Thinking

Population History, Climatic Adaptation, and Cranial Morphology

Physical anthropology in general and craniology in particular have quite a sordid history. The size of the skull was of great interest to scientific racialists, who seized on minor differences in averages as evidence of differential capacity of the “races” for civilization. As we have shown before and we shall see again in what follows, race gives us a very poor handle on cranial morphology, and human morphology more generally. This does not mean that there is little systematic variation; that all variation in between individuals within populations. To the contrary, human morphology is geographically patterned. Situated local populations or demes differ systematically and significantly from each other, generating smooth geographic clines. In particular, as the next figure shows, our species obeys Bergmann’s rule—bigger variants are found in colder climes.  

Ruff(1994)

Source: Ruff (1994)

Of course, skull size and body size scale together. We can read this off the US military anthropometric database. So we should expect the bigger people of colder climes to have bigger skulls as well. Indeed, if Ruff’s thermoregulatory theory is right, particular features of skull morphology should be adapted to the macroclimate even more than the postcranial skeleton (the bit below the head).  

It is easy enough to recover the monotonic relationship between climatic variables and cranial measurements. But there is a very serious problem. Suppose that we can rule out nutritional and other environmental influence, say because we know the parameter is very slow-moving. That does not mean that all systematic variation in that parameter is then due to the bioclimate. For it may instead be due to random drift. Indeed, we know that founder effects, isolation-by-distance, and genetic drift can generate geographic clines that confound the bioclimatic signal. There is mounting evidence that human morphology, just like the human genome and linguistic diversity, contains a strong population history signal. 

Population history signals in DNA, RBC polymorphisms, and craniometrics are congruent.

Segments of DNA under selection do not preserve the signal from population history well; the bit not under selection—”junk DNA”—does. Similarly, information on population history can be recovered from traits of cranial morphology that are neutral, ie not under selection. Scholars have used genetic distance to control for population history in order to recover information on bioclimatic adaptation from morphology. In what follows, I will show how we can use cranial morphology itself to the same effect.

Howells Craniometric Dataset contains 82 linear measurements 2,524 skulls from 30 populations located in five continents. We will be working exclusively with that data. Let us begin with skull size. The five continental “races” that Wade thinks are real—one each for Europe, Africa, Asia, Australia, and the Americas—explain 6 percent of the variation in skull size. Moreover, not even one of the mean differences between said “races” is statistically significant. See Table 1. 

Table 1. Mean cranial capacity by continental “race”.
ContinentMale meantStatStd
Europe1,3530.27573
Pacific1,3480.22097
Africa1,281-0.45895
Asia1,3340.074101
America1,322-0.04591
Source: Howells Craniometric Dataset. Estimates in Italics are insignificantly different from the global mean at the 5 percent level. 

While demes are lumpy, they can’t be called races; there are tens of thousands of them. And though races are useless fictions, demes or situated local populations are very useful fictions. Put another way, demes are to anthropology what particles are to physicists and representative rational agents are to economists. Recall the tyranny of distance over land before the late-nineteenth century. Under such conditions as prevailed well into the ethnographic present, populations were situated locally and isolated from nearby and far away demes; the latter more than the former and smoothly so. Dummies for the demes alone explain 44 percent of the variation. What needs to be explained is this systematic component. 

In order to understand variation in skull size we first note that Binford’s Effective Temperature (ET, estimated as a linear function of latitude) is a strong correlate of skull size (r=-0.547 for men, r=-0.472 for women) and orbital size, ie the size of the eye socket (r=-0.464 for men, r=-0.522 for women). See the top panel of next figure. Moreover, as you can see the bottom-left graph, orbital size is a strong predictor of skull size (r=0.506); raising an intriguing pathway of selective pressure that ties cranial variation not to thermal parameters but to variation in light conditions. Still, the bottom-right graph shows that ET is correlated with skull size even after controlling for orbital size. 

If we consider only the systematic component, orbital size alone explains half the variation in skull size. So we should try to understand variation in this mediating variable as well. 

