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TRANSCRIPT
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Brain architecture and social complexity in birds and dinosaurs
Mark J. Burish†, Hao Yuan Kueh‡, and Samuel S.-H. Wang*
Department of Molecular Biology, Princeton University, Princeton, NJ 08544.
*Corresponding author:Dr. Samuel WangPrinceton University Molecular BiologyLTL Bldg., Washington RoadPrinceton, NJ 08544 USATelephone: (609) 258-0388FAX: (609) 258-1035E-mail: [email protected]
†Current address: 340 Light Hall, Vanderbilt University, Nashville, TN [email protected]
‡Current address: Graduate Program in Biophysics, Harvard University, Cambridge, MA [email protected]
Running title: Avian brain architecture and social complexity
Key words: brain architecture, cognitive, social, Machiavellian intelligence, multivariate, birds, Archaeopteryx
6 figures, no tables
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Abstract. Vertebrate brains vary tremendously in size, but differences in form are
more subtle. To bring out functional contrasts that are independent of absolute
size, we have normalized brain component sizes to whole brain volume. The set
of such volume fractions is the cerebrotype of a species. Using this approach in
mammals we previously identified specific associations between cerebrotype and
behavioral specializations. Among primates, cerebrotypes shift in a progressive
manner, are linked principally to enlargement of the cerebral cortex, and are
associated with increases in the complexity of social structure. Here we extend
this analysis to include a second major vertebrate group, the birds. In birds, the
telencephalic volume fraction is strongly correlated with social complexity. This
correlation can explain nearly three-fourths of the observed variation in relative
telencephalic size and is not found for other avian behavioral specializations.
Interpolating this correlation for Archaeopteryx , an ancient bird, suggests that its
social complexity was likely to have been on a par with modern chickens. The
relative size of forebrain structures may be an anatomical substrate for social
complexity, and perhaps cognitive ability, that can be generalized across a range
of vertebrate brains, including dinosaurs.
Introduction
How can the architectures of brains be compared to bring out functional contrasts
independent of absolute size? For instance, among mammals, from the desman to the sperm
whale, brain size varies by over five orders of magnitude. At the same time, across phylogeny
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mammalian brains also show broad similarities in structure, internal connectivity, and anatomical
composition [Niewenhuys et al., 1998]. Thus, examining brain architecture to obtain functional
principles presents a great challenge to comparative neuroanatomy.
One approach to this problem is to make quantitative comparisons [Jerison, 1973;
Stephan et al., 1981; Armstrong, 1983; Jerison, 1991; Finlay and Darlington, 1995; Barton and
Dunbar, 1997]. Investigations have been directed at describing statistical trends and exceptions,
and also finding explanations for these observations. The result has been a variety of approaches
to comparing brains, each with its own strengths and weaknesses.
In one early approach, brain size was examined using body size as a reference measure
[Jerison, 1973; Jerison, 1991]. This approach revealed relationships that appear to follow power
laws; that is, a trend that is approximately fitted by a line in log-log coordinates. These are called
allometric scaling relationships [Thompson, 1942] and have been found repeatedly throughout
the animal kingdom for many anatomical components. Such scaling relationships, even if they
do not yet have a functional explanation, might be used as a baseline from which to compare
individual species. Such comparison has in turn been used to identify outliers – individual
species that depart from the scaling trend. This approach succeeds in identifying general
deviations, such as the extremes of human and dolphin brain size compared with other mammals
of similar body mass. These deviations, also known as residuals, are usually expressed as an
“encephalization quotient” [Jerison, 1973].
