O R I G I NA L A RT I C L E
doi:10.1111/j.1558-5646.2012.01598.x

EXPLOSIVE RADIATION OF A BACTERIAL
SPECIES GROUP
Hélène Morlon,1,2 Brian D. Kemps,3 Joshua B. Plotkin,3 and Dustin Brisson3,4
1

Center for Applied Mathematics, UMR 7641 CNRS, Ecole Polytechnique, 91128 Palaiseau, France
2

3

E-mail: helene.morlon@polytechnique.edu

Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
4E-mail: dbrisson@sas.upenn.edu

Received October 11, 2011
Accepted January 23, 2012
The current diversity of life on earth is the product of macroevolutionary processes that have shaped the dynamics of diversification.
Although the tempo of diversification has been studied extensively in macroorganisms, much less is known about the rates ofdiversification in the exceedingly diverse and species-rich microbiota. Decreases in diversification rates over time, a signatureof explosive radiations, are commonly observed in plant and animal lineages. However, the few existing analyses of microbial
lineages suggest that the tempo of diversification in prokaryotes may be fundamentally different. Here, we use multilocus andgenomic sequence data to test hypotheses about the rate of diversification in a well-studied pathogenic bacterial lineage, Borrelia
burgdorferi sensu lato (sl). Our analyses support the hypothesis that an explosive radiation of lineages occurred near the originof the clade, followed by a sharp decay in diversification rates. These results suggest that explosive radiations may be a generalfeature of evolutionary history across the tree of life.
K E Y W O R D S : Diversification, microbial macroevolution, phylogeny, radiation.

Explosive evolutionary radiations are one of the most conspicuousand intriguing features of evolutionary biology (Simpson 1953;
Schluter 2000; Rundell and Price 2009). Explosive radiations haveoccurred in a wide variety of animal and plant lineages, identified
using both fossil and molecular data, and may be a general pattern of the diversity of life (Sanderson and Donoghue 1994; Purvis
et al. 1995; Zink and Slowinski 1995; Alroy 1999; Harmon et al.
2003; McPeek 2008; Phillimore and Price 2008; Rabosky andLovette 2008; Morlon et al. 2010). In comparison with the wealthof evidence supporting rapid radiations of macroorganisms, very
little is known about the tempo of microbial diversification(Martin et al. 2004). Given the vast diversity of microbes andtheir crucial roles in human health and economics, as well as
in natural ecosystem processes, it is essential to investigate thepattern of species diversification in the microbial world.
Analyses of molecular phylogenies of extant taxa underbirth–death models of cladogenesis are widely used to investigate
diversification patterns. In particular, they are used to test the hy-

pothesis that diversification rates have decayed over time, whichis commonly interpreted as an ecological niche-filling process associated with radiations (Nee et al. 1994; Pybus and Harvey 2000;
Rabosky and Lovette 2008; Glor 2010; Morlon et al. 2010). Timedecays in diversification rates are often investigated by measuring
the relative position of nodes in a phylogeny using the gamma(γ) statistic (Pybus and Harvey 2000; Harmon et al. 2003; Martin
et al. 2004; Phillimore and Price 2008; Brock et al. 2011), or by
analyzing the likelihood of the waiting times between branching
events in a phylogeny under various birth–death models of
cladogenesis (Nee et al. 1992, 1994; Rabosky 2006; Rabosky
and Lovette 2008; Morlon et al. 2010; Morlon et al. 2011; Stadler2011). Such analyses have revealed time decays in diversification
rates in many plant and animal clades (Davis et al. 2005; Kozak
et al. 2006; McPeek 2008; Phillimore and Price 2008; Schuettpelz
and Pryer 2009; Morlon et al. 2010; Valente et al. 2010), and evenin the Kingdom Animalia itself (Rokas et al. 2005). By contrast,
the sole analysis of the tempo of diversification in prokaryotic

C 2012 The Author(s). Evolution 
C 2012 The Society for the Study of Evolution.

