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paleoanthropology, genetics and evolution

Photo Credit: Pre-Clovis Gault Assemblage artifacts. Thomas Williams et al. (2018) CC-BY-NC

A view of the Little Foot skeleton

Paleoanthropologist Ron Clarke and the University of the Witwatersrand made a big splash last week with the public unveiling of one of the most important hominin fossils ever discovered, known as “Little Foot”.

Little Foot skeleton
The Little Foot skeleton, photo: University of the Witwatersrand

The press announcement was not accompanied by new data or scientific studies on the skeleton, which were said to be forthcoming.

I’ve written some thoughts about this discovery and its scientific importance on Medium: “Will the “most complete skeleton ever” transform human origins?”

By all accounts, the Little Foot skeleton is not within our genus, Homo, nor is it confounded at the base of our genus, as Australopithecus sediba may be. Whatever its identity, whether it is Australopithecus africanus or “Australopithecus prometheus”, the Little Foot skeleton has an essential role for testing hypotheses of how species are related within Homo. For such hypotheses, Litttle Foot is what biologists call an “outgroup”, the most complete and closest one to our genus ever discovered.
Will it confirm old ideas, or overturn them? Obviously this one skeleton won’t answer every question. But now, no study will be sufficient without it.

The University of the Witwatersrand also released some videos giving background of the discovery and the press event last week, which are worth watching.

Should anyone be worried about the number of scientific research papers that are never cited?

Nature looks at the myth that a large fraction of scientific research goes uncited in a piece by Richard Van Noorden: “The science that’s never been cited”. Compiling a number of stories from researchers who have looked into citation rates, the piece concludes that around 10% of scientific research papers in journals tracked by Web of Science will go uncited, a bit more than this if self-citations are excluded. That number varies greatly by technical field, with engineering disciplines much higher.

None of these compare to the urban myth about citations, which is that half of papers go uncited. But really, I’ve heard that said about humanities papers, not scientific papers, and Van Noorden’s article acknowledges that research in the humanities tends to be more independent, with a higher fraction of research that goes uncited by other workers. The Nature piece traces to urban myth to Science:

The idea that the literature is awash with uncited research goes back to a pair of articles in Science — in the 1990 one and in another, in 1991. The 1990 report noted that 55% of articles published between 1981 and 1985 hadn’t been cited in the 5 years after their publication. But those analyses are misleading, mainly because the publications they counted included documents such as letters, corrections, meeting abstracts and other editorial material, which wouldn’t usually get cited. If these are removed, leaving only research papers and review articles, rates of uncitedness plummet. Extending the cut-off past five years reduces the rates even more.

Should we even worry about citations? After all, basic research is its own reward. The Nature article at some points reads like a support session for scientists who aren’t feeling the citation love, as it goes through one reason after another that research may be valuable even if no one ever cites it.

Most of the reasons discussed in the article are totally legitimate, and should be part of any conversation about the value of lines of research that don’t provoke lots of additional work on exactly the same model.

Still other articles might remain uncited because they close off unproductive avenues of research, says Niklaas Buurma, a chemist at Cardiff University, UK. In 2003, Buurma and colleagues published a paper about ‘the isochoric controversy’ — an argument about whether it would be useful to stop a solvent from contracting or expanding during a reaction, as usually occurs when temperatures change11. In theory, this technically challenging experiment might offer insight into how solvents influence chemical reaction rates. But Buurma’s tests showed that chemists don’t learn new information from this type of experiment. “We set out to show that something was not worth doing — and we showed it,” he says. “I am quite proud of this as a fully uncitable paper,” he adds.

A good amount of research in human evolution may fall into this category.

Case-control studies on excavation practices, for example, are very rare. It would be tremendously valuable to know whether some common variations in practice make a difference to data recovery, or whether innovations might result in better data. Failed experiments that nonetheless reinforce the value of existing practice are valuable knowledge, but probably shouldn’t need to be cited again and again.

All in all, it’s wrong to think about “landmark findings” giving rise to lots of citations. The most cited papers are those that establish new experimental (or computional) methods, and those that provide datasets useful for other researchers. Those are very good things, but not the only things!

Link: Language development in the Tsimané

A nice article in Scientific American by Dana Smith looks at a new study of language development in the Tsimané people of Bolivia: “Parents in a Remote Amazon Village Barely Talk to Their Babies—and the Kids Are Fine”.

The researchers observed, anecdotally, that language development appears to be slightly delayed in the Tsimané—but this does not seem to matter. The children grow up to be fully functioning, communicative and productive members of the community. In fact, as interactions between Tsimané and other Bolivians increase, many of the children are becoming bilingual in Spanish as well at their native Tsimané language.

This is a good story of the way that differences in childrearing across cultures have unpredictable outcomes, and what has been recommended within particular Western societies may not generalize to other places.

Link: An appreciation of Frank Brown

Nature last week published an appreciation by Bernard Wood of the life and contributions of the late Frank Brown, who died earlier this fall: “Frank Brown (1943-2017)”. The first paragraph gives a good summary of the importance of Brown’s work:

Although not a palaeoanthropologist, Frank Brown played a major part in unveiling the story of human evolution. He devoted half a century to working out the complex geology of the fossil-rich sediments of East Africa’s Omo–Turkana Basin, one of the key sources of information about early human evolution. By matching up the chemical signatures of volcanic ash layers identified at sites across the basin, Brown provided a reliable way to place fossil and archeological finds in chronological order, adding immeasurably to what we know about human origins.

Brown and his students and collaborators contributed the intellectual basis on which we now understand the Rift Valley chronology of human origins.

Link: A history of four scientists' fight against harassment in fieldwork

Kayla Webley Adler of Marie Claire magazine has just published an article recounting the history of the SAFE13 study about sexual harassment and assault in fieldwork sciences: “‘It Gnaws Away at Me’: Female Scientists Report a Horrifying Culture of Sexual Assault”.