Residuals analysis has also been attempted using individual brain components. In this
case the sizes of individual brain components are fitted against body size [Armstrong, 1983;
Harvey and Krebs, 1990], whole brain [Douglas and Marcellus, 1975], or brain subdivisions
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[Barton et al., 1995; Barton and Harvey, 2000] as reference measures using data for an entire set
of animals, and specializations in brain components are identified. In all cases, this approach has
several drawbacks. First, fit lines vary widely depending on how a data set is analyzed; both the
slope and intercept depend on the taxonomic level at which the data are grouped [Gould, 1975;
Martin and Harvey, 1985; Nealen and Ricklefs, 2001; Wang et al., 2002]. Therefore a linear fit
to pooled data contains the intrinsic ambiguity that no species is associated with a unique scaling
trend. Scaling trends derived this way are unlikely to capture the overall statistical properties of
the data set. Thus, residuals calculated relative to some fit may contain artifacts that are very
difficult to interpret [Wang et al., 2002]. Second, individual brain components each have their
own allometries [Fox and Wilczynski, 1986]; therefore, the combination of any two of them will
hardly ever be expected to follow a strict power law [Zhang and Sejnowski, 2000]. Finally, even
if taxonomic groups (clades) and brain components were subdivided to the point that basic size
trends could be identified clearly, few functional rationales have yet been found to account for
such relationships (though see [Harvey and Krebs, 1990; Stevens, 2001] and the extensive work
in circulatory systems [West et al., 1999]).
In a second type of approach, the use of allometric relationships has been bypassed by
analyzing absolute brain component volumes [Finlay and Darlington, 1995; Finlay et al., 2001].
Such an approach reveals that differences in component sizes across species tend to be correlated
to each other, consistent with a developmental principle of concerted brain growth. In this model
brain growth is regulated in part by a shared set of instructions that govern overall developmental
rates. This work has since been extended to include multivariate analysis as a means of
searching for patterns in growth spanning multiple brain regions [Finlay et al., 2001]. However,
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the results of such an analysis have been dominated by the effects of absolute size differences
among species. Also, most analyses of this type so far still begin by identifying common trends,
an approach that once again depends on initial examination of the data set as a whole.
Recently, as a means of eliminating the effects of absolute size we have normalized brain
component data on a species-by-species basis. We divide a component’s volume by whole brain
volume to obtain its volume fraction F [Jerison, 1991; Clark et al., 2001]. Although
normalization is straightforward, it differs from prior analyses. Most importantly, this definition
of relative component size does not depend on any cross-species fits. Volume fractions of all
components of the brain for a given species can be defined as the “cerebrotype” for that species,
and it is possible to quantify the dissimilarity in brain structure between two species by simply
calculating a Euclidean distance between the two cerebrotypes. These distances can be analyzed
easily using multivariate methods such as multidimensional scaling or principal component
analysis (also known as singular value decomposition).
Of the possible reference standards for size normalization, we have chosen the whole
brain as a baseline because generally, brain components connect principally with other brain
structures. Since larger targets are likely to require more neural projections, the combined size of
all targets (i.e. the whole brain) is a natural size standard. Another method for normalization has
been to divide the component size by brainstem size [Lefebvre et al., 1997; de Winter and
Oxnard, 2001]; however, that approach creates new interdependencies among components that
bias the outcome of multivariate analysis. Also, different brain components develop under
shared developmental growth mechanisms, suggesting that one way to identify size differences
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The resulting graphs facilitate the identification of finer taxonomic distinctions among
taxa. For instance, Scandentia (tree shrews), once categorized as primates [Stephan et al., 1981],
are quite well separated from them in cerebrotype (Figure 2a) and are in fact nearer to
insectivores. The insectivores themselves also can be subdivided into distinct groups of points
representing taxonomic subdivisions (Figure 2b), with the exception of the overlapping
cerebrotype distributions of Soricidae (shrews) and Tenrecidae (tenrecs). This exception is
instructive because it arose entirely from the cerebrotypes of three tenrecs that live and/or hunt in
the water. These tenrecs have reduced olfactory bulbs and piriform cortex, specializations that
may reflect differential needs of aquatic versus terrestrial lifestyles in tenrecs. Thus, cerebrotype
analysis provides a means of taxonomically grouping mammals by their brain structure.
In the case of primates, cerebrotype-based measures have helped identify coordinated
changes in brain architecture (Figure 2c). According to the social intelligence hypothesis of
primate brain evolution [Whiten and Byrne, 1988; Dunbar, 1995; Barton and Dunbar, 1997], a
larger cerebral cortex may confer selection advantages by providing increased cognitive capacity
for social dynamics. This social selection pressure would lead to progressive relative
enlargement of the cerebral cortex over time.
