Tag Archives: social network analysis

Social network analysis: Hypothesis testing and what-if projections

John Edward Terrell


Please note: this commentary, recovered on 28-Jan-2017, was originally published in Science Dialogues on 13-June-2014.


GIVEN A RELIABLE DATABASE of information and a good computer program (such as Microsoft Excel), it is possible today to simulate a broad range of hypothetical real-world situations under differing possible opening and subsequent conditions (Embrechts and Hofet 2014). Said differently, by changing the parameters and values of a spreadsheet in meaningful ways it is possible to do informative what-if analyses of many kinds of situations—thereby gaining better understanding not only of possible but also plausible outcomes.

Similarly, it is possible to use a good network analysis program (such as UCINET; Borgatti et al. 2013) to simulate differing social situations and their plausible impacts. Here briefly described is one example based on research currently being done to explore the history of social networks along the north coast of Papua New Guinea.

Research question

During the last glacial maximum (~21,000 BP), sea-levels were ~125 m (410 ft) lower than they are today. It is likely that New Guinea’s northern coast was mostly a steep rocky shoreline offering few resources supporting human settlements (Chappell 1982).  As one consequence, New Guinea during the last Ice Age served more as a vicariant barrier than a land bridge between Asia and island Oceania (Terrell 2004).

Figure 1. New Guinea is the second largest island in the world. The northern coastline is over 1,600 miles (2,600 km) long. Shown here in comparison with the 48 mainland states in the U.S.A.

Both historical and archaeological evidence (Welsch and Terrell 1998; Terrell and Schechter 2011) suggests that villages on the northern coastline of New Guinea and the nearby offshore islands have been linked with one another by far-reaching social and economic networks for the past 2,000 years. An obvious and historically important question, therefore, is whether people and places there in the more distant past were similarly integrated in comparable widely-distributed communities of practice (Terrell n.d.).After the last Ice Age, however, sea levels rose steadily and then began to stabilize around their modern levels ~6,000–7,000 years ago. The resulting formation of coastal plains and environmentally productive lagoons and estuaries led to peak biodiversity (Hope and Haberle 2005) and probably also peak human population densities along this coastline between ~4000–2000 BP.

Materials and methods

Figure 2 shows two mini-max networks (Cochrane and Lipo 2010) drawn using UCINET 6 (version 6.289) and NetDraw (version 2.109) with the edges weighted at two different thresholds. The upper network shows the connectivity of places in this region given a maximal customary voyaging distance of 220 km or less—the greatest distance known to have been locally traversed during the Pleistocene and the mid-Holocene prior to ~3300 BP (Golitko and Terrell n.d.). The lower network has a threshold of 360 km—the greatest voyaging distance (from Makira-Ulawa in the Solomon Islands to Temotu in the Reefs/Santa Cruz group) documented as having been crossed during the first settlement of Remote Oceania ~3300–3100 BP (Irwin 1992).

Also shown in this figure are (a) the network positions (blue) of this region’s major sources of obsidian, a volcanic glass widely transported both historically and prehistorically in this region of the world; and (b) the location of our study area (red) on this coast in the Aitape district (Terrell and Schechter 2011).

Figure 2. Connectivity of obsidian sources (blue nodes) and Aitape (red node) on the Sepik coast of Papua New Guinea. Top: when the edge distance is 220 km or less; bottom: when it is 360 km or less (baseline image source: Mark L. Golitko). Likely cut lines in these networks projected using the Girvan-Newman algorithm (Girvan and Newman 2002) are shown here as heavy black lines. The same four groupings in the upper mapping occur at any assumed what-if linkage distance between 186 and 270 km.
Network analysis

Given these analyses, it is readily apparent that the what-if connectivity of these mappings differs markedly. Under the upper scenario, it can be hypothesized that obsidian from sources southeast of Aitape (they are on New Britain Island) has probably been transported from place to place at least as far west as Aitape, but it is less likely that obsidian from the other sources—located in the Admiralty Islands—has also arrived there despite the fact that these sources are geographically closer to our study area. The situation is different in the lower mapping. Instead of four probable groupings within the network shown, there are only two, and given this scenario, when obsidian has reached Aitape, it is more likely to have been mined at the nearer Admiralty sources.

Hypothesis testing

The presence of Admiralty Islands obsidian at prehistoric sites has not been securely documented archaeologically outside the Admiralty Group earlier than the mid 2nd millennium B.C. Its widespread popularity at Aitape and elsewhere in this part of the world thereafter is generally associated with suspected improvements in canoe-making design and technology thought to have been introduced from Island Southeast Asia around this same time (Specht et al. 2014; Terrell n.d.). However, it is also generally accepted that the movement of animals, obsidian, and people between islands and coastal villages was characteristic of life in this part of the world for many millennia before then—in other words, the suspected improvements in watercraft design and voyaging prowess did not initiate coastal and inter-island mobility in this region but instead made longer-distance travel more feasible and routine (Specht et al. 2014).

Figure 3. The actual geographic locations of the obsidian sources (blue dots) and the study area (red dot) at Aitape on the Sepik coast of Papua New Guinea.

While obsidian from Admiralty sources has been found at archaeological sites on the north coast of New Guinea that are younger than ~2000 BP, almost all of the obsidian that has been recovered archaeologically on mainland New Guinea older than ~3,500 BP has been sourced to the the Kutau/Bao locality on the Willaumez Peninsula of western New Britain (Summerhayes 2009).

Our fieldwork at Aitape in 1993/1994 and 1996 supported by the National Science Foundation ((BNS-8819618 and DBS-9120301)  discovered large quantities of obsidian and chert at localities along the former mid-Holocene shoreline (which at Aitape is now located several kilometers inland) including assemblages with notably high frequencies of  obsidian from New Britain marked by large average flake sizes (Golitko 2011)—an archaeological signature consistent with pre-2000 BP obsidian assemblages found elsewhere in northern Melanesia (Summerhayes 2009).

Therefore, given our two what-if network analyses and this archaeological evidence it may be hypothesized that obsidian has probably been indirectly available to people living in what is now the Aitape district ever since the stabilization of world sea levels around 6,000–7,000 years ago, but it is likely that the major sources of this natural glass prior to ~3,500 BP were those located on New Britain.

