Brain size growth in wild and captive chimpanzees

I’m back in Astana, overcoming jet lag, after the annual conference of the American Association of Physical Anthropologists, which was held in my home state of Missouri. I’d forgotten how popular ranch dressing and shredded cheese is out there; but hey, at least you can drink the tap water! It was also nice to be immersed in a culture of evolution, primates and fossils, something so far lacking at the nascent NU.

Although I usually present in evolution and fossil-focused sessions, my recent interest in brain growth landed me in a session devoted to Primate Life History this year. The publication of endocranial volumes (ECVs) from wild chimpanzees of known age from Taï Forest (Neubauer et al., 2012) led me to ask whether this cross-sectional sample displays the same pattern of size change as seen in captive chimpanzee brain masses (Herndon et al., 1999). These are unique datasets because precise ages are known for each individual, and this information is generally lacking for most skeletal populations. We therefore have a unique opportunity to estimate patterns and rates of growth, and to compare different populations. Here are the data up to age 25 (the oldest known age of the wild chimps):

fig2 raw data copy

Brain size plotted against age in chimpanzees. Blue Ys are the Yerkes (captive) apes and green Ts are the Taï (wild) chimps. Note that Yerkes data are brain masses while the Taï data are endocranial volumes (ECVs). Mass and volume – as different as apples and oranges, or as oranges and tangerines? Note the relatively high “Y” at 1.25 years, who was omitted from the subsequent analysis.

This is an interesting comparison for a few reasons. First, to the best of my knowledge brain size growth hasn’t been compared between chimp populations (although it has been compared between chimps and bonobos: Durrleman et al., 2012). Second, many studies have found differences in tooth eruption, maturation and skeletal growth and development between wild and captive animals, but again I don’t think this has been examined for brain growth. Finally, and most fundamentally, it’s not clear whether ECV and brain mass follow the same basic pattern of change (brain mass but not ECV is known to decrease at older ages in humans and chimps, but at younger ages…?.

So to first make the datasets comparable, I used published data to examine the relationship between brain mass and ECV in primates, to estimate the likely ECV of the Yerkes brain masses. Two datasets examine adult brain size across different primate species (red and blue in the plot below), and one looks at brain mass and ECV of individuals for a combined sample of gorillas (McFarlin et al., 2013) and seals (Eisert et al., 2013). In short, ECV and brain mass in these datasets give regression slopes not significantly different from 1. One dataset has a negative y-intercept significantly different from 0, meaning that ECV should actually be slightly less than brain mass, but I think this pattern is driven by the really small-brained animals like New World Monkeys).


The relationship between endocranial volume and brain mass in primates (and Weddell seals). Solid lines and shaded confidence intervals are given for each regression, and the dashed line represents isometry, or a 1:1 relationship (ECV=brain mass). The rug at the bottom shows the range of the Yerkes masses. Note that the red and black regressions are not significantly different from isometry, while the blue regression is shifted slightly below isometry.

So let’s assume for now that the ECVs of the Yerkes apes are the same as their masses, meaning the two datasets are directly comparable. There are lots of ways to mathematically model growth, and as George Box famously quipped, “All models are wrong, but some are useful.” Here, I wanted to use something that explained the greatest amount of ontogenetic variation in ECV while also levelling off once adult brain size was reached (by 5 years based on visual inspection of the first plot above). This led me to the B-spline. With some tinkering I found that having two knots, one between each 0.1-2.5 and 2.6-5, provided models that fit the data pretty well, and I resampled knot combinations to find the best fit for each dataset. The result:

B-splines describing the relationship between ECV (or brain mass) and age in the TaÏ (green) and Yerkes (blue) data. Although resampling identified different knots for each sample, the regression coefficients are not significantly different.

B-splines describing the relationship between ECV (or brain mass) and age in the TaÏ (green) and Yerkes (blue) data. Note that although the Yerkes line is elevated above the Taï line after 4 years, the confidence intervals (shaded regions) overlap at all ages.

These models fit the data pretty well (r-squared >0.90), and nicely capture the major changes in growth rates. Resampling knot positions reveals best-fit models with different knots for each sample, but otherwise the two models cannot be statistically distinguished from one another: the 95% confidence intervals of both the model coefficients and brain size estimates overlap. So statistical modelling of brain growth in these samples suggests they’re the same, but there are some hints of difference.

Growth rates at each age calculated from the B-spline regressions. Note these are arithmetic velocities and not first derivatives of the growth curves.

Growth rates at each age calculated from the B-spline regressions. Note these are arithmetic velocities and not first derivatives of the growth curves. The dashed horizontal line at 0 indicates the end of brain size growth.

Converting the growth curves to arithmetic velocities we see what accounts for the subtle differences between samples. The velocity plot hints that, in these cross-sectional data, brain size increases rapidly after birth but growth slows down and ends sooner in Taï than among the Yerkes apes. I’m cautious about over-interpreting this difference, since there is great overlap between growth curves, and there is only one Taï newborn compared to about 20 in Yerkes: even just a few more newborns from Taï might reveal greater similarity with Yerkes.

