Relative Brain Size and Its Relation with the Associative Pallium in Birds

Abstract

Despite growing interest in the evolution of enlarged brains, the biological significance of brain size variation remains controversial. Much of the controversy is over the extent to which brain structures have evolved independently of each other (mosaic evolution) or in a coordinated way (concerted evolution). If larger brains have evolved by the increase of different brain regions in different species, it follows that comparisons of the whole brain might be biologically meaningless. Such an argument has been used to criticize comparative attempts to explain the existing variation in whole-brain size among species. Here, we show that pallium areas associated with domain-general cognition represent a large fraction of the entire brain, are disproportionally larger in large-brained birds and accurately predict variation in the whole brain when allometric effects are appropriately accounted for. While this does not question the importance of mosaic evolution, it suggests that examining specialized, small areas of the brain is not very helpful for understanding why some birds have evolved such large brains. Instead, the size of the whole brain reflects consistent variation in associative pallium areas and hence is functionally meaningful for comparative analyses.

Full text

E-Mail karger@karger.com
Original Paper
Brain Behav Evol
DOI: 10.1159/000444670
Relative Brain Size and Its Relation
with the Associative Pallium in Birds
Ferran Sayol a Louis Lefebvre a, c Daniel Sol a, b
a CREAF and
b CSIC, Cerdanyola del Vallès , Spain;
c Department of Biology, McGill University, Montréal, Qué. , Canada
size of the whole brain reflects consistent variation in asso-
ciative pallium areas and hence is functionally meaningful
for comparative analyses. © 2016 S. Karger AG, Basel
Introduction
The phylogenetic-based comparative approach has
become a major tool in investigating the evolution of the
vertebrate neural architecture. Much of past effort has
been devoted to assessing whether the existing variation
in brain size among species predicts differences in cogni-
tively demanding behaviours. This has yielded ample ev-
idence that larger brains are associated with enhanced
domain-general cognition [Lefebvre et al., 1997; Reader
and Laland, 2002; Reader et al., 2011; Benson-Amram et
al., 2016] and function to facilitate behavioural adjust-
ments to socio-environmental changes [Reader and La-
land, 2002; Sol et al., 2005, 2007; Sol, 2009; Schuck-Paim
et al., 2008]. Despite the progress, the biological signifi-
cance of brain size variation across species is not exempt
from criticism [Healy and Rowe, 2007]. A main argument
has been that, because brains are divided into function-
ally distinct areas, the analyses should focus on the areas
Key Words
Encephalization · Brain size · Cognition · Pallium · Mosaic
evolution · Concerted evolution
Abstract
Despite growing interest in the evolution of enlarged brains,
the biological significance of brain size variation remains
controversial. Much of the controversy is over the extent to
which brain structures have evolved independently of each
other (mosaic evolution) or in a coordinated way (concerted
evolution). If larger brains have evolved by the increase of
different brain regions in different species, it follows that
comparisons of the whole brain might be biologically mean-
ingless. Such an argument has been used to criticize com-
parative attempts to explain the existing variation in whole-
brain size among species. Here, we show that pallium areas
associated with domain-general cognition represent a large
fraction of the entire brain, are disproportionally larger in
large-brained birds and accurately predict variation in the
whole brain when allometric effects are appropriately ac-
counted for. While this does not question the importance of
mosaic evolution, it suggests that examining specialized,
small areas of the brain is not very helpful for understanding
why some birds have evolved such large brains. Instead, the
Received: November 10, 2015
Returned for revision: November 30, 2015
Accepted after revision: February 11, 2016
Published online: April 19, 2016
Ferran Sayol
CREAF
Campus UAB, Edifici C
ES–08193 Cerdanyola del Vallès, Catalonia (Spain)
E-Mail f.sayol  @  creaf.uab.cat
© 2016 S. Karger AG, Basel
0006–8977/16/0000–0000$39.50/0
www.karger.com/bbe
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Sayol/Lefebvre/Sol
Brain Behav Evol
DOI: 10.1159/000444670
2
to which a particular function could be ascribed [Healy
and Rowe, 2007].
In fact, the validity of the above criticism depends on
the classic, unresolved debate over the extent to which
brain areas evolve independently of each other in a mo-
saic fashion [Barton and Harvey, 2000; Iwaniuk and Hurd,
2005; Barrett and Kurzban, 2006] or in a concerted way as
a result of conserved developmental programs [Charvet et
al., 2011; Anderson and Finlay, 2013]. If information pro-
cessing in the brain is massively modular [Barrett and
Kurzban, 2006], then larger brains can evolve by the in-
crease of different brain regions in different species, mak-
ing comparisons of whole-brain size biologically mean-
ingless [Harvey and Krebs, 1990; Healy and Rowe, 2007].
