Morphological Phylogenetics in the Genomic Age

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Michael S.Y. Lee1,2,* and Alessandro Palci1,2 1Earth Sciences Section, South Australian Museum, North Terrace, Adelaide SA 5000, Australia 2School of Biological Sciences, University of Adelaide, SA 5005, Australia *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cub.2015.07.009

Evolutionary trees underpin virtually all of biology, and the wealth of new genomic data has enabled us to reconstruct them with increasing detail and conﬁdence. While phenotypic (typically morphological) traits are becoming less important in reconstructing evolutionary trees, they still serve vital and unique roles in phylogenetics, even for living taxa for which vast amounts of genetic information are available. Morphology remains a powerful independent source of evidence for testing molecular clades, and — through fossil phenotypes — the primary means for time-scaling phylogenies. Morphological phylogenetics is therefore vital for transforming undated molecular topologies into dated evolutionary trees. However, if morphology is to be employed to its full potential, biologists need to start scrutinising phenotypes in a more objective fashion, models of phenotypic evolution need to be improved, and approaches for analysing phenotypic traits and fossils together with genomic data need to be reﬁned.

Introduction remain a fundamental aim of biology. For example, tracing the The famous single illustration in Darwin’s The Origin of Species evolution of phenotypic traits along phylogenetic trees is essen- [1] is a phylogeny, or evolutionary tree. In the following century, tial for revealing the molecular basis of morphological change [8]. biologists reconstructed phylogenies using similar evidence as Similarly, fossils provide the best window into vast expanses did Darwin: phenotypic traits, especially morphology. However, of extinct biodiversity and associated evolutionary dynamics, from the 1960s onwards, scientists increasingly used diverse which are largely or totally inaccessible to genetic data [9]. types of genetic and molecular data (e.g. allozymes, chromo- But while biologists will always strive to interpret phenotypic somes, DNA–DNA hybridisation, nucleotide and amino acid se- traits in the context of evolutionary history (i.e. phylogeny), quences) for phylogenetic inference. The recent exponential such traits are becoming less central to reconstructing that growth in our ability to rapidly acquire vast amounts of DNA evolutionary history. data means that phylogenies are now routinely being con- In this review, we focus on a very specific type of phenotypic structed using genomic-scale molecular datasets exceeding analysis that might need more justification: morphological phylo- hundreds of genes and hundreds of thousands of base pairs genetics sensu stricto (Box 1), and how it enhances our knowl- [2,3]. These huge datasets pose computational challenges, but edge of both living and extinct biodiversity. In the genomic typically generate trees that are fully resolved (entirely bifur- age, is there any value in laboriously examining and analysing cating, i.e. dichotomous) and well-supported (most branches dozens to thousands of traits across the phenotype of living having maximum possible statistical significance). Molecular organisms, when much larger and more powerful genetic data- phylogenetics has now entered the phylogenomic age (Box 1), sets can be obtained more quickly and cost-effectively? and is often (with some justification) viewed as the most efficient Morphological phylogenetics remain vital for testing molecular and powerful approach to reconstructing evolutionary trees, at phylogenetic trees, and for time-scaling these trees by harness- least for living organisms. However, many problems remain, ing the fossil record, thus allowing inference of the dynamics of and the assumptions and biases of the analytic techniques for phenotypic and genomic evolution across both time and the handling vast genomic datasets are only beginning to be evalu- tree of life. These dated trees also form the basis of modern ated [4]. Despite this, morphological data are being increasingly comparative biology. Some shortcomings with traditional marginalised when it comes to phylogenetic inference: evolu- methods for putting timescales on phylogenetic trees might tionary trees are regularly constructed based entirely on large be overcome by promising (but largely untested) tip-dating genetic datasets, and morphology is often discussed only in approaches, where morphological data and analyses play inte- passing, if at all [5]. gral parts. However, for morphological phylogenetics to fulfill However, a comprehensive understanding of evolution re- these goals, modern morphologists need to scrutinise pheno- quires integration of both genetic and phenotypic information, types in a fundamentally different way, morphological evolution and fossil and living taxa. New approaches for gathering needs to be better modelled, and approaches for analysing morphological data from fossil and living taxa, such as laser morphology with genomic data need to be improved. microscopy and micro computer-tomography scanning, are expanding the universe of morphological characters [6]. These Simultaneous Analyses versus Molecular Scaffolds new data are now in turn being widely disseminated via image- The burgeoning amount of molecular data has highlighted rich online databases such as MorphoBank [7]. Understanding the prevalence of convergent evolution of phenotypic traits, the evolution of phenotypic traits — and their relationships to revealing that many proposed groupings based on morpholog- the genome, ontogeny, function and ecology — will always ical traits are artefacts of homoplasy (Box 1): insectivorous

