The Relative Importance of Body Size and Paleoclimatic Change as Explanatory Variables Influencing...

Syst. Biol. 57(1):116–130, 2008Copyright c© Society of Systematic BiologistsISSN: 1063-5157 print / 1076-836X onlineDOI: 10.1080/10635150801902193

The Relative Importance of Body Size and Paleoclimatic Change as Explanatory VariablesInfluencing Lineage Diversification Rate: An Evolutionary Analysis of Bullhead Catfishes

(Siluriformes: Ictaluridae)

MICHAEL HARDMAN AND LOTTA M. HARDMAN

Laboratory of Molecular Systematics, Finnish Forest Research Institute, Jokiniemenkuja 1, 01301, Vantaa, Finland; E-mail: [email protected] (M.H.)

Abstract.— We applied Bayesian phylogenetics, divergence time estimation, diversification pattern analysis, and parsi-mony-based methods of ancestral state reconstruction to a combination of nucleotide sequences, maximum body sizes,fossils, and paleoclimate data to explore the influence of an extrinsic (climate change) and an intrinsic (maximum body size)factor on diversification rates in a North American clade of catfishes (Ictaluridae). We found diversification rate to have beensignificantly variable over time, with significant (or nearly significant) rate increases in the early history of Noturus. Thoughthe latter coincided closely with a period of dramatic climate change at the Eocene-Oligocene boundary, we did not detectevidence for a general association between climate change and diversification rate during the entire history of Ictaluridae.Within Ictaluridae, small body size was found to be a near significant predictor of species richness. Morphological stasisof several species appears to be a consequence of a homoplastic increase in body size. We estimated the maximum stan-dard length of the ictalurid ancestor to be approximately 50 cm, comparable to Eocene ictalurids (Astephus) and similar tomodern sizes of Ameiurus and their Asian sister-taxon Cranoglanis. During the late Paleocene and early Eocene, the ictaluridancestor diversified into the lineages represented by the modern epigean genera. The majority of modern species originatedin the Oligocene and Miocene, most likely according to a peripheral isolates model of speciation. We discuss the difficul-ties of detecting macroevolutionary patterns within a lineage history and encourage the scrutiny of the terminal Eoceneclimatic event as a direct promoter of diversification. [Clade assymmetry; Eocene extinction; historical biogeography; NorthAmerican ichthyofauna; relaxed clock; speciation; Cenozoic freshwater fishes.]

Evolutionary biology aims to understand the factorsthat influence diversity and its distribution throughspace and time. Much of this understanding is soughtthrough the identification of extrinsic factors in the en-vironment, intrinsic factors of and among organisms,and the interaction of these factors through time (e.g.,Futuyma, 1998; Brock, 2000). As might be expected, theinfluence and interaction of extrinsic and intrinsic fac-tors varies among lineages and is not easily determinedwithout a comprehensive evolutionary analysis (Harveyand Pagel, 1991).

Although pervasive extrinsic factors such as cli-mate change and continental drift affect biotic systemsbroadly, component lineages can respond differentlyto a common stimulus (Prothero and Berggren, 1992;Bradshaw and Holzapfel, 2006). Intrinsic factors suchas body size and thermal tolerance have been the fo-cus of explanations for general trends observed in thespatial and temporal distribution of vertebrate pheno-types (Allen, 1877; Bergmann, 1847; Cope, 1887; Gloger,1833). Recent studies using phylogenetically correctedtests describe both general (Knouft and Page, 2003) andlineage-specific (Finarelli and Flynn, 2006) evolutionaryresponses among North American freshwater fishes andcaniform mammals, respectively. Of the intrinsic fac-tors, maximum body size is believed to be the mostimportant (Peters, 1983; Schmidt-Nielsen, 1984). How-ever, the relative importance of intrinsic and extrinsicfactors as drivers of diversification is poorly exploredother than for some recent studies of insect-plant inter-actions (Forest et al., 2007) and flowering plants (Mooreand Donoghue, 2007).

The North American Ichthyofauna and BullheadCatfishes (Ictaluridae)

The North American freshwater ichthyofauna is amodel system for the study of patterns and processesof diversification (Patterson, 1981; Wiley and Mayden,1985; Mayden, 1987, 1988). The fauna contains con-temporary representatives of early actinopterygian lin-eages (e.g., Acipenser, Polyodon, Amia, and Lepisosteus) aswell as descendants of all the freshwater teleost clades(osteoglossomorphs, elopomorphs, clupeomorphs, os-tariophysans, protacanthopterygians, paracanthoptery-gians and acanthopterygians; Lee et al., 1980; Page andBurr, 1991). Of the endemic lineages, bullhead catfishes(Ictaluridae) are the most species rich.

A recent review of catfish nomenclature consideredthere to be seven ictalurid genera containing 64 extantspecies and a single genus containing 14 species rep-resented by their fossilized remains (Ferraris, 2007).Egge and Simons (2006) described Noturus maydeni onthe basis of its geographic separation and distinctivemitochondrial DNA, bringing the total of the mostspecies-rich genus in the family to 29 binominals andwith at least one species in the Carolinas awaitingdescription (Page and Burr, 1991; Hardman, 2004).According to Ferraris (2007), Ameiurus is representedby seven extant and eight fossil species from Oligoceneto Pleistocene deposits in western North America, andIctalurus contains nine extant and four fossil species.However, J. G. Lundberg (personal communication)considers there to be an additional one to four Mexicanspecies awaiting description, I. ochoterenai to be asynonym of I. dugesi, and I. meridionalis to be a distinct

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species rather than a synonym of I. furcatus. Pylodictisolivaris is the sole representative of the genus and knownfrom Miocene fossils. No fossil material is known of thecave-dwelling Prietella (two species), Satan eurystomusor Trogloglanis pattersoni. Fossil ictalurids are knownonly from North America, though the family has a sistertaxon found today in southern China and Vietnam (Cra-noglanididae: Diogo, 2004; Peng et al. 2005; Hardman,2005; Sullivan et al., 2006).

Other than the claims made by Matsuo et al. (2001),ictalurid monophyly is undisputed (Taylor, 1969; Lund-berg, 1975, 1985, 1992; Mo, 1991; De Pinna, 1998; Diogo,2004; Hardman, 2004, 2005; Sullivan et al., 2006). Recentmolecular phylogenetic studies concerning or includingrepresentatives of the family (Hardman and Page, 2003;Hardman, 2004; Wilcox et al., 2004; Near and Hardman,2006; Sullivan et al., 2006; Egge and Simons, 2006) havecollectively compiled a nearly complete taxon samplefor mitochondrial gene cytochrome b (cytb) and the sec-ond subunit of the nuclear recombination activating gene(rag2). At the time of this study, sequences of both cytband rag2 were unavailable for the southwestern speciesof Ictalurus (I. australis, I. balsanus, I. dugesi, I. meridionalis,I. mexicanus, and I. pricei) and the four cave-dwelling taxa.

