Quantitative and PD Polly, Indiana University, Bloomington, IN, USA

(c) 2016 Elsevier Inc. All rights reserved.

Glossary theory onheritance of acquired characters. Neo- Adaptive peak A mathematical model for was abandoned in light of discoveries about toward an adaptive optimum symbolized by a hill peak, the and the development of population top of which represents the point of maximum fitness. genetic theory leading to the Modern Evolutionary Brownian motion A pattern of evolution in which a trait Synthesis. changes randomly from one generation to the next, thus Ornstein-Uhlenbeck process A process that produces a serving as a null hypothesis for the outcomes expected from pattern of evolution in which the phenotypes evolve around random evolutionary processes. Brownian motion produces an optimum and are constrained from diverging too far evolutionary outcomes that have similar statistical from it. Evolution on an adaptive peak is an example of an properties as diusion processes in physics and chemistry. Ornstein –Uhlenbeck process. Drift Random evolutionary change with a Brownian Orthogenetic An evolutionary process, now largely motion pattern. Neutral drift occurs because of chance disproven, in which the phenotype evolves inacontinuous sampling in the survival of an ospring generation, with a direction because oactors internal to the , rate that is determined by the reproductive population size. eventually driving the to as the phenotype Selective drift occurs by in which the becomes less and less fit. arose from early direction and magnitude of selection change randomly twentieth century discoveries about genetic inheritance, over time. especially homeotic , and largely replaced Neo- Evolutionary trajectory e evolutionary path of a Lamarckism until orthogenesis itself was replaced by the quantitative trait through a morphospace. Modern Synthesis evolutionary theory in which selection Macroevolution Evolution over longtime scales. It can and drift act on mutations within a population structure. refer to the long-term history of a population Parsimony An algorithm for reconstructing phylogenies through time (), to divergence between from discrete (noncontinuous) characters in which the populations after reproductive isolation, or to processes number of character changes is minimized. other than the microevolutionary processes associated with Phylogenetic divergence among drift, selection, and reproductive isolation. geographically separated populations that occurs within Morphospace A mathematical space, usually species and between closely related species. multidimensional, with axes that represent the range of Saltationist An evolutionary process in which phenotypic possible values of phenotypic traits. One of the simplest changes occur in large bursts without continuous morphospaces is a bivariate plot of simple measurements intermediates between ancestor and descendant. Like such as the length and width of a structure. Morphospaces orthogenesis, saltationism arose from twentieth century are frequently used to show the relative similarity of work on homeotic mutations and was replaced by Modern individual , populations, or species. Synthesis population-based theory. Neo-Lamarckian A school of evolutionary thought that UPGMA An early algorithm for constructing trees from was dominant in the late nineteenth and early twentieth continuous phenotypic traits, now superseded by Neighbor- centuries that placed importance on the now-discarded joining and maximum-likelihood algorithms.

Introduction applied the concept of an adaptive landscape to explain the origin of grazing horses by natural selection and environment- Whether quantitative genetic processes can explain macro- driven changes in fitness ( Simpson, 1944 ). While Simpson did evolutionary processes has been an important question in not literally apply quantitative genetic models to his fossil data, evolutionary since paleontologist George G. Simpson he did adopt quantitative genetic concepts like adaptive peaks

Encyclopedia of , Volume 2doi:10.1016/B978-0-12-800049-6.00055-X 409 410 Macroevolution, Quantitative Genetics and

Weak selection Strong selection , and macroevolution made him one of the architects of the Modern Synthesis theory of evolution, which in the 1930s and 1940s replaced the orthogenetic, saltationist, and neo-Lamarckian theories that were current at the beginning of the twentieth century (Bowler, 1992). Centripetal (stabilizing) Controversies The Modern Synthesis view that macroevolution is the result of population-level microevolutionary processes quickly dominated the field but not without controversy. Simply put, the Synthesis view is that evolution is a population-level process, that phenotypic variation in populations arises from and recombination, that population means change from one generation to the next by natural selection that dif- ferentially preserves those phenotypes that increase individual

