary, unchanging landscapes, an important new development is a focus on the evolution of the landscape itself. We also sketch an agenda of empirical issues that is inspired by theoretical developments.
As species evolve along a phylogenetic tree, we expect closely related species to retain some phenotypic similarities due to their shared evolutionary histories. The amount of expected similarity depends both on the hierarchical phylogenetic structure, and on the specific magnitude and types of evolutionary changes that accumulate during each generation. In this study, we show how models of microevolutionary change can be translated into the resulting macroevolutionary patterns. We illustrate how the structure of phenotypic covariances expected in interspecific measurements can be derived, and how this structure depends on the microevolutionary forces guiding phenotypic change at each generation. We then explore the covariance structure expected from several simple macroevolutionary models of phenotypic evolution, including various combinations of random genetic drift, directional selection, stabilizing selection, and environmental change, as well as models of punctuated or burst-like evolution. We find that stabilizing selection leads to patterns of exponential decrease of between species covariance with phylogenetic distance. This is different from the usual linear patterns of decrease assumed in most comparative and systematic methods. Nevertheless, linear patterns of decrease can result from many processes in addition to random genetic drift, such as directional and fluctuating selection as well as modes of punctuated change. Our framework can be used to develop methods for (1) phylogenetic reconstruction; (2) inference of the evolutionary process from comparative data; and (3) conducting or evaluating statistical analyses of comparative data while taking phylogenetic history into account.
The evolution of continuous traits is the central component of comparative analyses in phylogenetics and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models and we explore the effects of random errors in trait measurement on the accuracy of model selection. We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein- Uhlenbeck (OU) and accelerating/decelerating rates (ACDC). Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favor of more parameter rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection. Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support. Importantly, since measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.
Comparative studies tend to differ from optimality and functionality studies in how they treat adaptation. While the comparative approach focuses on the origin and change of traits, optimality studies assume that adaptations are maintained at an optimum by stabilizing selection. This paper presents a model of adaptive evolution on a macroevolutionary time scale that includes the maintenance of traits at adaptive optima by stabilizing selection as the dominant evolutionary force. Interspecific variation is treated as variation in the position of adaptive optima. The model illustrates how phylogenetic constraints nor only lead to correlations between phylogenetically related species, but also to imperfect adaptations. From this model, a statistical comparative method is derived that can be used to estimate the effect of a selective factor on adaptive optima in a way that would be consistent with an optimality study of adaptation to this factor. The method is illustrated with an analysis of dental evolution in fossil horses. The use of comparative methods to study evolutionary trends is also discussed.
Integration and modularity refer to the patterns and processes of trait interaction and independence. Both terms have complex histories with respect to both conceptualization and quantification, resulting in a plethora of integration indices in use. We review briefly the divergent definitions, uses and measures of integration and modularity and make conceptual links to allometry. We also discuss how integration and modularity might evolve. Although integration is generally thought to be generated and maintained by correlational selection, theoretical considerations suggest the relationship is not straightforward. We caution here against uncontrolled comparisons of indices across studies. In the absence of controls for trait number, dimensionality, homology, development and function, it is difficult, or even impossible, to compare integration indices across organisms or traits. We suggest that care be invested in relating measurement to underlying theory or hypotheses, and that summative, theory-free descriptors of integration generally be avoided. The papers that follow in this Theme Issue illustrate the diversity of approaches to studying integration and modularity, highlighting strengths and pitfalls that await researchers investigating integration in plants and animals.
Phenotypic integration is a pervasive characteristic of organisms. Numerous analyses have demonstrated that patterns of phenotypic integration are conserved across large clades, but that significant variation also exists. For example, heterochronic shifts related to different mammalian reproductive strategies are reflected in postcranial skeletal integration and in coordination of bone ossification. Phenotypic integration and modularity have been hypothesized to shape morphological evolution, and we extended simulations to confirm that trait integration can influence both the trajectory and magnitude of response to selection. We further demonstrate that phenotypic integration can produce both more and less disparate organisms than would be expected under random walk models by repartitioning variance in preferred directions. This effect can also be expected to favour homoplasy and convergent evolution. New empirical analyses of the carnivoran cranium show that rates of evolution, in contrast, are not strongly influenced by phenotypic integration and show little relationship to morphological disparity, suggesting that phenotypic integration may shape the direction of evolutionary change, but not necessarily the speed of it. Nonetheless, phenotypic integration is problematic for morphological clocks and should be incorporated more widely into models that seek to accurately reconstruct both trait and organismal evolution.
