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Primate brain size is predicted by diet but not sociality


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The social brain hypothesis posits that social complexity is the primary driver of primate cognitive complexity, and that social pressures ultimately led to the evolution of the large human brain. Although this idea has been supported by studies indicating positive relationships between relative brain and/or neocortex size and group size, reported effects of different social and mating systems are highly conflicting. Here, we use a much larger sample of primates, more recent phylogenies, and updated statistical techniques, to show that brain size is predicted by diet, rather than multiple measures of sociality, after controlling for body size and phylogeny. Specifically, frugivores exhibit larger brains than folivores. Our results call into question the current emphasis on social rather than ecological explanations for the evolution of large brains in primates and evoke a range of ecological and developmental hypotheses centred on frugivory, including spatial information storage, extractive foraging and overcoming metabolic constraints.
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NATURE ECOLOGY & EVOLUTION 1, 0112 (2017) | DOI: 10.1038/s41559-017-0112 | 1
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Primate brain size is predicted by diet but not
Alex R. DeCasien1,
2*, Scott A. Williams1,
2 and James P. Higham1,
The social brain hypothesis posits that social complexity is the primary driver of primate cognitive complexity, and that social
pressures ultimately led to the evolution of the large human brain. Although this idea has been supported by studies indicat-
ing positive relationships between relative brain and/or neocortex size and group size, reported effects of different social and
mating systems are highly conflicting. Here, we use a much larger sample of primates, more recent phylogenies, and updated
statistical techniques, to show that brain size is predicted by diet, rather than multiple measures of sociality, after controlling
for body size and phylogeny. Specifically, frugivores exhibit larger brains than folivores. Our results call into question the cur-
rent emphasis on social rather than ecological explanations for the evolution of large brains in primates and evoke a range of
ecological and developmental hypotheses centred on frugivory, including spatial information storage, extractive foraging and
overcoming metabolic constraints.
Primates, especially anthropoids, have relatively large brains
compared to other mammals. These observations have led
researchers to propose various explanations for the evolution of
increased brain size in the primate lineage. Accordingly, numerous
comparative analyses have been undertaken with the goal of iden-
tifying social and/or ecological variables that explain interspecific
variation in overall brain size, or of specific brain regions1.
Early studies suggested that ecological factors, such as diet,
explain relative brain size variation in non-human primates2–5. This
is consistent with the idea that processing of meat and other foods
contributed to subsequent increases in hominin brain size6,7 by ful-
filling corresponding higher energy requirements8–11. Later compar-
ative studies emphasized the role of social factors. In particular, the
social brain hypothesis posits that social complexity is the primary
driver of cognitive complexity among primates, and that social pres-
sures associated with maintaining group cohesion ultimately led to
the evolution of the large human brain12–15. This hypothesis has been
supported by studies indicating positive relationships between rela-
tive brain and/or neocortex size and mean group size2,14–17.
However, research investigating the relationships between rela-
tive brain size and different social and mating system types, which
may differ in their relative social complexity, has produced highly
conflicting results17,18. Some studies have shown that polygynan-
drous primate species have the largest brains3,17, consistent with the
idea that systems that promote the most interactions and relation-
ships between the greatest numbers of individuals might be the
most cognitively demanding. Conversely, other studies have shown
that monogamous species have the largest brains18, and have argued
that monogamy may require greater deception and manipulation
abilities18 for obtaining extra-pair copulations, produce a relatively
high cost of cuckoldry, and/or require conflict resolution and coor-
dination abilities for bond maintenance17.
These conflicting results suggest that methodological issues may
have led different researchers to different conclusions. Throughout
the comparative study of primate brain size evolution, species sam-
ple sizes used in analyses have been small and idiosyncratic, while
the statistical techniques available have improved considerably
since early analyses. For example, many early studies used residuals
as data, which can cause bias if the control variable co-varies with
other variables in the analysis; the use of multiple regression with
the confounding variable incorporated as a covariate is now recom-
mended instead19. In addition, many studies used a phylogeny20 that
has become outdated and set all branch lengths to 1—a relatively
radical branch length transformation that presumes an evolution-
ary pattern in which changes occur at the time of speciation in both
daughter species.
We assembled a much larger and more representative sample of
primates (> 140 spp., more than tripling the sample size of previous
studies) and tested whether multiple measures of sociality (mean
group size, social and mating system separately) explain variation
in brain size after controlling for body size, diet and phylogenetic
history. Although some studies have used relative neocortex size
rather than whole brain size, this information is not available for
a large sample of primate species; in any case, the neocortex scales
hyper-allometrically with brain size21. In its original form, the social
brain hypothesis was formulated to explain primate intelligence12,13
and was later discussed as an attempt to explain brain size14,15,22.
The subsequent focus on the neocortex was not always based on a
priori reasoning, but because neocortex analyses sometimes showed
the strongest correlations with the social variables under examina-
tion14. Regions outside the neocortex are also involved in complex
cognitive functions (for example, cerebellum23, hippocampus24,
striatum25) and studies show that overall brain size predicts global
cognitive ability across non-human primates24,26. Furthermore,
studies by the main proponents of the social brain hypothesis con-
tinue to present analyses of relative total brain size17,22,24, consistent
with the interpretation that the social brain hypothesis does indeed
aim to explain evolutionary increases not only in neocortex ratio,
but in overall brain size.
For each sociality measure, the full model (including all predictors)
included brain size as the dependent variable, and body size, either
diet category or percent frugivory and alternative sociality measures
1Department of Anthropology, New York University, 25 Waverly Place, New York, New York 10003, USA. 2New York Consortium in Evolutionary
Primatology, New York, New York 10024, USA. *e-mail:
2 NATURE ECOLOGY & EVOLUTION 1, 0112 (2017) | DOI: 10.1038/s41559-017-0112 |
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
as predictors. We employed phylogenetic generalized least squares
(PGLS) regression and incorporated phylogenetic uncertainty by
using two recent consensus phylogenies27,28 and testing across a
Bayesian block of alternative trees for one of them27, using maxi-
mum-likelihood model averaging, Bayesian phylogenetic mixed
models and a fully Bayesian phylogenetic regression analysis. We
compared different branch length transformations and full versus
reduced models using the Bayesian information criterion (BIC)29.
The possible effect of within-species group size variation was tested
via resampling. Consensus tree analyses were repeated within cer-
tain subgroups (anthropoids, catarrhines) and also using female-
only brain and body size data to account for possible effects of grade
shifts in the nature of sociality and sexual dimorphism, respectively.
