A new method for analyzing growth in extinct animals (dissertation summary 1)

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The last year and a half was a whirlwind, and so I never got around to blogging about the fruits of my dissertation: Mandibular growth in Australopithecus robustus… Sorry! So this post will be the first installment of my description of the outcome of the project. The A. robustus age-series of jaws allowed me to address three questions: [1] Can we statistically analyze patterns of size change in a fossil hominid; [2] how ancient is the human pattern of subadult growth, a key aspect of our life history;  and [3] how does postnatal growth contribute to anatomical differences between species? This post will look at question [1] and the “zeta test,” new method I devised to answer it.

Over a year ago, and exactly one year ago, I described some of the rational for my dissertation. Basically, in order to address questions [2-3] above, I had to come up with a way to analyze age-related variation in a fossil sample. A dismal fossil record means that fossil samples are small and specimens fragmentary – not ideal for statistical analysis. The A. robustus mandibular series, however, contains a number of individuals across ontogeny – more ideal than other samples. Still, though, some specimens are rather complete while most are fairly fragmentary, meaning it is impossible to make all the same observations (i.e. take the same measurements) on each individual. How can growth be understood in the face of these challenges to sample size and homology?

Because traditional parametric statistics – basically growth curves – are ill-suited for fossil samples, I devised a new technique based on resampling statistics. This method, which I ended up calling the “zeta test,” rephrases the question of growth, from a descriptive to a comparative standpoint: is the amount of age-related size change (growth) in the small fossil sample likely to be found in a larger comparative sample? Because pairs of specimens are likelier to share traits in common than an entire ontogenetic series, the zeta test randomly grabs pairs of differently-aged specimens from one sample, then two similarly aged specimens from the second sample, and compares the 2 samples’ size change based only on the traits those two pairs share (see subsequent posts). Pairwise comparisons maximize the number of subadults that can be compared, and further address the problem of homology. Then you repeat this random selection process a bajillion times, and you’ve got a distribution of test statistics describing how the two samples differ in size change between different ages. Here’s a schematic:

1. Randomly grab a fossil (A) and a human (B) in one dental stage (‘younger’), then a fossil and a human in a different dental stage (‘older’). 2. Using only traits they all share, calculate relative size change in each species (older/younger): the zeta test statistic describes the difference in size change between species. 3. Calculate as many zetas as you can, creating a distribution giving an idea of how similar/different species’ growth is.

The zeta statistic is the absolute difference between two ratios – so positive values mean species A  grew more than species B, while negative values mean the opposite. If 0 (zero, no difference) is within the great majority of resampled statistics, you cannot reject the hypothesis that the two species follow the same pattern of growth. During each resampling, the procedure records the identity and age of each specimen, as well as the number of traits they share in common. This allows patterns of similarity and difference to be explored in more detail. It also makes the program run for a very long time. I wrote the program for the zeta test in the statistical computing language, R, and the codes are freely available. (actually these are from April, and at my University of Michigan website; until we get the Nazarbayev University webpage up and running, you can email me for the updated codes)

The zeta test itself is new, but it’s based on/influenced by other techniques: using resampling to compare samples with missing data was inspired by Gordon et al. (2008). The calculation of ‘growth’ in one sample, and the comparison between samples, is very similar to as Euclidean Distance Matrix Analysis (EDMA), devised in the 1990s by Subhash Lele and Joan Richtsmeier (e.g. Richtsmeier and Lele, 1993). But since this was a new method, I was glad to be able to show that it works!

I used the zeta test to compare mandibular growth in a sample of 13 A. robustus and 122 recent humans. I first showed that the method behaves as expected by using it to compare the human sample with itself, resampling 2 pairs of humans rather than a pair of humans and a pair of A. robustus. The green distribution in the graph to the left shows zeta statistics for all possible pairwise comparisons of humans. Just as expected, that it’s strongly centered at zero: only one pattern of growth should be detected in a single sample. (Note, however, the range of variation in the green zetas, the result of individual variation in a cross-sectional sample)

In blue, the human-A. robustus statistics show a markedly different distribution. They are shifted to the right – positive values – indicating that for a given comparison between pairs of specimens, A. robustus increases size more than humans do on average.