Lundberg (1975, 1992) reviewed the fossil record ofIctaluridae. Though lacking the skull roof synapomor-phies of the extant genera, fragments of an undeterminedspecies of Astephus from late Paleocene-Eocene depositsof Wyoming provide a minimum age for the family ofapproximately 58 Ma. The extinct Ameiurus pectinatus,from Florissant lake deposits (Eocene-Oligocene bound-ary) in Colorado, provides a minimum age for Ameiurusof approximately 35 Ma. The oldest fossils of Ictalurus areEocene-Oligocene and from the Cypress Hills Formationin Saskatchewan. The extant Ictalurus punctatus and Py-lodictis olivaris are known to have existed at least as farback as the middle Miocene, as fossils identified as thesespecies have been found in beds of South Dakota andNebraska, respectively. Pleistocene Noturus are knownfrom South Dakota.

Given the nearly complete taxon sample of ictaluridsfor mitochondrial and nuclear sequence data (Hardmanand Page, 2003; Hardman, 2004; Wilcox et al., 2004;Near and Hardman, 2006; Sullivan et al., 2006; Egge andSimons, 2006), ample information concerning their bi-ology, distribution, and systematics (Taylor, 1969; Lee,et al., 1980; Page and Burr, 1991) and representation inthe fossil record (Lundberg, 1975, 1992), the family is anattractive candidate for an analysis of the possible rolesplayed by an extrinsic factor (climate change) and anintrinsic factor (maximum body size) on their diversifi-cation using parametric methods.

Evolutionary Analysis

Methodological developments in ancestral state re-construction (Harvey et al., 1994; Nee et al., 1994;Cunningham et al., 1998; Oakley and Cunningham, 2002;Oakley, 2003; Pagel et al., 2004), divergence time esti-mation (Drummond et al., 2006; Sanderson, 1998, 2002;

Thorne et al., 1998; Cutler, 2000; Kishino et al., 2001;Thorne and Kishino, 2002, 2005; Yang and Yoder, 2003),and diversification pattern analysis (Chan and Moore,2002; Harvey et al., 1994; Moore et al., 2004; Moore andChan, 2005; Moore and Donoghue, 2007; Nee et al., 1994,1996; Paradis, 1997, 1998, 2004) offer a toolbox with whichto obtain statistically robust estimates of ancestral valuesand their errors. These parametric approaches integratethe uncertainty associated with estimates of phylogeny,branch lengths, fossil ages, and the temporal fluctuationin rates of speciation, extinction, nucleotide substitution,and morphological change to reconstruct evolutionaryhistory and test hypotheses in a statistical framework.

By accommodating rate heterogeneity and fossil or pa-leogeographic calibration points in their maximum like-lihood (ML) or Bayesian inferences, recent studies havebeen able to test and develop scenarios of diversifica-tion for brachyceran flies (Wiegmann et al., 2003), extinctmoas (Baker et al., 2005), Neotropical placental mam-mals (Delsuc et al., 2004), modern bird orders in the lateCretaceous (Pereira and Baker 2006; Slack et al., 2006),and insect-plant interactions (Forest et al., 2007), as wellas to determine the influence of dispersal and pheno-typic innovations on diversification rates in floweringplants (Moore and Donoghue, 2007) and to explore bi-otic connections shown by catfishes found today in riversof southern Mexico and tropical Africa (Lundberg et al.,2007). To this list, we add Bullhead catfishes as the fo-cus of an examination of the respective roles played byCenozoic climate change and maximum body size ondiversification rate.

Briefly, the objectives of this study were to employprobabilistic methods of phylogenetic analysis, diver-gence time estimation, and ancestral state reconstructionto examine the possible relationships between changesin diversification rate, ancestral maximum body length,and paleoclimate during the evolutionary history ofmodern ictalurids. To meet these objectives, we used mi-tochondrial and nuclear protein coding sequences, sev-eral fossil calibration points, estimates of maximum bodysize from the literature, and a Cenozoic paleoclimate re-construction based on oxygen isotope data from deep-seacores (Zachos et al., 2001).

MATERIALS AND METHODS

Taxon Sampling

We assembled cytb and rag2 sequences (Table 1) de-posited at the National Center for Biotechnology Infor-mation by Hardman and Page (2003), Hardman (2004),Waldbieser et al. (2003), Wilcox et al. (2004), Peng et al.(2005), and Sullivan et al. (2006), aligned them withClustal X (Thompson et al., 1997), and checked for unex-pected stop codons in MacClade (v.4.0; Maddison andMaddison, 2000) using appropriate translation codes.The data set includes the monotypic Pylodictis olivaris,all species of Ameiurus, all extant species of Noturus ex-cept N. crypticus, N. gladiator, and N. stanauli, and threeof nine Ictalurus species. Following the results of Diogo(2004), Hardman (2005), Peng et al. (2005), and Sullivan


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TABLE 1. Species, GenBank accession numbers, and source of se-quence data. aPeng et al. (2002); bSullivan et al. (2006); cHardman andPage (2003); dWaldbieser et al. (2003); eHardman (2004); fWilcox et al.(2004).

Species cytb rag2

Cranoglanis bouderius AF416879a DQ492401b

Ameiurus brunneus AY184260c AY184251c

Ameiurus catus AY184267c AY184249c

Ameiurus melas AY184263c AY184252c

Ameiurus natalis AY184255c AY184248c

Ameiurus nebulosus AY184257c AY184250c

Ameiurus platycephalus AY184259c AY184247c

Ameiurus serracanthus AY184256c AY184246c

Ictalurus furcatus AF484159d AY327075e

Ictalurus lupus AY327267e AY327076e

Ictalurus punctatus AY184253c AY184245c

Noturus albater AY327268e AY327077e

Noturus baileyi AY327272e AY327079e

Noturus elegans AY327274e AY327080e

Noturus eleutherus AY327278e AY327082e

Noturus exilis AY327280e AY327083e

Noturus fasciatus AY327276e AY327081e

Noturus flavater AY327283e AY327084e

Noturus flavipinnis AY327284e AY327085e

Noturus flavus AY327288e AY327086e

Noturus funebris AY327291e AY327087e

Noturus furiosus AY327292e AY327088e

Noturus gilberti AY327294e AY327089e

Noturus gyrinus AY327295e AY327090e

Noturus hildebrandi AY327298e AY327091e

Noturus insignis AY327301e AY327092e

Noturus lachneri AY327304e AY327093e

Noturus leptacanthus AY327305e AY327094e

Noturus maydeni AY327271e AY327078e

Noturus miurus AY327306e AY327095e

Noturus munitus AY327309e AY327096e

Noturus nocturnus AY327311e AY327097e

Noturus phaeus AY327315e AY327098e

Noturus placidus AY327317e AY327099e

Noturus stigmosus AY327319e AY327100e

Noturus taylori AY327321e AY327101e

Noturus sp.(broadtail) AY32732e AY327102e

Pylodictis olivaris AY458887f AY327103e

et al. (2006), we used Cranoglanis bouderius (Cranoglani-didae) as a proximal outgroup. The sequence alignmentis available at http://www.systematicbiology.org.