Studies of evolutionary correlations commonly utilize phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly-multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high-dimensional datasets. The approach is derived from the statistical equivalency between parametric methods utilizing covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation and phenotypic change in high-dimensional datasets. This article is protected by copyright. All rights reserved.
Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. (2003) that is useful for quantifying and evaluating phylogenetic signal in highly-dimensional multivariate data. The method (Kmult) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of Kmult remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on Kmult and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of Kmult were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that Kmult provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.
Biologists employ phylogenetic comparative methods to study adaptive evolution. However, none of the popular methods model selection directly. We explain and develop a method based on the Ornstein-Uhlenbeck (OU) process, first proposed by Hansen. Ornstein-Uhlenbeck models incorporate both selection and drift and are thus qualitatively different from, and more general than, pure drift models based on Brownian motion. Most importantly, OU models possess selective optima that formalize the notion of adaptive zone. In this article, we develop the method for one quantitative character, discuss interpretations of its parameters, and provide code implementing the method. Our approach allows us to translate hypotheses regarding adaptation in different selective regimes into explicit models, to test the models against data using maximum-likelihood-based model selection techniques, and to infer details of the evolutionary process. We illustrate the method using two worked examples. Relative to existing approaches, the direct modeling approach we demonstrate allows one to explore more detailed hypotheses and to utilize more of the information content of comparative data sets than existing methods. Moreover, the use of a model selection framework to simultaneously compare a variety of hypotheses advances our ability to assess alternative evolutionary explanations.
We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion (BM) derived models like Pagel's lambda, kappa, delta and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression.
Many questions in evolutionary biology require the quantification and comparison of rates of phenotypic evolution. Recently, phylogenetic comparative methods have been developed for comparing evolutionary rates on a phylogeny for single, univariate traits (σ(2)), and evolutionary rate matrices (R) for sets of traits treated simultaneously. However, high-dimensional traits like shape remain under-examined with this framework, because methods suited for such data have not been fully developed. In this article, I describe a method to quantify phylogenetic evolutionary rates for high-dimensional multivariate data (σ(2)mult), found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices (R-mode and Q-mode methods). I then use simulations to evaluate the statistical performance of hypothesis testing procedures that compare σ(2)mult for two or more groups of species on a phylogeny. Under both isotropic and non-isotropic conditions, and for differing numbers of trait dimensions, the proposed method displays appropriate Type I error and high statistical power for detecting known differences in σ(2)mult among groups. By contrast, the Type I error rate of likelihood tests based on the evolutionary rate matrix (R) increases as the number of trait dimensions (p) increases, and becomes unacceptably large when only a few trait dimensions are considered. Further, likelihood tests based on R cannot be computed when the number of trait dimensions equals or exceeds the number of taxa in the phylogeny (i.e., when p ≥ N). These results demonstrate that tests based on σ(2)mult provide a useful means of comparing evolutionary rates for high-dimensional data that are otherwise not analytically accessible to methods based on the evolutionary rate matrix. This advance thus expands the phylogenetic comparative toolkit for high-dimensional phenotypic traits like shape. Finally, I illustrate the utility of the new approach by evaluating rates of head shape evolution in a lineage of Plethodon salamanders.
Many ecological and evolutionary studies seek to explain patterns of shape variation and its covariation with other variables. Geometric morphometrics is often used for this purpose, where a set of shape variables are obtained from landmark coordinates following a Procrustes superimposition.We introduce geomorph: a software package for performing geometric morphometric shape analysis in the r statistical computing environment.Geomorph provides routines for all stages of landmark-based geometric morphometric analyses in two and three-dimensions. It is an open source package to read, manipulate, and digitize landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform statistical analyses of shape variation and covariation, and to provide graphical depictions of shapes and patterns of shape variation. An important contribution of geomorph is the ability to perform Procrustes superimposition on landmark points, as well as semilandmarks from curves and surfaces.A wide range of statistical methods germane to testing ecological and evolutionary hypotheses of shape variation are provided. These include standard multivariate methods such as principal components analysis, and approaches for multivariate regression and group comparison. Methods for more specialized analyses, such as for assessing shape allometry, comparing shape trajectories, examining morphological integration, and for assessing phylogenetic signal, are also included.Several functions are provided to graphically visualize results, including routines for examining variation in shape space, visualizing allometric trajectories, comparing specific shapes to one another and for plotting phylogenetic changes in morphospace.Finally, geomorph participates to make available advanced geometric morphometric analyses through the r statistical computing platform.