 

We can also examine how zeta statistics are distributed between different age groups (above). I had broken my sample into five age groups based on stage of dental eruption – the plots above show the distribution of zeta statistics between subsequent eruption stages, the human-only comparison on the left and the human-A. robustus comparison on the right. As expected, the human-only statistics center around zero (red dashed line) across ontogeny, while the human-A. robustus statistics deviate from zero markedly between dental stages 1-2 and 3-4. I’ll explain the significance of this in the next post. What’s important here is that the zeta test seems to be working – it fails to detect a difference when there isn’t one (human-only comparisons). Even better, it detects a difference between humans and A. robustus, which makes sense when you look at the fossils, but had never been demonstrated before.

So there you go, a new statistical method for assessing fossil samples. The next two installments will discuss the results of the zeta test for overall size (important for life history), and for individual traits (measurements; important for evolutionary developmental biology). Stay tuned!

ResearchBlogging.org Several years ago, when I first became interested in growth and development, I changed this blog’s header to show this species’ subadults jaws – it was only last year that I realized this would become the focus of my graduate career.

References
Gordon AD, Green DJ, & Richmond BG (2008). Strong postcranial size dimorphism in Australopithecus afarensis: results from two new resampling methods for multivariate data sets with missing data. American journal of physical anthropology, 135 (3), 311-28 PMID: 18044693

Richtsmeier JT, & Lele S (1993). A coordinate-free approach to the analysis of growth patterns: models and theoretical considerations. Biological Reviews, 68 (3), 381-411 PMID: 8347767

Osteology everywhere: Muffin tops

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It’s become challengingly chilly here in Astana and my days of running outdoors are fading into memories redshifting into oblivion, so last weekend I went ice skating instead. Pulling off certifiably Scott Hamiltonian moves, I espy my silhouette and what hominid face is staring back?

That’s, right, Australopithecus boisei (right). Of course they’re not identical, but then they don’t really have to be when you see Osteology Everywhere.

But then again, when you’ve been doing this too long, you start to see Paleontology Everywhere, too. The shadow also reminded me of a time a few years ago, when we were picking through bags of backdirt at Dmanisi, foraging for micromammals, passing pachmelia and time with trivia. Someone posed the riddle, “What did one muffin say to the other muffin?” To which I responded:

Osteology Everywhere: Zubi

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We’re going over bone biology and bioarchaeology this week in my Intro to Bio class, and so I thought I’d open the unit with my patent-pending Osteology Everywhere series. I showed the students the various real-life objects from the series, and they kicked buttocks at seeing the bones in quotidian things. They even got this new one:

That yellow pepper is a ringer for a premolar crown, which hopefully was not as yellow. So I’m very proud of my students. I figure if I can make people see bones everywhere they look, well then I’ve done my job. But hopefully they don’t get as bad as me: a few months ago my friend bought one of those Kinder chocolate eggs with a prize inside. Shaking it, you could hear something rattling in there. It’s disconcerting that my mind immediately guessed, “Legos, or teeth.” At least legos came before teeth.

Also “zubi,” from the title, is the Croatian word for ‘teeth’ (and apparently also slang for ‘breasts’).

The beardless White House: Part I

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Something’s been bothering me about this election. No, it’s not the silence from both major parties on climate change. It’s the fact that neither Obama nor Romney (I accidentally just typed “RMoney”… accidentally?) sports facial hair. A friend and I were talking about this the other day, and a quick google search showed us there hasn’t been an appreciable furface sleeping at 1600 Pennsylvania Ave. since the mustachioed WH Taft (of butter and bathtub fame), 100 years ago. That is, unless any of these recent presidents was a closet homosexual (different meaning of “beard”).