Centrifugal fi (disruptive) tness or by the chance sampling process of drift. Coupled with factors that isolate one population from another, these processes are arguably sufficient to account for macro- evolutionary patterns observed at a geological time scale such Resultant = Fractionating = as the origin and diversification of major , changes in centripetal + linear centrifugal + centripetal taxonomic diversity through time, or phenotypic changes (directional) (diversifying) within a long-lived species through time. Challenges to Synthesis view have come primarily from scientists who have argued that additional, non-population processes are required to explain some macroevolutionary patterns. Examples that are relevant to this discussion are , the hypothesis that most species do not change much during their history except for rapid bursts of change at (Eldredge and Gould, 1972); devel- Mixed modes Mixed opmental constraints, the hypothesis that processes of organismal development channel and constraint variation and thus strongly influence macroevolutionary outcomes (Alberch, Figure 1 Modes of selection used by Simpson (1944) to 1982); neutral evolution, the hypothesis that most evo- fi explain macroevolutionary patterns, with today’s equivalent terms. The lutionary change occurs by random xation of mutations top four examples result in no net change in the group (red dot or variants rather than by selection based on improved fitness shows the group mode), but affect the pattern of within-group (Kimura, 1983); and species or selection, the hypothesis variation (cream colored area). Centripetal selection, which is the that selective processes not only at the level of individual same as stabilizing selection, pushes variation toward the mode; fitness but also on emergent traits of species and clades centrifugal selection, which is the same as , (Jablonski, 2008). Scientific debate about the impact of these pushes variation away from the mode equally in all directions. three processes on macroevolution has encouraged the devel- Simpson envisioned many combinations of selective patterns, two of opment of fully quantitative models for phenotypic evolution which are shown in the bottom panels. Centrifugal selection that is and their application to comparative and paleontological data unequal results in directional change in the group mode. A combination of centrifugal selection on individual groups but to test whether quantitative genetic patterns observed at the centripetal selection between groups, perhaps as a result of intergroup population level are commensurate with long-term macro- , results in fractionating selection (diversifying selection in evolutionary patterns observed in comparative phylogenetic current terminology). and paleontological data. from ’s work on mutation, allele frequencies, se- Nonmetric Traits lection, and population divergence (Wright, 1932). Simpson argued that the long-term evolution documented by the fossil Early applications of quantitative genetics to macroevolution record – macroevolution, including not only the origin fell into two categories: nonmetric trait analysis, which was and evolution of species, but also their phylogenetic and modeled on the analysis of allele frequencies, and continuous phenotypic diversification – could be explained by the same trait analysis, the theory for which was slower to develop. microevolutionary population processes that are studied by Nonmetric trait analysis focused on the frequencies of quantitative geneticists (Figure 1). Simpson’sgoalwasto minor variations of the phenotype, such as numbers of for- demonstrate that the varied rates of evolution in phenotypic amina (small openings in bones for vessels and nerves), traits measured from sequences of fossils could be explained by numbers of cusps on teeth, numbers of bristles on fruit varied regimes of natural selection. His integration of genetics, flies, and numbers of spots on moth wings. The frequencies of Macroevolution, Quantitative Genetics and 411 these traits, which were either known or assumed to be her- for the origin of higher taxa, or adaptive radiations, by col- itable, were compared within and between populations onization of new adaptive zones, an idea that has been tested and species to determine the amount of divergence and whe- by comparing rates of evolution (Kurtén, 1959), comparing ther drift alone, including founder effects, could explain modes of selection in a quantitative phylogenetic framework the divergence or whether selection needed to be invoked. The (Schluter, 2000), and by measuring phylogenetic evolution of nonmetric trait approach was grounded in the early genetic multivariate phenotypic traits in morphospaces (Polly, work of Sewell Wright (e.g., Wright, 1934) and extensively 2008a). The study of rates of evolution was formally placed in elaborated by Ford (1945), whose seminal evolutionary a quantitative genetic framework by Gingerich, who proposed work on industrial melanism in moths used it; Berry (1978), that rates be measured in units of haldanes, which is the who used it to study founder effect, selection, and phylogeo- average change in standard deviations per generation, that they graphy in mainland and island populations; and Sjøvold be scaled to take into account the probability of evolutionary (1977), who enlarged the statistical toolkit for analyzing reversals that occur over longtime periods, and that measure- nonmetric trait divergence at macroevolutionary scales. Theory ments of macroevolutionary divergence be scaled in a com- and process of the evolution nonmetric phenotypic traits were parable way (Gingerich, 1993, 2001). This work made it developed at length in the book Phenetics by Yablokov (1986), possible to convert phenotypic rates of evolution measured a student of the prominent geneticist Timofeev-Ressovsky. over thousands or millions of generations to quantitative Despite the potential of nonmetric traits for studying the re- genetic parameters such as heritability and selection intensity. lationship of quantitative genetic processes and macro- Selection has also been measured directly from sequences of evolution, this approach has received little attention since well preserved fossils from which demographic age structures the 1980s. and, hence, individual survivorship can be estimated (Van Valen, 1965; Bell, 1988).