With funding from the National Science Foundation my colleague Dr. Mark Golitko is currently (June–July 2014) leading a research team at Aitape that is surveying archaeological sites there on the mid-Holocene (~5000 BP) shoreline (as reconstructed from estimated local uplift rates and sea-level records) to document how far-reaching or alternatively how restricted were cultural and material exchanges on this coast at that time. Discovering how isolated or widely linked communities at Aitape were during the mid-Holocene is  critical to understanding the patterning of modern human diversity in northern New Guinea and elsewhere in the Pacific (Terrell 2010a, 2010b).

Conclusions

Obsidian has long been a popular although largely nonessential raw material in the Pacific (as elsewhere on earth) despite the fact that alternative and equally useful cutting materials (such as bamboo) are readily available. Hence the ancient transport of obsidian through inter-community networks is commonly interpreted by archaeologists as more a social phenomenon than a practical (“economic”) necessity (Torrence 2011). As suggested by the what-if analyses discussed here, we anticipate  that Golitko and his team will discover this summer that obsidian was reaching communities on the Sepik coast well before ~3,500 BP.

Funding for this research was provided by National Science Foundation Grant No. BCS-1155338–”Archaeological and Environmental Investigations along the mid-Holocene shoreline near Aitape, Northern Papua New Guinea,” Mark L. Golitko and John E. Terrell.
References:

Borgatti, S. P., M. G. Everett, and J. C. Johnson. 2013. Analyzing social networks. Los Angeles: Sage.

Chappell, J. 1982. Sea levels and sediments: some features of the context of coastal archaeological sites in the Tropics. Archaeology in Oceania 17:69–78.

Cochrane, E. E. and C. P. Lipo. 2010. Phylogenetic analyses of Lapita decoration do not support branching evolution or regional population structure during colonization of Remote Oceania. Philosophical Transactions of the Royal Society B 365:3889–3902.

Embrechts, P. and M. Hofet. 2014. Statistics and quantitative risk management for banking and insurance.  Annual Review of Statistics and It Application 1: 493–514.

Girvan, M. and M. E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99:7821–7826.

Golitko, M. 2011. Provenience Investigations of Ceramic and Obsidian Samples Using Laser Ablation Inductively Coupled Plasma Mass Spectrometry and Portable X-Ray Fluorescence. In Exploring prehistory on the Sepik coast of Papua New Guinea, J. E. Terrell and E. M. Schechter, eds., pages 251–287. Fieldiana Anthropology New Series No. 42. Chicago: Field Museum of Natural History.

Golitko, M. and J. E. Terrell. n.d. Modeling cultural patterning and prehistoric interaction along the “inland” Bismarck Sea using network analysis. Unpublished manuscript, 2012 NEOMAP Project “Inland Seas in a Global Perspective,” Leiden, Netherlands.

Hope, G. S. and S. G. Haberle. 2005. The history of the human landscapes of New Guinea. In Papuan Pasts: cultural, linguistic, and biological histories of Papuan-speaking peoples, A. Pawley, R. Attenborough, J. Golson, and R. Hide, eds., pages 541–554. Canberra: Pacific Linguistics.

Irwin, G. J. 1992. The prehistoric exploration and colonisation of the Pacific. Cambridge: Cambridge University Press.

Specht, J., T. Denham, J. Goff, and J. E. Terrell. 2014. Deconstructing the Lapita cultural complex in the Bismarck Archipelago. Journal of Archaeological Research 22:89–140.

Summerhayes, G. R. 2009. Obsidian network patterns in Melanesia—sources, characterisation and distribution. IPPA Bulletin 29:109–124.

Terrell, J. E. 2004. The “sleeping giant” hypothesis and New Guinea’s place in the prehistory of Greater Near Oceania. World Archaeology 36:601–609.

Terrell, J. E. 2010a. Language and material culture on the Sepik coast of Papua New Guinea: using social network analysis to simulate, graph, identify, and analyze social and cultural boundaries between communities. The Journal of Island and Coastal Archaeology 5:3-32.

Terrell, J. E. 2010b. Social network analysis of the genetic structure of Pacific Islanders. Annals of Human Genetics 74:211–232.

Terrell, John Edward. n.d. Understanding Lapita as history. In The Oxford handbook of prehistoric Oceania, Ethan Cochrane and Terry Hunt, eds. Oxford: Oxford University Press.

Terrell. J. E. and E. M. Schechter. 2011.Archaeological investigations on the Sepik coast of Papua New GuineaFieldiana: Anthropology42:1–303.

Torrence, R. 2011. Finding the right question: learning from stone tools on the Willaumez Peninsula, Papua New Guinea. Archaeology in Oceania 46: 29-41.

Welsch, R. and J. E. Terrell. 1998. Material culture, social fields, and social boundaries on the Sepik coast of New Guinea. In The archaeology of social boundaries, Miriam Stark, ed., pages 50–77. Washington: Smithsonian Institution Press.

© 2014 John Edward Terrell. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The statements and opinions expressed are those of the author(s) and do not constitute official statements or positions of the Editors and others associated with SCIENCE DIALOGUES.

Reconfiguring biological diversity 2. Coming to grips with diversity

John Edward Terrell


This is part 2 of a two part article

Coming to grips with diversity

Perhaps the greatest stumbling block to deciphering how biological diversity is patterned, or structured, in space and time within any given species is that most existing ways of modeling such diversity presuppose that genes are nested in some fashion within demonstrable and persistent primary units that can be labeled as populations, subpopulations, demes, communities, stocks, races, and like. Yet is this how biological reproduction works? Aren’t genes perfectly capable of “escaping,” so to speak, from such allegedly defining and confining “boxes” through the very acts of reproduction, reassortment, growth, and development?

It could be argued that there is irony in the fact that molecular genetics now has made it possible for scientists to map diversity at the genetic level. Yet many are still given to thinking about diversity as if they were compelled by the old limitations of their laboratory techniques to lump this new fine-grained evidence into inclusive nested sets (e.g., Pritchard et al. 2000; Greenbaum et al. 2016; Skoglund et al. 2016).

Perhaps it is not surprising, therefore, that some have concluded that “the observed pattern of global gene identity variation was produced by a combination of serial population fissions, bottlenecks and long-range migrations associated with the peopling of major geographic regions, and subsequent gene flow between local populations” (Hunley et al. 2009).

All three of these identified processes are plausible reasons for biological diversity in time and space. But aren’t all three of these population-level explanations ignoring individual agency and decision-making? Not to mention love, lust, and human compassion?

