Integrating Incomplete Fossils by Isolating Conflicting Signal in Saturated and Non-Independent...

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Syst. Biol. 0(0):1–19, 2014© The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved.For Permissions, please email: [email protected]:10.1093/sysbio/syu022

Integrating Incomplete Fossils by Isolating Conflicting Signal in Saturated andNon-Independent Morphological Characters

LILIANA M. DÁVALOS1,2,∗, PAÚL M. VELAZCO3, OMAR M. WARSI1, PETER D. SMITS4, and NANCY B. SIMMONS3

1Department of Ecology and Evolution, Stony Brook University, 650 Life Sciences Building Stony Brook, NY 11794, USA, 2Consortium forInter-Disciplinary Environmental Research, School of Marine and Atmospheric Sciences, Stony Brook University, 129 Dana Hall, Stony Brook, NY 11794,USA, 3Division of Vertebrate Zoology (Mammalogy), American Museum of Natural History, NY 10024, USA, and 4Committee on Evolutionary Biology,

University of Chicago, 1025 E. 57th Street, Culver Hall 402, Chicago, IL 60637, USA∗Correspondence to be sent to: Department of Ecology and Evolution, Stony Brook University, 650 Life Sciences Building, Stony Brook, NY 11794, USA;

E-mail: [email protected].

Received 20 March 2013; reviews returned 28 May 2013; accepted 18 March 2014Associate Editor: Mark Fishbein

Abstract.—Morphological characters are indispensable in phylogenetic analyses for understanding the pattern, process, andtempo of evolution. If characters are independent and free of systematic errors, then combining as many different kinds ofcharacters as are available will result in the best-supported phylogenetic hypotheses. But since morphological charactersare subject to natural selection for function and arise from the expression of developmental pathways, they may not beindependent, a situation that may amplify any underlying homoplasy. Here, we use new dental and multi-locus geneticdata from bats (Mammalia: Chiroptera) to quantify saturation and similarity in morphological characters and introduce twolikelihood-based approaches to identify strongly conflicting characters and integrate morphological and molecular data.We implement these methods to analyze the phylogeny of incomplete Miocene fossils in the radiation of Phyllostomidae(New World Leaf-nosed Bats), perhaps the most ecologically diverse family of living mammals. Morphological charactersproduced trees incongruent with molecular phylogenies, were saturated, and showed rates of change higher than mostmolecular substitution rates. Dental characters encoded variation similar to that in other morphological characters, whilemolecular characters encoded highly dissimilar variation in comparison. Saturation and high rates of change indicaterandomization of phylogenetic signal in the morphological data, and extensive similarity suggests characters are non-independent and errors are amplified. To integrate the morphological data into tree building while accounting forhomoplasy, we used statistical molecular scaffolds and combined phylogenetic analyses excluding a small subset of stronglyconflicting dental characters. The phylogenies revealed the Miocene nectar-feeding †Palynephyllum nests within the crownnectar-feeding South American subfamily Lonchophyllinae, while the Miocene genus †Notonycteris is sister to the extantcarnivorous Vampyrum. These relationships imply new calibration points for timing of radiation of the ecologically diversePhyllostomidae. [Chiroptera; conflict; dentition; morphology; Phyllostomidae; saturation; scaffold; systematic error.]

Fossil remains provide crucial data for understandingthe pattern, process, and tempo of evolution (Wienset al. 2010; Ronquist et al. 2012a), and for this reasonphylogenetic analyses of morphological characters areindispensable. Combining data from as many charactersystems as are available can also provide the mostpowerful inference of phylogeny, and the strongest testof evolutionary hypotheses (de Queiroz and Gatesy2007; Hermsen and Hendricks 2008). This rationale forintegrating morphological data through combinationcrucially depends on both the absence of systematic errorand independence among characters (de Queiroz andGatesy 2007; Springer et al. 2007). However, differentcharacter systems may have different properties ofphylogenetic signal (e.g., arising from very differentrates of evolution or the influence of natural selection).Integrating these systems in phylogenetic analyses thenrequires accounting for these differences (Wake 1991;Kangas et al. 2004; Eick et al. 2005; Springer et al. 2007).

When analyses fail to accurately model evolutionaryprocesses (e.g., by assuming neutral evolution whena character is under selection), additional data maymislead through bias. This is called systematic error(Phillips et al. 2004). Systematic errors have been foundwhen constraints on character states in nucleotides andmultiple substitutions at the same site produce endstates that are random with respect to shared history

(Simmons and Freudenstein 2003), when mutationalbiases are incorrectly modeled (Gruber et al. 2007), ifnatural selection for a specific function has resultedin adaptive convergence (Li et al. 2008; Liu et al.2010), and when correlated evolution at nearby sitesviolates the assumption of independence implicit inmost phylogenetic analyses (Davis et al. 2009). Ceilingson the number of character states, misleading adaptiveconvergence, and character dependency have all beenobserved in morphological characters (Wagner 2000;Wiens et al. 2003; Kangas et al. 2004; Dávalos et al. 2012).Further, evolutionary adaptation is expected to shapemorphological characters to a greater extent than manynucleotide substitutions because all morphologicalcharacters are phenotypic (Wake 1991; Springer et al.2007; 2008). Nonetheless, the potential for systematicerror to mislead morphological phylogenies andundermine data combination has not been as thoroughlyexplored as it has with molecular data. Crucially,comparisons among loci and models that account forbiased data make systematic error more tractable withmolecular data (Muse and Gaut 1994; Gruber et al. 2007;Blanquart and Lartillot 2008; Davies et al. 2012).

Molecular scaffolds in which trees derived frommolecular sequence analyses are used to constrainsearches for optimal morphological trees weredeveloped as a way to integrate data under the

1

Systematic Biology Advance Access published May 9, 2014

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2 SYSTEMATIC BIOLOGY

assumption that morphological characters may betoo homoplasious to be directly analyzed (Springeret al. 2001; 2007). Morphologists and paleontologistshave criticized scaffolds because they fail to conveyphylogenetic uncertainty arising from both themolecular and morphological data, and assign muchgreater weight to molecular sequences a priori (Manoset al. 2007; Hermsen and Hendricks 2008). By notanalyzing all characters on equal footing, scaffoldsoverlook potential support in molecular data sets forclades strongly supported by morphology (de Queirozand Gatesy 2007).

As models to analyze morphological charactershave been proposed, developed, and extended toinclude relaxed-clock analyses (Lewis 2001; Pyron 2011;Ronquist et al. 2012a), the question of how to integratemorphological characters in phylogenetic analyses hasbecome more pressing. Beyond providing constraintsfor dating divergence and understanding the tempo andmode of trait evolution, morphological characters canalso contribute to understanding the evolution of extanttaxa (Wiens 2004). In some cases, including fossil taxain combined analyses may actually change perceivedrelationships among extant lineages (Manos et al. 2007;Hermsen and Hendricks 2008). Uncovering errors inmorphological data and improving methods to integrateand analyze morphological characters are therefore ofgreat importance to fully inferring and analyzing theTree of Life (Wiens 2009).

