November 15, 2013
There are plenty of ways to study history. You can conduct archaeological digs, examining the artifacts and structures buried under the ground to learn about past lifestyles. You can read historical texts, perusing the written record to better understand events that occurred long ago.
But an international group of medical researchers led by Andrés Moreno-Estrada and Carlos Bustamante of Stanford and Eden Martin of the University of Miami are looking instead at a decidedly unconventional historical record: human DNA.
Hidden in the microscopic genetic material of people from the Caribbean, they’ve found, is an indelible record of human history, stretching back centuries to the arrival of Europeans, the decimation of Native American populations and the trans-Atlantic slave trade. By analyzing these genetic samples and comparing them to the genes of people around the world, they’re able to pinpoint not only the geographic origin of various populations but even the timing of when great migrations occurred.
As part of a new project, documented in a study published yesterday in PLOS Genetics, the researchers sampled and studied the DNA of 251 people living in Florida who had ancestry from one of six countries and islands that border the Caribbean—Cuba, Haiti, Dominican Republic, Puerto Rico, Honduras and Colombia—along with 79 residents of Venezuela who belong to one of three Native American groups (the Yukpa, Warao and Bari tribes). Each study participant was part of a triad that included two parents and one of their children who were also surveyed, so the researchers could track which particular genetic markers were passed on from which parents.
The researchers sequenced the DNA of these participants, analyzing their entire genomes in search of particular genetic sequences—called single-nucleotide polymorphisms (SNPs)—that often differ between unrelated individuals and are passed down from parent to child. To provide context for the SNPs they found in people from these groups and areas, they compared them to existing databases of sequenced DNA from thousands of people globally, such as data from the HapMap Project.
Tracing a person’s DNA to a geographical area is relatively straightforward—it’s well-established that particular SNPs tend to occur in different frequencies in people with different ancestries. As a result, sequencing the DNA of someone living in Florida whose family came from Haiti can reveal what proportion of his or her ancestors originally came from Africa and even where in Africa those people lived.
But one of the most amazing things about the state of modern genetics is that it also allows scientists to draw chronological conclusions about human migration, because blocks of these SNPs shorten over time at a generally consistent rate. ”You can essentially break the genome up into European chunks, Native American chunks and African chunks,” Martin says. “If each of these regions are longer, it suggests they arrived in the gene pool more recently, because time tends to break up the genome. If these chunks are shorter, it suggests there’s been a lot of recombination and mixing up of the genome, which suggests the events were longer ago.”
Modeling their DNA data with these assumptions built in, the researchers created a portrait of Caribbean migration and population change that stretches back to before the arrival of Columbus. One of their most interesting findings was just how few Native Americans survived the arrival of Europeans, based on the DNA data. “There was an initial Native American genetic component on the islands,” Martin says, “but after colonization by the Europeans, they were almost decimated.”
This decimation was the result of European attacks and enslavement, as well as the disease and starvation that came in their wake. The DNA analysis showed that the native population collapse of Caribbean islands happened almost immediately after the arrival of Columbus, within one generation of his first visits and the appearance of other Europeans. The gene pool on the mainland, by contrast, shows a more significant Native American influence, indicating that they didn’t die off at the same rates.
What replaced the missing Native American genes in island populations? The answer reflects the conquering Europeans’ solution to diminishing populations available for labor: slaves kidnapped and imported from Africa. The DNA analysis showed a heavy influence from characteristically African SNPs, but notably, it revealed two separate phases in the trans-Atlantic slave trade. “There were two distinct pulses of African immigration,” Martin says. “The first pulse came from one part of West Africa—the Senegal region—and the second, larger pulse came from another part of it, near the Congo.”
This corresponds to written records and other historical sources, which show an initial phase of slave trade starting around 1550, in which slaves were mostly kidnapped from the Senegambia area of the Mali Empire, covering modern-day Senegal, Gambia and Mali (the orange area in the map at right). This first push accounted for somewhere between 3 and 16 percent of the total Atlantic slave trade. It was followed by a second, much heavier period that made up more than half of the trade and peaked during the late 1700s, in which slaves were largely taken from what is now Nigeria, Cameroon, Gabon and the Congo (the red and green areas).
The genetic analysis can also look at genes that are passed down on the X chromosome in particular, revealing the historical influence of different ancestries on both the female and male sides of the genome. They found that, in the populations studied, Native American SNPs were more prevalent on the X chromosome than the others, reflecting the history of both marriage and rape of Native American women by Spanish men who settled in the area.
As medical researchers, the scientists are primarily interested in using the findings to advance research into the role of genetics in diseases that disproportionately affect Hispanic populations. Similar research on genetics and ethnicity has revealed that, for instance, Europeans are much more likely to suffer from cystic fibrosis, or sickle-cell anemia tends to strike people of African ancestry.
“Hispanics are extremely diverse genetically—they originate from countries all over the world,” Martin says. “So that poses great challenges in genetic studies. We can’t just lump all Hispanics into a group and think of them as homogenous, so we’re trying to look more deeply into their genetic heritage and where it came from.”
September 23, 2013
Most people think of history as a series of stories—tales of one army unexpectedly defeating another, or a politician making a memorable speech, or an upstart overthrowing a sitting monarch.
Peter Turchin of the University of Connecticut sees things rather differently. Formally trained as a ecologist, he sees history as a series of equations. Specifically, he wants to bring the types of mathematical models used in fields such as wildlife ecology to explain population trends in a different species: humans.