Phylogeny Estimation

The posterior probability distribution of tree topolo-gies was estimated with the Metropolis-coupled Markovchain Monte Carlo (MC3) algorithm implemented in Mr-Bayes (v3.1.2; Huelsenbeck and Ronquist, 2001; Ronquistand Huelsenbeck, 2003). Following the recommenda-tions of Shapiro et al. (2006), codon models were appliedto four partitions in the data: (1) first and second positionsof cytb, (2) third positions of cytb, (3) first and second po-sitions of rag2, and (4) third positions of rag2. ModelTest(v. 3.7; Posada and Crandall, 1998) identified the optimalmodel for each partition according to differences in theAkaike information criterion using parameter estimatesand likelihood scores calculated by PAUP* (v.4.0b10;Swofford, 2001; Table 2). Given the available settings inMrBayes, a GTR substitution model was specified whenModelTest selected submodels of three or more substitu-

TABLE 2. Optimal models identified according to differences inAkaike information criteria (�AIC) by ModelTest, their descriptions,and closest application in MrBayes as settings permit.

Data Optimal Base Substitution MC3

partition model frequencies classes model

cytb 1st and 2nd TVM+I+� Unequal 4Tv, 1Ti GTR+I+�

cytb 3rd GTR+I+� Unequal 4Tv, 2Ti GTR+I+�

rag2 1st and 2nd K80+� Equal 1 (Ti:Tv) K80+�

rag2 3rd TrN+� Unequal 1Tv, 2Ti GTR+�

tion categories. All state frequencies, substitution rates,distributions of rate heterogeneity, and the proportion ofinvariant sites were sampled separately (when includedin the model) for each data partition. All other priorswere specified with default settings. Two independentruns of 1 × 107 generations were completed, each con-taining four chains (three heated incrementally with atemperature of 0.2) initiated from random starting treesand sampled every 1 × 103 generations. Upon comple-tion of the runs, convergence to a stationary distributionwas examined by referring to the average standard devi-ation of split frequencies between paired cold chains andtime series plots of the tree scores (negative log likeli-hoods). The first 3000 samples (3 × 106 generations) werediscarded as burn-in and the maximum clade credibilitytree (MCC) calculated with TreeAnnotator (v1.4.5r516;Rambaut and Drummond, 2007). The MCC is preferredto the 50% majority consensus of post–burn-in samplesas it represents the maximum product of posteriorprobabilities of any single tree visited during the MC3

run. Branch support for nodes in the MCC was exploredthrough reference to nonparametric bootstrap propor-tions obtained according to ML estimates using the GTRmodel applied across all sites combined in a single parti-tion in GARLI (v0.95; Zwickl, 2006, at http://www.bio.utexas.edu/faculty/antisense/garli/Garli.html). Fivehundred pseudoreplicates were completed, each start-ing from a random tree and terminating after 1 × 105

generations.

Divergence Time Estimation

According to a likelihood-ratio test (LRT; Langley andFitch, 1974) of the MCC and log-likelihood scores esti-mated by PAUP* (according to models specified by Mod-elTest for each partition in turn), the ictalurid data setwas found to be overdispersed (2� lnL = 59.49–1136.91,df = 35, P < 0.001) with respect to a molecular clock.Given significant rate heterogeneity among lineages, the95% highest posterior density (HPD) of divergence timeswas estimated with a relaxed-clock MCMC approach(Drummond et al., 2002) using the uncorrelated log-normal (UCLN) model (Drummond et al., 2006) imple-mented in BEAST (v.1.4.5; Drummond and Rambaut,2006). Unlike other Bayesian divergence time estimatorsthe UCLN model does not assume autocorrelation ofsubstitution rates across nodes, though they are permit-ted. With respect to node age constraints and absoluterate calibration, we set conditional lognormal priors withmeans and standard deviations of 1.0 and offset values


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TABLE 3. Description and fossil basis of lognormal prior distributions of node age constraints specified in the BEAST MCMC analysis(MRCA = most recent common ancestor). All fossil identifications from Lundberg (1975, 1992) and zero offsets based on stratigraphic ages fromWoodburne (2004).

Constrained node Zero offset Mean ± SD Fossil justification

MRCA: Ictaluridae (root node) 58.0 1.1 ± 1.1 †Astephus sp.: Paleocene-EoceneMRCA: Ictalurus + Pylodictis + Noturus 35 1.0 ± 1.0 †Ictalurus rhaeas: Eocene-Oligocene †Ameiurus

pectinatus: Eocene-OligoceneMRCA: Ictalurus 19 1.0 ± 1.0 Ictalurus punctatus: MioceneMRCA: Pylodictis + Noturus 19 1.0 ± 1.0 Pylodictis olivaris: MioceneMRCA: A. melas + A. nebulosus 4 1.0 ± 1.0 †Ameiurus sawrockensis: Pleistocene

of 4.0, 19.0, 19.0, 35.0, and a root prior offset of 58.0 withmean and standard deviation of 1.1 (Table 3). Althoughother fossils are available, their morphological general-ity or distribution in time provide no further informa-tion concerning temporal constraints as they are eitheryounger than the four informative fossils or unable to beplaced phylogenetically.

