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Research
Cite this article: Chen L, Wiens JJ. 2021
Multicellularity and sex helped shape the Tree
of Life. Proc. R. Soc. B 288: 20211265.
https://doi.org/10.1098/rspb.2021.1265
Received: 3 June 2021
Accepted: 6 July 2021
Subject Category:
Evolution
Subject Areas:
evolution
Keywords:
bacteria, diversification, multicellularity,
phylogeny, sex, species richness
Author for correspondence:
John J. Wiens
e-mail: wiensj@email.arizona.edu
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.
c.5515170.
Multicellularity and sex helped shape
the Tree of Life
Lian Chen
1,2
and John J. Wiens
2
1
College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037,
People’s Republic of China
2
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721-0088, USA
JJW, 0000-0003-4243-1127
Across the Tree of Life, there are dramatic differences in species numbers
among groups. However, the factors that explain the differences among
the deepest branches have remained unknown. We tested whether multi-
cellularity and sexual reproduction might explain these patterns, since the
most species-rich groups share these traits. We found that groups with
multicellularity and sexual reproduction have accelerated rates of species
proliferation (diversification), and that multicellularity has a stronger effect
than sexual reproduction. Patterns of species richness among clades are
then strongly related to these differences in diversification rates. Taken
together, these results help explain patterns of biodiversity among groups
of organisms at the very broadest scales. They may also help explain the
mysterious preponderance of sexual reproduction among species (the ‘para-
dox of sex’) by showing that organisms with sexual reproduction proliferate
more rapidly.
1. Introduction
Explaining the dramatic differences in species diversity among groups of
organisms is a fundamental challenge in evolutionary biology [1]. Yet, few
studies, if any, have attempted to explain richness patterns among the deepest
branches of the Tree of Life. For example, animals, land plants and fungi
each have approximately 1.5 million, 350 000 and 140 000 described species
(figure 1), respectively. By contrast, most other major groups (e.g. bacteria,
archaeans, various protist clades) have far fewer described species (figure 1),
in the tens of thousands or less. What explains these striking differences
in diversity?
To our knowledge, no previous studies have attempted to explain patterns of
species diversity at this scale. Nevertheless, an earlier study [2] did examine
patterns of species richness among eight kingdom-level clades (e.g. animals,
fungi, plants, bacteria, archaeans, various protists). That study concluded that
most variation in species richness among these clades (approx. 55%) was
explained by differences in their rates of diversification and not their ages. Diver-
sification rates describe how quickly richness accumulated within clades, or the
rate of speciation minus the rate of extinction [3,4]. Thus, clades that are relatively
young and have high extant species richness (like animals, fungi and land
plants) have higher net diversification rates than those that are older or have
fewer living species (which then have lower diversification rates). This pattern
raises the obvious question: what trait or traits might explain these accelerated
diversification rates, especially among animals, plants and fungi?
Here, we test if two traits may help explain these patterns: multicellularity and
sexual reproduction. An obvious trait shared by animals, land plants and many
fungi is that they are multicellular, with adult bodies consisting of many connected
cells. Multicellularity has evolved many times, and is considered a major evol-
utionary transition [5–7]. Multicellularity is potentially important as a driver of
diversification because multicellularity may be a necessary precursor to overall
morphological complexity (e.g. allowing for different cell and tissue types with
© 2021 The Author(s) Published by the Royal Society. All rights reserved.
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different functions [6,7]). However, there have been few tests of
whether diversification rates and multicellularity are correlated,
and these have been at relatively small phylogenetic scales.
For example, a study within cyanobacteria found that multicel-
lularity might increase diversification rates within this group
[8]. Although this result is intriguing, studies within particular
clades cannot directly address whether multicellularity explains
variation in diversification rates across the entire Tree of Life.
The presence of sexual reproduction might also shed light
on these large-scale diversity patterns, as it is also present in
the three largest kingdom-level clades (animals, fungi, land
plants). Yet, the larger number of species that reproduce sexu-
ally (at least part of the time) relative to those that reproduce
only asexually is a long-standing puzzle in evolutionary
biology [9–13]. This puzzle is often framed in terms of the
costs of sexual reproduction relative to asexual reproduction,
and why sexual reproduction is maintained in sexual species
[12,13]. Differences in diversification rates associated with
each mode might also strongly influence the relative richness
of species with each reproductive mode. To our knowledge,
no studies have tested whether sexual and asexual reproduc-
tion are associated with different diversification rates across
the Tree of Life. However, as for multicellularity, there have
been important studies within smaller clades, such as rotifers
[14]. Much of this literature has focused on the persistence
of secondarily asexual lineages within ancestrally sexual
clades [15,16].
Possible links between sexual reproduction and multi-
cellularity have also not been tested at this deep scale.
Multicellularity has been hypothesized to precede and
underlie the evolution of differentiated sexes [11,17,18], if not
sexual reproduction itself.