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phylogenies that we know of did not detect a time decay indiversification rates (Martin et al. 2004), a surprising result
given that prokaryote lineages rapidly and adaptively diversify inexperimental systems (reviewed in MacLean 2005; Kassen 2009).
There are three main complications associated withphylogeny-based macroevolutionary analyses of microbial lineages. First, the true diversity is rarely well sampled as the majority of bacterial species have yet to be described; second, there
is no broadly accepted species concept for microbes; and lastly,
the horizontal gene transfer that is common among prokaryotes
renders birth–death models of cladogenesis less suited to analyze
diversification in microbial clades. The evolutionary clade containing the Lyme disease-causing spirochetes, Borrelia burgdorferi sensu lato (sl), avoids many of these complications and thus
provides an excellent opportunity to study rates of diversification
in a prokaryotic group.
Borrelia burgdorferi is obligatorily vectored among vertebrate hosts by ticks in the Ixodes ricinus species complex (Barbour
and Hayes 1986; Nuttall 1999; van Dam 2002; Piesman and Gern
2004). This host specialization potentially limits species diversity
by limiting habitat suitability, and makes scientific samplingeasier than for free-living groups. Further, the implications of B.
burgdorferi sl for human health has lead to an exhaustive examination of vertebrates and hematophagous arthropods around theworld for both human infectious and wildlife-specific Borrelia
species through both cultivation-dependent and cultivationindependent methods. These exhaustive field studies to identify
B. burgdorferi sl have resulted in a very high coverage of thediversity of this clade, revealing 15 described and two recently
proposed phylotypes that are readily distinguishable by DNA
sequence divergence and phylogenetic clustering (Gern 2008;
Margos et al. 2009; Rudenko et al. 2009, 2011). Genetic diversity
within each phylotype is small compared to divergence among
phylotypes, suggesting that each lineage is an independently
evolving, phylogenetically distinct group comparable to species
in eukaryotes (Avise and Wollenberg 1997; Richter et al. 2006;
Postic et al. 2007; Margos et al. 2011). Finally, horizontal
gene transfer among phylotypes is rare for chromosomal genes(Dykhuizen and Baranton 2001; Qiu et al. 2004) allowing
application of birth–death models of cladogenesis to study
diversification rates.
The present study provides a comprehensive assessment of
the pattern of phylotype diversification in B. burgdorferi sl. We
construct phylogenetic hypotheses for B. burgdorferi sl using multiple loci and chromosome sequences. We then analyze variationin the rate of diversification of B. burgdorferi phylotypes overthe evolutionary history of the species group using the γ-statistic
as well as a recently developed hypothesis-testing framework that
accounts for the possibility that some lineages have not been sampled (Morlon et al. 2010). Finally, we discuss our results in light

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diversification rates in this group.

Materials and Methods
LOCUS SEQUENCE DATAThe NCBI nucleotide database was searched in November 2010
for B. burgdorferi sl sequences that meet three criteria. First,
only DNA sequence data from chromosomal loci were used as
plasmid genes are more prone to horizontal gene transfer (Qiu
et al. 2004; Margos et al. 2009). Second, we used only locus datasets for which meaningful comparisons could be madeacross at least 11 B. burgdorferi sl taxa. Lastly, locus datasetswith homologs in at least two taxa in the relapsing fever Borrelia group (B. hermsii, B. duttonii, B. recurrentis, or B. turracatae) were desired, to root the trees. Nucleotide sequencesfrom seven loci—fla (flagellin, 122 sequences), hbb (histone-likeprotein, 77 sequences), rpoB (RNA polymerase, 66 sequences),
RRS (16S rDNA, 256 sequences), recA (recombinase A, 108 sequences), 5S–23S internal transcribed rDNA sequence (ITS, 798
sequences), and groEL (chaperone protein, 116 sequences)—fit
these criteria. Sequences for each locus in the NCBI nucleotide
database were downloaded and aligned using ClustalX (Higgins
and Sharp 1988; Larkin et al. 2007) and Muscle (Edgar 2004).
This resulted in a total of 1543 sequences. Data across all 15 recognized B. burgdorferi sl phylotypes were available for only threeloci (fla, groEL, and RRS). We thus constructed two phylogenies:
a phylogeny comprising all phylotypes based on these three loci,and a phylogeny using all seven loci comprising 11 phylotypes(hereafter referred to as “three-locus” and “seven-locus” trees,
respectively).

