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):

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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).

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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.

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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:

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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: toolmakers without tools?

Matt Skinner and colleagues report in today’s Science an analysis of trabecular bone structure in the hand bones of humans, fossil hominins and living apes. Trabecular bone, the sponge-like network of bony lattices on the insides of many of your bones, adapts during life to better withstand the directions and amounts of force it experiences. This is a pretty great property of the skeleton: bone is organized in a way that helps withstand usual forces, and the spongy organization of trabeculae also keeps bones fairly lightweight. Win-win.

An X-ray of my foot. Note that most of the individual foot bones are filled with tiny 'spicules' (=trabeculae) of bone. Very often they have a very directed, or non-random, orientation, such as in the heel.

An X-ray of my foot. The individual foot bones are filled with narrow spicules (=trabeculae) of bone. Very often they have a directed, or non-random, orientation: in the calcaneus, for instance, they are oriented mostly from the heel to the ankle joint.

This adaptive nature of trabecular bone also means that we can learn a lot about how animals lived in the past when all they’ve left behind are scattered fossils. In the present case, Skinner and colleagues tested whether tool use leaves a ‘trabecular signature’ in hand bones, looking then for whether fossil hominins fit this signature. Their study design is beautifully simple but profoundly insightful: First, they compared humans and apes to see if the internal structure of their hand bones can be distinguished. Second, they tested whether these differences accord with theoretical predictions based on how these animals use their hands (humans manipulate objects, apes use hands for walking and climbing). Third, they determined whether fossil hand bones look more like either group.

Comparison of first metacarpals (the thumb bone in your palm) between a chimpanzee (left), three australopithecines, and a human (right). In each, the palm side is to the left and the wrist end of the bone (proximal) is down. Image by Tracy Kivell, and found here.

Looking at the image above, it’s difficult to spot trabecular differences between the specimens with the naked eye. But computer software can easily measure the density and distribution of trabecular bone from CT scans. With these tools, researchers found key differences between humans and apes consistent with the different ways they use their hands. Neandertals (humans in the past 100 thousand years or so) showed the human pattern, not unexpected since their bones look like ours and they used their hands to make tools and manipulate objects like we do.

What’s more interesting, though, is that the australopithecines, dating to between 1.8-3.0 million years ago, also show the human pattern. This is an important finding since the external anatomy of Australopithecus hand bones shows a mixture of human- and ape-like features, with unclear implications for how they used their hands. Their trabecular architecture, reflecting the forces their hands experienced in life, is consistent with tool use.

This is a very significant finding. Australopithecus africanus fossils from Sterkfontein aren’t associated with any stone tools; bone tools are known from Swartkrans, though it is unclear whether Australopithecus robustus or Early Homo from the site made/used these. In addition, in 2010 McPherron and colleagues reported on a possibly cut-marked animal bone from the 3.4 million year old site of Dikika in Ethiopia, where Australopithecus afarensis fossils but no tools are found. Skinner and colleagues’ results show that at the very least, South African Australopithecus species were using their hands like tool-makers and -users do.

This raises many fascinating questions – were australopithecines using stone tools, but we haven’t found them? Were they using tools made of other materials? What do the insides of Australopithecus afarensis metacarpals look like? What I like about this study is that it presents both compelling results, and raises further (testable) questions about both the nature of the earliest tools and our ability to detect their use from fossils.

Another small Middle Pleistocene person

Last year I brought up the implications of the small female pelvis from Gona, Ethiopia for body size variation in Homo erectus (see previous post). This individual was much smaller than other Middle Pleistocene Homo fossils, indicating size variation comparable to highly sexually dimorphic gorillas and unlike recent human populations. Before this pelvis, most known Homo erectus fossils were fairly large (comparable to living people), with only a few hints of much smaller individuals (e.g., KNM-ER 427000, KNM-OL 45500). Now joining this petite party, this tiny troop, this little lot, this compact cadre, etc., is KNM-WT 51261, a 750,000 year old molar from Kenya (Maddux et al., in press).

Occlusal area for hominin first molars. The tooth is from Fig. 2 and the plot from Fig. 3 in the paper.

Occlusal area for first molars in the genus Homo. The tooth image is from Fig. 2 and the plot from Fig. 3 in Maddux et al. Lookit how tiny it is!