Finally, we reconstructed ancestral states to visualize the evolution-
ary context of brain size evolution and sociality.
Contrary to the predictions of the social brain hypothesis, our
results indicate that none of the sociality measures examined here
explain relative brain size variation in primates, which is predicted
only by diet, with frugivores having relatively larger brains than foli-
vores. Results from analyses across all primates incorporating the
10kTrees consensus tree27 are presented here in detail because this
set provides the largest species sample size. Maximum-likelihood
estimates of lambda were used for branch length transformations
because other models, particularly those with branch lengths set to
1 (as in most previous studies), were worse fitting (Tables1–4). In
all cases, Type I ANOVAs for models including all predictors indi-
cate that, while body size and diet each explain a significant amount
of brain size variation, none of the sociality measures examined
explain additional variation (Tables1–3). This pattern of significance
remains when percent frugivory is included in the model instead
of diet category (Supplementary Tables 1–3), and when the order
in which diet and sociality variables are entered into the model is
switched (Supplementary Tables 5–10). Phylogenetic uncertainty
does not affect these patterns, as both consensus trees (Tables1–3;
Supplementary Tables 1–18), most maximum-likelihood models
tested across the block of 1,000 trees (Supplementary Tables 19–26),
Bayesian phylogenetic mixed models (Supplementary Tables 30–37)
and fully Bayesian phylogenetic regression analyses (Supplementary
Tables 27–29) provide statistically indistinguishable results. Within-
species variation also does not affect these results, as the resampling
analysis of the full group size models confirmed the lack of effect for
group size (model with diet category: median estimate = 0.003,
95% confidence interval (CI) = 0.021 to 0.017; model with percent
frugivory: median estimate = 0.021, 95% CI = 0.008 to 0.047).
These results are not confounded by sexual dimorphism as analyses
run using female-only brain and body size data produced similar
results (Supplementary Tables 38–59). Although some sociality
measures seem to explain a significant amount of variation within
a small set of subgroup analyses, these are probably owing to model
assumption violations and are not consistent when different phylo-
genies or diet proxies are used (see Supplementary Text).
Furthermore, for each sociality proxy, the model including all pre-
dictors is not a good fit relative to models including either body size
alone or body size and diet (Tables1–3; Supplementary Tables 1–3,
11–13 and 15–17), whereas the last two are statistically indistin-
guishable from each other (Tables1–4; Supplementary Tables 1–4,
11–14 and 15–18). After correcting for multiple comparisons, fru-
givores and frugivore/folivores exhibit significantly larger brains
than folivores (Table 4), with model estimates suggesting that
frugivores exhibit 25% (95% CI = 8–44%) more brain tissue than foli-
vores of the same body weight. According to primate brain cellular
scaling rules as determined by the isotropic fractionator method30,
this predicted difference amounts to an increase of around 1.08 bil-
lion total neurons for a frugivore of average body weight. Relatively
more neurons for the same body mass probably indicates increased
processing power that is not simply related to maintenance and con-
trol of the body31. In some supplementary analyses, omnivores also
exhibit significantly larger brains than folivores (Supplementary
Tables 58, 66 and 80). These results are supported by the significant
Table 1 | Results for diet and group size models (spp. n=140):
brain (log) ~ body (log) + diet + group size (log).
Branch length comparisons dBIC Weight
Lambda=0.968 0.0 0.95
Kappa= 0.698 6.1 0.05
Delta= 1.469 11.1 < 0.01
All branch lengths= 1 42.1 < 0.001
Sequential SS ANOVA Mean sq. P value
Body (log) 0.2213 <2 × 1016
Diet 0.0012 0.02
Group (log) 0.0000 0. 74
Residuals 0.0004
Model comparisons dBIC Weight
Body (log) 0.0 0.92
Body (log) + diet 5.2 0.07
Body (log) + diet + group (log) 10.0 0.01
There is strong evidence that the model incorporating a maximum-likelihood value of
lambda provides relatively better model fit than models incorporating other branch length
transformations. Non-lambda models exhibit higher BIC values (by > 6) and very low weights,
which represent the relative likelihood of a model given the BIC values of all models in the
comparison. Body size and diet explain a significant amount of variation in brain size after
controlling for phylogeny and are highlighted in bold. Group size does not explain additional
variation, as this variable exhibits a low mean square value and a non-significant P value
(P > 0.05). Compared with a model including only body size, there is strong evidence that the
model including body size, diet and group size provides relatively poor fit, while there is only
weak evidence of such for the model including both body size and diet. The model including
group size exhibits a higher BIC value (by > 6) and very low weight, indicating low relative
likelihood of this model. Notes: branch length comparisons are estimated using maximum-
likelihood, with others set to 1. dBIC, difference in BIC value from best fit model (dBIC > 6
indicates strong evidence of relatively poor model fit)29; mean sq., mean squares. Sequential sum
of squares (SS) ANOVA represents Type I ANOVA of best fit model including all predictor
variables. Model comparisons were constructed using maximum-likelihood lambda.
Corrected significant P value cut-off= 0.05/6= 0.0083.
Table 2 | Results for diet and social system models (spp. n=142):
brain (log) ~ body (log) + diet + social system.
Branch length comparisons dBIC Weight
Lambda=0.961 0.0 0.98
Kappa= 0.635 7.7 0.02
Delta= 2.012 15.9 < 0.001
All branch lengths= 1 42.2 < 0.001
Sequential SS ANOVA Mean sq. P value
Body (log) 0.2232 <2 × 1016
Diet 0.0013 0.01
Social system 0.0002 0.60
Residuals 0.0003
Model comparisons dBIC Weight
Body (log) 0.0 0.91
Body (log) + diet 4.6 0.09
Body (log) + diet + social system 17. 6 < 0.001
See Table 1 legend for explanatory information. Social system does not explain additional
variation, as this variable exhibits a low mean square value and a non-significant P value
(P > 0.05). Compared with a model including only body size, there is strong evidence that the
model including body size, diet and social system provides relatively poor fit, while there is only
weak evidence of such for the model including both body size and diet.
NATURE ECOLOGY & EVOLUTION 1, 0112 (2017) | DOI: 10.1038/s41559-017-0112 | 3
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
and positive effect of percent frugivory (Supplementary Tables 1–4,
15–18, 23–29 and 34–37). Phylogenetic uncertainty does not affect
these differences between dietary categories (Supplementary Tables 14,
22 and 33). Ancestral reconstructions of relative brain size (encepha-
lization quotient, EQ) and the aforementioned sociality measures
(Fig.1; Supplementary Figs 1 and 2) highlight notable departures
from the predictions of the social brain hypothesis.