In this study, we analyse the relationships between diversi-
fication rates, multicellularity and sexual reproduction among
major clades across the Tree of Life. First, we estimate the pro-
portions of multicellular species and sexually reproducing
species in each of 17 kingdom-level clades (figure 1), based
on a literature survey spanning more than 1146 papers. These
17 clades include animals, land plants, fungi, bacteria, archae-
ans and 12 major groups of protists and algae (we also explore
subdividing some of these clades). Our definitions of these
traits are given in the Material and methods, and the electronic
supplementary material, appendix S1. Second, we estimate
rates of diversification for each of theseclades, using a standard
method for higher-level taxa [19]. Third, we test for relation-
ships between diversification rates and sexual reproduction
and multicellularity using phylogenetic regression. Using this
overall approach, we can address how much variance in diver-
sification rates each variable statistically explains (both alone
and in combination), not simply whether each trait has a sig-
nificant effect on diversification. We also address how much
variation in species richness among clades is explained by
this variation in diversification rates. Fourth, we test whether
sexual reproduction and multicellularity are significantly
related to each other at this scale. We perform these analyses
using both numbers of described species, and using projections
(estimates) of species richness that suggest there may be hun-
dreds of million (or even billions) of undescribed species,
especially of bacteria [20]. These projections are described in
the electronic supplementary material, appendix S2.
2. Material and methods
(a) Phylogeny
We used a previously assembled phylogeny that spanned the Tree
of Life [2], with the eukaryotic portion from Parfrey et al. [21]. We
initially used 17 major clades from this tree [2]. These clades were
either ranked askingdoms or were distinct clades outside the com-
monly recognized kingdoms. Scholl & Wiens [2] used eight major
non-overlapping clades in their kingdom-level analyses: Archaea,
Eubacteria, Animalia, Plantae (Embryophyta), Fungi, Amoebozoa,
Excavata andthe SAR clade. However, their tree also included nine
additional clades that did not overlap with each other or the other
eight clades. These nine clades are traditionally classified as pro-
tists and/or algae, and consisted of: Charophyta, Chlorophyta,
Choanoflagellatea, Cryptophyta, Filasterea, Glaucophyta, Hapto-
phyta, Katablepharidophyta and Rhodophyta. We pruned the
tree [2] to include only one species from each clade (the choice
has no impact). This tree was then used in the phylogenetic
regression analyses, and is given in the electronic supplementary
material, datafile S1.
Hypothetically, the Timetree of Life [22] could have been
used to construct an alternative tree. However, this tree did not
resolve relationships (or ages) among some relevant clades (e.g.
Glaucophyta, Excavata, Rhodophyta). Furthermore, many dates
were from Parfrey et al. [21], such that this source is not necessarily
an alternative estimate.
Charophyta was treated as a clade here, containing
Charophyceae, Chlorokybophyceae, Coleochaetophyceae and
Zygnemophyceae. However, this taxon may be paraphyletic
with respect to land plants [23,24]. Although this is problematic,
most species in Charophyta belong to Zygnemophyceae (4107 of
4867; electronic supplementary material, datafiles S2–S4), which
43210 0 0.4 0.8 1.2 1.6
million species
billion years ago
Charophyta
Embryophyta
Chlorophyta
Katablepharidophyta
Cryptophyta
Rhodophyta
Glaucophyta
Haptophytes
SAR
Excavata
Amoebozoa
Choanozoa
Animalia
Filasterea
Fungi
Archaea
Eubacteria
multicellular sexual
Figure 1. Summary of the 17 kingdom-level clades analysed in this study.
The time-calibrated phylogeny is shown on the left. For each clade, we show
the estimated proportion of species that are multicellular and the proportion
with sexual reproduction (in black). The species richness of each clade (based
on described species) is summarized in the graph on the right. Most clades
have so few species (relative to animals, fungi and land plants) that they are
not visible in this graph, but we used raw (not log-transformed) values to
better illustrate the actual disparity in richness. Embryophyta corresponds
to land plants. The tree is given in the electronic supplementary material,
datafile S1 and the data in the electronic supplementary material,
datafile S5. Note that we also tested the impacts of treating many prokaryotic
clades as separate units, and of including eukaryotes only (see Results).
(Online version in colour.)
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may be the monophyletic sister group to land plants [23,24].
Moreover, the estimated frequencies of multicellularity and
sexual reproduction are very similar for Zygnemophyceae (30.5
and 100%) and Charophyta (38.3 and 99.5%; electronic sup-
plementary material, datafiles S3 and S5). Therefore, treating
this taxon in our analyses as Charophyta or Zygnemophyceae
should have little impact on the results, because the phylogenetic
position (sister to Embryophyta), age, richness, diversification
rates and trait frequencies are similar either way. Furthermore,
other subgroups of Charophyta could also have been used as
terminal units in our analyses, given their phylogenetic distinct-
ness from land plants and other algal clades [23,24] but the age
and composition of these groups is still somewhat uncertain.
Besides the main analyses using 17 major clades, we also per-
formed alternative analyses in which we treated bacteria as 14
distinct clades ( phyla) and archaeans as two. This yielded 31
taxa overall, with a similar number of prokaryote and eukaryote
clades (n= 16 and 15, respectively). We used previously com-
piled data on the phylogeny, species richness and age of these
16 prokaryotic phyla, and their relationships to other taxa in
the tree [2]. Details are in the electronic supplementary material,
appendix S3, and the tree for all 31 taxa is given in the electronic
supplementary material, datafile S6. We were not able to project
the undescribed richness among these clades. However, results
were generally similar between the 31-clade and 17-clade ana-
lyses, suggesting that simply adding more bacterial species
would not overturn the results. We also performed analyses in
which we included only the 15 eukaryotic clades, which yielded
similar results. This allowed us to ensure that our main con-
clusions were not an artefact of comparing the (mostly)
unicellular and asexual prokaryotes to the eukaryotes (which
are variable for both traits). The tree for these 15 clades is
given in the electronic supplementary material, datafile S7.