Box 1. Glossary. Autapomorphy: an evolutionary change that characterises a single taxon (tip) in a phylogenetic analysis. Homoplasy: shared similarity that is not due to common ancestry, but rather the result of convergent evolution or reversal (loss). Molecular scaffold: molecular phylogeny onto which certain taxa (typically extinct forms) are inserted based on phenotypic (e.g. morphological) information. Morphological phylogenetics: inference of evolutionary trees using anatomical traits. Node: a branching point in an evolutionary tree, where an ancestral lineage diverges into two (or more) daughter lineages. Node-dating: time-scaling a phylogeny by enforcing ages or age ranges on certain divergences (calibration nodes), typically based on information from the stratigraphic record. Phylogenomics: inferring evolutionary trees using genome-scale molecular data (typically dozens to hundreds of genetic loci). Simultaneous analysis: inferring phylogeny by combining multiple sources of information (e.g. morphology, genes) for the same set of taxa, and co-estimating phylogeny using this ‘total evidence’. Tip: a terminal taxon (smallest grouping of organisms) used in a phylogenetic analysis. Tip-dating: time-scaling a phylogeny by directly using the ages of sequentially-sampled terminal taxa or ‘tips’, e.g. historical samples of viruses, or fossils from different rock strata. Topology: the branching order of an evolutionary tree.

mammals [10], legless reptiles [11], waterbirds [3], and metamer- molecular trees is rather pointless if these are essentially iden- ically segmented invertebrates [12] are now known to be hetero- tical. geneous assemblages of distantly-related lineages that have Yet, integrating morphology into phylogenetic analyses — evolved similar traits. However, while analyses of morphology despite its decreasing impact on relationships between living alone might retrieve inaccurate trees, it has been argued that taxa — remains important, as it reveals the suites of phenotypic morphology might still have a positive impact on phylogenetic novelties that characterise molecular groupings, thus helping accuracy when analysed in combination with other (principally systematists to conceptualise species and clades [20]. But molecular) data. This issue has long been central to the debate tracing the evolution of large sets of phenotypic traits along all about whether to analyse morphology and molecular data using branches of a phylogeny also helps address important scientific simultaneous analysis (letting both influence the resultant trees; questions. It allows extinct taxa — the vast majority of life — to Box 1), employing molecular scaffolds (forcing morphology to be integrated into trees of living taxa, revealing past diversity conform to a molecular tree or sample of trees; Box 1), or using and transitional forms largely inaccessible to genomics [11,18]. a combination of both approaches [13]. One argument for simul- Even if the focus is exclusively on living taxa, morphological taneous analysis was that permitting morphology to contribute phylogenetics remains important for testing and dating molecu- to tree reconstruction might increase accuracy — helping to lar topologies. These two areas are the focus of this review. resolve ‘bushy’ areas of the tree intractable to molecular data [14] or interacting with molecular data to retrieve novel clades Morphology as an Independent Test not discoverable by morphology or molecules alone [11,15].An Genomic data are not immune from processes that can mislead argument against simultaneous analysis was the potential circu- phylogenetic inference, such as saturation, long branch attrac- larity of tracing the evolution of characters on a phylogeny which tion, paralogy, lineage sorting, horizontal transfer, rate heteroge- was itself partly based on those characters [16]. neity across lineages and across sites, base composition bias, These debates were initiated when morphological and codon usage bias, autocorrelation of adjacent sites, and even molecular datasets were often broadly comparable in terms of large-scale adaptive convergence [21,22]. Two striking exam- numbers of traits and phylogenetic signal, but now are becoming ples of genome-scale convergence involve echolocation [23] moot. Morphological traits now typically comprise less than 2% and marine habits [24]: for example, bats and dolphins share of characters in combined analyses (Figure 1), and this percent- similarities in over 200 gene loci, many related to hearing age will continue to decline. The increasing disparity between and other senses [23]. The huge numbers of DNA sites now morphological datasets (typically containing fewer than several used for phylogenetic analyses means even tiny biases might hundred traits) and current phylogenomic datasets (up to mil- generate very strong statistical support for erroneous relation- lions of nucleotide positions) means that ‘simultaneous’ and ships, a problem exacerbated by datasets so large that they ‘scaffold’ analyses will yield increasingly similar trees. In simulta- are difficult to scrutinise visually. For example, saturation and neous analyses of some of the largest known morphological da- base composition bias [25] and adaptive evolution [26] pervad- tasets and with relatively modest genomic datasets (few dozen ing entire vertebrate mitogenomes have distorted the resultant genes), the genomic data still largely dictated tree topology phylogenies; these errors were highlighted when genomic-scale (Box 1) [17,18]. With the addition of much larger relevant nuclear datasets retrieved conflicting trees. But when a tree is genomic datasets [10,19], tree topology will be almost totally based on the bulk of available relevant DNA sequence data, dictated by DNA, and the results of a simultaneous analysis how can we assess its accuracy? It seems overly optimistic will be equivalent to mapping morphology onto a molecular scaf- to assume that larger samples of broadly similar data will be fold. Similarly, debating whether phenotypic traits should be infallible; similar biases potentially pervade all DNA sequence traced on combined morphological and molecular or exclusively data [21–26].