In this study, we introduce and analyze multi-locusDNA sequences and a set of >270 dental characters forthe mammalian family Phyllostomidae with the goalof investigating the phylogenetic signal and featuresof morphological data. Phyllostomid bats are perhapsthe single most ecologically diverse mammalian family,including specialized lineages that feed exclusivelyon nectar, blood, hard or soft fruits, in addition totaxa that are carnivores and/or gleaning insectivores(Rojas et al. 2011; Santana et al. 2011; Dumont et al.2012). Fossil phyllostomids are known from the middleMiocene of northern South America (Czaplewski et al.2003b) and extensive Pleistocene deposits in Centraland South America, and the Caribbean (Czaplewskiet al. 2003a; Fracasso and Salles 2005; Dávalos andRussell 2012; Dávalos and Turvey 2012). The outstandingdiversity of phyllostomids makes the evolution oftheir taxonomic diversity, feeding specializations, andbiogeography of interest to systematists, evolutionaryecologists, and functional morphologists (Rojas et al.2011, 2012; Dumont et al. 2012; Santana et al. 2012). Thephylogenetic placement of Miocene fossils, in particular,is critical to understanding the tempo of phyllostomiddiversification. To date, only one analysis includingone (of three) Miocene fossil lineages has ever beenconducted, and it encompassed only 7 of 58 extant genera(Czaplewski et al. 2003b). Successfully integratingmorphological characters into our analyses is thereforecrucial to understanding the evolution of phyllostomids.

Given their outstanding ecological diversity in a singleunarguably monophyletic group, phyllostomids are

uniquely suited for testing for signals of systematic errorin morphological characters driven by functional anddevelopmental constraints (Springer et al. 2007). Sincethese constraints affect multiple traits simultaneously(Wiens et al. 2003; Kangas et al. 2004; Carrier et al.2005), combinations of character states will end upbeing similar to one another and the observed signalwill amplify any underlying errors. The occurrenceof identical character states that do not arise bycommon descent but by other processes is homoplasy.One form of homoplasy involves rates of change sohigh that the observed distribution of states cannotbe distinguished from a random assignment, andthis can be diagnosed by examining saturation incharacter state changes. Specifically, in this studywe aimed to: (1) measure saturation and similarityin morphological characters, as well as phylogeneticconflict with molecular trees; (2) find ways tolocate phylogenetic conflicts among morphologicalcharacters (if substantial homoplasy is detected); and (3)extend methods to integrate morphological data withmolecular sequence data to account for systematic errorsuncovered.

MATERIALS AND METHODS

Taxonomic SamplingPhyllostomidae (Mammalia: Chiroptera:

Noctilionoidea) is a group of Neotropical batswhose monophyly is strongly supported by bothmorphological and molecular synapomorphies(Simmons 1998; Baker et al. 2000, 2003; Wettereret al. 2000). Phyllostomid bat species were traditionallyclassified into subfamilies based on dietary habitsand associated morphologies (Wetterer et al. 2000).We sampled densely among lineages characterized byinsectivory and nectarivory because Miocene fossils(†Palynephyllum, †Notonycteris) have been assigned togroups sharing these dietary specializations based onsimilarity to extant taxa and, for †Notonycteris, analysesof 48 dental characters in seven genera (Czaplewski et al.2003b). Two subfamilies formerly recognized based onmorphological characters and later shown by moleculardata to be paraphyletic were sampled. The subfamilyGlossophaginae sensu Wetterer et al. (2000) comprisedprimarily nectarivorous lineages whose morphologicalsynapomorphies are associated with feeding ecology,implying ecological convergence (Dávalos et al. 2012).This group has subsequently been divided into twomonophyletic groups, Glossophaginae sensu stricto andLonchophyllinae (Baker et al. 2003). No similar patternsof support from ecologically driven character changeshave been found for the subfamily Phyllostominaesensu Wetterer et al. (2000), another group shown tobe paraphyletic with molecular data (Baker et al. 2003;Datzmann et al. 2010). Lineages of phyllostominessensu Wetterer et al. (2000) retain the primitive feedingecology of insectivory (Rojas et al. 2011).

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Sampling densely among primarily nectarivorousand insectivorous lineages enabled inclusion of allthe potential relatives of four fossil species. Three ofthese taxa are extremely incomplete Miocene fossiltaxa—†Notonycteris magdalenensis, †N. sucharadeus, and†Palynephyllum antimaster—known only from isolatedteeth, partial mandibles, and partial postcranial bones.We also included the better-preserved Quaternary†Phyllonycteris major (Choate and Birney 1968).Six additional noctilionoid species in the familiesMormoopidae, Noctilionidae, and Thyropteridae wereincluded as outgroups. Thyroptera tricolor (Chiroptera:Thyropteridae) was used to root trees. The total numberof genera sampled was 50, with both morphologicaland molecular data for 85 extant species. An additional28 species were represented only by morphologicaldata including fossils and extant species and subspeciesknown only from the type of material and lacking tissuevouchers at this time.

Morphological DataThe new morphological data generated to place

the fossils comprised representatives of all currentlyrecognized phyllostomid subfamilies, including 107species and subspecies in 46 phyllostomid genera.We identified 278 dental characters of potentialphylogenetic significance based on comparisonsamong ingroup taxa. Characters were generatedusing reductive character coding sensu Wilkinson(1995). We used combinations of two character statesto indicate taxonomic polymorphism in compositeterminal taxa (species or subspecies). Of the 278characters scored, 112 were treated as ordered inphylogenetic analyses. These multistate charactersdescribe apparently progressive gradations in size,shape, or relative position, and were coded as orderedto preserve hypotheses of homology of states andtransformations. We assembled the morphologicalmatrix using MorphoBank (www.morphobank.org), apublic online database for assembling and managingmorphological matrices (O’Leary and Kaufman 2012).Each cell in the matrix is documented with a labeledimage that can be zoomed into or downloaded to betterobserve the structure in question. The morphologicalmatrix is available at MorphoBank as project P891.

OverviewOur analyses aimed to find the best way to investigate

the data structure of morphological data and integratemorphological characters with DNA sequences. Figure 1summarizes these analyses of morphological characterstructure and data integration and serves as a generalguide to the methods implemented here.

Measuring saturation in morphological character statechanges.—A maximum parsimony (MP) phylogenetic

approach and curve fitting were used to assess theextent of saturation in changes in morphologicalcharacter states using the state: step relationship (Fig. 1,upper right; Wagner 2000). Saturation indicates onekind of homoplasy, as character states end up beingidentical without common descent (Dávalos et al.2012). To quantify saturation, we first inferred amorphological phylogeny using the Unix version ofPAUP* v. 4b10 (Swofford 2002) in MP heuristic searchesincluding ordered characters and 1000 replicates ofrandom sequence addition, followed by tree–bisection–reconnection (TBR). All maximally parsimonious treeswere retained.

