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.

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.


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


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© 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.

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