Moving beyond population modeling

Current population-level modeling based on molecular genetics is arguably an advance over older metapopulation models framing diversity as an ever-changing flux within species among discrete subpopulations inhabiting separate habitat patches linked by migration and extinction (Fig. 2). Certainly few today would accept that diversity within any species can be adequately explained solely or even largely as the product of fluctuating colonization and extinction events.

Figure 2. A simple metapopulation model at two time periods (A and B) attributing spatial diversity to a shifting dynamic of colonization and extinction events.

Similarly, the concept of the fitness landscape (also known as as an adaptive landscape; see Fig. 3) introduced by the geneticist Sewell Wright in 1932 is another long-debated way of modeling the dynamic interplay—or balance—of a number of plausible determinants of genetic variation in space and time. As Wright explained in 1932:

The most general conclusion is that evolution depends on a certain balance among its factors. There must be gene mutation, but an excessive rate gives an array of freaks, not evolution; there must be selection, but too severe a process destroys the field of variability, and thus the basis for further advance; prevalence of local inbreeding within a species has extremely important evolutionary consequences, but too close inbreeding leads merely to extinction. A certain amount of crossbreeding is favorable but not too much. In this dependence on balance the species is like a living organism. At all levels of organization life depends on the maintenance of a certain balance among its factors. (Wright 1932)

Figure 3. “Field of gene combinations occupied by a population within the general field
of possible combinations. Type of history under specified conditions indicated by relation
to initial field (heavy broken contour) and arrow.” Source: Wright 1932, fig. 4.

A “balance of factors” sounds right and reasonable, but are the ones he mentions the only major factors that must be taken into account? Surely adaptation is not the only driving force of evolution?

Agency and social networks

Consider the observation that human beings are notably variable in stature, weight, and other characteristics of their appearance. Clearly the gene mutations supporting such phenotypic variation have not resulted in what Wright would describe as “an array of freaks.” Evidently such diversity is not selected against—to use Wright’s way of framing the discussion. Why? Because much of the burden of human adaptation does not need to be genetically endowed. Instead, as most social scientists would insist, much of what we do supporting our survival and reproduction is accomplished using socially learned skills rather than by genetically inherited biological means.

Recently Greenbaum and his colleagues observed that the research strategies and tools of modern network analysis are increasingly being used to explore genetics questions in genomics, landscape genetics, migration-selection dynamics, and the study of the genetic structure of species more generally speaking (Greenbaum et al. 2016).

Adopting a networks approach to genetics makes it possible to come to grips not only with the ways in which racism—to return to Roseman’s point raised earlier—has shaped human variation in the past few hundred years, but also how our species’ mobility, adaptive skills, technologies, and social behaviors have been configuring human variation throughout the history of our species.

Figures 4 and 5 illustrate the potential value of using of network analysis in the study of genetic diversity. The first figure is a network mapping of localities reported in a genome scan published in 2008. While the patterning is complex, there is an obvious geographic signal in the genetic linkages shown. Figure 5 resolves the relationships among a smaller subset of the localities that had been sampled, specifically those in the Bismarck Archipelago-North Solomons region of the southwest Pacific.

Figure 4. Spring-embedding network mapping of the localities sampled in a genome scan of autosomal markers (687 microsatellites and 203 insertions/deletions) on 952 individuals from 41 Pacific populations). Mapping derived from the mean STRUCTURE assignment probabilities when K = 10 reported by Friedlaender at al. (2008) color-coded by geographic location. Blue-white = Asia; blue = Taiwan; black = Europe; red = Polynesia; pink = Micronesia; yellow = New Britain; purple = New Guinea; dark green = North Solomons; green = New Ireland; light green = New Hanover; pale green = Mussau. Source: adapted from Terrell 2010b, fig. 3.

 

Figure 5. Nearest-neighbor structuring of interaction among the localities sampled in the Bismarck Archipelago and North Solomons color-coded to show genetic clustering (blue nodes represent locations not represented in the genetic scan). Source: Terrell 2010b, fig. 11.Both network mappings suggest that geography has influenced the structuring of genetic similarities among people living in the sampled localities shown. Yet it also is apparent that the linkages shown may often be closer than geographic distance alone would lead us to expect. Judging by figure 5, the effect of isolation by distance is evidently constrained by social networks (as projected in this figure using nearest-neighbor linkages). Hence while geographic distance may be contributing to the patterning of genetic diversity among people in this part of the world, geography is by no means the whole story.
Conclusions

The network analysis briefly introduced in figures 4 and 5 had two principal aims, one phylogenetic, the other tokogenetic (Terrell 2010b). Do people living today in the Pacific segregate genetically along lines concordant with the reputedly separate (i.e., cladistic) histories of languages spoken there, principally the divide drawn by linguists and others between speakers of Austronesian and non-Austronesian (Papuan) languages (Terrell 2006)? To what extent does the genetic similarity among people living in different residential communities correlate with the nearest-neighbor propinquity of these sampled places?

Neither of these aims presuppose that the research goal is to define genetically discrete human populations (or subpopulations, demes, groups, communities, races, and the like) either a priori or by using, say, individual-based clustering (IBC) methods (e.g., Ball et al. 2010).

These two aims have more in common with those of the emerging field of landscape genetics (Dyer and Nason 2004; Garroway et al. 2008) than with most previous research in population genetics. However, both of these aims focus more directly on the genetic consequences of the behavior of organisms in space and time—in this case, humans—than on the geography, ecology, and environmental history of the locales where the people in question reside.

Both can also be seen as stepping back from Roseman’s observations about the impact of racial politics and social practices on the human genome in the past few centuries to underscore a more general issue in evolutionary biology: How much do the mobility and social behavior of individuals within any given animal species structure the genetic variation of that species?

As Dyer and Nason (2004) have remarked: “The evolution of population genetic structure is a dynamic process influenced by both historical and recurrent evolutionary processes.” Using network theory and visualization techniques to map the genetic structure of a species in space and time is still in its infancy. Reconfiguring how science grapples with the inherent complexity of evolution as an ever unfolding process using network approaches has the promise of making it easier to explore how comparable or dissimilar species are in their strategies for survival and reproduction (Fortuna et al. 2009).

Looking long and hard at what other species do to survive and reproduce may make it easier for us to see just how toxic our own social strategies—and the assumptions supporting them—can be.

Acknowledgements

I thank Neal Matherne and Tom Clark for their comments on a draft of this commentary.