Continuous Quantitative Phenotypic Traits Adaptive Peaks Selection Regimes and Rates of Evolution Although the adaptive landscape was used as a metaphor for the processes governing variation in rates of phenotypic evolution While nonmetric traits were important in early research on and the origin of taxa, it was not placed in a fully quantitative evolution of the phenotype, most studies of genetic processes framework until Lande’s work in the late 1970s. Lande presented and macroevolution have been based on quantitative traits. equations that describe the intensity and direction of selection Advances in multivariate morphometrics and phylogenetic on the population mean of a quantitative phenotypic trait at any theory of quantitative traits have allowed complex new ques- point on an adaptive peak (Figure 2), derived estimates of the tions to be asked, as described in more detail below. Simpson’s early work, and those who followed his lead, involved relatively simple phenotypes, usually sizes of teeth and bones that could be measured in both living and fossil , and focused 4 on questions that could be answered by comparing rates of evolution, direction of selection, or intensity of selection. Simpson, Haldane, and Kurten made early contributions to our understanding of how selection changes as environments 2 change by measuring rates of evolution in living taxa from different environmental regimes and measuring sequences of fossils through major environmental transitions (Simpson, 0 1944; Haldane, 1949; Kurtén, 1959). Simpson (1944) classi- Trait 2 fied evolutionary rates into three classes based on the distri- bution of their magnitudes within a taxonomic group or clade: horotelic distributions are the norm, with many bursts of rapid –2 evolution interspersed with lower rates; bradytelic distri- butions are characterized by slow rates with most near zero, arising in situations where evolution is constrained by stabil- –4 izing selection, diminished , or other factors; Population tachytelic distributions are unusually rapid and include ex- ‘ ’ ceptionally fast bursts of as lineages cross –4 –2 0 2 4 from one adaptive zone to another. Simpson explained all Trait 1 three of these modes of evolution – stabilizing, random, and Figure 2 Graphic depiction of evolution on an adaptive peak. Joint directional modes of selection are their parallels – in terms of fitness of two phenotypic traits defines the contoured peak (lighter population genetic processes, especially variation in the en- colors indicate higher fitness). Selection vectors (small arrows) show vironmental and evolutionary context controlling the intensity the direction and intensity of selection at different points on the peak. of selection (Figure 1). Wright’s (1932) shifting balance ex- A population is shown as a smaller set of contours that show the planation for how a species could shift from one adaptive peak mean and variance of phenotype. Over time, selection will push the to another was an important model for Simpson’s explanation population in a predictable path up the peak (heavy arrow). 412 Macroevolution, Quantitative Genetics and rate and direction of evolutionary response to that selection, and their can be estimated, the likely ancestral described the trade-offs between drift and selection on the evo- phenotypes can be estimated, and the covariances that arise lution of the phenotype (Lande, 1976). Lande’sequationswere from shared common ancestry can be removed in to based firmly in population quantitative genetic principles of study non-phylogenetic factors that cause trait covariance, such population variance, heritability, fitness, and multilocus genetic as genetic correlations or developmental constraints (Felsen- underpinnings of phenotypic traits. One of the goals of the stein, 1988; Harvey and Pagel, 1991). Furthermore, the stat- quantitative framework was to determine whether challenges to istical framework can be parameterized so that non-Brownian the Modern Synthesis, especially the hypotheses that most evo- motion modes of evolution can also be evaluated (Martins lution was neutral or random, that developmental and other and Hansen, 1997; O’Meara et al., 2006; Revell and Collar, constraints channel the direction of long-term evolution, and 2009; Hunt and Rabosky, 2014). These other modes include that most phenotypic change occurs at speciation, were consist- Ornstein–Uhlenbeck processes that arise from centrifugal ent with quantitative genetic principles (Charlesworth et al., (stabilizing) selection associated with an adaptive landscape 1982), so it was elaborated to include adaptive peak shifts as with a broad peak, and diversifying (directional) selection. model for long-term changes in the environmental context that Collectively, these tools for analyzing phenotypic traits among defines fitness (Lande, 1986) and for correlated phenotypic traits taxa whose phylogeny