On field sites with clear codes of conduct and supervisors who enforced the rules, women thrived, but on the sites where rules did not exist or were ambiguous and there were no consequences for wrongdoers, they found instances of unwanted flirtation or physical contact and intimidation, verbal sexual advances, sexist jokes and comments about physical appearance, forced kissing, attempted rape, and rape. One respondent said field site leaders insisted on conducting conversations while naked. Another said the head of her field site “would systematically prey on women” to the point that some women in her group chose to sleep on the floor in the same room rather than their own beds: “I had to serve as a kind of bodyguard.”

The details uncovered in the course of this research have been outrageous. We must work to make scientific research safe for everyone, including local communities where the research happens, local collaborators, students, and early career scientists.

I recommend this article and plan to assign it for students next semester to highlight the importance of this issue.

What anthropology loses when we pigeonhole public engagement as “service”

Today at the meetings of the American Anthropological Association, Caroline VanSickle and Natalia Reagan organized a panel entitled, “Biological Anthropology and the Public”. The session featured the work of six innovative early- and mid-career anthropologists, each of whom has found new ways of interacting with a broader public beyond university students and fellow researchers.

I wasn’t at the conference in Washington DC, but I was able to follow along with the session because of its novel approach, including filmed segments with each of the presenters and a lively Twitter stream at the hashtag #bioanthpub. The organizers are making the filmed segments at the National Museum of Natural History available on the BOAS Network on YouTube, where anyone can watch them.

The burden of service work

One topic that came up in the Twitter stream really irked me, and I want to talk about it. Jess Beck, a great science communicator herself, captured the question and answer about public engagement as “service work”:

“Audience member of #bioanthpub asks why panel is all female? Is it because it’s service work? @KateClancy talks about need for tenure & promotion guidelines that incorporate outreach. @SusanGSheridan points out that burden of service work falls disproportionately on women.”
Jess Beck (@BoneBroke9) on Twitter

I cannot disagree with Kate or Susan on their comments. Influential senior people decide grant funding, research opportunities, jobs, awards, and promotions, and too many of these people ignore innovative and effective work in public engagement. Too many devalue innovations contributed by women and all other anthropologists who talk effectively with non-academics about anthropology.

Let’s end the misconception that building meaningful public engagement with anthropological research is “service work”. It is no accident that this wrong idea is so widespread. Spreading this misconception serves the purposes of lazy ivory tower academics who want to treat public engagement as window dressing in “broader impacts” statements rather than develop evidence-based strategies for building public trust in their work.

Margaret Mead with journalists
Margaret Mead talking with journalists at a "Town Meeting of Science" in 1968, with Linus Pauling in the background. Photo courtesy of Smithsonian Institution.

Here’s the reality: Public engagement was always part of anthropology’s past, and without effective public engagement, anthropology will have no future.

Bioanthpub panel
The #bioanthpub panel. From left: Agustín Fuentes, discussant, Caroline VanSickle, Briana Pobiner (with guest), Kate Clancy, Natalia Reagan, Becca Peixotto, Susan Guise Sheridan, and Julie Lesnik. Photo: Jess Beck (via Twitter)

Experiments in engagement

Take a look at the six anthropologists in this one session at the AAA meetings. Every one of them is an innovator running a live experiment in building real public engagement in their work.

  • Susan Guise Sheridan has built the largest social media forum connecting research in biological anthropology with the public, with 20,000 followers and counting.

  • Natalia Reagan has brought a scientific anthropology perspective to television audiences of hundreds of thousands of people.

  • In addition to her work advancing public understanding of science at the National Museum of Natural History, Briana Pobiner has undertaken an enormous experiment in K-12 evolution education in the state of Alabama.

  • Julie Lesnik has found new ways to connect communities and people with human evolution by putting their hands (and tongues!) on the dietary evidence.

  • Becca Peixotto has connected with schoolkids and more than 100,000 people worldwide from the unbelievably inaccessible chambers of the Rising Star cave, finding new ways to document this work with video, and new ways to tell the story of archaeological fieldwork.

  • Kate Clancy has changed the academic landscape of biological anthropology with her engaged research on sexual harassment and abuse in fieldwork settings, and she is breaking new ground in public dialog with her podcasts.

None of them have done it all alone, but each of them is a leader. Every one of these projects started as an experiment. Some of them have progressed far enough to fundamentally change the way that we work, either with the public or with each other. Many of the experiments are still running—the results are not in yet!

Not a single one of these ongoing experiments should be pigeonholed as “service work”. Sure, they may perform a valuable service for other anthropologists by increasing the profile of the field with the public. Every one of these anthropologists should be commended for that valuable role. But calling them “service work” devalues the real academic contribution that underlies each of these experiments in effective engagement.

From experiment to evidence-based engagement

Even though public engagement has been so important to the history of anthropology, it is only today that we are seeing the first steps toward evidence-based public engagement.

So many times, I have seen anthropologists interested in developing new teaching practices for K-12 students, do a couple of workshops, discover that their ideas simply don’t connect with the needs of K-12 teachers and classrooms, and then walk back to their laboratories. So many times, I’ve seen science festivals presenting the same tired activities for kids. So many times, I’ve seen the same tired public lectures from leading researchers.

Every one of those has been a “broader impacts” activity for somebody’s research grant. I suspect their net effect is negative. Anthropologists get people’s attention and then bore them.

We must start building a knowledge base about which public engagement activities are effective. That means going beyond simply telling stories about what we did last summer for public engagement. We must look beyond informal assessment and anecdotes, toward real evidence. We must build communities of practice to enable more researchers to be a part of effective public and community engagement. We must recognize excellence in assessment, documentation, and replication, not just anecdotal stories.

Only by respecting the academic effort in public and community engagement can we elevate anthropology to the next level.

The good news is that we have come a long way in the last fifteen years toward recognizing innovative public engagement as part of tenure and promotion in American universities. For example at my institution, the University of Wisconsin-Madison, tenure guidelines specifically discuss outreach and community engagement activities. The guidelines emphasize that a candidate’s record must present evidence of excellence in outreach and public engagement, as demonstrated by leadership in developing innovations and transferring technology and science from research programs into the community. We look for documentation of the intellectual contribution of this outreach into the community, beyond the campus.

What can we do to build upon these first steps?

