The Complexity of Social Complexity: A Quantitative
Multidimensional Approach for Studies of Social Organizationvol . 1
96 , no . 5 the amer ican natural i st november 2020
Synthes is
Jacob G. Holland*,† and Guy Bloch*
Department of Ecology, Evolution, and Behavior, Alexander Silberman
Institute of Life Sciences, Hebrew University of Jerusalem,
Jerusalem, Israel
Submitted August 15, 2019; Accepted June 9, 2020; Electronically
published September 11, 2020
Dryad data: https://doi.org/10.5061/dryad.x3ffbg7g6.
abstract: The rapid increase in “big data” during the postgeno- mic
era makes it crucial to appropriately measure the level of social
complexity in comparative studies. We argue that commonly used
qualitative classifications lump together species showing a broad
range of social complexity and falsely imply that social evolution
always progresses along a single linear stepwise trajectory that
can be deduced from comparing extant species. To illustrate this
point, we compared widely used social complexity measures in
“primi- tively eusocial” bumble bees with “advanced eusocial”
stingless bees, honey bees, and attine ants. We find that a single
species can have both higher and lower levels of complexity
compared with other taxa, depending on the social trait measured.
We propose that mea- suring the complexity of individual social
traits switches focus from semantic discussions and offers several
directions for progress. First, quantitative social traits can be
correlated with molecular, develop- mental, and physiological
processes within and across lineages of so- cial animals. This
approach is particularly promising for identifying processes that
influence or have been affected by social evolution. Second, key
social complexity traits can be combined into multi- dimensional
lineage-specific quantitative indices, enabling fine-scale
comparison across species that are currently bundled within the
same level of social complexity.
Keywords: social complexity, social evolution, primitive
eusociality, social insects, bumble bees.
Introduction
What Is Social Complexity?
Although there is no universal definition of “complexity,” in
biological systems functional complexity commonly
* Corresponding authors; email:
[email protected],
guy.bloch@mail .huji.ac.il. † Present address: Institute of
Evolutionary Biology, School of Biological Sciences, University of
Edinburgh, Edinburgh, United Kingdom. ORCIDs: Holland,
https://orcid.org/0000-0002-4064-0630; Bloch, https://
orcid.org/0000-0003-1624-4926.
Am. Nat. 2020. Vol. 196, pp. 000–000. q 2020 by The University of
Chicago. 0003-0147/2020/19605-59421$15.00. All rights reserved.
DOI: 10.1086/710957
implies that a system is made from many specialized and interacting
parts that together contribute to a function (e.g., a complex
tissue is composed of more cell types than a simple tissue, and a
complex organism has more tissue and cell types than a simple
organism; Valentine et al. 1994; McShea 2000). Studies of insect
sociality have typi- callyusedadifferent approach that is basedona
small number of qualitatively defined social traits, including
reproductive division of labor within a colony, cooperative brood
care, and an overlap of multiple adult generations (table 1). This
approach has been central to defining the “eusocial” (truly social)
insects (Wilson 1971;Michener 1974; Batra 1966) as well as other
classifications and subdivisions. Within an evolutionary framework,
social complexity is typically used as a proxy for the degree of
social group transformation, de- scribing how groups of formerly
independent entities (such as cells or multicellular organisms)
become new organism- like units (Maynard Smith and Szathmáry 1995;
Keller 1999; Bonner and Brainerd 2004; Bourke 2011; Szathmary 2015;
West et al. 2015). The richness and diversity of social insects
have provided fertile grounds for cultivating ideas on the
evolution of complexity and sociality. Social insects have formed
the primary examples of the major evolution- ary transition from a
multicellular organism to a society of multicellular organisms and
for the kin and multilevel se- lection theories
(Hamilton1964;Maynard Smith and Szath- máry 1995; Keller 1999;
Bourke 2011).
Limitations with the Common Classifications of Social Complexity in
Social Insects
The framework in which we assess social complexity is critical for
the interpretation of comparative studies. Well- resolved
phylogenies and the rapid increase in sequencing the genomes,
transcriptomes, and epigenomes of an in- creasing number of social
and solitary animals has set the stage for a new wave of studies
comparing molecular,
Table 1: Common qualitative classifications of social
complexity
Least complex
Most complex
Eusociality: Behavioral division of labor and cooperative brood
care between mono- morphic parents and offspring.
Hypersociality: Queen and worker castes differ in morphology,
physiology, and be- havior. Colony repro- duction by
swarming.
Wilson 1971
Quasisociality: Cooper- ative brood care in nest of same-generation
individuals.
Semisociality: Repro- ductive division of la- bor and cooperative
brood care in nest of same-generation individuals.
Eusociality: Reproduc- tive division of labor and cooperative brood
care between parents and offspring.
Michener 1974 (social bees)
Subsociality: Lone foundress mother provisions and protects brood
until emergence of adults or foundress death.
Primitive eusociality: Queens found alone and morphologically
similar to worker caste. Some reproduc- tive and behavioral
division of labor. Col- ony size typically in tens to
hundreds.
High eusociality: Queen and worker castes differ in morphology,
physiology, and be- havior. Colony repro- duction by swarming.
Colony size typically in thousands or higher.
Crespi and Yanega 1995
Quasisociality: Some temporary reproduc- tive division of labor,
but lifetime repro- ductive success is unimodal. All indi- viduals
remain totipotent.
Semisociality: Tempo- rary reproductive di- vision of labor with
bimodal lifetime re- productive success. All individuals remain
totipotent.
Facultative eusociality: The worker caste (but not the reproductive
caste) has lost behav- ioral totipotency.
Obligate eusociality: Both worker and reproductive castes have lost
behavioral totipotency.
Toth et al. 2016 (vespid wasps; also see Jandt and Toth 2015)
Subsociality: Foundress mother builds and inhabits nest alone.
Shows extended maternal care.
Facultative sociality: Some foundresses in a population may nest
with worker daughters.
Primitive eusociality: Founding by lone or small groups of females.
Queen and worker castes highly flexible.
Advanced eusociality: Lone foundress. High queen-worker differ-
entiation. Possible colony reproduction by swarming.
Boomsma and Gawne 2018
Cooperative breeding: No castes. All indi- viduals can mate.