We acknowledge that we focused on a single estimate of phy-
logeny and divergence times for this study [2,21]. We know of
few other estimates that are both time-calibrated and include
all the relevant clades. Further, our phylogenetic regression ana-
lyses found little phylogenetic signal in the relationships between
these variables (table 1), making these phylogenetic regression
analyses equivalent to non-phylogenetic analyses. Thus, the
details of the phylogeny should have little impact on the results.
On the other hand, alternative phylogenies might influence the
ages of clades, and thereby the estimated diversification rates.
However, alternative trees should also show that land plants, ani-
mals and fungi are relatively young, and thus have high rates
relative to other kingdom-level clades (given the high species
richness of these three clades; figure 1), regardless of the details
of the ages and phylogeny.
We note that we could have analysed the data at a lower
taxonomic level. However, our primary interest in this study
was in finding the traits that explain variation in diversification
rates among the largest branches of the Tree of Life. Therefore,
Table 1. Relationships among diversification, sexuality, multicellularity and species richness. (Analyses are performed using the number of described species in
each of the 17 kingdom-level clades (electronic supplementary material, datafile S5), a relatively low projected number of species for several major clades
(electronic supplementary material, datafile S8) and a relatively high projected number of species (electronic supplementary material, datafile S9). Diversification
rates were estimated using ε= 0.5. Results using alternative values are very similar (electronic supplementary material, tables S1 and S2). Lambda is the
estimated phylogenetic signal for the relationship. Values in parentheses for the multiple regression model are the standardized partial regression coefficients for
each independent variable. Akaike information criterion (AIC) values were often negative when the dependent variable was diversification rates. We did not
compare positive and negative AIC values. Italicized p-values are significant after a table-wide, sequential Bonferroni correction.)
dependent variable independent variable AIC λr
2
p-value
described richness
div. rate multicellularity −160.7763 0
a
0.5453 0.0007
div. rate sexuality −156.3670 0
a
0.4107 0.0056
div. rate multicellularity (0.73) + sexuality (0.27) −159.5671 0
a
0.5660 0.0029
multicellularity sexuality 164.6609 0
a
0.5418 0.0008
ln-richness div. rate 82.5635 0
a
0.5384 0.0008
ln-richness ln-age 95.0779 0
a
0.0364 0.4636
projected richness (low)
div. rate multicellularity −157.1000 0
a
0.4905 0.0017
div. rate sexuality −154.2875 0
a
0.3988 0.0065
div. rate multicellularity (0.65) + sexuality (0.35) −156.3698 0
a
0.5272 0.0053
multicellularity sexuality 163.4575 0
a
0.5025 0.0014
ln-richness div. rate 100.2158 0.826 0.5369 0.0008
ln-richness ln-age 110.0502 0
a
0.0115 0.6825
projected richness (high)
div. rate multicellularity −156.0320 0
a
0.5033 0.0014
div. rate sexuality −153.2296 0
a
0.4143 0.0053
div. rate multicellularity (0.64) + sexuality (0.36) −155.4694 0
a
0.5436 0.0041
multicellular sexuality 163.2765 0
a
0.5004 0.0015
ln-richness div. rate 103.6561 0.86 0.5651 0.0005
ln-richness ln-age 114.4356 0
a
0.0164 0.6245
a
Aλof 1 was significantly rejected ( p< 0.05).
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we did not focus on explaining variation among subclades (e.g.
within animals, plants or fungi). Of course, it would be interest-
ing to analyse multicellularity and sexual reproduction within
those clades that are variable for one or both traits (e.g. fungi).
However, the results at lower taxonomic scales do not necessarily
explain the large-scale patterns, and these patterns at the largest
phylogenetic scales were our focus here. In other words, we
chose the traits based on the phylogenetic scope: we did not
choose the phylogenetic scope based on the traits.
(b) Multicellularity
After selecting the 17 focal clades, we estimated the frequencies of
multicellularity and sexual reproduction among their species. The
estimates are summarized inthe electronic supplementary material,
datafiles S5, S8 and S9, and supportingdata and references are given
in the electronic supplementary material, datafiles S2–S4.
We first estimated how many species in each clade were multi-
cellular. We defined a multicellular organism as having cell-to-cell
adherence and cell-to-cell communication [25]. In general, we con-
sidered a species to be multicellular if it was multicellular during at
least part of its life cycle (given that most organisms which are con-
sidered multicellular are also unicellular during part of their life
cycle, if only briefly). Some fungus species are dimorphic and
have two different phenotypes: a mycelial, multicellular form
and a yeast-like, unicellular form [26]. If these species had a multi-
cellular, mycelial form, they were considered multicellular. Species
characterized instead as exclusively colonial or yeast-like were con-
sidered unicellular, as the usual, dominant form in yeast is
unicellular [27]. Colonial organisms have cell-to-cell adherence
but not cytoplasmic (symplastic) continuity among adjoining
cells [25]. Species in which individuals were generally unicellular
but occasionally aggregated (but not as part of the regular life
cycle) were not considered multicellular.