However, if only some areas evolve in a concerted way, but
together occupy a large part of the brain, then a dispropor-
tionate increase in these brain areas would be reflected in
a larger brain regardless of the fact that smaller, more spe-
cialized, brain regions might evolve independently. This
could be the case of brain areas like the avian mesopallium
and nidopallium (which together form the associative pal-
lium) and the mammalian isocortex [Rehkämper et al.,
1991]. If the most important part of whole-brain size vari-
ation is driven by these large, concertedly evolving areas,
then focusing on the whole brain in comparative studies
would be a good proxy for variation in these areas. Com-
parative evidence suggests that taxonomic variation in the
size of the primate isocortex and the avian associative pal-
lium is associated with variation in a suite of correlated,
domain-general cognitive abilities [Lefebvre et al., 2004;
Reader et al., 2011] that include feeding innovation and
tool use [Timmermans et al., 2000; Lefebvre et al., 2002;
Reader and Laland, 2002; Mehlhorn et al., 2010]. En-
hanced demands on domain-general cognition could thus
be reflected in an enlarged cortex and associative pallium,
as well as an enlarged brain.
The debate over models of brain size evolution has not
yet been settled in part due to disagreements on how
brain size should best be quantified. In primates, as many
as 26 different metrics have been used in large-scale stud-
ies exploring ecological, life history and cognitive corre-
lates of encephalization [reviewed in Lefebvre, 2012]. The
Table 1. Encephalization metrics used in the comparative literature on birds
Metric References
Frequently used
Log brain mass Lefebvre and Sol, 2008; Shultz and Dunbar, 2010
Res log (brain) log (body) Isler and van Schaik, 2006; Franklin et al., 2014
Res log (tel) log (body) Nicolakakis and Lefebvre, 2000; Lefebvre and Sol, 2008; Iwaniuk and Wylie, 2006
Res log (tel) log (rest of brain) Iwaniuk and Wylie, 2006
Volume tel/brainstem Lefebvre et al., 1997
Volume tel/brain Burish et al., 2004
Volume tel/rest of brain Shultz and Dunbar, 2010
Log region Lefebvre and Sol, 2008
Res log (region) log (body) Timmermans et al., 2000; Mehlhorn et al., 2010
Res log (region) log (body) log (other regions) Iwaniuk et al., 2004
Res log (region) log (tel) Fuchs et al., 2014
Res log (region) log rest of brain) Iwaniuk and Wylie, 2006; Gutierrez-Ibanez et al., 2014
Res log (region) log (rest of tel) Iwaniuk and Wylie, 2006; Iwaniuk et al., 2008
Volume region/brainstem Lefebvre and Sol, 2008
Volume region/brain Iwaniuk and Hurd, 2005; Fuchs et al., 2014
Rarely used
Martin EQ Lefebvre and Sol, 2008
Head volume Møller et al., 2010
Shape based on absolute values Kawabe et al., 2013
Shape based on regressions against body size Kawabe et al., 2013
Telencephalon/brainstem of Galliformes Lefebvre et al., 1997; Zorina and Obozova, 2012
Log tel/brainstem of Galliformes Lefebvre et al., 1998
Skull height Winkler et al., 2004
Res = Residual; tel = telencephalon; region = varies according to study (e.g. mesopallium, nidopallium, hyperpallium and visual ar-
eas); rest of brain or tel = volume of the brain or telencephalon minus the volume of the region studied.
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comparative literature on birds is similarly based on a va-
riety of metrics, which go from residuals to fractions and
proportions of the whole or of parts of the brain ( table1 ).
The different ways in which the data are combined in the
analyses adds additional uncertainties about what the size
of the whole brain really means [Healy and Rowe, 2007].
In this paper, we use the most complete dataset on avi-
an brain regions currently available [Iwaniuk and Hurd,
2005] to ask what the variation in brain size really means
in terms of underlying structures. We use phylogeneti-
cally controlled analyses based on the current Bird Tree
project [Jetz et al., 2012] to examine inter-relationships
between brain size, body size and the volume of 6 major
brain parts and to assess the validity of several data trans-
formation metrics used to control for allometry. We pre-
dict that a bigger brain should mainly correspond to an
increase in associative pallium, and hence that variation
in these areas would strongly predict variation in the
whole brain when using appropriate methods to remove
allometric effects.
