American Scientist

Paleoanthropologists have long established that the earliest humans came from Africa, but new discoveries are complicating the narrative of exactly where in Africa they originated. Cecilia Padilla-Iglesias is melding environmental data and studies of modern Central African hunter-gatherer populations to learn about ancient humans and how these early populations interacted. Her models have indicated that the first people lived in communities that were far more connected than previously thought, sharing both genetic material and cultural traditions. Padilla-Iglesias, who is a doctoral candidate in evolutionary anthropology at the University of Zürich, combines genetic, archaeological, ecological, and ethnographic techniques to better understand the processes that shape human diversity. She spoke with special issue editor Corey S. Powell about her research. This interview has been edited for length and clarity.

Your work diverges from the idea that modern humans emerged from a single population in East Africa. At what point did you start to think that there were details that didn’t match the conventional models?

There is a long tradition of paleoanthropology in East Africa. We know that some of the oldest human fossils were found there, so we must have come from there and left. But in the past 10 to 15 years, very old, humanlike things were found in Southern Africa and in Morocco. We saw that some of the oldest genetic lineages were from populations that now live in other parts of Africa. Some researchers started developing a theoretical model in which humans came from more than one place in Africa.

As I was reading this literature, I wondered whether the Central African hunter-gatherers I worked with fit this model. If there was one place that could connect Morocco with Southern Africa and East Africa, it was Central Africa, and I also knew that hunter-gatherers had networks created for mobility.

I started looking at the genetic evidence and environmental models and asking, Where do contemporary Central African hunter-gatherers live? Where are the fossils, the few that we’ve found in Central Africa? Under which environmental conditions? Then I created a model using data on contemporary hunter-gatherers from Central Africa to try to predict where we’ve found fossils and genetic divergences in the past.

I found that the moments in which my environmental model predicted fragmentation coincided with points where I could see genetic splits between populations. I’m also doing analysis that can trace the evolution of certain words and objects—almost doing a gene tree, but with objects.

Even if these populations now speak languages that are unrelated, it does seem that these people had a very deep past in which they were connected to one another. They were exchanging genetic material, and I’m finding that they exchanged culturally with one another as well. And if the whole stretch of Central Africa was exchanging genes and culture, they must have been also exchanging genes and culture with people a bit further north, a bit further south, and to either side.

The idea of connectivity networks is very important in your research. Do genetic connectivity and cultural connectivity go hand in hand?

Not necessarily. In order to have a diverse set of solutions to complex problems, two things are important: innovation and collaboration. When I talk about connectivity being the key to genetic and cultural diversity and an important element of human evolution, it’s the idea that different populations living in different environments that come into contact every once in a while are able to optimize these two processes. The separate populations are each innovating, and when they come together and exchange those ideas, they’re able to get the best of what the other one has.

What is the role of partial connectivity in this process?

If you have a population that’s fully connected from the beginning, everybody would get stuck on the idea that came first or that was from the most prestigious person. Partial connectivity allows populations to explore and build on a set of solutions to build a greater repertoire.

For example, you might have a population that has optimized fishing methods and another one that has great antelope hunting techniques. You can recombine and even trade these ideas. And with genes it happens in a similar way. You’re able to access a greater gene pool, even if you’re adapted to a local environment.

We conducted a study in which I compared the bits of DNA that I knew had been exchanged between Central African hunter-gatherers with the structural diversity of their musical instruments and of their foraging tools. These two sets of objects have completely different purposes; the musical instruments might serve as identity markers, whereas the tools might be specific to a foraging niche. When I did a relation test between the structure of genetic diversity and the structure of musical instrument diversity, the two matched almost perfectly—populations that were exchanging genetically were also exchanging musically. But for the foraging tools, there was no relationship at all. It just may not make sense to use the tools that someone in a different environment is using.

You are describing deeper and wider levels of connectivity than what’s commonly represented in the scientific literature. Are there barriers that have kept people from taking this broader view?

Traditionally ethnographers who worked with hunter-gatherers would go to one place and spend a lot of time there. They would live with a group for a couple of months and note everything about rituals and about the physical appearance of a population. But nobody would spend long enough to understand the big aspects of mobility, of how far people are moving.