ABFigure 1. The increasing dominance of molecular over morphological phylogenetic 8 datasets.

7 (A) Decreasing percentage of morphological 100000 characters in analyses (since 2000) that consider 6 both molecular and morphological datasets

5 (usually, though not always, employing simulta-

60000 neous analyses). (B) Increasing size (number of 4

Nucleotides nucleotide positions) of molecular phylogenetic datasets. Points are annual averages based on a 3 3-year sliding window; green lines are least- % Morphological Data 20000 square regressions. For the 50 studies sampled, 2

0 see Supplemental information. 2000 2005 2010 2015 2000 2005 2010 2015 Year Year Current Biology

If an unexpected branch on a phylogenomic tree is real, A and C). Of the various approaches to put absolute timescales then the DNA substitutions that have occurred along it were on molecular phylogenies, the fossil record is usually the primary probably accompanied by other types of evolutionary change. source of evidence. However, this crucial temporal information is Congruence with independent evidence is crucial: such traits only useful when the phylogenetic position of fossils is accurately include genomic elements, such as SINEs, LINEs, microsatel- known. Ideally this involves quantitative analysis of morpholog- lites and microRNAs, biogeographic evidence, stratigraphy ical datasets of fossil and living taxa in combination with DNA ev- and phenotypic traits. For instance, when multiple nuclear idence for living taxa, using simultaneous or scaffold approaches genes retrieved a grouping of disparate mammals (including [35]. The branching pattern of living taxa is then robustly deter- aardvarks, elephants and golden moles), congruence with inde- mined (largely or entirely) by molecular data, and morphological pendent data was compelling evidence that this was an evolu- evolution is traced onto this framework, which allows fossils tionary entity rather than an analytical artefact [27]. The clade to be placed into their optimal positions [36,37]. Inserting in question — Afrotheria — was independently supported by fossils into a molecular tree based on a few selected ‘key’ char- biogeography, and subsequent studies identified additional acters is potentially inadequate, as it does not fully consider congruent traits from genetic architecture, morphology and contradictory evidence and phylogenetic uncertainty. Analysing eventually genomes [10,28,29]. fossil and living taxa using morphology alone is similarly subop- Conversely, if a heterodox branch on a phylogenomic tree is timal — the fossils might be inserted into a morphology-based an artefact, it is unlikely that datasets with different evolu- tree of living taxa that is contradicted by genomic data, such tionary dynamics, such as morphology, will also retrieve a as a phylogeny of mammals that lacks Afrotheria, or a phylogeny compelling suite of congruent changes [30]. A paucity of of squamate reptiles that unites distantly-related serpentine lin- such independent support for a hypothesised phylogenomic eages such as snakes, amphisbaenians and ‘legless lizards’. clade should serve as a warning, especially if alternatives Inclusion of molecular data improves estimates of relationships have stronger independent support. Within arthropods, among living taxa, and this in turn results in (re-)optimisation genomic analyses usually place myriapods (centipedes and of morphological characters that improves their ability to millipedes) either with chelicerates (spiders, scorpions, etc.) accurately place fossils [11,15,36,37]. It also allows phylogenet- [31], or with pan-crustaceans (crustaceans and insects) [19]. ically misleading morphological traits to be identified and The first grouping is sometimes termed Paradoxopoda [32], excluded, potentially further refining morphology-based place- reflecting a paucity of morphological support, whereas the ments of fossil taxa [13]. The three principal methods of dating second grouping is named Mandibulata, reflecting congruence molecular trees all require accurate phylogenetic placement of with the possession of unique jaw morphology (arthropod fossils, most powerfully inferred via quantitative morphological mandibles) and other phenotypic traits, along with genomic analyses. architecture [19]. ‘Node-dating’ (Box 1) remains the most widely-used approach Consilience [33,34] is one of the fundamental criteria for to incorporating temporal information from fossils into molecular judging scientific hypotheses: evolutionary trees supported by phylogenetic trees, and is facilitated by expanding databases diverse sources of data are more likely to be correct. The value such as fossilcalibrations.org [35]. The phylogenetic position of of morphology as a test of phylogenomic trees is increased by the fossils is first determined, using methods that only evaluate the relative distance between the phenotype and the genome, topology (e.g. parsimony). These fossils are then employed to and by the very different evolutionary dynamics of morphology temporally constrain (calibrate) particular nodes (Box 1)ina and DNA [30]. tree, either during or after phylogenetic analysis of molecular data. These calibrations exploit the truism that a clade must Node-dating and Tip-dating Approaches to the History be at least as old as its oldest known fossil. Thus, this fossil of Life sets the minimum age for the clade’s ancestral node. While Molecular phylogenies provide explicit information only on the this remains the most widely-used time-scaling method, and relative order of divergences between lineages (e.g. split be- has been extensively evaluated [35,38], it has four potential tween lineages A and B occurred after split between lineages drawbacks [39,40]. First, the initial analysis of the phylogenetic