I plan to keep pushing back against senior anthropologists who pigeonhole valuable public engagement experiments as “service work”. I also recognize that I can do more by publishing the assessment of some of my own work in outreach and engagement, and by encouraging others to publish theirs. I will keep writing letters to support the innovative work of early career anthropologists who are advancing public engagement in our field. And I will continue to lobby grant agencies and foundations for real support for evidence-based “broader impacts”.

This post first appeared on my Facebook page, where a lively conversation of more than 50 comments from many professional anthropologists accompanies it.

Dilemma of the obstetrical dilemma

During the past few years, anthropologists have been questioning the long-held idea that human birth is uniquely risky for mothers and infants because of the narrow size of the human pelvis. This week, Josie Glausiusz has an article for Undark that reviews the topic: “Of Evolution, Culture, and the Obstetrical Dilemma”.

The assumption that “women are compromised bipedally in order to give birth,” is widely accepted says anthropologist Holly Dunsworth of the University of Rhode Island. But Dunsworth sees flaws in this premise. Women already have a range of dimensions in their birth canal, she thought, and they are all walking just fine. Indeed, research on human skeletons by anthropologist Helen Kurki of the University of Victoria in Canada has shown that the size and shape of the human birth canal varies very widely, even more so than the size and shape of their arms.

The article provides a fair summary of the conversation about birth and human evolution happening now in the field. It focuses on Dunsworth’s ideas about metabolic limits on gestation, and Jonathan Wells’ hypothesis that most complications with birth seen today are results of postagricultural changes to human nutrition and subsistence patterns.

The topic of the obstetrical dilemma illuminates many ways that people become confused about what evolution means to us in our lives.

Human babies are born relatively helpless compared to other primate babies, and they are born with a smaller proportion of their adult brain mass, leaving more of their brain growth for the first year of life. But human babies are not born early compared to other primates. Adult body mass predicts gestation length in primates pretty well and human babies are born about when we would expect for a primate with human body mass.

People make a huge deal out of the difficulty of human childbirth. Traditional people around the world recognize that human childbirth is difficult compared to many other animals. In the Christian tradition, the difficulty of human childbirth is even recognized in the bible, with Genesis 3:16 saying, “I will greatly multiply Your pain in childbirth, In pain you will bring forth children.”

Still, although childbirth can be very difficult for both mother and child, the extent of this difficulty varies greatly among women and among births by the same mother. Pain is hard to compare across species, as non-human primates cannot report what they are experiencing during uterine contractions.

The dimensions of the pelvic inlet and the average newborn head are much easier to compare objectively. Compared to great apes like chimpanzees and gorillas, human infants have larger heads, and the maternal pelvic inlet is much smaller. But many other species of smaller primates also have relatively large infant heads compared to the maternal pelvic inlet:

Infant head size at birth compared to maternal pelvis size, from Rosenberg and Trevathan 1995
Infant head dimensions at birth (black outline) compared to average maternal pelvic inlet dimensions (open outline) in various primate species and humans. This figure is from Rosenberg and Trevathan (1995), using data from Adolph Schultz.

What makes humans different from macaques, as Rosenberg and Trevathan pointed out, is not only the small size of the maternal pelvic inlet relative to infant head size, but also that its long axis is side-to-side instead of front-to-back. This means that most infants must rotate as they pass through the birth canal, while the smaller primates are typically born with the back of infant heads facing toward the back of the mother.

Still, the large heads of small primates show that there’s nothing inherently paradoxical about the human “obstetrical dilemma”. Trade-offs shape the timing of life history events. In both small-scale human societies and in primates, mortality at the time of birth is slight compared to infant mortality during the first year of life. Babies could be born earlier and smaller, but both have substantial costs that balance the occasional mortality from cephalopelvic disproportion.

References

Rosenberg, K., & Trevathan, W. (1995). Bipedalism and human birth: The obstetrical dilemma revisited. Evolutionary Anthropology: Issues, News, and Reviews, 4(5), 161-168.

Considering rodent eating by the Flores hominins

A nice article by Anna Goldfield in Sapiens today profiles the work of zooarchaeologist Grace Veach, who is examining the remains of rodents in Liang Bua Cave, on the island of Flores. “Can Rat Bones Solve an Island Mystery?” This site is otherwise well-known as the discovery locality of Homo floresiensis.

By looking at the markings and textures, such as gouges or scrapes, on the surfaces of the rat bones, Veatch can tell whether the animal was butchered or whether it passed through the digestive tract of one of the local birds of prey. Most of the small- and medium-sized rats from Liang Bua seem to have been consumed by birds. However, Veatch was surprised to find some unexpected cut marks on one small specimen from the material associated with H. floresiensis. This introduced the possibility that the “hobbit” diet included all sizes of rat.

The article makes note of a paradox in archaeological thinking. Archaeologists often interpret small mammal remains as evidence for advanced behavioral solutions like nets and snares, at least when they find such remains in sites where they think modern humans were active. But when the find small mammal remains in Neandertal or archaic human sites, they have often dismissed or ignored them.

That is changing, at least with respect to Neandertals, as a newer generation of archaeologists has revisited the importance of small mammals and birds in Neandertal foraging strategies. Human foragers today rely upon a wide array of animal

But we are only starting to learn about the foraging behavior of the Flores hominins. I’ll be looking forward to seeing more of this research.

How long ago did Neandertals and Denisovans part ways?

We have learned an immense amount about Neandertal population history from their genomes. But many old questions and some new ones remain unanswered.

Among the most basic: How long ago did Neandertal populations become separate from other populations, including Denisovans and ancestral Africans?

Reconstruction of the Neandertal skeleton, from the Neandertal Museum

In early August, Proceedings of the National Academy of Sciences published a paper by Alan Rogers, Ryan Bohlender and Chad Huff that gave a startling new perspective on the origin of Neandertal and Denisovan populations: “Early history of Neanderthals and Denisovans”. At the time, I wrote a perspective piece on the research article, focusing on the implications for rapid dispersal of the ancestral Neandertal-Denisovan population: “Neanderthals and Denisovans as biological invaders.” According to Rogers, Bohlender, and Huff, the Neandertal-Denisovan colonization of Eurasia was a fast expansion and dispersal of a small founder population. It appears to have been an early Middle Pleistocene version of the later colonization of Eurasia by modern humans.