The goal of the state: step plot is to test thehypothesis that new character states accumulatecontinuously throughout evolutionary history.Character optimization and ordering could affectthis relationship: accelerated transformation (acctran)would place changes earlier in the evolutionary historyof the clade, and ordered steps would lengthen thetree compared with unordered steps. These choiceswould skew results in the direction of rejecting the nullhypothesis of continuous accumulation of characterstates. For this reason, the resulting trees were used tooptimize morphological character state changes usingthe delayed transformation option (deltran) and allunordered characters. Each step of the MP phylogeniesbeginning with the first transformation starting fromthe root was then matched to its beginning and endcharacter state. We could then determine whether theend character state had been previously observed or not.The age of each node was determined by the relativebranching of the MP morphological phylogenies, andwhen identical relative ages were found, using the datesof divergence estimated by Dumont et al. (2012).

The resulting table includes the step in the phylogeny,the cumulative number of character states observed,and whether or not that particular step resultedin a new state or not (Supplementary Table S1,http://dx.doi.org/10.5061/dryad.pg4c0). The steps ofthe phylogeny were divided into two sets: thosethat result in new states and those that do not. Ifnew character states were distributed throughout thesequence of steps in the MP tree, then there shouldbe no significant difference in the distributions of stepnumbers from both groups. That is, if new characterstates are equally probable throughout evolutionaryhistory, then steps that result in new states shouldnot be concentrated at low or high step numbers, andboth kinds of steps would have similar step ranks. Wetested this null hypothesis using a Wilcoxon signed-rank test (Wilcoxon 1945) as implemented in R v.2.15.1(R Development Core Team 2012). The inflection pointbeyond which the accumulation of new character statesslows down relative to the steps of the phylogenywas identified by fitting a segmented linear modelusing the segmented R package v.0.2-9.4 (Muggeo 2008).We compared predictions from two hypotheses ofnon-linear accumulation of character states: a finite-state models in which there is an upper boundary

www.morphobank.org

http://dx.doi.org/10.5061/dryad.pg4c0



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Analyses

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FIGURE 1. Graphic summary of analyses performed to estimate character state exhaustion in morphological characters, quantify rates ofevolution in molecular and morphological partitions, and integrate molecular and morphological data. The workflow begins with molecularand morphological data matrices, and subsequent analyses: (1) examine saturation and independence in the morphological data (upper right),(2) examine character independence (upper center), (3) build a phylogenetic constraint from molecular sequences and produce a constrainedintegrated phylogeny or statistical scaffold (left half), (4) generate unconstrained combined phylogenies (lower left), (5) identify nodes thatconflict between constrained and unconstrained phylogenies (middle center), and (6) use conflicting nodes and simulations to identify outliermorphological characters supporting conflicting nodes and exclude these from an alternative set of combined phylogenies (middle and lowerright).

on the total number of character states, and theordered-state model in which each state has a finitenumber of descendent states (Wagner 2000). Non-linearrelationships describing the finite-state and ordered-state hypotheses were fitted to the data using theequations of Dávalos et al. (2012) and the nls commandin R (Fig. 1, upper right). The fit of the non-linear modelsto the data was compared using the Akaike InformationCriterion (AIC), with a lower AIC indicating better fit ofthe curve to the data (Burnham and Anderson 2002).

Quantifying similarity between characters.—Selectionfor function under similar ecological pressureshas been proposed as the mechanism producingadaptive convergence in character states (Wienset al. 2003; Springer et al. 2007). Correlationsbetween morphological character states resultingfrom development are also expected to generatehomoplasy by limiting both character states andcombinations of character states (Wake 1991). Both ofthese mechanisms are known to operate on mammaliandentition (Kangas et al. 2004; Evans et al. 2007) andwill result in non-independence of character states.To explore the independence of character states, wecalculated the Gower pairwise dissimilarity among

all morphological and molecular characters (Gower1966). The Gower dissimilarity has been adopted incommunity ecology and genomics, transcriptomics,and proteomics to explore correlations in variablescomprising continuous measures, categories (e.g.,different base pairs), binaries, and ordinals (Legendreand Gallagher 2001; Marengo et al. 2003; Peterson 2003;Alheit et al. 2011). It ranges from 0 to 1, with 0 indicatingcomplete identity and 1 complete dissimilarity inpatterns found in pairs of variables.

The coding of states using similar symbols fromone morphological character to the next does notimply an equivalence of states between characters.To overcome this challenge posed by morphologicaldata, we recoded each character as a binary matrix ofsimilarity and difference between species. For example,if a morphological character had states 0012 for thefirst four species, the comparison between the first twospecies would yield a 0; between species 2 and 3, a 1;and between species 3 and 4, a 1. This transformationmade all recoded characters directly comparable to oneanother, and Gower dissimilarities were calculated fromthese recoded characters.

To generate dissimilarity distributions comparablebetween morphological and molecular data, invariable



base pairs were excluded from calculations. Allambiguous characters of both kinds of data wereconverted to empty cells, and nucleotides were coded ascategorical variables. The daisy command in the clusterR package v.1.14.3 (Maechler et al. 2012) was then usedto calculate the Gower dissimilarities (Fig. 1, uppercenter). This implementation ignored missing valuesin calculating the dissimilarities for a given row ofcharacters. The frequency distributions of dissimilaritieswere then compared by plotting histograms andby estimating and visualizing the relative frequencydistribution using the R package reldist v.1.6-1 (Handcockand Morris 1999; Handcock 2013).

Molecular DataSmall pieces of tissue (∼2–10 mg) were used for DNA

extraction using the QIAmp DNA Micro Kit (QiagenN.V. Catalogue no. 56304). To maximize DNA recovery,we followed the manufacturer’s tissue protocol with anextra step of using carrier RNA after lysis. All DNA waseluted in molecular-grade water. DNA yields from tissueextractions ranged from 20–100 ng/�l.

We collected new partial sequences from seven nuclearloci: two introns, thy or thyrotropin beta chain, stat5aor signal transducer and activator of 5A; one 3′-untranslated region (UTR), plcb4 or phospholipase Cbeta 4; three autosomal exons, bdnf or brain-derivedneurotrophic factor, ttn6 or titin 6, rag2 or recombination-activating protein 2; and the X-chromosome exonatp7a or ATPase-7A. New complete sequences weregenerated for the mitochondrial locus cytb or cytochromeb. To maximize sampling of nectarivorous lineages,published sequences from the mitochondrial genes cox1or cytochrome oxidase I, cytb, and the ribosomal RNAs12S, tRNAval and 16S were also included (Baker et al.2003; Clare et al. 2007; 2011). All sequences analyzed inthis study are shown in Table 1.

We used primers designed by Eick et al. (2005) toamplify and sequence introns, those by Teeling et al.(2005) for the 3′-UTR, those by Murphy and O’Brien(2007) for the nuclear exons; and the combination ofprimers by Jansa et al. (1999) to amplify the cytb gene.To increase efficiency in amplification with noctilionoidtaxa, we redesigned the bdnf and ttn6 primers, andused newly designed internal primers for sequencingthe ttn6 locus. All primers used in this study areshown in Supplementary Table S2. Touchdown PCRamplifications were used for all genes, except thy,for which a single annealing temperature was used(Supplementary Tables S3 and S4). All amplificationswere carried out using the Taq PCR Core Kit (QiagenN.V. Catalogue no. 201223) with the reagent mixshown in Supplementary Table S5. Fragments werecycle- sequenced in both directions using external andsometimes internal primers (Supplementary Table S2).Automated Applied Biosystems 3730 sequencers wereused to analyze the sequenced fragments. Contigs werealigned using the assembly tool in Geneious v.5.3.6

(Drummond et al. 2011). Quality scores and visualinspection of chromatograms were used to identifyheterozygous positions.

