chimpanzee

Did GDF6 “gene tweak” allow humans to become upright?

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The short answer is, “Not really.” But as is often the case, the real story behind so many headlines last week is a bit more complicated.

smh.

smh. Links to the first, second, third, and fourth stories.

What are they talking about, Willis?

These headlines, each saying something slightly different, are referring to a study by Indjeian and colleagues published in Cell.  Researchers identified a stretch of DNA that is highly conserved across mammals, or in other words, it is very similar between very different organisms. In humans, however, this conserved region is actually missing (“hCONDEL.306”):

Fig. 4A from Indjeian et al. 2016. A stretch of DNA, "hCONDEL.306" is completely missing in humans (as is another stretch, hCONDEL.305) but otherwise very similar between chimpanzees, monkeys and mice.

Fig. 4A from Indjeian et al. 2016. A stretch of DNA on Chromosome 8, “hCONDEL.306,” is very similar between chimpanzees, macaque monkeys, and mice, but is completely missing in humans (as is another stretch, hCONDEL.305).

That a stretch of DNA should be highly conserved across diverse animal groups suggests purifying natural selection has prevented any mutations from occurring here – alterations to this stretch of DNA negatively affected fitness. But that humans should be missing such a highly conserved region suggests that this deletion came under positive natural selection at some point in human evolution. This strategy, of seeking stretches of DNA that are similar between many animals but very different in humans, has led to the identification of hundreds of genetic underpinnings of human uniqueness. Some of these, such as the case in question, involve deleted sequences and have been termed “hCONDELs,” for “regions with high sequence conservation that are surprisingly deleted in humans” (McLean et al., 2011: 216). Others involve the accumulation of mutations where other animals show few or none (e.g., HACNS1; Prabhakar et al. 2008). In many (most?) cases these are “non-coding” sequences of DNA.

How can “non-coding” DNA help make humans upright?

As was predicted 30 years ago (King and Wilson, 1975), what makes humans different from other animals isn’t so much in the protein-coding DNA (the classical understanding of the term, “genes”), but rather in the control of these protein-coding genes. “Non-coding” means that a stretch of DNA may get transcribed into RNA but is not then translated into proteins. But even though these sequences themselves don’t become anything tangible, many are nevertheless critical in regulating gene expression – when, where and how much a gene gets used. It’s wild stuff. Indeed, “Many human accelerated regions are developmental [gene] enhancers” (Capra et al., 2013).

In the present case, hCONDEL.306 refers to the human-specific deletion of a developmental enhancer located near the GDF6 gene, which is a bone morphogenetic protein. The major finding of the paper, as stated succinctly in the Highlights title page, is that “Humans have lost a conserved regulatory element [hCONDEL.306] controlling GDF6 expression…. Mouse phenotypes suggest that [this] deletion is related to digit shortening in human feet.”

How do they link this “gene tweak” to digit shortening?

Since humans have lost this gene enhancer that is highly conserved in other mammals, Indjeian and team reasoned that the chimpanzee DNA sequence associated with this deletion, retaining the enhancer sequence, is likely the ancestral condition from which the human version evolved. They inserted the chimpanzee version into mouse embryos and watched what happened as they developed. The enhancer was only active in the mice’s back legs, specifically in the cartilage that would later become the lateral toe bones and cells that would become a muscle of the big toe (abductor hallucis). These are areas where humans and chimpanzees differ: our lateral toes are shorter than chimps’, and we only have one abductor hallucis muscle whereas chimpanzees have an additional, longer abductor hallucis  (Aiello and Dean, 2002). So, we’re on our way to seeing how hCONDEL.306 might relate to our big toe or upright walking, as the headlines say.

But this still doesn’t explain how this deletion affects GDF6 gene expression, and therefore what this does for our feet. Pressing onward, the scientists compared the size of certain bones in mice with a normal Gdf6 gene, and those in which the Gdf6 gene was completely turned off (or “knocked out”).  The Gdf6 knock-out mice had shorter lateral toe bones than regular mice, but they also had shorter big toes as well – the previous experiment staining mouse embryos showed the ancestral enhancer was expressed more in the latter toes, not so much the big toe.

Figures 5-6 from Indjeian et al. (2016) sum up the findings. Figure 5 (left) shows that the ancestral version of the GDF6 enhancer (blue staining) is most strongly expressed in the lower limb, especially the fifth toe bone. Figure 6 (right) shows that a lack of GDF6 expression (black bars) results in shorter skull and toe bones. Combining these findings, humans lack a gene enhancer associated with the development of long lateral toes.