Methods
Data Sources and Phylogenetic Hypotheses
Data on the whole brain and on the volume of 6 brain parts
were taken from Iwaniuk and Hurd [2005]. Three regions part of
the telencephalon: the nidopallium, which also includes all of the
nidopallial subregions [but see Iwaniuk and Hurd, 2005, for more
details], the mesopallium and the hyperpallium. Three other non-
telencephalic regions include the cerebellum, the diencephalon
and the brainstem – which is the sum of the mesencephalon and
the myelencephalon. The 6 areas together form between 70 and
87% of the avian brain volume. Body mass data (g) were obtained
from Dunning [2007]. The phylogenetic hypotheses we used were
taken from the Bird Tree project [Jetz et al., 2012], where random-
ly sampled trees were taken from 2 different backbones coming
from independent studies [Hackett et al., 2008; Ericson, 2012]. We
removed one species (Pavo meleagris) from the database of Iwan-
iuk and Hurd [2005], as in this set of phylogenetic trees it is con-
sidered the same species as Meleagris gallopavo, already present in
the database (see online suppl. fig. S1 for an example of one of the
phylogenetic hypotheses used; for all online suppl. material, see
www.karger.com/doi/10.1159/000444670).
Statistical Analyses
We first calculated a correlation matrix between the 6 brain
areas. We used the ‘phyl.vcv’ function in R software [R, 2013] with
optimization of the parameter lambda using maximum likelihood
criteria [Revell, 2012] to account for phylogenetic non-indepen-
dence of the data. We then compared different ways of removing
allometric effects for each brain part, using body mass, volume of
the entire brain or volume of a basal part, i.e. the brainstem. For a
given brain part, e.g. the nidopallium, we tested the following mea-
sures: (1) absolute nidopallium volume; (2) residuals of nidopal-
lium volume from a log-log regression against body mass or (3)
brainstem volume; (4) nidopallium volume divided by brainstem
volume, similar to the executive brain ratio used for primates, and
(5) nidopallium volume divided by the volume of the rest of the
brain (fraction) or (6) by the volume of the entire brain (propor-
tion). Measures 2 and 3 are thus residuals of log-log regressions
and measures 4, 5 and 6 can be calculated using untransformed or
log-transformed volumes. We thus had 9 different measures that
we compared and tested for potential remaining effects of body
size using phylogenetically corrected least-squares regressions
(PGLS) with the R package ‘caper’ [Orme, 2013]. This method,
compared to a non-corrected regression, controls for the non-in-
dependence of data due to shared ancestry. Contrary to indepen-
dent contrasts, however, it first determines the strength of the phy-
logenetic signal in the data (parameter lambda, which varies be-
tween 0 and 1 and is calculated using maximum likelihood [Pagel,
1999]) and controls it accordingly, without assuming, as do con-
trasts, that lambda is 1. For this purpose, we used a set of 20 phy-
logenetic trees and calculated means over the 20 models.
For all further analyses, we used residuals only, as other metrics
do not eliminate the effect of body mass (see Results). We next
analysed the extent to which each brain region is associated with
body size using PGLS models with log-transformed variables. To
see which brain part best predicts whole-brain variation, we took
the residuals of whole-brain volume against body mass and exam-
ined their relationship with the residuals of each brain part re-
gressed against body mass. To illustrate these relationships, we
plotted positive and negative whole-brain residuals in different
shades (black for positive and white for negative) and graphed
them against brain part residuals. A brain part that predicts whole-
brain size well will yield clearly separated clouds of white and black
points; in contrast, a brain part that does not predict whole-brain
size well will yield overlapping black and white data points. The
extent to which positive and negative whole-brain residuals are
well separated in each graph can then be expressed by a histogram
illustrating overlaps. We also used a set of PGLS models to deter-
mine which allometrically corrected brain part best explains varia-
tion in allometrically corrected whole-brain size. A possible prob-
lem with the last two analyses is that we are correlating two vari-
ables that are residuals from the same predictor (body size), which
might lead to some circularity. However, when using brainstem to
remove allometry in the brain regions and body size to remove al-
lometry in the whole brain, we obtained exactly the same results in
terms of which parts explain most variation in the whole brain.
Finally, we conducted a phylogenetic reconstruction of whole-
brain residuals and associative pallium residuals – all corrected for
body mass by taking phylogenetic residuals – on a sample tree us-
ing the ‘contMap’ function of the ‘phytools’ R package [Revell,
2012]. This technique combines data on phylogeny and trait vari-
ation between clades to estimate evolutionary increases or decreas-
es in different lineages.
Results
In terms of absolute size, all brain areas are positively
associated with each other in phylogenetically corrected
analyses ( fig.1 a; online suppl. table S1). Much of this trend
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4
is due to body size allometry, however, so we next exam-
ined the way different transformations of the original data
affect the body size confounder. Of all of the metrics we
tested, only those based on residuals and the executive
brain ratio calculated on log-transformed data completely
removed the effects of body size (online suppl. table S2).
Analyses based on metrics such as fractions and propor-
tions therefore do not deal exclusively with brain part vari-
ation but also include body size.
When allometric effects are taken into account by es-
timating residuals, some areas show stronger inter-rela-
tionships than others, suggesting a combination of con-
certed and mosaic evolution ( fig.1 b, online suppl. table
S3). Concerted evolution is particularly evident for the
areas forming the associative part of the telencephalon,
notably the nidopallium and the mesopallium (r = 0.94).