Describing this parallel was easy, but coming to a full understanding of the implications is not. If a model like Rogers, Bohlender, and Huff’s is close to reality, then we will need to radically change some of the ways we look at the fossil and archaeological records.

I’ll describe some of the ways I think this will shake out in several posts over the next few weeks. First, I want to look carefully at the strength of the evidence presented by Rogers and colleagues.

Nothing about interpreting ancient DNA is easy, and I don’t think our current “standard” approaches are adequate to capture the complexity of human prehistory. Most of these interpretations are attempts to fit a model to some statistical summary of the data. By showing that some combinations of parameters fit the data much better than others, it is sometimes possible to reject hypotheses about past populations.

But models are inevitably oversimplifications, and sometimes adding more complexity can resurrect hypotheses that seem inconsistent with simpler models. I’ll go through the model used by Rogers and colleagues, and then point out some of the things that it has omitted.

How it was done

Rogers, Bohlender, and Huff examined the pattern of shared derived mutations in four genomes: an African modern human, a Eurasian modern human, the high-coverage Altai Neandertal genome, and the Denisovan genome. These genomes share different mutations with each other: some are shared between the Neandertal and the Eurasian modern humans, some are shared between the Neandertal and Denisovan, some are shared by three different genomes, and so on.

Here are the data from Rogers and colleagues:

Rogers site frequency results
Site frequency pattern from figure 2a of Rogers and colleagues (2017a). I've added labels indicating which pairs and trios of genomes are which. African and Eurasian modern human genomes share the most derived alleles with each other, Neandertals and Denisovans share many fewer, while all modern and archaic pairs and trios share relatively few derived alleles. However, the modern Eurasian and Neandertal genomes share slightly more than the other combinations of modern and archaic.

In around a third of cases, the two modern humans share the derived mutation. These shared mutations reflect the common shared heritage of these people from Africa, prior to 100,000 years ago.

The Neandertals and Denisovans share fewer mutations with the modern humans; each combination of modern and archaic genomes account for around three or four percent of all the shared mutations. Mostly, the mutations shared by Neandertals or Denisovans and modern humans come from the common origin of all these populations in Africa long before 500,000 years ago. The Neandertal and Eurasian genomes share a small proportion more with each other than the Neandertals and Africans do, and this small proportion reflects the introgression of Neandertal genes into modern human populations.

Shared mutations between the Neandertal and Denisovan genomes account for around 20 percent of the total number of shared mutations from any of the samples. That 20 percent comes from the shared common ancestral population that gave rise to these two archaic populations. Rogers, Bohlender, and Huff wanted to find out how large this ancestral population may have been, and how long it existed before it separated into Neandertal and Denisovan branches.

One innovation of this approach is that it uses more information from the data than the usual method, the “D-statistic” or “ABBA-BABA” method of examining mixture from ancient genomes. With the usual method, researchers are looking at models with mixture and introgression of populations that historically were separated by isolation and genetic drift. By fitting these models, they are trying to estimate the proportion of admixture or introgression, and also measuring the original level of difference between the populations.

Applying more information from the same samples allows Rogers, Bohlender, and Huff to potentially look at richer historical population models. They chose to consider the question of how the Neandertal and Denisovan populations initially became different from each other. For this purpose, they formed a population model that encompasses the separation of a Neandertal-Denisovan ancestral population from the ancestral population of African modern humans, the than the simple mixture and introgression models

Population model from Rogers et al. 2017
Figure 1 from Rogers et al., (2017). Original caption: "Fig. 1. (A) Population tree representing an African population, X; a Eurasian population, Y; Neanderthals, N; and Denisovans, D. The model involves admixture, mN; time parameters, Ti; and population sizes, Ni. (B) Population tree with embedded gene tree. A mutation on the solid red branch would generate site pattern yn (shown in red at the base of the tree). One on the solid blue branch would generate ynd. Mutations on the dashed black branches would be ignored. “0” and “1” represent the ancestral and derived alleles."

Although they can look at more complex models than some other approaches, there is still a limit to what can be discovered from four genomes. When the model involves many more parameters, many different combinations of those parameters may be found to fit the data.

Rogers, Bohlender, and Huff looked at each possible pattern of shared mutations among the four genomes, and this includes ten possible combinations. Each of them accounts for a fraction of the total number of shared mutations, giving ten values for the model to fit. The model shown above includes nine parameters—the effective sizes of four of the ancestral populations, the proportion and timing of introgression from Neandertal into Eurasian populations, and the times of separation of three of the populations. Trying to include more parameters would result in many different parameter combinations being equally good fits to the model—the problem of overfitting.

So this model does not include every aspect of population history that might have been important to the ancestors of the four genomes. It doesn’t include any introgression from ancestral African modern humans into the Neandertals, for example – such as the mixture that must have given rise to the similarity between modern and Neandertal mtDNA. It also doesn’t include introgression into Denisovans from Neandertals or from a “hyperarchaic” ghost population, both of which have been inferred from other kinds of comparisons. These patterns of mixture might throw off the model.

Each of these phenomena might affect the proportion of shared mutations found in both the Neandertal and Denisovan genomes, and that’s potentially important considering that the time of separation of Neandertals and Denisovans is such an important new conclusion.

The results of Rogers and colleagues’ analysis

What is so interesting about Rogers, Bohlender, and Huff’s conclusions is that they find the separation time between Neandertals and Denisovans to be very close after the separation of the Neandertal-Denisovan ancestral population from ancestral Africans:

Rogers and colleagues Neandertal-Denisovan timeline figure
Model for relationships of ancestral Neandertals, Denisovans, and ancestral African modern humans, according to Rogers and colleagues (2017). This model focuses on timeline and does not portray differences in effective population size.

This figure doesn’t encompass every detail of the model investigated by Rogers, Bohlender, and Huff. Probably most important, I have not depicted the estimates of ancestral effective population sizes for each of these populations. And I haven’t included the proportion of admixture that they estimate between Neandertal and ancestral Eurasian populations. In this figure, I’ve only focused on the timeline.