References

Ball, Mark C., Laura Finnegan, Micheline Manseau, and Paul Wilson. 2010. Integrating multiple analytical approaches to spatially delineate and characterize genetic population structure: An application to boreal caribou (Rangifer tarandus caribou) in central Canada. Conservation Genetics 11, 6: 2131-2143.

Dyer, Rodney J., and John D. Nason. 2004. Population graphs: The graph theoretic shape of genetic structure. Molecular ecology 13, 7: 1713-1727.

Fortuna, Miguel A., Rafael G. Albaladejo, Laura Fernández, Abelardo Aparicio, and Jordi Bascompte. 2009. Networks of spatial genetic variation across species. Proceedings of the National Academy of Sciences 106, 45: 19044-19049.

Friedlaender, Jonathan S., Françoise R. Friedlaender, Jason A. Hodgson, Matthew Stoltz, George Koki, Gisele Horvat, Sergey Zhadanov, Theodore G. Schurr, and D. Andrew Merriwether. 2007. Melanesian mtDNA complexityPLoS One 2, 2: e248.

Friedlaender, Jonathan S., Françoise R. Friedlaender, Floyd A. Reed, Kenneth K. Kidd, Judith R. Kidd, Geoffrey K. Chambers, Rodney A. Lea et al. 2008. The genetic structure of Pacific IslandersPLoS Genet 4, 1: e19.

Garroway, Colin J., Jeff Bowman, Denis Carr, and Paul J. Wilson. 2008. Applications of graph theory to landscape genetics. Evolutionary Applications 1, 4: 620-630.

Greenbaum, Gili, Alan R. Templeton, and Shirli Bar-David. 2016. Inference and analysis of population structure using genetic data and network theory. Genetics 202.4: 1299-1312.

Hellenthal, Garrett, George BJ Busby, Gavin Band, James F. Wilson, Cristian Capelli, Daniel Falush, and Simon Myers. 2014. A genetic atlas of human admixture history.” Science 343, 6172: 747-751.

Hunley, Keith, Michael Dunn, Eva Lindström, Ger Reesink, Angela Terrill, Meghan E. Healy, George Koki, Françoise R. Friedlaender, and Jonathan S. Friedlaender. 2008. Genetic and linguistic coevolution in Northern Island MelanesiaPLoS Genet 4, no. 10 (2008): e1000239.

Hunley, Keith L., Meghan E. Healy, and Jeffrey C. Long. 2009. The global pattern of gene identity variation reveals a history of long‐range migrations, bottlenecks, and local mate exchange: Implications for biological race. American Journal of Physical Anthropology 139, 1: 35-46.

Kelly, Kevin M.,  2002. Population. In Hart, J. P. & Terrell, J. E. (eds.) Darwin and Archaeology: A handbook of key concepts, pp 243–256. Westport, Ct: Bergin & Garvey.

Moore, John H. 1994. Putting anthropology back together again: The ethnogenetic critique of cladistic theory. American Anthropologist (1994): 925-948.

Posada, David, and Keith A. Crandall. 2001. Intraspecific gene genealogies: Trees grafting into networks. Trends in Ecology & Evolution 16, 1: 37-45.

Pritchard, Jonathan K., Matthew Stephens, and Peter Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155, 2: 945-959.

Rieppel, Olivier. 2009. Hennig’s enkaptic system. Cladistics 25, 3: 311-317.

Roseman, Chartes C. 2014. Troublesome Reflection: Racism as the Blind Spot in the Scientific Critique of Race” Human biology 86, 3: 233-240.

Roseman, Charles C. 2014. “Random genetic drift, natural selection, and noise in human cranial evolution. Human Biology 86, 3: 233-240.

Skoglund, Pontus, Cosimo Posth, Kendra Sirak, Matthew Spriggs, Frederique Valentin, Stuart Bedford, Geoffrey R. Clark et al. 2016. Genomic insights into the peopling of the Southwest Pacific. Nature 538: 510-513.

Terrell, John Edward. 2006. Human biogeography: Evidence of our place in nature. Journal of Biogeography 33, 12: 2088-2098.

Terrell, John Edward. 2010a. Language and material culture on the Sepik coast of Papua New Guinea: Using social network analysis to simulate, graph, identify, and analyze social and cultural boundaries between communities. Journal of Island & Coastal Archaeology 5, 1: 3-32.

Terrell, John Edward. 2010b. Social network analysis of the genetic structure of Pacific islanders. Annals of human genetics 74, 3: 211-232.

Terrell, John Edward. 2015. A Talent for Friendship: Rediscovery of a Remarkable Trait. Oxford University Press.

Terrell, John Edward, and Pamela J. Stewart. 1996. The paradox of human population genetics at the end of the twentieth century. Reviews in Anthropology 25, 1: 13-33.

Wade, Nicholas. 2014. A Troublesome Inheritance: Genes, Race and Human History. Penguin.

Wilson, David Sloan, and Edward O. Wilson. 2008. Evolution for the Good of the Group”: The process known as group selection was once accepted unthinkingly, then was widely discredited; it’s time for a more discriminating assessment. American Scientist 96, 5: 380-389.

Wright, Sewall. 1932. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proceedings of the Sixth International Congress of Genetics , Vol. 1: 356-366.

© 2017 John Edward Terrell. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The statements and opinions expressed are those of the author(s) and do not constitute official statements or positions of the Editors and others associated with SCIENCE DIALOGUES.

In the works: Mating, variation, and speciation: An interdisciplinary conversation

Source: https://commons.wikimedia.org/wiki/File:Limenitis_archippus_mating_2.jpg

While using network theory and visualization techniques to map the genetic structure of species in space and time is in its infancy, reconfiguring how science grapples with the inherent complexity of evolution as an ever unfolding process using network approaches has the promise of making it easier to explore how comparable or dissimilar species are in their strategies for survival and reproduction. Looking long and hard at what other species do to survive and reproduce may also make it easier for all of us to see just how toxic our own social strategies—and the assumptions supporting them—can be.

Reconfiguring biological diversity 1. Toxic and obsolete assumptions

John Edward Terrell


This is part 1 of a two part article

IN AN INSIGHTFUL REVIEW of Nicholas Wade’s recent book A Troublesome Inheritance: Genes, Race and Human History (Wade 2014), the anthropological geneticist Charles C. Roseman concluded that current scientific arguments against biological racism are weak and scattered. These failings—my word, not Roseman’s—are far more than just scientifically troubling. “To recuperate a useful scientific critique of race,” he argues, “we need to come to grips with ways in which the political processes of racism have shaped human organisms over the last few hundred years” (Roseman 2014).