These two areas show much larger amounts of variation
independently of body size than do basal brain areas such
as the brainstem ( fig.2 ; online suppl. table S4). Phyloge-
netically corrected variation in nidopallium and meso-
pallium size correctly classifies 95 and 92%, respectively,
of the positive and negative residuals of whole-brain size
regressed against body size (fig. 2a, b). In contrast, brain-
stem volume is strongly related to body size and does not
discriminate between species with large versus small
brain residuals ( fig.2 e). As a consequence, brain to body
size residuals are better predicted by variation in associa-
tive pallium residuals (mesopallium + nidopallium) than
by other brain parts ( fig.3 ), regardless of whether allom-
etry is corrected by body mass (online suppl. table S5) or
brainstem volume (online suppl. table S6). In fact, brain
size and associative pallium (after corrections for allome-
tric effects) are almost indistinguishable measures of en-
cephalization ( fig.4 ; PGLS: R 2 = 0.91, p < 0.001). Inferring
the evolution of avian brains with phylogenetic recon-
structions yields virtually identical results with the two
metrics ( fig.5 ), where we can see independent shifts in
the increase of both relative brain and associative pallium
sizes in crows and parrots and the reduction of these two
measures in three practically independent clades (rheids,
galliforms and swifts).
Discussion
Our analyses lead to three main conclusions regarding
the evolution of the avian brain. First, all 6 brain parts
ana lysed here tended to increase in a concerted way, a
trend that was not simply a consequence of allometry or
phylogeny. Second, some areas, notably those belonging
Cerebellum
Hyperpallium
Mesopallium
Diencephalon
Nidopallium
Brainstem
Nidopallium
Cerebellum
Hyperpallium
Mesopallium
Diencephalon
a
b
Brainstem
Fig. 1. Phylogenetic correlations between different brain regions,
using absolute values (
a ) or residuals from log-log regressions ( b )
against body size.
Fig. 2. Log size of the 6 brain parts against log body mass, distin-
guishing species with positive brain residuals (closed data points)
and species with negative brain residuals (open data points). To
the right of each plot, we present two histograms, one for each set
of dots from the plots (closed and open), corresponding to positive
and negative brain residuals.
(For figure see next page.)
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6
to the associative pallium, evolved in a more concerted
way than others. Finally, large brains primarily resulted
from a disproportionate increase in these pallial areas.
These areas are not only anatomically well delineated
(thus minimizing measurement error) but also comprise
a large fraction of the brain, in particular the nidopallium.
Thus, the same proportional increase of these areas is
likely to have a stronger effect on the size of the whole
brain than on that of smaller areas, an idea previously
proposed by Rehkämper et al. [1991].
The associative pallium areas are known to have key
roles in avian cognition. The nidopallium, in particular
its caudolateral part, the NCL, is the closest avian equiva-
lent of the mammalian pre-frontal cortex. Several lines of
evidence, using different approaches and techniques
(connectome [Shanahan et al., 2013], single-unit record-
ing [Rose and Colombo, 2005; Veit and Nieder, 2013;
Lengersdorf et al., 2015], receptor architecture [Rose et
al., 2010; Herold et al., 2011], temporary inactivation
[Helduser and Güntürkün, 2012] and lesions [Mogensen
and Divac, 1993]) point to the importance of the NCL in
avian executive control. Comparative work also suggests
that the nidopallium is the brain area most closely corre-
lated with avian tool use [Lefebvre et al., 2002], while the
other part of the associative pallium, i.e. the mesopallium,
is most closely correlated with innovation rate [Timmer-
mans, 2000]. The mesopallium is significantly enlarged in
the bird with the most sophisticated form of tool use, i.e.
the New Caledonian crow (Corvus moneduloides) [Mehl-
horn et al., 2010]. The very tight relationship between ni-
dopallium and mesopallium size, once phylogeny and al-
lometry have been removed, further suggests that evolu-
tionary changes in the two structures are strongly linked.
Together, the two structures are the closest avian equiva-
lent to the mammalian non-visual cortex. These areas ap-
pear to be crucial to domain-general cognitive abilities.
Our results suggest the need for caution in the use of
absolute brain size to study the neural basis of cognitive
skills, at least in birds. Given that this measure is con-
founded by body size, traits associated with body size (e.g.
range, energetics and prey size) will confound any com-
parative test of brain size correlates. Using relative mea-
sures could be a solution to remove allometric effects, but
we found here that dividing brain part volume by the vol-
ume of the whole (proportions) or the rest of the brain
(fractions), with or without prior log transforms of the
volumes, leaves significant body size confounders ( ta-
ble1 ). Studies using these metrics [e.g. Clark et al., 2001;
Burish et al., 2004] thus contain a hidden confounder that
might affect conclusions about evolutionary trends.