Clades reported to be entirely unicellular or multicellular
were coded as such (electronic supplementary material, datafile
S2). If a clade included both unicellular and multicellular taxa,
we estimated the frequency of multicellularity among species,
starting at the phylum level (electronic supplementary material,
datafile S3). We searched the literature using Google Scholar,
with the name of each higher taxon and ‘unicellular’and then
‘multicellular’as keywords. We used a list of phyla within
each clade [2], but with four additional phyla in fungi (Chytri-
diomycota, Microspordia, Mucoromycota, Zygomucota). If a
phylum included both unicellular and multicellular classes, we
estimated the proportion of species with each state based on
their frequency among classes. Similarly, we searched at the
level of orders, families and genera when these taxa were vari-
able. If a genus was not described as being multicellular or
unicellular overall, we searched for data at the species level.
We estimated the frequency of each state in that genus based
on species with known states. For bacteria, which are predomi-
nantly unicellular, we performed a more limited search to
estimate the frequency of multicellularity (details in the
electronic supplementary material, appendix S4).
For each clade, the frequency of each state was estimated
based on the proportion of species with each state in the
higher taxa within the clade, and the richness of those taxa.
Specifically, we multiplied the proportion by the richness of
that higher taxon and then summed the richness for each state
across the taxa within that clade. This was done at all taxonomic
scales (e.g. phyla to genera) that were variable for this trait. Taxa
with no data were excluded when calculating the proportion of
multicellular species.
In general, we used species richness data summarized for
major clades [2]. However, we also used the Catalogue of Life
[28] (CoL), and other databases (see below) to estimate the rich-
ness of lower-level taxa. This sometimes led to slight differences
in richness for phyla and other higher taxa (relative to [2]), and
we used these updated numbers instead.
For fungi, we used the CoL [28] for five phyla (Ascomycota,
Basidiomycota, Glomeromycota, Microsporidia, Zygomycota).
This database included more species of these phyla and the taxo-
nomic composition of each phylum was relatively clear (e.g. in
terms of family and genera). Taxonomic information above the
family level was relatively clear in the MycoBank Database [29].
However, below the family level, the taxonomy was more complex
(e.g. given different placements of species among genera). We used
MycoBank [29] for three phyla (Blastocladiomycota, Chytridiomy-
cota, Mucoromycota). Mucoromycota was not found in the CoL
[28]. Blastocladiomycota was a class of Chytridiomycota in the
CoL [28], not a separate phylum. We checked data carefully to
avoid replicated genera. Subspecies, varieties and synonyms
were not counted as species. Because two databases were used
for the eight fungal phyla, we compared genera, families, orders
and classes among these phyla in the two databases to avoid
including the same taxon in different phyla.
Data were lacking in the CoL [28] for the algal phyla Bacillario-
phyta, Charophyta, Chlorophyta, Euglenozoa and Rhodophyta. We
instead used AlgaeBase [30] for these taxa. AlgaeBase includes ter-
restrial, marine and freshwater algae, and is particularly complete
for marine algae.
The phyla Apicomplexa, Dinophyta, Fornicata, Haptophytes
and Rhizaria were not found in the CoL [28]. Furthermore, rich-
ness data for Amoebozoa were limited. Therefore, we used the
National Center for Biotechnology Information (NCBI) taxon-
omy database [31] for these six phyla. Only species with
formal scientific names in the NCBI taxonomy database were
used to estimate species richness of phyla, classes, orders,
families and genera. Entries based on environmental samples,
varieties and unverified species were not included.
(c) Sexual reproduction
We also estimated the proportion of sexual and asexual species in
eachkingdom. Our definitions of asexual versus sexual reproduction
followed Kondrashov [32]. Thus, we define asexual reproduction as
reproduction in which each new individual originates from mitotic
cell division, which does not change the genotype. Sexual reproduc-
tion is defined as the alteration of meiosis and syngamy with
attendant segregation and recombination. Meiotic cell division
involves halving the amount of DNA, genetic recombination from
independent segregation of nonhomologous chromosomes and
crossing over between homologous chromosomes. Syngamy
involves the fusion of two haploid gametes. We give more detailed
descriptions of different terms used to characterize reproduction in
the electronic supplementary material, appendix S1.
Overall, we considered a species to be sexual if it was
described as having sexual reproduction, or both sexual and
asexual reproduction, or having a sexual stage. Only species
described as being entirely asexual were considered asexual.
We calculated the proportion of sexual speciesin each kingdom
following the general methodology described for multicellularity.
However, for animals and land plants, large-scale summaries of
trait frequencies were available. We describe how we estimated fre-
quencies for these two clades in the electronic supplementary
material, appendix S5. We also confirmed that similaroverall results
were obtained using a different frequency of sexual reproduction in
land plants (electronic supplementary material, appendix S5).