Figures 5-6 from Indjeian et al. (2016) sum up the findings. Figure 5 (left) shows that the ancestral version of the GDF6 enhancer (blue staining) is most strongly expressed in the lower half of the body, especially the fifth toe bone. Figure 6 (right) shows that a lack of Gdf6 expression (black bars) results in shorter skull and toe bones. Combining these findings, humans lack a gene enhancer associated with the development of long lateral toes.

hCONDEL.306 doesn’t completely turn off GDF6, so this second experiment doesn’t really tell us exactly what the hCONDEL does. But the results are highly suggestive. Indjeian and team showed that Gdf6 affects toe length, among other skeletal traits, in mice. The ancestral enhancer that humans are missing seems to affect GDF6 activity in the leg/foot only. This illustrates a mechanism of modularity – as the authors state, “Loss of this enhancer would thus preserve normal GDF6 functions in the skull and forelimbs, while confining any … changes to the posterior digits of the hindlimb.” In other words, developmental enhancers allow different parts of the body to evolve independently despite being made by some of the same genes (such as GDF6).

As with any good study, results are intriguing but they raise more questions for future studies. The researchers conducted two experiments to investigate the function of hCONDEL.306: first putting the chimp version in mouse embryos to see where the ancestral enhancer is expressed, and then turning off Gdf6 completely in mice to see what happens. A more direct way to see what hCONDEL.306 does might be to put a longer stretch of the human sequence surrounding GDF6 containing (or rather missing) the ancestral enhancer into mouse embryos. I’m not a molecular biologist so maybe this isn’t possible. But this is important because the ancestral (chimpanzee) enhancer appeared to be most strongly expressed in the little toe, but of course this isn’t our only toe that is short compared to chimps. Similarly, how hCONDEL.306 relates to the abductor hallucis muscle remains in question – does it reduce the size of the intrinsic muscle present in both humans and chimps, or does it prevent development of the longer muscle that chimps have but we lack? We can expect to find hCONDEL.306 in the genomes of Neandertals (and Denisovans?), since they also have short toes, but what would it mean if they retained the ancestral enhancer?

So how does this gene tweak help with upright walking?

This is a really cool paper with important implications for human evolution, but something seems to have been lost in translation between the paper and the headlines (the news pieces themselves are good, though). The upshot of the study is that humans lack a stretch of non-coding DNA, which in chimpanzees (or chimp-ified mice) promotes embryonic development of the lateral toes and a big toe muscle. This may be a genetic basis for at least some aspects of our unique feet that have evolved under natural selection for walking on two legs.

But the headlines misrepresent this result, with words like “led to,” “allowed,” and “caused,” especially when these are followed by “big toe” or “upright walking.” hCONDEL.306 doesn’t really have anything to the big toe bone itself, although it might relate to a muscle affecting our big toe. The only sense in which the “Gene tweak led to humans’ big toe” (first title above) is that hCONDEL.306 might be responsible for our short lateral toes, which make our first toe look big by comparison. The other headlines are misleading since we know from fossil evidence that hominins walked upright long before we have evidence for short toes:

These little piggies get none. Fourth toe bones of living apes and humans (left) and possible hominins from 3-5 million years ago (right).

These little piggies get none. Fourth toe bones of living apes and humans (left) and (probable) hominins from 3-5 million years ago (right). I did my best to get all images to scale.

“Epigenetic,” from the fourth article headline, is simply wrong. Modern day epigenetics is a field concerned with the chemical alterations to the structure of DNA. Even the broad concept of epigenetic as originally devised by Conrad Waddington was about how environments (cellular or outside the body) influence development.

ResearchBlogging.orgIt’s hard to fit all the important and interesting information from scientific papers into news headlines. Still, it would be good if headlines more accurately portrayed scientific findings, especially avoiding such definitive verbs as “caused.” Especially in the realm of biology, people should know that there’s a lot that we still don’t know, that there’s lots more important work left to be done.

References

Aiello and Dean, 2002. Human Evolutionary Anatomy. Academic Press.

Capra et al., 2013. Many human accelerated regions are developmental enhancers. Philosophical Transactions of the Royal Society B 368: 20130025.

