Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology || Preparing...

Chapter 22Preparing Paleontological Datasetsfor Phylogenetic Comparative Methods

David W. Bapst

Abstract The fossil record holds considerable promise for furthering our under-standing of macroevolutionary patterns, particularly allowing us to analyzehypotheses which cannot be tested with phylogenies of extant taxa alone. How-ever, although there is a growing number of paleontological studies that usephylogenetic comparative methods to address questions of trait evolution, there islittle documentation on obtaining the timescaled phylogenies of fossil taxarequired for such analyses. This chapter is an attempt to introduce interestedreaders to the issues involved with that process, including the uncertainties andbiases involved with fossil data, which some might inadvertently overlook. Inaddition, I illustrate how the fossil records of different groups can be very differentin terms of the datasets available, including the issues of that data, and stress thatthere is no one size fits all solution. Instead, for several hypothetical examples,I recommend several approaches that explicitly consider potential uncertainties,unavailable data, and biasing factors.

22.1 Introduction

Phylogeny-based analyses of evolution have proven critical to testing for macro-evolutionary processes and measuring the tempo of diversification and trait evo-lution. Increasingly, biologists working on extant taxa (i.e., neontologists) haveavailable a large number of timescaled, ultrametric phylogenies or, if not, toolsare readily available to obtain such a phylogeny, given the necessary data.However, information from the fossil record can reveal patterns that cannot evenbe predicted from datasets consisting of extant taxa alone (Finarelli and

D. W. Bapst (&)South Dakota School of Mines and Technology, Rapid City, SD, USAe-mail: [email protected]

L. Z. Garamszegi (ed.), Modern Phylogenetic Comparative Methods and TheirApplication in Evolutionary Biology, DOI: 10.1007/978-3-662-43550-2_22, Springer-Verlag Berlin Heidelberg 2014

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Flynn 2006; Losos 2010; Slater et al. 2012; Slater 2013). Working with fossil datais the only avenue available to paleontologists who wish to understand evolution inextinct groups. Unfortunately, the typical procedures for obtaining and preparing aphylogeny for analyses often do not apply to fossil datasets. This chapter isintended to fill this paleontological gap in the primers that are presently availablefor preparing data for general comparative analyses and to introduce users to thebasic concepts and necessary tools for evaluating the fossil record.

This chapter is structured in a series of discussions with the reader. First, Iaddress the current state of analyses in paleontology that depend on phylogeneticdatasets, particularly comparative studies of trait evolution, as it is currently anactive area of interest and the theme of this book. Secondly, using several hypo-thetical workers with similar research questions, I illustrate how the informationand data obtainable for different groups of organisms can vary drastically, duesimply to differences in morphological completeness, incompleteness of theassociated geologic record, taphonomy and the historical contingencies of thepaleontological approaches used in studying that group. Finally, I discuss severalapproaches that take into consideration uncertainties and biases of fossil data,rather than ignoring such issues.

22.1.1 Questions for Phylogeny-Based Analysesin Paleobiology

For decades, paleobiologists have used phylogenetic and non-phylogenetic data-sets to understand a myriad of topics in macroevolution. To cover every sort ofanalysis that a paleobiologist might attempt with a phylogenetic dataset would beimpossible. In line with the theme of this volume, this chapter will specificallyfocus on preparing fossil datasets for analyzing trait evolution, which are oftenapproached with a family of analyses referred to as phylogenetic comparativemethods. Borrowing phylogenetic comparative methods from the neontologicalliterature to study trait evolution has recently become a popular paleobiologicalpursuit (for some recent examples: Friedman 2009; Hunt and Carrano 2010;Hopkins 2011, 2013; Benson et al. 2012; Fusco et al. 2012; Sallan and Friedman2012; Benson and Choinere 2013; Raia et al. 2013; Zanno and Makovicky 2013).The majority of the methods used were generally developed with only ultrametricmolecular phylogenies in mind and so it is often not apparent what issues need tobe considered before applying these methods to fossil data. Some typical questionsaddressed with these analyses of trait evolution are Was there a directional biasover time in the evolution of a trait, such as is postulated for body size underCopes Rule? Were rates of trait change relatively faster or slower in a particulargroup or interval? or What is the relationship among traits or between a trait andan environmental predictor? Analyses for trait evolution are often quite similar toanalyses used in model-based studies of phylogeography, which have also recently

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been applied to fossil data (Gates et al. 2012), and such biogeographic analysesusually require similar types of phylogenetic datasets.

Why would we want to analyze trait evolution with information from the fossilrecord, given that including this information can introduce issues not encounteredwith extant lineages? Other than the obvious reason that the clade of interest mightbe extinct and only known from the fossil record, we also know that paleonto-logical data can reveal patterns that are not apparent in analyses including onlyliving species. Constant, directional trait evolution cannot be reconstructed ordetected on ultrametric phylogenies, unless a strong prior is put on the root state(Felsenstein 1988; Cunningham and Oakley 2000). Finarelli and Flynn (2006)showed that a trend of increasing body size could not be reconstructed in canidmammals without fossil data. Slater et al. (2012) used simulations to show thateven small amounts of fossil data can assist in identifying patterns of trait evo-lution, such as trends.

Additionally, comparative analyses that utilize the fossil record can also revealchanges in patterns of evolution through time that would not necessarily bedetectable from extant taxa alone, even when active trends are not involved. Forexample, Slater (2013) used a phylogenetic dataset of extinct and extant mammalsto show that Mesozoic mammal appears to have been constrained to evolve onlysmall body sizes. Similarly, Benson and Choinere (2013) used a dataset of extinctMesozoic theropods to identify a shift toward increased rates of forelimb evolutionin early avian dinosaurs. Wagner and Erwin (2006) used a phylogeny and a datasetof discrete ecomorphological characters to test whether Paleozoic gastropod shellswere constrained by intrinsic or extrinsic factors. Additionally, paleontologicalcomparative studies can utilize information unavailable for extant species, such asthe longevity of morphospecies in the fossil record as a predictor for extinctionrisk. For example, Hopkins (2011) phylogenetic study of Cambrian trilobitesfound that species longevity was correlated with both geographic range and(surprisingly) reduced intraspecific variation in morphology. In a similar study,Roy et al. (2009) used analyses of phylogenetic signal to show that extinction ratesamong bivalve families were predicted by their relatedness, suggesting thatextinction risk is tied to heritable traits.

However, if we have a dataset from the fossil record, why do we need to studytrait evolution in phylogenetic context? Most of the questions considered abovewere first addressed by paleobiologists without the explicit data structures that werefer to as phylogenies. One particular predecessor to phylogenetic approaches wascomparing ancestordescendant pairs to estimate trends (Alroy 1998, 2000). Alroy(1998) used taxonomic structure and the order of appearance within genera toeffectively average over the uncertainty in ancestordescendant relationshipswithin higher taxa. Similar approaches have also been developed to get at rates oftrait change in the absence of phylogenetic information (Bapst et al. 2012; Evanset al. 2012). However, these non-phylogenetic approaches still require infor-mation about relationships, often using taxonomy as a rough proxy for the phy-logenetic hierarchy, assuming that taxonomy is both trustworthy and that earliertaxa are probably ancestors to later appearing taxa.

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There are many other popular questions that are not the focus of this chapter; infact, there are probably enough topics concerning the use of fossil data in phy-logeny-based analyses to fill an entire book. However, many of the related anal-yses are either quite simple and thus do not warrant extended discussion, or themethods available are incomplete and further development of methods is needed.