Two independent and identical BEAST analyses werecompleted, each of 1 × 107 generations during whichthe posterior probability density of divergence timeswas estimated with a UCLN relaxed clock modelingGTR+I+�. All parameters were unlinked and appliedseparately to both of two partitions: (1) all first- andsecond-codon positions combined, and (2) all thirdpositions. The prior for the branching process was setaccording to a pure birth (Yule) model assuming a con-stant speciation rate per lineage. A starting chronogramthat satisfied the node age priors was generated byfirst importing the MCC annotated with mean branchlengths from the MC3 analysis into PAUP*, deleting theoutgroup (Cranoglanis bouderius), resetting the root in itsplace, and enforcing the resulting phylogram and fossilcalibrations described above in R8S (v. 1.71; Sanderson,2003). The nonparametric rate smoothing (NPRS)method (powell algorithm, otherwise default settings)was used to estimate unknown node ages and providean ultrametric tree. The resulting chronogram waschecked in TreeEdit (v1.0a10; Rambaut and Charleston,2002) to ensure node ages and clades were among thosespecified in the prior distributions of the BEAST MCMCanalysis. This chronogram, in newick format, replacedthe UPGMA starting tree in the BEAST input file andthe pertinent text was edited appropriately.

Following completion of the runs, stationarity of eachposterior distribution was examined according to themarginal probabilities of sampled parameters usingTracer (v1.3; Rambaut and Drummond, 2003). Given sta-tionary parameter estimates and effective sample sizes(ESS) >1000, samples from both runs were combined us-ing LogCombiner and the MCC calculated in TreeAnno-tator. Samples of the distribution taken prior to station-arity were discarded as burn-in. FigTree (v1.0; Rambaut,2006) was used to visualize the chronogram and 95%HPD of divergence times.

Patterns and Rates of Lineage Diversification

Following Moore and Donoghue (2007), shifts in lin-eage diversification rate through time were evaluated us-

ing both temporal and topological approaches. Temporalmethods involve the distribution of node ages throughtime and calculation of the relative cladogenesis statistic(Nee et al., 1996; Moore and Donoghue, 2007) in END-EPI(Rambaut et al., 1997). The topological method examinedthe MCC branching pattern and a sample of 1000 treesdrawn from the stationary distribution of the MC3 anal-ysis for significant changes in diversification rate by cal-culating shift statistics (�1) and performing whole-treetests of diversification rate variation using SymmeTREE(v1.1; Chan and Moore, 2005). The null distribution foreach test statistic was generated by Monte Carlo simu-lation of 1 × 106 trees containing the same number oftaxa as the test tree and branching according to an equal-rates Markov (ERM) model. In order to explore issues as-sociated with taxon sampling in the diversification rateanalysis (e.g., Nee et al., 1996), an experiment followingthat outlined by Moore and Chan (2005) and Moore andDonoghue (2007) was completed by assigning the miss-ing extant species to their respective supraspecific taxaas far as they are known. The missing extant Ictalurusspecies were assigned according to the synthetic phy-logeny of Lundberg (1992); i.e., I. balsanus and I. meridion-alis in a trichotomy along with I. furcatus; and I. australis,I. dugesi (including I. ochoterenai), I. mexicanus, and I. pri-cei in an unresolved node subtending these species aswell as the sampled I. punctatus and I. lupus. Similarly,Noturus crypticus and N. stanauli were assigned to a poly-chotomous node subtending these two species; and N.baileyi, N. elegans, N. fasciatus, N. hildebrandi, and N. glad-iator as sister to N. stigmosus following Hardman (2004),Near and Hardman (2006), and Egge and Simons (2006).Soft polytomies were randomly resolved 1 × 106 timesaccording to the taxon-size-sensitive ERM model (Chanand Moore, 2002; Moore and Chan, 2005).

Reconstruction of Ancestral Body Size and ItsCorrespondence to Species Richness

Body size has been identified as one of the mostimportant intrinsic factors governing animal biology(Schmidt-Nielsen, 1984) and one that has exhibited di-rectional evolutionary trends among families of NorthAmerican freshwater fishes (Knouft and Page, 2003).The extant and reconstructed evolution of maximumstandard length (MSL; body length excluding the cau-dal fin) was examined with respect to the MCC fromthe BEAST MCMC analysis and compared to the


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results of the diversification rate analysis and changesin Cenozoic climate. We collected MSLs from Page andBurr (1991), Thomas and Burr (2004), Burr et al. (2005),and Egge and Simons (2006; see Fig. 2). These contin-uous data were natural-log–transformed prior to beinganalyzed according to MCMC methods in BayesTraits(http://www.evolution.reading.ac.uk/; Pagel et al.,2004) and weighted-squared-change parsimony (WSP;Maddison, 1991) in Mesquite (v. 1.12; Maddison andMaddison, 2006). Additionally, MacroCAIC (v. 1.0.1;Agapow and Isaac, 2002) was used to evaluate the re-lationship between species richness and lnMSL throughthe comparison of independent contrasts (Felsenstein,1985; Harvey and Pagel, 1991; Garland et al., 1992).

BayesTraits was used to examine the evolution of ln-MSL across the post–burn-in phylograms from the MC3

analysis. Prior to their input to BayesTraits, the sample ofphylograms was thinned to 4.0 × 103 and rooted accord-ing to the outgroup (Cranoglanis bouderius) in Mesquiteso that the ingroup node was dichotomous and the out-group had a positive branch length. A total of 2 × 107

iterations, sampled every 1 × 103, were completed andthe number of generations required to reach stationar-ity of the posterior distribution detected by examiningmarginal probabilities plotted as time series in TRACER.The “ratedev” prior was set heuristically so that the meanacceptance of the proposed state was at least 25% in theposterior distribution. This parameter enables the chainto be effectively mixed when stationary. All other pri-ors were uniformly distributed. Three replicate MCMCruns were completed to estimate the distribution of like-lihood scores of nested hypotheses in which the lnMSLdata evolved according to a random-walk or a direc-tional model of evolution across the samples of MC3

phylograms. Bayes factors (BFs) were used to assess rel-ative support for the alternative (directional) and null(random-walk) hypotheses, calculated as twice the dif-ference between the harmonic mean of the marginal loglikelihood under the corresponding model (e.g., Suchardet al., 2001). The preferred evolutionary model was thenused to estimate the mean values of posterior distribu-tions of three scaling parameters: delta (δ; ancient or re-cent trait evolution), kappa (κ ; rate heterogeneity in traitevolution), and lambda (λ; phylogenetic signal in traitevolution; Pagel, 1994, 1997, 1999).

Unfortunately, BayesTraits can only reconstruct an-cestral states for discretely coded data; e.g., binary ormultistate. In its place, the WSP method was used toreconstruct ancestral states of lnMSL on the MCC inMesquite. WSP minimizes the sum of squared changesoccurring tip-to-node and node-to-node weighted ac-cording to their branch length (Maddison, 1991). By in-corporating branch lengths, WSP mimics an ML estimateof the same assuming a Brownian motion evolutionaryprocess (Finarelli and Flynn, 2006). Following the detec-tion of significant phylogenetic structure (λ ≈ 1.0), thePDAP module (Midford et al., 2005) was used to esti-mate standard errors and confidence intervals for theancestral lnMSLs following Garland et al. (1999).