Indjeian et al. 2016. Evolving new skeletal traits by cis-regulatory changes in bone morphogenetic proteins. Cell http://dx.doi.org/10.1016/j.cell.2015.12.007

King and Wilson, 1975. Evolution at two levels in humans and chimpanzees. Science 188: 107-116 DOI: 10.1126/science.1090005

McLean et al., 2011. Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature 471: 216-219.

Prabhakar et al., 2008. Human-specific gain of function in a developmental enhancer. Science 321: 1346-1350.

Brain size growth in wild and captive chimpanzees

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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).

Untitled

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.

#AAPA2015

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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…

eFfing #FossilFriday: toolmakers without tools?

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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.

2015 AAPA conference: More brain growth

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The American Association of Physical Anthropologists is holding its annual meeting next year in St. Louis, in my home state of Missouri (I’m from Kansas City, which is by far the best city in the state, if not the entirety of the Midwest). I’ll be giving a talk comparing brain size growth in captive and wild chimpanzees, on Saturday 28 March in the Primate Life History session. Here’s a sneak peak:

Velocity curve for brain size from birth to 5 years in wild (green) and caprive (blue) chimpanzees. For the captive models, the dashed line is fit to the raw brain masses, and the solid line is fit to the estimated endocranial volumes.

Velocity curves for brain size growth from birth to 5 years in wild (green) and captive (blue) chimpanzees. The wild data are endocranial volumes, but the captive specimens are represented by brain masses. So the captive data are modeled for both the original masses (dashed) and estimated volumes (solid). Wild data are from Neubauer et al. 2011, captive data from Herndon et al., 1999.

Abstract: This study compares postnatal brain size change in two important chimpanzee samples: brain masses of captive apes at the Yerkes National Primate Research Center, and endocranial volumes (ECVs) of wild-collected individuals from the Taï Forest. Importantly, age at death is known for every individual, so these cross-sectional samples allow inferences of patterns and rates of brain growth in these populations. Previous studies have revealed differences in growth and health between wild and captive animals, but such habitat effects have yet to be investigated for brain growth. It has also been hypothesized that brain mass and endocranial volume follow different growth curves. To address these issues, I compare the Yerkes brain mass data (n=70) with the Taï ECVs (n=30), modeling both size and velocity change over time with polynomial regression. Yerkes masses overlap with Taï volumes at all ages, though values for the former tend to be slightly elevated over the latter. Velocity curves indicate that growth decelerates more rapidly for mass than ECV. Both velocity curves come to encompass zero between three and four years of age, with Yerkes mass slightly preceding Taï ECV. Thus, Yerkes brain masses and Taï ECVs show a very similar pattern of size change, but there are minor differences indicating at least a small effect of differences in habitat, unit of measurement, or a combination of both. The overall similarity between datasets, however, points to the canalization of brain growth in Pan troglodytes.

Friday excitement: Panoramic data inspection

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I teach Tuesdays and Thursdays this year, leaving Fridays welcomely wide open for non-teaching related productivity. Today’s task is arguably the most exhilarating aspect of doing Science – inspecting raw data to make sure there are no major errors or problems in the dataset, so I can then analyze it and change the world. The excitement is truly hard to contain.

Delectable dog food is the dataset; I’m the dog.

No, it’s not the funnest, but it’s an important part of doing Science. To make your life easier, you should inspect data daily as you collect them. This way, you can identify mistakes and make notes about outliers early on, so that you are not stupefied and stalemated by what you see when you sit down to begin analysis.

You (corgi) are getting ready to analyze and you find an anomalous observation (door stop) you didn’t notice when you were collecting data.

Today I’m looking at measurements I took from ape mandibles housed in an English museum last summer; I inspected data before I left the UK for KZ, so today should be a breeze. But no matter how meticulous you are in the field/museum, you still need to inspect your data before analyzing them, just to be safe. If you’re as disorganized as I am, there will be lots of programs each with lots of windows. Here’s a tip: plug into multiple monitors (or at least one big ass monitor), so you can easily espy all open windows and programs in prodigious panorama.

Using two monitors helps when checking data for errors and patterns

Using two monitors helps when checking data for errors and patterns. On my left screen I’m using R to visualize and examine the raw data open in Excel on the right screen. If something seems off on the left screen, I can quickly consult the original spreadsheet on the right.

Barely visible in the above screenshot, these are chimpanzee (red) and gorilla (black) mandible measurements plotted against a measure of body size, preliminarily described in this post from last August. I’m looking at whether any mandibular measurements track body size across the subadult growth period, in hopes that bodily growth can be studied in fossil species samples dominated by kid jaws. As you can (barely) see, some jaw measurements correlate with body size better than others, and sometimes the apes follow similar patterns but other times they don’t.