CHROMOSOME SEQUENCE DATA

Primary shotgun sequences from 15 Borrelia genome projects,
11 of which are from the B. burgdorferi sl group, were acquired from NCBI. These 11 genome projects covered six ofthe recognized phylotypes: three B. burgdorferi sensu stricto (ss)genome projects (B31, CA-11.2, ZS7), three B. garinii (Far04,
PBi, PBr), two B. afzelii (ACA-1, PKo), one B. sp SV1 (SV1),
one B. valaisiana (VS116), and one B. spielmanii (A14S). The
chromosome sequences were aligned to the annotated chromosome of the B. burgdorferi sensu stricto (ss) type-strain (B31)
using Blast (Altschul et al. 1990) and Muscle (Edgar 2004). All
ambiguous nucleotides were excluded from analyses. The data
represent orthologous genes shared across all sampled organisms.
The final dataset comprised 551,152 nucleotides (58% of the approimately 0.95 Mb B31 reference chromosome). Of the 551,152
chromosomal nucleotides, 95,018 were polymorphic (14.7%) of
which 41,042 (7.4%) were parsimony informative. Sites with

E X P L O S I V E D I V E R S I F I C AT I O N I N BAC T E R I Aevidence of multiple mutations—those with three or more divergent bases—made up only 7021 of the polymorphic sites suggesting that mutation saturation is unlikely. The tree was rootedusing 118,709 nucleotides from B. hermsii, B. duttonii, B. reccurentis, and B. turicatae genomic sequences, hereafter referred
to as the “chromosome” tree.
DELIMITING PHYLOTYPES

Although there is no universally accepted species definition forprokaryotes, there are well-recognized phylotypes in B. burgdorferi sl (Richter et al. 2006; Margos et al. 2009; Rudenko et al.
2009, 2011). To test that the genetic diversity of each of theseven loci clusters into independently evolving lineages that correspond to the recognized phylotypes, we first built single-locus
ultrametric trees using MrBayes and penalized likelihood (PL),as described below. We then used the Generalized Mixed Yule
Coalescent model, implemented in the GMYC package in R
(splits http://r-forge.r project.org/projects/splits/), which tests the
hypothesis that the data represent several independently evolving
populations against the null hypothesis that it represents a single
population (in practice, that the tree conforms to a neutral coalescent, Pons et al. 2006; Barraclough et al. 2009). The GMYC
model estimates a threshold that defines clusters of sequences
corresponding to independently evolving populations. The clusters formed with this approach matched the phylotypes given by
GenBank for each locus, which were used to infer the multilocus
trees described below.
PHYLOGENETIC RECONSTRUCTION