Eusociality: Some caste distinction. Mating chance correlated with
age or body size. Queens maintain dominance hierarchies.
Superorganismality: All individuals belong to permanent castes
lacking totipotency, with no mating for the worker caste.
Richards 2019 (social bees)
Sociality: Nest with social interactions between adult
conspecifics, with no castes.
Eusociality: Behavioral division of labor and cooperative brood
care between mono- morphic parents and offspring (after
Batra).
Hypersociality: Colo- nies with discrete castes, with no mating in
the worker caste.
Note: Shown is a summary of criteria and traits suggested by some
key studies for classifying the level of sociality. The term used
for each level is highlighted in boldface. The solitary and
aggregate levels are omitted. Where studies have primarily focused
on certain taxa, this is shown in parentheses in the leftmost
column.
Quantitative Social Complexity 000
organismal, and ecological data across different levels of social
complexity (e.g., Cardinal and Danforth 2011; Kap- heimetal.
2015;RehanandToth2015;Tothet al. 2016;Glastad et al. 2017; Wittwer
et al. 2017; Shell and Rehan 2018; Sum- ner et al. 2018). These
comparative studies can drive the understanding of which traits and
mechanisms influence, or are influenced by, the evolution of social
complexity (Fischman et al. 2011). For example, molecular
signatures of caste differentiation, an important component of
social complexity, have already begun to be identified in diverse
taxa (Sumner et al. 2018). However, the array of terms used to
describe the levels of social complexity, such as “primi- tively
eusocial,” “advanced eusocial,” “hypersocial,” and “superorganism,”
are not always consistently used in the literature or across social
lineages (table 1), leading to diffi- culties in comparisons across
studies and in evolutionary interpretation of the data (Boomsma and
Gawne 2018; Richards 2019 and references therein). We argue that a
re- assessment of theway social complexity is defined andmea- sured
is especially timely in the current genomic and post- genomic
(“-omics”) era in which new molecular data open unprecedented
opportunities for understanding the evolu- tionof sociality.We
argue that in order tomeet this goal, the field needs to move from
inconsistently defined and some- times unclear qualitative
classifications into approaches that account for multiple
quantitative traits as components of social complexity. The common
qualitative classifications of social insects
(table 1) are built on the presumption of a single axis of so- cial
complexity, which is assumed to be supported by evi- dence for
correlations between a number of social traits (Bourke 1999, 2011;
Anderson and McShea 2001). For ex- ample, multiple social traits
have been shown to be associ- ated with colony size (Thomas and
Elgar 2003; Holbrook et al. 2011; Kramer and Schaible 2013;
Ferguson-Gow et al. 2014;Amador-Vargas et al. 2015) or thedegree of
caste dimorphism (e.g., Fergusson-Gow et al. 2014; Sumner et al.
2018). Thus, while some classifications may consider mul- tiple
traits, the assumption of strong correlationmeans that only a
single axis of variation is considered. By using a sin- gle scale,
these classifications implicitly assume that the im- portant traits
underlying social complexity always evolve in synchrony with each
other in a single narrow trajectory and faithfully reproduce
specific stages along a ladderlike evolutionary pathway progressing
from solitary to interme- diate sociality (commonly termed
“primitive eusociality”) to elaborated societies (e.g., defined as
“highly eusocial,” “superorganismal,” or “hypersocial”; see
examples above and in table 1). We herein term this approach the
“linear stepwise model.” This common view also asserts, intention-
ally or otherwise, that extant solitary species or species with
simple societies faithfully represent the ancestors (i.e., lower
rungs on a ladder) of species with more complex societies.
However, theoretical considerations and empirical stud- ies suggest
that social evolution (or other complex evolu- tionary traits) is
not constrained in such a way that a pre- dictable and stepwise
trajectory is consistently followed across independent lineages
(reviewed in Linksvayer and Johnson 2019). Instead, studies with
diverse social insects point to distinct, lineage-specific
trajectories that may have been shaped bydifferent costs or
benefits of eusociality (e.g., Fischman et al. 2011; Sumner 2014;
Shell and Rehan 2018). There is evidence that key traits of
“primitive eusociality” have been maintained for millions of years
and so likely represent successful evolutionary strategies
(Linksvayer and Johnson 2019 and references therein). Moreover, the
as- sumption of a linear stepwise progression of traits is not
consistent with species having different levels of social
complexity across their social traits in a multidimensional mosaic
fashion (fig. 1)—for example, if species A is more socially complex
than species B in trait X but is less socially complex than species
B in trait Y. Such a phenomenon, where different lineages vary in
the level of complexity for different social traits, could result
from selection pres- sures that are specific to certain ecological
niches, preadap- tions, or life-history traits (e.g., predation
pressure, latitudi- nal clines, habitat selection, nesting biology,
or diapause; Rehan et al. 2012; Kocher et al. 2014). In this case,
a more nuanced understanding of social evolution would be nec-
essary, where different components of social complexity can evolve
at different rates across, or even within, distinct phylogenetic
lineages. We refer to this as the “multidi- mensional model.”
A Test Case: Are Bumble Bees Primitively Eusocial?
Bumble bees (tribe Bombini) form an especially useful focal lineage
because there ismuch research on bumble bee phys- iology and
sociobiology as well as that for other apid bees to which they are
often compared. Bumble bees are com- monly considered an
intermediate stage of social complex- ity between the orchid bees,
which are considered solitary, “primitively social,” or
“facultatively eusocial” (Andrade et al. 2016), and the “advanced
eusocial” honey bees and stingless bees. Indeed, Apidae are the
most commonly used lineage for comparative studies of social
complexity (Kocher et al. 2014; Richards 2019). However, it is not
clear how well bumble bees really fit this assumed posi- tion.
Bumble bees are also a good taxon with which to demonstrate
inconsistency in the field because their level of social complexity
has been variously described as “eu- social” (Wilson 1971),
“primitively eusocial” (Michener 1974), “highly social” (Kocher et
al. 2014), “superorgan- ismal” (BoomsmaandGawne2018),or
“hypersocial” (Rich- ards 2019), among other terms. Below, we use
published
000 The American Naturalist
and new data to compare social traits of bumble bees with other
social insect taxa (see the appendix for a description of our
methods).