The results presented here are consistent with a range of ecologi-
cal and developmental hypotheses centred on frugivory, including:
(1) necessity of spatial information storage and retrieval3,4; (2) cognitive
demands of ‘extractive foraging’ of fruits and seeds5; and (3) higher
energy turnover and enhanced diet quality for energy needed dur-
ing fetal brain growth8–11. Together, it seems that frugivory not only
provides selective pressures on cognitive processing3–5, but compen-
sates for the costs of a metabolically expensive brain via facilitating
higher energy turnover and/or lower energy allocation to diges-
tion8–11. There are many examples of closely related species with
similar body sizes, group sizes and social systems that exhibit cor-
responding brain size and dietary differences (for example, Ateles
versus Alouatta; Supplementary Table 107). Notably, ancestral
reconstructions elsewhere suggest these changes were not driven
by reductions in body size32. Results suggesting that omnivorous
anthropoid, but not strepsirrhine, species have large brains relative
to folivores probably reflect different behavioural strategies related
to faunivory. While omnivorous strepsirrhines usually feed on
insects, a relatively abundant food source, omnivorous anthropoids
often additionally hunt vertebrates (such as Cebus capucinus33).
Although frugivores tend to have relatively smaller guts, recent work
indicates no negative correlation between relative size of the brain
and digestive tract after controlling for fat-free mass34. The lack of
relationship between relative brain size and sociality indicated here
is consistent with previous studies limited to strepsirrhines35 and on
non-primate taxa (for example, cetaceans36, carnivores37, birds38),
and suggests that assumptions underlying the primate social brain
hypothesis and its associated predictions should be re-evaluated.
First, complex social behaviours (for example, coalitions, recip-
rocation) that were previously assumed to be unique to primates
have now been found in other taxa that do not exhibit relatively
large brains compared to other members of their order (such as
spotted hyenas39). Therefore, the premise that social complexity
necessarily requires cognitive complexity may not always hold, as
social living challenges might not require flexible cognitive solu-
tions in real-time, but could be solved using simpler evolved rules-
of-thumb40. Observational and simulation studies have suggested
that simple associative rules may actually explain many complex
patterns of behaviour40.
Second, the hypothesis that complex social environments are
more cognitively demanding than properties of the physical envi-
ronment is partially derived from the idea that the former is more
unstable than the latter and requires more processing power to navi-
gate41. This has not been demonstrated quantitatively41, and stud-
ies indicating a positive relationship between relative brain size and
survival in mammalian species introduced into new environments
suggest that long-term environmental variability could select for
behavioural versatility42.
Difficulties associated with assigning appropriate proxies of
social complexity and cognitive complexity should not be underes-
timated. For example, mean group size is, ‘at best, a crude proxy’ of
social complexity43, because larger groups may not be characterized
by a corresponding increase in the number of differentiated rela-
tionships/interactions44. Future studies using more sophisticated
proxies may provide better support for the social brain hypoth-
esis. However, our results call into question the current emphasis
on social rather than ecological explanations for the evolution of
large brains in primates. Rapid expansion of the hominoid cer-
ebellum suggests technical intelligence was at least as important as
social intelligence in human cognitive evolution45. Furthermore,
numerous studies suggest that the neural substrates of tool use may
represent evolutionary precursors for the evolution of language in
humans46,47. Technical innovations also allowed for the increased
incorporation of meat in the diet, and the advent of cooking meat
Table 4 | Results for diet models (spp. n=144):
brain (log) ~ body (log) + diet.
Branch length comparisons dBIC Weight
Lambda=0.967 0.0 0.97
Kappa= 0.637 6.7 0.04
Delta= 1.703 15.5 < 0.001
All branch lengths= 1 43.2 < 0.001
Sequential SS ANOVA Mean sq. P value
Body (log) 0.2248 <2 × 1016
Diet 0.0012 0.02
Residuals 0.0004
Model comparisons dBIC Weight
Body (log) 0.0 0.91
Body (log) + diet 4.7 0.09
Multiple comparisons Estimate P value
Frugivores vs folivores 0.097 0.002
Frugivore/folivores vs folivores 0.099 0.004
Omnivores vs folivores 0.074 0.037
Frugivore/folivores vs frugivores 0.002 0.931
Omnivores vs frugivores 0.023 0.269
Omnivores vs frugivore/folivores 0.025 0.297
See Table 1 legend for explanatory information. Compared with a model including only body size,
there is only weak evidence that a model including body size and diet provides relatively poor
fit. Only frugivores and frugivore/folivores exhibit significantly larger relatively brain size than
folivores, after correcting for the number of comparisons, and these comparisons are highlighted
in bold. The estimated intercept differences between categories are also provided here.
Table 3 | Results for diet and mating system models (spp. n=142):
brain (log) ~ body (log) + diet + mating system.
Branch length comparisons dBIC Weight
Lambda=0.963 0.0 0.98
Kappa= 0.653 7.3 0.03
Delta= 1.533 15.7 < 0.001
All branch lengths= 1 41.8 < 0.001
Sequential SS ANOVA Mean sq. P value
Body (log) 0.2241 <2 × 1016
Diet 0.0013 0.01
Mating system 0.0005 0.19
Residuals 0.0003
Model comparisons dBIC Weight
Body (log) 0.0 0.91
Body (log) + diet 4.6 0.09
Body (log) + diet + mating system 17.9 < 0.001
See Table 1 legend for explanatory information. Mating system does not explain additional
variation, as this variable exhibits a low mean square value and a non-significant P value
(P> 0.05). Compared with a model including only body size, there is strong evidence that the
model including body size, diet and mating system provides relatively poor fit, while there is only
weak evidence of such for the model including both body size and diet.
4 NATURE ECOLOGY & EVOLUTION 1, 0112 (2017) | DOI: 10.1038/s41559-017-0112 |
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
and other foods6,7. Together with the present study, this body of
comparative work suggests that both human and non-human pri-
mate brain evolution was primarily driven by selection on increased
foraging efficiency, with associated changes then perhaps providing
the scaffolding for subsequent development of social skills.