The phylogenetic history of B. burgdorferi sl was inferred by
Bayesian inference in MrBayes 3.0 version 4 (Huelsenbeck andRonquist 2001). Two types of phylogenetic trees were constructed, corresponding to the multilocus and partial-chromosome
sequence data (the three- and seven-locus trees, and the chromosome tree, respectively). The multiloci trees were obtained byconcatenating the individual gene sequences. In practice, for agiven phylotype, a concatenated sequence was obtained by randomly sampling one sequence per locus in the cluster of sequences
corresponding to that phylotype. We checked that the resulting
trees were robust to the set of sequences sampled, and used one
set for further analyses. This procedure resulted in an average ofapproximately five concatenated sequences per phylotype. To circumvent any potential complications that could arise through theconcatenation of loci with different phylogenetic signals, we also
analyzed the diversification rates in each of the seven individual
locus trees.
For both the multilocus and chromosomal sequence data, weused the HKY85 model of molecular evolution with invariantsites and γ distributed rates, as selected by the Akaike Informa-tion Criterion by MrModeltest version 2.2 (Nylander 2004). Thishighly parameterized model of molecular evolution is necessary
to avoid biasing branch length estimates that could result in biases
in diversification rate estimates (Revell et al. 2005). Bayesian reconstructions using a Markov Chain Monte Carlo (MCMC) algorithm with four chains were run for 15,000,000 generations eachand sampled every 250 generations. The first five million generations were discarded as burn-in, and convergence of the model
was detected by assessing stationarity of logL values, plotting
the posterior probabilities of nodes as a function of the numberof generations, and by examining standard deviations of split frequencies for independent runs (Huelsenbeck et al. 2001; Rambautand Drummond 2007). Three Bayesian runs using different seeds
were implemented to verify convergence to the same posterior
distribution of trees. To overcome uncertainty in the branchingtimes and branching order, all further analyses were conductedon the 100 trees with the greatest posterior probability support
from the Bayesian phylogenetic reconstruction (Huelsenbeck andRonquist 2001). Ultrametric trees were obtained by applying PL
to each phylogram using the software package r8s (Sanderson
2003). In the resulting trees, the nodes corresponded to divergences among phylotypes as well as divergences within phylotypes. Because we were interested in the diversification patternsof independently evolving lineages comparable to macroeukaryote species, that is, the diversification patterns of phylotypes, we
had to clump within-phylotype sequences into a single lineage.
We thus ran the GMYC model on the ultrametric trees containing
all sequences, and checked that they formed clusters consistent
with the accepted phylotypes. One branch from each phylotype
was then retained for subsequent analyses.
The sensitivity of our results to the method of phylogenetic
reconstruction was assessed by constructing ultrametric trees with
two additional methods. The Bayesian Estimation of Species
Trees (BEST) leverages multilocus data while accounting forthe fact that gene trees are embedded within a common phylogeny (Edwards et al. 2007; Liu and Pearl 2007; Liu 2008).
The BEST analyses using all sequences from the seven loci were
initiated from random starting trees and sampled every 1000 generations over a 100 million generation run. The posterior distribution of species trees (post burn-in) was summarized as the
50% majority-rule consensus tree to obtain posterior probability values for species relationships. We also constructed phylogenetic trees using maximum-likelihood methods assuming amolecular clock (MLMC) in PAUP∗ version 4.0b10 (Swofford
2003) for both the partial-chromosome and concatenated multilocus datasets. MODELTEST version 3.06 (Posada andCrandall 1998) was used to choose the best evolutionary model(HKY85+I+). Maximum-likelihood analyses were subjected
to 1000 bootstrap replicates.

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DETECTING TIME VARIATION IN DIVERSIFICATIONRATESBoth the γ-statistic (Pybus and Harvey 2000) and the coalescent approach of Morlon et al. (2010, codes available
at http://www.cmap.polytechnique.fr/∼morlon/resources.html)
were employed to test for temporal variation in diversification
rates. Both methods are robust to absolute age estimates and require only relative divergence times. These approaches also allow
us to statistically account for phylotypes that are not included in
the dataset. Accounting for unsampled phylotypes is essential as
missing phylotypes can cause the spurious detection of declines
in diversification rates (Pybus and Harvey 2000). Completing theanalyses of γ with another method is also essential, as a negative
γ does not necessarily indicate an early burst of diversification(Fordyce 2010). Other methods that allow for periods of diversity decline (Morlon et al. 2011) and rate heterogeneity acrosslineages (Alfaro et al. 2009; Morlon et al. 2011) are also applicable, but not required to address our central question of whether
diversification rates have varied over time.
To assess the robustness of our results to incomplete phylotype sampling, we analyzed all datasets using both the γ and thecoalescent with the assumption that the true number of existing
phylotypes (N) is 15 (the current number of recognized phylotypes), 17 (the current number of recognized phylotypes plus thetwo newly proposed phylotypes), 20, 30, 50, 100, 150, 200, and250. An upper limit of 250 phylotypes, more than 15-fold thetotal number of currently described B. burgdorferi sl phylotypes,
seems a reasonable upper bound on the true number of phylotypes
given the extensive effort that has been made to sample this clade.
We used the Monte Carlo Constant Rates (MCCR) γ approach detailed in Pybus and Harvey (2000) to test for a decline
in diversification rates while accounting for potentially missing
taxa. This approach assumes that missing taxa are randomly distributed in the tree. Violations of this assumption could lead tospurious detection of rate declines (Cusimano and Renner 2010),
extensions of the approach exist that can account for overdispersed or underdispersed sampling (Brock et al. 2011). However,
we chose not to employ these methods here as we had no a priori
expectations of the form of the sampling. For each phylogeny, we
calculated the γ-value of the 100 Bayesian phylogenies with bestposterior support. We then compared these values to the critical
MCCR corrected γ-value indicating a significant decay in diversification rate (P < 0.05). The critical MCCR corrected γ-value
was estimated for each phylogeny and each assumed number of
total phylotypes (N).
The coalescent approach employed yields an approximate
likelihood expression for internode distances in a phylogeny under a variety of diversification scenarios (Fig. 1 and Table 1 from
Morlon et al. 2010). The coalescent framework was introduced in
population genetics to study the genealogy of samples, and is thus