Rogers, Bohlender, and Huff place the common ancestry of the Neandertal-Denisovan branch and the African ancestors of modern humans at 744,000 years ago. They acknowledge that this estimate depends on assumptions about the mutation rate, and those assumptions leave a lot of possibility for error—for example, they show that a faster mutation rate as preferred by some geneticists (5 × 10−10 per year) gives rise to a more recent estimate of only 616,000 years. That faster mutation rate is probably not correct, but whatever the mutation rate, it’s not appropriate to talk about 3 significant digits with estimates that have so many uncertain inputs. I prefer to say that the date is around 700,000 years.

The most striking aspect of the population model described by Rogers, Bohlender, and Huff is that Neandertals and Denisovans divided into separate populations quite rapidly after their common origin. This conclusion contrasts with earlier thinking, which suggested a slow divergence of Neandertals and Denisovans from each other after their common origin. Rogers and colleagues also find that the effective population size of this ancestral Neandertal-Denisovan population was very small. They propose one explanation is a population bottleneck and founder effect in the origin of these populations.

Another finding, which I have not depicted in the figure above, is that the Neandertal population had a quite large effective population size, maybe as large as 15,000 to 30,000 effective individuals. This also contrasts with previous estimates of Neandertal effective size.

I don’t consider this contrast to be very surprising because this new method is measuring something different from the earlier ones. Both this and earlier estimates are measures of the strength of genetic drift within Neandertals, but previous estimates from Neandertal genomes are measures of inbreeding within the ancestors of that genome, which is strongly affected by local population histories if the Neandertal population was subdivided. If introgression into Eurasians came from a different subpopulation of Neandertals than the Altai genome, Rogers and colleagues’ estimate of effective size will refer to the overall metapopulation of Neandertals, not the local history of the Altai genome, and will therefore be much larger. Neither of these estimates says much about the actual number of Neandertals that walked the earth, the census population size of Neandertals.

A critique based on singletons

How strong is any of this?

Today, PNAS issued a one-page comment by Fabrizio Mafessonia and Kay Prüfer on this work, together with a one-page reply by Rogers, Bohlender, and Huff. Mafessonia and Prüfer suggest that Rogers and colleagues overlooked one category of genetic variation in their analyses, which would affect the results.

As described above, Rogers, Bohlender, and Huff studied the patterns of variations that are shared among genomes, Neandertal, Denisovan, and modern. Mafessonia and Prüfer suggest it is necessary also to look at the genetic variations that are unique within one of the genomes and therefore are not shared among them. They provide a brief analysis that suggests looking at these “singleton” variations changes the results, making the data more consistent with a fairly long time of shared ancestry by Neandertals and Denisovans, and a smaller effective size for Neandertals than Rogers and colleagues had found.

In their reply Rogers, Bohlender, and Huff provide a new analysis that includes the singleton variants, from the exact same genomes used by Mafessonia and Prüfer. They show it is correct that using all the singletons makes a lot of difference to the outcome—although even in this case their results still suggest a much more ancient separation of Denisovans and Neandertals than has previously been found by other researchers.

But the singleton data generate some estimates that cannot be reconciled with the fossil samples. For example, the Denisovan genome includes many more singletons than the Neandertal genome. Looking at these singletons, it would appear that the Denisovan genome must come from an individual that lived much later in time than the Altai Neandertal—maybe within the last 4000 years. This is way off compared to the geological history of the site, which shows the Denisovan genome to be much older. There must be some other source of singletons in this genome other than the mutational history proposed in the population model.

One possible explanation is a factor that the model does not include: the introgression into the Denisovan genome by a “hyperarchaic” ghost population. Some portions of the Denisovan genome accumulated many more mutations than expected because they actually come from a population that diverged from ancestral Africans much earlier in time.

In their reply, Rogers, Bohlender, and Huff conclude that including the singletons is a bad idea because of such possible biases that are not included in the model.

However, this explanation points back to a problem with every method of examining these ancient genomes. A model that includes all the possible interactions between every population has more parameters than can be fitted to the data. Looking at singletons in these four genomes provides four more data points, and may open a view into models that have four more parameters. But to do substantially better, we will need many additional ancient high-coverage genomes, and we will need to look more closely at genetic variation among more modern human populations.

With all this considered, there are good reasons to hesitate before accepting the exact values proposed by Rogers and colleagues. The model is leaving out important aspects of population history.

The fossil record speaks

Still, there is another source of evidence about Neandertal origins, and it also suggests a much earlier timeline than previously thought.

Recent genetic and geochronological findings from the Sima de los Huesos sample show that early Neandertals were not what we once assumed. Meyer and colleagues (2016) showed that Sima de los Huesos nuclear genetic samples are unambiguously close to Neandertals, and not closely connected with Denisovans. This shows that the Neandertal and Denisovan populations must already have separated substantially earlier than the deposition of the Sima de los Huesos hominin remains.

The geochronology of Sima de los Huesos provides evidence that the fossils were deposited around 430,000 years ago (Arsuaga et al. 2014; Arnold et al. 2014). Meyer and colleagues (2016) looked at the chronology from the point of view of genetic data and came to a somewhat weaker conclusion:

Although it is difficult to determine the age of Middle Pleistocene sites with certainty, geological dating methods, as well as the length of the branches in trees relating the mtDNAs from femur XIII and an SH cave bear to other mtDNAs, suggest an age of around 400,000 years for the SH fossils. This age is compatible with the population split time of 381,000–473,000 years ago estimated for Neanderthals and Denisovans on the basis of their nuclear genome sequences and using the human mutation rate of 0.5 × 10−9 per base pair per year. This mutation rate also suggests that the population split between archaic and modern humans occurred between 550,000 and 765,000 years ago.

That is, the mtDNA timeline gives approximately the same result as the geochronology, placing the fossils’ age around 400,000 years, and Meyer and colleagues suggest this does not contradict the notion that Neandertals and Denisovans parted ways only between 381,000 and 473,000 years ago.