In contrast, residual brain size seems to better describe
how brains increase due to a disproportionate enlarge-
ment of specific, large brain areas. Using residuals com-
pletely removes allometric effects on the brain but might
pose a problem of interpretation, as it is unclear what a
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–1
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Brain residual
Associative pallium residual
R2 = 0.91
p value < 0.001
Brain residual
Hyperpallium, R2 = 0.65
Mesopallium, R2 = 0.84
Nidopallium, R2 = 0.90
Diencephalon, R2 = 0.79
Brainstem, R2 = 0.36
Cerebellum, R2 = 0.70
Fig. 3. Relationship between residuals of different brain parts and
whole-brain residuals, all regressed against log body mass, with the
R
2 for PGLS models represented on a schematic avian brain [re-
drawn based on Nottebohm, 2005].
Fig. 4. Residual of whole-brain size against body size plotted
against residual of associative pallium size against brainstem size.
The data points represent actual species, while the line represents
the PGLS model. The slightly lower slope of the regression with
respect to the cloud of data points is due to the phylogenetic cor-
rections.
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disproportionately large area means in functional terms.
The underlying assumption for existing variation in brain
size among species is that any increase in size provides
some increase in function. Although this is supported by
growing evidence linking residual brain to enhanced cog-
nition [see a revision by Lefebvre and Sol, 2008], why
should a disproportionate increase matter at all? Because
the brain processes information, and this is done by dis-
crete neurons acting together via neurotransmitters and
receptors, the functional significance of volume differ-
ences might not be clear. In mammals, different orders
have different scaling relationships of neuron numbers to
brain area volume [Herculano-Houzel, 2011, 2012]. Sim-
ilar differences might well characterize bird brains. One
can imagine, for example, that a corvid or a parrot meso-
pallium might have more neurons per cubic millimetre
than a quail brainstem. Knowing this would obviously be
important, but it would not change correlational trends
of the type we report here, or the associations with cogni-
tion reported in the literature. We might in fact be under-
estimating selection on brain areas associated with cogni-
tion by focusing on mass or volume rather than neuron
numbers if differences in density go in the same direction
as differences in classical metrics of encephalization. This
also assumes that the number of neurons is the main de-
terminant of information processing capacity, not their
connectedness or the density and type of neurotransmit-
ters and receptors. Comparative studies of receptor den-
sity and gene expression in brain areas will shed new light
on the functional significance of enlarged brains [Good-
son et al., 2012].
The finding that enlarged brains have primarily
evolved by the concerted increase of certain brain re-
gions does not deny the importance of mosaic evolution.
Indeed, the fact that some areas evolve more concert-
edly than others can be interpreted as a combination of
mosaic and concerted evolution. Theoretical work on
other biological systems (e.g. metabolic networks [Ra-
vasz et al., 2002]) suggests that modular units are orga-
nized into hierarchical clusters, a principle that might
reconcile modular and concerted views on the way in
which the neural substrate of cognitive abilities operates
and evolves. Moreover, mosaic evolution could be more
important for small areas specialized in particular be-
Rhynchotus rufescens
Rhea americana
Dendrocygna eytoni
Anser anser
Anas platyrhynchos
Ortalis canicollis
Numida meleagris
Colinus virginianus
Coturnix coturnix
Alectoris chukar
Meleagris gallopavo
Phasianus colchicus
Chrysolophus pictus
Perdix perdix
Gallus gallus
Phaps elegans
Patagioenas leucocephala
Streptopelia roseogrisea
Columba livia
Spheniscus magellanicus
Ardea cinerea
Nycticorax caledonicus
Phalacrocorax auritus
Puffinus tenuirostris
Charadrius vociferus
Vanellus miles
Calidris minutilla
Limnodromus griseus
Sterna hirundo
Entomyzon cyanotis
Strepera versicolor
Garrulus glandarius
Corvus corone
Taeniopygia guttata
Passer domesticus
Calyptorhynchus funereus
Nymphicus hollandicus
Cacatua roseicapilla
Melopsittacus undulatus
Glossopsitta concinna
Trichoglossus haematodus
Psephotus haematonotus
Platycercus eximius
Platycercus elegans
Agapornis roseicollis
Agapornis personatus
Neopsephotus bourkii
Polytelis swainsonii
Alisterus scapularis
Psittacula krameri