This ‘new’ specimen substantially increases the range of size variation among early African H. erectus molars, although the expanded range isn’t remarkable compared with later Homo samples such as from Zhoukoudian cave in China or Neandertals. What is different, though, is that most of the highly variable samples show a fairly continuous range of variation, while the WT 51261 molar is a considerable outlier from the rest of the African Middle Pleistocene sample (a lot like the situation with the Gona pelvis). So this tooth re-raises an important question: were smaller specimens like Gona and WT 51261 as rare in life as they are in the fossil record, or was such great size variation common in the Middle Pleistocene? How we reconstruct what kind of animal Homo erectus was differs depending on the answer to this question.

Driving nails into the 2014 Lawn Chair

It’s that time again, when we come to bury the year we’ve just defeated, only to celebrate the zombie birth of a new onslaught of days to clobber. In the spirit of auld lang syne, let’s recap the highlights of Lawn Chair in 2014.Georgia dinos 2014

Osteology was everywhere: although I am wont to see bones everywhere in everyday life, this year I only wrote about it four times. First there were the baby bones in cafe upholstery in my hometown of Kansas City, then the giant sheep bones in my new home of Astana. I discovered that animal bones littered the landscape of desert Mangystau, and then I spotted a vertebra hiding in a helmet at a conference in Italy. I also tweeted about a false femur head from a karaoke bar in Astana. You can’t escape. 2015 is sure to be more osseous.BONES!
eFfing Fossil Friday reboot: This old series focusing on fossils furtively restarted on a plane, when I uncovered the conspiracy that the Australopithecus africanus cranium Sts 71 was actually the Kryptonian codex. I later wrote about the Sima de los Huesos skulls, Neandertal poop, the origins of feathers on badass dinosaurs, the 45,000 year old Ust’-Ishim femur and its delicious DNA, and facial flanges in early mammals and nearly modern baboons. Fossils are the best, and 2015 is bound to be as fossiliferous as last year.Ancient DNA was boss: In addition to the earliest ‘modern’ human DNA from Ust’-Ishim, 2014 also witnessed a swath of studies early on attesting to the success of paleogenomics. We also got a first glimpse into epigenetics of ancient humans, and the potential importance this will have in uncovering how our DNA makes us human. Along these lines, for 2015, I’d be keen to see more work on miRNA and other aspects of gene regulation in ancient genomes.

Screen Shot 2014-10-24 at 11.26.31 AMR codes: I’ve posted R code for the analysis from my paper that came out this year, comparing mandibular growth in humans and Australopithecus robustus (I didn’t get to talk about that paper when it came out because I was in the middle of the Rising Star Workshop. Things to look forward to in 2015…). I’ll also be posting code for the analysis of brain growth in Homo erectus once that paper is published, and I have already posted code for creating the pretty pictures from the paper.

Brain size data (left) and the average annual rates from birth calculated from pairs of specimens (right). Black=humans, green=chimpanzees, red=gorillas, blue=Homo erectus.
Body size variation in Homo erectus: A response to a response to a paper led me to reexamine sexual dimorphism in body size in our early ancestor – seems it was higher than has lately been appreciated, and there are many potential reasons for this. I presented the initial results of this investigation on the blog and at a conference, and am now writing this up for publication. This investigation is based on resampling statistics, nothing as new and flashy as in the growth studies. I will post code for these analyses on the R Codes page in due time.

Dimorphism ratios copy

Resampled ratios of dimorphism, calculated by dividing the average of six randomly selected male body masses by a randomly selected female mass. The blue star in each plot is the empirical ratio of average male mass/average female mass. For all species the average resampled ratio is almost identical to this empirical value. The red line marks the ratio of the six largest (male?) Homo erectus mass estimates divided by the estimated mass of the Gona (female?) pelvis. The Homo erectus male/female difference is rarely observed in chimps and humans, but is common in gorillas. Gorillas display high levels of sexual dimorphism, suggesting this may have been the case for Homo erectus as well.

Classroom lab activities: This year I added a lab components to my courses here at NU, and I posted up two of the lab activities I did in my classes this semester. Last spring, I got the idea for an activity in which students measure toe joint angles on digital images, to test whether Ardipithecus kadabba and other hominin toes can be distinguished from apes’. This semester, students in my human evo-devo class did this study, and generally found hominin toes to be more angled than apes’. Hypothesis tested. My Intro to Bio Anthro class tested whether their limb proportions fit expectations based on Allen’s Rule, and mystery ensued. My classes next term aren’t as conducive to lab activities, but if I come up with any good assignments I’ll be sure to post them.class models both copy

Now that 2014 is laid to rest, here’s to a bright and successful zombied 2015! Жаңа Жылыңызбен!