is known are called phylogenetic com- (Lande and Arnold, 1983). Lande’s own conclusions were that parative methods. drift, selection, and a phenotypic version of Wright’s shifting balance theory were adequate to explain, perhaps even to predict, patterns of punctuated equilibrium and that developmental and Hypothesis Testing other constraints on evolution were consistent with quantitative genetic models for multivariate phenotypic traits (Lande, 1985, Because they are underpinned by a common quantitative 1986). Importantly, expansions of Lande’s model now form the genetic framework, the tools derived from Lande’s quantitative basis for most of the quantitative analysis of phenotypic evo- theory for the evolution of phylogenetic traits and Felsenstein’s lution (Arnold et al., 2001; Gingerich, 2009; Hansen, 2013; phylogenetic methods can be combined to test quantitative Doebli, 2013; Bell, 2013). genetic hypotheses about macroevolution, or to generate hypotheses about population-level genetic processes from comparative phenotypic data. Population-level parameters Phylogenetic Comparative Methods such as phenotypic variance, heritability, population size, In parallel to adaptive peak models, methods for analyzing and selection intensity can be extrapolated over hundreds, traits in a phylogenetic context were also developed. Felsen- thousands, or millions of generations using any one of stein (1973) proposed a maximum-likelihood algorithm for the several existing models of evolutionary pattern (Brownian reconstructing phylogeny from continuous phenotypic traits motion, stasis or stabilizing selection, directional or di- when they have evolved by a Brownian motion process such versifying selection, shifting adaptive peak, etc.) and the re- as drift or randomly changing selection. Unlike previously sulting predictions can be compared to data collected from existing morphometric tree-building algorithms such as the species with comparable phylogenetic divergence times. The unweighted pair-group averaging (UPGMA) method, or then- expected rate of phylogenetic divergence in the phenotype will emerging discrete-trait algorithms such as parsimony, Felsen- be a of population-level phenotypic variance, popu- stein’s method was explicitly grounded in probabilistic lation size, and heritability (Lande, 1976). If these parameters evolutionary quantitative genetic theory and balanced the are known for all the species being compared, then the probability of character reversals with character change using average selection intensity and the long-term pattern of the statistical properties of Brownian motion. In the simplest selection can be inferred. Conversely, the population par- situations, the phenotypic divergence between taxa evolving ameters can be inferred if the long-term patterns are known by Brownian motion is a function of their rate of evolution from paleontological or phylogenetic data and tested at the and shared phylogenetic history. A single phenotypic trait population level. evolving in an ancestor-descendant lineage by Brownian mo- An example of macroevolutionary hypothesis testing based tion will have a possible distribution of descendant pheno- on quantitative genetic predictions is Gingerich’s (2001) study types whose mean is equal to the ancestor’s phenotype rates of evolution in lineages of fossil species tracked through and whose variance is equal to the squared rate of change millions of years. He found that average per-generation se- per generation times the number of generations elapsed lection intensity was about 0.2 phenotypic standard deviations (Felsenstein, 1988). Because the statistics of Brownian motion per generation, similar to the rapid rates of change measured are nondirectional, the same distribution describes the range in lab selection experiments for large and small body size. of possible ancestor phenotypes given an observed descendant Interestingly, long-term diversification (maximum range of phenotype. The likelihood of phenotypes of the ancestor phenotypes in a clade) was less than expected if evolution of two observed descendants is described by the joint prob- continued directionally at this rate over millions of gener- abilities of the descendants. By taking into account the an- ations, suggesting that, while evolution is not constrained over cestral likelihood distributions of many taxa, the overall short timescales, there are large-scale constraints on pheno- likelihoods of competing phylogenetic hypotheses can be es- typic disparity, a finding that has been replicated in other timated and the most likely one identified. Using the same macroevolutionary studies covering many taxonomic groups logic, the likelihood that traits evolved by Brownian motion at a several phylogenetic timescales (e.g., Hunt, 2007; Evans can be evaluated if the is already known, et al., 2012). Macroevolution, Quantitative Genetics and 413