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particularly well suited to the study of phylogenies that may not becompletely sampled. We compared the fit of four diversification
models to the empirical data: the Yule model (constant diversification rate, no extinction), the YuleVAR model (varying diversification rate, no extinction), the Birth–Death model (constant diversification and extinction rates), and the BirthVAR-Death model(varying diversification rate, constant extinction rate). When rates
varied over time, we used an exponential form of rate variation
with time, t. Time is measured from the present to the past, suchthat t = 0 denotes the present.
To test the hypothesis that diversification rates have varied
over time, we used likelihood ratio tests for the two pairs of models with constant versus varying rate (i.e., Yule vs. YuleVAR,
and Birth–Death vs. BirthVAR-Death). The possibility that phylotypes exist but are not represented in our datasets was accounted
for by computing the sampling fraction f (Morlon et al. 2010) for
each reconstructed phylogeny and each hypothesized number ofphylotypes (N) and fitting the various diversification models withthe corresponding sampling fraction. We tested the robustness of
the results to topological and branch-length uncertainty by running the likelihood ratio tests on the 100 Bayesian phylogenies
with best posterior support.

Results
The Bayesian inference of the evolutionary relationships among
the three-locus tree revealed 15 phylotypes separated by long
branches with relatively little variation among sequences withineach phylotype (Fig. 1A). This visual inspection was supported
by results from the Generalized Mixed Yule Coalescent (GMYC)
model, which identified a threshold at 1.4% sequence divergence
resulting in 15 independently evolving clusters. Hence, the number of phylotypes delimited with our phylogenetic reconstruction
matched the number of described B. burgdoferi sl phylotypes.
Similarly, the number of phylotypes delimited by the GMYC
model for the seven-loci tree (11 phylotypes with less than 1.3%
sequence divergence, Fig. 1B) and the partial chromosome tree(six phylotypes with less than 1.9% sequence divergence, Fig. 1C)matched the number of described B. burgdoferi sl phylotypes expected given the sequences used to reconstruct the phylogenies.
Importantly, the GYMC models clustered sequences by the currently accepted genospecies names in all cases. These results indicate that each phylotype can be treated as an independently evolving lineage comparable to species in sexual eukaryotes (Avise andWollenberg 1997; Richter et al. 2006; Postic et al. 2007).
The Bayesian trees reconstructed from the three-locus
dataset, the seven-locus dataset, and the partial-chromosome
dataset featured similar topologies and shapes, with many nodes
toward the root and long branches leading to each phylotypiccluster of sequences (Fig. 1). It is unlikely that the long terminal

E X P L O S I V E D I V E R S I F I C AT I O N I N BAC T E R I A

Figure 1. Bayesian phylogenetic reconstruction (left) and ultrametric phylogenies (right) of all available B. burgdorferi sensu lato (sl)phylotypes, based on a total of 1543 gene sequences. The triangles represent the maximum genetic distance among sequences within
phylotypes in the Bayesian phylogenies. The majority of among-phylotype diversification events occur near the root of the phylogenies
suggesting a rapid burst of diversification early in the evolutionary history of the group. Phylogenies were constructed using: (A) the
three loci for which data were available from 15 phylotypes, (B) seven loci for which data were available from 11 phylotypes, and (C) the
partial chromosome sequence data (chromosome). Nodes marked with asterisks are supported by posterior probabilities of more than
0.8 (∗ ), 0.9 (∗∗ ), or 0.95 (∗∗∗ ).