ABFigure 2. Time-scaling a hypothetical evolutionary tree of trilobites using a 400 400 f Slower-Evolving Faster-Evolving tip-dating. a 400Ma 400Ma f Clade Clade For simplicity, tree topology and numbers of 420 e changes on each branch are assumed to be known 440 and ﬁxed; in practice they are typically co-esti- b 440 440 d mated along with divergence dates and all other 420Ma e 460 c 460 460 free parameters [45,50]. (A) Phylogeny of six trilo- bite species A–F, along with ages for each taxon 480 (Ma: million years ago), and an evolutionary tree 440Ma with inferred morphological changes indicated on b 440Ma 440Ma d 500 each branch. Younger taxa have accumulated C a 400Ma 400Ma f more changes; for instance, the ancient taxon C Slower-Evolving Time Interval (460 Ma) has acquired only 4 changes from the 460Ma 420Ma e base (root) of the tree, whereas younger taxa B c 460Ma 460Ma and D (440 Ma) have each acquired 6 changes. 440Ma Black Numbers = b 440Ma 440Ma d This relationship between morphological change fossil dates (observed) (anagenesis) and age — 1 change every 10 million Grey Numbers = 460Ma c 460Ma 460Ma 480Ma branching dates (inferred) years — can be used to time-scale the tree. This

Evolutionary changes: 480Ma Faster-Evolving method requires sampling of all morphological synapomorphies Time Interval changes, including unique changes on terminal

autapomorphies 500Ma branches (autapomorphies). (B) The same tree to- 500Ma (unique changes on pology as in A, where rates of evolution vary across terminal branches) clades. Here, evolution is faster on the right clade Current Biology (1 change every 5 million years) and slower on the left clade (1 change every 10 Ma); an appropriate ‘autocorrelated relaxed clock’ [44,46] would accommodate this rate variation when time-scaling this tree. (C) The same tree topology as in A, where rates of evolution vary across time. Here, evolution is faster in the earlier time slice (1 change every 5 million years) and slower in the later time slice (1 change every 10 million years); an appropriate ‘epoch relaxed clock’ [47] would accommodate this rate variation when time-scaling this tree.