A major issue for a biological audience would be the selection of fossil cali-brations for obtaining a timescaled molecular tree, which can be sensitive to thechoice of fossil dating constraints (Benton and Donoghue 2007; Heath 2012;Warnock et al. 2012). This has motivated the development of approaches that relyon additional data from the fossil record for calculating more informed priors (e.g.,Nowak et al. 2013). Similar questions in paleobiology simply do not require themethodological preparation discussed in this chapter, such as When did a par-ticular clade first diverge from their sister lineage? or How incomplete is thefossil record, given a phylogenetic hypothesis for a group? Analyzing the misfitbetween phylogeny and the appearance times of taxa generally only requires acladogram and set of first appearance times (e.g., Norell and Novacek 1992;Huelsenbeck 1994; Benton and Storrs 1994; Benton and Hitchin 1997; Siddall1996; Wills 1999; Pol and Norell 2001; Wills et al. 2008; Boyd et al. 2011).Scientists interested in these questions, however, may benefit from the discussionof timescaling approaches in this chapter.

The diversification of lineages has long been a focus of paleobiology, withconsiderable interest in how information from relationships might be used in ourestimates of past diversity and rates of origination (branching) and extinction.Phylogenetic corrections to diversity estimates mainly rely on long establishedmethods (Norell 1992; Smith 1994), although there is debate about the conditionsunder which phylogenetic information improves taxonomic estimates of diversityand the degree to which potential ancestordescendant relationships need to beconsidered (Wagner 1995, 2000; Norell 1996; Lane et al. 2005; Guinotet al. 2012; Bapst 2014). Biologists have developed a wide range of diversificationanalyses for molecular phylogenies based on tree imbalance (e.g., Mooers andHeard 1997; Chan and Moore 2002) or using branch times from the present (e.g.,Nee et al. 1992; Alfaro et al. 2009). None of these methods are entirely appro-priate for fossil data due to incomplete sampling, even when using a time-sliceapproach to create ultrametric phylogenies (Tarver and Donoghue 2011). How-ever, this topic is still in its infancy, with some models recently introduced forpaleontological phylogenies that can directly parameterize sampling processes(Stadler 2010; Didier et al. 2012) and new attempts to combine typical taxonomicestimates for obtaining diversification rates from phylogenetic datasets (e.g.,Simpson et al. 2011; Ruta et al. 2011). In addition, Ezard et al. (2011) took a verydifferent approach from those mentioned and applied hazard models to a phy-logeny of living and extinct planktonic foraminifera to estimate the effect of traitsand environment on diversification and extinction.

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22.2 The Fossil Record is Differentand FossilRecords are Different

There are numerous reasons why the typical dataset for extant taxa and any givenpaleontological dataset might require different approaches. Not all of these issuesare immediately obvious, but they all require special consideration. A key elementof paleontological data is that the available fossil record for each group oforganisms can be very different in terms of the type and amount of data available.This means the methods that we can apply to analyze and interpret phylogeneticpatterns in one clade may not work at all for a different groups fossil record.

To illustrate how very different the information available can be for differentgroups in the fossil record, Id like to introduce you to four imaginary friends, allof whom want to analyze paleontological data in a phylogenetic framework(Table 22.1). Brock wants to test if there are trends in body size in planktonicmicrofossils from the Late Cenozoic, which have an extremely well-sampled fossilrecord owing to their abundance in deep-sea ocean cores. Misty wants to usecomparative methods to test if there is a relationship between body size and taxonlongevity in Mid-Paleozoic brachiopods. Lance wants to use phylogenetic data tostudy the patterns of evolution in biomechanical traits among Mesozoic avians andother theropod dinosaurs. Erika intends to study the rate of evolution of floral traitsand already has a well-supported molecular phylogeny for the living members ofan angiosperm clade, but she also wants to include information from a fewextremely well-preserved specimens from a unique fossil bed in the Mid Cenozoic.

Although some readers might assume I am referring to specific individuals inpaleontology with Erika, Lance, Misty, and Brock, I would like to stress that thesecombinations of groups and research interests were chosen purely out of therhetorical concern of what best illustrates the issues presented by the fossil record.I do not provide citations here to real analyses similar to these examples because Ido not want readers to mistakenly wonder if I am targeting anyone for the defi-ciencies of their group. I myself study macroevolution in the extinct Graptoloidea,a group of fossil plankton which present their own issues; however, these issues aremore specific to graptoloids and thus are less useful for examples in this chapter.

Brock, Misty, Lance, and Erika all have extremely different datasets, becausethe fossil records they are working with are so very different. All four of them willhave to deal with issues not encountered if they were only analyzing a molecularphylogeny of extant taxa. As stressed above, even though they share the same goalof applying comparative methods that require a time-calibrated phylogeny, all fourwill likely end up using very different solutions to achieve that goal. To understandthe inherent differences, we will consider the major issues of the fossil record anddiscuss how each of our four friends is affected.


22.2.1 An Incompletely Sampled Geologic Record

First and foremost, anyone who wishes to analyze anything about the fossil recordhas to understand the fundamental nature of the fossil record: Everything isincompletely sampled. While sampling issues are certainly important to every fieldof science, the incomplete gaps and tattered remains of the fossil record areimmediately apparent to most observers. To paraphrase a colleague, this has madepaleontology the science of studying a degraded biological record. How we dealwith this degradation is the art of paleontology.

The key to understanding the incompleteness of the fossil record is stratigraphy.Stratigraphy, for those without a geological background, is the order and positionof sedimentary rocks in geologic outcrops (Fig. 22.1). Sedimentary rocks formwhen sediments such as sand, mud, and silt are deposited in some environmentwhere they become buried, compacted, and eventually lithify (become rock).Fossils are found within units of sedimentary rock, a series of stratigraphic layersthat share similar geologic characteristics, probably reflecting a similar environ-ment of deposition (series of related units are called formations). Many units arebarren, reflecting environments in which skeletonized organisms did not live orcould not be preserved in (or both), with a very small number capable of pre-serving non-skeletonized organisms. The vast majority of sedimentary units arealso highly incomplete, each containing and often separated by innumerous sed-imentation hiatuses and intervals of erosion, reflecting the discontinuity of sedi-mentation rate (Sadler 1981). The formation of sedimentary rocks and theirpreservation to the present ultimately is a reflection of the ever shifting balancebetween deposition and erosion, resulting in many regions of the world thatcompletely lack any rock record for a particular interval of time. Finally, there are

Table 22.1 Summary of our hypothetical paleobiology-inclined friends, with their questions andgroups of study

Name ofhypotheticalworker

Research topic Group of research interest

Brock Trends in body sizeevolution

Cenozoic planktonic microfossils

Misty Relationship between bodysize and taxon longevity

Mid-Paleozoic brachiopods

Lance Testing patterns ofbiomechanical traitevolution

Mesozoic avians and other theropod dinosaurs

Erika Rate of evolution of floraltraits

An extant angiosperm clade with fossil taxafrom a locality of exceptional preservation

As discussed throughout this chapter, their group of interest will present various challenges toobtaining a timescaled phylogeny. Thus, their choice of group may create more difficulties andrequire more research effort than the application of comparative methods to address these dif-ferent research topics

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many aspects of the relationship between the sediments buried and the fossils wefind which reflect the interaction between biotic communities and their environ-ment (see Patzkowsky and Holland 2012, for a comprehensive discussion), andthese relationships impact when and how often we sample particular taxa (e.g.,Holland 2003).