For other clades, we searched the literature for data on reproduc-
tion in taxa within that clade. Specifically, we used the name of the
taxon (e.g. phyla, classes, orders) and ‘sexual’and then ‘asexual’as
keywords to search the literature using Google Scholar. However,
there were fewer data available on sexuality than cellularity.
In cases in which we had to search for data at the genus
level (i.e. for variable families), only the five largest genera in
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the family were used when the family included more than five
genera (the largest genera will be the most influential for deter-
mining the family-level frequency). If we could not find
information for the five largest genera in the family, we included
the sixth largest genus (or seventh, etc.), until we found data for
at least five genera. For most fungal phyla, we searched for repro-
ductive data at the species level.
Bacteria are not generally considered to have sexual reproduc-
tion, but they do have genetic exchange with other conspecific
individuals. Therefore, we also tested if our results would be
impacted by considering all bacteria to have sexual reproduction.
(d) Estimating diversification rates and species richness
We used a well-established approach for estimating diversi-
fication rates of higher-level taxa, the method-of-moments
estimator for stem-group ages (MS estimator hereafter [19]).
This approach only requires information on the age and species
richness of each clade, rather than requiring a detailed time-cali-
brated phylogeny within each clade (which is needed for most
alternative methods, and is lacking for many clades analysed
here). Using the stem-group estimator, only one species must
be included in the phylogeny per clade, and the approach can
also be robust to incomplete sampling among clades [2]. This
approach yields strong relationships between true and estimated
rates in simulations, even when rates vary strongly between sub-
clades [33] and when rates vary strongly within clades over time
[34]. Therefore, it does not require constant rates within clades
over time to be accurate, despite many assertions to the contrary.
The approach also remains accurate when there are faster rates in
younger clades [35], a pattern found to be widespread across the
Tree of Life [2]. This approach does not attempt to disentangle
the contribution of speciation and extinction rates to diversifica-
tion rates, nor estimate variation in rates at different timepoints
within a clade. We focus on explaining the current patterns
of richness among these clades, even if richness within clades
changed extensively over time.
The MS estimator uses a correction for clades that are
entirely unsampled because they are extinct (ε, extinction frac-
tion [19]). Therefore, a single value of εis typically applied
across all clades. Simulations show that the MS estimator is accu-
rate when a single εis assumed but speciation and extinction
rates vary among clades [33,34]. Following standard practice,
we used three values (0, 0.5, 0.9) but emphasize those from
the intermediate value (0.5) in the main text. All three yielded
similar relationships between traits and diversification rates.
Again, simulations show that different values also yield similar
relationships between true and estimated rates for the stem-
group estimator, even when extinction rates vary from clade to
clade [33,34].
We used three approaches for estimating the species richness
of each clade for calculating diversification rates. First, we used
the described species richness of each clade [2], as described
above. We then used two alternative estimates [20] that incorpor-
ate projections of undescribed species (but focusing on specific
clades projected to be particularly species rich). We describe
these estimates in the electronic supplementary material, appen-
dix S5. Note that these projections include bacteria, protists,
fungi and animals.
Importantly, the use of these projections should account for
potential bias if there is a greater propensity for researchers to
describe multicellular rather than unicellular species. Specifically,
most of the undescribed (projected) richness is among unicellu-
lar species of bacteria, protists and fungi. We think that using
specific estimates of undescribed richness is the best way to
deal with this potential bias. Note also that the projections
used [20] were based on a review of all major groups across
the Tree of Life (and only some of them were projected to have
millions of undescribed species).
(e) Testing relationships between variables
We used phylogenetic generalized least-squares regression
(PGLS) to test relationships between traits and diversification
[36]. PGLS was implemented in the R package caper [37]
v. 0.5.2. Following standard practice, we used the maximum-
likelihood transformation of branch lengths, based on the
estimated values of phylogenetic signal (λ) [38]. The use of λ
estimates and corrects for the observed level of phylogenetic
signal in the data. PGLS is valid when all variables are continu-
ous, and when independent variables are categorical and the
dependent variable is continuous [36], like diversification rates.
Statistical significance was assessed using a sequential Bonferroni
correction [39,40] for each table of regression results.
We tested for relationships between diversification (dependent
variable) and multicellularity and sexual reproduction (indepen-
dent variables). We tested each independent variable separately
and then in combination. We then compared the AIC (Akaike
information criterion [41]) for all three models to evaluate which
had the best fit. Models within four AIC units of each other were
not considered to have significantly different fit [41]. For the
multiple regression model (including both multicellularity and
sexual reproduction), we also calculated the standardized partial
regression coefficients (using R code from [42]), to evaluate the
contribution of each independent variable to the overall variance
explained. The ability of this general approach to infer how
much variance in diversification rates is explained by each variable
(alone and in combination) is a major advantage relative to other
approaches to studying diversification.
In addition to testing relationships between traits and diversifica-
tion, we also used PGLS to evaluate how much variance in species
richness among clades was explained by variation in diversification
rates. A significant relationship between richness and diversification
rates is not inevitable [2,35]. We also tested fora relationship between
species richness and each clade’s stem age, given that clades may be
more species rich because they are older [2]. Finally, we tested for a
relationship between multicellularity (independent variable) and
sexual reproduction (dependent variable). Sexual reproduction
might (hypothetically) drive multicellularity instead of the converse,
but this should have little impact on the PGLS results.