A straightforward way to extract the population history signal is to isolate morphological parameters that are uncorrelated with climatic variables and use these to construct a neutral phenotypic distance measure. We identity 14 linear measurements in the dataset that are uncorrelated or very weakly correlated with ET. Assuming that these are neutral traits not under selection, we use them to compute phenotypic distance from the San. (We use Pearson’s correlation coefficient between standardized 14-vectors for the San and each of the 30 populations as our measure of phenotypic distance.) Basically we are using the fact that the San are known to have been the first to diverge from the rest of us so that the degree of correlation in these neutral measures contains information on the genetic distance between the two populations. We also know that genetic distance is proportional to geographic distance from sub-Saharan Africa due to our specific population history. So if our measure is capturing population history, it should be correlated with geographic distance. The next figure shows that this is indeed the case. 

If we are right about our reasoning, we are now in a position to decompose morphological variation into neutral, bioclimatic and non-systematic factors. We begin with our estimates of pairwise correlation between our neutral factor (phenotypic distance from San) and ET on the one hand and selected morphological variables on the other. 

Table 2. Spearman’s correlation coefficients.
  Skull sizeOrbital sizeCranial IndexNasal Index
MenNeutral-0.488-0.5750.2150.257
ET-0.547-0.464-0.0610.583
WomenNeutral-0.437-0.5700.1170.301
ET-0.472-0.522-0.0250.543
Source: Howells Craniometric Dataset. Estimates in Bold are significant at the 5 percent level. 

We see that Cranial Index (head breadth/head length) is uncorrelated with both ET and Neutral, while orbital size and skull size are strongly correlated with both. Interestingly, the Nasal Index (nasal breadth/nasal length) is a strong correlate of ET but not our neutral factor, implying that nasal morphology contains a strong bioclimatic signal and a weak population history signal. These results are only suggestive however. In order to nail down the bioclimatic signal we must control for population history and vice-versa. 

Table 3. Spearman’s partial correlation coefficients.
  Skull sizeOrbital sizeCranial IndexNasal Index
Neutral controlling for ETMen-0.533-0.6090.2210.260
Women-0.488-0.6590.1180.348
ET controlling for neutralMen-0.584-0.513-0.0800.584
Women-0.517-0.625-0.0270.564
Source: Howells Craniometric Dataset. Estimates in Bold are significant at the 5 percent level.

We see that both population history and bioclimatic signals are present in skull size and orbital size; neither is present in the Cranial Index; and only the bioclimatic signal is present in the Nasal Index. This is consistent with known results in the field. Not just the Nasal Index but a bunch of other traits in facial morphology exhibit a strong bioclimatic signal, suggesting strong selective pressure on the only part of the human body exposed to the elements even in winter gear and even in the circumpolar region. 

Table 4 shows the percentage of variation explained by phenotypic distance from the San and ET. We see that Neutral and ET explain roughly 11 percent of the variation in skull size and orbital size each; neither explains CI; and ET explains 15.6 percent of the variation in the Nasal Index. More than three-fourths of the variation in these variables in not explained by either. 

Table 4. Apportionment of individual craniometric variation.
 Skull sizeOrbital sizeCranial IndexNasal Index
Neutral11.1%11.7%1.5%4.5%
ET11.8%10.2%1.2%15.6%
Error77.1%78.1%97.3%80.0%
Source: Howells Craniometric Dataset. OLS-ANOVA estimates after controlling for sex. 

Table 5 displays the portion of systematic (interdeme or interpopulation) variation explained by population history and ET. Interestingly, the population history signal is stronger than the bioclimatic signal for systematic variation in skull size and especially orbital size. Neutral phenotypic distance from the San, our population history variable, explains 35 percent of the systematic variation in orbital size and 28 percent in skull size. ET explains 22 percent in both. Population history and ET explain more than half the systematic variation in both size variables. ET explains 27 percent of the systematic variation in the Nasal Index likely reflecting morphological adaptation to the macroclimate. The less said about the Cranial Index the better. 

Table 5. Apportionment of systematic craniometric variation.
 Skull sizeOrbital sizeCranial IndexNasal Index
ET24.0%22.1%1.5%28.5%
Neutral26.9%36.8%4.2%10.0%
Error49.1%41.1%94.4%61.5%
Source: Howells Craniometric Dataset. OLS estimates adjusting for sex-ratio.