As Roseman notes, nobody seriously contests that human variation “is structured in geographic space, through time, and across many social divisions.” What is still up for grabs is how to explain this observable diversity. And as Roseman emphasizes, how we explain human variation cannot ignore the divisive and often destructive power of racism as a potent driver of human evolution. “Without incorporating the effects of racism into models of human variation today, we will not be able to have a cohesive theory of genes and race, and the scientific critique of race will continue to have no teeth.”

While Roseman’s observations focus on human biological diversity, the weaknesses and uncertainties he has highlighted about our explanations for variation within our species apply also to modern science’s grasp of biological diversity more broadly speaking. From this more inclusive point of view, racism is just a particularly invidious human form of social behavior capable of patterning our genetic diversity in time and space. If so, what about other species? How does the patterning of their mobility and social behavior shape their genetic diversity?

“Populations,” “admixture,” and conventional wisdom

Although the human brain can be coaxed into paying close attention to detail and nuance,  as a thinking machine it generally favors expediency and the utility of knowledge over precision and accuracy.  It is not altogether surprising, therefore, that even scientists often still take it for granted that biological species are naturally subdivided into separate “populations” or “subspecies” that  may occasionally—say under changing demographic or environmental conditions—meet and mix, and thereby produce more or less isolated “admixed” new hybrids (e.g., Moore 1994; Hellenthal et al. 2014). The question being overlooked or at any rate downplayed is how real and persistent are these assumed “populations” (Terrell and Stewart 1996; Kelly 2002).

This question may sound academic, but it is not trivial, as Charles Roseman has underscored. When it comes to human beings, the favored word in scholarly circles may be the word population or perhaps deme, group, or community, but for the chap on the street, the more likely choice wouldn’t be one of these formal terms, but rather the more down-to-earth word race. (I still vividly remember being scolded by a famous biological anthropologist decades ago when I was an undergraduate for using this particular “r” word. “We don’t use that word anymore,” he told me. “We use the term stock  instead.”)

What’s at stake here

It has been a foregone assumption in most genetics research for years that different species are by definition and by their biology isolated reproductively from one another, i.e., individuals in different species cannot mate and give birth to viable offspring capable of sustaining life for longer than a single generation. However, even the most committed cladist accepts that biological relationships below the level of the species are tokogenetic, not phylogenetic (Posada and Crandall 2001; Rieppel 2009).

Figure 1. “Tokogeny versus phylogeny. (a) Processes occurring among sexual species (phylogenetic processes) are hierarchical. That is, an ancestral species gives rise to two descendant species. (b) Processes occurring within sexual species (tokogenetic processes) are nonhierarchical. That is, two parentals combine their genes to give rise to the offspring. (c) The split of two species defines a phylogenetic relationship among species (thick lines) but, at the same time, relationships among individuals within the ancestral species (species 1) and within the descendant species (species 2 and 3) are tokogenetic (arrows).” Source: Posada and Crandall 2001, fig. 1.

Here, therefore, is the conundrum. Call them what you want, populations within any given species are not inherently isolated reproductively either by definition and by their biology. Hence to treat populations as natural units, they must first be defined and demonstrated to be isolated and discernible as such in some other way, or ways. Can this be done?

Here is one favored way when the species in question is ourselves. Many people believe that the language you speak is a reliable sign or marker of your true ethnicity and even your race. Is this right?

Hardly. As both fable and risqué jokes alike would have it, any sailor arriving in a strange port of call is likely to discover soon enough that you don’t really need to speak the local language to enjoy a good time while ashore as long as you have a few coins in your pocket. Yet scholars have long written about people living in what some see as the “underdeveloped” regions of the world as being subdivided into recognizable ethnolinguistic groups, language communities, and the like despite the fact that such euphemisms for the old-fashioned word race pigeonhole rather than map the realities of their lives (Terrell 2010a).

But if neither biology nor language inherently—i.e., “naturally”—isolates and thereby subdivides human beings as a species into different populations, subpopulations, demes, communities, stocks, or races, is there anything that does? And what about other species on earth?

Competition and tribalism, or isolation-by-distance?

As Roseman has remarked: “All analyses of human variation make strong assumptions about the mode, tempo, and pattern whenever they interpret statistical results to make evolutionary conclusions” (Roseman 2016). Favored explanations for or against the assumption that our species can be subdivided into enduring natural populations largely fall into one or the other of two basic sorts.

On the one hand, there has long been anecdotal and scholarly evidence, too, that geography and topography can limit how well and how often people are able to stay in touch with one another socially and intellectually as well as sexually. As the authors of one recent study commented, research has shown that there is a strong positive correlation between global genetic diversity within our species and geographic distance. The correlations observed have often been interpreted “as being consistent with a model of isolation by distance in which there are no major geographic discontinuities in the pattern of neutral genetic variation” (Hunley et al. 2009).

As these same authors note, however, discordant gene frequency patterns are also common within our species. It is obvious, too, that physical and social impediments to gene flow have regularly produced both larger discontinuities as well as concordant allele frequency patterns than would be expected based solely on isolation-by-distance (clinal) models of variation (Ibid.).

Adding social impediments to the mix of possible explanations brings into play the second way many have tried to explain why people around the globe appear to be so diverse. While there are many variants of this alternative argument, the essential ingredients are the baseline assumptions that (a) competition between individuals and groups is the main driving force of evolution, (b) human beings are by nature selfish and aggressive creatures, and (c) until recently humans lived in small tribal groups that were not just suspicious of strangers and other communities near and far, but were frequently at war them them, too. All of these claims are not only questionable, but are arguably contrary to the fundamental evolved characteristics of our species (Terrell 2015).


Part 2: Coming to grips with diversity 


References

Ball, Mark C., Laura Finnegan, Micheline Manseau, and Paul Wilson. 2010. Integrating multiple analytical approaches to spatially delineate and characterize genetic population structure: An application to boreal caribou (Rangifer tarandus caribou) in central Canada. Conservation Genetics 11, 6: 2131-2143.

Dyer, Rodney J., and John D. Nason. 2004. Population graphs: The graph theoretic shape of genetic structure. Molecular ecology 13, 7: 1713-1727.