Thus, the fossil record is incomplete because the rock record is incomplete:Some intervals of time have almost no fossil record available, while for otherintervals, the rock record only captures a small number of regions and habitats,with varying degrees of completeness. Except in groups with extremely highsampling potential that lived in environments of high and constant deposition,there will always be some fraction of species missing from the fossil record. Evenfor taxa sampled in the fossil record, we almost certainly sampled them someunknown time after they originated and an equally unknown time before they wentextinct, making their first and last appearances biased estimates for those events. Inaddition, the sparseness of rocks we can absolutely date means that for most fossilrecords, first and last appearances are always known with some imprecision(Figs. 22.1 and 22.2).

Knowing that the fossil record is incompletely sampled, the question becomeshow we can quantify and account for the uncertainty introduced by that incom-pleteness (e.g., Strauss and Sadler 1989). In order to understand sampling beyondjust assuming it exists, we must choose a model of sampling processes. A com-monly assumed and mathematically tractable model is that sampling (both pres-ervation and collection of specimens) is a Poisson process occurring at some time-homogenous rate (e.g., Huelsenbeck and Rannala 1997; Solow and Smith 1997;Foote 1997; Stadler 2010), much like the birth and death models used to studydiversification (e.g., Kendall 1948; Raup et al. 1973; Raup 1985; Nee et al. 1992;Foote 2000). A Poisson process is a statistical model of some process that pro-duces a rare event occurring over some interval at a constant rate: The number ofevents that occur in an interval is described by the Poisson distribution, and thewaiting times between events is described by the exponential distribution.Although such a simple, uniform model is almost certainly incorrect, only a fewstudies have explored the limitations of assuming uniform sampling or havepresented alternative models (e.g., Holland 2003; Liow et al. 2010; Wagner andMarcot 2013). Others have split sampling into two separate processes, modelingpreservation and collection of preserved specimens individually (Heath 2012).Alternatively, some approaches do not model sampling rate or probabilityexplicitly; instead, sampling intensity is modeled indirectly using various proxies,such as the amount of exposed rock outcrop (Raup 1976; Peters and Foote 2001;Smith and McGowan 2007; Lloyd 2012).

The incomplete sampling of the fossil record is entangled with another basiclimit of the rock record: the limits of geologic timescales. The vast majority offossil finds cannot be assigned with precision to a particular date, but rather tosome discrete interval, often several million years long. Some exceptional sedi-mentary records exist in which events can be pinned with very precise dates as aresult of great diligence and the luck to find a very complete rock record, such as


with many deep ocean cores. Most fossil finds lack the potential to be resolved sofinely. This temporal uncertainty represents the lack of biostratigraphic, litholog-ical, and geochemical evidence to further resolve the age of a sedimentary unit.For example, if some fossil outcrop is composed of only long-lived taxa from arock formation bounded by geochemically obtained absolute dates that areextremely distant, then the age of that outcrop will be very poorly constrained(e.g., Fig. 22.1).

Our ability to resolve the appearance dates of a fossil can differ widely becausethe stratigraphic quality of each particular locality can be very different, such thatresolution can vary greatly within a group of fossil taxa, even among close rela-tives. One fossil taxon might be known to first occur within a two-million-year-long interval in the Early Triassic, while its sister taxon may be very hard to

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Fig. 22.1 The relationship between the fossil record and the rock record is most obvious at thelevel of a single hypothetical outcrop, seen here as an idealized stratigraphic section. At thisparticular locality, horizontal sedimentary rock units of different composition are found stackedvertically, with oldest sediments at the bottom (a common feature of outcrops but not commonenough). The doughnut, triangle, and double triangle represent fossils found at different beds,split into separate morphotaxa by differences in their morphology. The collection of taxa in thesection is patchy and unevenly distributed, with some units apparently lacking fossils, and somecollections within a taxons range lacking that particular fossil. Atypically, this section has notjust one but two ash beds that can be absolutely dated, revealing a span of more than 15 millionyears. As shown from the range bars shown on the right, the double-triangle taxon is firstsampled and the triangle taxon is last sampled within this time span, but whether they trulyoriginated/went extinct during that interval is complicated by the incomplete sampling. Omittedfrom this simplified example is the common association between rock types and faunas and thetendency for rock types to reoccur in a cyclical manner (see Patzkowsky and Holland 2012, fornumerous empirical examples)

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pin down, maybe known from a single site constrained only as far as beinganywhere within the fifty-million-year-long Triassic interval. Furthermore, a giventaxon may be sampled many times over within a single interval (as exemplified inFig. 22.1), to a degree which may be unexpected under homogenous models ofsampling. Particularly in the marine fossil record, the number of finds or collec-tions can be so dense that they are often not measured (or possibly not evenquantifiable), meaning that the only recorded information is the first and lastintervals in which those morphotaxa appear.

Our understanding of the timing of appearances in the fossil record can also differconsiderably across groups and environments (e.g., Wagner and Marcot 2013).Occurrences listed from discrete time intervals and the low probability of samplingthe true first or last appearance of a given taxon can combine to provide considerableuncertainty and bias in when observed morphotaxa originate and go extinct.

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Fig. 22.2 An example model of phylogeny and sampling in the fossil record. a In this diagram, aclade of persistent morphotaxa are separated by anagenesis and budding cladogenesis events(Foote 1996). b Sampling events are distributed randomly across the evolutionary history of thisgroup. c This means only a small proportion of taxa are sampled, and their observed ranges aretruncated with respect to their true ranges. Furthermore, we may only be able to resolve first andlast appearance dates to within stratigraphic intervals, often of uneven duration. d The nestedcladogram that would be ideally obtained for the sampled taxa. Patterns of branching andancestry among taxa can lead to intrinsic polytomies, such as the one containing taxa C, E, and H,which have no correct resolution on an unscaled cladogram (Bapst 2013b)


This provides a sometimes unexpected hurdle to those who want to make use oftiming information from the fossil record. Marine records tend to be much morecomplete and known with much better precision than terrestrial records, because ofhigher and more constant sedimentation rates which allow for more complete rockrecords. Brocks microfossils will probably have excellent stratigraphic recordsavailable from a variety of deep-sea cores, with exceptional dating precision andextremely dense sampling through time. The first and last appearances of MistysPaleozoic brachiopods will probably be resolved to geologic stages, if not to shorterintervals. Lances theropods are probably much more incompletely known. Mor-phospecies in many terrestrial tetrapod groups are known from only a single col-lection or formation, unlike the long-ranging microfossils and marine invertebrates,suggesting these fossil records are more incomplete and making it difficult to inferthe relative stratigraphic order of these collections. Ultimately, the majority ofLances taxa may be known only from a set of earliest and latest dates for eachgeologic stage they were collected in. Erikas plant fossils will likely depend on thegeologic context of those beds of exceptional preservation they are found in. If Erikais lucky and the fossil bed is perhaps associated with several volcanic ash layers, shemay have very specific dates for when these fossils were buried. Otherwise, shemight know only that her fossils are from some point in a lengthy interval, perhapsspanning several million years (e.g., the Early Miocene).