I disagree. The population split must in fact be substantially earlier than the fossils’ age to give rise to the pattern of shared alleles between the Sima de los Huesos sequences and the later Neandertals.

One way of looking at how early the Neandertal population arose would be to examine the number of derived alleles shared by the Sima de los Huesos genetic data and the other Neandertals, in comparison to modern humans and the Denisovan genome. This is not straightforward, though, because if the Neandertals or the Neandertal-Denisovan ancestral populations did experience bottlenecks and founder effects, the Neandertals will share a higher fraction of derived alleles as a result of suppressed incomplete lineage sorting, in addition to new mutations early in their evolutionary history. The Sima de los Huesos specimens show around 40 percent derived allele sharing with later Neandertals, in comparison to 70 percent or more derived allele sharing of later Neandertal specimens with each other. By contrast, the Sima de los Huesos specimens share only around 7-9 percent derived alleles with Denisovans—way less than they share with Neandertals, and reflecting a substantial shared history of drift between the Sima de los Huesos and later Neandertal samples. All this shared drift happened earlier than 400,000 years ago, but it’s not clear how much of that shared drift is sheer time, and how much may have occurred quickly during bottlenecks.

Genetic structure within the ancient Neandertals makes a difference. The later Neandertal population had strong regional differences separating the Altai and other genetic samples. The Denisovan population also seems to have had strong regional structure, reflected in the differences between the present-day introgressed sequences and the Denisovan genome we have from the fossil record. Did the earlier Neandertal population also have strong regional structure? If so, the Sima de los Huesos population itself may have been quite distinct from other contemporary Neandertals, and the shared ancestry of these regional populations might have preceded the Sima de los Huesos deposition by a hundred thousand years or more. If all the shared derived alleles of Sima de los Huesos and later Neandertals date to earlier than 500,000 years ago, their common evolution must have started even earlier.

Another element of the quote from Meyer and colleagues is their use of the higher 5 × 10−10 per year mutation rate, compared to 3.8 × 10−10 used by Rogers, Bohlender, and Huff. A 25 percent higher mutation rate obviously leads to a 25 percent lower estimate of genetic divergence.

All of this suggests that the separation time of Neandertals and Denisovans was indeed quite a bit older than most sources have suggested up to now. I do not believe that the estimate of separation time proposed by Mafessonia and Prüfer (2017), only around 460,000 years ago, can possibly be true. If Sima de los Huesos actually dates to around 430,000 years ago, as in the present geological chronology, the previous genetic estimates are simply too young.

The value for Neandertal-Denisovan separation time reported by Rogers, Bohlender, and Huff is one possibility, placing this divergence almost as old as the initial separation of the Neandertal-Denisovan ancestral population from ancestral Africans. That means Neandertals have existed as a population for more than 700,000 years. Or, as Rogers, Bohlender, and Huff find in their singleton analysis, the Neandertal-Denisovan separation time might be as recent as 630,000 years ago.

More recent than this seems doubtful in light of the shared genetic history of Sima de los Huesos and later Neandertals. But where to draw a line indicating a minimum possible date for the Neandertal-Denisovan separation is not clear.

What is clear is that the origin of Neandertal and Denisovan populations is much older than previously assumed. And that timeline makes a hash out of many long-standing ideas about the fossil and archaeological records. I’ll be writing about some of these ideas over the next few weeks, with some ideas about where the science must go next.

References

Arnold, L. J., Demuro, M., Parés, J. M., Arsuaga, J. L., Aranburu, A., de Castro, J. M. B., & Carbonell, E. (2014). Luminescence dating and palaeomagnetic age constraint on hominins from Sima de los Huesos, Atapuerca, Spain. Journal of human evolution, 67, 85-107.

Arsuaga, J. L., Martínez, I., Arnold, L. J., Aranburu, A., Gracia-Téllez, A., Sharp, W. D., ... & Poza-Rey, E. (2014). Neandertal roots: Cranial and chronological evidence from Sima de los Huesos. Science, 344(6190), 1358-1363.

Hawks, J. (2017). Neanderthals and Denisovans as biological invaders. Proceedings of the National Academy of Sciences, 201713163. doi:10.1073/pnas.1713163114

Meyer, M., Arsuaga, J. L., de Filippo, C., Nagel, S., Aximu-Petri, A., Nickel, B., ... & Viola, B. (2016). Nuclear DNA sequences from the Middle Pleistocene Sima de los Huesos hominins. Nature, 531(7595), 504-507.

Rogers, A. R., Bohlender, R. J., & Huff, C. D. (2017). Early history of Neanderthals and Denisovans. Proceedings of the National Academy of Sciences, 114(37), 9859-9863. doi:10.1073/pnas.1706426114


This is a nice piece in ChronicleVitae by Terry McGlynn: “Why Blogging Is Still Good for Your Career”.

Regardless, in every field, scholars run academic blogs that reflect the professional discourse, and sometimes those blogs will drive the broader conversation. Even if you don’t read academic blogs, they may be driving the conversation in your discipline. It typically takes several months for traditional peer-reviewed journals to publish research and then publish rebuttals and responses. In blogs, the same kind of academic conversation can take place over the course of days, or even hours.

I find that the blogging environment has changed enormously since Facebook became ubiquitous. People are discussing blogs and blog posts in their own networks with other professionals. Those conversations often happen in places separate from the blog posts themselves, and not followed by the blog author.

I think that’s generally healthy, because it enables people to talk (really, write) through issues with people they know and trust.

But these decentralized conversations within the discipline have a big downside. What seems like “common knowledge” actually may only be shared among a small group of people, and they reinforce each other’s voices like an echo chamber.

I’ve spent less time blogging during the last couple of years, because my fieldwork and research commitments have taken a lot of my energy. But I can say that blog posts—whether here or at Medium—are having a greater readership and impact than ever before.

Dinosaur phylogeny woes

This is a nice write-up by Laura Geggel of a current exchange of comments in Nature about dinosaur phylogeny: “Dino Family Tree Overturned? Not Quite, But Changes May Lie Ahead”.