position of the fossil excludes temporal information. Although a longer root-to-tip distance: a range of clock models can use topology can be, and often is, estimated without any consider- this relationship (age versus anagenesis) to infer the rates of evo- ation of time, temporal information might be relevant under lution. These estimated rates, along with the known ages of the certain circumstances. For instance, if a very ancient fossil has historical samples, together allow inference of divergence dates conflicting traits which place it either low (‘rootward’ or basal) across the phylogenetic tree. Variation in rates of evolution — or high (‘crownward’ or nested) in a tree, its antiquity might across clades and across time intervals — can be accommo- preclude a crownward position. Second, using only the age dated using relaxed clock models [44–47]. When tip-dating is and phylogenetic position of this fossil to calibrate the ancestral used for deeper divergences in paleontology [39,48,49], fossil node for a clade in a molecular analysis now excludes poten- taxa are treated like historical virus samples, and morphology tially relevant morphological information: if the earliest known is treated like RNA. The relationship between stratigraphic age fossil for that clade is already highly divergent and specialised, and accumulated morphological change permits estimates of this would suggest that the clade is probably somewhat older. rates of morphological evolution (Figure 2); these inferred pheno- Third, a fragmentary fossil of uncertain phylogenetic relation- typic rates and the stratigraphic ages of the fossil ‘tips’ then ships can potentially calibrate several alternative nodes, but automatically time-scale the tree. this phylogenetic uncertainty is cumbersome to incorporate Tip-dating is most often applied to living and extinct taxa using [41]. Finally, while the oldest fossil sets an objective minimum datasets containing both morphological and molecular data, in possible age for a clade, the maximum possible age (and the which case it is termed ‘total evidence dating’ (Figure 3) [39]. Un- intervening probability distribution) is typically rather subjective like node-dating, phylogenetic inference and dating are typically [35,38–40]. conducted simultaneously: phylogenetic analysis explicitly con- Fossilised birth–death dating [40] circumvents the last issue. siders stratigraphic age, with likelihood-based clock-models The phylogenetic position of each fossil is first assessed, and and substitution models applied to the morphological and ge- its age and occurrence is then assigned to the relevant clade. netic data. The analysis retrieves the dated trees — topology The temporal pattern of clade-specific fossil occurrences, and branch length — most congruent with the morphological together with molecular data and an appropriate lineage diversi- and molecular traits, the chosen clock and substitution models, fication model, enables time-scaling a tree. This method as well as the stratigraphic ages of all taxa. In particular, the ages currently can only be implemented using a fixed tree topology, of all divergences in the tree are made to be maximally consistent and is only beginning to be tested [42]. with both the ages of the fossil ‘tips’ and the inferred rates of ‘Tip-dating’ (Box 1) [43,44] is an increasingly used approach morphological evolution; all these variables are co-estimated. that contrasts with node dating (Figure 2). This method was Total-evidence dating essentially treats morphology as an un- developed for calibrating shallow, recent evolutionary trees of vi- usual locus that can be obtained from both living and very ruses using genetic (RNA) samples taken at different years: these ancient historical samples, and thus used to directly time-scale sequential samples (‘tips’) provide unique information for time- a deep phylogenetic tree. scaling a phylogeny. Recent samples will have accumulated Tip-dating, including total-evidence dating, cannot yet be more substitutions (changes) than ancient samples, resulting in considered a robust method, as many of its assumptions and

Figure 3. The total-evidence approach to Molecular (e.g. DNA sequence) and phenotypic (e.g. morphological) data time-scaling phylogenetic trees. collected for identical terminal taxa This new but relatively untested approach [39] uses tip-dating and potentially overcomes some Taxon Molecular data Phenotypic data shortcomings of older node-dating methods. Species a TCCTAGATATGACTAACC etc 11010101 etc Top: the method requires phenotypic (typically Species b TCCTAGATATGACTAACC etc 11000001 etc morphological) and molecular data to be gathered for identical taxa, and analysed using equivalent Species c GCCTAGATATGACTAACC etc 01010101 etc likelihood-based methods. Middle: during these Species d GCCTCGATATGCCTAACC etc 00001200 etc combined morphological and molecular analyses, Species e GCCTCGATATGCCTAACC etc 00001200 etc morphology is treated as an unusual locus that † can be sampled in long-extinct taxa (much like Species f None (extinct) 01?001?0 etc historical virus samples or ancient DNA); the rela- † = 20 million-year-old fossil. tionship between age and evolutionary change thus provides temporal information. In sampled Tree 1, for instance, all living taxa (a–e) have Clock, substitution and diversification models acquired two extra morphological changes y chosen for both phenotypic and molecular data compared to 20 million year old fossil f (six versus four changes, relative to the common ancestor at the base of the tree). As in Figure 2, this relationship between morphological change (anagenesis) and age — one change every 10 million years — Tree topology and divergence dates are inferred simultaneously can be used to time-scale the tree. Bottom: - typically a sample of trees is retrieved using Bayesian MCMC approaches consensus statistics (e.g. topology, divergence - no age constraints required apart from actual age of fossil. dates, and evolutionary rates) across all sampled trees accommodate and thus quantify uncertainty Tree 1 Tree 2 etc caused by all freely-estimated parameters. These a b c d e abc d e include the phylogenetic position of the fossils, the 0 clock and substitution models applied to the * * * * * * * * * * Molecular Phenotypic change change morphological and molecular data, and the diver- † † sification (speciation-extinction-sampling) model. * * f * * * * * * f * * 20 Phenotypic change unique to terminal taxon (autapomorphy) * * * * * * * * * Branch length based on * * * * * * molecular and phenotypic 40 changes (divergences) simultaneously tries to find the tree topol- * * ogy and divergence dates that best fit the Branch length based on phenotypic data and stratigraphic ages. 60 phenotypic changes (divergences) only Thus, if a very old fossil has conflicting