Queen-Worker Variation
Morphological differentiation between queens and work- ers has been
often used as a key criterion for the highest level of social
complexity (table 1). In bumble bees, queens are typically much
larger than workers, and in many spe- cies the size ranges of
workers and queens do not overlap (Cumber 1949; Michener 1974;
Goulson 2010; Prs-Jones and Corbet 2011). We quantified the size
ratio of bumble bee queens to workers and compared these ratios
with those of the honey bee Apis mellifera, stingless bees, and
attine ants (fig. 2; see the appendix for methodological de-
tails). These ratios were higher than those in the honey bee
(although not statistically significant, probably as a re- sult of
the small sample size; t-test assuming equal variance,
t p 1:36, n p 7, P p :23) and comparable to those of many stingless
bees (Wilcoxon rank sum test, W p 55, n p 27, P p :46), the
“advanced eusocial” close relatives of bumble bees (Cardinal and
Danforth 2011; Bossert et al. 2019). Notably, bumble bee
queen-worker size di- morphism was significantly larger than that
of attine ant species taken together (Wilcoxon rank sum test, W p
151, n p 36, P p :008); the degree of bumble bee dimor- phism was
actually numerically similar to that of the most extreme attines,
Atta and Acromyrmex (the leaf-cutting ants), which have been used
to exemplify an extremedegree of social complexity (e.g.,
Holldobler and Wilson 1990). Bumble bee queens and workers
apparently differ in a few additional morphological traits (such as
color patterns and possibly ommatidia number and the density of
anten- nal sensilla; Goulson 2010; Chole et al. 2019) but do not
show the strong allometric variation typical to queens of some
“highly” social species (Wilson 1971; Michener 1974). Queens of
many ants, honey bees, and stingless bees show more substantial
morphological variation, reflecting
In fe
rre d
p ro
g re
s s
io n
Figure 1: Models for the evolution of increasing social complexity,
represented by hypothetical social insect phylogenies. Different
shapes refer to different social traits (e.g., queen-worker
dimorphism, across-worker differentiation, or colony size). The
common ancestor in each model is assumed to have the least socially
complex traits (pale colors), whereas darker colors represent an
evolutionary increase in quan- titative (or qualitative) complexity
for specific traits (see the main text for details). For
simplicity, these models assume that traits cannot become less
complex. At the tip of each branch, a present-day species is
represented by a letter and its complement set of traits. In the
linear stepwise model, the tree can be arranged so that social
traits evolve increasing complexity in tandem (e.g., increase in
colony size and in worker-queen differentiation), as depicted by
the arrow. Under this model, it may be tempting to conclude that
species A approximates the ancestral traits of species B, C, and D.
Note that lineages that branched early in evolution share many
features with the ancestor of the other lineages but themselves are
not ancestral or more “primitive” to the other lineages. Moreover,
given that nodes in phylogenetic trees can be freely rotated,
arrow-like evolutionary inferences are arbitrary. In the
multidimensional model, different social traits do not necessarily
evolve synchronously along a single trajectory, meaning that
species B may be more socially complex than species A in several
traits but not in others (e.g., as a result of differing selection
pressures). In the multidimensional model, there is no way to
rearrange the tree such that it is consistent with a stepwise
linear increase in social complexity.
Quantitative Social Complexity 000
selection on the same genome to produce two distinct de-
velopmental trajectories. Allometric or other morpholog- ical
variability may be used for characterizing the degree of caste
differentiation (Wilson 1971; Michener 1974), but it has not been
precisely quantified and currently cannot be used in our analysis.
Division of labor in reproduction is a hallmark of insect
sociality, and various measures of reproductive skew have been used
as proxies for social complexity (e.g., the propor- tion of
reproductive females, the proportion of queen- derived males, and
worker ability to mate; e.g., Sherman et al. 1995; Rubenstein et
al. 2016). In bumble bees, queens and workers have a similar number
of ovarioles, but the queen has larger ovaries with a higher
maximal number of mature oocytes, and she is significantly more
fertile. Only queens attract and copulate with males and are
physiologi- cally capable of diapause (Röseler and Röseler 1986;
Am- salem et al. 2015). Nevertheless, in sharp contrast to honey
bees, a large proportion of bumble bee workers activate their
ovaries and attempt to reproduce at later stages of col-
ony development, even though relatively few worker off- spring seem
tomake it to adulthood in queenright colonies (Owen and Plowright
1982; Bloch et al. 1996; Bloch and Hefetz 1999; Brownet al. 2003;
Takahashi et al. 2008).How- ever, worker egg laying may be
similarly (and even more) substantial in many species of the
“highly social” stingless bees. In some species workers lay
numerous “trophic” eggs for the queen diet (Michener 1974), and in
other species workers lay many male eggs (Toth et al. 2004). Thus,
the highly eusocial stingless bees and the “primitively eusocial”
bumble bees cannot be clearly separated on the basis of the degree
of worker egg laying or the proportion of repro- ductive workers.
Morphological differences between adult queens and
workers (including in overall body size) are determined by
developmental processes during the embryo (egg), larva, or pupa
stages. In particular, early differentiation is typi- cally
associatedwithmore distinct queen-worker phenotypes (Wheeler 1986)
and has been suggested as a component of social complexity (Bourke
2011). In Bombus terrestris, the
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Figure 2: Queen-worker body size dimorphism in selected social
insect genera. Bumble bees (red) are compared with 11 attine ants
(black), 10 stingless bees (blue), and Apis mellifera (green). Each
point represents queen-worker size ratio (mean queen size/mean
worker size) for a single species of the genus indicated on the
X-axis. Bombus data are from Cumber (1949; raw data) and from I.
Medici de Mattos and G. Bloch (unpublished data). Bombus terrestris
is highlighted with a black outline. Stingless bee and attine ant
data are from Toth et al. (2004) and Ferguson-Gow et al. (2014),
respectively. A phylogeny is shown to highlight relationships.
Additional details on our methods are pro- vided in the
appendix.
000 The American Naturalist
known critical period for caste determination occurs early in
development, a few days after larval hatching (Cnaani et al. 2000;
Bortolotti et al. 2001). The paragraphs above indicate that bumble
bees show
substantial queen-worker body size polymorphism, few
worker-produced adults, caste-specific physiology, and early caste
determination, which are comparable to those found in some “highly
eusocial” species.On the other hand, they do not show
caste-specific structures or significant al- lometric differences,
and compared with honey bees (but notmany stingless bees)
reproductive bias is relatively small in terms of the number of
eggs laid by workers and the pro- portion of reproductive
females.