The data look good, so now I can go on to examine relationships between mandible and body size in more detail. Stay tuned for results!

Open wide for open access: chimpanzee tooth eruption

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Two anthropology papers came out yesterday in advance print at the Proceedings of the National Academy of Sciences. I’d like first to draw your attention to the fact that they’re open access – normally such scientific papers are behind a paywall, but these two can be obtained by anyone (well, anyone with internet). One is about the chronology and nature of Acheulean technology at the 1.7-1.0 mya site of Konso in Ethiopia. The other, and the subject of this post, is about life history in wild chimpanzees from Uganda.

Tanya Smith and colleagues analyzed behavior of chimps and photographs of chimps’ erupting first molars (“M1”) to determine a] the age at which these events happen in the wild (in this population at least), and b] whether M1 eruption is tightly linked with other important life history variables, such as the adoption of adult foods, as had previously been claimed. What an adorable study – check out figure 1 from the paper (right):

Figuring out age at M1 eruption in wild, healthy chimps is important because there has been debate about whether wild chimps actually erupt their teeth at as young of ages as they do in captivity – not natural conditions. This question has recently been investigated in a skeletal sample of wild chimps of known age, from Tai forest in Cote d’Ivoire (Zihlman et al. 2004, T Smith et al. 2010), but somehow these studies raised more questions than they answered (e.g. BH Smith and Boesch 2011). So TM Smith and colleagues decided to further address this question with photographic evidence of living, arguably healthy chimps. I’m kicking myself in the ass because I had this exact same idea a few months ago but had a bit too much on my plate to tacklet it at the time. Life.

Anyway, Smith and pals showed found that M1 eruption occurred anywhere from 2.8-3.3 years of age in their sample of 5 cuddly infants, consistent with estimates from captivity. I have to say I’m a bit surprised it wasn’t later (but what fun is science if it’s not surprising?). Of course, this is based on 5 infants from one population, so it could stand to be reinvestigated in other chimp populations, as the authors note; variation is, after all, key for evolution and a key problem for evolutionary biologists. Maybe I’ll get another crack at a photo-based eruption study after all…

Smith et al’s second task was to see how well age at M1 eruption coincided with other life history variables – this is supposed to be an important event, alleged to coincide with cessation of weaning and the adoption of adult foods. Moreover, since a mother is no longer nursing her infant, M1 eruption “should” also be roughly contemporaneous with a mother’s return to estrus cycling and subsequent re-pregnancy. Many infants were observed to begin eating adult-like foods prior to M1 eruption, around 3 years. Unexpectedly however, infants also nursed for a while even after M1 eruption. In fact, time spent nursing actually increased for a brief period around 3 years of age, possibly because their mothers’ milk was not as nutritious as at younger ages.

Now, what interests me most about this are possible implications for my research on the evolution of growth and life history. Many researchers have argued that extinct hominids, like the australopithecines, would have grown up relatively rapidly like apes, rather than slowly like humans. This claim has been based pretty much entirely on dental development, until my dissertation research. There, I’ve shown that one hominid, Australopithecus robustus, probably experienced greater jaw growth than humans prior to eruption of the M2. Now, if this hominid erupted its teeth as fast as apes, and grew more than humans, this implies really really high growth rates for A. robustus (that is, if we can extrapolate from the jaw to the overall body size).

ResearchBlogging.orgI’ll be working a bit more on this latter point in the near future. In the mean time, let’s hear it for bioanthro dominating open access today!

References
Smith BH, & Boesch C (2011). Mortality and the magnitude of the “wild effect” in chimpanzee tooth emergence. Journal of human evolution, 60 (1), 34-46 PMID: 21071064

Smith TM, Smith BH, Reid DJ, Siedel H, Vigilant L, Hublin JJ, & Boesch C (2010). Dental development of the Taï Forest chimpanzees revisited. Journal of human evolution, 58 (5), 363-73 PMID: 20416929

Smith, T., Machanda, Z., Bernard, A., Donovan, R., Papakyrikos, A., Muller, M., & Wrangham, R. (2013). First molar eruption, weaning, and life history in living wild chimpanzees Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1218746110

Zihlman A, Bolter D, & Boesch C (2004). Wild chimpanzee dentition and its implications for assessing life history in immature hominin fossils. Proceedings of the National Academy of Sciences of the United States of America, 101 (29), 10541-3 PMID: 15243156

Genes and culture in animals

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Recent UM Ph.D. Kevin Langergraber and others (including UM primatologist John Mitani; 2010) recently reported on a high correlation between genetic relatedness and ‘cultural’ behavioral repertoire in wild chimpanzees.

