Tag Archives: populations

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.

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.