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john hawks weblog

paleoanthropology, genetics and evolution

Photo Credit: Hand remains of Homo naledi. John Hawks CC-BY 2.0

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.


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.


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”

Nature Genetics wants more context for citations, but doesn't notice that bad context comes from word limits

Nature Genetics has a remarkable editorial in the current issue that makes a point of criticizing citation practices by authors in “articles we have recently published”: “Neutral citation is poor scholarship”.

Neutral citation, for example, “this field exists (refs. 1–20),” may on the face of it seem to be a fair practice, giving evenhanded and minimal citation credit to a range of preexisting works as background to the current report. But it can also be malpractice, artificially inflating the metrics of irrelevant or trivially related works by including them in lists of relevant publications....

Those are tough words. The editors’ recommendation is to add more background that gives necessary context for cited works:

Best citation practice is to summarize the claim made in the cited work without distorting whether it was of cause, correlation or conjecture, much as you would for your own findings (Nat. Genet. 47, 305, 2015). The relevant reasons for citing pertinent publications should also be introduced early in the article rather than discussed as late afterthoughts. This best practice will often entail making statements that are strongly supported by prior publications in the background introducing your findings. We believe this is key to writing research papers with impact that can benefit from peer review, as it encourages explanation of the knowledge gap that motivates the research as well as clear explanation of the conceptual advances made by the main findings of the new research.

I’m strongly in favor of this approach. But take a look at the Instructions to Authors that lists the requirements for papers submitted to Nature Genetics:

The text is limited to 1500 words, excluding the introductory paragraph, online Methods, references and figure legends.
An Article is a substantial novel research study, with a complex story often involving several techniques or approaches. The main text (excluding abstract, online Methods, references and figure legends) is 2,000-4,000 words.

In a letter with only 30 references, giving appropriate context for each reference would take up 500 words – a third of the paper. For an article of 2000 words with 100 references, it would likewise take up 2/3 to half of the paper.

I agree that adding better context for citations would make better scientific papers. Adding more citations would in many cases improve papers. This is especially true in areas of science that lie on the boundaries of disciplines, where many readers will need more context to understand where citations fit together. But word limits and limits on citation number prevent authors from adding such context!

If Nature Genetics wants well-written and well-referenced papers, it is going to have to change its editorial practices to enable authors to spend the necessary words.

I especially like citation formats that enable authors to add context in the reference section—listing a short synopsis or reason why each citation is valuable. Books have that kind of facility in endnotes, so it’s a fairly widespread practice in scholarship. More scientific journals should enable greater context in references.

Reviewing the September excavation in the Rising Star cave system

In September, the team was underground in the Rising Star cave system, working at new excavations in the Lesedi Chamber and Dinaledi Chamber. I posted updates on the excavation goals and the progress at the end of the month on Medium, and I thought it would be helpful to provide links to those articles here.

A look at some photos of the live National Geographic Classroom events in the Rising Star cave system
A look at some photos of the live National Geographic Classroom events in the Rising Star cave system

In the first week, I reviewed the hypotheses that we set out to test: “Renewed excavations in the Rising Star cave”. Key among them is the formation of the fossil assemblage within the Dinaledi Chamber.

Many people have been curious whether some other entrance to this chamber may have existed in the past. So far, geological work in the chamber has found no other passage that might have allowed hominins or their bodies to get in. There’s a lot of evidence that the chamber must have been very inaccessible when the remains of H. naledi arrived — especially the clear difference in sediment composition between Dinaledi and other nearby chambers, and the lack of evidence for any other medium or large animal remains. It appears that the hominin remains must have entered the chamber in the same way we do today, down this Chute.
But nearly all of the hominin remains so far come from a tiny area of excavation, only 0.8 square meters, at the far end of the chamber more than 10 meters from the Chute.

Later, near the end of the excavation work, I reviewed some of the discoveries the team made: “What we’ve learned from the Rising Star cave system this month”. Perhaps the most interesting is a feature just beneath the base of the Chute:

This mass of bone has emerged just at the base of the Chute, the narrow entry channel into which the team enters the chamber. After weeks of careful excavation, the team can now see part of an articulated hand, ribs and a possible shoulder, even some teeth in what appears to be proper anatomical order.
This may be the partial skeleton of a single hominin individual. We do not know how much additional bone may yet remain just beneath the surface.

It will be some time before we know fully the results of the September excavation. We will need to consolidate the feature at the base of the Chute and extract it to the laboratory for further preparation. We will also need to get chemical and biological results back from samples taken inside both chambers. We have a lot of work ahead of us, and we’ll update as we can.