Fortuna, Miguel A., Rafael G. Albaladejo, Laura Fernández, Abelardo Aparicio, and Jordi Bascompte. 2009. Networks of spatial genetic variation across species. Proceedings of the National Academy of Sciences 106, 45: 19044-19049.

Friedlaender, Jonathan S., Françoise R. Friedlaender, Jason A. Hodgson, Matthew Stoltz, George Koki, Gisele Horvat, Sergey Zhadanov, Theodore G. Schurr, and D. Andrew Merriwether. 2007. Melanesian mtDNA complexityPLoS One 2, 2: e248.

Friedlaender, Jonathan S., Françoise R. Friedlaender, Floyd A. Reed, Kenneth K. Kidd, Judith R. Kidd, Geoffrey K. Chambers, Rodney A. Lea et al. 2008. The genetic structure of Pacific IslandersPLoS Genet 4, 1: e19.

Garroway, Colin J., Jeff Bowman, Denis Carr, and Paul J. Wilson. 2008. Applications of graph theory to landscape genetics. Evolutionary Applications 1, 4: 620-630.

Greenbaum, Gili, Alan R. Templeton, and Shirli Bar-David. 2016. Inference and analysis of population structure using genetic data and network theory. Genetics 202.4: 1299-1312.

Hellenthal, Garrett, George BJ Busby, Gavin Band, James F. Wilson, Cristian Capelli, Daniel Falush, and Simon Myers. 2014. A genetic atlas of human admixture history.” Science 343, 6172: 747-751.

Hunley, Keith, Michael Dunn, Eva Lindström, Ger Reesink, Angela Terrill, Meghan E. Healy, George Koki, Françoise R. Friedlaender, and Jonathan S. Friedlaender. 2008. Genetic and linguistic coevolution in Northern Island MelanesiaPLoS Genet 4, no. 10 (2008): e1000239.

Hunley, Keith L., Meghan E. Healy, and Jeffrey C. Long. 2009. The global pattern of gene identity variation reveals a history of long‐range migrations, bottlenecks, and local mate exchange: Implications for biological race. American Journal of Physical Anthropology 139, 1: 35-46.

Kelly, Kevin M.,  2002. Population. In Hart, J. P. & Terrell, J. E. (eds.) Darwin and Archaeology: A handbook of key concepts, pp 243–256. Westport, Ct: Bergin & Garvey.

Moore, John H. 1994. Putting anthropology back together again: The ethnogenetic critique of cladistic theory. American Anthropologist (1994): 925-948.

Posada, David, and Keith A. Crandall. 2001. Intraspecific gene genealogies: Trees grafting into networks. Trends in Ecology & Evolution 16, 1: 37-45.

Pritchard, Jonathan K., Matthew Stephens, and Peter Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155, 2: 945-959.

Rieppel, Olivier. 2009. Hennig’s enkaptic system. Cladistics 25, 3: 311-317.

Roseman, Chartes C. 2014. Troublesome Reflection: Racism as the Blind Spot in the Scientific Critique of Race” Human biology 86, 3: 233-240.

Roseman, Charles C. 2014. “Random genetic drift, natural selection, and noise in human cranial evolution. Human Biology 86, 3: 233-240.

Skoglund, Pontus, Cosimo Posth, Kendra Sirak, Matthew Spriggs, Frederique Valentin, Stuart Bedford, Geoffrey R. Clark et al. 2016. Genomic insights into the peopling of the Southwest Pacific. Nature 538: 510-513.

Terrell, John Edward. 2006. Human biogeography: Evidence of our place in nature. Journal of Biogeography 33, 12: 2088-2098.

Terrell, John Edward. 2010a. Language and material culture on the Sepik coast of Papua New Guinea: Using social network analysis to simulate, graph, identify, and analyze social and cultural boundaries between communities. Journal of Island & Coastal Archaeology 5, 1: 3-32.

Terrell, John Edward. 2010b. Social network analysis of the genetic structure of Pacific islanders. Annals of human genetics 74, 3: 211-232.

Terrell, John Edward. 2015. A Talent for Friendship: Rediscovery of a Remarkable Trait. Oxford University Press.

Terrell, John Edward, and Pamela J. Stewart. 1996. The paradox of human population genetics at the end of the twentieth century. Reviews in Anthropology 25, 1: 13-33.

Wade, Nicholas. 2014. A Troublesome Inheritance: Genes, Race and Human History. Penguin.

Wilson, David Sloan, and Edward O. Wilson. 2008. Evolution for the Good of the Group”: The process known as group selection was once accepted unthinkingly, then was widely discredited; it’s time for a more discriminating assessment. American Scientist 96, 5: 380-389.

Wright, Sewall. 1932. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proceedings of the Sixth International Congress of Genetics , Vol. 1: 356-366.

© 2017 John Edward Terrell. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The statements and opinions expressed are those of the author(s) and do not constitute official statements or positions of the Editors and others associated with SCIENCE DIALOGUES.

Network science: The language of integrative research


Please note: this commentary, recovered on 9-Jan-2017, was originally published in Science Dialogues on 16-May-2015.


Mathematics, they say, is the language of science. When it comes to what is happening—or has happened—down here on earth, it is beginning to look like the right dialect of mathematics to learn is what is now being called (somewhat confusingly) network science.

When the goal is integrating research discoveries across disciplines as diverse as archaeology, primatology, neurobiology, and geochemistry, the mathematics of networks is the Esperanto of choice.

Field Museum in Chicago is one of the world’s largest natural history and anthropology museums. Scientists working there study the world and its human inhabitants from scores of different research directions, both pure and applied. Integrating these often seemingly disparate specialities so that the results of so much scholarship can be communicated to the public through exhibits and publications has always been a problem.

Under the leadership of Thorsten Lumbsch, Ph.D., the Director of Integrative Research at the Museum, “The Field” as it is affectionately known in Chicago is pushing back against research specialization using network science. Here is one example.

A social network is a set of actors defined by their ties, links, or relationships with one another (e.g., friendship networks, ecological networks, global trade networks, and protein interaction networks) rather than by their individual characteristics (attributes) as actors. Since the research focus is on relationships rather than on characteristics, statistical methods  in network science are being developed that do not need to assume—unlike in traditional statistical analyses—that the observations being studied are independent of one another.

Dr. Termeh Shafie, who is currently a Visiting Bass Scholar at the Field,  arrived in mid April from the Algorithimics Department at the University of Konstanz in Germany to help the Field’s scientists apply the statistical methods and models of network science to their research datasets which are as seemingly dissimilar as gorilla social interactions, sharks swimming in the ocean, the genetics of lichens, and the decorations on prehistoric American potshards.