Age (million years before present) traits suggesting either a very basal or 80 very nested position, the basal position would be favoured if it is associated with Tree consensus methods and more sensible global divergence dates summary statistics and evolutionary rates. Second, phenotypic information in fossils is integral to estimating divergence dates: if the Consensus tree(s) and summary statistics: earliest fossil in a clade already has - topology With error estimates accounting for all estimated parameters including many unique specialisations, then the in- - divergence dates } phylogenetic uncertainty of fossil(s) ferred age of that clade would be pushed - molecular and phenotypic evolutionary rates deeper. Third, the effects of phylogenetic uncertainty on inferred divergence dates ab c d e are readily accommodated. Bayesian 0 Markov-chain Monte-Carlo implementa- r1 r2 r8 r9 Rate of molecular evolution for branch n tions of tip-dating methods [45,50] sam- r r r 1 2 † r r n (with error estimate) f 8 9 ple alternative tree topologies, including 20 rn Rate of phenotypic evolution for branch n 0.96 r6 (with error estimate) potentially different fossil positions, and r r3 r 6 4 0.96 Error in divergence estimate associated divergence dates (temporal r 3 1.0 (e.g. 95% interval) 40 branch lengths), thus producing more 0.52 r5 r5 Error in topology estimate realistic error estimates for the age of r10 (posterior probability of clade) 1.0 r7 r each node. Finally, tip dating does not r 10 Age (million years before present) 60 7 require (though it permits) enforcing sub- 1.0 Current Biology jective age maxima and distributions for particular nodes; the exact ages of biases remain poorly tested (see below). However, if these is- the fossil tips, the morphological and molecular characters, sues can be overcome, it potentially addresses the above short- and the chosen models provide sufﬁcient data to generate dated comings of node-dating. First, the age of a fossil is considered trees. However, tip-dating faces some major challenges, as when inferring its phylogenetic afﬁnities: a tip-dating analysis discussed below.