22.2.2 The Availability of Paleontological Phylogenies

To apply phylogeny-based approaches, you have to have some sort of branchingtopology. By necessity, paleontological phylogenies utilize datasets of morpho-logical features to reconstruct relationships. Many issues have been raised withphylogenies inferred from morphological characters, and those seeking to applyphylogenetic comparative methods to fossil data should understand these potentialdrawbacks (e.g., Rieppel and Kierney 2002; Scotland et al. 2003; Wagner 2000,2012). Independently of issues with morphological systematics, making a newphylogeny from a new character matrix of morphological data is a time-consumingtask that can take a career to do properly. As it is, scientists who want to usephylogeny-based approaches often plan on finding trees in the literature rather thanmaking them entirely anew. Unfortunately, published phylogenetic hypotheses inpaleontology often predate modern cladistic practices or are not available for agiven set of taxa. Current phylogenetic effort is also unevenly distributed amonggroups. The relationships of many marine invertebrate groups receive relativelylittle attention compared to vertebrate groups. For example, Lances theropodsmay have an abundance of published cladograms relative to Mistys Paleozoicbrachiopods. Among invertebrate groups, somewhat more cladistic effort is beendevoted to arthropods, echinoderms, and graptolites than others, based on a per-publication metric (Neige et al. 2009). In some groups, traditional taxonomy alonemay be the closest approximation to information on the relationships among taxa.

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The methods used to infer relationships also vary considerably among groups,with many complete phylogenies for a fossil group constructed by hand, based ona mlange of stratigraphic order, morphology, and expert opinion, rather than theproduct of computational algorithms (e.g., Pearson 1998). Additionally, somestudies that use an explicit parsimony criterion may not have been applied viaoptimization algorithms, instead involving the comparison of a small set of can-didate topologies by hand (e.g., Fortey and Cooper 1986). Non-computational,hand-drawn trees make up the majority of published topologies for some groups,particularly microfossils. These are sometimes referred to as lineage or stra-tophenetic phylogenies (Gingerich 1979), although that term is also used forsome computational clustering approaches (e.g., Wei 1994; Roopnarine 2005;Hannisdal 2006, 2009). Some microfossil workers (e.g., Aze et al. 2011; Ezardet al. 2011) have preferred stratophenetic phylogenies over results from compu-tational cladistics analyses of molecular and morphological data. This preferenceis generally argued on the basis that cladistics analyses greatly disagree with othersources of evidence, such as stratigraphic order, or that formal analyses are toounder-sampled taxonomically to provide consistent results.

Unfortunately, handmade lineage phylogenies are often much more inclusive ofknown taxa than trees constructed from formal character matrices and computa-tional analyses and often represent the most taxonomically complete phylogenies.The incompleteness of computational phylogenies is partly an issue of taxonomiclevel, with such analyses often performed in the fossil record only at the supra-specific level, with genera or families. In other groups, the taxonomic sampling ofcladograms is simply patchy because the fossil record leaves only incompletespecimens, and some taxa are much more incomplete than other. Thus, in somefossil groups, a few species may be known in exceptional detail, while the majorityof taxa are known from much more incomplete material. The taxa included inphylogenies tend to be the more complete, well-preserved taxa. This is problem-atic, as preservation potential can vary non-randomly with ecology, morphology,and habitat (e.g., Valentine et al. 2006), such that available cladograms mayunwittingly represent biased taxon sampling with respect to these factors. For ourintrepid comparative paleobiologists, this can produce biases in any attempt tostudy the evolution of ecologies and morphologies that correlate with preservation.

Of course, incomplete taxon sampling and a lack of prior phylogenetic work arealso an issue in molecular phylogenetics, but the reasons for those biases in thefossil record are very different and so these biases must be evaluated on their ownterms. Furthermore, the broad scale of quality and repeatability of what counts aspaleontological phylogenetics for many groups is very different from conflicts inmolecular biology, such as the issue of whether a mitochondrial tree is comparableto a nuclear gene tree. Ultimately, any worker interested in applying comparativemethods in the fossil record will have to decide on their minimum criteria for anacceptable, useable hypothesis of relationships.


To illustrate how different the phylogenetic data available for different groupsin the fossil record can be, let us consider what our four friends would find in astereotypical situation. Lance is probably doing the best: The most detailed spe-cies-level phylogenies are often available for tetrapod vertebrates. Misty mightlocate cladograms made from a computational algorithm, but they might includeonly a portion of the brachiopods in her dataset: Maybe a species-level cladogramfor a single genus or a cladistic analysis conducted at the family or genus level.Brock will likely find only some detailed (but probably handmade) stratopheneticlineage phylogenies for his microfossil group. Finally, Erika may discover that herplant fossil taxa have only been loosely placed in the taxonomic hierarchy andtheir position never indicated in even the most informal of phylogenies.

22.2.3 A Timescaled Tree

One of the biggest hurdles to any worker looking to use paleobiological data in aphylogeny-based analysis is that the vast majority of such analyses require treeswhich are scaled to time (or some measure of expected evolutionary change;Garland et al. 1992). Modern biological studies often use dates from molecularclock analyses, calibrated using constraints from the fossil record when available.While many groups in the fossil record have extant members, allowing cladedivergence dates to be taken directly from molecular clock analyses, not all ofthem do. Less obviously, even in those groups that do have extant members, anumber of sub-clades will probably lack extant taxa. Without extant members and(ignoring the rare cases with ancient DNA), molecular clock dates do not directlyaid the inference of branching times in an extinct clade (although they can indi-rectly inform other dating approaches for fossils; e.g., Ronquist et al. 2012). Evenassuming a phylogeny where every extinct taxon had an extant sister, the nodesplitting each of these sister pairs cannot be dated under a molecular clockapproach. Thus, dates derived from molecular clocks may be difficult to relate todivergence times in a paleontological dataset, unless there is a clear distinctionbetween stem and crown taxa for all living clades.

Existing phylogenies that contain fossil taxa are, with very few exceptions as ofthe moment, not timescaled. Although methods exist to simultaneously inferrelationships among fossil taxa and timescale the resulting phylogeny (e.g., Marcotand Fox 2008; Pyron 2011; Ronquist et al. 2012), they are not yet widely used.Instead, the majority of phylogenies found in the paleontological literature areunscaled cladograms, usually obtained via parsimony analyses. Thus, any phy-logeny of fossil taxa constructed or found by searching the literature is likely anunscaled cladogram (like Fig. 22.1d). The divergences among fossil lineagescannot be dated with molecular clocks, and, until recently, there was relativelylittle information on how to proceed with timescaling the available cladogram withstratigraphic occurrence data. Although the fossil record can be read at face value,this is probably unrealistic in all but the most exquisitely sampled of groups

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and brings unwelcome artifacts when more derived taxa appear earlier than moreprimitive taxa. To deal with these issues, numerous methods were developed totimescale a cladogram and are discussed in detail later in this chapter.

In a stereotypical scenario, three of our four scientists probably will not readilyobtain a timescaled tree. If Brocks microfossil taxa were on a stratophenetic tree,then that tree is already timescaled. Misty and Lance probably have cladograms (ofsome sort), but these cladograms probably would not be timescaled. Instead, theywill have to utilize the available stratigraphic information. Assuming Erika hasfound a phylogeny that includes her plant fossils, she will probably also need toutilize stratigraphic data to obtain divergence dates for extinct taxa or she will beunable to place her fossil taxa on a timescaled phylogeny.