So there you have it, it looks like the wild Taï and captive Yerkes chimps follow basically the same pattern of brain growth, despite living in different environments. Whereas the generally greater stressors in the wild often lead to different patterns of skeletal and dental development in wild vs. captive settings, brain growth appears pretty robust to these environmental differences. That brain growth should be canalized is not too surprising, given the importance of having a well-developed brain for survival and reproduction. But it’s cool to see this theoretical expectation borne out with empirical observations.


Tomorrow I’m heading to St. Louis, MO for the annual meeting of the American Association of Physical Anthropologists. I’ll be giving a talk on Saturday presenting results of a comparison of brain size growth between captive and wild chimpanzees. Some recent work has highlighted differences between captive and wild animals in terms of bodily growth and maturation, but so far as I know brain development has not been part of this. Here’s a teaser plot, showing how the captive (blue) and wild (green) datasets deviate from a piecewise linear regression of brain size against age (for the combined wild+captive sample):

Rplot copyThe dashed black line is zero, or no deviation from the model. This plot shows that each dataset deviates little from the model at younger ages (when the brain is growing rapidly), but at older ages the captive animals have larger brains, and the wild animals have smaller brains, than predicted by the model. What’s the meaning of this? Find out Saturday afternoon at 3 pm…

Talk at UW Madison tomorrow

I’ve just flown some 7,000 miles for a 2-week stint in the USA. I’m first spending a week in Madison, WI as part of the faculty exchange between Nazarbayev University and the Unversity of Wisconsin Madison. Next week I will be in St. Louis for the AAPA conference, catching up with colleagues and presenting an analysis of brain growth in chimpanzees. Highlight of the trip so far: potable tap water (I can’t stress enough the importance of staying hydrated).

Image from Wolfram Alpha.

Image from Wolfram Alpha. Actual route is through Frankfurt, Germany.

Tomorrow I will be giving a talk here at UWM about wrangling important information out of a secretive fossil record. If you’re in the area, please come check it out! Here’s a flier with more info:


Australopithecus boisei bites

I always wondered what our extinct relative, Australopithecus boisei tasted like, until I moved to Kazakhstan.

2015-03-11 21.38.26

Mini calotte, or manti?

Here, dumplings with various fillings are called “manti” and usually have a distinct crimping running across the top. Along with their broad flaring bases and dome-like shapes, this gives manti the appearance of miniature A. boisei brain cases replete with sagittal crests:

They all look so delicious!

They all look so delicious! Fillings from left to right: lamb, pumpkin+lamb, mushrooms ewwwww.

In case you had trouble discerning braincase from блюдо, calotte from закуски in the pic, check out and see if their handy, free 3D scans of fossils OH 5 and ER 406 help you figure it out.

Shockingly alarming pedagogical discovery

You heard it here first: class attendance is correlated with test performance. The discovery was made in two undergraduate anthropology courses in Astana, Kazakhstan, though the findings can probably be replicated elsewhere. This result runs counter to the widely held consensus among undergraduate students, that it is not important to attend lectures.

Midterm exam scores (out of 32 points) plotted against class attendance (left) and participation grades (right). Participation is based on in-class quizzes over readings, and so measures students exposure to both lecture and reading.

Figure 1. Midterm exam scores (out of 32 points) plotted against class attendance (left) and participation grades (right), for one biological anthropology class. Correlations and regressions slopes are significantly higher than zero.

Highly paid scientists collected data on students’ midterm exam scores, the number of sessions students were physically present at a lecture (“attendance”), and how they performed on in-class quizzes (“participation”). As quizzes are based on course readings, participation measures active investment beyond simply attendance.

Figure 2. Same variables plotted as in the previous figure, but for a second class.

Figure 2. Same variables plotted as in the previous figure, but for a second class (exam out of 25 points). In addition to linear regression lines (solid black), polynomial regressions (dashed red) were also fit for this class. Polynomial regressions have slightly lower standard errors and slightly higher coefficients of determination. Linear regressions have slopes significantly different from zero while polynomial coefficients are not statistically significant. Either way, more investment generally translate into higher grades.

The researchers were shocked to find positive relationships between students’ exam performance and measures of course participation and active participation. “With the rise of unsourced information on the internet, we assumed students didn’t need to go to class – what could a professor possibly say in lecture that can’t hasn’t already been said on ‘the Net’,” said an out of touch analyst who wasn’t involved in the analysis. The lead investigator of the study remarked, “All college students are hard-working and motivated, so we figured they would read and come to lectures if they knew they’d benefit. Our findings hint that maybe they don’t know everything after all.”

Scientists think these findings have important implications for students everywhere. An empirical link between active participation in class and grades mean that a student’s chances of doing passing or even excelling in a class can improve dramatically with increased attendance. So take note, students: read and go to class! Who knows, you might even learn something from it.

* These are my students’ actual grades and attendance this semester. No undergraduates were harmed in this study.

A neonatal perspective on Homo erectus brain growth

The Mojokerto infant Homo erectus. The fossil as preserved is on the left, and on the right is the reconstructed brain based of CT scans of the fossil (Figure x from Balzeau et al., 2005). The fossil and endocast are viewed from the right side so the front of the fossil is to the right.