Alignment.—We used the Perl script transAlign v1.2(Bininda-Emonds 2005) to align all nuclear exons andprotein-coding mitochondrial genes. The multiplesequence alignment method multiple alignmentthrough fast Fourier transform (MAFFT) v6.710bapplying the einsi protocol was used to align non-codingnuclear sequences (Katoh et al. 2005; Katoh and Toh2008). An alignment of mitochondrial ribosomal RNA(mtrDNA) sequences based on the secondary structureof the genes was obtained directly from Dávalos et al.(2012). After trimming ends to reduce missing data, theconcatenated alignment comprised 9584 base pairs.

Selection of partition scheme and optimal models ofDNA evolution.—The molecular data were dividedinto genomic and functional partitions. The maximumnumber of partitions (nine data blocks) comprisedthree codon positions each for mitochondrial andnuclear protein-coding genes, mtrDNA loops and stems,and introns together with one 3′ UTR. The optimalpartitioning scheme and model of DNA evolution forindividual partitions were selected using the BayesianInformation Criterion (BIC) in the PartitionFinderpython script v.1.0.0 (Lanfear et al. 2012). Briefly,PartitionFinder takes the predefined partitions in thealignment and compares combinations of data blocksand models of evolution for those data blocks usingML phylogenies as implemented in PhyML (Guindonet al. 2010). The approximate gamma distributionaccommodates sites with very low rates of change (Yang1994). To avoid possible dependencies between gammaand the proportion of invariable sites (Gu et al. 1995), therange of models of sequence evolution was restricted toeither parameter, but not both. The greedy algorithm,which uses the BIC to guide the search for optimalpartitioning schemes, was used to reduce computationaltime. Results from PartitionFinder comprised both theoptimal partitioning scheme and the best-fit models ofevolution for its individual partitions, and these wereapplied in subsequent ML and Bayesian phylogeneticanalyses. Downstream analyses included tree topologyand branch length searches. Partitioned alignments andresulting trees were deposited in TreeBASE under ID13970.

Phylogenetic AnalysesThe central goal of our phylogenetic analyses was to

integrate morphological and molecular data (Fig. 1). Twoapproaches were used: (1) statistical molecular scaffoldsand (2) combined analyses. Previous analyses have useda phylogeny derived from molecular data as a backboneconstraint in MP searches using morphological data—a molecular scaffold (Springer et al. 2001; Manoset al. 2007). Besides a priori assigning greater weight



to molecular sequences (Hermsen and Hendricks2008), this approach has two disadvantages. First, itdoes not account for the phylogenetic uncertaintyassociated with the summary molecular phylogeny, asa single majority-rule consensus tree resulting from MPbootstrap analyses is generally used (Springer et al.2001; Manos et al. 2007). Second, measures of branchsupport for the morphological data are superimposedon the molecular scaffold phylogeny through bootstrapsor matrix representation parsimony, again losing thephylogenetic uncertainty associated with both types ofdata. We extended the scaffolding approach to overcomeboth of these issues by generating statistical scaffolds.With our approach morphological characters mayinfluence the phylogeny of taxa represented in molecularposterior phylogenies if these are in poorly supportednodes, but not if nodes are strongly supported.

Statistical molecular scaffolds.—A combination of Bayesianand ML approaches were used to obtain statisticalmolecular scaffolds (Fig. 1, left half). First, a posteriordistribution of molecular phylogenies was obtainedthrough Metropolis-coupled Markov chain MonteCarlo Bayesian analyses applying the best partitioningscheme and optimal models of sequence evolution foreach partition using MrBayes v.3.2.0 (Ronquist et al.2012b). Most MrBayes runs were completed in theCIPRES Science Gateway (Miller et al. 2010). Nucleotidefrequencies, shape of the discrete approximation ofthe gamma distribution of rates across the alignment,transition/transversion rate ratios, and rate matrixparameters were all unlinked between partitions; and therate of evolution was allowed to vary between partitions(prset ratepr = variable). Four independent searchesran for 10 million generations with a starting treederived from maximum likelihood (ML) analyses. Thisstarting tree was obtained through a ML search usingthe Randomized Axelerated ML (RAxML) algorithmv.7.2.7 (Stamatakis 2006). The preliminary analysiswas conducted on a maximally partitioned alignment(nine data blocks) and implemented the general timereversible (GTR) model of nucleotide evolution witha discrete approximation of a gamma distribution toaccount for variation in rates between sites for all datablocks (Tavaré 1986; Yang 1994).

To improve chain mixing, the temperature of theheated chains was lowered from the MrBayes default of0.02 to 0.01. Burn-in of Bayesian searches was assessedby examining log-likelihood and tree length estimatesusing Tracer v.1.5 (Rambaut and Drummond 2007).Convergence within searches was assessed through thestandard deviations of split frequencies, and betweensearches by comparing posterior estimates of log-likelihood, tree lengths, and relative rate parameters.Standard deviations of split frequencies <0.01 wereinterpreted as indicative of within-search convergence.Parameters were assessed to have converged betweenruns if their median posterior distributions differed byno more than 1 standard deviation from one another.

Estimates of autocorrelation time between posteriorsamples were used to subsample posterior phylogeniesto obtain an uncorrelated posterior sample of 1000phylogenies.

Second, each phylogeny in the uncorrelated posteriorsample of Bayesian molecular phylogenies was usedas a backbone constraint in ML searches in RAxMLusing the Markov k-state variable model with a gammadistribution of rate variation among characters toanalyze the morphological data (Mkv + gamma) (Fig. 1;Lewis 2001). Branch lengths and model parameters forthe combined data were optimized on each resultingtree using a partition file specifying different ratematrices and approximate gamma distribution foreach molecular partition, and the Mkv+gamma modelfor the morphological model (i.e., f e, -t each_tree,-q file_with_partitions, -s combined_data, and -K MKcommands). The resulting trees were then summarizedinto a maximum clade credibility tree with medianbranch lengths using TreeAnnotator v.1.6.1 (Rambautand Drummond 2010).

Unconstrained combined data analyses.—Theunconstrained combined morphological and moleculardata set were analyzed using ML and Bayesianapproaches. To obtain a ML estimate of the combinedphylogeny, we used RAxML with a partition file, and theMkv+gamma model to analyze the morphological data.The rapid bootstrap algorithm of RAxML was followedby thorough ML searches (i.e., f a, -q file_with_partitions,-s combined_data, and -K MK commands) (Stamatakiset al. 2008). One hundred bootstrap pseudoreplicateswere used to estimate branch support.