Fossil and molecular evidence and morphological evidence from non-brain characters
shows that successively more derived primate taxa have arisen over time: lemurs/lorises,
followed by tarsiers, New World monkeys, Old World monkeys, and then hominoids. The
arrangement of the corresponding cerebrotypes is collinear with this order, with the exception of
some overlap between New World and Old World monkeys. Interestingly, this overlap arises
from the four New World monkeys in this database – Ateles, Lagothrix, Cebus, and Saimiri –
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with larger group sizes and complex social structures resembling those of Old World monkeys
[Kinzey, 1997]. The cerebrotypes of large group-size New World monkeys differ from other
New World monkeys principally in F neo (large group-size 69.3±0.5%, other 61.8±1.7%) but are
similar to Old World monkeys (70.4±2.4%). Taken together, these results suggest that increased
derivation was indeed accompanied by progressive changes in the cerebrotype.
Birds
Like mammals, birds are highly diverse and have spread to fill a variety of aerial,
terrestrial, and marine niches throughout the planet. However, birds and mammals have diverged
for more than 300 million years since their common tetrapod ancestor, yielding brain plans that
differ in fundamental ways. Each lineage has distinctive brain structural specializations
[Niewenhuys et al., 1998], such as the retention of nuclear organization in birds and the
appearance of a prominent laminated cerebral cortex in mammals.
Birds and mammals also share a number of convergent features. Both groups are warm-
blooded and have a higher basal metabolic rate than most other vertebrates. Compared to other
vertebrates, both groups have large brains relative to body weight; this relationship is nearly the
same for these two groups but is significantly offset compared with reptiles and amphibians
[Jerison, 1991]. Bird body sizes span most of the range for mammals (though mammals can be
larger). Finally, certain bird and primate lineages (songbirds and primates) have been suggested
to have evolved with great rapidity [Wyles et al., 1983]. Given these similarities, birds present
an attractive comparison group relative to mammals.
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Materials and Methods
We used brain measurements from 33 bird species [Nottebohm, 1981; Rehkämper et al.,
1991; DeVoogd et al., 1993; Boire and Baron, 1994]. Body weights were taken from the same
literature sources except for the canary [Harper and Turner, 2000]. Measurements of brain
parameters in theropod dinosaurs and Archaopteryx came from reports using CT scanning of
endocasts for three-dimensional reconstruction [Rogers, 1998; Larsson et al., 2000].
Birds were sorted into categories of social structure using flock size and interactions
between individuals [del Hoyo et al., 1992; Cramp, 1994; Zann, 1996]. Solitary birds (group
size: 1 to 3 birds) are often territorial, with the majority of intraspecies interactions occurring
during the mating season. Covey birds (group size: 5 to 50 birds) commonly show dominance
structures such as pecking orders and leks. Colonial birds (group size: hundreds to thousands of
conspecifics) exhibit communal structures such as gregarious roosts and large foraging flocks.
Transactional birds (group size: variable, often found in large flocks) show a high level of
communal structure and strong one-on-one interactions outside the mating season. In this data
set, the transactional birds were the mallard ( Anas platyrhynchos), a highly gregarious species
with socially coordinated loafing and other comfort activities [Desforges and Wood-Gush, 1975;
Brodsky et al., 1988; Cramp, 1994]; two songbirds (Corvus c. corone, carrion crow; Garrulus
glandarius, jay), which exhibit social learning and unusual spring social gatherings [Goodwin,
1986; Cramp, 1994]; the magellanic penguin (Spheniscus magellanicus), which is highly
communal and altruistically defends the nests of unrelated neighbors [Boersma, 1988; Capurro et
al., 1988; del Hoyo et al., 1992]; and two parrots ( Melopsittacus undulatus, budgerigar; Pionus
menstruus, blue-headed parrot), which display a hierarchy based on age, association, and
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experience that has been compared to primate fission-fusion social structures [Ingels, 1978;
Lowry, 1991; del Hoyo et al., 1992].
For multivariate analysis, Euclidean cerebrotype distances were calculated as previously
described [Clark et al., 2001] among 28 species [Boire and Baron, 1994] using telencephalon,
diencephalon, mesencephalic tegmentum, optic tectum, cerebellum, and myelencephalon. For
the display of these distances, multidimensional scaling was used as a means of representing the
variation in brain structure in two dimensions. Multidimensional scaling was performed as
previously described [Clark et al., 2001] using MATLAB (The Mathworks, Inc., Natick, MA).