Between-Worker Morphological Variation
Highly eusocial societies typically show clear division of labor
between workers specializing in different tasks, and in some highly
social ants and termites this is asso- ciated with morphological
worker caste differentiation (Wilson 1971). At intermediate levels
of social complexity (e.g., “primitive eusociality”), division of
labor between workers is assumed to be less structured and
morpholog- ical variation is expected to be absent or weak (table
1). It is then perplexing to note that bumble bees exhibit pro-
found variation in worker size, with up to eightfold vari- ability
inmass in B. terrestris (Goulson 2010), tenfold var- iability in B.
lucorum (Cumber 1949) and B. impatiens (Mares et al. 2005), and
probably even more in “pocket- making” species such as B. pascorum
and B. hortorum (Alford 1975; Pouvreau 1989; Prs-Jones and Corbet
2011). This variation is mediated by juvenile hormone and social
inhibition, among other factors (reviewed in Chole et al. 2019). To
investigate differences in this component of so- cial complexity,
we compared variation in worker body size between bumble bees and
“highly eusocial” honey bees, stingless bees, and attine ants (see
the appendix for de- tails). Following previous studies, we used
the coefficient of variation as a simple way to compare all genera
(includ- ing those with morphological worker castes, such as Atta
and Acromyrmex). We found that bumble bee colonies have a
significantly larger variation in worker body size compared with
their close relatives, the stingless bees (Wilcoxon rank sum test,
W p 280, n p 47, P ! :0001; fig. 3). Bumble bee size variation was
also larger than that in A. mellifera (although not quite
statistically significant, probably as a result of the small sample
size; t-test assum- ing equal variance, W p 2:39, n p 8, P p :054)
and overall was comparable to that in attine ants (Wilcoxon rank
sum test,W p 125, n p 43, P p :98). In fact, bum- ble bees show
higher variation than all attine ant genera studied except the
leafcutter ants.
Variation in body size by itself is not necessarily linked to
social complexity because itmay not relate to functional variation
in behavior. However, the profound worker body size variation in
bumble bees has been repeatedly shown to relate to the propensity
of workers to perform different tasks (reviewed in Michener 1974;
Alford 1975; Goulson 2010; Chole et al. 2019). Specifically, many
stud- ies have shown that small workers disproportionately per-
form in-nest tasks, such as brood care, whereas larger workers
disproportionately perform foraging activities. This behavioral
variability is associated with a range of size-related
morphological and physiological features (summarized in Chole et
al. 2019) that apparently make larger workers more efficient
foragers than smaller work- ers (Goulson et al. 2002; Spaethe and
Weidenmuller 2002). There is also evidence consistent with a
reduction in the performance of free-foraging B. terrestris
colonies when the largest and smallest workers are replaced with
middle-sized workers, at least under some conditions (Holland et
al. 2020). This association with task perfor- mance links body size
variation to social complexity be- cause division of labor is a key
organizational principle of insect societies. Other social insect
taxa also exhibit an association between worker size variation and
task spe- cialization, which can be functionally significant even
without allometric distinctions (Trible and Kronauer 2017). Thus,
the high degree of between-worker body size varia- tion coupled
with its associated task specialization is not consistent with most
definitions of primitive eusociality. Instead, it bears some
similarity to the morphological worker castes found in highly
eusocial societies of some ants and termites, despite having a
monomorphic rather than polymorphic body size distribution, as in
some ants. It should be appreciated, however, that division of
labor can also be efficient without morphological variation, as
seen in many other taxa.
Colony Size
Colony size (i.e., the total number of individuals, or only
workers, in a colony) is broadly correlated with various indices of
social complexity and has been commonly used as a proxy for the
degree of social complexity (Wilson 1971; Bourke 1999; Bonner and
Brainerd 2004). Social bum- ble bees meet the upper range of
Michener’s description stating that primitively eusocial bees
typically have!20 work- ers and no more than a few hundred
(Michener 1974). These numbers contrast with “advanced eusocial”
colonies of honey bees and many stingless bees that can be two
orders of magnitude larger (Michener 1974, 2007); ant and termite
societies can be even larger still (Wilson 1971). Nota- bly,
however, other stingless bee or ant species may have
Quantitative Social Complexity 000
smaller colonies than bumble bees, indicating the colony size does
not always separate “advanced” from “primitively” eusocial species
(e.g., Wille 1983; Holldobler and Wilson 1990).
Multidimensional Patterns of Social Complexity in Other Social
Insects
Our analyses show that social bumble bees, compared with some honey
bees, stingless bees, and attine ants, in some traits are less
socially complex (generally smaller colony size, less extreme
reproductive division of labor between queens and workers) but in
other traits are com- parable or even more socially complex
(similar or larger queen-worker body size ratio, similar or larger
between-
worker morphological variation and specialization, larger colony
size than some stingless bee and ant species, lower proportion of
egg-laying workers than some stingless bees; fig. 4A). No matter
how trait variables are rescaled or classifications repositioned,
these taxa will not neatly fit into a classification system that
assumes that all social traits evolve in synchrony and in which one
taxon is more socially complex than another in all traits. The
literature suggests that a multidimensional mosaic
combination of social traits is not unique to comparative studies
within the Apidae. Examples are implied in An- derson and McShea’s
(2001) classification of social com- plexity among ants, in which
they categorized some species as having an overall higher level of
social complexity than others on the basis of such factors as
greater polymorphism, complex division of labor, and large colony
size. One such
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Figure 3: Log10 coefficient of variation of worker body size in
selected social insect genera. Bumble bees (red) are compared with
12 attine ants (black), 16 stingless bees (blue), and the honey bee
Apis mellifera (green). Each point represents the coefficient of
variation for a single species of the genus indicated on the X-axis
(using the sample-size-weighted mean of study populations). Workers
in each case included both in-nest and foraging workers. The Bombus
terrestris data represent more than 6,000 workers from three
studies (Cumber 1949; Goulson et al. 2002; Holland et al. 2020) and
are highlighted with a black outline. Additional Bombus data (five
more species) are from Cumber (1949) and Couvillon et al. (2010).