Data collection and compilation. We compiled all species averaged data on brain
and body weights from published literature sources (Supplementary Data: ‘Brain
Data’ and ‘Body Data’ tabs). Brain weights represent averages of the following:
(1) brain weights recorded in several original sources (compiled in ref. 48); and
(2) endocranial volumes (ECV) recorded in ref. 49, which were converted to
masses by multiplying by a factor of 1.036 g cm3 (the specic gravity of brain
tissue50). e nal value used for each species represents an average of the
values provided across studies, weighted according to the study sample size
(Supplementary Data: ‘Brain Pivot’ and ‘Brain Final’ tabs). is dataset was
supplemented with secondary source data51. e female-only dataset represents
brain and body weights of sexed specimens from ref. 49. e subset of species for
which both brain weight and ECV were available (n = 79) was tested for bias due
to dierent collection methods. Agreement was concluded given that: (1) corrected
ECV and weight measurements were highly correlated (R2= 0.99); (2) the mean
dierence (corrected ECV – weight) was 0.62 g with a 95% CI including zero
( 1.81 to 3.05 g); and (3) there was low correlation (R2= 0.20) between the mean
value of the two methods and the dierence. As body weights were unavailable
for many specimens in the original studies, they represent averages from
the CRC Handbook of Mammalian Body Masses52, and the AnAge53 and
PanTHERIA54 databases. Additional data from secondary sources51,55,56 were
added to supplement this dataset, and were calculated as the mean values of
males and females. In the course of compiling this dataset, we excluded juveniles,
emaciated individuals and ‘low-quality’ data (indicated in the AnAge database53)
when this information was available.
We assigned dietary categories according to previous designations in the
published literature3,55,57–61 and used a four category scheme, which includes
folivore, frugivore/folivore, frugivore and omnivore (Supplementary Data:
‘Diet Data’ tab). Percent frugivory per species was obtained from ref. 62
(Supplementary Data: ‘Diet Data’ tab). We assigned social and mating systems
after consolidating assignments listed in published literature sources3,17,18,35,63–66
(Supplementary Data: ‘System Data’ tab). Social system categories represent the
four-way categorization scheme typically used in primate studies67, including
solitary, pair-living, harem polygyny and polygynandry. Mating system
categories include spatial polygyny, monogamy, polyandry, harem polygyny
and polygynandry. These two categorization schemes, social system and mating
system, were used to conform to previous studies using either one, and because
primate social organizations and mating systems describe distinct categorization
schemes that are not always congruent, although are often used interchangeably.
Group size data were collected from numerous primary and secondary
sources35,59,67–74. Average group size per species was calculated after removing
duplicate values (Supplementary Data: ‘Group Size Pivot’ tab). This resulted
in an average of 4.7 group size data points per species.
No simple universally accepted rule exists regarding the ratio of sample
size to the number of predictors; however, a commonly used rule states that the
Haplorhini Strepsirrhini
EQ Mean group
Cercopithecoidea Ceboidea
Myr ago
60 40 20 0
Figure 1 | Ancestral reconstructions of primate EQ (left) and mean group size (right). Lower values for EQ and mean group size are shown in redder
colours; higher values are shown in blue (n= 140 species). Examples of departures from predictions of the social brain hypothesis include gibbons, which
exhibit a decrease in group size since their last common ancestor with the great apes (associated with transitioning to monogamy) and an increase in
relative brain size (concurrent with decreased body size and maintenance or slight increase in absolute brain size32). Similarly, although cebids and the
aye-aye have not increased group size, their relative brain size has increased (possibly in relation to their extractive foraging behaviours5).
NATURE ECOLOGY & EVOLUTION 1, 0112 (2017) | DOI: 10.1038/s41559-017-0112 | 5
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
MCMC diagnostics were run using the ‘coda’ package in R82. We report the
posterior means of the variables included in each model and their 95% CI83,
and the probability that each explanatory parameter value is > 0 (PMCMC) as
all have been hypothesized to have positive associations with brain size.
Finally, we considered the possible effect of phylogenetic uncertainty by
using Bayesian mixed models to test across a random sample of 100 different
trees from the previous set of 1,000 10kTrees phylogenies. This was implemented
using the R package ‘MCMCglmm84 and modified R code from ref. 85. This
procedure uses an MCMC estimation approach and accounts for phylogenetic
non-independence by including the phylogenetic relationships among species
as a random variable. We confirmed convergence between model chains using
the Gelman–Rubin statistic, with all models required to have a potential scale
reduction factor below 1.1 (ref. 86). The effective sample sizes for all terms
across all models were > 1,000. In line with previous work84,85, we used an
uninformative inverse-Wishart distribution and a parameter expanded
previously, with a half-Cauchy distribution for the random factor. Each model
was fitted to each of the 100 trees, and the model outputs were combined
to create coefficient distributions.
We implemented a resampling procedure as a way to incorporate within-
species variation in group size. This procedure involves randomly choosing one
group size datum for each species and setting it as the species-specific estimate16,87.
Using this resampling scheme, we created 1,000 species-specific datasets that
were subsequently analysed using the full (all predictors) group size PGLS model.
Some iterations encountered optimization errors, which relate to calculating the
maximum-likelihood of lambda. These were ignored and resampling continued
until we produced 1,000 models16. To make inferences across these models, we
determined the 95% CI of the derived group size term coefficients.
Before ancestral reconstruction analyses, polytomies in the phylogenetic tree
were resolved using the ‘multi2di’ function in the R package ‘ape’88. Maximum-
likelihood ancestral state reconstructions of continuous traits (relative brain size,
mean group size) were estimated using the ‘fastAnc’ function in the R package
‘phytools’89. The EQ31 was used as a measure of relative brain size, and the
equation was derived from our dataset (n= 144 species) using the allometric
formula E = kPα, where Eisbrain mass, Pisbody mass, kis theproportionality
constant, αis theallometric exponent and the final equation is E= 0.085 × P0.775.
The root node was set22 to reflect a species characterized by solitary foraging
(group size = 1) and a relative brain size (EQ= 0.41) based on estimates of early
Eocene fossil primate brain and body weights (Smilodectes gracilis90: brain= 9.84 g,
body= 1,600 g, EQ= 0.38; Tetonius homunculus91: brain= 1.55 g, body= 161 g,
EQ= 0.45; see Supplementary Data: ‘Fossil Data’ tab).
Reconstructions for discrete variables (social and mating system) were
conducted using an empirical Bayesian method in which the transition matrix
is fixed at its most likely value, executed by the ‘make.simmap’ function in
R package ‘phytools89. The function first fits a continuous-time reversible Markov
model for the evolution of the trait in question, and then simulates stochastic
character histories using that model and the tip states on the given tree89.
To provide information regarding reconstruction uncertainty, marginal ancestral
reconstructions were performed at each node by computing the set of empirical
Bayes posterior probabilities that each node is in each state over 500 simulations.