Units of Analysis multivariate structures such as these to be studied quantita- tively in large numbers (Bookstein, 1991; Dryden and Mardia, An important requirement for comparing data drawn from 1998). Studies of the genetics, development, geography, different evolutionary scales is that the units of analysis must phylogeny, and phenomics of complex traits have abounded be comparable (Gingerich, 2001; Houle et al., 2011). Evo- in the last decade and the opportunity for studying phenotypic lutionary divergence times, for example, can be measured in evolution across these scales has never been greater. units of millions of years (meganna), in molecular sequence Complex traits present new challenges, both in terms of differences, or in phenotypic character counts, but the time analytical methods and evolutionary theory. Morphometric scale of the evolutionary process is the generation. Similarly, analysis of the shapes of two-dimensional structures, even phenotypes can be measured in millimeters, kilograms, or extraordinarily complex ones, has become trivially easy, merely Procrustes units, but it is the amount of change proportional requiring a source of digital photos and software for capturing to the genetic and phenotypic variance that is relevant to Cartesian coordinates that represent the structure of interest processes such as selection and drift. In order to compare (Bookstein, 1991). The mathematical dimensionality of the re- population-level and macroevolutionary processes, the units sulting morphometric traits is often very large, but nevertheless of analysis must be standardized across the scales on which easily tractable in mathematical terms. The analysis of three- data are observed. Rates of phenotypic evolution are most dimensional shape, which includes most real biological struc- easily compared across scales when they are measured as tures, is far more challenging. Capturing three-dimensional data standard deviations per generation, a unit known as the ‘hal- requires specialized equipment such as CT-scanning or laser dane’ (Gingerich, 1993), which is the same unit as quantitative scanning, and the methods available for representing the full genetic parameters such as selection intensity. three-dimensional structure of a trait as Cartesian coordinates, An example of how scaling matters is determining whether whose orientation on the object must be homologous, are dif- an observed Brownian motion pattern of species divergence ficult to apply to varied structures and fraught with concerns arises from neutral or selective drift. Neutral about biological , algorithmic optimization, and un- genetic drift is a process due to chance sampling from parent intentional weighting of one part of a structure over another to offspring generation and is dependent on population size. (Gunz et al.,2005; Polly, 2008a; Shen et al.,2009). Divergence between species due to drift is therefore a function The high dimensionality of morphometric traits itself is an of the average population size over time, the number of gen- issue, because the number of mathematical variables repre- erations since common ancestry, and the genetic variance in sented by the landmark coordinates used to represent the the trait and will have a Brownian motion pattern (Lande, structure may be much greater than the trait’s biological di- 1976). Selective drift is a process in which a trait is evolving by mensionality. Three-dimensional