Psittacula eupatria
Eclectus roratus
Pionus menstruus
Amazona aestiva
Pyrrhura molinae
Psittacus erithacus
Falco longipennis
Falco cenchroides
Podargus strigoides
Chlorostilbon mellisugus
Chaetura pelagica
Accipiter fasciatus
Ninox novaeseelandiae
Todiramphus sanctus
Dacelo novaeguineae
Caprimulgus vociferus
Rhynchotus rufescens
Rhea americana
Dendrocygna eytoni
Anser anser
Anas platyrhynchos
Ortalis canicollis
Numida meleagris
Colinus virginianus
Coturnix coturnix
Alectoris chukar
Meleagris gallopavo
Phasianus colchicus
Chrysolophus pictus
Perdix perdix
Gallus gallus
Phaps elegans
Patagioenas leucocephala
Streptopelia roseogrisea
Columba livia
Spheniscus magellanicus
Ardea cinerea
Nycticorax caledonicus
Phalacrocorax auritus
Puffinus tenuirostris
Charadrius vociferus
Vanellus miles
Calidris minutilla
Limnodromus griseus
Sterna hirundo
Entomyzon cyanotis
Strepera versicolor
Garrulus glandarius
Corvus corone
Taeniopygia guttata
Passer domesticus
Calyptorhynchus funereus
Nymphicus hollandicus
Cacatua roseicapilla
Melopsittacus undulatus
Glossopsitta concinna
Trichoglossus haematodus
Psephotus haematonotus
Platycercus eximius
Platycercus elegans
Agapornis roseicollis
Agapornis personatus
Neopsephotus bourkii
Polytelis swainsonii
Alisterus scapularis
Psittacula krameri
Psittacula eupatria
Eclectus roratus
Pionus menstruus
Amazona aestiva
Pyrrhura molinae
Psittacus erithacus
Falco longipennis
Falco cenchroides
Podargus strigoides
Chlorostilbon mellisugus
Chaetura pelagica
Accipiter fasciatus
Ninox novaeseelandiae
Todiramphus sanctus
Dacelo novaeguineae
Caprimulgus vociferus
−0.93 1.79Associative pallium residual−0.70 1.51Brain residual
Fig. 5. Phylogenetic reconstruction in a sample phylogenetic hypothesis of birds in our dataset, representing re-
sidual brain size evolution and residual associative pallium size evolution.
Color version available online
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Sayol/Lefebvre/Sol
Brain Behav Evol
DOI: 10.1159/000444670
8
haviours, which have not been evaluated here. A case in
point is the network of song nuclei that has been exten-
sively studied in oscines. Nuclei of this type are absent
in non-oscines, with the exception of parrots and hum-
mingbirds [Jarvis, 2007], and at least one of them, i.e. the
HVC, varies strongly as a result of sexual selection on
repertoire size [DeVoogd et al., 1993; Moore et al., 2011].
If there is one clear case of adaptive specialization of
brain areas in birds, it is the case of oscine song nuclei,
which could evolve independently from other brain re-
gions. However, these findings do not deny that, as our
study suggests, the main variation in whole brain size is
due to concerted changes in pallial areas, allowing the
use of relative brain size as a proxy for relative pallium
size in comparative studies.
Acknowledgements
We are grateful to members of the laboratory of D. Sol for help-
ful discussions. We also thank two anonymous reviewers for their
comments, and José Luis Ordóñez for his help with the figures.
This research was supported by funds from the Spanish govern-
ment (CGL2013-47448-P) to D.S. F.S. was supported by a PhD
fellowship (FI-DGR 2014) from the Catalan government.
References
Anderson ML, Finlay BL (2013): Allocating struc-
ture to function: the strong links between
neuroplasticity and natural selection. Front
Hum Neurosci 7:
918.
Barrett HC, Kurzban R (2006): Modularity in cog-
nition: framing the debate. Psychol Rev 113:
628.
Barton RA, Harvey PH (2000): Mosaic evolution
of brain structure in mammals. Nature 405:
1055–1058.
Benson-Amram S, Dantzer B, Stricker G, Swan-
son EM, Holekamp KE (2016): Brain size pre-
dicts problem-solving ability in mammalian
carnivores. Proc Natl Acad Sci USA 113:
2532–2537.
Burish MJ, Kueh HY, Wang SS-H (2004): Brain
architecture and social complexity in modern
and ancient birds. Brain Behav Evol 63:
107–
124.
Charvet CJ, Striedter GF, Finlay BL (2011): Evo-
devo and brain scaling: candidate develop-
mental mechanisms for variation and con-
stancy in vertebrate brain evolution. Brain
Behav Evol 78:
248–257.
Clark DA, Mitra PP, Wang SS (2001): Scalable ar-
chitecture in mammalian brains. Nature 411:
189–193.
DeVoogd TJ, Krebs JR, Healy SD, Purvis A (1993):
Relations between song repertoire size and
the volume of brain nuclei related to song:
comparative evolutionary analyses amongst
oscine birds. Proc R Soc Lond B Biol Sci 254:
75–82.
Dunning JB (2007): Handbook of Avian Body
Masses, ed 2. Boca Raton, CRC Press.
Ericson PGP (2012): Evolution of terrestrial birds
in three continents: biogeography and paral-
lel radiations. J Biogeogr 39:
813–824.