The root node prior probabilities were set to assume spatial polygyny and solitary
foraging as the ancestral states for mating system and social system, respectively.
Different transition rate models were considered using BIC, including: (1) an
equal rates model, in which a single parameter governs all evolutionary transition
rates; (2) a symmetrical rates model, in which forward and reverse evolutionary
transitions between states are constrained to be equal; and (3) an all-rates-different
model, where each rate is given a unique parameter. The symmetrical rates model
was ultimately used for both reconstructions as it exhibited the lowest BIC value.
Data availability. The authors declare that all data supporting the findings of this
study are available within the paper and its Supplementary Information files.
Received 21 September 2016; accepted 7 February 2017;
published 27 March 2017
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number of cases should be at least 10 times the number of estimated terms75.
Across all models included in these analyses, the maximum number of terms to
be estimated is 10 (mating system models including all predictors: 1= intercept,
1= body size (log) coefficient, 3= diet category coefficients, 4= mating system
coefficients, 1= branch length transformation parameter), and all sample sizes
exceed 100 species.
Statistical analyses. All statistical analyses were carried out in R 3.2.2. Humans
(Homo sapiens) were excluded from all analyses because we are an outlier with
regard to brain size and, consequently, excluding humans or presenting results with
humans omitted is common practice in comparative studies of brain size18.
For each of the three sociality measures (mean group size, social system,
mating system), two sets of models were constructed to incorporate either dietary
category or percent frugivory as the diet measure. In each set, three different
models were constructed, each of which had brain size as the dependent variable
and either body size, body size + diet, or body size + diet + sociality proxy as
predictors. All continuous variables except percent frugivory were log-transformed
before analyses. Interaction terms were not included for the sake of interpretability
and to prevent over-parameterization76. Assumptions of the linear model, with the
exception of uncorrelated errors (see below), were tested and confirmed. Although
the expected relationship between body size and diet77 was confirmed in this
sample, all variance inflation factors throughout the linear models did not exceed
3.3, a cut-off commonly employed78 as it indicates the point at which R2= 0.70
between variables. Although residual variances tend to differ between dietary
categories and social and mating systems, multiple regression models assume the
error variance is constant across values predicted from the model, and plots of
residuals versus predicted values support this assumption.
Species represent non-independent cases because they may share traits due
to phylogenetic inertia, so we tested for phylogenetic signal in linear model
residuals by estimating values of Pagel’s lambda (λ). Although it has been common
practice in comparative biology to test the independent and dependent variables
for phylogenetic signal to justify analysis using phylogenetic methods, PGLS
assumes that the regression model residuals, not the traits under investigation,
follow a multivariate normal distribution with variances and covariances that are
proportional to the species’ phylogenetic relationships. As significant phylogenetic
signal was detected, PGLS regression was employed in all cases. We used the
topologies and branch lengths from the GenBank taxonomy consensus tree
provided on the 10kTrees website27 (version 3) and also repeated all analyses
using the molecular phylogeny from ref. 28.
Model comparisons were conducted using BIC, rather than Akaike
information criterion, as the former uses a more conservative penalty for
additional terms and is more likely to suggest the most parsimonious model,
or the one with the fewest number of parameters that need to be estimated.
Sequential analysis of variance (Type I ANOVA) was used to identify variables
that explained a significant amount of brain size variation.
In some of the analyses limited to catarrhines, maximum-likelihood
estimations of lambda produced by the PGLS models resulted in a value of zero.
It is unlikely that these traits should be modelled using ordinary least squares
regression (equivalent to lambda = 0) and that this result is due to decreased
sample size. The log-likelihood plots of lambda illustrate this, as they are very flat
(see Supplementary Fig. 3 for example). Consequently, these models were run
using a value of lambda obtained by calculating its 95% CI (represented by red
lines in Supplementary Fig. 3), extracting 100 equally spaced values of lambda
within this interval and averaging them with each value weighted according
to its likelihood.
We also considered the influence of uncertainty in phylogenetic relationships
by using 1,000 different trees from 10kTrees, which were created using Bayesian
phylogenetic methods and sampled in proportion to their probability27. We
examined the full (all predictors) PGLS models for each sociality and diet measure
combination separately for each tree. Type I ANOVA was conducted on each of the
resulting models, and the range of P values for each predictor was compared with
those produced by analyses incorporating the consensus tree. We also examined
the PGLS model including body size and diet separately for each tree to confirm
brain size differences between dietary categories. We applied model averaging, as
this procedure takes into account the varying degree of fit of the models to estimate
regression coefficients79. For each model, we allowed the phylogenetic scaling
factor (lambda) to take the value of its maximum-likelihood19.
We also fitted regression models using the ‘Continuous’ program in
BayesTraits V280. As this function can use only continuous variables, we ran
models using percent frugivory and group size (log) as proxies of diet quality and
sociality, respectively. This program allowed us to generate posterior distributions
of PGLS regression models (regression coefficients and scaling parameters) that
account for phylogenetic non-independence of species data. The analysis sampled
the tree block of 1,000 trees in proportion to their posterior probability to account
for phylogenetic uncertainty, and the scaling parameter lambda was sampled
during the Markov chain Monte Carlo (MCMC) regression analysis. Uniform,
uninformative priors were used, as these reflect the assumption that all possible
values of the parameters are equally likely a priori81, and this analysis was run
for 2 million iterations, sampling every 200 iterations, with a burn-in of 200,000.
6 NATURE ECOLOGY & EVOLUTION 1, 0112 (2017) | DOI: 10.1038/s41559-017-0112 |
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
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We thank M. Shattuck for help with data compilation, H. Kaplan for providing
access to additional data, R. Raaum for statistical advice, and R. Peterson
and M. Petersdorf for encouragement and feedback on previous versions of the
manuscript. For training in phylogenetic comparative methods, J.P.H. thanks the
AnthroTree Workshop, which is supported by the National Science Foundation
(NSF; BCS-0923791) and the National Evolutionary Synthesis Center (NSF grant
EF-0905606). This material is based on work supported by the NSF Graduate
Research Fellowship (grant DGE1342536).
Author contributions
A.R.D. designed the project and performed the analyses with input from
J.P.H. and S.A.W. A.R.D. and S.A.W. collected the data. All three authors wrote
the manuscript.
Additional information
Supplementary information is available for this paper.
Reprints and permissions information is available at
Correspondence and requests for materials should be addressed to A.R.D.
How to cite this article: DeCasien, A. R., Williams, S. A. & Higham, J. P. Primate brain
size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112 (2017).
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims
in published maps and institutional affiliations.