22.2.4 Morphotaxa, Ancestors, and Intrinsic Polytomies

Fossil taxa are almost entirely delimited based on their morphology. While allpaleontologists would prefer to work with taxonomic units comparable to bio-logical species, this is not possible for every group. Even ignoring the mercurialnature of species-level taxonomy in some groups, taxa equivalent to biologicalspecies sometimes are not distinguishable given the traits that readily preserve.Many groups are analyzed at the lowest taxonomic level that can be consistentlyrecognized by experts: in some groups, that is species, in others, genera. However,even in those groups which can be split to species, it can be difficult to supportthe claim that such morphospecies represent reproductively isolated biologicalspecies, especially given the frequency of cryptic speciation observed in modernstudies of some groups (Pfenniger and Schwenk 2007; Trontelj and Fiser 2009)and the morphological variability exhibited by single, reproductively isolatedspecies (Wayne 1986). For groups that are entirely extinct, there may not even bean appropriate modern benchmark to set our expectation of what morphologicalvariation a species might have, meaning that the use of the term species isentirely disconnected from any special connotation that term has when used forliving species, for which reproduction isolation can be tested.

Furthermore, the morphotaxa of the fossil record are often not limited to singlemoments in time. Many morphospecies of marine invertebrate groups, microfossilgroups, and even Cenozoic mammals persist across stretches of geologic time(Foote and Raup 1996). Although the fossil specimens may not be perfectlyidentical, these morphospecies are clearly recognizable from one fossil collectionto another. In some cases, discrete morphological characters do not noticeablychange over tens of millions of years, as is apparent from the longevity of fossilmorphospecies in many invertebrate groups (Eldredge 1971; Van Valen 1973;Stanley 1979; Eldredge et al. 2005). This is at odds with the modern interpretationof a phylogeny in comparative biology, for which taxonomic identity is onlyappended to terminal tips. These terminal tips represent populations observed at aspecific instance in time (usually the present day), not morphologically persistent


lineages that may have been present over several million years. For Misty andBrock, who likely will be dealing with morphospecies that persist across geo-logical time, this issue will need to be dealt with.

At present, we do not have comparative methods that can take into account ourobservation that morphotaxa can persist without much morphological change.Instead, a particular times of observation has to be selected within each mor-photaxons duration (Bapst 2013a, 2014). The choice of these dates is not arbi-trary: They may have a considerable impact on the resulting branch lengths(compare parts d and e in Fig. 22.3, which contrast the use of first and lastappearances as the times of observation). Preferably, these times of observationshould be the dates corresponding to the specimens used for trait measurement,although this distinction is unhelpful for a static discrete trait. Hunt (2013) tookmultiple samples from the same morphotaxa, treated as independent observations,to test for relative stasis, but the majority of comparative paleontologists do notcollect multiple samples from the same morphospecies across time. Even then,multiple observations of the same discrete trait value across an anagenetic lineagemay be unexpected under typical models of discrete trait change and may produceunexpected artifacts of model fitting.

Morphotaxa can also potentially be ancestors to one another. For example, alineage might be sampled as one morphotaxon and then sampled later as a differentmorphotaxon, through either anagenetic or cladogenetic change. There may evenbe multiple intervening morphotaxa that are not sampled, yet the first taxon is stillan ancestor of the later (indirect ancestry; Foote 1996; for example, taxa C andF in Fig. 22.2). Furthermore, evidence from the fossil record suggests morphotaxaoften persist unchanged through speciation events, with analyses supporting amodel in which only one daughter lineage accrues immediate morphologicalchange (Wagner and Erwin 1995). This means a single morphotaxon might be theancestor to a large number of morphologically distinguishable but independentlyderived descendants. In addition to this, you could have scenarios in which youhave sampled an ancestral morphotaxon. Although the incomplete sampling of thefossil record might lead us to predict that the chance of observing ancestors is quiterare, modeling suggests that the possibility of sampling ancestors is quite high(Foote 1996).

Although paleobiologists are not required to make any explicit assumptionsabout ancestordescendant relationships in comparative methods, these relation-ships are likely present and cannot safely be ignored. Many studies ignore thepotential for ancestral taxa, often treating the concept of ancestors as philosoph-ically inconsistent with a cladistic motive. In studies which do consider ancestors,one commonly applied rule of thumb treats early appearing, autapomorphy-lack-ing sister taxa as ancestors (Smith 1994). However, this protocol has beenchallenged on the grounds that such an assumption ignores the potential forcharacter reversal or loss (Wagner 1996; Polly 1997). Many stratophenetic lineagephylogenies include extensive sets of ancestordescendant relationships basedsolely on expert opinion (e.g., Pearson 1998), while a few phylogenetic methodsexist that allow ancestors to be inferred based on a compromise of stratigraphy and

528 D. W. Bapst

morphology (e.g., Fisher 2008). Ultimately, none of these methods account for thegeneral lack of positive evidence for any particular set of ancestordescendantrelationships. Actually, identifying single ancestors with certainty may beimpossible in practical terms. In a phylogenetic comparative framework, ancestralpopulations can be described in a typical phylogeny structure by placing ancestorson a zero-length branch, effectively attributing zero divergence between a taxonand the lineage that produces later appearing taxa (Bapst 2013a, 2014).

AC

H

F AC

H

F

B B

AC

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A L CL

H L

FL

BF BLT

ime

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(a) (b) (c)

(d) (e)

Fig. 22.3 Attempts to timescale phylogenies of fossil taxa for comparative analyses arechallenged by the production of ZBLs and the choice of times of observation. a The truetimescaled phylogeny of the sampled taxon ranges from Fig. 22.2. b The timescaled tree inferredgiven the cladogram and ranges from Fig. 22.2. Taxon B appears to be part of the polytomycontaining C, E, and H because of an internal zero-length branch. c The basic timescaled treewith all edges constrained to be some minimum branch length, with the resulting extensionsshown as dotted lines. This both reveals the presence unapparent ZBLs and emulates theminimum branch length timescaling method (Laurin 2004). de Using the minimum branchlength timescaled tree from (c), we can contrast the impact on the resulting branch lengths ofusing the first appearance dates or the last appearance dates as times of observation. Dependingon the analyses used, this difference in branch lengths could have an impact on the results of aphylogenetic comparative study (Bapst 2014). In addition, note that these branch lengths differfrom those which would be inferred if the true timescaled tree in (a) was known


Ancestors have implications beyond their mere existence. If either (a) ancestorsare sampled or (b) single morphotaxa have multiple sampled descendants, theexpected maximum resolution decreases for a given cladogram of these taxa. Thisdecrease occurs because either scenario introduces true sets of relationships thatcannot be reduced to binary branching nodes in a cladogram, producing an intrinsicpolytomy (Wagner and Erwin 1995; Bapst 2013b). A lack of intrinsic resolution isparticularly expected in well-sampled groups in the fossil record, and thus, we wouldpredict relatively unresolved topologies to result from analyses of such groups.While this introduces another reason to expect phylogenetic uncertainty in paleon-tological data, the greatest implication is that any polytomy found among a con-sensus tree could indicate complex scenarios of ancestordescendant relationships(such as the apparent polytomy in Fig. 22.2d). This possibility should not be ignoredwhen we encounter polytomies in the course of a comparative analysis.