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branches observed in the ultrametric trees (Fig. 1, right column)
resulted from high among-lineage variation in molecular evolutionary rates, because the nonclocklike trees (Fig. 1, left column)
also featured long terminal branches and likelihood ratio tests did
not detect deviations from clocklike evolution (P > 0.1 for allcomparisons). Further, the phylogenetic trees inferred by maximum likelihood with a forced molecular clock (Fig. S1) and theBEST tree (Fig. S2) displayed internal nodes near the root andlong branches leading to each phylotype. The evolutionary relationships differed slightly among the phylogenies in some cases(ex B. valaisiana, Fig. 1) and statistical support for several internal nodes was not significant. However, the consistent shapeacross trees indicates phylotype diversification occurred early andrapidly in the history of the clade, potentially explaining the uncertainty in phylogenetic resolution and incongruence between
trees.
Macroevolutionary analyses of the phylogenies support the
hypothesis that the present-day diversity of B. burgdorferi sl results from rapid-early diversification (Figs. 2 and 3). The γ-values
calculated from the three- and seven-locus phylogenies were lessthan the smallest γ-value of the phylogenies simulated underconstant rate (hence, the null hypothesis of constant diversification rates was rejected, P < 0.001), even when assuming atrue diversity of as many as 250 B. burgdorferi sl phylotypes(Fig. 2A,B). The MCCR analyses of the partial chromosome phylogeny also significantly supported a time-decaying diversification rate (P < 0.05) unless the currently described phylotypes
represent less than 10% of the true number of extant phylotypes(Fig. 2C). These results strongly suggest that diversification occurred rapidly in the early history of the clade and has since
slowed.
The time-decaying diversification rate hypothesis was further
supported by the coalescent-based analyses. B. burgdorferi sl phylogenies were consistent with models that included time-varying
rates. Both the pure-birth and the birth-death models with varying
diversification rates were significantly more likely than models
with constant diversification rates (Fig. 3). Both models posit thatdiversification rates have decayed sharply since the diversification
events that happened early in the history of the clade. The strong
support we found for time-decaying rates is all the more striking
given that there are few B. burgdorferi sl phylotypes and thus a priori little statistical power to select complex (time-varying) models
over simpler (time-constant) models. Our conclusions were robust
to potential biases introduced by missing phylotypes as the models
with time-varying rates were consistently supported even when we
assumed that as many as 250 B. burgdorferi sl phylotypes exist today (Fig. 3). The conclusions were also supported by our analysesof the individual-locus trees (Fig. S3), suggesting that our results
are not an artifact of concatenating loci with different phylogenetic
signal.

50
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150
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250

Figure 2. Monte Carlo Constant Rates (MCCR) tests reject the hy-

pothesis that diversification rates have been constant over timeeven if considerable species diversity in the B. burgdorferi sl clade
has yet to be discovered. Black lines represent the γ-values of the
100 phylogenies with maximal Bayesian support. Black dots represent the critical γ-values of the constant-rates test corresponding to each assumed number of phylotypes. γ-values below thedots indicate significant rate-decay ( P < 0.05). The MCCR analysesof the three-locus (A) and seven-locus (B) phylogenies support atime-decaying diversification rate, even if the currently described
phylotypes represent less than 6% of the true number of extant
phylotypes. Similarly, the hypothesis of constant diversification
rate can be rejected for the chromosome phylogeny even if theactual number of existing phylotypes exceeds 100 (C).

Discussion
Although explosive radiations followed by rapid decreases indiversification rates is a common pattern for plant and animallineages, little is known about the macroevolutionary patterns
of prokaryotes, which constitute most of the diversity of life onearth. Contrary to a previous result suggesting that the tempo
of diversification in microorganisms is fundamentally different
from that of macroorganisms (Martin et al. 2004), our study
suggests that the variation in the rates of cladogenesis overtime in bacteria can be comparable to those reported in classic
examples of radiations (Harmon et al. 2003; Phillimore and Price

E X P L O S I V E D I V E R S I F I C AT I O N I N BAC T E R I A

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Figure 3. Likelihood ratio tests indicate that models with varying
diversification rates are significantly more likely than models withconstant rates regardless of the assumed number of species in the1
B. burgdorferi sl clade. The likelihood ratios (2 log LL H
H2 ) between
models with constant versus varying diversification rates are plotted as a function of the assumed number of extant phylotypes. The
dashed lines indicate the chi-square values corresponding to P <0.001, 0.01, and 0.05 (from top to bottom). The pure-birth modelwith decaying diversification rate is more likely given the data
than a pure-birth model with constant rates (left column), and thebirth–death model with decaying diversification rate is more likely
than the constant-rate birth–death model (right column). Models
with time-varying rates were consistently supported even when
we assumed that as many as 250 B. burgdorferi sl phylotypes exist
at present.