Competing interests
The authors declare no competing financial interests.
NATURE ECOLOGY & EVOLUTION 1, 0122 (2017) | DOI: 10.1038/s41559-017-0122 | 1
news & views
Humans exist in a complex social
world, far more complex than any
other primate, as the late American
singer–songwriter Lou Reed describes in just
two lines of his song ‘New York Telephone
Conversation’: “Did you see what she did
to him, did you hear what they said. Just a
New York conversation, rattling in my head.”
Humans have developed such extraordinary
cognitive complexity that we even have the
intelligence to write songs about the familiar
situation where two people are talking about
what numerous other people have said and
done, using a device specically invented to
communicate with people who are far away.
ere is a long-standing notion in
evolutionary biology that social complexity
is linked to cognitive complexity. In the
mid-1990s, drawing inspiration from earlier
work on the subject, this idea was solidied
and has become known as the social brain
hypothesis (SBH)1,2. is controversial
hypothesis asserts that social complexity
is the predominant evolutionary force
driving intelligence. Its supporters argue
that it can explain the purported elevated
level of cognitive complexity observed
in primates and that the exceptional
evolutionary pressures associated with
human sociality eventually resulted in
our unique intelligence and astonishingly
large brains. Writing in Nature Ecology &
Evolution, DeCasien etal.3 test SBH using
the largest dataset ever brought to bear on
this question, analysing more than three
times the number of primate species than
have previously been used. Aer accounting
for species body size, they nd no support
for SBH.
Instead they nd that what a species eats
predicts its brain size — specically that
species whose diet is predominantly made
up of fruit have, on average, larger brains
than those that specialize on eating leaves
(Fig.1). e idea that the diet of a species
is important is by no means a new one and
is likely to be associated with spatial and/or
temporal memory demands associated with
foraging. Diet is an important component
of what is oen referred to as the ecological
or foraging hypothesis4 — an alternative
explanation for the evolution of cognitive
complexity— which researchers have found
evidence for time and time again5,6. But the
study by DeCasien et al. is novel in that it
simultaneously tests between social and
ecological hypotheses across a large number
of primate species.
At rst glance, this study would seem to
put an end to SBH. However, I doubt that
this will be the last word on the matter.
DeCasien etal. should be applauded for
the construction of their dataset and
their methodology, which accounts for
phylogenetic uncertainty and interspecic
variation in brain size. I feel condent that
their study will refocus and reinvigorate
research seeking to explain cognitive
complexity in primates and other mammals.
But many questions remain.
Like many before them, this study
assumes that whole brain size is a good
proxy for cognitive complexity. While
our intuition might be satised with this
assumption, evidence shows that the brain
is a complex compartmentalized organ,
and that these compartments evolve in a
mosaic fashion7 with natural selection acting
to change some parts while others remain
unaltered. So then, does the evolutionary
signature of the social brain reside in one of
these compartments? e main contender
here is likely to be the neocortex. is region
of the brain is considered key in facilitating
complex cognitive tasks and has been
described as the “crowning achievement
of evolution and the biological substrate of
human mental prowess”8. e problem is
that we don’t have enough data on primate
neocortex size to test the idea that it is
related to social complexity on anything like
the same scale as the whole brain — this is
something DeCasien etal. recognize. With
this in mind, can our last goodbye to the
social brain really come until we rectify
DeCasien etal. employ social group
size as their measure of social complexity,
which, although widely used, is by no
means universally accepted as a good
proxy. Firstly, it is very variable within a
species. For example, a recent study shows
that chimpanzees (Pantroglodytes) live
in groups that range in size from 21 to
187individuals9. In addition to its lability
some criticize the use of social group size
owing to the fact that that it does not inform
about interactions within the group. It has
been noted recently that an objective and
universal measure of social complexity
is lacking — but the number of distinct
relationships an individual of a group has is
likely to be important10.
So where does this leave us in our quest
to understand the evolutionary forces
driving cognitive complexity or intelligence?
DeCasien etal. may have delivered a blow
to SBH that has it reeling, and if future
work irons out some of the remaining
methodological creases it may be down and
out. en we will be le in the extraordinary
Eating away at the social brain
Primates, especially humans, have large brains and this is thought to reflect our level of cognitive complexity or
‘intelligence’. Could this all be down to what we eat?
Chris Venditti
Figure 1 | Rhesus macaque (Macaca mulatta), a
primarily frugivorous Old World primate (top) and
mantled howler monkey (Alouatta palliata), a New
World folivore (bottom). Photo credits: Mauritius
images GmbH / Alamy Stock Photo (top), and
Diana Rebman / Alamy Stock Photo (bottom).
2 NATURE ECOLOGY & EVOLUTION 1, 0122 (2017) | DOI: 10.1038/s41559-017-0122 |
news & views
position of trying to explain primate
cognition without sociality. But surely diet
cannot be the whole story.
Chris Venditti is in the School of Biological Sciences,
University of Reading, Reading RG6 6AS, UK.
1. Barton, R.A. & Dunbar, R.I. in Machiavellian Intelligence II:
Extensions and Evaluations (eds Whiten, A. & Byrne, R.W.) Ch.9,
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Competing interests
e author declares no competing nancial interests.
... Also within primates these traits vary, showing an increase on the great ape branch, but also decreases in several New World monkey species. Using comparative, that is, phylogenetic, approaches across primates and mammals, these variations have been correlated with different life history traits, such as longevity, diet, or energetic constraints (DeCasien et al., 2017;DeCasien et al., 2022;Heldstab et al., 2022) revealing underlying ecological factors that drive selection for larger brains. ...
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Brain size and cortical folding have increased and decreased recurrently during mammalian evolution. Identifying genetic elements whose sequence or functional properties co-evolve with these traits can provide unique information on evolutionary and developmental mechanisms. A good candidate for such a comparative approach is TRNP1, as it controls proliferation of neural progenitors in mice and ferrets. Here, we investigate the contribution of both regulatory and coding sequences of TRNP1 to brain size and cortical folding in over 30 mammals. We find that the rate of TRNP1 protein evolution (ω) significantly correlates with brain size, slightly less with cortical folding and much less with body size. This brain correlation is stronger than for >95% of random control proteins. This co-evolution is likely affecting TRNP1 activity, as we find that TRNP1 from species with larger brains and more cortical folding induce higher proliferation rates in neural stem cells. Furthermore, we compare the activity of putative cis-regulatory elements (CREs) of TRNP1 in a massively parallel reporter assay and identify one CRE that likely co-evolves with cortical folding in Old World monkeys and apes. Our analyses indicate that coding and regulatory changes that increased TRNP1 activity were positively selected either as a cause or a consequence of increases in brain size and cortical folding. They also provide an example how phylogenetic approaches can inform biological mechanisms, especially when combined with molecular phenotypes across several species.