Bayesian estimates of phylogeny were obtained byrunning four independent analyses for 10 milliongenerations with the optimal molecular partitionscheme, the best-fit models of sequence evolution, andunlinked parameters for partitions (Fig. 1, lower leftand center). The Mkv+gamma model of evolution withsymmetric rates of change between character states(default, or Prset symdirihyperpr = fixed(infinity)) was usedto analyze the morphological partition. Burn-in andconvergence were assessed as with the molecular dataset (see the “Statistical molecular scaffolds” section). Thetemperature of the heated chains was lowered to 0.001to improve chain mixing.

Analyses of morphological data.—To make our analysescomparable to the majority of published morphologicalphylogenies (e.g., Wetterer et al. 2000; Dávalos et al. 2012),we analyzed the morphological data using MP, ML,and Bayesian methods. MP heuristic searches used 20replicates of random sequence addition followed by TBRin PAUP*. ML optimizations applied the Mkv+gammamodel in 100 searches using the rapid bootstrapalgorithm followed by a thorough search for the optimaltree. Three independent Bayesian analyses ran for 40million generations with the Mkv+gamma model ofcharacter evolution and a prior on the distribution of


2014 DÁVALOS—SATURATED AND NON-INDEPENDENT MORPHOLOGICAL DATA 7T

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branch lengths that was shorter than the MrBayes default(prset Brlenspr = Unconstrained:Exp(100)).

Comparisons between PhylogeniesWe carried out simulations and implemented a

likelihood approach to determine which morphologicalcharacters significantly supported incorrect nodes(Fig. 1, middle and lower right). Three nodes recoveredin combined data analyses, deemed incorrect basedon the molecular statistical scaffold and previouslypublished molecular and morphological phylogenies(Simmons 1996; Baker et al. 2003; Datzmann et al. 2010;Dávalos et al. 2012), were used to develop this method.First, we calculated the per-character likelihoods of themorphological data given the scaffold tree, and giventhe same tree modified only to contain the incorrectnode (Fig. 1, middle right). This was accomplished byusing the computation of per-site log likelihoods andthe Mkv+gamma model in RAxML (i.e., using the -f g, -z file_with_trees, -s morphological_data, and -K MKcommands) (Stamatakis 2006). Then, null distributionsof differences between the fit of characters to the treewith and without the incorrect node were generated foreach of the 278 morphological characters. Morphologicalcharacters were simulated using the rTraitDisc functionin the ape R package v.3.0-5 (Paradis et al. 2004).Simulating discrete characters requires a phylogenywith branch lengths and a transition matrix of ratesof change between character states (Pagel 1994). Thestructure of each transition matrix assigned both thenumber of character states and whether or not thecharacter was ordered. To facilitate simulation, transitionmatrices consisted only of 0/1 values. This meantthat the branch lengths of each phylogeny used assimulation input had to be multiplied by the rate ofcharacter evolution so that the rate of change in thecorresponding transition matrix equaled 1. The posteriordistributions of Bayesian morphological phylogeniesand evolutionary rate parameter (m in MrBayes) wereused to generate these scaled phylogenies. Finally, theper-character log-likelihoods of simulated data sets wereobtained using RAxML and the resulting distributionsof differences in site likelihoods were used to determinewhich morphological characters were outliers in theirsupport for particular nodes (Fig. 1, lower right).

RESULTS

Characteristics of the Morphological DataThe state: step plot showed the accumulation of

character states slowed down as steps accumulated inthe phylogeny (Fig. 2). The inflection point of the curvewas estimated at 878 steps (standard error = 2.625).Steps in which a new character state was first observedoccurred earlier in the phylogeny and were ranked lowerthan other steps (W =203,034, P-value = 6.587E-05). The

0

200

400

600

0 1000 2000

No. of steps in MP phylogeny

No.

of s

tate

s ob

serv

ed

Character change

Finite states model

Observed

Ordered states model

FIGURE 2. State: step plot for morphological data optimized usingMP. Lines fitted using the finite-states (or rarefaction) and ordered-states (or hypervolume) models are shown for all points beyond thelinear accumulation of character states.

ordered-character relationship provided a substantiallybetter fit to the data (AIC = 11,081) than the finite-staterelationship (AIC = 18,662). Curves predicted using thefitted models are shown in Figure 2.

The frequency distributions of pairwise dissimilaritiesfrom molecular and morphological data were markedlydifferent (Fig. 3A). Relative distribution analysesrevealed the greatest difference between morphologicaland molecular distributions consisted of an excess ofvery low dissimilarities, and lack of dissimilaritiesabove 0.2 among morphological characters (Fig. 3B).Dental characters encoded variation similar to thatin other morphological characters, whereas molecularcharacters encoded highly dissimilar variation incomparison. Phylogenetic analyses of the dental dataresulted in weakly supported trees that were markedlydifferent from previous morphological and molecularphylogenies (Supplementary Fig. S1). Optimal treesrecovered using each approach were used in subsequentsite-likelihood comparisons.

Phylogenetic AnalysesThe optimal partition found using PartitionFinder had

seven partitions: each of the three codon positions for themitochondrial data, mt rRNA loops, mt rRNA stems,introns and the 3′ UTR, and protein-coding nuclear



0

Fre

quen

cy (

perc

ent)

Morphological

0

5.2

10.4

15.6

Molecular

2.1

4.2

6.3

8.5

Dissimilarity between characters

Rel

ativ

e de

nsity

bet

wee

n m

orph

olog

ical

cha

ract

ers

0.0 0.2 0.4 0.6 0.8 1.0

0

2

4

6

8

10

A

B

FIGURE 3. Frequency distributions and relative frequencydistribution of Gower dissimilarities from molecular andmorphological characters. (A) Frequency distributions of dissimilarityof morphological (upper) and molecular (lower) characters. (B)Frequency distribution of dissimilarities between morphologicalcharacters relative to dissimilarities between molecular characters.Solid bars show the observed relative density, and the solid black lineshows the smoothed density of morphological character dissimilaritiesrelative to the dissimilarity among variable molecular characters.The dashed black line indicates the expected relative density if thefrequency distributions of dissimilarities from morphological andmolecular data were not significantly different. Significant differencesbetween frequency distributions occur in intervals above and belowthe dashed line.

sequences. The optimal model of sequence evolutionfor nuclear protein-coding genes, mitochondrial secondand third codon positions, and mt rRNA loops wasthe GTR+gamma (Tavaré 1986; Yang 1994). For intronsand the 3′ UTR, mitochondrial first positions and mtrRNA stems, the optimal model was the symmetrical(SYM)+gamma (Zharkikh 1994).

After discarding a burn-in of 1 million generations,Bayesian analyses of the molecular data using optimalpartitions and models yielded a resolved and generallywell-supported phylogeny (Supplementary Fig. S2).Examination of the posterior distribution of log-likelihoods and tree lengths showed that theseparameters were autocorrelated up to every 7000generations. The posterior distribution of trees wassampled every seven trees to obtain an uncorrelatedposterior for subsequent analyses. A random sample of1000 trees was used to constrain ML searches to generatethe statistical scaffold. The resulting phylogeny reflectedthe phylogenetic uncertainty and support from both theBayesian molecular posterior and the morphological MLsearches (Fig. 4A). The placement of incomplete Miocenefossils received high support, corresponding to 99–100%of sampled trees.