2008; Rabosky and Lovette 2008; Morlon et al. 2010). The rapid
diversification of B. burgdorferi phylotypes early in the history of
the clade followed by a decrease in diversification rates observedin this study closely resembles the patterns described in many
plant and animal lineages.
Our results conflict with previous findings that suggest thatthe diversification rates of microorganisms are constant over time(Martin et al. 2004). This discrepancy may stem from either
methodological artifacts or differing biological characteristics ofthe clades analyzed. Martin et al. (2004) pointed out that thebroad phylogenetic clade they investigated was substantially undersampled and exhibited significant substitution rate variation
among lineages. Undersampling and substitution rate variation
are thought to bias γ negatively rather than positively (Revell
et al. 2005), which would lead to a false detection of rate declines
rather than the opposite. These two factors nevertheless create
biases in the distribution of node heights, which weakens the general confidence one can have in the inferred patterns of lineage
diversification. By contrast, the focus on a narrow clade in thepresent study resulted in no departure from clocklike assumptions, allowing a more accurate assessment of node heights andthus the relative timing of diversification. Further, it is likely thatthe B. burgdorferi sl clade has been thoroughly sampled due to itsrole in human Lyme disease and because of its obligatory association with arthropod vectors and vertebrate hosts that are easy tocapture and investigate for infection (Barbour and Hayes 1986;
Nuttall 1999; van Dam 2002; Piesman and Gern 2004). Althoughnovel phylotypes will doubtless be described in the future, it isunlikely that the 15 phylotypes analyzed here represent a smallenough fraction of the total diversity to lose the statistical support
we found for the decay in diversification rate over time (Figs. 2
and 3).
One difference between our analyses and that of Martin et al.
(2004) is that we grouped individual sequences into phylotypes
before analyzing diversification patterns. Our resulting phylogenies therefore represent divergences among phylotypes. By contrast, Martin et al. (2004) analyzed the lineage-through-time plotof a phylogeny without grouping sequences; their phylogeny represents divergences both within and between phylotypes. Hence,a potential slowdown in the diversification rate of phylotypes in
Martin’s study could have been masked by recent divergencesof individuals within phylotypes. Because our goal was to analyze the diversification patterns of independently evolving bacterial lineages—comparable to species in macroorganisms—wedefined the independently evolving lineages (the phylotypes) prior
to analyzing their phylogenetic relationships and diversification
dynamics. The 15 phylotypes we defined, based on molecular datafrom multiple loci, conform to the 15 B. burgdorferi sl phylotypes
recognized in the literature.
The patterns of diversification observed in the B. burgdorferi clade may indicate that time-decaying diversification rates
are a common pattern in the microbial world, as they are in
macroeukaryote clades. However, the rapid-early diversification
observed here may result from specific biological features of B.
burgdorferi that differ from free-living microbes like those previously analyzed (Martin et al. 2004). B. burgdorferi are obligatorily associated with vertebrates and hematophagous arthropods and thus may be subject to similar ecological processes
that structure the macroevolutionary patterns of macroeukaryotes. For example, the geographical processes that influence