morphometric represen- directional natural selection, but the direction and magnitude tations of teeth, for example, can easily have several of selection are changed randomly over time (Kimura, 1954). hundred mathematical dimensions, each of which can be Polly (2004) used this scaling relationship to show that di- thought of as a potentially independent sub-trait, but quan- vergence in tooth in a species flock of shrews titative trait locus (QTL) analysis and molecular develop- whose last common ancestor lived about 40 million years ago mental studies suggest that a much smaller number of genes had diverged randomly (Brownian motion) but at a rate much may be involved in variation in tooth phenotypes (Workman faster than expected from neutral genetic drift, even if herit- et al., 2002; Harjunmaa et al., 2014). Because rates of evo- ability in tooth structure was exceptionally high and popu- lution, standard deviation units, trait covariances, and other lation sizes were consistently small. Only selection could fundamental parameters are themselves dimensional, a new explain the observed divergences, yet there was no evidence consensus about the concept of trait dimensionality is urgently that selection followed a consistent, directional trend, despite needed. Multivariate traits might be thought of as coherent the clear functional role that teeth have. individual phenotypes with no sub-traits (Adams, 2014), they might be thought of in terms of the number of different genes underpinning them (Workman et al., 2002), they might be Morphometrics and Multivariate Phenotypes thought of in terms of the number of developmental factors that control them (Salazar-Ciudad and Jernvall, 2010), or they This comparative framework for hypothesis testing is espe- might be thought of in terms of their number of functional cially powerful when it can be applied to complex traits that units (Stinchcombe et al., 2012). are commonly preserved in the fossil record. Quantitative An important challenge for quantitative phenotypic evo- genetic studies have traditionally focused on traits that are easy lution is the study of evolutionary novelties – the gain and loss to measure in lab or field settings, especially univariate traits of limbs and digits, the multiplication of body segments, and such as body size, color, bristle number, and fecundity, but the origin of eyes. The origin of novel structures is one of the these traits are rarely preserved in fossils or in the museum most interesting aspects of macroevolution, and a better research collections that are used for large-scale comparative understanding of which is the primary goal of evolutionary phenotypic studies. Instead, complex structures such as teeth, developmental biology (Raff, 1996). However, the origin of bones, shells, and leaves are commonly studied in macro- new structures is almost impossible to quantify in morpho- evolution, even though we know comparatively little about metrics because each homologous point on the structure must their developmental and genetic underpinnings. Develop- be present in all structures in the analysis. Some morphometric ments in geometric morphometrics now allow extraordinarily approaches, such as outlines and surfaces, are ‘homology free’ complicated structures to be measured efficiently, allowing in that they can accommodate the gain and loss of features on 414 Macroevolution, Quantitative Genetics and trait 1 trait Phenotypic