For our four friends, Brock alone probably would have explicit ancestordescendant relationships from the lineage phylogenies he obtained from thepublished literature. Misty and Lance probably do not have information on impliedancestordescendant relationships, although there may be some lineage phyloge-nies for Mistys brachiopods with such. Erika, who would be lucky to have aphylogeny, will be very unlikely to have any information on ancestordescendantrelationships, although given that her fossil come from a single stratigraphichorizon, perhaps the probability of sampling an ancestor of her extant taxa isnegligible.

22.3 Preparing Datasets from the Fossil Record

As with any scientific analysis, paleontological or otherwise, every worker will haveto consider whether the available data is sufficient and how to account for uncer-tainties in any downstream analysis. If the phylogenetic and stratigraphic infor-mation available is not sufficient for a scientist to be comfortable with applying aphylogenetic comparative analysis, the only alternative is to do primary data col-lection and analysis to get better data, like a better phylogeny. Some groups may justbe too incompletely known at present to draw deeper understanding about traitevolution in those groups, at least from within a phylogenetic context.

If we decide our data are sufficient, we still have to account for the potentialuncertainties listed above: topological relationships, chronostratigraphy, times ofobservation for persistent taxa, branching times of divergence, and ancestral taxa.Ignoring these similar uncertainties by taking the most likely result to each, oreven taking the best solution across all the uncertainties, could be misleading andmask potential uncertainties. For example, under a simple sampling model, themost likely time of extinction for any single taxon is its time of last appearance inthe fossil record even though we simultaneously expect, under the same model,that a large proportion of taxa go extinct after they are last observed (Strauss andSadler 1989).

530 D. W. Bapst

We need to take such uncertainties into account, not ignore them. For eachuncertainty, we can produce multiple likely solutions, and combine them, ulti-mately expressing our uncertainties as a large number of possible timescaledphylogenies (iterations of this idea can be found in Pol and Norell 2006; Boydet al. 2011; Bell and Braddy 2012; Lloyd et al. 2012; Sallan and Friedman 2012;Bapst 2013a). We can then analyze across a sample of phylogenies to understandthe impact of these various unknowns on an analysis. If our analyses require atimescaled tree, using a sample of multiple phylogenies may be the only avenue toaccount for the many forms of uncertainty unique to the fossil record.

22.3.1 Combining Taxonomy and Phylogeny

For Misty and her brachiopods, we postulated that perhaps she was not able to findcomplete phylogenetic information. Perhaps the hypotheses she wishes to test willrequire collecting character data and inferring new phylogenies, composed of thetaxa she is interested in. Yet, there may be a few alternative solutions to considerfirst. If her main issue is that her taxa are spread across a number of overlappingbut conflicting cladistic studies, formal supertree methods have proven to be useful(e.g., Sanderson et al. 1998; Bininda-Emonds et al. 2007; Ruta et al. 2007; Lloydet al. 2008; Bronzati et al. 2012; Brocklehurst et al. 2013; also see Chap. 3 byBininda-Emonds in this volume). However, particularly in invertebrate groupswhere cladistic analyses can be sparse, a different set of issues emerges. MaybeMisty could find trees that cover only a portion of the group she wanted to workon, or the cladograms she found were at the genus level although she aims to testhypotheses at the species level. Phylogenies that include a large majority of taxamay be necessary to avoid results unbiased by taphonomic variation and, yet, suchtrees are difficult to obtain.

One solution might be to consider the gray phylogenetic data: The relation-ships implied by stratophenetic diagrams or ancestordescendant relationships,generally provided in older literature. Similarly, traditional Linnean taxonomyitself is generally assumed to represent a rough first approximation of relation-ships. The quality of such gray phylogenetic data is extremely variable and needsto be considered carefully.

Scientists who want to have more inclusive datasets of relationships withoutbuilding new trees must seek compromises between the trees produced usingexplicit quantitative phylogenetics and the gray phylogenetic literature, includingtaxonomy. Hybrid informal trees that merge phylogenetic and taxonomicinformation are becoming increasingly common, particularly among scientistsstudying patterns of macroevolution and macroecology. Recent examples includethe widely used Phylomatic for plant relationships (Webb and Donoghue 2005),which uses phylogenetic relationships when available but defaults to taxonomic


http://dx.doi.org/10.1007/978-3-662-43550-2_3http://dx.doi.org/10.1007/978-3-662-43550-2_3

relationships when they are not, or the inclusive, global phylogeny of birds whichmerges taxonomic and phylogenetic data (Jetz et al. 2012). Some analyses haveeven used phylogenetic datasets constructed entirely from traditional taxonomichierarchies (e.g., Green et al. 2011).

If Misty can find a reasonable genus-level tree for her group, she might be ableto use taxon lists of genera and species as a way of artificially expanding herphylogenys taxonomic resolution to the species level. Each genus can be replacedwith a polytomy of the species listed for it, reflecting the lack of knowledge forrelationships within that taxon.

The main drawback of using taxonomic data as a replacement for phylogeny isthat the each higher taxon is assumed to be a valid monophyletic clade. Thisassumption will need to be considered carefully by each scientist. The monophylyof the traditional taxonomic hierarchy can vary considerably across different fossilgroups; for example, Bulman (1970) explicitly defined several form-taxa forgraptolites that he considered polyphyletic and many others which were clearlyparaphyletic. If we can reject polyphyly but not paraphyly for a group, we caninclude the possibility of such in our taxonomicphylogenetic summaries bycollapsing the nodes uniting potentially paraphyletic taxa. This effectively pro-duces much larger polytomies, acknowledging the uncertainty in the relationships.However, although this approach is becoming more popular, we badly needanalyses comparing phylogenetic trees and taxonomic hierarchies for the samegroups and testing their relative capability to address common macroevolutionaryquestions.

Ultimately, such taxonomicphylogenetic summaries may become dominatedby unresolved polytomies, reflecting the phylogenetic uncertainty of this approach.Phylogenetic uncertainty is certainly not limited to datasets that make use oftaxonomic information as a replacement for phylogenetic data; for instance, manypaleontological cladograms are also poorly resolved. By randomly resolvingpolytomies in our dataset many times, we can test whether a particular analysisconsistently produces the same result across the potential range of topologies.Unfortunately, some common comparative analyses are positively misled byphylogenetic uncertainty, particularly tests of phylogenetic signal (Davieset al. 2011). Furthermore, such effects may vary from dataset to dataset, dependingon the distribution of phylogenetic uncertainty. Approaches such as parametricbootstrapping on phylogenetic datasets (e.g., Boettiger et al. 2012) may be usefulfor identifying whether phylogenetic uncertainty could be biasing analyticalconclusions.

22.3.2 Timescaling in the Fossil Record

Once we have a cladogram containing the taxa we want (maybe not fullyresolved), we next have to worry about how we will timescale this topology. Thisissue is confounded with the need to consider potential ancestordescendant

532 D. W. Bapst

relationships and the requirement that tip taxa represent populations, if examinedtaxa persist over considerable geologic time.

All timescaling methods are dependent on the quality of the stratigraphic dataavailable. Data on temporal occurrences for taxa should, at least, record whichintervals taxa first and last occur in and the dates for those intervals. If precisedates are available, or dates for individual samples of the fossil taxa, this infor-mation can also be useful, particularly if timescaling involves parameterizedmodels of sampling. Such detailed data may be available in online databases, butoften the intervals in which taxa are found and dates for those intervals are foundin separate references.