Attine ant data and methods are from Ferguson-Gow et al. (2014).
Stingless bee data are from Waddington et al. (1986) and Grüter et
al. (2017). A phylogeny is shown to highlight relationships.
Additional details on our methods are provided in the
appendix.
000 The American Naturalist
example is the genus Cataglyphis, which they classify as having a
lower level of social complexity because of its au- tonomous
individuals and a lack of complex recruitment behavior in foraging
but which has a larger colony size (es-
timated at 5,000) than many species they classify as more socially
complex. The inverse is true for another species, Acromyrmex
landoli, which they classify as having a higher level of social
complexity because of fungus farming and
0.00 0.25 0.50 0.75 1.00
Colony size
Colony longevity
Reproductive skew
Queen-worker dimorphism
Worker variation
Rescaled value
Figure 4: Possible strategies for using quantitative
multidimensional approaches for social complexity. We used real
data in five social traits from six species of bees and ants. A,
Separately comparing social traits across all six species. Colony
size p number of workers at peak colony size; colony longevity p
estimated lifespan of colony; reproductive skew p the proportion of
males produced by the queen/ foundress in queenright colonies;
queen-worker dimorphism p mean queen size/mean worker size; worker
size variation p coefficient of variation in worker size. Log10
values are used for worker size variation and colony size. All
values are rescaled so that the minimum and maximum across species
are 0 and 1, respectively. Each symbol represents a different
species, as shown at the bottom of B and C. The social complexity
rank of the species differs for each measured social trait. B, C,
Example of combining the traits in A into a single social
complexity index, with traits weighted equally and rescaled
separately for ants (B) and for bees (C). Illustrations are by Guy
Bloch. Additional details on our methods are provided in the
appendix.
Quantitative Social Complexity 000
worker polymorphism but which has a colony size of only about 1,000
workers. Consistent with this evidence, some studies with ants have
failed to find any relationship be- tween colony size and worker
reproduction (Hammond and Keller 2004; Helantera and Sundstrom
2007), despite both being considered important measures of social
com- plexity. In vespine wasps, worker-queen differentiation is
restricted, since queens establish nests alone and workers can lay
eggs (Evans and West-Eberhard 1973), but colony sizes can be very
large, including the production of nearly 10,000 larvae in Vespula
colonies (Donovan et al. 1992). By contrast, the paper wasp
Ropalidia ignobilis forms small colonies of 100 or fewer adults and
yet has female castes differing in size, wing length, coloration,
and allometric relationships among body parts (Wenzel 1992). Lower
ter- mites live in complex societies but still show little, if any,
brood care, which is one of the defining characters of euso-
ciality. This lack of brood care probably relates to their
wood-dwelling lifestyle (Korb et al. 2012). The multidimensional
model of social evolution is also
congruent with findings of substantial variation in mo- lecular
mechanisms regulating social traits in different taxa, even within
the same lineages (Sumner 2014; Shell and Rehan 2018). For example,
there is evidence that var- ious bee species differ in whether
queen-biased genes or worker-biased genes undergo higher levels of
selection (Harpur et al. 2017) and in the microRNAs associated with
caste determination (Collins et al. 2017), while some caste-related
genes in wasps also appear to be taxon spe- cific (Ferreira et al.
2013). In other studies, a mixture of shared and lineage-specific
pathways and genes are asso- ciated with social regulation across
taxa (e.g., Woodard et al. 2011; Berens et al. 2015; Kapheim et al.
2015;Warner et al. 2019), suggesting that it may be fruitful to
test cor- relations of these patterns with different components of
social complexity rather than with the common qualitative stages
along the social ladder. To test whether these cor- relations hold
over broad taxa, such comparisons should also be phylogenetically
corrected to control for pseudo- replication of clades.
A New Approach for Comparative Studies of Social Complexity
A Need to Move from Stepwise Qualitative Definitions to
Quantitative Multidimensional Approaches
The “-omic” era presents an unprecedented opportunity to study
social evolution in molecular terms as a wealth of new studies
using large amounts of data become avail- able (e.g.,
transcriptomics, genomics, proteomics, and epigenomics). Many of
these studies rely on the compar- ative approach in which
behavioral, physiological, or mo-
lecular features are compared for species differing in their level
of social complexity. As illustrated above, the com- monly used
linear stepwise qualitative classification suffers from several
important limitations. First, there is inconsis- tency in the way
different studies label qualitatively defined levels of social
complexity for the same taxon (table 1). Sec- ond, much of this
classification is very crude and limits the resolution of the
analyses. For example, small Halicitinae, Euglossini, and
Xylocopinae societies composed of few in- dividuals are frequently
placed at the same level of social complexity as bumble bee
societies numbering several hun- dred individual workers and
showing substantial caste and between-worker variability.
Similarly, ant societies showing orders of magnitude variability in
colony size and consider- able variation in the degree of
queen-worker and worker- worker differentiation are all classified
as having the same level of sociality (e.g., “advanced eusocial”).
Third, social com- plexity is commonly treated as a single
trajectory passing through similar stages that bundles multiple
social traits together, but in reality these traits are not always
tightly correlated (fig. 4A). There is currently no convincing the-
oretical or empirical support for the notion that social com-
plexity evolves along the same narrow restrictive stepwise
trajectory in different social lineages (Linksvayer and John- son
2019). Fourth, low social complexity in one or more traits should
be seen as a successful evolutionary strategy (adaptation) rather
than a transitional or “primitive” stage representing a low rung in
the ladder of social evolution. Fifth, the current approach may
advocate focus on species that neatly fit the common
classifications, potentially ob- scuring important natural
variation among species. For some studies, such as those
specifically focusing on
a “point of no return” to eusociality (e.g., Holldobler and
Wilson1990; Boomsma2009), on the initial transition from solitary
to social life, or on species differing greatly in several social
complexity components, using broad qualitative clas- sifications
may be useful. Nonetheless, we argue that for many studies,
particularly those using molecular and phys- iological variables,
it will be more fruitful to measure social complexity on a
trait-by-trait basis. The logic is that molec- ular, developmental,
and physiological processes affect spe- cific organismal traits,
such as behavior, communication systems (e.g., pheromonal or
odorant receptor repertoire), body size, morphology, or
reproductive potential. Many of these traits underlie key
components of social complex- ity, and it is important to know
whether they are regulated by similar or different processes across
different lineages of social insects and how this regulation is
modified through evolution. For example, the developmental
processes that determine body size underlie the variability between
queens and workers and among workers. The number of enzymes that
are involved in pheromone biosynthesis and the num- ber of odorant
receptors may determine communication
000 The American Naturalist
complexity. Likewise, the molecular processes that control ovary
size and oogenesis influence fertility and the degree of
reproductive skew. Therefore, molecular processes such as the
expression of genes in certain pathways or the degree of epigenetic
methylation are likely to show clearer associ- ations, within and
across taxa, with such quantitative com- ponents of social
complexity thanwith the commonbroadly defined qualitative states
that may stem from diverse com- binationsof social traits. Indeed,
studies that have attempted to link molecular processes to the
commonly used levels of social complexity revealed weak
associations at best (e.g., Woodard et al. 2011; Kapheim et al.