This is hairy dearth is deplorable. Just look at this pic of portraits of past presidents:

You’re probably thinking, “Where’s all the virile scruff?” Well, no, you’re probably thinking, “There’s a lot of dudes / white ppl there.” But your next thought is probably, “Where’s all the virile scruff?” However, from Abe Lincoln through Bill Taft there’s a fairly flagrant concentration of beards, mustaches and whatever you call the thing hiding Chester A. Arthur’s charming smile (squared off in red); only W McKinley and A Johnson dared rain on this badass parade. Yes, there are some audacious sideburns on John Q. Adams and Martin Van Buren, but otherwise all Executive facial hair is concentrated between 1860 and 1913. What gives?

It looks like there’s a fairly clear pattern: voters loathed and distrusted facial hair for the first nearly 100 years of American history, followed by a brief period in which facial hair was loved and trusted, which may then have been ruined by Taft and after which there’s been nary a stache nor goat sitting in the oval office to the present day. Is this a real pattern, or could some other random process produce this same distribution of scruff? (for simplicity’s sake, we’ll pretend no president served more than 1 term…) Could random sampling of 43 (mostly white) men give us a clump of 9/13 with facial hair? (side burns don’t count) If there’s a 50/50 chance of a man growing facial hair, is 9/43 Prezes unusually high or low? I’ll let you know after I write and run some tests!

microRNAs punch Plasmodium parasites in the face

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This is the first time I’m teaching Introduction to Biological Anthropology here at Nazarbayev University. It’s exciting and curious that for nearly every class session, I’m able to find a very recent outside article or blog post that’s relevant to the field and/or something we’re talking about at the moment. For instance, the 30-paper barrage of the ENCODE project came out right as we were beginning the unit focused on evolution and genetics. Serendipity!

Recently in this first unit, we covered one of the classic anthro examples illustrating principles of both genetics and evolution: sickle-cell anemia and malaria resistance. And right on cue, a brief review about the actual molecular basis for this phenomenon was just published in Nature Genetics (Feliciano, 2012, reviewing LaMonte et al., 2012).

Briefly, sickle-cell anemia is an iron deficiency caused by having aberrant hemoglobin, and characterized by sickle-shaped red blood cells (“erythrocytes”). The sickle cell trait is caused by a simple point mutation on the 11th chromosome, at a locus termed the hemoglobin S (or HbS) allele; the ‘normal’ allele is designated A (or HbA). If you have two A alleles you have normal hemoglobin, whereas two S alleles result in sickle cell, which is generally fatal. You don’t want to have two S alleles. The deleterious S allele is nevertheless maintained in the population because heterozygous individuals (AS genotype) have basically normal red blood cells and resistance to malaria, a disease caused by the parasite Plasmodium falciparum. P. falciparum loves red blood cells, and so in populations where malaria is endemic, having normal hemoglobin can actually be a health risk because of stupid smelly P. falciparum. Natural selection therefore maintains both the normal A and sickle S alleles in malarial areas because of a heterozygote advantage.

The outstanding question, however, is how having both an A and an S allele confers resistance to malaria. The textbook explanation (e.g. Larsen, 2010) is that sickle cells are poor in oxygen, and therefore poor hosts for stupid smelly P. falciparum. A recent study, however, points to a much more badass mechanism of resistance.

LaMonte and colleagues (2012) show a role for microRNAs (miRNA) in sickle cell-mediated resistance to malaria. miRNAs are small strands of RNA (21-25 base pairs long) that do not get translated into proteins, but are nevertheless important in regulating gene expression. This mechanism is called RNA interference (RNAi) – check out this sweet slideshow and animation from Nature for more info. What LaMonte and colleagues found was that SS and AS red blood cells had higher concentrations of certain variants of miRNA, which were then transferred into P. falciparum parasitizing these cells. These miRNA-enriched parasites, in turn, showed reduced growth compared to those parasitizing normal cells. It remains to be seen, however, just how these human miRNAs are disrupting development of Plasmodium, since these parasites do not produce the same genetic machinery that utilizes the miRNA used in human RNAi (Feliciano, 2012).