Genetic factor 1

factor 2 Environmental

Genetic factor 1 trait 1 trait Phenotypic Genetic factor 2 Phenotypic trait 2 Genetic factor 1 trait 1 Phenotypic

Genetic factor 2 (a) (b) (c) Figure 3 Diagram showing how genotype–phenotype maps might be combined with geometric morphometrics to model evolutionary trajectories in which developmental interactions result in discontinuous phenotypic change. (a) Continuous, linear evolution toward an adaptive peak in genetic space. (b) Two genotype–phenotype maps that describe the relationship of phenotypic traits to genetic and environmental factors. The same evolutionary trajectory is shown crossing both surfaces. (c) The genotype–phenotype maps result in a nonlinear, discontinuous distribution of phenotypes in the morphospace shown in this panel. This figure is based on ideas presented in Polly, 2008b. a larger homologous structure, but even these cannot be used advances in both now offer opportunities for comprehensively to represent an evolutionary transformation such as the evo- addressing these questions. For instance, the preponderance of lution of limbs (Polly, 2008b). The most promising approach data suggest that phenotypic evolution is usually more rapid for studying evolution that involves the origin and loss of traits than expected by truly neutral processes like drift over short is the nonmetric approach described above, but no new de- timescales, but over broad timescales there appear to be con- velopments have been made in this area. straints operating that prevent lineages from becoming as dis- An even more important challenge is that the evolutionary parate as they might, given their per-generation rates of transformations that are reconstructed as shortest paths evolution (Gingerich, 2001; Hunt, 2007; Evans et al., 2012). through a multivariate mathematical morphospace may However, despite the huge variety of data that underpins that not be the shortest biological paths from one phenotype to observation, most of the traits that have been analyzed are another (Polly, 2008b). The quantitative genetic and mor- essentially proxies for body size. Body size has fairly obvious phometric frameworks that were introduced above are based constraints imposed on the small end by the requirements of on trajectories through mathematical spaces, but as evo- cellular function and on the large end by the force of gravity lutionary distances increase to the macroevolutionary scale, (McNeill Alexander, 1998). Data for other kinds of traits are the correspondence between mathematical and biological incomplete, but those that are available suggest that the pattern transformations becomes less certain. Dynamic relationships is different. between tissues and gene signaling during development are Rates of evolution in complex traits, such as the form of known to produce nonlinear transformation in phenotypes teeth and bones, for example, is still usually faster than ex- that jump across gaps in morphometric spaces (Salazar- pected from drift and long-term divergence often appears to be Ciudad and Jernvall, 2010). Thus, a continuous linear constrained; however, the constraints appear to change to change in the level of expression of a developmental signaling allow lineages to move in new directions that their ancestors gene may result in a discontinuous jump in the phenotype, and relatives could not. The lifting of constraints is associated one that may involve the origin of novel features. This with large, seemingly correlated changes in form and function. phenomenon has important implications for quantitative For example, ankle structure in mammalian carnivores has theory for phenotypic evolution and is the object of ongoing evolved for about 50 million years constrained to a relatively study (Wolf et al., 2001; Hansen, 2008; Rice, 2008). Mor- small area of morphospace within which lineages have phometric algorithms may require elaboration to provide a evolved and revolved similar structures; only the nonlinear mapping between developmental genetic spaces and lineage (seals and sea lions) have broken out of those con- morphospaces (Figure 3). straints, and out of the terrestrial locomotor system of their relatives (Polly, 2008a), consistent with Simpson’s concept of adaptive zones (Simpson, 1944; Figure 4). The evolutionary Conclusion dynamics of developmental systems appear to generate similar patterns in which phenotypes evolve within constrained areas The challenges raised about the ability of the Modern Synthesis of morphospace then jump to new areas at points where to reconcile microevolutionary processes with macroevo- the system passes a threshold (Salazar-Ciudad and Jernvall, lutionary patterns have as yet only been partially answered, in 2010). Added to the complexity of these processes is the large part for want of data and analytical methods. The rapid complexity of selection surfaces and adaptive landscapes for Macroevolution, Quantitative Genetics and 415

More mobile LAJ Less mobile LAJ

0.2 More digitigrade More plantigrade

0.1 Mustela Felis Lynx Leptailurus Crocuta Meles Paradoxurus Ailurus Lynx Bassaricyon Felis 0.0 Node 4 Crocuta Leptailurus Calcaneum PC 2 Lutra Canis