Convergence in combined Bayesian analyses wasachieved after 1 million generations, and the summaryof multiple independent runs resulted in a well-resolvedand weakly supported phylogeny (Fig. 4B). The relativelevels of support were similar to those of the combinedML phylogeny (cf. Fig. 5A), with two importantdifferences in resolution. The combined ML phylogeny,statistical scaffold, and morphological phylogeniesplaced the monotypic extant genus Neonycteris (knownonly from morphology) in a clade with Trinycteris (and,often, Glyphonycteris Figs. 4A and 5A and SupplementaryFig. S1). The combined ML phylogeny and statisticalscaffold placed †Palynephyllum in a clade with Platalinaand Xeronycteris (Figs. 4A and 5A). In contrast,the combined Bayesian phylogeny placed Neonycteriswithin Micronycteris with a posterior probability of0.91, and †Palynephyllum in a clade of all nectar-feeding phyllostomids (Fig. 4B; Bayesian posteriorprobability = 0.91). The phylogenetic position of thetwo †Notonycteris species was unchanged in combinedphylogenies relative to the statistical scaffold andwas also supported in morphological phylogenies. MLanalyses of the combined data resulted in a phylogenysimilar to Bayesian analyses, crucially differing inplacing Lonchorhina as sister to Phyllostominae (Fig. 5A).

Comparisons between PhylogeniesThree nodes highlighted in Figures 4B and 5A

were identified as incorrect based on the statisticalscaffold: (1) the node uniting all nectar-feedingphyllostomids (MRCA of Lonchophylla and Glossophagain Fig. 4B), (2) Lonchorhina as sister to the subfamilyPhyllostominae, and (3) Neonycteris nested withinMicronycteris. The first two nodes have also been rejectedin previous phyllostomid phylogenies generated usingdata included in our analyses (Baker et al. 2003; Dávaloset al. 2012), and a combination of overlapping and non-overlapping loci (Datzmann et al. 2010). No previouscombined analysis has included Neonycteris, which isknown only from the type series (Simmons 2005).Molecular data are not available for this monotypic



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genus. Both previous morphological analyses and MLanalyses of combined data (Fig. 5A) suggest thatNeonycteris is more closely related to Trinycteris andGlyphonycteris than to Micronycteris (Simmons 1996).

The log-likelihood of character node support wascompared with the null distribution of per-characterdifference in log-likelihood. The distribution of supportor rejection for incorrect nodes from individualdental characters is shown in Figure 6. The threenodes received different levels of support from themorphological data, with overwhelmingly more outlierssupporting than rejecting the monophyly of nectar-feeding phyllostomids (10:1), fewer outliers supportingthan rejecting Lonchorhina as sister to the Phyllostominae,and 10 outliers supporting and five rejecting theinclusion of Neonycteris in Micronycteris (13:14; Fig. 5).

Thirty-two significant positive outliers (onecharacter overlapped in support for two nodes)were then excluded from the combined data matrix(Supplementary Table S6). The resulting phylogenywas generally weakly supported, especially within thesubfamily Phyllostominae and between Lonchophyllaand its relatives (Fig. 5B). However, support valuesacross nodes from analyses of the trimmed data setwere often higher than those from analyses including allthe data (Fig. 5B). The position of †Palynephyllum wasequivalent to that found in the statistical scaffold (i.e.,within the crown Lonchophyllinae sensu Baker et al.2003) and †Notonycteris remained sister to Vampyrum(Fig. 5B).

DISCUSSION

Integrating Morphological and Molecular DataWe propose new methods to integrate morphological

and molecular characters that more rigorously addressconflict caused by morphological homoplasy. Ina previous contribution (Dávalos et al. 2012), weexamined saturation in both morphological andmolecular character states and applied likelihoodapproaches to identify strongly conflicting molecularcharacters. Here, we focus on diagnosing homoplasy inmorphological data, and implementing new methodsto isolate significantly conflicting signal and integratemorphological and molecular characters. These newmethods were applied to an empirical data set toplace the Miocene fossils in the phylogeny of NewWorld leaf-nosed bats and understand the evolutionaryhistory of the family. Instead of either automaticallyconstraining the signal from morphological data toconform to a single molecular scaffold (Springer et al.2001), or unconditionally combining the data (Hermsenand Hendricks 2008), we aimed to uncover evidenceof homoplasy in morphological data, and identifyand exclude characters involved in conflicts arisingfrom homoplasy (Wiens et al. 2003; Dávalos et al.2012). To accomplish these objectives, we exploredthe patterns of variation in morphological data,extended molecular scaffolds to account for variation

Dental characters ●

●

MandibularMaxillary

● CanineIncisors

MolarsPremolars

● Significant

B

A

−4

−2

0

2

Sup

port

for

Neo

nyct

eris

in M

icro

nyct

eris

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2

Sup

port

for

nect

ar−

feed

ing

clad

e

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

Sup

port

for

Lonc

horh

ina

sits

ter

to P

hyllo

stom

inae

−2

0

2

●●●

●

●

●●●●●

●

●

●●●●●●●

●●

●

●

●●

●

●

●

●

●

0 100 200

Character

4

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●●

●

●●

●

●

●

●

●

FIGURE 6. Observed differences in morphological per-characterlog-likelihood between trees containing the clades and the statisticalscaffold tree. Values above the 0 line indicate support, below indicaterejection. Large symbols indicate statistically significant outliers. (A)Morphological support or rejection for a clade of nectar-feedingphyllostomids. (B) Morphological support or rejection for a cladeof Lonchorhina and Phyllostominae. (C) Morphological support orrejection for Neonycteris nesting within Micronycteris.



in phylogenies estimated from DNA sequences, andintroduced a site-likelihood approach to identifycharacters supporting particular nodes. These methodsrespond to a major challenge in phylogenetics: howto integrate morphological and molecular data andestimate relationships between fossil and extant taxa(Manos et al. 2007; Wiens 2009).

Molecular scaffolds and combined data analysesreflect two divergent views on integratingmorphological data in phylogenetic analyses. Scaffoldswere originally introduced to overcome two analyticalchallenges in optimizing morphological and moleculardata: the use of MP and the lack of molecular data forextinct taxa (Springer et al. 2001). Since then, models ofmorphological evolution have been proposed, ML andBayesian methods to analyze combined data have beenimplemented (Lewis 2001; Ronquist and Huelsenbeck2003), and the impact of missing data on phylogenies hasbeen found to be small (Wiens and Moen 2008; Smithet al. 2009; Wiens 2009; Wiens and Tiu 2012). Instead,biological drivers of homoplasy, including ecologicalconvergence and correlations among character statesarising through development (Kangas et al. 2004; Evanset al. 2007; Dávalos et al. 2012), have emerged as themain reasons to use scaffolds (Springer et al. 2007; 2008).