Franklin DC, Garnett ST, Luck GW, Gutierrez-
Ibanez C, Iwaniuk AN (2014): Relative brain
size in Australian birds. Emu 114:160–170.
Fuchs R, Winkler H (2014): Brain geometry and
its relation to migratory behavior in birds. J
Adv Neurosci Res 1: 1–9.
Goodson JL, Kelly AM, Kingsbury MA (2012):
Evolving nonapeptide mechanisms of gregar-
iousness and social diversity in birds. Horm
Behav 61:
239–250.
Gutiérrez-Ibáñez C, Iwaniuk AN, Moore BA,
Fernández-Juricic E, Corfield JR, Krilow JM,
Kolominsky J, Wylie DR (2014): Mosaic and
concerted evolution in the visual system of
birds. PLoS One 9:e90102.
Hackett SJ, Kimball RT, Reddy S, Bowie RCK,
Braun EL, Braun MJ, Yuri T (2008): A phy-
logenomic study of birds reveals their evolu-
tionary history. Science 320:
1763–1768.
Harvey PH, Krebs JR (1990): Comparing brains.
Science 249:
140–146.
Healy SD, Rowe C (2007): A critique of compara-
tive studies of brain size. Proc R Soc Lond B
Biol Sci 274:
453–464.
Helduser S, Güntürkün O (2012): Neural sub-
strates for serial reaction time tasks in pi-
geons. Behav Brain Res 230:
132–143.
Herculano-Houzel S (2011): Scaling of brain me-
tabolism with a fixed energy budget per neu-
ron: implications for neuronal activity, plas-
ticity and evolution. PLoS One 6:e17514.
Herculano-Houzel S (2012): The remarkable, yet
not extraordinary, human brain as a scaled-
up primate brain and its associated cost. Proc
Natl Acad Sci USA 109:
10661–10668.
Herold C, Palomero-Gallagher N, Hellmann B,
Kröner S, Theiss C, Güntürkün O, Zilles K
(2011): The receptor architecture of the pi-
geons’ nidopallium caudolaterale: an avian
analogue to the mammalian prefrontal cor-
tex. Brain Struct Funct 216:
239–254.
Isler K, van Schaik C (2006): Costs of encephaliza-
tion: the energy trade-off hypothesis tested on
birds. J Hum Evol 51: 228–243.
Iwaniuk AN, Dean KM, Nelson JE (2004): A mo-
saic pattern characterizes the evolution of the
avian brain. Proc Biol Sci 271 Suppl:
S148–S151.
Iwaniuk AN, Hurd PL (2005): The evolution of
cerebrotypes in birds. Brain Behav Evol 65:
215–230.
Iwaniuk AN, Wylie DRW (2006): The evolution
of stereopsis and the Wulst in caprimulgiform
birds: a comparative analysis. J Comp Physiol
A Neuroethol Sens Neural Behav Physiol 192:
1313–1326.
Jarvis ED (2007): Neural systems for vocal learn-
ing in birds and humans: a synopsis. J Orni-
thol 148:
35–44.
Jetz W, Thomas GH, Joy JB, Hartmann K, Mooers
AO (2012): The global diversity of birds in
space and time. Nature 491:
444–448.
Kawabe S, Shimokawa T, Miki H, Okamoto T,
Matsuda S, Itou T, et al (2013): Relationship
between brain volume and brain width in
mammals and birds. Paleontol Res 17: 282–
293.
Lefebvre L (2012): Primate encephalization; in
Hofman MA, Falk D: Progress in Brain Re-
search, ed 1. Amsterdam, vol 195.
Lefebvre L, Gaxiola A, Dawson S, Timmermans S,
Rosza L, Kabai P (1998): Feeding innovations
and forebrain size in Australasian birds. Be-
haviour 135: 1077–1097.
Lefebvre L, Nicolakakis N, Boire D (2002): Tools
and brains in birds. Behaviour 939–973.
Lefebvre L, Reader SM, Sol D (2004): Brains, in-
novations and evolution in birds and pri-
mates. Brain Behav Evol 63:
233–246.
Lefebvre L, Sol D (2008): Brains, lifestyles and
cognition: are there general trends? Brain Be-
hav Evol 72:
135–144.
Lefebvre L, Whittle P, Lascaris E (1997): Feeding
innovations and forebrain size in birds. Anim
Behav 53:
549–560.
Lengersdorf D, Marks D, Uengoer M, Stüttgen
MC, Güntürkün O (2015): Blocking NMDA-
receptors in the pigeon’s ‘prefrontal’ caudal
nidopallium impairs appetitive extinction
learning in a sign-tracking paradigm. Front
Behav Neurosci 9:
85.