G. G. Simpson, one of the chief architects of evolutionary biology's modern synthesis, proposed that diversification occurs on a macroevolutionary adaptive landscape, but landscape models are seldom used to study adaptive divergence in large radiations. We show that for Caribbean Anolis lizards, diversification on similar Simpsonian landscapes leads to striking convergence of entire faunas on four islands. Parallel radiations unfolding at large temporal scales shed light on the process of adaptive diversification, indicating that the adaptive landscape may give rise to predictable evolutionary patterns in nature, that adaptive peaks may be stable over macroevolutionary time, and that available geographic area influences the ability of lineages to discover new adaptive peaks.
The comparison of additive genetic variance-covariance matrices (G-matrices) is an increasingly popular exercise in evolutionary biology because the evolution of the C-matrix is central to the issue of persistence of genetic constraints and to the use of dynamic models in an evolutionary time frame. The comparison of G-matrices is a nontrivial statistical problem because family structure induces nonindependence among the elements in each matrix. Past solutions to the problem of G-matrix comparison have dealt with this problem, with varying success, but have tested a single null hypothesis (matrix equality or matrix dissimilarity). Because matrices can differ in many ways, several hypotheses are of interest in matrix comparisons. Flury (1988) has provided an approach to matrix comparison in which a variety of hypotheses are tested, including the two extreme hypotheses prevalent in the evolutionary literature. The hypotheses are arranged in a hierarchy and involve comparisons of both the principal components (eigenvectors) and eigenvalues of the matrix. We adapt Flury's hierarchy of tests to the problem of comparing G-matrices by using randomization testing to account for nonindependence induced by family structure. Software has been developed for carrying out this analysis for both genetic and phenotypic data. The method is illustrated with a garter snake test case.
The comparison of additive genetic variance-covariance matrices (G-matrices) is an increasingly popular exercise in evolutionary biology because the evolution of the G-matrix is central to the issue of persistence of genetic constraints and to the use of dynamic models in an evolutionary time frame. The comparison of G-matrices is a nontrivial statistical problem because family structure induces nonindependence among the elements in each matrix. Past solutions to the problem of G-matrix comparison have dealt with this problem, with varying success, but have tested a single null hypothesis (matrix equality or matrix dissimilarity). Because matrices can differ in many ways, several hypotheses are of interest in matrix comparisons. Flury (1988) has provided an approach to matrix comparison in which a variety of hypotheses are tested, including the two extreme hypotheses prevalent in the evolutionary literature. The hypotheses are arranged in a hierarchy and involve comparisons of both the principal components (eigenvectors) and eigenvalues of the matrix. We adapt Flury's hierarchy of tests to the problem of comparing G-matrices by using randomization testing to account for nonindependence induced by family structure. Software has been developed for carrying out this analysis for both genetic and phenotypic data. The method is illustrated with a garter snake test case.
Is your phylogeny informative? Measuring the power of comparative methods
Phylogenetic comparative methods are one of the most important parts of the morphometric toolkit for studies of morphological evolution. The assessment of repeated independent events of evolution of phenotypic and associated ecological-functional traits is still a starting point for the study of adaptation, but modern comparative approaches go beyond correlative methods, allowing for the modeling of evolutionary scenarios and analyses of trait evolution patterns. The evidence for adaptive change due to ecological diversification is stronger (even if still circumstantial) if models that predict increases in diversification rate fit the data well and the morphological changes are associated with ecological and functional changes. A large body of literature is dedicated to methodological and theoretical aspects of comparative methods, but in the context of univariate traits. On the other hand, biological shape is a complex trait, and morphometric data is essentially multivariate. Whereas most comparative methods allow for direct multivariate extensions, dimension reduction is an almost certain requirement due to the high dimensionality of morphometric data sets and the large number of evolutionary parameters that need to be estimated by comparative methods. Objective methods with considerable statistical support to determine data dimensionality exist, but the applied literature usually relies on subjective criteria to assess how many shape dimensions should be retained. The most appropriate calculation and interpretations of principal components, the most popular dimension reduction method, are also topics that should be considered more carefully in applications. The maturity of comparative methods and the development of model-based approaches linking macroevolutionary patterns and microevolutionary processes provide an exciting perspective for the study of morphological evolution.