... Кроме того, компьютерные программы, симулирующие виртуальных автономных агентов с простым набором функций, воспроизводят сложные социальные структуры, описанные приматологами (Pfeifer, Bongard, 2006). Сравнение диет у разных видов приматов, описанных Дунбаром, показывает, что выявленная им корреляция между размером мозга и социальной группы легко объясняется различиями в диете (DeCasien et al., 2017). Кроме того, корреляция не объясняет направление каузальных связей и естественней предположить, что высокий интеллект является причиной формирования сложных социальных групп, а не наоборот (Kluger, 2010). ...
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What is the meaning of life? What is the nature of the human mind, love, morality? All of these questions tend to be answered and explained in "natural science" terms. Life arose out of inanimate nature by random physical and chemical factors and one should hardly look for any sense in it; man is the product of natural selection and reason, love and morality, are the result of chemical and electrical processes in the brain. Since these questions are among the most important for human beings, due to the unquestionable authority of the natural sciences, the proposed answers can and already do have a major impact on all areas of human life, from economics and politics to mental health and the subjective well-being of the individual. Does this perception of the world and one's place in it make one happy? Sociological studies clearly say no. Adherents of this worldview, however, argue that no matter how unpleasant it may seem, one should have the courage to accept it, because it is consistent with the scientific evidence. But is this true? Does the modern scientific picture of the world really allow for all these far-reaching conclusions? People who are professionally involved in science know that it almost never provides answers to worldview questions. All empirical facts and scientific theories can be interpreted in different ways and the choice of one interpretation or another is largely determined by one's worldview position, not vice versa. Although the book is called "Worldview Problems of Neuroscience", and most of it is indeed devoted to neuroscientific problems and their philosophical interpretation, it deals with a wider range of questions, which form the basis of the worldview of most modern people. Author tries to understand whether the reductionist materialistic worldview that dominates today, especially in the neurosciences, is really capable of plausibly explaining the current evidence about the nature of the relationship between mental processes and the physical world. The book concludes by outlining modern philosophical positions alternative to orthodox physicalism and tries to summarize them in a unified system.
... The brain volume of animal species that frugivorous is on average 25% larger than that of species that carnivorous or omnivores. But the social structures of the researched species do not play a role in their brain development (DeCasien et al., 2017). The main diet of primates are frugivorous. ...
... If we see an enrichment for a specific class of receptors with increasing social complexity across biological species, this would be an important finding. It might also be possible to reintegrate hypotheses vis-a-vis alternative explanations that propose ecological (instead of social) drivers of brain evolution (DeCasien et al., 2017). Specifically, reliance on spatially and temporally distributed food sources (e.g., especially seasonal fruit) seems to be a better explanation of brain evolution across a larger number of biological species compared to group size alone. ...
... Our brains and intelligence are finely honed to function in some of the most complex social environments found on the planet. Some have argued that primate evolution has delivered our type of brain precisely because of the computational demands of our highly complex social environments, especially the dynamics of in-group social life (Adolphs, 2009;Dunbar, 2009; but for dissent see Ashton, Kennedy, and Radford, 2020;DeCasien, Williams, and Higham, 2017;Lockwood, Apps, and Chang, 2020;Rosati, 2017). One significant aspect of those environments is the notion of social status. ...
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This essay focuses primarily on Fury (2014) and to a lesser extent on Saving Private Ryan (1998), two popular war films. I suggest cognitive mechanisms that make the actions of main characters biologically plausible and intelligible. The principal ones have to do with social status and especially prestige, which is based on certain kinds of competence, especially in heroic leaders. Prestige is understood to be co-created by leaders and followers together. Heroism is understood to be depicted in these films as effective leadership in extreme circumstances so as to solve collective action problems. Heroic qualities are signaled (honestly) by means of wounds and scars. These signals are received and understood (both in the films and in their viewers) by means of social learning and social comparison judgments that track prestige. Group cohesion is also explored, especially in Fury, in terms of shared emotions, mimicry, rituals and commitments to a common task. Hatred of morally defined out-groups is also seen at work. I conclude that these films posit warfare as one of a small number of "biologically possible arrangements" that accomplish a rare concatenation of fitness promoting goals. Some consideration is also given to what makes consilience between empirical science and cinematic imagination possible.
The expensive tissue hypothesis proposes that humans increased the quality of their diet and reduced the size of high‐energy consuming digestive organs to afford the metabolic needs of a large brain. In larger mammalian brains, like those observed in large‐bodied primates, the distance between brain areas increases, which introduces new challenges to coordinating neuronal signaling between functionally linked regions. Dynamics of gray matter growth relative to white matter have been theorized to influence folding patterns of the cerebral cortex. Early primates were small‐bodied, nocturnal, and solitary creatures that evolved to fill the fine‐branch niche in tropical forest habitats. Comparative studies have shown that modern humans are exceptionally encephalized compared to other primates and the majority of hominins. Although many primates display social behaviors, modern humans are characterized by their extremely prosocial way of life and cultural practices.
Primates are among the most well‐known animals; however, compiling a list of their distinctive characters is challenging. Most mammal groups have acquired enough unique modifications to make distinguishing one group from another relatively straightforward. The stakes are significant because they bear directly on the priority given to features that bracket the Order as well as where we draw the primate–non‐primate boundary. Descriptions of primates often begin with statements similar to the following: primates are generalized mammals that have retained a primitive body plan with comparatively few modifications. Primates have evolved an array of locomotor adaptations that include quadrupedalism, forelimb suspension, knuckle‐walking, vertical clinging and leaping, and bipedality. Primate teeth are characterized by a combination of trends generally not found in other mammals. Strepsirrhines possess the primitive primate features such as a simple post‐orbital bar, two mobile bones in the forearm, and five digits.
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Large brains provide adaptive cognitive benefits but require unusually high, near-constant energy inputs and become fully functional well after their growth is completed. Consequently, young of most larger-brained endotherms should not be able to independently support the growth and development of their own brains. This paradox is solved if the evolution of extended parental provisioning facilitated brain size evolution. Comparative studies indeed show that extended parental provisioning coevolved with brain size and that it may improve immature survival. The major role of extended parental provisioning supports the idea that the ability to sustain the costs of brains limited brain size evolution.