Downloaded by:
McGill University Health Center
132.216.236.68 - 4/21/2016 3:38:48 PM

Brain Size and Pallium Areas Brain Behav Evol
DOI: 10.1159/000444670
9
Mehlhorn J, Hunt GR, Gray RD, Rehkämper G,
Güntürkün O (2010): Tool-making New
Caledonian crows have large associative brain
areas. Brain Behav Evol 75:
63–70.
Mogensen J, Divac I (1993): Behavioural effects of
ablation of the pigeon-equivalent of the mam-
malian prefrontal cortex. Behav Brain Res 55:
101–107.
Møller AP (2010): Brain size, head size and behav-
iour of a passerine bird. J Evol Biol 23: 625–
635.
Moore JM, Székely T, Büki J, Devoogd TJ (2011):
Motor pathway convergence predicts syllable
repertoire size in oscine birds. Proc Natl Acad
Sci USA 108:
16440–16445.
Nicolakakis N, Lefebvre L (2000): Forebrain size
and innovation rate in European birds: feed-
ing, nesting and confounding variables. Be-
haviour 137: 1415–1429.
Nottebohm F (2005): The neural basis of bird-
song. PLoS Biol 3:
0759–0761.
Orme D, Freckleton R, Thomas G, Petzoldt T,
Fritz S, Isaac N, Pearse W (2013): Caper:
Comparative Analyses of Phylogenetics and
Evolution in R. R package (version 0.5.2).
Pagel M (1999): Inferring the historical patterns
of biological evolution. Nature 401:
877–884.
Ravasz E, Somera AL, Mongru DA, Oltvai ZN,
Barabási A-L (2002): Hierarchical organiza-
tion of modularity in metabolic networks. Sci-
ence 297:
1551–1555.
RCT (2013): R: A language and environment for
statistical computing. R Found Stat Comput.
Vienna, Austria.
Reader SM, Hager Y, Laland KN (2011): The evo-
lution of primate general and cultural intelli-
gence. Phil Trans R Soc B Biol Sci 366:
1017–
1027.
Reader SM, Laland KN (2002): Social intelligence,
innovation, and enhanced brain size in pri-
mates. Proc Natl Acad Sci USA 99:
4436–4441.
Rehkämper G, Frahm HD, Zilles K (1991): Quan-
titative development of brain and brain struc-
tures in birds (Galliformes and Passeriformes)
compared to that in mammals (insectivores
and primates) (part 2 of 2). Brain Behav Evol
37:
135–143.
Revell LJ (2012): phytools: an R package for phy-
logenetic comparative biology (and other
things). Methods Ecol Evol 3:
217–223.
Rose J, Colombo M (2005): Neural correlates of
executive control in the avian brain. PLoS Biol
3:
1139–1146.
Rose J, Schiffer A-M, Dittrich L, Güntürkün O
(2010): The role of dopamine in maintenance
and distractability of attention in the ‘pre-
frontal cortex’ of pigeons. Neuroscience 167:
232–237.
Schuck-Paim C, Alonso WJ, Ottoni EB (2008):
Cognition in an ever-changing world: climat-
ic variability is associated with brain size in
neotropical parrots. Brain Behav Evol 71:
200–215.
Shanahan M, Bingman VP, Shimizu T, Wild M,
Güntürkün O (2013): Large-scale network or-
ganization in the avian forebrain: a connectiv-
ity matrix and theoretical analysis. Front
Comput Neurosci 7:
89.
Shultz S, Dunbar RIM (2010): Social bonds in
birds are associated with brain size and con-
tingent on the correlated evolution of life-his-
tory and increased parental investment. Biol J
Linn Soc 100: 111–123.
Sol D (2009): Revisiting the cognitive buffer hy-
pothesis for the evolution of large brains. Biol
Lett 5:
130–133.
Sol D, Duncan RP, Blackburn TM, Cassey P, Le-
febvre L (2005): Big brains, enhanced cogni-
tion, and response of birds to novel environ-
ments. Proc Natl Acad Sci US A 102: 5460–
5465.
Sol D, Székely T, Liker A, Lefebvre L (2007): Big-
brained birds survive better in nature. Proc R
Soc Lond B Biol Sci 274:
763–769.
Timmermans S, Lefebvre L, Boire D, Basu P
(2000): Relative size of the hyperstriatum ven-
trale is the best predictor of feeding innova-
tion rate in birds. Brain Behav Evol 56:196–
203.
Veit L, Nieder A (2013): Abstract rule neurons in
the endbrain support intelligent behaviour in
corvid songbirds. Nat Commun 4:
2878.
Winkler H, Leisler B, Bernroider G (2004): Eco-
logical constraints on the evolution of avian
brains. J Ornithol 145: 238–244.
Zorina ZA, Obozova TA (2012): New data on the
brain and cognitive abilities of birds. Biol Bull
39: 601–617.
Downloaded by:
McGill University Health Center
132.216.236.68 - 4/21/2016 3:38:48 PM