ResearchBlogging.orgNot being a geneticist, I’m really enjoying how complicated the genome is proving to be. The example here illustrates not only our increased appreciation for RNA and especially non-protein-coding elements, but also the dynamic genetic interactions between different species.

Better explanations than I was able to give
Feliciano P (2012). miRNAs and malaria resistance. Nature genetics, 44 (10) PMID: 23011225

Lamonte G, Philip N, Reardon J, Lacsina JR, Majoros W, Chapman L, Thornburg CD, Telen MJ, Ohler U, Nicchitta CV, Haystead T, & Chi JT (2012). Translocation of Sickle Cell Erythrocyte MicroRNAs into Plasmodium falciparum Inhibits Parasite Translation and Contributes to Malaria Resistance. Cell host & microbe, 12 (2), 187-99 PMID: 22901539

Bonobo survival strategy

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A paper was just released that showcases the technological prowess of two captive bonobos (Pan paniscus), the famous Kanzi and the less famous Pan-Banisha (Roffman & al. in press). It’s a neat paper, and I don’t really have much to say about it, but I will pass on what I enjoyed most about it (abstract and keywords):

It sounds like a rock band or something. You don’t see key words/phrases like that every day!

ResearchBlogging.org
Read for yourself
Itai Roffman, Sue Savage-Rumbaugh, Elizabeth Rubert-Pugh, Avraham Ronen, & Eviatar Nevo (2012). Stone tool production and utilization by bonobo-chimpanzees (Pan paniscus) Proceedings of the National Academy of Sciences, in press DOI: 10.1073/pnas.1212855109

These new fossils are intriguing as hell

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Some big changes here at Lawnchair Anthropology. I just successfully defended my dissertation (Mandibular Growth in Australopithecus robustus, more info on that to come), and moved to Kazakhstan to begin my new job in the School of Humanities and Social Sciences at Nazarbayev University. I landed in Astana about 22 hours ago, so I should be asleep, battling (or succumbing to) jetlag, but some friends have pointed me to newly published early Homo fossils from Kenya, dating to between 1.9-1.6 million years ago (Leakey et al., 2012). See Adam Van Arsdale’s blog, the Pleistocene Scene, for great historical background and perspective on these new fossils.

Now, one of the themes of my dissertation is that there is lots of interesting information to be gleaned from fossils that we’ve known about for a long time (many of the A. robustus mandibles featured in my research have been known for decades). But dammit if some of these much more recently discovered fossils point to tantalizing variation in hominids just later than 2 million years ago (note I’m careful to say “variation” rather than “diversity”). In light if this variation, Adam discusses the similarities between one of these Kenyan fossils (KNM-ER 60000) and the large mandible from Dmanisi, which was discovered in only in the year 2000 (Gabunia et al., 2002).

Piggy-backing off Adam, I’d like to point out similarities between another of the new fossils, the KNM-ER 62000 face of a juvenile, and the recently discovered A. sediba juvenile face (Berger et al., 2010). These two fossils are at the same stage of dental development, so they’re roughly at the same stage of life. They are close in geological age, but A. sediba is from South Africa. Below are figures of A. sediba (left) and the ER 62000 face (right). The pics should be to scale, modified from the original publications. (sorry I couldn’t remove the background from the top left one)

What do you think? Pretty different, right? WRONG! Below I’ve superimposed the ER 62000 face onto A. sediba (slightly recolored and transparented for contrast). Remember that these are to scale.