2015; Glastad et al. 2017).
Can Components of Social Complexity Be Reliably Quantified?
Previous studies have already compiled and compared commonly used
quantitative traits that are components of social complexity
(Michener 1974; Anderson and McShea 2001; Gardner et al. 2007;
Aviles and Harwood 2012). These include reproductive skew,
variation in body size, colony size, behavioral repertoires,
behavioral spe- cialization (i.e., division of labor), the number
of genes differentially expressed between queen- and worker-
destined larvae, the richness of communication signals (e.g.,
pheromones, antennation, trophallaxis, “dances” etc.), the fraction
of the life cycle that individuals remain in their social group,
and the efficacy of nest homeostasis (e.g., Sherman et al. 1995;
Gorelick and Bertram 2007; Avilés and Harwood 2012; Sumner et al.
2018). However, to realize the full potential of the
multidimensional ap- proach it is important to develop quantitative
and stan- dardized measures for additional components of social
complexity, such as the degree of morphological differ- entiation
other than body size (including allometry and other morphological
differences among workers and be- tween queens and workers).
What Are the Best Quantitative Measures of Social Complexity?
The selection of the most appropriate indices of social
complexity—and in some cases also the best ways to quantify them—is
not always easy andmay differ between lineages of social insects.
There is good agreement and much literature supporting the
importance of several com- monly used metrics. For example, the
degree of reproduc- tive skew is considered in many studies to be
particularly important for determining social complexity overall
(Sher- man et al. 1995; Crespi and Yanega 1995; Hughes et al. 2008;
Boomsma 2009; Boomsma andGawne 2018). Similar arguments have been
made for the causal importance of
colony size, termed the “size-complexity hypothesis” (Bon- ner and
Brainerd 2004; Bourke 2011). There is less agree- ment concerning
other metrics. For example, some re- searchers may argue that size
variation among workers is not a good proxy of social complexity if
it is not associ- ated with discrete (polymorphic) size
distribution or that morphological/allometric differences between
queens and workers should receive more weight than mere differences
in body size or physiology. Likewise, variation in behavioral
repertoire (as seen in honey bees) may be seen as a better proxy
for social complexity than morphological variability (as seen in
some ants, termites, and bumble bees). Deciding between these
conflicting opinions requires good under- standing of the social
biology, natural history, and life his- tory of the studied
species. The value of different traits prob- ably differs between
lineages of social insects. Thus, further development of our
proposed approaches requires addi- tional discussion that is based
on a deep understanding of the natural history and life history of
the different social lineages. There is also an obvious need for
more empirical and theoretical studies that are beyond the scope of
the current article.
How to Use Quantitative Multidimensional Approaches?
As an illustrative example of a quantitative multidimen- sional
approach, we have used data available from the literature to
compare the relative levels of five commonly used quantitative
components of social complexity in six species of bees and ants
(fig. 4A; see the appendix for de- tails). This approach reveals
the uncorrelated nature of social traits, showing that the order of
relative complex- ity between species is different for each trait,
which em- phasizes the multidimensional pattern and highlights the
need to take multiple quantitative traits into account. To this
end, we suggest two approaches for using quan-
titative multidimensional data on social complexity to un- derstand
the causes and consequences of social evolution. The first
approach, which explicitly recognizes the multi- dimensional
pattern of social complexity, is to separately focus on individual
components of social complexity. In the ideal case, this involves
comparing species that vary in one focal social trait but not in
others (e.g., where colony size varies but queen-worker
differentiation is comparable), which allows the importance of the
focal trait to be assessed empirically. A clear separation of
social complexity compo- nents facilitates the recognition of
alternative preadapta- tions ormodifications thatmay be important
for social evo- lution. For example, the annual colony life
histories of some bees andwasps restrict increases in colony size
andmay also restrict queen-worker differentiation (because the
queen needs to care for the brood and forage effectively during
col- ony foundation), but they do not appear to constrain the
Quantitative Social Complexity 000
complexity of other social traits, such as between-worker
differentiation (as seen in bumble bees). In addition, it will also
help in the recognition of different selection pressures in
different lineages. For example, most bumble bee species are
principally adapted to cold climates, whereas stingless bees and
honey bees are mostly tropical. This may account for some of the
differences they show in social traits, such as queen-worker
variation, which may be related to diapause in bumble bees but not
in honey bees or stingless bees. The same logic has been used to
assess the effect of abiotic factors (variation in temperature and
precipitation) on col- ony size, queen-worker dimorphism, and
worker size vari- ation in attine ants (Fergusson-Gow et al. 2014).