New project to scan vertebrate diversity across thousands of samples

Science reports on a new initiative to provide 3D scan data on thousands of vertebrates: New 3D scanning campaign will reveal 20,000 animals in stunning detail.

Then last year David Blackburn, a herpetologist at the Florida Museum of Natural History in Gainesville, saw Summers's #scanAllFish hashtag on Twitter and light-heartedly countered that he would "scan all frogs." Blackburn had just chatted with museum curators about starting a new digitization effort, so he also called Summers. They decided to up the ante and seek money to "scan it all." Now they have $2.5 million in National Science Foundation funding in hand, and on 1 September they will launch their project: oVert, for "Open Exploration of Vertebrate Diversity in 3D." Many scientists simply know it as the "scan-all-vertebrates" project.

The project, “oVert”, will deposit scans of museum specimens onto the MorphoSource site, so that people can download and use them. This is a tremendous win for open access science, and for the value of the MorphoSource repository.

Can you imagine the projects that will be fueled by this dataset? Amazing. It will become an essential morphological research tool–probably setting a new standard. And it will enable massive educational projects.

This is such an effective expenditure of money, and I wish that other NSF-funded projects would follow this model!

Arguing about species: Is it evidence, or ego?

For some people who follow human evolution news, recognizing “species” is really just about whether you’re a lumper or a splitter. Many people assume that the names of species are about ego, not evidence.

But nature presents us with real challenges, which still cause different scientists to approach the past with different assumptions. Let me give some examples.

Just today, I got notification of a new paper by Walter Neves and colleagues, in which they suggest that Australopithecus sediba and Homo naledi are actually South African representatives of Homo habilis. Some people might scoff at this—after all, the Dinaledi fossils are only 236,000–335,000 years old, while the latest-known H. habilis is around 1.6 million. But a young date for some fossils doesn’t bar them from from membership in a species with much older fossil representatives. Identity is tested with morphological evidence, not geological age.

Now, I disagree with the idea that H. naledi is the same species as H. habilis—Neves and colleagues have come to this taxonomic conclusion by neglecting all the morphological evidence showing H. naledi is different from H. habilis. But it’s not so easy to reject the idea that these species might be close relatives. As we pointed out earlier this year, H. naledi might even conceivably be a descendant of H. habilis or Au. sediba. On the other hand, Mana Dembo and colleagues showed last year that H. naledi seems to be closer to modern and archaic H. sapiens than to H. erectus (and much closer than H. habilis). These are stark differences of interpretation, from similar parts of the skeleton.

[see my article: “The plot to kill Homo habilis”]

At the other extreme, this week Jeffrey Schwartz is set to present results of his own examination of the Dinaledi Chamber sample. According to his abstract, all the teeth belong to one species, but some of the skulls represent another—two species in this assemblage, not just one. I haven’t seen the details of this analysis, but I’m pretty sure I disagree with this one, too.

I admit that it would be fatuous to say that ego plays no role in paleoanthropology. Scientists express provocative opinions that will draw attention from the press.

Still, the trouble with taxonomy isn’t just about new fossil discoveries like H. naledi or Au. sediba. We have seen similarly broad and vociferous diversity of opinions in the last few years about H. erectus, Au. deyiremeda, Au. anamensis, H. floresiensis, Denisovans, Neanderthals, H. heidelbergensis, Kenyanthropus platyops, and others. These are species new and old, and the same issues keep arising again and again.

Many people would say that taxonomic debates just reflect basic philosophy about variation—again, lumping versus splitting. But that’s really only one of the dimensions:

  1. How much variation should a species include? Broadly, all of us recognize that some species are polytypic (as humans are today), but small and fragmentary samples make it very hard to distinguish polytypy from distinct species. There are living polytypic species that include very extensive variation, and living sister species that barely differ from each other, making model selection difficult.

  2. What kind of data provide evidence of similarity or difference? Some researchers rely mainly on phenetic similarity measures, using geometric morphometrics, principal components or canonical variates approaches. Others examine discrete (or threshold) traits, counting shared derived traits as evidence of similarity and ignoring shared primitive traits. This group was once dominated by cladists, but in recent years Bayesian approaches have become more and more common.