In front view (left), the ER 62000 face is almost identical to A. sediba, right down to the positions of the teeth. THIS DOES NOT MEAN THAT I THINK THESE TWO FOSSILS REPRESENT THE SAME SPECIES. In side view, however, some differences do become apparent. Notably, the front of the A. sediba maxilla projects a bit further forward than ER 62000, and the nasal and orbital anatomy are also fairly different. THIS DOES NOT MEAN THAT I THINK THESE ARE DIFFERENT SPECIES. (although I would be surprised if these fossils turned out to be the same animal)

Leakey et al. liken these new Kenyan fossils to the cranium KNM-ER 1470, from the same region and at 1.9 million years old. But what’s weird to me is that ER 1470 actually looks a bit more like the juvenile A. sediba in the side view (as reconstucted; the face and braincase of ER 1470 are actually separated, leaving it unclear just how the two parts fit together). Here are all three specimens, to scale:

From left to right: ER 62000, A. sediba, ER 1470

Now, the ER 1470 comparison isn’t really fair – ER 1470 is an adult and it is much larger: the bottom of ER 1470’s eye socket is about the same height as the top of A. sediba‘s. The size difference is probably the main reason why its face below the nose sticks out as much as A. sediba‘s, even though the latter is smaller. (I should note, too, that the adult A. sediba mandible is superficially very similar in gonial and ramus anatomy to another of the recently published Kenyan specimens, ER 60000).

The point of all these comparisons is not to say whether these fossils are the same species, but rather to point out that there are actually striking similarities between fragmentary fossils, and it’s not clear what exactly these similarities (or differences, for that matter) mean. Maybe my eye was drawn to the ER 62000-A. sediba comparison not because of any evolutionary relationship, but because these fossils are in similar stages of growth and development – if it weren’t waaaaay past my bedtime I’d love to compare these fossils with other similarly-aged fossils (like D2700 from Dmanisi and KNM-WT 15000, also from Kenya).

All of these fossils (except ER 1470) were discovered in the past few years. I’ve said it before and I’ll repeat it now: this is a great time to study paleoanthropology.

ResearchBlogging.orgRead more NOW
Berger L, de Ruiter DJ, Churchill SE, Schmid P, Carlson KJ, Dirks PHGM, and Kibii JM. 2010. Australopithecus sediba: A New Species of Homo-like Australopith from South Africa. Science 328: 195 – 204.

L. Gabounia, M.-A. de Lumley, A. Vekua, D. Lordkipanidze, and H. Lumley. 2002. Découverte d’un nouvel hominidé à Dmanissi (Transcaucasie, Géorgie). Comptes Rendus Palevol 1(4):243-253

Meave G. Leakey, Fred Spoor, M. Christopher Dean, Craig S. Feibel, Susan C. Antón, Christopher Kiarie, & Louise N. Leakey (2012). New fossils from Koobi Fora in northern Kenya confirm taxonomic diversity in early Homo Nature, 408, 201-204 DOI: 10.1038/nature11322

Osteology Everywhere: I’ve been doing this too long

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I was trying to squeeze a nice picture of the African continent into the Gall-Peters projection, and I suddenly saw something I hadn’t seen there before:

The image at right is the fossil, KNM-ER 3228 a 1.9 million year old right pelvic (innominate) bone of Homo (erectus?) from Kenya. Not 100% identical, maybe rotate ER 3228 medially a bit. You know you’ve been doing it too long when you start to see Osteology Everywhere.

The most wonderful teeth ever seen

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I came across an interesting quote from Dr. Robert Broom (quoted in Brain’s Hunters or Hunted? monograph), a great South African paleontologist of the first half of the 20th century. He’s recounting how he came upon the Kromdraai site, which produced the very first Australopithecus (a.k.a. Paranthropus) robustus fossils:

I went off to the school. About a mile of the way was so rocky that it was impossible to go by car. It was playtime, about 12.30 pm., when I arrived. I saw the principal, and told him what I had come about. Gert was found, and drew from his trouser pocket four of the most wonderful teeth ever seen in the world’s history. [emphasis added]

Lots of funny things about that story, most notably that Broom basically took this kid, Gert, from school  to show him where Kromdraai was. And Broom’s so excited by these teeth! I don’t know the last time I was that excited about anything.