When asked about her work at the Field Museum in Chicago, Termeh Shafie explains:

"The first step will be to learn more about the empirical data at hand, the hypotheses about these data being considered, and how to embed a network approach to them. The second step will be to develop network models based on these hypotheses. This requires the mathematical formulation of models, programming these models using statistical software, and then running simulations. Goodness-of-fit tests can be used to test the fit of the models to the data. Once suitable models are identified, statistics can be used to measure different properties of the networks under study and unlock information in them using the models as predictive tools. Within a level of certainty, we can then predict trends and behavior patterns even for parts of the networks we don’t yet have data for."

On Wednesday, May 13th, Dr. John P. Hart (Director, Research & Collections Division, New York State Museum), Dr. Mark Golitko (Regenstein Research Scientist), and James Zimmer-Dauphinee (2015 Regenstein Intern) participated with Shafie in a small-group Network Science Workshop at the Museum exploring ways to apply network analysis to a large database of information about pottery designs on ancient vessels from 102 archaeological sites to help unravel how communities across southern Ontario coalesced between ca. A.D. 1350 and 1650 into the larger regional populations that ultimately became the historically documented Huron confederacy.

Left to right: John Hart, Termeh Shafie, James Zimmer-Dauphine, Mark Golitko
Left to right: John Hart, Termeh Shafie, James Zimmer-Dauphinee, Mark Golitko

Shafie will be at the Field until August 15th, but even after she returns to Germany, she will continue to be the “networks link” between scientists at the Museum and the Algorithmics Unit under the direction of Professor Ulrik Brandes in the Department of Computer & Information Science at the University of Konstanz.

 

Migration, admixture, and human populations

Mark L. Golitko


Please note: this commentary, recovered on 8-Jan-2017, was originally published by Mark L. Golitko on Science Dialogues on 30-Jun-2015.


Cellarius_ptolemaic_system
The Ptolemaic universe as depicted by Johannes van Loon, ca. 1611–1686.

TWO NEW STUDIES IN EUROPEAN PREHISTORY have recently made headlines. The first (Haak et al. 2015) purports to show, using genetic data from ancient skeletons, that massive migration from the Central Asian Steppes into Europe during the Bronze Age likely introduced Indo-European languages, thus supporting the venerable “Kurgan” hypothesis championed by Marija Gimbutas decades ago. The second study (Smith et al. 2015) identified DNA from domesticated wheat (triticum) in submarine peat deposits off the southern coast of England dating to 8000 years ago, two millennia before such plants formed an identifiable component of crop assemblages in known terrestrial sites in England, and thus well ahead of the Neolithic agricultural “front.”

The validity of the later results remains to be seen—the DNA in question was cored out of the ocean bottom, and while the published results appear robust, it is unlikely that these data will single-handedly overturn the long-standing archaeological narrative of the Neolithic. That study does however provide a convenient point of digression for reexamining the first study and other similar studies of ancient genetics. Archaeologists have typically used two kinds of models to explain the past—diffusionist models in which ideas, things, and practices move, and migrationist models in which ideas, things, and practices move because people move. The movement of domesticated plants and animals of Near Eastern origins into Europe—the so-called “Neolithic Revolution”—has been the bell-weather case for testing these two types of explanations in archaeology. In the last three decades or so, human genetics has entered the picture as a way of testing competing hypotheses, first using modern DNA samples from living people in Europe and the Near East, and increasingly in the last decade, using ancient DNA (aDNA) extracted from archaeological burials (Pinhasi et al. 2012).

European population genetics, modern and ancient

Early studies of modern biological patterning (initially using blood types and other proteins) suggested a broad SE-NW trend in frequencies (Ammermann and Cavalli-Sforza 1984), one that was later confirmed when DNA sequencing became possible. This pattern was immediately interpreted as the outcome of a Neolithic period migration out of the Near East into Europe beginning after 8000 BC, swamping out “indigenous” European peoples (and their genes) that had been in place since at least the end of the last ice age (c. 12,000 years ago or longer). Vigorous debate ensued as some researchers argued that this trend could have resulted from a much earlier peopling of Europe by modern humans c. 45,000 years ago, or possibly during the reoccupation of Europe after the last glacial maximum (c. 22,000 years ago) by people who had occupied glacial refugia further south in Europe (see Pinhasi et al. 2012 and Deguilloux et al. 2012 for reviews of this work).

In 2005, the first study of DNA from actual early Neolithic skeletons was published (Haak et al. 2005), and the results were quite different from what most researchers had expected. As it turns out, early Neolithic skeletons, at least in central Europe (associated with an archaeological culture called the Linienbandkeramik or LBK) contain gene frequencies that are quite unlike those found in modern European populations. Specifically, mitochondrial DNA (mtDNA) haplogroups (sets of genomes related by shared mutations at particular locations on the genome suggesting common origins) thought to be clear markers of Neolithic population growth and movement were only present at relatively low frequencies, while one particular haplogroup—N1a—present at extremely low frequencies anywhere in modern day Eurasia and Africa, was quite common in the early Neolithic genepool. In the ensuing ten years, there has been a rapidly growing set of aDNA analyses performed in Europe, both on mtDNA (tracing descent through females) and Y-chromosome aDNA (tracing descent through males). As with modern DNA, measuring descent through males and females provides somewhat different answers, and suggests that on a whole, women have been more mobile than men in Europe (likely indicating a very old predominant pattern of patrilocality, e.g., Seielstad et al. 1998). In some places (parts of Northern Spain, for instance—see Sampietro et al. 2007), early Neolithic gene frequencies are not that different from earlier ones or modern ones, while in central Europe at least, the early Neolithic did witness a massive reshaping of the genetic landscape from a relatively genetically homogenous late-Paleolithic and Mesolithic background to a much more diverse Neolithic one, and little similarity is evident between the Neolithic and the present day (see Pinhasi et al. 2012 and Deguilloux et al. 2012 for reviews of this work).

The Haak et al. study (published earlier this month) identifies gene flow between central Asia and Europe in the Bronze Age, and a series of other recent studies also clearly demonstrate that the Neolithic is not the end of the story either. The researchers postulate a massive migration of Steppe populations into Europe associated with the Yamnaya archaeological culture, one that has been previously hypothesized to have spread Indo-European languages both eastwards and westwards out of Central Asia. aDNA research is for the most part slowly hammering the nail in the coffin of diffusionist models for the spread of agriculture, and for many, is now offering strong support for the spread of languages through massive migratory events (including Renfrew’s [1988] hypothesized spread of Indo-European during the early Neolithic).