Most existing methods for modeling trait evolution are univariate, while researchers are often interested in investigating evolutionary patterns and processes across multiple traits. Principal components analysis (PCA) is commonly used to reduce the dimensionality of multivariate data as univariate trait models can be fit to the individual principal components. The problem with using standard PCA on phylogenetically structured data has been previously pointed out yet it continues to be widely used in the literature. Here we demonstrate precisely how using standard PCA can mislead inferences: the first few principal components of traits evolved under constant-rate multivariate Brownian motion will appear to have evolved via an "early burst" process. A phylogenetic PCA (pPCA) has been proprosed to alleviate these issues. However, when the true model of trait evolution deviates from the model assumed in the calculation of the pPCA axes, we find that the use of pPCA suffers from similar artifacts as standard PCA. We show that datasets with high effective dimensionality are particularly likely to lead to erroneous inferences. Ultimately, all of the problems we report stem from the same underlying issue-by considering only the first few principal components as univariate traits, we are effectively examining a biased sample of a multivariate pattern. These results highlight the need for truly multivariate phylogenetic comparative methods. As these methods are still being developed, we discuss potential alternative strategies for using and interpreting models fit to univariate axes of multivariate data.
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A model of long-term correlated evolution of multiple quantitative characters is analyzed, which partitions selection into two components: one stabilizing and the other directional. The model assumes that the stabilizing component is less variable than the directional component among populations. The major result is that, within a population, the responses of characters to selection in the short term differ qualitatively from those in the long term. In the short term, the responses depend on genetic correlations between characters, but in the long term they are only determined by the fitness functions of stabilizing and directional selection, independent of genetic and phenotypic correlations. Treating the stabilizing component as a constant and assuming the directional component to vary among populations, I present formulas for the interpopulation covariation and interspecific allometry, which are functions of the intensity matrix of stabilizing selection. Particular attention is paid to the relationship between intra- and interpopulation correlations.
George Gaylord Simpson famously postulated that much of life’s diversity originated as adaptive radiations—more or less simulta- neous 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.
A modified minimum evolution approach is used to estimate covariance matrices for hypothetical ancestors. Branch lengths are calculated as the mean disparity in corresponding ancestor-descendent covariances. Branches are longest leading to terminal populations and subspecies, while interspecific branches are relatively short, indicating a general conservation of covariance structure among species despite a high degree of intraspecific variability. Absolute deviations in covariance structure are not correlated with phenotypic divergence. Interpreted in light of other studies, the analyses suggest that deviations in covariance structure are most strongly associated with the formation of diagnosably distinct taxa and stochastic sampling of genotypes at the population level. There is no evidence for restructuring of phenotypic covariance structure in association with reproductive isolation. The results suggest that phenotypic covariances are dynamic over short time scales and do not support attempts to extrapolate genetic covariance structure to explain or predict macroevolutionary change. This study further demonstrates that branch lengths, which are not usually analyzed in detail, contain valuable evolutionary information complementary to that residing in the branching pattern.
1.For the study of macroevolution, phenotypic data are analyzed across species on a dated phylogeny using phylogenetic comparative methods. In this context, the Ornstein-Uhlenbeck (OU) process is now being used extensively to model selectively-driven trait evolution, whereby a trait is attracted to a selection optimum μ.2.We report here theoretical properties of the maximum likelihood (ML) estimators for these parameters, including their non-uniqueness and inaccuracy, and show that theoretical expectations indeed apply to real trees. We provide necessary conditions for ML estimators to be well-defined and practical implications for model parametrization.3.We then show how these limitations carry over to difficulties in detecting shifts in selection regimes along a phylogeny. When the phylogenetic placement of these shifts is unknown, we identify a “large p - small n” problem where traditional model selection criteria fail and favor overly complex scenarios. Instead, we propose a modified criterion that is better adapted to change-point models.4.The challenges we identify here are inherent to trait evolution models on phylogenetic trees when observations are limited to present-day taxa, and require the addition of fossil taxa to be alleviated. We conclude with recommendations for empiricists.This article is protected by copyright. All rights reserved.
1. Models of trait macroevolution on trees (MOTMOT) is a new software package that tests for variation in the tempo and mode of continuous character evolution on phylogenetic trees. MOTMOT provides tools to fit a range of models of trait evolution with emphasis on variation in the rate of evolution between clades and character states.