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diversification in macroeukaryotes by creating ecological opportunity and reducing gene flow among populations (Day 2000)
may be less important for many free-living prokaryotes that are
more readily transported over large distances. The constant association of B. burgdorferi with either ticks or vertebrate hosts(Barbour and Hayes 1986; Nuttall 1999; van Dam 2002; Piesman
and Gern 2004) may limit gene flow between isolated populations, rendering macroevolutionary patterns more similar to thoseof macroorganisms. Hence, time variation in diversification rates
may be rare among free-living bacteria as previously suggested(Martin et al. 2004) but common in bacteria that are obligatorily
associated with macroorganisms. This hypothesis could be tested
using a broader array of well-sampled free-living and nonfreeliving bacterial phylogenies.
The fast-early diversification followed by a strong decay indiversification rates observed in the B. burgdorferi phylogenies
is consistent with the hypothesis that ecological opportunity promoted diversification (Schluter 2000; Yoder et al. 2010), that is,that diversification is constrained by ecological factors (Rabosky
and Lovette 2008; Rabosky 2009; Morlon et al. 2010; Rabosky
and Glor 2010). An intriguing hypothesis is that diversification of
B. burgdorferi phylotypes may have resulted from adaptive specialization to ecological niches, such as specific vertebrate species
or tick vectors (Kurtenbach et al. 2002, 2006; Xu et al. 2003). Indeed, each phylotype infects only a subset of vertebrate species
collectively infected by all B. burgdorferi phylotypes (Kurtenbach
et al. 2002, 2006); for example, human infectiousness is restrictedprimarily to three of the 15 phylotypes (Baranton et al. 1992). Although current data suggest that B. burgdorferi phylotypes partition potential niches only into broad categories such as association
with either birds, mammals, or both (Kurtenbach et al. 2002), additional studies on host species specificity or organ tropism mayreveal further niche partitioning. Recent evidence suggests thatlineages within two of the human infectious B. burgdorferi phylotypes specialize on specific host species and maybe organs within
hosts (Kurtenbach et al. 2002; Hanincova et al. 2003; Brisson
and Dykhuizen 2004; Hanincova et al. 2006; Brisson et al. 2008;
Brisson et al. 2011). Finer scale investigation into host species use
by B. burgdorferi phylotypes that are not medically relevant may
also reveal greater specialization than is currently acknowledged.
Evidence of rapid diversification in bacteria gathered fromexperimental microcosm studies (reviewed in MacLean 2005;
Kassen 2009) is consistent with the analyses supporting timedecaying diversification rates in B. burgdorferi sl. A wide varietyof bacterial lineages readily and repeatedly diversify to adapt toa myriad of structured environmental and biotic niches, and to
specialize on alternative resources during experimental evolution
studies (Hardin 1960; Friesen et al. 2004; Kassen and Rainey
2004; Barrett et al. 2005; Barrett and Bell 2006; Brockhurst et al.
2006; Habets et al. 2006; Jasmin and Kassen 2007). These exper-

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iments suggest that diversification rates should be rapid initiallywhen ecological opportunities are abundant and decrease as opportunities become less abundant (MacLean 2005; Kassen 2009).
Additionally, nonadaptive radiations, potentially caused by vicariance processes, could also lead to a time decline in diversification
rates as space is occupied (Kozak et al. 2006). Therefore, the signature of decaying diversification rates is likely to be common
and readily observable in many prokaryotic phylogenies.
There are many challenges associated with the study ofmacroevolutionary patterns in microorganisms. Although many
microbial groups must be analyzed to establish that the rapid-early
diversification hypothesis is a common macroevolutionary featureacross the entire tree of life, the analyses presented here suggestthat rapid-early diversification can occur in bacterial lineages.
ACKNOWLEDGMENTS
We thank P. Sniegowski and D. Dykhuizen for helpful advice and comments on the manuscript. We also thank P. Turner, M. Pennell, and two
anonymous reviewers for comments that improved the manuscript. This
work was supported by grants from the NIAID (AI076342) to DB. HM
acknowledges support from the Centre National de la Recherche Scientifique and the Agence Nationale de la Recherche (ECOEVOBIO—CHEX2011). JBP acknowledges support from the James S.
McDonnell Foundation, the Alfred P. Sloan Foundation, the David andLucille Packard Foundation, the Burroughs Wellcome Fund, and the Defense Advanced Research Projects Agency (HR0011–05-1–0057).
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Associate Editor: P. TurnerSupporting Information
The following supporting information is available for this article:
Figure S1. Phylogenetic trees inferred by maximum likelihood with a forced molecular clock for: (A) the three-locus tree
(multilocus3), (B) the seven-locus tree (multilocus7), and (C) the chromosome tree (chromosome).
Figure S2. Phylogenetic tree inferred by Bayesian Estimation of Species Trees (BEST).
Figure S3. For most of the seven individual locus phylogenies trees, likelihood ratio tests indicate that the pure-birth model withdecaying diversification rate is significantly more likely than a pure-birth model with constant rate regardless of the assumed
number of species in the B. burgdorferi sl clade.
Supporting Information may be found in the online version of this article.
Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by theauthors. Any queries (other than missing material) should be directed to the corresponding author for the article.

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