In order to evaluate the extent to which lnMSL pre-dicts species richness among ictalurids, least-squares re-gression through the origin (Garland et al., 1992) of 24independent contrasts (sister-species comparisons areuninformative) was completed in MacroCAIC using theMCC with branch lengths in units of time following therecommendations of Isaac et al. (2003). Differences inclade species richness were measured according to therelative rate difference statistic (RRD = ln[ni /nj ], whereni is the number of species in the larger clade, and njthe number in the smaller clade; Gittleman and Purvis,1998; Isaac et al., 2003). The ancestral reconstruction of ln-MSL was made according to a Brownian motion model ofcharacter change, as in WSP, with branch lengths takenbetween point estimates of node ages from the BEASTMCMC analysis. Similar to Gittleman and Purvis (1998),the extent to which species richness was dependent on aparticular value of lnMSL was tested by regressing RRDagainst reconstructed values of lnMSL. Also, to examinethe extent to which the association was constant throughtime, RRD was regressed against node age.

Paleoclimatic Change and Its Relationshipto Diversification Rate

To explore the correspondence of possible associationsbetween changes in climate and natural log per lineageaccumulation rate (lnλ) during the Cenozoic, mean oceantemperature (MOT) changes were collected from thepaleoclimate reconstruction of Zachos et al. (2001) andchanges in the lnλ from the MCMC chronogram for eachof 58 one-million-year intervals. Given that the analysisdoes not include an estimate of the per lineage extinc-tion rate, lnλ is based solely on lineage birth and thusassumes either positive (speciation) or neutral (no spe-ciation) values. Changes in MOT on the other hand canbe positive, neutral or negative. Observations of the sixpossible pairings were scored and exposed to a standardχ2 test of the null hypothesis of no association betweenthe change in MOT and change in lnλ. Additionally, thecorrespondence between these two variables based onthe absolute rate of change in MOT (±◦C per millionyears) was examined according to a two-tailed test us-ing Kendall’s τb correlation coefficient.

RESULTS

Phylogeny

After approximately 3 × 106 generations, the averagestandard deviation between split frequencies of the cou-pled runs stabilized with stochastic fluctuation between0.0045 and 0.007, suggesting convergence to the station-ary distribution. Accordingly, the first 3 × 104 sampleswere discarded as burn-in. All parameter estimates of thepost burn-in sample had ESS values >1000. The MCC ofthe MC3 analysis was made ultrametric in r8s and spec-ified as starting tree for the MCMC analysis in BEAST.Given the inclusion of additional prior information, i.e.,node age distributions, the MCC obtained from the


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post–burn-in sample of the BEAST MCMC analysis wasconsidered the best estimate of phylogeny. Figure 1 dis-plays the results of the divergence time reconstruction,and Fig. 2 the cladogram containing node posterior prob-abilities (PPs) and proportion of 500 ML bootstrap pseu-doreplicates (MLBPs) estimated by GARLI.

In the MCC, all genera were recovered monophyleticwith convincing support (PPs and MLBPs of 1.0 and0.99 to 1.0, respectively). Ameiurus was resolved as thebasal genus and Ictalurus as sister to Noturus and Py-lodictis. Though intergeneric nodes all had PPs of 1.0,only the clade comprising Noturus and Pylodictis wasfound in more than 0.5 of MLBPs (0.72). Relationshipsamong Ameiurus species were resolved as in Hardmanand Page (2003) and relationships among the incom-pletely sampled Ictalurus were consistent with those ofprevious studies based on morphology (Lundberg, 1970,1992). Ictalurus furcatus was found to be sister to a cladeof the widespread I. punctatus and the southwestern I.lupus. Pylodictis is monotypic and its recovery as sisterto Noturus is consistent with the morphological studiesof Taylor (1969) and Lundberg (1975, 1982, 1992). Rela-tionships among Noturus species were similar to thoseinferred from analyses of similar data sets (Hardman,2004; Burr et al., 2005; Near and Hardman, 2006; Eggeand Simons, 2006) and a survey of allozyme and chro-mosomal variation (Grady and LeGrande, 1992).

Divergence Time Estimates

Stationarity of each of the BEAST MCMC posteriordistributions was examined by plotting time series of themarginal probabilities of estimated parameters. Samplesof the first 2.0 × 106 generations were discarded as burn-in and subsequent ESSs of all parameters were at least1.3 × 103. The MCC of combined samples with the sta-tionary distribution is shown in Fig. 1. The mean ± stan-dard deviation width of the 95% HPDs was 11.8 ± 4.7 Ma.Error widths were positively correlated with node age(B = 2.22, SE = 0.237, t = 9.38, P < 0.001); thus, moreancient nodes were less precisely estimated. Overall, thechronology describes a scenario in which the modernictalurid lineages originated early in the Eocene andthat the majority of extant species originated during theOligocene and Miocene.

Lineage Diversification Rates

The temporal method employed in END-EPI identi-fied four nodes in the MCC with significant rate increases(P < 0.05), all located in the early diversification of theNoturus lineage (Fig. 1). The topological method em-ployed in SymmeTREE identified significant rate vari-ation among lineages (P = 0.0002–0.002) and two near-significant rate shifts (P = 0.07) in the MCC, one of whichwas also identified by END-EPI. Summarized whole-tree rate variation tests based on 1 × 104 samples fromthe stationary distribution of the MC3 analysis wereof reduced significance (P = 0.07–0.03) with respect tothe tests based on the MCC, though they similarly im-plied or identified significant diversification rate hetero-

geneity. Although test statistics increased slightly in sig-nificance, including the unsampled taxa did not affectthe conclusions based on the original matrix and MCC.The taxon-inclusion experiment did however identifythe node subtending all non-Ameiurus ictalurids as thelocation of a significant diversification rate shift (P =0.03). This clade received all of the unsampled taxa inthe experiment.

Reconstruction of lnMSL Evolution and Influenceon Species Richness

Convergence of the MCMC analyses in BayesTraitswas assessed by plotting the loglikelihood scores ofeach sampled generation as time series and discardingthose beneath the asymptote (2.0 × 106 generations) asburn-in. The BF (= 3.62) obtained from the comparisonof harmonic means of the marginal log likelihoods re-jected the null hypothesis of a random walk in favor ofa directional model of evolution. Under the directionalmodel, mean ± standard deviations of the scaling pa-rameters δ (0.88 ± 0.26), κ (0.70 ± 0.17), and λ (0.82 ±0.10) suggested the majority of lnMSL evolution to havetaken place early in the lineage history, to have remainedfairly constant following those early changes, and tobe structured phylogenetically. Collectively, these val-ues describe a punctuated model of evolution for MSL inIctaluridae.