Of course, depending on what sort of tree we are using it may already betimescaled, as is the case with Brocks lineage phylogeny of his microfossil group.Such trees are generally produced by hand and not using computational algorithmsor a measure of optimality, but there are now a number of exceptions to thisstatement. Phylogenetic methods which simultaneously infer topology andtimescale the divergences between lineages have increasingly gained popularityover the past two decades. The earliest approaches, sometimes referred to asstratocladistics or strato-likelihood, applied a hybrid of maximum parsimonyphylogenetics with an algorithm that could choose less parsimonious phylogeniesif they exhibited a better fit to the temporal order of taxa in the fossil record(Fisher 1991, 1994, 2008; Wagner 1998; Marcot and Fox 2008). More recently, itwas proposed to infer timescaled phylogenies under both probabilistic models formorphological character change (Lewis 2001) and models of taxonomic samplingin the fossil record, within a likelihood or Bayesian framework (Wagner andMarcot 2010, 2013). A parallel effort has also seen the development of simulta-neous methods which use morphological clocks in a Bayesian framework toproduce tip-dated timescaled phylogenies of fossil and extant taxa (Pyron 2011;Ronquist et al. 2012; as used in Slater 2013; Wood et al. 2013). In the majority ofthese applications, an uninformative tree prior is used that allows divergence datesto be mostly informed by the combined molecular and morphological clocks andthe first appearance dates, instead of a model of sampling in the fossil record(e.g., Ronquist et al. 2012). Probabilistic models of sampling through time(Stadler 2010) have been implemented for use in estimating viral phylogenies,where samples were taken throughout an epidemic, but were recently applied todatasets containing fossil taxa (Alexandrou et al. 2013). As these Bayesianmethods develop further, hopefully adding the ability to assign taxa as ancestrallineages, they will likely become the methods of choice for generating timescaledphylogenies of fossil taxa.

For now, most phylogenetic datasets in paleontology will likely consist ofunscaled cladograms, such as the datasets of Misty and Lance. This means thatthey will need to apply some type of post-inference timescaling algorithm to theircladogram, using their collected stratigraphic data. Most of these methods arefairly ad hoc, with no underlying statistical model. The most basic timescalingmethod proposes that clades are as old as the oldest taxon within that clade(Norell 1992; Smith 1994; example in Fig. 22.3b). Ancestral taxa are sometimes


inferred, but only if such taxa lack autapomorphies and are sampled earlier thantheir sister lineage (e.g., Wickstrom and Donoghue 2005).

The greatest issue with this basic method (as I have termed it; Bapst 2013aand 2014) is the presence of artificial zero-length branches (ZBLs) in theresulting timescaled phylogenies (Hunt and Carrano 2010). These are introducedin two different ways: First, if taxa are known from single occurrences or if the treeis being timescaled so the tip-dates used are the first appearance times, then everysister pair of taxa will produce at least one terminal ZBL, as there is zero timebetween this earliest first appearance and the branching time for each clade (e.g.,the branch leading to taxon B in Fig. 22.3c). Secondly, ZBLs are introduced whenmore derived taxa appear earlier than more primitive taxa in a clade, causinginternal branches to be zero-length (e.g., the branch ending in the clade containingtaxa B, C, E, and H in Fig. 22.3c). Such mismatches between phylogeny andstratigraphy are expected under any scenario of incomplete sampling, but ZBLspose a number of issues. First, they may pose algorithmic difficulties for somecomparative software, but secondly, such ZBLs are generally interpreted bycomparative analyses as implying multiple, simultaneous branching events. Taxaplaced on a zero-length terminal branch are effectively assumed to represent directancestors of their sister lineage (Hunt and Carrano 2010). Several arbitrarytransformations to deal with this issue have been used, such as moving branchingtimes earlier in time, such that all branches have some minimum branch length(Laurin 2004; example in Fig. 22.3c).

One timescaling approach assumes a morphological clock and uses the numberof character state transitions (estimated during phylogeny inference) as a proxy forthe relative length of timescaled branches (e.g., Ruta et al. 2006; Brusatteet al. 2008; Lloyd et al. 2012). A related approach extends branch lengths tomaximize the fit to a model of Brownian motion for some continuous trait, such asbody size (Laurin 2011). Caution is necessary in applying such strict morpho-logical clock approaches, as considerable evidence exists from the fossil record forthe heterogeneity of rates of trait evolution (e.g., Wagner 1995; Bapst et al. 2012;Lloyd et al. 2012). The simultaneous Bayesian methods described above alsomake use of a morphological clock, but allow for the relaxed clock modelsimplemented previously for molecular data, thus allowing for some heterogeneityof rates of character change (Ronquist et al. 2012).

Some paleontological studies have opted to avoid the need for a timescaledphylogeny for analyses of trait evolution (e.g., Friedman 2009; Pyenson andSponberg 2011; Smith 2012; Pittman et al. 2013). While some of these studies usethis merely for convenience or because stratigraphic information is unavailable orincomplete, other studies argue that if all branches are set to unit-length (i.e., 1,or some other nonzero value), than the resulting tree has branch lengths that simplyreflects the evolutionary potential under a speciational model of evolution, asmight be expected under punctuated equilibrium. This assumption is somewhatanalogous to the explanation given for the commonly used kappa transformation(Pagel 1999), which scales the set of branch lengths in a comparative analysisfrom their true length to 1, and is thus considered to reflect a measure of

534 D. W. Bapst

speciational trait change. Unfortunately, the branching nodes of any cladogramcomposed of extant, extinct, or any combination thereof are probably a poorreflection of the speciation events within a group (Pennell et al. 2014). Extantphylogenies are missing the branching events that lead to extinct lineages, andeven phylogenies that include extinct taxa will be missing those unknown lineagesthat we have never sampled, due to the capricious nature of the fossil record.Given that the necessary biological and geological assumptions are not wellsupported, using equal branch lengths should be avoided except in analyses wherethe choice of branch lengths demonstrably has little or no effect on the result.

Very recently, parameterized models of sampling in the fossil record have beenused to provide a context in timescaling cladograms (Bapst 2013a; Wagner andMarcot 2013). A considerable advantage of these model-based post-inferencemethods is that they can consider taxa as potential ancestors based on the calcu-lated probability of sampling ancestors. Unfortunately, these same methods alsorequire a priori estimates, as either rates or probabilities, of sampling (Wagner andMarcot 2013), or extinction and branching in addition (Bapst 2013a). Such esti-mates are often available for marine invertebrate records, such as Mistys bra-chiopods, but not for more poorly sampled fossil records, like Lances theropods.The proposed simultaneous Bayesian methods avoid this issue by simultaneouslysampling these parameters simultaneously from the data during analysis (Wagnerand Marcot 2010).

A major issue of timescaling in the fossil record is that unless a morphologicalclock is assumed, there is not any information that allows us to narrow down whena branching event occurred. Similarly, nothing indicates whether a specific taxon islikely to be an ancestor not. Even with divergence dating informed by a mor-phological clock, uncertainty will be present even if morphology does change at aconstant rate, as there will always be only a finite number of morphologicalcharacters to measure change in. Thus, considerable uncertainty in divergencetimes exists in general, even when probabilistic models of sampling and mor-phology are utilized. As mentioned previously, the majority of such samplingmodels treat the first and last appearance times of specific taxa as the most likelytimes that they also originated and went extinct (Strauss and Sadler 1989). For thepurposes of inferring branching times, a single set of divergence times obtained viamaximum likelihood with such models will be highly misleading. Additionally, itis difficult to utilize the information from confidence intervals on branching timesin typical comparative methods, particularly given that divergence dates for nestednodes on a single phylogeny constrain each other, such that more nested (derived)nodes have to occur later in time relative to ancestral nodes.