  3. What temporal or geological information is sufficient to justify pooling fossil specimens into a single sample? Some scientists are willing to assume that fossils from the same 500,000-year period belong to a single population, even if they preserve different parts of the skeleton or minimally overlap. Others draw trees that separate every specimen into its own “operational taxonomic unit”. The concept of a paleodeme is based on lumping specimens by date and geography, an approach that has come more and more into question as the fossil record increases.

Anthropologists may get a bad rap from other biologists for arguing about taxonomy so much, but in reality many areas of taxonomy are undergoing seismic shifts following more widespread application of genetics and phylogeographic analyses.

For example, bovid systematists have been arguing for the last few years about whether to double the number of species they recognize—a debate about living species with abundant morphological samples and genetic data. Meanwhile, living and fossil elephants are on the verge of a complete revision of relationships, based on ancient DNA and the appreciation of deep diversity between forest and savanna African populations. Similar examples are unfolding across mammalian systematics.

Neandertal and Denisovan DNA has shown us that hominins also exchanged genes by introgression, occasionally but recurrently despite hundreds of thousands of years on their own trajectories. Genetic evidence of African “ghost lineages” means other long-lasting Pleistocene populations once existed.

H. naledi might potentially be one such lineage. I have no idea what the closest relative of H. naledi will prove to be. Whether it reproduced with human populations or not, it shows that many cherished human features may not have been uniquely derived evolutionary developments.

I can’t help but feel that we are standing at a special moment in the history of paleoanthropology. New data give us the opportunity to make progress on old areas of disagreement about species and phylogeny. We have to start by taking what we now know about the later Pleistocene, and seriously appling these lessons to earlier periods of human evolution. Our assumptions about the past really are changing.

Whatever we choose to call species won’t change their nature. But our assumptions determine the way we frame our future studies, including our attempts to find more fossil evidence. That makes it important to communicate clearly about what these ancient species mean, both with each other and with the public.

Is there a trade-off between publication impact and open approaches?

Two Dutch biomedical researchers discuss how they are trying to move their institution away from mere quantity of research and citations, and toward real clinical impact: “Do our measures of academic success hurt science?”. They begin their essay with a scenario that reminds me of human evolution research:

A Ph.D. student wants to submit his research to a journal that requires sharing the raw data for each paper with readers. His supervisors, however, hope to extract more articles from the dataset before making it public. The researcher is forced to postpone the publication of his findings, withholding potentially valuable knowledge from peers and clinicians and keeping useful data from other researchers.

I agree with much of what they say in this essay. But I think their opening scenario doesn’t really express a trade-off they are trying to illustrate between an artificial measure of “impact” and real impact.

What we keep finding in human evolution research is that sharing the data leads to higher impact. Papers are published faster, they are cited more widely, and they lead to career advancement for the authors.

It is true that some scientists try to keep datasets private so that other researchers cannot replicate their work. But that is counterproductive to their own research, not only to the field. Researchers who are publishing slowly, not distributing data in a way that can be inspected and used, are not achieving publications, citations, or “impact” even in the artificial, publication-oriented sense.

Using open approaches is not just the way to advance science and its impact on the public, it is also the way to advance careers. There is no trade-off here, not that I’ve experienced at least.

Genomes of straight-tusked elephants

Earlier this month in eLife, Matthias Meyer and colleagues published a cool paper: “Palaeogenomes of Eurasian straight-tusked elephants challenge the current view of elephant evolution”.

The straight-tusked elephants lived in Europe and western Eurasia as far east as India during the Pleistocene. Most people are familiar with other extinct elephant relatives, such as mammoths or mastodons. The straight-tusked elephants were not mammoths, and they are assumed to be much more like living elephants because they seem to have entered more northerly parts of Europe mainly during interglacial times. Paleontologists have noted that the straight-tusked elephants share some morphological features with Asian elephants, as mammoths do also. For some paleontologists, these similarities are so compelling that they have classified Palaeoloxodon as part of the Asian elephant genus, Elephas.

Ancient DNA evidence is breaking open the study of how the extinct relatives of living elephants moved, interacted, and evolved. First mitochondrial, and more recently nuclear gene sequences have revealed different populations that lasted more than a million years, yet hybridized and mixed where they met.

During the last ten years, it has become clear that populations of elephants in the central African forest have a long history as an evolving lineage distinct from savanna elephants across most of Africa. Forest and savanna elephants have increasingly been recognized as two species, Loxodonta cyclotis in the forest, and L. africana across the rest of Africa. No fossils have been attributed to L. cyclotis, and the L. africana fossil record is quite sparse up until 20,000 years ago.