Regression tree analysis was performed to analyze the relationship between telencephalic
volume fraction and behavioral categories. Regression tree analysis [Venables and Ripley, 1994]
is a form of multivariate analysis in which a grouping (in this case, by behavioral category) is
found that accounts for the most variance in a set of observations; the data are split according to
the grouping; and the procedure is repeated. Regression tree analysis was implemented using the
R language (The R Project, http://www.r-project.org/).
Results
We used brain measurements from 33 species [Nottebohm, 1981; Rehkämper et al., 1991;
DeVoogd et al., 1993; Boire and Baron, 1994; Clark et al., 2001]. The variation in volume
fractions among species ranged from 0.9% to 8.3% (standard deviation) of total brain volume.
The greatest variation occurred in the volume fraction of telencephalon ( F tel ), 58.4 ± 8.3% (mean
± s.d.; n=33 species). The overall range for F tel , 44% to 78%, is quite similar to the range found
in mammals, 50% to 85% [Clark et al., 2001]. Likewise, for F cbl , the overall range (7 to 21%)
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and variation (14.2 ± 3.4%, 28 species) are similar to mammals (range 6 to 23% by individual
species, variation 13.5 ± 2.4% across 19 mammalian taxa; see Figure 2 of Clark et al., 2001).
The variability in telencephalon and non-telencephalic brain components can also be
quantified relative to body weight. Using a power law model (that is, a linear fit in log-log
coordinates), the correlation of body size with brain size (r 2=0.822, n=33) leaves 17.8% of the
variance unexplained. The amount of unexplained variance (Figure 3) is greater for
telencephalon (23.8%) than for non-telencephalic regions (11.1%; P =0.05, one-tailed z-test).
The relative strictness of scaling in non-telencephalic brain components is consistent with the
idea that a given body size requires a certain amount of “basal” brain [Portmann, 1947a,b; Boire
and Baron, 1994]. Conversely, the observed variations in telencephalic volume suggest that this
structure may reflect functional variations that are not dependent on body size.
Because of the correspondence between telencephalic size and cognitive complexity in
primates [Reader and Laland, 2002], we looked for quantifiable analogous parameters of bird
behavior. As one measure, we assigned species to categories based on social interaction. We
focused on the qualities of group size and inter-individual interaction because they are relatively
straightforward to observe and can be directly compared to other behaviors such as eating habits
and mating type. Other measures relating to cognitive complexity have previously been used,
such as foraging and nesting innovations [Lefebvre et al., 1997; Lefebvre et al., 1998;
Nicolakakis and Lefebvre, 2000] and tool-making [Hunt et al., 2001; Weir et al., 2002]. These
qualities and social complexity are likely to share common neural substrates. In primates,
various measures of cognitive ability are strongly correlated with each other and with indices of
cerebral cortical size [Reader and Laland, 2002]. Furthermore, group size has been used in
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primates as an indicator of social complexity [Dunbar, 1995]. We therefore considered social
complexity as a rapid means of sorting species that does not require an exhaustive survey of
known behaviors [Lefebvre et al., 1997; Lefebvre et al., 1998; Nicolakakis and Lefebvre, 2000;
Reader and Laland, 2002].
Sorting the birds into categories of social structure revealed a strong correspondence
between telencephalic volume fraction ( F tel ) and level of social complexity (Figure 4a).
Progressively increasing ranges of F tel were seen for solitary (49.9 ± 2.2%; mean ± s.d., n=4),
covey (53.9 ± 4.6%, n=13), colonial (59.8 ± 5.1%, n=10), and transactional birds (71.5 ± 4.0%,
n=6) (ANOVA: P<0.001). Pairwise comparison showed significant differences (two-tailed t-
test, P<0.02) between all groupings except for solitary vs. covey birds ( P=0.12). Thus,
increasing degrees of social complexity are accompanied by an increased relative volume of the
telencephalon.