The upshot is that last spring, Matthew Baron and colleagues (2017) claimed that the traditional groupings of dinosaurs were all wrong. For more than a hundred years, paleontologists have grouped theropods together with sauropods, as “saurischians”, based on pelvic morphology. Baron et al. suggested that the theropods are instead relatives of the ornithischians—including duckbills and ceratopsians.

These branches are within the deepest part of the dinosaur phylogeny, and many of the fossil groups in the dataset lived much later and have many derived traits that would have been absent in their common ancestors. This makes it harder test their relationships than one might expect. The problem is analogous to determining relationships among the very deepest nodes of the mammal phylogeny—for example, do we group together primates, bats, and rodents into a higher level taxon, and are insectivores really a single group? Paleontologists have radically revised some ideas about early mammal diversification in the wake of genetic comparisons of living species, because these relationships just are not well reflected by morphological traits. For dinosaurs, there are no genetic comparisons, and we shouldn’t be very surprised that morphology might not be a straightforward indication of the deepest relationships.

But the new exchange of comments, initiated by Max Langer and colleagues, shows that the dinosaur phylogeny is not going to be overturned easily. In their assessment, Baron and coworkers scored some characters incorrectly. They suggest that the correct data still support the traditional hypothesis that connects the theropods and sauropods.

I don’t have any deep insight about dinosaur phylogeny. But I am interested in the case because it reflects a singular problem with phylogenetic analyses that we are also seeing expressed in the study of hominin relationships.

Many empirical sciences are going through a “replication crisis”, as statisticians are showing that studies are systematically underpowered and results driven by false positives and p-hacking. We can’t precisely compare phylogenetic methods to the kind of statistical analyses underlie many hypothesis tests in other branches of science.

But something very similar is true in phylogenetics. Scientists working on fossil relationships are working with sparse data matrices, many key taxa are very poorly represented, with samples that often include only a single individual, and many interesting questions involve deep nodes. The advent of genetics in the phylogenetics of mammals, birds, and many other groups has shown just how badly morphological data represent deep relationships.

The adoption of Bayesian methods has helped a bit, in that the Bayes factor provides at least a way of saying that the data don’t clearly distinguish hypotheses from each other. I think that today many scientists working on hominin relationships have a fairly healthy attitude, that we just do not know how some key species should be arranged in a phylogeny.

Certainly we face that problem with species like Homo naledi and Australopithecus sediba. These species are exceptionally well represented across the skeleton by fossils, but their placement cannot be determined with any confidence except in very broad terms.

For dinosaurs, I expect that this phylogenetic problem will continue for quite a while, as the current exchange shows that the phylogenetic methods are very sensitive to small changes in the datasets.

References

Baron, M. G., Norman, D. B., & Barrett, P. M. (2017). A new hypothesis of dinosaur relationships and early dinosaur evolution. Nature, 543(7646), 501-506.

Langer, M. C., et al. (2017). Untangling the dinosaur family tree. Nature 551, E1–E3.

The ad that started the Human Genome Project

Via Jay Shendure, who shared this ad on Twitter this weekend:

Original advertisement that brought in the donors for Human Genome Project (Buffalo News, 3/23/1997), h/t Pieter de Jong, who placed the ad
Buffalo News ad for Human Genome Project

People who worked with HGP data in the early days will remember how the entire genome appeared to be designed by committee. Genetic samples from around thirty people were ultimately included, so different parts actually reflected the genetic heritage of entirely different individuals.

These were chosen to be “representative” of the genetics of the U.S., meaning that some parts of the draft genome were African in ancestry, most were European, and a few were Asian. But the identities of the individuals were anonymous, and the first draft of the genome was being completed at a time when the diversity of most parts of the genome was unknown (by definition, since they hadn’t ever been sequenced in anybody!).

Given the incredible expense of the project, I think this was an appropriate (if unavoidable) decision, but it did make some kinds of population genetic analysis very difficult to carry out. In genetics, how variation was first identified–the “ascertainment” of a variant–exerts a statistical bias on results. To understand the significance of variations, first it is necessary to know the direction of this bias. Many of us did a lot of complicated modeling to try to work around this aspect of the Human Genome Project draft.

The decision had a legacy that lived on for the first few generations of microarrays, because the single nucleotide polymorphisms (SNPs) that these microarrays tested were found in human samples that were initially very small, many of them HGP samples. When applying a microarray to individuals from a population, it is very important to know whether the SNPs were ascertained within the same population or a different population–a microarray will always miss rare variation in a sample, but it will miss much more common variation in a sample from a different population than the ascertainment sample.

Over time, microarray SNPs began to be ascertained on broader samples of populations, and resequencing–especially the 1000 Genomes Project–began to address the problems of representation that were insoluble in the HGP. But it’s interesting to see this historical ad that put into motion a long-lasting statistical problem.

Link: The discovery story of the LB1 skeleton

Paige Madison pointed me today to her post from 2015 recounting the discovery of the LB1 skeleton, from Liang Bua, Flores: “The Moment the Hobbit was Discovered”. Better known as the type specimen of the species, Homo floresiensis, the first description of the fossil was published on this day in 2004.

LB1 skeleton cast
Cast of LB1 skeleton, at Belgian Academy of Sciences. Photo: Ghedoghedo (CC-BY-SA 4.0, Wikimedia Commons)

Recent, unconsolidated sediments like those in the Liang Bua cave are among the most challenging situations to excavate skeletal remains, and the story of this discovery emphasizes those challenges. Madison also discusses the way that chance was involved in the discovery. All in all, fascinating context.

Link: How scientific societies are moving to combat sexual harassment

Cris Russell has a very strong piece in Scientific American covering the ways that some scientific societies are responding to combat sexual harassment and assault in scientific fields: “Confronting Sexual Harassment in Science”.

She focuses on quotes from Marcia McNutt, former editor of Science, now president of the U.S. National Academy of Sciences, and recent statements from the American Geophysical Union.