The Future of Morphological Phylogenetics especially extinct terminal branches (which provide the temporal Morphological data are central to harnessing the fossil record to information for time-scaling, and for which molecular evidence is time-scale molecular trees of living taxa, regardless of which non-existent). Failure to sample autapomorphies results in un- dating approach is employed. These dated phylogenies in turn derestimation of morphological change along fossil and recent shed crucial light on the dynamics of morphological and molec- terminal branches, potentially compromising inferred divergence ular evolution over time and across the tree of life [17,51]. More dates. Proposed tree-wide corrections for this sampling bias [50] generally, dated evolutionary trees greatly increase the power have been evaluated with respect to topology and tree-wide of all inferences based on comparative phylogenetic methods evolutionary rates [60], but might not ‘lengthen’ artefactually [52]. Thus, the temporal information provided by morphological short terminal branches. Thus, before existing morphological da- data is ultimately vital across biology, for inferring diversification tasets can be used in tip-dating, whole suites of undersampled dynamics, adaptation, trait associations, modes of speciation, characters (autapomorphies) should be added — a task which niche conservatism, as well as measuring and prioritising biodi- requires morphological and taxonomic expertise. versity [53]. Encouragingly, traditional morphological datasets, despite the Traditional morphological phylogenetics — despite some- above problems with character delineation and selection, often times being considered unfashionable — thus remains vital for capture morphological disparity that is congruent with morpho- testing and rigorously dating the tree of life, and ultimately under- metric characters explicitly selected for that purpose [61]. The pins much of biology. Yet, there is a dwindling number of taxon- objectivity of morphological datasets might also be improved omists and morphologists able to gather and analyse phenotypic by automated methods to identify and add characters implicitly data [54,55]. The need for such expertise is pressing, because referenced but not explicitly included [62]. most published morphological datasets are ill-suited for modern There are at least two further difficulties in tip dating, this time analyses and integration with genomic data for several reasons: relating to the analytic approaches employed. The appropriate- older morphological analyses have typically scored phenotypic ness of many of the likelihood models for morphology is only traits at the level of higher taxa (e.g. families, genera), often beginning to be tested [60,63]. Morphological traits are modelled resulting in many traits being coded as polymorphic due to be- using stochastic substitution- and clock-models, analogous tween-species variation. In contrast, molecular data are gath- to those used for DNA. However, the processes underlying ered at the level of single species or individual specimens. morphological evolution are more difficult to model. Morphology Thus, for morphological data to be dovetailed with genomic da- differs from DNA sequence data in that states across characters tasets, which is essential for either node-, fossilised birth–death are not readily comparable (a ‘1’ for two different morphological or tip-dating, the morphological traits sampled by previous gen- characters cannot be equated in the same way as a ‘C’ for two erations of biologists for supraspecific taxa need to be reeval- different nucleotide positions). The models typically used in uated and scored for the individual species sequenced [56]. morphology are thus much more simple (and likely simplistic) Tip-dating, although a promising new method to timescale than those in molecular biology: morphologists still employ phylogenies, raises at least four additional issues. Two concern ‘Jukes-Cantor’-type models that assume all characters and the nature of morphological data, which typically comprise character states follow a single evolutionary model [59–60], while discrete traits collected originally for phylogenetic analysis — molecular biologists are using increasingly complex models that though in principle continuous traits can be readily incorporated capture different categories of change using complex substitu- [48]. First, a broadly consistent division of an organism into traits tion matrices that can vary across lineages [64]. Patterns of is required to quantify evolutionary change, but it is unclear how rate variation in morphological datasets — both across charac- one can objectively delineate a ‘‘unit morphological trait’’ — ters, and across lineages — are only beginning to be investigated analogous to a base pair in nucleotide sequences. This is a [63]. The available models for accommodating these patterns long-standing issue affecting morphological studies in general were developed for DNA sequence data, for example the [57], not just phylogenetics and tip-dating. discrete gamma distribution for among-character rate variation Second, even if organisms can be atomised into broadly justi- [65], and relaxed clocks for among-lineage rate variation fiable unit characters, nearly all existing morphological studies [44,46]. They might not be very appropriate for morphological have sampled these characters in a skewed manner that is prob- characters, which likely have very different dynamics of rate lematic for tip-dating approaches. Existing morphological data- heterogeneity, for instance stronger links between functionally sets were typically gathered under a parsimony framework, correlated characters, greater rate variation across lineages, which aimed to retrieve topology and not divergence dates or and different adaptive constraints [13,63]. The tree shape prior amounts of evolutionary change on each branch. Autapomor- can also greatly influence divergence dates, and the most prom- phies (Box 1) — traits unique to single taxa, which change on ising models take into account sequential sampling (fossilization terminal branches (Figure 2) — contribute no topological infor- probability) in addition to speciation and extinction rates [43]; mation under parsimony, and thus are usually ignored or under- again, these are only beginning to be empirically tested. sampled [58]. But in tip-dating approaches, autapomorphies Finally, tip-dating is computationally demanding [66], and has improve parameterisation of models of morphological evolution only thus far been applied to relatively small genetic datasets [59,60]. Furthermore, tip-dating requires accurate estimates of [39,42,67]. Efficient, highly parallelised algorithms for analysing the amount of morphological and — where available — molecu- genomic-scale data do not yet simultaneously infer topology lar change across every branch, in order to estimate all diver- and divergence dates, although some can accommodate gence dates [39]. Autapomorphies provide essential information morphological data in (undated) simultaneous analyses [68]. on the changes along and thus duration of terminal branches, Genomic datasets are thus typically analysed stepwise, with

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