To test for possible links between F tel and traits not explicitly cognitive in nature, we also
divided the species in five other ways (Figure 4a): 1) Eating habits, into herbivores, omnivores,
and carnivores (specifically, fish-, insect-, and/or marine invertebrate-eating birds). 2) Migration
pattern, into sedentary (permanent residents), migratory (seasonal residents), and nomadic (long-
distance travelers that establish new home territories) [Faaborg, 1988; Zann, 1996]. 3) Flight
capacity, into flightless/terrestrial (no flight or short and heavy flight) and aerial. 4) Mating type,
into monogamous or polygynous [Faaborg, 1988; del Hoyo et al., 1992]. 5) Learned
vocalization: no mimicry (no known capacity for learned vocalizations) or vocal learning
(capable of intraspecies or interspecies learned vocalizations).
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One difficulty in quantitatively determining the contributions of these traits to F tel is the
possibility that memberships in different categories may co-vary with one another. This problem
can be addressed with regression tree analysis [Venables and Ripley, 1994], a form of
multivariate analysis that is particularly appropriate for small data sets. For this data set, the
largest contributions to variation in F tel came from social structure (Figure 4b): the first and
second branch points separated birds into transactional , colonial , and covey/solitary and
accounted for a combined total of 71.2% of the variance in F tel . Learned vocalization and flight
accounted for a small amount of the remaining variance in F tel (3.2% and 4.4% of total variance,
respectively; rightmost branch points, Figure 4b). The low contribution of learned vocalization
is consistent with the observation that the song system comprises less than 10% of the
telencephalon [DeVoogd et al., 1993]. Therefore, the bulk of the apparent contribution of these
traits to variation in F tel can most parsimoniously be accounted for by a single general factor,
social complexity as defined by our groupings.
We also searched for correlates of behavioral categories in other patterns of brain
architecture. Analysis of non-telencephalic volume fractions revealed no significant correlation
between behavioral groupings and any single brain component (ANOVA and t -test; data not
shown). In search of more subtle relationships, we then performed multidimensional scaling on
the cerebrotypes. The resulting relational maps showed little or no clustering by phylogenetic
grouping (Figure 5a). The dependence of F tel on phylogeny was tested specifically by arranging
species according to a published phylogeny [Sibley and Ahlquist, 1991] and calculating, at each
node, differences in DNA hybridization temperature [Sibley and Ahlquist, 1991] as a measure of
evolutionary distance. This quantity and differences in F tel were uncorrelated (r S = –0.14),
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indicating that variations in telencephalic size cannot be explained by random evolutionary
divergence [Harvey and Pagel, 1991].
Species were separable by social structure (Figure 5b) in a pattern that matched the
regression tree analysis. Re-analysis done after exclusion of the telencephalon [Clark et al.,
2001] eliminated this relationship (Figure 5c), indicating that interspecies variations related to
social complexity are confined to the telencephalon and not other brain divisions. No other
behavioral trait was associated with distinct groupings of either whole-brain or telencephalon-
excluded cerebrotypes (data not shown).
Dinosaurs
Our findings for birds and mammals indicate that the telencephalon's role in guiding
social interactions has either evolved independently multiple times or, more likely, has persisted
throughout hundreds of millions of years of evolution. In the case of birds, the relationships we
found are so strong that we were motivated to examine their predecessors, the dinosaurs. This
case is of particular interest because behavioral data for these animals is speculative, based on
fossil footprints or physical features [Hopson, 1977]. Thus brain structure may provide a major
source of information [Edinger, 1961].
Dinosaurs (Saurischia and Ornithischia) arose in the late Triassic era approximately 230
million years ago. Until their extinction approximately 65 million years ago, along with other
reptiles ( Pterosauria and Crocodylia) they were the dominant terrestrial vertebrates [Hopson,
1977; Sereno, 1999]. They spanned a large range of body sizes, from 45 kg to nearly 100,000 kg;
the lower end of this range overlaps with the upper ranges of living reptiles and birds [Hopson,
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1977]. Approximately 150 million years ago, during the Late Jurassic, they gave rise to an
ancient bird, Archaeopteryx.
Some dinosaur skulls have been preserved with sufficient integrity that endocasts reveal
the size and external form of the brain [Edinger, 1961; Edinger, 1964]. The general form of
dinosaur brains is reptilian [Jerison, 1969; Hopson, 1977; Larsson et al., 2000]; for instance, the
features of Allosaurus brains are very similar to those of modern crocodilians [Rogers, 1998].