Before that time, Africa itself was rich in elephants attributed to Palaeoloxodon, especially P. recki, which many sources identify as Elephas recki. P. recki has been identified from fossils as early as 4 million years ago, and as late as 300,000 years ago. Other African species of Palaeoloxodon (again, often classified as Elephas) have been interpreted as part of a single P. recki lineage, including the earlier P. ekorensis and the later P. iolensis. P. iolensis survived until around 30,000 years ago. With so many species identified as Elephas or closely related to Elephas, and with mammoths sharing so many features with living Asian elephants, the basic idea has been the Asian elephant branch of the elephant phylogeny was once global, with mammoths spread across the northern tier of Eurasia and across the Americas, extinct P. antiquus in western Eurasia, extinct P. namascus further east in Asia, and extinct P. recki in Africa. Paleontologists have speculated that P. namascus may itself be the immediate ancestor of living Asian elephants, Elephas maximus. Living African elephants, Loxodonta, were the odd elephants out.

Meyer and colleagues obtained mitochondrial genomes from four individuals of P. antiquus, three of them from Neumark-Nord, Germany, and one from Weimar-Ehringsdorf, Germany. They find that the Neumark-Nord elephants probably date to the last interglacial, around 120,000 years ago, while the Weimar-Ehringsdorf elephant dates to the previous interglacial, around 230,000 years ago. This is a pretty small section of the overall geographic range covered by P. antiquus:

Palaeoloxodon sites across western Eurasia, from Meyer et al. 2017
Figure 1 from Meyer et al. 2017, original caption: "Palaeoloxodon antiquus, geographic range based on fossil finds (after Pushkina, 2007). White dots indicate the locations of Weimar-Ehringsdorf and Neumark-Nord."

What they found was that P. antiquus mitochondrial genomes are not related to Elephas at all; they’re related to forest elephants:

Surprisingly, P. antiquus did not cluster with E. maximus, as hypothesized from morphological analyses. Instead, it fell within the mito-genetic diversity of extant L. cyclotis, with very high statistical support (Figure 2). The four straight-tusked elephants did not cluster together within this mitochondrial clade, but formed two separate lineages that share a common ancestor with an extant L. cyclotis lineage 0.7–1.6 Ma (NN) and 1.5–3.0 Ma (WE) ago, respectively.

That’s not a small difference. Living Asian and African elephants came from a common ancestral population more than 6 million years ago, during the Late Miocene. They are about as different from each other genetically as humans and chimpanzees. The fossil story was just wrong–and it’s as big a difference as misidentifying a Neanderthal as a fossil chimpanzee.

Elephant phylogeny from Meyer et al. 2017
Elephant mtDNA and nuclear DNA phylogeny from Meyer et al. 2017. The Neumark-Nord (NN) and Weimar-Ehringsdorf (WE) straight-tusked elephants are indicated. The mtDNA tree has a time scale (bottom) but the nuclear DNA tree has no time scale associated with it.

All four of the P. antiquus mitochondrial lineages are on the same branch as living forest elephants, and in fact some forest elephants have mtDNA genomes that are closer to P. antiquus than to some other forest elephants. In other words, the mitochondrial genomes of P. antiquus fall within the variation of L. cyclotis. Within this variation, the mitochondrial lineage of the earlier Weimar-Ehringsdorf elephant is part of a different clade than the three Neumark-Nord elephants, so the P. antiquus mitochondrial genomes are not a monophyletic group.

Now, we might well expect the story with the nuclear genome would be different for elephants. We know that the story for Neandertals is different considering the mitochondrial and nuclear genomes: the Sima de los Huesos nuclear genome groups clearly with later Neandertals even though the mtDNA of later Neandertals is more similar to that of living humans.

There is another reason why elephant nuclear and mtDNA genomes might be discordant. In humans, mtDNA is markedly less diverse than most parts of the nuclear genome, and mtDNA types occur across wide geographic areas. Elephants are the opposite. Their mitochondrial DNA exhibits substantially greater variation among populations than the average for the nuclear genome, because female elephants very rarely transfer between groups. Most gene flow in elephants is male-mediated, and male elephants sometimes disperse over very long distances. These contrasting patterns of nuclear and mitochondrial diversity in elephants are consistent enough to provide a way to “triangulate” the region that ivory samples originated (Ishida et al. 2013).