2. We introduce a new method, trait MEDUSA, to identify the location of major changes in the rate of evolution of continuous traits on phylogenetic trees. We demonstrate trait MEDUSA and the other main functions of MOTMOT, using body size of Anolis lizards.
3. MOTMOT is open source software written in the R language and is freely available from CRAN (http://cran.r-project.org/web/packages/).
1. Modern comparative approaches use model-based methods to describe evolutionary processes. Generalised least squares calculations lie at the heart of many methods; however, they can be computationally intensive. This is because it is necessary to form a variance–covariance matrix, then to calculate the inverse and determinant of this.
2. Based on an algorithm provided by Felsenstein (American Journal of Human Genetics, 1973, 25, 471), I show how to perform comparative calculations that avoid these computational steps.
3. I apply the method to several problems in comparative analysis, including calculating likelihoods, estimating Pagel’s λ for one or several traits and fitting linear models.
4. R code is provided, which implements the algorithm described. Examples are included to demonstrate the computational gains possible for several commonly used comparative methods.
Phylogenetic comparative methods provide a powerful way of addressing classic questions about tempo and mode of phenotypic evolution in the fossil record, such as whether mammals increased in body size diversity after the Cretaceous‐Palaeogene (K‐Pg) extinction.Most often, these kinds of questions are addressed in the context of variation in evolutionary rates. Shifts in the mode of phenotypic evolution provide an alternative and, in some cases, more realistic explanation for patterns of trait diversity in the fossil record, but these kinds of processes are rarely tested for.In this study, I use a time‐calibrated phylogeny of living and fossil Mammaliaformes as a framework to test novel models of body size evolution derived from palaeontological theory. Specifically, I ask whether the K‐Pg extinction resulted in a change in rates of body size evolution or release from a constrained adaptive zone.I found that a model comprising an Ornstein–Uhlenbeck process until the K‐Pg event and a Brownian motion process from the Cenozoic onwards was the best supported model for these data. Surprisingly, results indicate a lower absolute rate of body size evolution during the Cenozoic than during the Mesozoic. This is explained by release from a stationary OU process that constrained realized disparity. Despite a lower absolute rate, body size disparity has in fact been increasing since the K‐Pg event.The use of time‐calibrated phylogenies of living and extinct taxa and realistic, process‐based models provides unparalleled power in testing evolutionary hypotheses. However, researchers should take care to ensure that the models they use are appropriate to the question being tested and that the parameters estimated are interpreted in the context of the best fitting model.
The aim of macroevolutionary research is to understand pattern and process in phenotypic evolution and lineage diversification at and above the species level. Historically, this kind of research has been tackled separately by palaeontologists, using the fossil record, and by evolutionary biologists, using phylogenetic comparative methods.Although both approaches have strengths, researchers gain most power to understand macroevolution when data from living and fossil species are analysed together in a phylogenetic framework. This merger sets up a series of challenges – for many fossil clades, well‐resolved phylogenies based on morphological data are not available, while placing fossils into phylogenies of extant taxa and determining their branching times is equally challenging. Once methods for building such trees are available, modelling phenotypic and lineage diversification using combined data presents its own set of challenges.The five papers in this Special Feature tackle a disparate range of topics in macroevolutionary research, from time calibration of trees to modelling phenotypic evolution. All are united, however, in implementing novel phylogenetic approaches to understand macroevolutionary pattern and process in or using the fossil record. This Special Feature highlights the benefits that may be reaped by integrating data from living and extinct species and, we hope, will spur further integrative work by empiricists and theoreticians from both sides of the macroevolutionary divide.
Abstract There are many methods for making evolutionary inferences from phylogenetic trees. Many of these can be divided into three main classes of models: continuous-time Markov chain models with finite state space (CTMC-FSS), multivariate normal models, and ...
1. Here, I present a new, multifunctional phylogenetics package, phytools, for the R statistical computing environment.
2. The focus of the package is on methods for phylogenetic comparative biology; however, it also includes tools for tree inference, phylogeny input/output, plotting, manipulation and several other tasks.
3. I describe and tabulate the major methods implemented in phytools, and in addition provide some demonstration of its use in the form of two illustrative examples.
4. Finally, I conclude by briefly describing an active web-log that I use to document present and future developments for phytools. I also note other web resources for phylogenetics in the R computational environment.