The sizes of dinosaur brains are also consistent with predictions made by extrapolating values
from modern reptiles along allometric straight-line fits [Jerison, 1969; Hopson, 1977]. An
exception to these patterns in shape and size is found in the case of Archaeopteryx [Edinger,
1926; Hopson, 1977], whose brain has more prominent hemispheres and follows the external
contours of the skull, similar to modern birds. Archaeopteryx’s body was an estimated 300-500 g
[Jerison, 1969; Hopson, 1977] and its brain was approximately 1.1 cm3 [Larsson et al., 2000],
placing it much closer to the brain-body allometric relationship for living birds than for living
reptiles.
Because impressions found in endocasts can reveal surface features such as the
boundaries between brain components, gross aspects of dinosaur brain architecture can be
compared with living forms (Figure 6). One feature that can be distinguished is the
telencephalon [Rogers, 1998; Larsson et al., 2000]; this allows estimation of F tel . The
telencephalons of theropods have been measured using three-dimensional reconstruction by CT
scanning of endocasts [Rogers, 1998; Larsson et al., 2000]. This method has found F tel values of
24% (Carcharodontosaurus), 28% ( Allosaurus), and 33% (Tyrannosaurus) [Larsson et al.,
2000]. These values are at or below the range of living reptiles (33% to 56%) [Platel, 1976] and
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considerably smaller than any bird. In contrast, an Archaeopteryx endocast shows a value for F tel
of 45%. This value is within the range of solitary and covey birds. In particular, the brain
proportions of Archaeopteryx are similar to chickens, which have the smallest F tel (44%)
considered here. Thus, extrapolation from the known behavior and brain structure of living
forms suggests that brains of dinosaurs (and thus social complexity, and perhaps other cognitive
traits) were comparable to those of reptiles living today, and that Archaeopteryx is likely to have
been a solitary or covey bird, on a par with chickens.
Discussion
Previous analysis has suggested a central role for the cerebral cortex in the rise of social
or “Machiavellian” intelligence in primates [Whiten and Byrne, 1988; Dunbar, 1995; Barton and
Dunbar, 1997]. Our recent results show that in birds as well, the degree of social complexity
correlates strongly with relative telencephalic size. The telencephalon therefore plays a central
role in guiding social interactions in two highly divergent vertebrate taxa.
Variation in the architecture of bird brains has been considered previously by Portmann
[Portmann, 1946; Portmann, 1947a; Portmann, 1947b]. He found that taxa with relatively large
adult brains (scaled to body size) tend at hatching to be altricial, reaching physical independence
late and thus requiring intensive parental care. The adult brains of altricial birds are also those
with large hemispheres, that is, with a large F tel . Conversely, precocial birds tend to have smaller
adult brains and smaller values of F tel . Similarly, in primates, late postnatal maturation is
correlated with relative enlargement of the neocortex [Allman and Hasenstaub, 1999]. Taken
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together, these results suggest that accelerated telencephalic growth, altriciality, and social
complexity are generally linked phenomena in warm-blooded vertebrates.
Our findings support the view that a major functional role of the vertebrate telencephalon
is the regulation of social interaction. They also suggest that a layered cerebral cortex is not
necessary for such telencephalic functions. This may be true for other cognitive skills besides
social intelligence: a number of the birds described in our study as “transactional” are known for
their reasoning abilities. For instance, crows can modify objects into tools without prior
experience [Weir et al., 2002] and can manufacture functionally lateralized (“handed”) tools
using skilled, multiple-step crafting procedures [Hunt, 2000; Hunt et al., 2001]. Parrots are
capable of impressive feats of communication and reasoning and can master concepts such as
number, relative comparison, and object permanence [Pepperberg and Brezinsky, 1991;
Pepperberg, 2002]. These birds have large relative telencephalic volume, and their cognitive
capabilities include planning, reasoning, and prediction functions generally considered the
province of the mammalian cerebral cortex.
We have applied our analysis of living birds to an extinct bird, Archaeopteryx, and have
placed putative limits on its degree of social complexity based on extrapolation from its related
living successors. Although until now published descriptions of fossil braincases [Molnar, 1985;
Wittmer, 1990; Chatterjee, 1991; Elzanowski, 1991; Elzanowski and Galton, 1991; Currie and
Zhao, 1993] have only rarely included volume estimates of divisions of the brain, our work
indicates that such measurements may be of interest.