This ap- proach, for example, sets the stage for asking questions
such as whether similar processes (e.g., molecular) are asso-
ciated with colony size in meliponine stingless bees and attine
ants or whether the degree of caste differentiation is regulated by
similar molecular pathways in bees and wasps. This approach will be
particularly powerful with molecular data, such as the level of DNA
methylation or differential gene expression. Rather than
correlating the molecular variableswith qualitatively defined
stagesof social complex- ity (e.g., Kapheim et al. 2015; Glastad et
al. 2017; Sumner 2018), the same data could be separately
correlated to each relevant component of social complexity (e.g.,
reproductive skew, queen-worker size variation, colony size). A
second approach is to combine multiple components
of social complexity into a single quantitative measure of social
complexity and use this multidimensional index to identify new
features and processes correlated with social complexity. This
would be similar to the approach of pre- vious comparative studies
(see the introduction) but using a quantitative and less ambiguous
measure of social com- plexity. An important advantage of this
approach over qualitatively defined levels of sociality is that it
allows finer analyses and distinctions between species that are
cur- rently bundled within the same level of sociality. A first
move in this direction was suggested by Avilés and Har- wood
(2012), who combinedmultiple quantitative indices of social
complexity into a single value to compare social spiders. We used a
similar approach to demonstrate an- other example using several
species of bees and ants (see the appendix for details on our
methods). As in Avilés andHarwood (2012), we assigned the
sameweight to each of the social complexity components in figure 4A
to gen- erate a social complexity metric (fig. 4B, 4C). Such
overall social complexity metrics could use a complement of dif-
ferent social traits that are tailored to the study in question but
would be most reliable if they explain as much varia- tion as
possible. Our example demonstrates an important advantage of this
approach, which is the ability to separate the level of social
complexity for species that are com- monly considered to have the
same level of social com-
plexity (e.g., under some classifications, all of these species
except Euglossa viridissima would be considered hyper- social or
superorganismal; table 1). Another advantage is for lineages, such
as spiders, in which there are no accepted classifications for the
overall level of social com- plexity (Avilés and Harwood 2012).
However, studies us- ing this approach should acknowledge the
intrinsic lim- itations of assuming a linear stepwise model that we
discuss above aswell as the need to carefully consider theweighting
of different social traits according to the biology and natu- ral
history of the studied lineages. Our example in figure 4
demonstrates some of these difficulties—for example, by showing
bumble bees as more similar to stingless bees and honey bees than
is commonly accepted. We propose that this approach is especially
powerful for comparing species within the same lineage (e.g.,
Attini, Halictinae, Vespidae) by allowing for a finer resolution
that can be later used to identifymolecular or other processes that
have been impor- tant in the evolutionof social complexity. For
this reason,we separately compared our basic “social complexity”
metric for ants and bees as a simple form of phylogenetic control.
Using this metric to compare bees, in which all five traits were
equally weighted, Apis mellifera, Bombus terrestris, andMelipona
fasciata all showed very similar levels of com- plexity, with E.
viridissima having a relative complexity of zero.
Conclusions and Future Perspectives
The remarkable increase in the number of species for which “-omics”
data are available, coupled with improved phylog- enies and
steadily growing knowledge on the social biology of species showing
diverse forms of social lifestyles, calls for new methods and
strategies for studies of the evolution of social complexity.We
suggest thatmultidimensional arrays of quantitative metrics more
precisely describe the diverse forms of social complexity than the
broad and inconsistent qualitative classifications that are
currently used. Moving the field into a
quantitativemultidimensional approachwill enablefiner-scale
analyseswithin lineages and a better com- parison of processes
influencing or influenced by different components of social
complexity across lineages, recogniz- ing that social evolution is
not always strictly linear and stepwise. The proposed approach is
particularly appropri- ate for linking molecular or physiological
processes, pre- adaptations, selection pressures, and regulatory
processes to specific phenotypic social traits, such as colony
size, reproductive skew, or the degree of caste differentiation.
The various quantitative measures of complexity can po- tentially
be further combined into unifiedmultidimensional indices or models
for overall social complexity. Although we believe that some
previous and current comparative studies can already significantly
benefit from relating
000 The American Naturalist
molecular (or other) data to individual indices of social
complexity (fig. 4A), we advocate that additional theoreti- cal and
empirical work is needed in order to realize the full potential of
quantitative multidimensional approaches. Some of the most pressing
challenges are to identify the most important components of social
complexity and find the best ways to quantify them.Developing
social complex- ity indices, while potentially useful, is
challenging because it requires deciding not only which traits to
include but also how much weight to assign to each social trait.
These de- cisions will require deep knowledge on the biology,
behav- ior, natural history, and life history of the studied
species and will likely depend on the specific lineages and ques-
tions in focus. The value of a quantitative multidimensional
approach will increase as more data are collected for more species
along gradients of social complexity. This will al- low for better
resolution in comparative studies and for better understanding of
the interactions between different components of complexity.We hope
that our contribution will encourage additional researchers to
contendwith these challenges.
Acknowledgments
We thank Jenny Jandt and Igor Medici de Mattos for sharing
unpublished data used for the analyses. We also thank Daniel
Bolnick, Timothy Linksvayer, and two anon- ymous reviewers for
detailed and helpful comments on the manuscript. Financial support
was provided by grants from the US-Israel Binational Agricultural
Research and Development Fund (BARD; project IS-4418-11 and IS-
5077-18) and the US-Israel Binational Science Founda- tion (BSF) to
G.B. as well as a Lady Davis Postdoctoral Fellowship to J.G.H.
Finally, we thank Eamonn Mallon’s group for hosting J.G.H. during
much of the development of this article.
Statement of Authorship
The ideas in this piece were conceived and developed by J.G.H. and
G.B., data gathering and analyses were per- formed by J.G.H., and
the manuscript was written by J.G.H. and G.B.
Data and Code Availability
Data in support of this publication have been deposited in the
Dryad Digital Repository (https://doi.org/10.5061 /dryad.x3ffbg7g6;
Holland and Bloch 2020).
APPENDIX
Methods
To quantify bumble bee queen-worker size differentia- tion, we used
data from Cumber (1949) and new data.
In Cumber’s publication, wing lengths of females (n p 944) present
in nine nests of six species were provided without distinguishing
between workers and queens. However, all colonies were stated to
contain queens and the size distribution was clearly bimodal in
each colony, which we assumed to represent worker and queen modes.
In the pollen-storing species (Bombus lapidarius, Bombus lucorum,
Bombus terrestris, Bombus pratorum), there was clear separation of
worker and queen distributions. For the pocket-making species
(Bombus agrorum, Bombus hortorum), where queen-worker size
distinction is less obvious, the sizemeasurement with theminimum
amount of records between the worker and queen modes (a clear
trough in all three colonies) was assumed to consist of workers and
queens equally, with all values smaller and larger than this
assumed to be workers and queens, re- spectively. Additional size
data using thorax width were taken from all emerging workers (n p
434) and gynes (n p 26) in five B. terrestris colonies kept
enclosed in standard laboratory conditions from incipient colonies
to queen death (I. Medici de Mattos and G. Bloch, unpub- lished
data). A B. terrestris value was calculated from both studies using
ameanweighted by sample size.Queenworker size ratio (queen mean
size/worker mean size) was calcu- lated for comparison with
stingless bee data from Toth et al. (2004) and attine ant data from
Fergusson-Gow et al. (2014). Additionally, a value for Apis
mellifera was calcu- lated using thorax size and taking a mean of
values from European and African strains in DeGrandi-Hoffman et al.