In a recent paper (Slater 2013), I described and implemented two “mode shift” models, where a continuous trait evolves under an Ornstein-Uhlenbeck (OU) process over the basal portion of a phylogenetic tree and shifts to an unconstrained Brownian Motion (BM) process, with or without a novel variance parameter, at a user specified time before the present day. There were two related errors in this paper that are here corrected. First, the equations describing elements of the phylogenetic variance-covariance matrices for these models (Slater 2013, Equations 2 & 4) were incorrect.This article is protected by copyright. All rights reserved.
1. The R package ‘diversitree’ contains a number of classical and contemporary comparative phylogenetic methods. Key included methods are BiSSE (binary state speciation and extinction), MuSSE (a multistate extension of BiSSE), and QuaSSE (quantitative state speciation and extinction). Diversitree also includes methods for analysing trait evolution and estimating speciation/extinction rates independently.
2. In this note, I describe the features and demonstrate use of the package, using a new method, MuSSE (multistate speciation and extinction), to examine the joint effects of two traits on speciation.
3. Using simulations, I found that MuSSE could reliably detect that a binary trait that affected speciation rates when simultaneously accounting for additional thats that had no effect on speciation rates.
4. Diversitree is an open source and available on the Comprehensive R Archive Network (cran). A tutorial and worked examples can be downloaded from http://www.zoology.ubc.ca/prog/diversitree.
Correlation between life-history or ecological traits and genomic features such as nucleotide or amino-acid composition can be used for reconstructing the evolutionary history of the traits of interest along phylogenies. Thus far, however, such ancestral reconstructions have been done using simple linear regression approaches that do not account for phylogenetic inertia. These reconstructions could instead be formalized as a genuine comparative regression problem, such as formalized by classical generalized least-square comparative methods, in which the trait of interest and the molecular predictor are represented as correlated Brownian characters co-evolving along the phylogeny.
Here, a Bayesian sampler is introduced, representing an alternative and more efficient algorithmic solution to this comparative regression problem, compared to currently existing generalized least-square approaches. Technically, ancestral trait reconstruction based on a molecular predictor is shown to be formally equivalent to a phylogenetic Kalman filter problem, for which backward and forward recursions are developed and implemented in the context of a Markov chain Monte Carlo sampler. The comparative regression method results in more accurate reconstructions and a more faithful representation of uncertainty, compared to simple linear regression. Application to the reconstruction of the evolution of optimal growth temperature in Archaea, using GC composition in rRNA stems and amino-acid composition of a sample of protein-coding genes, confirms previous findings, in particular, pointing to a hyperthermophilic ancestor for the kingdom.
The program is freely available at www.phylobayes.org.
nicolas.lartillot@univ-lyon1.fr.
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.
1. We present a method, 'SURFACE', that uses the Ornstein-Uhlenbeck stabilizing selection model to identify cases of convergent evolution using only continuous phenotypic characters and a phylogenetic tree. 2. SURFACE uses stepwise Akaike Information Criterion first to locate regime shifts on a tree, then to identify whether shifts are towards convergent regimes. Simulations can be used to test the hypothesis that a clade con-tains more convergence than expected by chance. 3. We demonstrate the method with an application to Hawaiian Tetragnatha spiders, and present numerical sim-ulations showing that the method has desirable statistical properties given data for multiple traits. 4. The R package surface is available as open source software from the Comprehensive R Archive Network.
Are measurements of quantitative genetic variation useful for predicting long-term adaptive evolution? To answer this question, I focus on g(max), the multivariate direction of greatest additive genetic variance within populations. Original data on threespine sticklebacks, together with published genetic measurements from other vertebrates, show that morphological differentiation between species has been biased in the direction of g(max) for at least four million years, despite evidence that natural selection is the cause of differentiation. This bias toward the direction of evolution tends to decay with time. Rate of morphological divergence between species is inversely proportional to theta, the angle between the direction of divergence and the direction of greatest genetic variation. The direction of greatest phenotypic variance is not identical with g(max), but for these data is nearly as successful at predicting the direction of species divergence. I interpret the findings to mean that genetic variances and covariances constrain adaptive change in quantitative traits for reasonably long spans of time. An alternative hypothesis, however, cannot be ruled out: that morphological differentiation is biased in the direction g(max) because divergence and g(max) are both shaped by the same natural selection pressures. Either way, the results reveal that adaptive differentiation occurs principally along ''genetic lines of least resistance.''