In the future, quantitative approaches to neuroanatomy and behavior can be improved in a
number of ways. From an ethological standpoint, it would be useful to arrive at a measure of
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behavioral complexity that can be applied across species [Bullock, 1993]. An ideal solution to
this problem would allow comparisons not only within large clades (e.g. birds, mammals) but
also across all animals. However, even though problem-solving ability and social complexity
have been observed in animals as divergent as crows, octopuses, dolphins, and humans,
comparing them with each other is, at present, quite difficult.
Another unresolved issue is the detailed functional interpretation of volume fraction. In
other words, what is the relationship between a structure’s relative or absolute size and its
functional role in the entire nervous system? Underlying the simple measures of macroscopic
volume is a wide array of changes that also vary systematically across species (see Harrison et al.,
in press). For instance, the cellular components of cerebral cortex change in number [Changizi,
2001; Elston et al., 2001], size [Elston et al., 2001] and connectivity [Braitenberg, 2001]; the
relationship between these parameters to function is unknown. With sufficient understanding of
the underlying cellular and network processes, it may be possible to rationalize size in terms of
neural processing.
Acknowledgements. We thank P.P. Mitra and D.A. Clark for collaborative efforts; R. Jornsten for advice on
statistical analysis; M. Hau, P.R. Hof, S. Tavazoie, and M. Wikelski for stimulating discussions; and T.A. Barney for
excellent secretarial aid. M.J.B. is supported by the Howard Hughes Medical Institute. S.S.-H.W. is supported by
the Alfred P. Sloan Foundation.
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Figure legends
Figure 1 Constancy of cerebellar volume fraction and variability of cerebral cortical volume
fraction across mammalian taxa. Cerebellar volume fractions ( F cbl ) are plotted at order level
except for the primates, which are divided into Strepsirhini (lemurs and lorises), Platyrrhini (New
World monkeys), Cercopithecoidea (Old World monkeys), and Hominoidea (apes and human).
F cbl values are indicated by solid symbols; cerebral cortical volume fractions ( F neo), by open
symbols. Error bars indicate standard deviation and the shaded bar indicates mean +/- s.d. for the
pooled data. Statistically significant differences between an individual taxon and the entire
group are identified in bold (asterisk, P<0.02; double asterisk, P<0.01). Modified from [Clark et
al., 2001].
Figure 2 Clustering of cerebrotypes by taxon, plotted using multidimensional scaling. (a)
Variation in the 11-component cerebrotype for insectivores, tree shrews, and primates. (b)
Insectivores divided into taxa. (c) Primates divided into taxa. Modified from [Clark et al., 2001].
Figure 3 Relative variation in brain components compared with body size. Volumes of
telencephalon (filled symbols) and non-telencephalic brain components (open symbols) as a
function of brain size. Brain and body measurements came from published data on 33 species
[Nottebohm, 1981; Rehkämper et al., 1991; DeVoogd et al., 1993; Boire and Baron, 1994;
Harper and Turner, 2000]. The telencephalon is significantly more variable than non-
telencephalic brain components.
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Figure 4 Analysis of telencephalic volume fraction and social complexity in birds. (a)
Relationships between avian behavioral characteristics and telencephalic volume fraction ( F tel ).
Boxplots of F tel values indicate median (midline), 25% and 75% quartiles (box edges), minimum
and maximum values (whisker ends), and outliers residing more than 1.5 times the box length
beyond the upper or lower quartile (dots). The numbers below boxes indicate number of species
in each category. (b) Regression tree showing optimal separation of F tel . For partitioning, all six
behavioral characteristics from (a) were used except nomadic migration, for which the data set
contained only two parrots, M. undulatus and P. menstruus. The regression tree gave splits by
social structure at the first and second nodes that accounted for major components of variance in
F tel . Values at the nodes in bold indicate the fraction of total variance in F tel accounted for by the
node. The branch length is proportional to the amount of variance reduction. For each leaf,
percentages give mean F tel and (number of species in the leaf). The minimum deviance
[Venables and Ripley, 1994] was set at 0.01 and the minimum leaf size was set at 3 species.