On the other hand, archaeologists looking at comparative pottery analysis and ochre transport have shown that people were using nonlocal materials for a very long time. We’ve found shell beads in the middle of the continent where there are no shells. It must be that people were trading. Perhaps we’ve overlooked the scale.

There is nonanecdotal evidence that big networks were in place. Maybe it’s not just a consequence of people being friendly or moving around. Maybe those networks were the reason people moved around, to maintain them. Maybe they were way bigger than we’ve ever thought.

Your studies indicate that foraging is central to hunter-gatherer social networks. Why is foraging so important?

Foraging is an aspect of hunter-gatherer social life that’s been studied a lot, traditionally, from an anthropological perspective, because it’s a behavior that separates humans from, say, great apes. If chimpanzees find food in the forest, they’ll just grab and consume it where they find it. When night comes they’ll build a sleeping nest where they are and then abandon it.

Researchers working with hunter-gatherers from an archaeological perspective found that humans didn’t just consume food when and where they found it. They would take it back to a central place or a camp where they would share it with others and consume it together. This practice is a huge aspect of hunter-gatherer social life because it allows for the division of labor. You have people who remain at the camp and others who forage, and then the food brought back is for everyone. It’s this element of cooperation that, as we often say in anthropology, separates humans from other members of our family tree. It allows people to survive even when they don’t forage. One day I get yours and one day you get mine. And it allows people to tell stories and talk about one another.

“If the whole stretch of Central Africa was exchanging genes and culture, they must have been also exchanging genes and culture with people a bit further north, a bit further south, and to either side.”

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An interesting aspect my team is exploring is not just the implications of going back to a single place or not, but variations within that spectrum. We know that hunter-gatherer societies around the world vary in how much time they spend in these central places, and how far away from them they’re willing to travel to forage. It depends a lot on ecological parameters. If there’s plenty of forage close to you, you won’t go so far away. If there’s less, you will deplete the things that are close by faster, and therefore you potentially need to move your home base earlier in order to not starve.

How do you put your ideas about social networks to the test?

Our team built a model to ask, What are the implications of this changing behavior for early human social networks compared with those of other animals, such as great apes? And, if we find differences in the network properties, what would be the consequences for cultural transmission, which is one thing we know that human societies are exceptionally good at doing?

In our model, people move based on resources. Then we calculated the efficiency of the resulting network. We found that the hunter-gatherer way of moving, compared with the great ape way of moving, led to networks that revealed signs of being partially connected. They had very densely connected nuclei according to what we would observe of camps or groups of camps, and they would be embedded in large regional networks.

We also ran a contagion simulation, which is a method that comes from epidemiology that assesses how fast and how far a new innovation or idea travels in a network. We found, again, that the hunter-gatherer networks were particularly efficient at transmitting these new innovations quickly in a way that could reach the network nodes. It’s almost as if they were adapted to be efficient at diffusing advantageous information across a network. Then we played around with what happens when the environment is more heterogeneous or more homogeneous. We found that—as we would expect—a more heterogeneous, rich environment would facilitate this information transmission and these network efficiencies.

How have computer models influenced your ideas about human development?

The great thing about computer models is that you can explicitly test mechanisms. If I just rely on a statistical model, I could say: “I think that because this river was dry 7,000 years ago, these people stopped exchanging genes,” and then compare gene exchange before and after the river dried. But it could have been the river, or it could have been seven other things. With computational modeling, you can create a world in which the only thing that changes is that river. If you have sufficient processes in place that mimic the real world, you can see how big an impact the river drying would have had. Then you can test your findings against your data.

For example, our lab simulated something simple, such as the development of a plant medicine that involved innovating with different plants and recombining them. We simulated that process in different types of networks, each with the same number of nodes and edges but different structures. One network was fully connected, and in another every edge and every node could be connected or not in a random fashion.

We also ran the simulation on real networks collected from hunter-gatherers. We could compare directly—not varying anything else—how the network structure affected the speed at which a group would reach the right medicine. The hunter-gatherer version was much more efficient than a random configuration or a configuration that was fully connected. There’s something mechanistic in the structure of this network that makes it efficient at discovering a complex solution through recombination. For my work, especially when you add the spatial component, it opens an entire new realm of possibilities.

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