(2004). To statistically compare bumble bees to stingless bees and
(separately) to attine ants, we used species-level Wilcoxon rank
sum tests (given the nonnormality of the data). For comparing
bumble bees and A. mellifera (single value only), aWilcoxon rank
sum test does not provide suf- ficient power to allow such
comparisons; thus, we assumed a normal distribution and equal
variances in a t-test.
To quantify bumble bee among-worker size differentia- tion, we used
the following: B. terrestris data obtained from (i) wing marginal
cell length of all workers (n p 1,832) produced by nine freely
foraging colonies over themajority of colony growth (Holland et al.
2020), (ii) thorax width of all workers (n p 4,492) found in28
freely foraging colonies around the peakof colony growth (Goulson
et al. 2002), and (iii) wing length of workers (n p 139) found in
wild colo- nies (Cumber 1949). Bombus impatiens data were obtained
from thorax width of all workers (n p 1,133) produced by 12
enclosed colonies over the later stages of colony growth (Couvillon
et al. 2010; J. Jandt, personal communi- cation). Other Bombus spp.
data were obtained from wing lengths of workers (n p 570) found in
seven wild colonies of five species (Cumber 1949). For the data
from Cumber’s (1949) study, workers were determined as described
above. The coefficient of variation, 100 # (worker head width
Quantitative Social Complexity 000
standard deviation/worker head width mean), was calcu- lated for
comparison with attine ant data from the meta- study of Fergusson
Gow et al. (2014). In addition, stingless bee data from Waddington
et al. (1986) and Grüter et al. (2017) were also used to calculate
coefficients of variation (there was no overlap in the species
covered by each study). A value for A. mellifera was obtained from
Roulston and Cane (2000). Although size in bumble bees was measured
as thoraxwidth orwingmarginal cell length,we assume this to be
comparable to head width (which was used for ants and stingless
bees) because both of these measures are strongly correlated with
head width across the full size range of B. terrestris workers
(Pearson correlation, thorax width-head width, r p 0:92, R2 p 0:85;
wing cell length- head width, r p 0:95, R2 p 0:90; n p 69; J. G.
Holland, unpublished data). To statistically compare bumble bee
species to stingless bees and (separately) to attine ants, we used
species-level Wilcoxon rank sum tests (given the nonnormality of
the data). As with the queen-worker comparison, for comparing
bumble bees and A. mellifera (single value only), a Wilcoxon rank
sum test does not provide sufficient power to allow such
comparisons; thus, we assumed a normal distribution and equal
variances in a t-test.
We produced a genus-level phylogenetic tree for graph- ical
comparison using relationships from the following studies: within
Attini, Fergusson-Gow et al. (2014); within Meliponini, Rasmussen
and Cameron (2009); amongMeli- ponini, Bombini, andApini, Cardinal
andDanforth (2011); and between Apidae and Formicidae, numerous
studies (e.g., Johnson et al. 2013). The trees were produced,
without differences in branch length, using TreeGraph2.
As an example of comparing social complexity be- tween species, we
chose six species (A. mellifera, Atta sex- dens, B. terrestris,
Euglossa viridissima, Melipona fasciata, and Trachymyrmex
septentrionalis) for which quantitative data were available in five
social traits. The traits were quantified as follows: (1) worker
size variationp log10 co- efficient of variation in worker head
width (A. melliferap Roulston and Cane 2000; A. sexdens, T.
septentrionalisp Fergusson-Gow et al. 2014; B. terrestris p see
above; M. fasciata p Waddington et al. 1986; E. viridissima p
variation among females, Eltz et al. 2011); (2) queen-worker body
size dimorphism p mean queen size/mean worker size (A. mellifera p
DeGrandi-Hoffman et al. 2004, see above; A. sexdens, T.
septentrionalis p Fergusson-Gow et al. 2014;B. terrestrisp see
above;E. viridissimap equal size assumed since all females can mate
and disperse; M. fasciata p Toth et al. 2004); (3) reproductive
skew p the proportion of adult males that are queen produced rather
than worker produced in queenright colonies (A. mellifera, B.
terrestris, Trachymyrmex spp. p Wenseleers and Ratneiks 2006; A.
sexdensp 1 implied given no viable
worker-laid eggs in Dijkstra et al. 2005; E. viridissima p the mean
proportion of eggs laid by dominant mother, Cocom Pech 2008;M.
fasciatap Toth et al. 2004); (4) col- ony size p log10 number of
workers at peak colony size (A. mellifera p approximated at 10,000
workers; A. sex- dens, T. septentrionalis p Fergusson-Gow et al.
2014; B. terrestris p approximated at 200 workers; E. viridissima p
Cocom Pech 2008; M. fasciata p Toth et al. 2004); and (5) colony
longevity p best estimate of lifespan in colo- nies surviving
foundation (A. melliferapmean of feral col- onies, Seeley 1978; A.
sexdensp Keller 1998; B. terrestrisp approximated at 0.5 years; E.
viridissima p longevity of adults, Skov and Wiley 2005; M. fasciata
p Roubik 1983; T. septentrionalisp approximated at 2 years, on the
basis of comments in Beshers and Traniello 1996 and Weber 1966).
For plotting, traits were rescaled across species so that the
minimum was set to 0 and the maximum was set to 1. This rescaling
was performed both for all species together (presented in fig. 4A)
and for ants and bees sep- arately (fig. 4B, 4C). To produce the
combined indices (fig. 4B, 4C), unweighted means of the six scaled
trait values were then calculated. This is not an exclusive list of
traits that could be quantified as components of social complexity,
and several of these traits could be quantified by alternative or
complementary methods. These compar- isons are obviously limited by
the amount of comparable data available.
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Bolnick