Figure 2 shows scaled point estimates and 95% con-fidence intervals of the reconstructed ancestral MSLs.Based on the WSP analysis, the ancestral ictalurid hada mean MSL of 50.9 cm (with a 95% confidence inter-val of 27.8 to 93.1 cm), which is similar to the largerspecies of modern Ameiurus. Similar ancestral conditionswere reconstructed for the common ancestors of the non-Ameiurus ictalurids (60.3 [29.7–122.4] cm) and of the lin-eage leading to Pylodictis and Noturus (68.1 [35.4–130.7]cm). Although modern Ameiurus exhibit a range of MSLs,the condition within the lineage was reconstructed as sta-ble during the last 35 Ma, with the larger MSL of A. catusreconstructed as autapomorphic in this early Miocenelineage. Similarly, the large maximum size of Pylodic-tis olivaris was reconstructed as autapomorphic to thisearly Eocene lineage rather than as an ancestral con-dition shared among other modern species with largeMSLs (Ictalurus furcatus: 165 cm; I. punctatus: 127 cm).Within the Noturus lineage, MSL was reconstructed asreducing rapidly to a value more typical of the modernspecies early in their history and which changed ratherlittle after that, echoing the results of the MCMC analysesin BayesTraits.

Least-squares regression through the origin identifiedlnMSL as a nearly significant predictor of species rich-ness (B = −0.470, SE = 0.251, t = −1.869, r2 = 0.132,P =0.07). Fifteen of 24 contrasts were negative, 8 were pos-itive, and 1 was neutral (sign test: not significant, P =0.21). The regression of contrast against node age (height)was positive, indicating a decrease in lnMSL throughthe history of Ictaluridae, corroborating the results ofBayesTraits and the WSP reconstruction. Additional tests


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FIGURE 1. Maximum credibility chronogram resulting from the combined samples of the stationary posterior distributions estimated in theBEAST MCMC analysis. Branch lengths are in units of time and error bars represent the 95% highest posterior densities. Nodes labeled withsymbols were identified as the locations of significant (P < 0.05: filled circles and star) and nearly significant (P = 0.07: filled square and star)diversification rate shifts in END-EPI and SymmeTREE, respectively. Lower bounds for the five constrained nodes are labeled in Ma. Inset showsthe plot of log lineage accumulation rate against the mean ocean temperature (climate) reconstruction of Zachos et al. (2001). Image of Ictalurusfurcatus reproduced from Etnier and Starnes (1993).


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FIGURE 2. Cladogram corresponding to the maximum credibility tree resulting from the BEAST MCMC analysis with posterior probabilitiesdisplayed above branches and the proportion of 500 maximum likelihood bootstrap pseudoreplicates below (not shown if recovered in less than0.50 of pseudoreplicates). Black silhouettes are scaled maximum standard lengths (MSLs) of the extant ictalurid fauna. Grey silhouettes are scaledpoint estimates of ancestral MSLs with 95% confidence intervals in the accompanying shaded boxes. Filled circles, square, and star correspondto the diversification rate shifts noted in Fig. 1 and text.


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suggested independence of the value of lnMSL andspecies richness but significant variation in the relation-ship between lnMSL and species richness through time.

Correspondence of Diversification Rateand Paleoclimatic Change

Figure 3 plots the rate and direction of MOT changefor the Cenozoic paleoclimate reconstruction of Zachoset al. (2001) and the lnλ inferred from point estimates

FIGURE 3. Time-series plots comparing the rate and direction of climate change and log per lineage accumulation rate over 58 one-million-year intervals. Both chi-square and Kendall’s τbfailed to reject the null hypothesis of no association between these two variables, but see text for adiscussion of the possible type II error. The abscissa on the upper plot represents no change in mean ocean temperature over 1 million years. Thesmoothed line above zero represents climate warming, below represents climate cooling and its distance from zero describes the rate of changein ◦C/Ma. Filled symbols correspond to the diversification rate shifts noted in Fig. 1 and text.

of node ages obtained from the BEAST MCMC analysis.Although variation in the direction and rate of changewas noted for both reconstructions, the approaches em-ployed to explore their correlation failed to reject the nullhypothesis of no association between the variables, ei-ther analyzed with respect to the basic observation ofpaired changes (χ2 = 2.96, df = 2, P = 0.227) or with re-spect to the rate of MOT change (Kendall’s τb = 0.065,P = 0.529).


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DISCUSSION

Phylogeny

In evolutionary studies, phylogeny is of profound im-portance. In the majority of cases the branching patternis unknown but taxonomic congruence among phyloge-nies obtained from diverse data sources is consideredcompelling evidence for their accuracy. In this study, re-sults of the phylogenetic analysis were understandablysimilar to earlier studies focused on subsets of the dataset analyzed here (Hardman and Page, 2003; Hardman,2004; Near and Hardman, 2006; Egge and Simons, 2006).Comparison with results obtained from morphologicaldata sets is restricted due to minimal overlap of taxonsample, but with respect to intergeneric relationships,significant differences have been observed (Hardmanand Page, 2003; Hardman, 2004). Previous phylogeneticstudies including exemplars of all ictalurid genera werebased on morphological characters polarized accordingto their commonality in other fish groups; i.e., an im-plicit outgroup comparison (Lundberg, 1975, 1982, 1992).Although J. G. Lundbergs’ dendrograms place Ictalu-rus sister to (Ameiurus (Pylodictis, Noturus)), the appar-ent conflict may be due to placement of the root ratherthan significantly incongruent signal. Other morpholo-gists have sampled catfishes broadly but did not includeexemplars of all epigean ictalurids (e.g., Mo, 1991; DePinna, 1998; Diogo, 2004) and are thus uninformativewith respect to intergeneric relationships. A recent sur-vey of nuclear gene sequences among catfishes includedsamples of the epigean ictalurids but failed to resolverelationships among them convincingly (Sullivan et al.,2006). Hence, we have no basis to consider the inter-generic resolution recovered here as inaccurate, thoughit is without convincing MLBP support.