The AGU is also part of a new collaborative research project, funded by a $1.1-million, four-year grant from the National Science Foundation Foundation, that will update the teaching of research ethics by addressing sexual harassment as scientific misconduct. Led by University of Wisconsin–Madison researcher Erika Marín-Spiotta, the project will produce more effective training materials in Earth, space and environmental sciences that may serve as a model for other STEM fields. This includes development of tested bystander intervention workshops to help academic leaders respond to and prevent sexual harassment. There is limited data on the effectiveness of existing training programs and a sense that many were designed primarily to meet legal liability concerns.

I was really happy to read in Russell’s article that my own university, the University of Wisconsin-Madison, has a leadership role in the AGU effort to update teaching of research ethics. In my experience during the last several years, UW-Madison has been uniform in its message, from the Chancellor’s office through all levels of administration, that sexual harassment is unacceptable. The workplace training (required of all employees) on sexual harassment and assault is in my view very effective, and this year it is being supplemented by workshops to address implicit bias. In other words, I think my institution is very serious in its response to these issues.

But many of the problems in science are trans-institutional. Sexual harassment and assault often happen in settings removed from formal workplaces like universities and research institutes. Fieldwork is a special problem in anthropology and archaeology, with many practitioners adopting an attitude that “what happens in the field, stays in the field”. Professional conferences have also been locations where harassment and assault occur outside the bounds of their institutions. Professional associations can make a difference, by reinforcing professional standards of conduct among researchers outside of their own institutions.

Sexual misconduct graphic from Science story
Figure from Science magazine story on sexual misconduct in anthropology, illustrating some results of the Survey on Academic Fieldwork Experiences (2014).

The American Association of Physical Anthropologists responded strongly to this issue starting in 2015 and 2016, and I’m proud of the association for its strong stance. That response came in the wake of reported cases of sexual harassment at the professional conference of the AAPA. Another professional meeting, that of the European Society for the Study of Human Evolution, was the occasion of an alleged case of sexual assault in 2014.

Sexual harassment, assault, and other abuses during anthropological and archaeological fieldwork have driven talented people out of anthropology and archaeology for years. I have heard first-hand accounts of some of these abuses from colleagues, and I believe their personal stories. I have heard many more rumors of abuses second-hand or third-hand from many people—often with corroborating details that suggest that they are true. I have also seen directly the effects of misogyny and implicit bias by scientific referees, both as a coauthor of papers and as an academic editor.

I’m pleased that NSF is spending money to help develop better training in professional ethics and to study the effects of that training. It is important to the future of science that students and postdoctoral trainees be given the tools to defend themselves from professional misconduct of all kinds. It would be helpful for professional associations to develop ombudperson positions to help trainees find solutions when they are subjected to harassment and assault.

But I would further encourage NSF to investigate how it has awarded funds to abusers in the past.

We know from the 2014 SAFE study that harassment and assault have been very common in recent and existing field programs in archaeology and anthropology. Millions of dollars of funding have gone to researchers who maintain field projects that are widely rumored to be sites where abuses have happened for years. Researchers have used this support to intimidate and silence the targets of their abuse, and have evaded scrutiny from institutions because of the federal dollars they bring in (“Why do universities cover up high-profile harassment? Look for the money”). Meanwhile, the institutions who received 50% or more overhead on these NSF grants did not maintain minimal levels of professional standards by the site directors.

I hope that more of these stories will be made public so that the broader community of scientists can acknowledge this history and commit to stop covering up the unethical and immoral behavior by supposed leaders in the field.

Why is open science important in archaeology?

Are you curious about open science, but don’t really know what it means? The September issue of The SAA Archaeological Record includes an article that reviews “open science” approaches in archaeology: “Open Science in Archaeology”.

Marwick et al 2017 article header

This article was the brainchild of Ben Marwick, who has helped to organize the new Open Science Interest Group within the Society for American Archaeology. I’m proud to be able to support and participate in this group, and to have joined with 48 other professionals in this paper. I work at all levels within my scientific research to advance the principles that the article describes.

The paper begins with a short discussion of what “open science” actually means.

Often described as “open science,” these new norms include data stewardship instead of data ownership, transparency in the analysis process instead of secrecy, and public involvement instead of exclusion.

I approve strongly of this definition. Open science is nothing radically new, it reflects a recognition that responsible scientific approaches lie at one end of an axis, where the opposite end is really an antiscientific attitude of exclusion.

The new paper refers to open access (in publication), open data, and open methods. All these tend to increase transparency and replicability in the production of knowledge.

I would add a couple of aspects that the article doesn’t discuss in detail.

Archaeological sites are not merely data sources, they are physical places. Allowing colleagues and the public to see the sites and inspect work at sites is part of providing confidence and transparency in archaeologists as stewards of heritage. That access can be provided today with technology, as many projects (including our Rising Star project) are doing. Or access to sites can be provided in cooperation with national heritage authorities through responsible tourism and site visits.

Scientific projects are complex social undertakings that involve power and funding, and open collaboration may be just as important as open methods and open data in providing transparency of scientific processes.

Some people have the misconception that open approaches are less rigorous compared to approaches that involve long gestation of ideas in relative secrecy. Unfortunately, this misconception is still actively promoted by a few irresponsible scientists. Spreading such a misconception is much like the strategy of “fear, uncertainty, and doubt” that was once deployed by software companies in their battle for market share against open source software projects.

In my experience, open approaches are more rigorous than secretive ones. Open approaches rely strongly upon establishing transparent methods that emphasize replicability. When researchers follow through on a commitment to provide the data that underlie their analyses, they provide the means for independent researchers to check their results and conclusions. It’s a basic principle of scientific credibility: Conclusions that cannot be checked should not be believed.

The new paper is available and is a great resource. I know that academic articles about how to do academic work are not always exciting, but these articles are necessary to build the scholarly background for changing practices, especially in building support for responsible practices among institutions and grant agencies. I applaud the Society for American Archaeology for supporting this initiative.

There is no such thing as inertia—some people and institutions actively maintain processes that exclude colleagues and the public. Let’s subject those practices to examination and let institutions justify them if they are necessary. Meanwhile we must make the real costs of closed systems explicit, not hide them.

More: I’ve long been an advocate for open data practices, which I describe in my white paper, “Public interests in data from federally funded research”