The alternative to scaffolds is combining andsimultaneously analyzing morphological and moleculardata on equal footing (Hermsen and Hendricks 2008).The most compelling argument for using all availabledata (or the total evidence) is that all phylogeneticallyinformative characters should be used in estimatingphylogenies. In the absence of systematic error andif each character provides independent evidence forrelationships, combining all the data will then providethe most powerful inference of phylogeny (de Queirozand Gatesy 2007). The profusion of genomic data hasrevealed that systematic error from mutational bias canbe common (Phillips et al. 2004; Baurain et al. 2007;Dávalos and Perkins 2008), and natural selection cangenerate significant support for incorrect relationships(Castoe et al. 2009; Li et al. 2010; Liu et al. 2010; Davieset al. 2012). Constraints imposed by the genetic code,molecular interactions, selection, and developmentalprocesses imply that many molecular and morphologicalcharacters are not independent (Emerson and Hastings1998; O’Keefe and Wagner 2001; Carrier et al. 2005; Skellyet al. 2013). Critically, models that account for varioussources of systematic error with molecular sequencesare available and can be deployed when saturation ormutational biases are suspected (Muse and Gaut 1994;Schöniger and Von Haeseler 1994; Gruber et al. 2007;Blanquart and Lartillot 2008). Comparisons between locimake the detection of convergence shaped by adaptationpossible with genetic data (Castoe et al. 2009; Li et al.2010; Liu et al. 2010; Dávalos et al. 2012; Davies et al.2012).

Models of morphological evolution have been slowerto develop, as states across different morphologicalcharacters are fundamentally incomparable, an obstaclethat is largely absent for molecular characters. The

most widely applied model (Mkv), for example,is an extension of the Jukes-Cantor model firstpublished in 1969 (Jukes and Cantor 1969). Under thesecircumstances, methods that analyze the accumulationof homoplasy and correlated evolution in morphologicalcharacters can be helpful in deciding whether systematicerror is present, or if the assumption of independence iswarranted (Emerson and Hastings 1998; Wagner 2000;O’Keefe and Wagner 2001). Detecting and correctingfor the consequences of ecological convergence drivenby morphological adaptation is more difficult becauseit requires comparisons to independent phylogenies(Wiens et al. 2003; Holland et al. 2010). Either way,investigating the data structure and phylogenetic signalof morphological characters can help illuminate thebiological processes that shape phenotypic variation(Dávalos et al. 2012).

As in previous analyses of the phylogeny of extantmammals (Springer et al. 2007; 2008; Lee and Camens2009), morphological characters rejected relationshipsestimated with multiple loci (Supplementary Fig. S1).Based on their results, Springer et al. (2007) concludedthat homology and homoplasy were confounded inmorphological characters, with homoplasy prevailing,and cautioned the need to reexamine both charactercoding and models of morphological evolution.Hermsen and Hendricks (2008) and Asher et al.(2008) have nonetheless argued for combining databecause the evidence of morphological homoplasy arosethrough incongruence with molecular phylogenies, andincongruence may also be observed among differentgenes. The data structure of our new morphologicalcharacters, however, revealed the randomization ofphylogenetic signal and the potential for amplificationof homoplasy before any comparisons with molecularphylogenies were undertaken.

Just as the randomization of phylogenetic signal can beassessed in molecular data by examining the saturationof changes through time, the plateauing of new characterstates relative to changes in the phylogeny or characterexhaustion indicates homoplasy in morphological data(Wagner 2000). The pattern of character state changesfound in the dental character data in phyllostomidsindicates saturation (Fig. 2). Another indication ofsaturation in molecular data is a high rate of changeinferred using model-based methods. We comparedthe relative rates of change estimated for each of thepartitions in Bayesian combined analyses (mi parametersin MrBayes output) to compare the morphologicalrate of change to molecular substitution rates. With amedian rate of 2.9 substitutions/character, the relativerate of change for the morphological partition wasthe second highest observed. Only mitochondrial thirdcodon positions had higher rates of change (median = 7.2substitutions/site), and morphological rates of changewere consistently at least one order of magnitude higherthan all nuclear partitions and most mitochondrial data(Table 2). Both saturation and high rates of changeindicate homoplasy is present in the morphologicaldata.



TABLE 2. Posterior estimates of relative rates of change fromnon-clock combined Bayesian analysis of combined data, ranked fromfastest to slowest

Partition Median 95% HPD lower 95% HPD upper

Mt pos. 3 7.244 6.710 7.801Morphology 2.889 2.553 3.240Mt rRNA loops 1.875 1.718 2.033Mt pos. 1 0.478 0.429 0.533Mt rRNA stems 0.364 0.327 0.403Introns 0.263 0.241 0.285Mt pos. 2 0.128 0.101 0.160Nuclear 0.118 0.108 0.128

Notes: Rates are in substitutions/site for molecular data andchanges/character for morphology. HPD = high probability density,mt = mitochondrial, pos. = position, rRNA = ribosomal RNA.

High rates of change alone cannot explain howsaturation in morphological changes affected thecombined phylogeny. If changes from one character stateto another were random relative to common descent,and independent from one another, then resulting treesshould be unresolved or weakly supported. Stronglysupported conflicts must arise through additionalprocesses besides high rates of change. One of thenodes uncovered in combined analyses and identifiedas conflicting with prior hypotheses was stronglysupported (Bayesian posterior probability >0.90 inFig. 4B and ML bootstrap >75% in Fig. 5A). Evolutionaryprocesses that superimposed on saturated changes resultin strong homoplasious signal have been extensivelystudied in molecular data (Phillips et al. 2004). Insome lineages, for example, mutational biases towardAT at 4-fold degenerate positions produce significantlyconflicting signal at saturated sites (Gruber et al. 2007;Dávalos and Perkins 2008). This process generatescharacter states that are both homoplastic and correlated,as all sites throughout the genome experience similarmutational bias and end up having similar characterstates.

One consequence of proposed functional anddevelopmental constraints on the phenotype is thenon-independence of morphological character states.As expected if functional and developmental processesoperated simultaneously on multiple aspects ofmorphology, dental characters were much more similarto one another than molecular characters (Fig. 3).These analyses were not meant to infer particularassociations between character states. There arehypothesis-testing approaches to infer such associations(Huelsenbeck et al. 2003; Pagel and Meade 2006),but these were not designed to conduct all possiblepairwise comparisons (Bollback 2006). The dissimilarityanalyses do suggest pervasive non-independencein morphological characters and, together with thesaturation of character state changes, have implicationsfor integrating morphology in phylogenetic analyses.

The morphological characters employed in this studywere saturated and recurrently encoded similar patternsof variation, indicating direct unweighted analyses of

these data would be inappropriate. The frequencydistribution of morphological dissimilarities suggeststhat new models of evolution will need to account fornon-independence between morphological characters.For molecular data, such models (e.g., doublet and codonmodels) are computationally intensive and knownbiochemical properties of nucleotides and proteins helpdefine and estimate parameters of correlated evolution(Muse and Gaut 1994; Schöniger and Von Haeseler1994). The processes that generate correlations amongmorphological traits are understood best for mammalianteeth and angiosperm leaves, and these might be thebest targets for developing and testing more complexmodels (Kangas et al. 2004; Geeta et al. 2012). Finally,the assumption of independence among charactersthat underlies the key argument for total evidence isviolated to a greater extent with morphological thanwith molecular data. These findings warrant closeexamination of the morphological characters to betteraccount for their combination of phylogenetic andhomoplasious saturated signal.