Human “populations”

Care needs to be taken in interpreting these results, however. Population geneticists model the human past as a series of admixture events between discrete populations (see Hellenthal et al. 2014 for a recent attempt to define how many such populations there are). These populations may be defined in a number of ways—by geography (typically by continent), by language, by self-defined or externally perceived ethnicity, and in the case of palaeogenetics, by archaeological culture. There is thus an “LBK” or a “PPNB” set of gene frequencies which can admix or not (see for instance Fernández et al. 2015). This is a convenient shorthand, because it allows a small number of analyses (aDNA studies have sampled at most a few hundred individuals to date, while even modern studies are based on only thousands of individuals) to be taken as representative of some larger analytically meaningful population.

By Mike88n (Mike88n) [GPL (http://www.gnu.org/licenses/gpl.html)], via Wikimedia Commons (originally from Novembre et al. 2008).
Modern genetic map of Europe. (By Mike88n (Mike88n) [GPL (http://www.gnu.org/licenses/gpl.html)], via Wikimedia Commons (originally from Novembre et al. 2008)).

But what is a human population? That we have many ways of categorizing each other is unquestioned—we divide people up by race, income, clothing style, dialect, neighborhood, country, and a thousand other ways. It is also not implausible to imagine that real geographical boundaries such as major mountain chains, oceans, deserts, and so forth, may produce long-term vicariant barriers inhibiting interaction (i.e., people having sex with one another). That this is so is clearly demonstrated by the fact that modern gene frequencies are strongly patterned by geography in Europe, so much so that a multi-dimensional scaling plot (a way of representing many axes of variability on a single two-dimensional plot) of gene frequencies virtually recreates the geographic shape of Europe (Novembre et al. 2008). It is also everyone’s experience that humans live in social groups that can feel very real and rigid, and thus it might seem clear that human populations can be defined. However, the issue in palaeogenetics is different, namely, whether people live in sexual groups impermeable and long-lasting enough to explain the long-term configuration and development of gene frequencies, as well as serving as the basic scaffolding for other forms of human identity including the transmission of learning through time (i.e., culture and languages, including Indo-European ones).

Analytical simplification and historical reality

If the goal is simply to abstractly model how genes may have moved across the landscape historically, then perhaps an analytical fiction of discrete human “populations” is adequate for the job, similar to the use of the “gene” as analytical shorthand for modeling the complex network of DNA-RNA-protein interactions that drive biological function (e.g., Dawkins 2009). In econometrics, Friedman (1970) argued that it didn’t matter whether models were based on plausible assumptions, as long as those theories generated testable predictions that matched observations and resulted in predictive power. However, while predictive power may result even from a model with unrealistic starting assumptions, if social scientists want to explain what actually happened, our starting assumptions do matter. Their plausibility must be evaluated by examining how consistent they are with our knowledge of the world, updated in light of new information—if those assumptions are subsequently found wanting, we must reject the basic plausibility of our models, even if they produce outcomes consistent with empirical data (Nooteboom 1986). This is simply another way of stating that the same outcome can often be generated by several different models, and we need to turn to other lines of information to choose between them. In a recent paper, Pickrell and Reich (2014) use simulation to demonstrate that a number of opposing population genetic models used to explain human genetic patterning can produce the exact same results when operating over long periods of time.

As more aDNA analyses are published, the number of population migrations required to explain observed palaeo- and modern-gene frequencies in Europe (and by implication elsewhere) appears to be steadily increasing, in some cases seemingly at a rate of one per study (e.g., Hervella et al. 2015). This situation reminds me somewhat of the addition of spheres to the Ptolemaic system of planetary motions. Eventually, the Ptolemaic system grew so ponderous that some doubted it merely on the principal of parsimony. It took a radical rethinking of planetary positioning to generate a far simpler explanation of planetary motion. In the case of palaeogenetics (and other explanations of the past), perhaps a similar shift in thinking is required, one that moves away from the monolithic “billiard-ball” model of cultures and populations to something more plausible.

The human network

What should be the unit of analysis in historical genetics (and historical explanation more generally), and how do we create models that are consistent with other observations about human social structure and sexual behavior? In other words, how do we distinguish between competing historical genetic models by evaluating the basic plausibility of those models? One promising avenue comes from the recent explosion of interest in network analysis, which provides a robust method and body of knowledge for describing human social structure and comparing it to genetic patterning (e.g., Terrell 2010), and which does not necessarily require that one define broader units of analysis in advance, such as archaeological cultures. The challenge is to combine our knowledge of network structure in the human population (small-worlds and the like) with our understanding of genetics to create more plausible models of the human past. How this is to be accomplished in a formal mathematical sense remains to be seen.

This is more than just an academic concern—the popular media picks up on these studies and reinforces the viewpoint that humans do in fact come in particular “types” that can be identified through the new science of genetics—for instance, a recent distillation of one such aDNA study in a major media outlet described the results as indicating that modern Europeans derive from “three tribes” of ancient people, one of whom may be previously “unknown” to science (Rincon 2014). Do we really need “pulse-stasis” models for human population structure in the past? How do we adequately account for the fact that archaeological evidence suggests expansive social networks wherever and whenever we look, and that modern political/continental boundaries and perceived historical and cultural areas are not adequate units of analysis for splitting populations then or now? What happens if we resample our data and begin arbitrarily drawing lines that don’t correspond to these perceived political, geographical, linguistic, or archaeological categories? Does the story stay the same? A social networks perspective on the past is one way to transcend these problematic but common-sense ideas of human population(s) structure. If wheat can move beyond “Neolithic” communities thousands of years earlier than previously supposed, what else was moving?

References

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Hellenthal, G., G.B.J. Busb, G. Band, J.F. Wilson, C. Capelli, D. Falush, and S. Myers (2014). A Genetic Atlas of Human Admixture History. Science 343: 747-751.

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Terrell, J.E. (2010) Social Network Analysis of the Genetic Structure of Pacific Islanders. Annals of Human Genetics 74: 211-232.

© Mark L. Golitko. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The statements and opinions expressed are those of the author(s) and do not constitute official statements or positions of the Editors and others associated with SCIENCE DIALOGUES.