Chimpanzees, and other animals, have been observed to display behaviors that appear ‘cultural,’ since the behaviors are variously a) learned from other individuals, b) specific to certain chimp populations, and/or c) not recognizably adaptive. Such behavioral variants include, for example, how hands are clasped during grooming (photo from Whiten, 2005), or whether/how insects are acquired.

Researchers have debated whether these behavioral variants actually represent culture, in the sense that humans have culture. This itself is tough because anthropologists have had a helluva time defining what ‘culture’ is simply for humans, let alone animals. I’m a bit anthropocentric myself, and I’m wont to view culture as something uniquely human, the adaptation (or set of adaptations) that has essentially shaped our evolution for over a million (2 million?) years.
Anyway, back to Pan, Langergraber and colleagues set out to test whether genetic variation may help explain some of the behavioral variation between different chimp populations. Lo and behold! there was a significant correlation between groups’ genetic dissimilarity and behavioral dissimilarity. This isn’t at all to say the authors have found the genetic basis for cultural behaviors, but rather that some genetic variation may underlie some behavioral variation we see in chimpanzees. Indeed, the authors note that the mtDNA used in the study doesn’t ‘code for’ any of the putatively cultural behaviors; it’s a proxy for genetic relatedness. However, there was no clear pattern of which types of behaviors (e.g. grooming- vs. feeding-related) correlate with genetic relatedness.
The results are a bit tough to interpret. The authors state that the finding of a correlation does not mean that many chimp behaviors analyzed are not cultural. But it doesn’t necessarily mean that the behaviors are cultural, either. This gets really tricky for a number of reasons.
First, identifying “the” or “a” genetic basis for phenotypes is difficult, and it’s especially difficult for complex phenotypes like behaviors (in general, if you ever hear about a “gene for” some behavior, immediately disbelieve it). The analysis uses an allegedly neutral DNA marker, that admittedly does not ‘code for’ any of the behaviors in question. All the DNA can do here is attempt to indicate relatedness among groups. To say that “genetic differences cannot be excluded as playing a major role” in patterning behavioral variation (p. 7), basically means that some unexamined genetic region may be patterned among populations the same way as the mtDNA marker, and might be responsible for specific, fine-tuned, non-adaptive aspects of their behavior. The authors discount the possibility of the link being due to kin teaching behaviors to kin, but I would suppose a higher resolution (like looking at relatedness and behavior between individuals rather than groups) would put that matter to rest.
Next, how much of a correlation is biologically (and here culturally?) meaningful? In various permutations of their analysis, the correlations between the behavioral and genetic dissimilarity matrices ranged from r = 0.37 – 0.52, most of which were significant. “Significant” here means that the correlation coefficients, r, are different enough from zero – there isn’t no relationship between the variables (I mean to say the double negative). Put another way, we can square the r coefficients to get the ‘amount of variance explained’: 13.7 – 27.0% of the behavioral dissimilarity can be ‘explained’ by genetic dissimilarity. What if the correlation coefficients had been higher – would this be better evidence for some genetic basis for chimp behavioral variants? I love correlation as much as the next guy, but aside from significance level, variation in linearity is not always completely understandable.
So, regardless of the results of the analysis, do apes (or other non-human animals) have culture? An interesting conundrum is that when people describe the subtle variants of behavior as cultural, they’re assuming the variation itself is non-adaptive, while the grand behavior itself purportedly is. Can things that are readily adaptive (ecological explanation) not also be cultural? Moreover, how widespread in a population must a behavioral variant be to be cultural? How many variants on a theme are permissible within a population? Questions like these are why I tend to shy away from the topic of culture, in humans and animals.
References
Langergraber K, Boesch C, Inoue E, Inoue-Murayama M, Mitani JC, Nishida T, et al. Genetic and ‘cultural’ similarity in wild chimpanzees. Proc R Soc B in press. Proc. R. Soc. B doi:10.1098/rspb.2010.1112 (2010)
Whiten A. 2005. The second inheritance system of chimpanzees and humans. Nature 437: 52-55.