We introduced a new method to identify stronglyconflicting morphological characters and reduce theimpact of homoplasy on combined phylogeneticanalyses. Unfortunately, the characters supporting thosenodes could not have been identified a priori. Forexample, nectar feeding in phyllostomids is associatedwith modifications to the incisors (Carstens et al. 2002),but none of the characters supporting the nectar-feeding clade involved the incisors (Fig. 6A). In aprecedent for these results, Carstens et al. (2002) foundno significant conflict between the phylogenetic signalsfrom incisor, hyoid, and tongue characters involvedin nectarivory and signal from other morphologicalcharacters. Upper and lower dentition characters, mostlyfrom the premolars and molars, supported the other twonodes. Despite the focus on only three nodes, excludinga subset of characters a posteriori reduced the number ofstrongly supported conflicts across the entire phylogeny(Supplementary Table S6, cf. Fig. 4A and 5).

Instead of providing the most powerful phylogenetichypothesis, our results suggest that combined analysesmay reflect homoplasy in the morphological data.Given the saturation, high rates of evolution, non-independence, conflicting signal, and inadequacy ofcurrent models of evolution for inferring phylogeny fromdental characters, the statistical scaffold summarizes thephylogenies that best capture phylogenetic signal in thedata (Fig. 4A).

Phylogeny of Miocene PhyllostomidaeWe undertook the first family-wide analyses of

Miocene fossil phylogeny. The integrative phylogeniesanalyzed here confirmed the close relationship betweenthe two †Notonycteris species and the carnivorousgenera Vampyrum and Chrotopterus (Czaplewski et al.2003b) (Figs. 4 and 5). Since its original description,†Palynephyllum has been hypothesized to be a nectar-feeding lineage based on its morphology (Czaplewski



et al. 2003b). That hypothesis was supported in allphylogenetic analyses (Figs. 4 and 5). The resolutionvaried between †Palynephyllum being nested within aspurious nectar-feeding clade in combined Bayesiananalyses (Fig. 4B), or nesting among the lonchophyllinesPlatalina, Xeronycteris, and Lionycteris (Figs. 4A and5). The first resolution is equivalent to the datingconstraint imposed in several previous dating analysesfor phyllostomids (Baker et al. 2010; Dávalos 2010; Rojaset al. 2011; Dumont et al. 2012), while the second isunprecedented and may change ingroup divergencedates. The difficulties with integrating morphologicalcharacters in phylogenetic analyses and fragmentarynature of the †Palynephyllum remains suggest cautionin concluding that this fossil is a crown lonchophylline.An independent line of evidence—biogeography—supports the latter conclusion.

Biogeography of †Palynephyllum.—In glossophagines,known diversification events among genera areexclusively restricted to Mesoamerica and the WestIndies (Dávalos 2010; Rojas et al. 2012). In contrast, bothgeneric and species-level diversity of lonchophyllinesis concentrated in South America (Gregorin andDitchfield 2005; Mantilla-Meluk 2007). In the Miocene,the La Venta site was isolated from Mesoamerica andinterconnected to South America (Hoorn et al. 1995).A phylogenetic placement of the fossil †Palynephyllumas a stem glossophagine or a stem plant-visitingphyllostomid would imply that, outside the genusAnoura, glossophagines either failed to diversify on theSouth Americas continent or did diversify and thenwent extinct. These hypotheses are both plausible, butrequire additional explanations. †Palynephyllum tracesthe evolutionary history of the lonchophyllines to SouthAmerica, where all known diversification events in thatclade have unfolded and is thus consistent with boththe paleogeography of the region and the biogeographyof diversification in nectar-feeding phyllostomids.

CONCLUSIONS

By investigating the rates of evolution and quantifyingthe similarity between dental characters scored inphyllostomid bats and their relatives, we demonstratethat these morphological data show saturation andnon-independence. We introduce two approaches tocompensate for these model violations, one extendingmolecular scaffolds to posterior series of trees in alikelihood framework, and the other identifying stronglyconflicting characters for subsequent character analyses.Applying these new methods to analyses integratingmolecular and morphological data, we confirm closerelationships among the extant carnivorous generaChrotopterous and Vampyrum, and †Notonycteris; andsupport the novel conclusion that †Palynephyllum is amember of the nectarivorus crown Lonchophyllinae.Both fossils trace the history of their clades to the middleMiocene of northern South America. By quantifyinghomoplasious signal and similarities, and identifying

strongly conflicting characters, the approaches wedeveloped here are one step toward future methodsto model the evolution of saturated and correlatedmorphological characters.

SUPPLEMENTARY MATERIAL

Data available from the Dryad Digital Repository:http://dx.doi.org/10.5061/dryad.pg4c0.

FUNDING

The National Science Foundation (DEB-0949759 toL.M.D. and DEB-0949859 to N.B.S.).

ACKNOWLEDGMENTS

We thank Noa Krawczyk, James Proffitt, Dani Saks,and Susan Tsang for collection of morphological data,and the NSF Research Experience for Undergraduates(REU) program at the AMNH for supporting Saks,Krawczyk, and Proffitt. This work would have beenimpossible without the work of collectors and theinfrastructure provided by natural history collections.For access to morphological specimens we thank thestaff at the Mammalogy Department of the AmericanMuseum of Natural History (AMNH); the Field Museumof Natural History (FMNH); the Florida Museum ofNatural History (FLMNH); the Instituto de CienciasNaturales (ICN. Bogotá, Colombia); the InstitutoNacional de Investigaciones Geológico-Mineras (IGM,Bogota, Colombia); the Museo de Historia Natural,Universidad Nacional Mayor de San Marcos (MUSM,Lima, Peru); the Museum of Vertebrate Zoology at theUniversity of California, Berkeley (MVZ); the RoyalOntario Museum (ROM); the Natural Science ResearchLaboratory at Texas Tech University (TTU); Bill Clemensat the University of California Museum of Paleontology(UCMP); and Kris Helgen at the Smithsonian Institution(USNM). For tissue loans, we thank the staff at theAmbrose Monell Cryo-Collection at AMNH, and BrucePatterson and John D. Phelps at the FMNH. For greatideas and editing for Figure 1, we thank Romain Dahan,Samantha DelSerra, Jacob Roday, Danny Rojas, Laurel R.Yohe, and Xiaoyu Zhang. We thank Nicole M. Gerardofor finding a way to render morphological characterstates comparable. Part of this work was performedon the Abel Cluster, owned by the University of Osloand the Norwegian metacenter for High PerformanceComputing (NOTUR), and operated by the ResearchComputing Services group at USIT, the University ofOslo IT department.

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