Within the convincingly monophyletic genera, rela-tionships among species of Ameiurus were identical tothose reported by Hardman and Page (2003). Relation-ships among Noturus species were similar to those ofearlier studies based on the same or a subset of the dataanalyzed here (Hardman, 2004; Burr et al., 2005; Nearand Hardman, 2006; Egge and Simons, 2006). Thoughconvincing support of the basal nodes in Noturus re-mains elusive, the pattern emerging from studies oftheir phylogeny is that the subgenus Rabida is mono-phyletic, whereas Schilbeodes is not. Within Rabida, thegeographic distribution of several species groups cor-responds to what might be expected under a peripheralisolates model of speciation (Grady and LeGrande, 1992).Species groups of Schilbeodes also correspond rather wellto geography: two species from the Coastal Plain, fourspecies from Atlantic Slope, and finally a clade with abroad distribution throughout eastern North America.The basal lineage of Noturus was recovered as the slendermadtom (N. exilis). This species has a rather large (536,600km2; Grady and LeGrande, 1992) but fragmented dis-tribution in streams of the Midwestern and southernUnited States. Noturus exilis has been characterized ashaving considerable genetic structure, with differencessuggesting diversification of the extant members of the

lineage to have taken place prior to or early in the Pleis-tocene (Hardy et al., 2002). Our estimate of the split be-tween the lineage represented today by N. exilis and allother Noturus is early Oligocene (mean 35.8; 95% HPD26.8 to 41.9 Ma) during a period of significant (or nearlysignificant) diversification rate increase (Figs. 1 to 3) anddramatic climate cooling (Fig. 3).

Comparison of the Molecular Chronogramand the Fossil Record

Although the comparative richness of the ictalurid fos-sil record (Lundberg, 1975, 1992) cannot be fully em-ployed as constraints in divergence time estimation, thetemporal distribution of the unused fossils offers a checkof the chronology shown in Fig. 1.

Unfortunately, fossil Ameiurus species are difficult tointerpret in the present context because Hardman andPage (2003) demonstrated significant phylogenetic con-flict between the morphological data of Lundberg (1975,1982, 1992) and the same sequences upon which our phy-logeny and chronology are based. Thus, we were reluc-tant to use fossil species to provide indirect age minimafor their sister taxa according to morphology. However,although no fossils of any extant Ameiurus species occuroutside of the estimates provided in Fig. 1, divergencetimes suggest that the minima provided by the major-ity of Ameiurus fossils and those used to justify lowerbounds of lognormal priors considerably underestimatelineage birth times.

The fossil record of Ictalurus (Lundberg, 1975, 1992) isconsistent with the DNA chronology in Figure 1. Fos-sils identified as I. punctatus have been found in middleMiocene deposits of South Dakota and Nebraska (Lund-berg, 1975, 1992) and were used to set a lower bound toa lognormal prior specified in the UCLN MCMC analy-sis. These fossils predate the upper 95th percentile of theHPD for the divergence time of I. punctatus and I. lupusby at least 15 Ma. A possible explanation for this observa-tion is that I. lupus represents a recently evolved speciesthat has diverged from an ancestral form that was mor-phologically indistinguishable from modern I. punctatus.Thus, the Miocene fossils of I. punctatus correctly predatethe origin of I. lupus and characterize the evolution of I.punctatus as one with a stable morphology similar to thatobserved for P. olivaris (Lundberg, 1975, 1992).

We would like to stress that several species of Ictaluruswere not available for inclusion in this study. Though thediversification rate experiment including these lineagesdid not identify the node subtending all Ictalurus speciesas the location of a significant shift in diversification rate,their future analysis might offer some insight into theprocesses of diversification in this larger-bodied clade inMexico and the southwestern United States.

Correspondence of Diversification Rateand Paleoclimatic Change

Near et al. (2005) used a set of consistent fossil cali-bration points and mitochondrial and nuclear gene se-quences to reconstruct the evolutionary chronology of


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sunfishes and basses (Centrarchidae) in Cenozoic NorthAmerica. According to a penalized likelihood analysis,Near et al. (2005) estimated the root node age of centrar-chids to be 33.59 ± 3.58–5.77 and suggested that the dra-matic cooling of the climate at this time may have playeda role in their early diversification as well as the NorthAmerican ichthyofauna more generally. In line with thehypothesis of a general response of the North Americanbiota, Webb and Opdyke (1995) characterized the evolu-tion of land mammals as irregularly distributed in timeand tied to Cenozoic climate change.

Although the coincidence of the terminal Eocene eventand the significant (or nearly significant) increase in di-versification rate of Noturus is striking (Fig. 1 inset; Fig.3), our attempt to quantitatively examine a general asso-ciation between diversification rate and climate changedid not detect a significant relationship. According tothe reconstruction of Zachos et al. (2001), the Cenozoicclimate was almost constantly changing, although therate and direction of change varied (Fig. 3). The signifi-cant shifts were tightly bounded in time, specific to earlybranches of the Noturus lineage, and apparently coin-cided with the greatest period of climatic shift in theCenozoic. However, if a causal relationship exists be-tween climate change and diversification in ictalurids,why is it that only the Noturus lineage showed a sig-nificant response? The Ameiurus, Ictalurus, and Pylodictislineages were present at the time but (unless the fossilrecord for these lineages is misleading) did not respondsignificantly to the environmental change. Furthermore,the dramatic cooling of the terminal Eocene event wasalmost matched by an equally dramatic warming of theclimate in the Middle-Late Oligocene (Fig. 1 inset; Fig.3) but no significant response in diversification rate wasnoted.

We did not estimate the type II error rate (false neg-ative) in the correspondence analysis of climate changeand diversification rate. Failure to reject the null may bedue to our use of point estimates of node ages that are,in fact, inaccurate or that our arbitrary choice of one-million-year measurement intervals is a poor reflectionof the time scale over which the effect operates. Addi-tionally, diversification events may be the product of aset of cumulative events that collectively create the cir-cumstances under which the event can take place, anddivision of that set into million-year slices effectively dis-rupts its detection. And although climate change appearsto have been the norm during the Cenozoic, warmingclimates pose different changes to the environment thandoes climate cooling so each climatic event likely poseda different set of selective pressures (with different out-comes) that our rather coarse analysis failed to discrim-inate. Perhaps it was naıve of us to look for a generalrelationship between an extrinsic factor and diversifica-tion rate within a lineage history, though we might expectit on the basis of phylogenetic constraint. Or perhaps theresponse was only effective on ancestors with a particu-lar body size and, therefore, different biology to the otherlineages. All these things (and many more) are possi-ble, but accommodating the errors will decrease preci-

sion and power to reject or reveal explicit evolutionaryphenomena.