Remarkably, both ET and population history’s average share is more than a sixth but shy of a fifth, adding up to 37 percent of systematic variation of all four variables. If we drop the Cranial Index and average the other three morphological variables, they explain 24 percent of the variation each. In the horse race between population history and ET, we have a draw. The balance is more uneven in cranial size variables where population history has the upper hand. What happens if we introduce race dummies? 

Table 6. Apportionment of systematic craniometric variation.
 Skull sizeOrbital sizeCranial IndexNasal Index
ET23.8%31.2%1.1%16.3%
Neutral24.4%19.5%1.2%5.1%
Europe dummy4.1%0.0%0.7%7.0%
Asia dummy0.0%0.0%16.5%6.1%
America dummy0.0%3.4%0.2%3.9%
Pacific dummy0.3%0.9%0.9%0.5%
Error47.4%45.0%79.5%61.3%
Source: Howells Craniometric Dataset. Estimates in bold are significant and those in italics are insignificant at the 5 percent level. OLS estimates adjusting for sex-ratio.

We see that race is pretty much a useless fiction. It gives us no handle at all on craniometric variation. The best we can say is that Asian heads are more globular. Interestingly, ET and population history exchange rankings in explaining orbital size after controlling for race. But the overall picture is unchanged. 

In order to be sure than we are not picking up spurious correlations, we fit linear mixed-effects models. We allow for random-effects by deme and admit fixed-effects for sex and race. We report the number of continental race dummies (out of four) that are significant in each regression. 

Table 6. Linear mixed-effects model estimates.
 Skull sizeOrbital sizeCranial IndexNasal Index
InterceptYesYesYesYes
Sex dummyYesYesYesYes
Deme random effectYesYesYesYes
Neutral-7.153-1.2080.9722.620
ET-0.895-0.209-0.1550.590
Number of race dummies significant0012
Source: Howells Craniometric Dataset. Estimates in bold are significant at the 5 percent level.

Our main results are robust to the inclusion of random effects for demes. The Cranial Index is bunk. The Nasal Index contains a strong bioclimatic signal but an insignificant population history signal. The gradients of ET and phenotypic distance from the San are significant for skull size and orbital size. Note that the sex dummy is always significant due to dimorphism—the dimorphism index for skull size in the dataset is around 1.15. But race dummies are rarely significant. Indeed, of the 16 dummies for race in the above regressions only 3 were significant. And these had mostly to do with “the wrong latitude problem”: New World population morphology can be expected to be adapted to the paleoclimate of Siberia so that it is not surprising that the coefficient parameter of the dummy would absorb that systematic error. 

The results presented above are congruent with known results from dental, cranial, and postcranial morphology. The basic picture that is emerging suggests that some skeletal traits are developmentally-plastic so that they reflect health status (eg stature, femur length); some are selectively neutral (eg temporal bone, basicranium, molars) so that they can be used to track population history; and finally, some have been under selection and likely reflect bioclimatic adaptation (eg, nasal shape, orbital size, skull size, pelvic bone width). 

In the 1990s and the early 2000s there was a sort of panic in physical anthropology related to genetics. The genomic revolution threatened to put people out of business. But it has become increasingly clear that the genomic revolution has turned out to be a dud. Most efforts to tie phenotypic variation to genomic variation have failed utterly. So far the best use of DNA for understanding human variation has turned out to be just a fancy version fingerprinting. So if you have ancient DNA samples, you can track population history. It has since been shown that morphological variation itself can be used to track population history just as effectively as DNA markers. With the advent of new techniques such as geometric morphometrics, the resurgence of interest in understanding morphological variation, and the manifest failure of DNA as the key to understanding variation in human morphology, we are truly in the midst of an unannounced golden age in physical anthropology. 


In lieu of references: See the splendid work by, among others, Brace (1980), Beals (1983, 1984), Ruff (1994), Relethford (2004, 2010, 2017), Roseman (2004), Harvati and Waever (2006), von Cramon-Taubadel (2014), and Betti et al. (2010). 

 

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