Testing relationships between trait frequencies and diversifica-
tion rates among higher-level clades has been done in many
previous studies [42–45], including analyses across animals [43]
and plants [44]. Nevertheless, there are scenarios wherebytrait fre-
quencies might appear to be related to diversification, but without
a causal relationship. For example, a clade might consist of two
subclades (A and B), with the trait of interest present only in sub-
clade A but with increased diversification rates only in subclade B
[43]. However, this problematic scenario would need to be
repeated across multiple clades to generate a strong relationship
between the trait and diversification rates, which seems unlikely.
Note that the frequencies of traits within clades are not necessarily
related to the number of trait origins. Thus, there could be multiple
origins of a trait in a clade, but that trait could still be present at low
frequencies among species (especially if the trait did not increase
diversification rates). Alternatively, a trait could arise only once
within a clade and be present in almost all the species, or may
have arisen before the origin of that clade.
We used PGLS regression because this is a standard approach
for analysing comparative data. However, the data were not
normally distributed. We describe the normality tests and our
non-parametric analyses in the electronic supplementary material,
appendix S6, tables S7–S10.
3. Results
The estimated trait frequencies for each clade for multi-
cellularity and sexual reproduction are given in the
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electronic supplementary material, datafile S5, along with the
estimated species richness, age and diversification rate of
each clade. Data on trait frequencies, clade ages and richness
are summarized in figure 1, along with the phylogeny. The
baseline results are based on the number of described species
in each clade and an intermediate extinction fraction (ε=0.5)
for estimating diversification rates (εis the assumed ratio of
extinction to speciation [19]). However, we also explored the
robustness of the results to alternative extinction fractions,
different projections of species richness and other assumptions.
These baseline results showed significant, positive relation-
ships between multicellularity and diversification rates and
between sexual reproduction and diversification (figure 2a,b
and table 1). Multicellularity explained 54% of the variation
in diversification rates among clades, whereas sexual reproduc-
tion explained 41%. A phylogenetic multiple regression model
including both variables explained 57% of the variation in
diversification rates among clades, but had slightly poorer fit
than the one based on multicellularity alone (table 1). In the
context of the model including both traits, 73% of the variance
in diversification rates explained by the model was explained
by multicellularity, and 27% by sexual reproduction. Variation
in diversification rates also explained the majority of the var-
iance in richness among these clades (r
2
= 0.54), but the ages
of clades did not (r
2
= 0.04). There was a significant, positive
relationship between the proportion of sexual and multicellu-
lar species among clades (r
2
= 0.54; figure 2c).
Results were generally similar using projected species
numbers in major clades, rather than described species rich-
ness (table 1). Using a relatively low projected number of
undescribed species (282 million species total, 77% bacteria;
electronic supplementary material, datafile S8) showed
more variance in diversification rates explained by multi-
cellularity (49%) than sexuality (40%). A model including
both variables again had poorer fit than the one based on
multicellularity alone, and explained 53% of the variance in
diversification rates. In this model, multicellularity again
explained more variance than sexuality (65 versus 35%).
The relationship between multicellularity and sexuality was
also similar (r
2
= 0.50).
Results were also similar (table 1) using much larger pro-
jected species numbers (2.238 billion species total; 78%
bacteria; electronic supplementary material, datafile S9).
However, less variance in diversification rates was explained
by multicellularity alone (50%). A model including both
multicellularity and sexuality explained 54% of the variance
in diversification rates (but again with poorer fit than the
model including multicellularity alone). Multicellularity
again explained more variance than sexuality (64 versus
36%) in this two-trait model. Note that in both of these ana-
lyses using projected richness, we included large projected
numbers for bacteria, protists, fungi, and animals, and not
only bacteria.
These general results were robust to changing other assump-
tions, beyond species richness. There was very little effect of
using alternative extinction fractions (ε= 0 and 0.9) to estimate
diversification rates, for all three sets of species numbers (elec-
tronic supplementary material, tables S1 and S2). Assuming
that bacteria all have sexual reproduction also had relatively
minor effects (electronic supplementary material, table S3).
Overall, the variance in diversification rates explained by
sexual reproduction declined slightly when bacteria were
considered to have sexual reproduction.
We also performed analyses in which we treated prokar-
yotes as 16 separate clades (i.e. phyla) instead of two
(archaeans, bacteria), yielding 31 clades in total and similar
numbers of prokaryotic and eukaryotic clades (electronic
supplementary material, datafile S10). The baseline results
(ε= 0.5, described richness; electronic supplementary
material, table S4) were similar to those using 17 clades
1.0
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
proportion multicellular
proportion multicellular
proportion sexual
proportion sexual
diversification rate diversification rate
(b)
(a)
(c)
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0
0 0.2 0.4 0.6 0.8 1.0
Figure 2. Plots showing relationships between (a) estimated diversification
rates of the 17 clades and their estimated proportions of multicellular species,
(b) estimated diversification rates and their proportions of species with sexual
reproduction, and (c) the estimated proportions of sexual and multicellular
species. Results are based on described species richness and diversification
rates estimated using ε= 0.5. Results are similar using projected richness
(table 1) and alternative εvalues (electronic supplementary material,
tables S2 and S3). For the ease of interpretation, results based on raw
values are shown. The main results (table 1) are based on phylogenetic
regression, but are almost identical.