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Humans are distinguished from the other living apes in having larger brains and an unusual life history that combines high reproductive output with slow childhood growth and exceptional longevity. This suite of derived traits suggests major changes in energy expenditure and allocation in the human lineage, but direct measures of human and ape metabolism are needed to compare evolved energy strategies among hominoids. Here we used doubly labelled water measurements of total energy expenditure (TEE; kcal day(-1)) in humans, chimpanzees, bonobos, gorillas and orangutans to test the hypothesis that the human lineage has experienced an acceleration in metabolic rate, providing energy for larger brains and faster reproduction without sacrificing maintenance and longevity. In multivariate regressions including body size and physical activity, human TEE exceeded that of chimpanzees and bonobos, gorillas and orangutans by approximately 400, 635 and 820 kcal day(-1), respectively, readily accommodating the cost of humans' greater brain size and reproductive output. Much of the increase in TEE is attributable to humans' greater basal metabolic rate (kcal day(-1)), indicating increased organ metabolic activity. Humans also had the greatest body fat percentage. An increased metabolic rate, along with changes in energy allocation, was crucial in the evolution of human brain size and life history.
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Although the behaviour and ecology of primates have been more thoroughly studied than that of any other group of mammals, there have been very few attempts to compare the communities of living primates found in different parts of the world. In Primate Communities, an international group of experts compares the composition, behaviour and ecology of primate communities in Africa, Asia, Madagascar and South America. They examine the factors underlying the similarities and differences between these communities, including their phylogenetic history, climate, rainfall, soil type, forest composition, competition with other vertebrates and human activities. As it brings together information about primate communities from around the world for the very first time, it will quickly become an important source book for researchers in anthropology, ecology and conservation, and a readable and informative text for undergraduate and graduate students studying primate ecology, primate conservation or primate behaviour.
Comparative studies have become both more frequent and more important as a means for understanding the biology, behaviour and evolution of mammals. Primates have complex social relationships and diverse ecologies, and represent a large species radiation. This book draws together a wide range of experts from fields as diverse as reproductive biology and foraging energetics to place recent field research into a synthetic perspective. The chapters tackle controversial issues in primate biology and behaviour, including the role of brain expansion and infanticide in the evolution of primate behavioural strategies. The book also presents an overview of comparative methodologies as applied to recent primate research which will provide new approaches to comparative research. It will be of particular interest to primatologists, behavioural ecologists and those interested in the evolution of human social behaviour.
Phylogenetic comparative approaches are powerful analytical tools for making evolutionary inferences from interspecific data and phylogenies. The phylogenetic toolkit available to evolutionary biologists is currently growing at an incredible speed, but most methodological papers are published in the specialized statistical literature and many are incomprehensible for the user community. This textbook provides an overview of several newly developed phylogenetic comparative methods that allow to investigate a broad array of questions on how phenotypic characters evolve along the branches of phylogeny and how such mechanisms shape complex animal communities and interspecific interactions. The individual chapters were written by the leading experts in the field and using a language that is accessible for practicing evolutionary biologists. The authors carefully explain the philosophy behind different methodologies and provide pointers – mostly using a dynamically developing online interface – on how these methods can be implemented in practice. These “conceptual” and “practical” materials are essential for expanding the qualification of both students and scientists, but also offer a valuable resource for educators.
The continued growth of emerging nations depends largely on the development of their built infrastructures and communities. Roads, dams, bridges, hospitals, schools, and housing are all examples of the built environment that impacts economic improvements in the developing world.Decision Support for Construction Cost Control in Developing Countries explores how the construction industry contributes to a nation’s GDP and the related cost issues and proposed cost reduction solutions for construction projects and initiatives in developing regions. Emphasizing the role of decision support systems for reducing and managing the costs associated with construction projects, this title is an essential reference source for civil engineers, business and engineering managers, project managers, researchers, and professionals in the construction industry.
Phylogenetic comparative methods have become standard for investigating evolutionary hypotheses, including in studies of human evolution. While these methods account for the non-independence of trait data due to phylogeny, they often fail to consider intraspecific variation, which may lead to biased or erroneous results. We assessed the degree to which intraspecific variation impacts the results of comparative analyses by investigating the "social brain" hypothesis, which has provided a framework for explaining complex cognition and large brains in humans. This hypothesis suggests that group life imposes a cognitive challenge, with species living in larger social groups having comparably larger neocortex ratios than those living in smaller groups. Primates, however, vary considerably in group size within species, a fact that has been ignored in previous analyses. When within-species variation in group size is high, the common practice of using a mean value to represent the species may be inappropriate. We conducted regression and resampling analyses to ascertain whether the relationship between neocortex ratio and group size across primate species persists after controlling for within-species variation in group size. We found that in a sample of 23 primates, 70% of the variation in group size was due to between-species variation. Controlling for within-species variation in group size did not affect the results of phylogenetic analyses, which continued to show a positive relationship between neocortex ratio and group size. Analyses restricted to non-monogamous primates revealed considerable intraspecific variation in group size, but the positive association between neocortex ratio and group size remained even after controlling for within-species variation in group size. Our findings suggest that the relationship between neocortex size and group size in primates is robust. In addition, our methods and associated computer code provide a way to assess and account for intraspecific variation in other comparative analyses of primate evolution.
The origins of the genus Homo are murky, but by H. erectus, bigger brains and bodies had evolved that, along with larger foraging ranges, would have increased the daily energetic requirements of hominins. Yet H. erectus differs from earlier hominins in having relatively smaller teeth, reduced chewing muscles, weaker maximum bite force capabilities, and a relatively smaller gut. This paradoxical combination of increased energy demands along with decreased masticatory and digestive capacities is hypothesized to have been made possible by adding meat to the diet, by mechanically processing food using stone tools, or by cooking. Cooking, however, was apparently uncommon until 500,000 years ago, and the effects of carnivory and Palaeolithic processing techniques on mastication are unknown. Here we report experiments that tested how Lower Palaeolithic processing technologies affect chewing force production and efficacy in humans consuming meat and underground storage organs (USOs). We find that if meat comprised one-third of the diet, the number of chewing cycles per year would have declined by nearly 2 million (a 13% reduction) and total masticatory force required would have declined by 15%. Furthermore, by simply slicing meat and pounding USOs, hominins would have improved their ability to chew meat into smaller particles by 41%, reduced the number of chews per year by another 5%, and decreased masticatory force requirements by an additional 12%. Although cooking has important benefits, it appears that selection for smaller masticatory features in Homo would have been initially made possible by the combination of using stone tools and eating meat.