We are left with an understanding that evolutionaryevents operate on the standing diversity at a point in timethat is the sum of all prior events and their influence onshaping its species, their communities and distributions.In effect, no two evolutionary events within a lineage arecomparable and although significant diversification rateshifts may be detected, their causes may be due to en-tirely different or ephemeral processes that operate onlyunder certain circumstances on a particular distributionof extant diversity. Perhaps we might find general rela-tionships across rather than within lineages, and runningmultiple contemporary and sympatric clades throughthe analytical procedure described here and by others(Moore and Donoghue, 2007) might help to reveal a gen-eral model of macroevolutionary change in the Cenozoic,if there is one to be found.

Interestingly, although Near et al. (2005) emphasizedthe influence of the Eocene-Oligocene climatic shift onNorth American freshwater fish diversification, most ofthe extant centrarchid genera and species were recon-structed as late Oligocene, Miocene, and Pliocene in age.So, perhaps the importance of the terminal Eocene eventas a driver of diversification has been overemphasizedand we should consider it more as an important period ofextinction rather than an important period of diversifica-tion; i.e., newly vacated niches being reoccupied ratherthan increasing diversity within an already diverse sys-tem. Furthermore, based on the distribution of diversi-fication events presented here and by Near et al. (2005),we might find more evidence of an association betweenclimate change and diversification rate in the Oligoceneand Miocene.

Reconstruction of Ancestral MSLs and Correspondenceto Diversification

The MSLs of the early nodes in ictalurid history werereconstructed as being ca. 50 to 60 cm, similar to those ofthe larger modern Ameiurus. The much larger MSLs ofthe extant Ictalurus furcatus (165.0 cm), Pylodictis olivaris(155.0 cm), and I. punctatus (127.0 cm) were reconstructedas autapomorphic increases from smaller ancestors, sug-gesting a homoplastic evolution of increasing body sizewithin the family. The Paleocene-Eocene Astephus weresimilar in MSL (estimated 44.0 cm; Grande and Lund-berg, 1988) to that estimated at the root node, suggestingthat the addition of proximal fossil lineages would havelittle impact on its estimation and, therefore, support itsaccuracy (Oakley and Cunningham, 2000; Finarelli andFlynn, 2006). Additionally, the MSLs of extant Cranogla-nis (43.0 cm; Zheng, 1990) further reinforce the ances-tral MSL of Ictaluridae as an approximately half-meterfish. The positive correlation of body size and dispersalis a well known phenomenon (Ware, 1978; Bernatchezand Dodson, 1987) and a half-meter ancestral ictaluridcould presumably navigate the physical and physiologi-cal barriers met in a dispersal route through a Beringianland bridge during the Late Cretaceous or Early Tertiary(Hardman, 2005).


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With respect to the species richness discrepancy ob-served between Pylodictis and Noturus, the increasedMSL of P. olivaris (155.0 cm) from an ancestral condi-tion of 68.1 (35.4–122.4) cm could explain the failure ofthis lineage to diversify. The combination of a preferencefor habitats that typically connect adjacent drainages intimes of abundant rainfall coupled with its breathtak-ing dispersal within and among seeded systems (Guieret al., 1981; Kwak et al., 2004; Brown et al.,2005) likelymaintains and promotes gene flow among populationsof P. olivaris and offers an explanation of the species’morphological homogeneity over time (Lundberg, 1975,1992).

A larger MSL for the ancestral Noturus than shownby the majority of extant species also makes sense giventhe large distribution of a genus that today is composedof rather small and fragmented ranges of many small-bodied species. Of the Noturus species with large dis-tributions, N. gyrinus is atypical for the genus in that itis commonly found in lowland habitats through whichits dispersal is presumably enhanced (Taylor, 1969; Pageand Burr, 1991). Noturus flavus is the largest memberof the genus at 31.0 cm, so its range can be explainedin terms of a larger MSL. Noturus eleutherus, N. miu-rus, and N. stigmosus are all less than 15.0 cm but arefound in large streams through which they presumablyenjoy dispersal throughout the Ohio and Mississippidrainages. The low genetic differentiation (Hardman,2004) and large range of the Atlantic Slope N. insignis(MSL of 15.0 cm) is anomalous and a subject for furtherstudy.

CONCLUSIONS

If we ignore our concerns and let the results tell theirstory, the analyses of Ictaluridae found diversificationrate to have varied significantly during the Cenozoic andsignificant (or nearly significant) increases were impliedfor early nodes in the Noturus lineage. The evolution oflnMSL followed a directional (decreasing) model andwas found to be a nearly significant predictor of speciesrichness, with clades composed of small species beingproportionally more species rich than expected under anull model. The chronology of diversification describesthe origins of the extant genera during the Paleoceneand early Eocene and the majority of extant species tobe Oligocene and Miocene in age. Climate change didnot significantly affect diversification rate throughoutthe history of the lineage.

We estimated the maximum standard length of the ic-talurid ancestor to be approximately 50 cm, comparableto Eocene ictalurids (Astephus) and similar to modernsizes of Ameiurus and the Asian sister taxon Cranoglanis.Based on the properties of modern fishes of similar size,the hypothetical ancestor would be suitably sized to dis-perse from Asia into North America through freshwatersystems of Beringia (Hardman, 2005).

The covariation of increasing size, large distribution,and low diversity appears to be a general one shown by

Pylodictis olivaris, Ictalurus furcatus, and I. punctatus. Allthree species regularly grow well over a meter and havelarge natural distributions. Additionally, all three appearto be rather ancient species with conservative morpholo-gies that have independently increased their maximumstandard lengths from smaller ancestors, as implied bythe fossil record and reconstructions presented here. Pre-sumably, the common diversification process of periph-eral isolation fails to operate when species evolve largebody sizes and, consequently, expand their distributionswithin which dispersal and migration maintain or in-crease gene flow. The small-bodied lineage, on the otherhand, has experienced a diversification rate significantlyhigher than its larger cousins and the patchy distribu-tions of many modern Noturus suggest a separate set ofevolutionary processes have applied to this clade.

ACKNOWLEDGMENTS

Special thanks to B. Moore and J. Lundberg for their detailed,thoughtful, and tremendously helpful comments on an earlier versionof this paper. Also, J. Knouft, L. Page, and A. Summers provided excel-lent counsel on macroevolutionary matters, North American freshwa-ter fishes, and their thoughts on the importance of body size. N. Isaac,A. Meade, B. Moore, and A. Rambaut provided software and technicalsupport. We thank H. Henttonen for providing logistic and informaticssupport during the course of this project. M.H. was supported by theAll Catfish Species Inventory (NSF: DEB 0315963) and L.H. by a FinnishAcademy Postdoctoral Research Grant (no. 108372; Wickstrom et al.,2005).

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First submitted 28 May 2007; reviews returned 16 August 2007;final acceptance 14 November 2007

Associate Editor: Allan Baker


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