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(table 1), but the impact of multicellularity was weakened. We
found significant relationships between multicellularity and
diversification (r
2
= 0.40) and sexuality (r
2
= 0.52). A model
with both variables explained more variance (58%) and had
the best fit. We also included the eukaryotic cell as a separate
trait in these analyses (electronic supplementary material,
table S4). This trait was significantly related to diversification
rates (r
2
= 0.24), but an analysis with all three variables had
poorer fit than the one with just multicellularity and sexuality.
When we treated all bacteria as having sexual reproduction
in this analysis (electronic supplementary material, table S5),
the effect of sexuality on diversification rates was weaker
(r
2
= 0.18), and the best-fitting model included multicellularity
alone. These alternative analyses with 31 clades also showed
strong, positive relationships between species richness and
diversification rates (r
2
= 0.61) but not clade ages (r
2
= 0.23;
negative relationship).
Results were also similar to the main results (table 1) when
analysing eukaryotes alone (15 clades; electronic supplemen-
tary material, table S6), with a strong relationship between
multicellularity and diversification (r
2
=0.52),aweakerrelation-
ship with sexuality (r
2
= 0.37) and a combined model that was
dominated by multicellularity and had poorer fit than the one
including multicellularity alone. Again, there were strong
relationships between species richness and diversification
rates (r
2
= 0.59) but not clade ages (r
2
=0.11).
Relationships between multicellularity and diversification
were broadly similar among the main analyses (r
2
=0.50–
0.54; table 1) and those using 31 clades (r
2
= 0.40; electronic
supplementary material, table S4) or only the 15 eukaryotic
clades (r
2
= 0.52; electronic supplementary material, table S6).
The relationship between sexuality and multicellularity was
weaker using 31 clades and treating all bacteria as sexual
(r
2
= 0.29; electronic supplementary material, table S5),
but similar to the 17-clade analyses making this assumption
(r
2
= 0.42; electronic supplementary material, table S3).
Finally, we performed a non-parametric version of our
main analyses. These analyses yielded similar results to those
from PGLS, supporting the influence of multicellularity
(strongly) and sexuality (more weakly) on diversification
rates, and the strong correlation between multicellularity and
sexuality (electronic supplementary material, appendix S6).
4. Discussion
In this study, we provide evidence that multicellularity and
sexual reproduction both strongly influenced diversification
rates of the major clades across the Tree of Life. Together,
these two variables statistically explain more than half of
the variance in diversification rates among these clades. Vari-
ation in diversification rates then explains most variation in
species richness among these clades. We also find that multi-
cellularity is generally more important than sexuality in
explaining these diversification patterns, and that multicellu-
larity and sexual reproduction are strongly related to each
other in their distribution among clades.
Our results offer possibly the first analysis of the traits that
may explain diversity patterns across the entire Tree of Life,
and should open the door for future studies that address the
specific mechanisms by which these traits might increase spe-
ciation and/or reduce extinction within clades. For example,
multicellularity allows for differentiation into many different
tissue types, which might increase organismal complexity
and thereby possibly accelerate rates of divergence among
species within clades [6,7].
We acknowledge that our results are based on a statistical
relationship between these variables, and so causation is not
proven. This will be the case for almost any empirical macro-
evolutionary study, regardless of trait, taxa or scale. Similarly,
it is possible that other traits explain these diversification
patterns instead of multicellularity or sexual reproduction.
However, it is unclear what these traits would be, especially
given the lack of obvious commonalities uniquely shared
among the fastest diversifying clades (animals, plants,
fungi). It is also possible that there are simply unique traits
within each of these clades that explain their rapid diversifi-
cation. Yet, we suspect that many potential candidate traits
would be contingent on multicellularity and/or sexual
reproduction (e.g. flowers in plants).
Our findings may be relevant to explaining the paradox
of sex [9–13]. This paradox is usually stated as: why is
sexual reproduction so prevalent among species despite its
apparent disadvantages? Much literature on this topic focuses
on empirical and theoretical investigations of closely related
species and populations (typically multicellular organisms)
and why sex is maintained over time [13]. Our results suggest
that sexual reproduction may also be numerically widespread
among species (at least in part) because it increased rates of
diversification among major clades at deep timescales.
How might sexual reproduction increase diversification?
Previous theoretical research has suggested that asexual
lineages may have higher extinction rates than sexual
lineages, potentially caused by the accumulation of deleter-
ious mutations (Muller’s ratchet [46,47]) or a lack of genetic
diversity that hinders their evolutionary response to biotic
and/or abiotic threats [10], including parasites [11]. Thus, sec-
ondarily asexual eukaryotes are thought to be ‘evolutionary
dead ends’, although the evidence is somewhat mixed [16].
Sexual reproduction might also increase speciation rates,
since it may increase overall rates of adaptation and evolution
[9–11]. A simulation study found that sexual reproduction
increased speciation rates (relative to asexual lineages) but
did not lead to higher richness of sexual species [48].