Meyer and colleagues cannot assess yet whether the Weimar-Ehringsdorf elephant would yield a divergent nuclear genome, because they didn’t get nuclear evidence from it. But two of the Neumark-Nord P. antiquus specimens yielded nuclear genome data and they are a close sister group compared to all the forest elephants. That is, the African forest elephants were much broader in their mtDNA phylogeny, and tighter together in their nuclear genome, just as one would expect from the mass of evidence about them.

Palaeoloxodon antiquus tooth, by Khruner (Wikimedia)
P. antiquus tooth. Photo credit: Khruner, CC-BY.

So, that’s an interesting data point about elephant evolution. A widespread extinct species of elephant, which on morphological grounds was interpreted as an Asian elephant relative, is actually related to forest elephants within Africa. Forest elephants today are a relative island species in central Africa, surrounded by savanna elephants. So from today’s standpoint, forest elephants look like a geographic and phylogenetic relict of a much more diverse lineage that once existed.

We already know that today’s situation did not exist earlier in the Pleistocene. In the past, many parts of Africa were inhabited not by today’s savanna elephants but instead by other extinct species, for much of the Early and Middle Pleistocene, P. recki. Savanna elephants are found in the fossil record as early as 500,000 years ago, but they are a relatively rare component of the elephant diversity in comparison to the extinct Palaeoloxodon species.

Of course, without ancient DNA evidence, it’s not certain that these other extinct Palaeoloxodon species are closely related to the forest elephants and P. antiquus.

Furthermore, the nuclear genome evidence presented by Meyer and colleagues does not establish whether the P. antiquus population may have exchanged genes with Asian elephants, thereby accounting for some of its anatomical resemblance to them. Hybridization has already been found to be widespread among the varieties of mammoths, and it continues to occur between savanna and forest elephants despite what appears to be a multi-million year separation. We might expect the same of other extinct elephant species.

When Eleftheria Palkopoulou presented on some of these data at a conference in 2016, she did talk about hybridization. Ewen Callaway reported on that conference presentation at the time: “Elephant history rewritten by ancient genomes”.

Palkopoulou and her colleagues also revealed the genomes of other animals, including four woolly mammoths (Mammuthus primigenius) and, for the first time, the whole-genome sequences of a Columbian mammoth (Mammuthus columbi) from North America and two North American mastodons (Mammut americanum). The researchers found evidence that many of the different elephant and mammoth species had interbred. Straight-tusked elephants mated with both Asian elephants and woolly mammoths. And African savannah and forest elephants, who are known to interbreed today — hybrids of the two species live in some parts of the Democratic Republic of Congo and elsewhere — also seem to have interbred in the distant past. Palkopoulou hopes to work out when these interbreeding episodes happened.

None of these scientific results concerning interbreeding and hybridization are in the new paper by Meyer and colleagues. So I expect we will see much more from these new genome sequences.

W. W. Howells, in the conclusion of the 1980 review, Homo erectus–Who, When and Where: A Survey”:

So we might be wise to be continually careful in writing about Homo erectus, making clear whether one is referring to a population or taxon with a workable definition (such as might embrace all the Chou-k'ou-tien and Javanese fossils), or to a grade taken broadly, or to a time zone (Campbell, 1972). The history of argument about the "Neanderthal phase" should show what the problems may be. As to subspecies of H. erectus, there [sic] are of course legitimate and what we should look for; we should expect their development and their survival over considerable periods. There is no reason to suppose that H. erectus as a species did not include all hominids for a long interval. But the bestowing of names, like having a child, carries responsibilities. To be too liberal with subspecific names, even awarding them to single specimens (e.g., H. e. leakeyi), rather than to recognizable populations, is both to injure their use and to confuse the search for real lineages.

I’m quoting Howells not to endorse this view but because he expresses clearly one opinion about the goals of naming species and subspecies.

A short piece “On the evolution of the science blogosphere” by the Andy Extance of the ScienceSeeker team has some interesting notes on current statistics in blogs.

A quote from late in the article:

It’s notable that despite this exorcism of sci-comm ghosts, the overall number of blogs aggregated by ScienceSeeker has fallen by less than 5%. Our new blogs are frequently soon featured in our weekly picks. Often, new additions are young scientists motivated by the issues of our times. They emphasise the powerful tools science has for illuminating the truth, and argue for national policies to adopt them.

As they point out, the lives of science bloggers change, and things become more or less active as they fit into other parts of life.

For myself, I’ve been very busy with the Homo naledi work and that has reduced the effort I can put into cleaning up my notes about the other parts of science for public consumption. Also, I have a book out, and that took some time to write and edit!