Canis Ailurus Bassaricyon Mustela –0.1


–0.2 –0.2 –0.1 0.0 0.1 0.2 Calcaneum PC 1

Paradoxurus Meles Lutra Phoca Figure 4 Complex patterns of evolution in multivariate phenotypes illustrated with the evolution of the ankle in mammalian carnivores. The calcaneum, the main supporting bone in the mammalian hind foot, of 12 taxa are shown at left, including cats (Felis), dogs (Canis), otters (Lutra), weasels (Mustela), and seals (Phoca). The morphospace at the right shows the distribution of their shape. Most of these species have evolved in a limited area of the morphospace defined by the functional constraints of supporting their weight and moving in their terrestrial environment (ellipse). The dark arrow shows an evolutionary trajectory estimated with maximum-likelihood for the seal lineage from its last common ancestor with terrestrial carnivores (Node 4). The rate of evolution of ankle structure in the seal lineage is similar to the rates in terrestrial species, but the seal’s ankle is able to become more different by passing out of the constraints of terrestrial locomotion. This figure is based on research presented in Polly (2008a). highly multivariate traits, which may have ‘holes’ that cannot most of the quantitative apparatus that is available for be crossed, isometric lines of equal fitness in which pheno- estimating rates and modes of phenotypic evolution are types can wander randomly, and complex peaks and valleys algorithms that find a single optimal parameter. The complex that defy the simple metaphor of a landscape (Gavrilets, patterns and processes of multivariate evolution require 1999). Some processes, such as species selection, are only methods that allow the relative support for competing hy- starting to be formulated in a quantitative framework that will potheses to be compared, ones that use a maximum- allow their effects to be compared in the same equations. likelihood or Bayesian statistical framework. Some such Notably, Simpson (2011) has used a modified version of methods are already available (Schluter et al., 1997; O’Meara Price’s (1972) evolutionary equation to partition phenotypic et al., 2006; Goldberg and Igić, 2008; Slater et al., 2012), change in clades into change arising from evolution within but more are needed, as are development of new models for lineages and change that arises from turnover in traits due to the evolution of nonmetric traits and, ideally, synthesis with differential survival of lineages. continuous multivariate traits. Opportunities exist not only for empirical research on how evolution works, but on methodological developments to deal with the evolution of complex traits. In addition to the Supplementary Material morphometric challenges raised above, new ways of con- ceiving evolutionary modes are needed for multivariate [Note: This multimedia item is essentially an animated version traits. For univariate traits, the only alternatives to Brownian of Figure 4.] Multimedia Animation 1 related to this article motion are directional evolution and stasis. Multivariate traits can be found online at doi:10.1016/B978-0-12-800049-6. evolving on multivariate selection surfaces behave in ways 000555-X. that hardly fit the latter definitions. The models used to estimate evolutionary modes are therefore difficult to apply to multivariate evolution in a meaningful way, even though See also: Adaptive Landscapes. Divergence and Diversification, considerable progress has been made to develop multivariate Quantitative Genetics of. Systems in Evolutionary equations (Arnold et al., 2001; Gavrilets, 1999). Furthermore, 416 Macroevolution, Quantitative Genetics and

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Further Reading Jablonski, D., 2008. Species selection: Theory and data. Annual Review of Ecology and Systematics 39, 501–524. Arnold, S.J., Pfrender, M.E., Jones, A.G., 2001. The adaptive landscape as a Lande, R., 1976. Natural selection and random genetic drift in phenotypic evolution. – conceptual bridge between micro- and macroevolution. Genetica 112−113, 9–32. Evolution 30, 314 334. Bell, M.A., 1988. Stickleback fishes: Bridging the gap between population biology Polly, P.D., 2008b. Developmental dynamics and G-matrices: Can morphometric and paleobiology. Trends in Ecology and Evolution 3, 320–324. spaces be used to model evolution and development? Evolutionary Biology 35, – Felsenstein, J., 1988. Phylogenies and quantitative characters. Annual Review of 83 96. Ecology and Systematics 19, 445–471. Simpson, G.G., 1944. Tempo and Mode in Evolution. New York, NY: Columbia Gingerich, P.D., 2009. Rates of evolution. Annual Review of Ecology, Evolution, and University Press. Systematics 40, 657–675. Yablokov, A.V., 1986. Phenetics. New York, NY: Columbia University Press. Hansen, T., 2013. Adaptive landscapes and macroevolutionary dynamics. In: Svensson, E.I., Calsbeek, R. (Eds.), The Adaptive Landscape in Evolutionary Biology. Oxford: Oxford University Press, pp. 205–226.