Our results raise another possible explanation for the
prevalence of (described) sexual species. Sexual reproduction
may be widespread (at least in part) because sexual reproduc-
tion is associated with multicellularity, and multicellularity is
instead the main driver of these large-scale patterns of
diversification and richness among major clades.
We also note that sexual reproduction is only predomi-
nant based on described species numbers: some projections
suggest that approximately 78% of all species are bacteria
[20]. If these projections are accurate, then species with
sexual reproduction represent only a minority of all species.
Indeed, the paradox of species is specifically that the majority
of species have sexual reproduction, despite its apparent
disadvantages [12].
We found strong relationships between multicellularity
and sexual reproduction among these major clades. Our
results do not resolve which trait drives the evolution of the
other. Nevertheless, they do imply a possible causal relation-
ship between these traits at broad phylogenetic scales. Some
previous hypotheses and theory have proposed that multicel-
lularity precedes and drives the evolution of differentiated
male and female sexes, starting with differentiated gametes
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211265
7
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[11,17]. There is some empirical support for this hypothesis
within volvocine algae [18]. Further testing of the causes of
the observed relationship between sexuality and multicellu-
larity among these major clades will be an important area
for future research.
Many other questions are raised by these results. Our ana-
lyses explain roughly 50% of the variance in diversification
rates among these clades, but considerable variance remains
unexplained. What explains the rest? Some of the unex-
plained variance may be attributed to the exceptionally
high diversification rate in land plants (Embryophyta),
which is almost certainly related to the very fast diversifica-
tion rate in angiosperms [44]. This very high rate in land
plants (coupled with lower species richness than in animals)
may also weaken the overall relationship between diversifica-
tion rates and species richness among these clades (table 1).
Considerable unexplained variance in diversification rates
might also result from groups that have multicellularity
and/or sexual reproduction but unexceptional diversification
rates. For example, the marine algae clade Rhodophyta
has both traits at high frequencies but modest diversifica-
tion rates. Why has this group failed to radiate as rapidly
as other clades with these traits? Studies in animals [43]
suggest that predominantly marine clades have lower
diversification rates than mostly terrestrial ones (and land
plants, animals and fungi are predominantly terrestrial).
A similar explanation may apply to largely freshwater
algae groups (Charophyta, Chlorophyta), given results in
vertebrates showing lower rates in aquatic groups in general,
not just marine groups [45]. An extensive dataset on the habi-
tats of these clades will be needed to test these patterns. We
also found mostly unicellular clades which have sexual repro-
duction at high frequencies but relatively low diversification
rates (Amoebozoa, SAR clade). These groups may help
explain the weaker relationships between diversification
and sexual reproduction, relative to multicellularity.
Finally, we recognize that readers may have a diversity of
valid concerns about the methods of our study. In addition to
the Material and methods section, we address these at length
in the electronic supplementary material, appendix S7. These
involve potential errors in estimating trait frequencies, varia-
bility in reproductive modes, whether bacteria have sexual
reproduction, the problem of comparing species across all
of life, and concerns about the diversification-rate estimators.
We urge readers that are concerned about these issues to
consult the electronic supplementary material, appendix S7.
5. Conclusion
We found that much of the variation in diversification rates
(and species richness) among the major branches of the Tree
of Life is related to the positive effects of multicellularity and
sexual reproduction on diversification rates. These results
help explain why three disparate groups of organisms
(animals, land plants, fungi) have been so evolutionarily suc-
cessful in terms of species numbers. Moreover, our results
may have implications for the ‘paradox of sex’(i.e. the domi-
nance of sexual reproduction among species, despite its
disadvantages). Sexual reproduction may be widespread
relative to asexual reproduction (in part) because it increases
diversification rates among major clades, and possibly because
of its association with multicellularity ( given that multicellular-
ity has a stronger impact on diversification rates). We show that
these overall results are robust regardless of whether overall
species richness on Earth is in the millions or billions.
Data accessibility. All data are available as electronic supplementary
material, datafiles S1–S10 and are also available on the Dryad Digital
Repository: https://doi.org/10.5061/dryad.866t1g1r1 [49].
Authors’contributions. L.C.: conceptualization, data curation, formal
analysis, writing—original draft, writing—review and editing; J.J.W.:
conceptualization, project administration, visualization, writing—
original draft, writing—review and editing. All authors gave final
approval for publication and agreed to be held accountable for the
work performed therein.
Competing interests. We declare we have no competing interests.
Funding. L.C. thanks the Jiangsu Provincial Government Scholarship
Program for supporting her trip to Tucson to work with J.J.W. L.C.
was funded by the National Natural Science Foundation of China
(31770402 and 31670422), the Natural Science Foundation of Jiangsu
Province (BK20171407) and the fifth phase of ‘333 High-Level Talents
Training Project’of Jiangsu Province, Qinglan Project of Jiangsu pro-
vince, and the Priority Academic Program Development of Jiangsu
Higher Education Institutions (PAPD). J.J.W. thanks the US National
Science Foundation for support (DEB 1655690).
Acknowledgements. We thank C.W. Birky and anonymous reviewers
helpful comments on the manuscript.
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