The Homo erectus infant from Mojokerto. The fossil as preserved is on the left, and on the right is the brain cast reconstructed from CT scans of the fossil (Figure 7 from Balzeau et al., 2005). The fossil and endocast are viewed from the right side so the front is on the right and back is on the left.

My paper (coauthored with Jeremy DeSilva) about brain growth in Homo erectus will be coming out soon in Journal of Human Evolution. I’ve been working on this study for a while now, so it feels good to’ve turned in the approved copy edits at long last. I’ve discussed this work a bit while it was in progress (here, here, and here), and the final version is a little different from what I posted back then, but I won’t rehash everything here. The take home message is that by around 1 million years ago, Homo erectus from Java probably had brain growth rates during early infancy in the modern human range. Really rapid early brain size growth is a unique feature of humans, and our analysis shows this trait, and many other correlates of it, were likely present early in our evolutionary history.

Our results are based on a custom resampling test, the codes for which I’ve posted here on my R Codes page. Now you can do this kind of analysis yourself!

Until the paper actually comes out, here’s the abstract:

The Mojokerto calvaria has been central to assessment of brain growth in Homo erectus, but different analytical approaches and uncertainty in the specimen’s age at death have hindered consensus on the nature of H. erectus brain growth. We simulate average annual rates (AR) of absolute endocranial volume (ECV) growth and proportional size change (PSC) in H. erectus, utilizing estimates of H. erectus neonatal ECV and a range of ages for Mojokerto. These values are compared with resampled ARs and PSCs from ontogenetic series of humans, chimpanzees, and gorillas from birth to six years. Results are consistent with other studies of ECV growth in extant taxa. There is extensive overlap in PSC between all living species through the first postnatal year, with continued but lesser overlap between humans and chimpanzees to age six. Human ARs are elevated above those of apes, although there is modest overlap up to 0.50 years. Ape ARs overlap throughout the sequence, with gorillas slightly elevated over chimpanzees up to 0.50 years. Simulated H. erectus PSCs can be found in all living species by 0.50 years, and the median falls below the human and chimpanzee ranges after 2.5 years. Homo erectus ARs are elevated above those of all extant taxa prior to 0.50 years, and after two years they fall out of the human range but are still above ape ranges. A review of evidence for the age at death of Mojokerto supports an estimate of around one year, indicating absolute brain growth rates in the lower half of the human range. These results point to secondary altriciality in H. erectus, implying that key human adaptations for increasing the energy budget of females may have been established by at least 1 Ma.

eFfing #FossilFriday: Pleistocene ppl blowin up this week

This was a big week for Middle-Late Pleistocene fossil humans. Chun-Hsiang Chang and colleagues describe a mandible dredged up off the western coast of Taiwan, which they note in the title as, “The first archaic Homo” fossil known from the region. The geological context makes it difficult to date the specimen precisely, but authors argue it is probably younger than 190 thousand years old.

The Penghu mandible. Figure 3. From Chang et al.

In life, this individual was fully grown but appears never to have developed third molars (the “wisdom teeth”). Such “third molar agenesis” is relatively rare before modern times, but is also seen in the D2735 Homo erectus mandible from Dmanisi. I wouldn’t make much of this coincidence, but it does raise the question of whether the cause of agenesis, not uncommon today, was the same then as now.

Shortly after the announcement of the Penghu mandible, Israel Hershkovitz and colleagues presented a 55,000 year old brain case from Manot Cave in the Levant. The calvaria (fancy word for brain case) looks very similar to the skulls of the slightly younger “anatomically modern” humans of the Upper Paleolithic in Europe, albeit with a few Neandertal-like traits here and there (hey, just like many of the Upper Paleolithic humans).

The Manot calvaria (Figure 2 from Hershkovitz et al.) The views are (a-d) from the top with front to the left; from the left; from the front; and from the back. Extra credit: In the top view (a), can you identify the features telling that the front is to the left?

John Hawks has good posts dedicated to both Penghu and Manot. The upshot of these discoveries is that Middle and Late Pleistocene human population diversity, and the interactions between these populations, are probably much more complicated and interesting than the old model of ‘modern’ humans arising singly in Africa and replacing ‘archaic’ humans in different parts of the globe. With the technological advances and fossil discoveries of the past decade, the rather simple Replacement model has given way to a better appreciation of true complexity of human evolution toward the end of the Ice Age. Both of these new papers reflect this new perspective.

Along these lines, accompanying the Manot paper in Nature is an editorial, “Human history defies easy stories.” What caught my attention reading this (anonymous?) commentary is that it puts scientific interpretations of the past into a social and historical context. The author notes that the traditional story of modern humans arising, spreading and eradicating other groups of human has “imperialist framing, in which evolution and replacement can be justified after the fact as a kind of manifest destiny.” Science doesn’t occur in a vacuum, it’s done by people whose minds and creativities are molded in specific historical, economic and cultural contexts. This editorial comment makes one wonder how the human fossil record would have been interpreted, had most of it not discovered against the social backdrop of ruthless capitalism.