Figure 5 Multidimensional scaling of avian brain architecture. Multidimensional scaling of
avian cerebrotypes was performed using an iterative bootstrapping procedure [Clark et al., 2001];
the figure shows the mapping with lowest root-mean-squared (rms) error in 10 or more trials.
The color-coded regions represent minimum convex polygons for each group. Scale bars
represent 10% Euclidean distance. a, Taxonomic clustering of bird cerebrotypes. Ga.,
Galliformes: pheasants/guineafowl/turkeys; Ap., Apodiformes: swifts/hummingbirds; Co.,
Columbiformes: pigeons/doves; Ch., Charadriiformes: shorebirds; Ps., Psittaciformes: parrots.
The rms fractional error is 7.2%. b, Social clustering of same bird cerebrotypes. c, Social
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clustering when telencephalon was excluded from cerebrotype data and the remaining brain
regions renormalized. The rms fractional error for the renormalized scaling is 11.1%.
Figure 6 Comparison of dinosaur telencephalic volume fractions with living forms. Dinosaur
telencephalic volume fractions F tel were estimated from CT scan reconstructions of endocasts of
four dinosaurs [Larsson et al., 2000]. Boxplots are calculated from bird data as cited in the text,
from 35 reptiles [Platel, 1976], and 70 fishes [Ridet and Bauchot, 1990].
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0.8
0.6
0.4
0.2
0
F r a c t i o n
o f t o t a l b r a i n
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o i d e a
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i r e n i a
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t e r a
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21 117 232 471522523 414122 1 8 15 12 6
E d e n t a t a
Burish, Kueh and Wang
Figure 1
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Insectivores
PrimatesTree shrews
Lemurs/ lorises/ tarsier
Monkeys/ apes
10% Euclideandistance
Tarsier
Great apes
Old World monkeysNew World monkeys
Lemurs/lorises
Pan
Homo
Gorilla Pongo
Tenrecs
Tree shrewsSolenodon
Golden moles
Hedgehogs
Elephant shrewsShrews
Moles
Potamogale velox Limnogale mergulus
Micropotamogale lamottei
a
b
c
1 0 % E
u c l i d e a n d i s t a n c e
At.Lag.
Ceb.
Sai.
Burish, Kueh and Wang
Figure 2
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4
3
2
1 2 3 4
Telencephalon
Non-telencephalic
components
Log (body mass) (g)10
L o g
( b r a i n v o l u m e ) ( m m
)
1 0
3
Burish, Kueh and Wang
Figure 3
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|
Aerial 48.6% (4)
Terrestrial/
Flightless
No mimicry 58.0% (7)
Learned
Vocalizations
Transactional 71.6% (6)
Social structure
0.577
Social structure
0.135
Vocalizations
0.032
Flight
0.044
Solitary/Covey/Colonial
Col.
Sol./
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54.2% (13)
63.9% (
a b
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0.50
0.55
0.60
0.65
0.70
0.75
Telencephalic
fraction of total brain
Social
Structure
Eating
HabitsMigration Flight Mating
TypeSong
T r a n s a c t i o n a l
S o l i t a r y
C o v e y
C o l o n i a l
H e r b i v o r o u s
O m n i v o r o u s
C a r n i v o r o u s
S e d e n t a r y
M i g r a t o r y
N o m a d i c
T e r r e s t r i a l / F l i g h t l e s s
A e r i a l
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N o m i m i c r y
L e a r n e d v o c a l i z a t i o n s
613104 21120 9249195 1914 825
Burish, Kueh and Wang
Figure 4
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10%
bTransactional
Colonial
Covey
Solitary
10%
a
Ga.
Ps.
Ap.
Ch.
Co.
c
TransactionalColonial
Covey
Solitary
10%
Burish, Kueh and Wang
Figure 5
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0.5
0.6
0.7
T e l e n c e p h a l i c f r a c t i o n o f t o t a l
b r a i n
Birds
T r a n s a c t i o n a l
S o l i t a r y
C o v e y
C o l o n i a l
613104
0.4
0.3
0.2
Archaeopteryx
Tyrannosaurus
35
0.8
R e p t i l e s
0.1
0
Cold-blooded
vertebrates
Carcharodont.
Allosaurus
72
F i s h e s
Burish, Kueh and WangFigure 6