Bayesian approaches that simultaneously infer topology and timescale allow forthe direct consideration of uncertainty by returning a large posterior sample oftimescaled phylogenies. A stochastic alternative for post-inference timescalingmethods is to generate many timescaled trees, each one with node ages sampledfrom some probability distribution, with node ages for a single tree sampledsequentially for consistency. Analyses can then be run over the sample oftimescaled phylogenies. Tomiya (2013) constrained a small number of divergence


dates using estimates from molecular clock studies and then sampled (andresampled) ages for the remaining nodes under a uniform probability distribution. Irecently introduced the cal3 method (Bapst 2013a), which stochastically samplesnode ages from the probability density implied by a probabilistic model of sam-pling in the fossil record.

This stochastic approach brings other benefits, beyond just divergence data. Thecal3 method extends this stochastic algorithm and the sampling model to sto-chastically assign ancestral taxa and resolve soft polytomies relative to the amountof stratigraphic misfit expected given the sampling rate (Bapst 2013a). Addition-ally, the uncertainty in fossil appearance dates from discrete intervals can beaccounted for by randomly resampling dates from within those intervals for eachgenerated timescaled phylogeny (e.g., Pol and Norell 2006; Lloyd et al. 2012;Sallan and Friedman 2012). Stochastic timescaling can also account for issuesrelated to the times of observation of morphotaxa. While typical choices have beento use either the first or last appearance times to date terminal tips (Fig. 22.3de),these may not be the most reasonable choices in analyses of trait evolution. Ide-ally, times of observations for such analyses should represent the precise date ofthe specimens used for measuring traits, but this information is often not available.One alternative is to randomly resample dates from within a taxons stratigraphicrange, like the solution for obtaining dates from discrete intervals. Stochastictimescaling provides a framework for considering all these diverse sources ofuncertainty in our analyses.

Bayesian tip-dating or post-inference stochastic methods are ideal approaches totimescaling if the ultimate goal is to apply phylogenetic comparative methods. In arecent study, I compared the performance of the basic method, the minimum branchlength transformation and the stochastic cal3 method in a series of simulationswhere comparative data were generated and then analyzed on trees timescaledusing these different methods (Bapst 2014). The non-stochastic timescalingmethods were allowed a partial stochastic element by randomly resolving polytomiesand resampling dates from within simulated discrete intervals, such that all theanalyses were done on samples of multiple timescaled trees. Although someanalyses were not particularly affected by the differences in these timescalingmethods, this was not true for all comparative analyses. Estimates of the rate of traitevolution and comparisons between models of trait evolution were particularlybiased by artifacts introduced by the basic and minimum branch length timescalingmethods. This suggests that the choice of an appropriate timescaling method maybe just as important for some phylogeny-based analyses as choosing an acceptablecladogram.

Unfortunately, differences in datasets entail differences in methods. At present,only scientists who work on well-sampled fossil records, such as Misty, can utilizethe cal3 method and other methods because of the need for detailed informationabout the sampling intensity of the fossil record. Workers like Lance will have toapply methods that are more arbitrary, while being aware that the methods they usemay be introducing unwanted artifacts, like erroneously inferring very short

536 D. W. Bapst

branches. These timescaling artifacts may propagate errors and biases whenapplying comparative methods (Bapst 2014).

In the online practical material associated with this chapter (located at http://www.mpcm-evolution.org), I demonstrate the timescaling approaches available inthe freely available software package paleotree (Bapst 2012) for the statisticalprogramming language R (R Core Team 2013). These examples use a real datasetof retiolitid graptolite genera, consisting of a consensus tree from Bates et al.(2005) and chronological data from Sadler et al. (2009).

22.3.3 Cheating with the Fossil Record: Not Placing FossilData Directly on Trees

Clearly, obtaining a timescaled tree of fossil taxa is a difficult and sometimes time-consuming process, but is it absolutely necessary to ask the evolutionary questionswe are interested in? For the moment, let us ignore the approaches discussed sofar, which require us to have a timescaled phylogeny of fossil taxa.

Fossil data certainly inform us about past patterns, but it is clear that we maynot always have sufficient information to place extinct taxa with certainty within aphylogeny, neither topologically nor with respect to when a taxon diverged fromother lineages. If we do not feel certain enough about the information at hand, wecannot feel confident to proceed with analyses of evolutionary history. Our friendErikas flower fossils may simply be known to have features indicative of herstudy group and nothing else. However, there are ways of including informationfrom the fossil record without having fossil taxa placed as terminals within a tree.

Slater et al. (2012) introduced a promising approach for studying trait evolutionby treating fossil data as informative priors in a Bayesian framework. In theirmethod, trait values known from the fossil record only needed to be looselyassociated with some clade on a molecular phylogeny of extant taxa. These traitvalues were then used an informative priors on the trait value at a particular node,essentially using a fossil as an estimate of ancestral morphology. The procedurefor setting these priors at nodes is analogous to the application of fossil calibrationsas node age priors in molecular clock analyses. Using this approach in bothsimulations and mammalian fossil data, Slater et al. found that even weaklyconstrained trait data like this could allow for considerably more power in dis-tinguishing patterns of trait evolution. However, simulations by Slater et al. foundthat the fossil-prior approach had less power than including a fossil as an actual tip,suggesting that it is still preferable to include fossil taxa as tip taxa if possible.

Approaches, such as this informative prior framework, can avoid placing fossiltaxa on a timescaled tree and shall probably become valuable for groups which arediverse in the modern but have poor fossil records. I would recommend thisapproach for scientists like Erika, who only have some fossils loosely known torepresent early representatives of a group much better known from the modern.


http://www.mpcm-evolution.orghttp://www.mpcm-evolution.org

However, just like setting fossil calibrations, there has to be reasonable evidencethat a fossil specimen represents an early member of a particular extant clade.Hence, the Slater et al. method does not allow us to consider information fromfossils that represent distantly related and/or extinct clades.

22.4 Conclusion

Using the fossil record in comparative analyses is a pursuit that requires consid-erable care, both with respect to issues common to most of the fossil record, butalso for the aspects specific to each group of fossil taxa. The issues of the fossilrecord cannot be solved without pluralism, as no single workflow can satisfy everyscientist seeking to utilize paleontological data. The four individuals we followedhad the same motivation but different types of data available, requiring them toadopt very different procedures just to reach the stage where they could applyphylogenetic comparative methods. In the long term, methods will hopefully bedeveloped that can deal with more of the differences among different paleonto-logical datasets, particularly Bayesian phylogenetic inference with a tip-datingapproach. Eventually, development of methods might provide a single frameworkfor paleontological comparative analyses, but until that point, forcing a singlemethodology across datasets will restrict our ability to utilize the advantages of thefossil record.

Acknowledgments Id like to thank D. Wright and P. Smits for their comments on an early draftof this manuscript. Suggestion from two anonymous reviewers and the editor greatly improvedthis chapter. Many of the ideas came from conversations with G. Lloyd, G. Slater, L. Soul, A.Wright, N. Matzke, J. Mitchell, K. Larson, M. Pennell, and E. King.

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