In a paper published with colleagues today in the Proceedings of the National Academy of Sciences, he presents a mathematical model (shown on the left of the video above) that correlates well with historical data (shown on the right) on the development and spread of large-scale, complex societies (represented as red territories on the green area studied). The simulation runs from 1500 B.C.E. to 1500 C.E.—so it encompasses the growth of societies like Mesopotamia, ancient Egypt and the like—and replicates historical trends with 65 percent accuracy.
This might not sound like a perfect accounting of human history, but that’s not really the goal. Turchin simply wants to apply mathematical analysis to the field of history so that researchers can determine which factors are most influential in affecting the spread of human states and populations, just as ecologists have done when analyzing wildlife population dynamics. Essentially, he wants to answer a simple question: Why did complex societies develop and spread in some areas but not others?
In this study, Turchin’s team found that conflict between societies and the development of military technology as a result of war were the most important elements that predicted which states would develop and expand over the map—with those factors taken away, the model deteriorated, describing actual history with only 16 percent accuracy.
Turchin began thinking about applying math to history in general about 15 years ago. “I always enjoyed history, but I realized then that it was the last major discipline which was not mathematized,” he explains. “But mathematical approaches—modeling, statistics, etc.—are an inherent part of any real science.”
In bringing these sorts of tools into the arena of world history and developing a mathematical model, his team was inspired by a theory called cultural multilevel selection, which predicts that competition between different groups is the main driver of the evolution of large-scale, complex societies. To build that into the model, they divided all of Africa and Eurasia into gridded squares which were each categorized by a few environmental variables (the type of habitat, elevation, and whether it had agriculture in 1500 B.C.E.). They then “seeded” military technology in squares adjacent to the grasslands of central Asia, because the domestication of horses—the dominant military technology of the age—likely arose there initially.
Over time, the model allowed for domesticated horses to spread between adjacent squares. It also simulated conflict between various entities, allowing squares to take over nearby squares, determining victory based on the area each entity controlled, and thus growing the sizes of empires. After plugging in these variables, they let the model simulate 3,000 years of human history, then compared its results to actual data, gleaned from a variety of historical atlases.
Although it’s not perfect, the accuracy of their model—predicting the development and spread of empires in nearly all the right places—surprised even the researchers. “To tell the truth, the success of this enterprise exceeded my wildest expectations,” Turchin says. “Who would have thought that a simple model could explain 65% of variance in a large historical database?”
So why would conflict between societies prove to be such a crucial variable in predicting where empires would form? “To evolve to a large size, societies need special institutions that are necessary for holding them together,” Turchin proposes. “But such institutions have large internal costs, and without constant competition from other societies, they collapse. Only constant competition ensures that ultrasocial norms and institutions will persist and spread.”
The model shows that agriculture is a necessary but not sufficient precondition for a complex society, he says—these states can’t form without farming, but the persistent presence of competition and warfare is necessary to forge farming societies into durable, large-scale empires. Conventional analyses of history could come to this same conclusion, but they wouldn’t be able to demonstrate it in the same mathematically-based way. Using this approach, on the other hand, Turchin’s group could remove the influence of warfare and see the model’s accuracy in describing real historical data plummet.
Of course, there are limitations to viewing history through math—humans are more complicated than numbers. “Differences in culture, environmental factors and thousands of other variables not included in the model all have effect,” Turchin says. “A simple general model should not be able to capture actual history in all its glorious complexity.”
Still, the model is a unique and valuable tool. Going forward, Turchin’s team wants to develop it further—adding more nuance (such as including the quality of agricultural productivity, rather than merely toggling if farming exists in a given area or not) to improve on that 65 percent accuracy. Additionally, they’d like to expand the model, applying it to more recent world history and also pre-Columbian North America, if they can find relevant historical data.
Based on his experiences so far, Turchin thinks they’ll be successful in developing a model that better reflects the the rise and fall of civilizations. “It turns out that there is a lot of quantitative data in history,” he says, “you just have to be creative in looking for it.”
July 1, 2013
Developing nations often have bigger problems to worry about than protecting wildlife. The limited resources available are directed towards fulfilling basic human needs such as food, sanitation, shelter and disease treatment and prevention. Rather than taking away from those human-oriented endeavors, developing countries rely upon donations largely from North America and Europe to address conservation. But the international donor community, it turns out, plays favorites when it comes to doling out funding for environmental protection–and those biases don’t necessarily have anything to do with the biodiversity at stake.
Until now, attempts to identify highly underfunded yet biodiverse countries have been hampered by poor and incomplete data on actual spending. To figure out which countries are the biggest losers when it comes to conservation, researchers decided to build the most complete database of global conservation funding to date.
To explore how international donors, governments and various organizations invested in conservation each year from 2001 to 2008, an international team of researchers analyzed around-the-world donations on a country-to-country basis. The database included all money a country spends on conservation, including funds procured from both outside and within the country. Those expenditures totaled $19.8 billion and represented the most complete database of conservation spending ever assembled. They created a statistical model that took into account factors ranging from country size, government effectiveness, political stability, GDP and biodiversity. Using statistical analyses, the authors teased out the underlying reasons driving whether countries do or don’t get funding.
For measuring biodiversity, they calculated the proportion of a species an individual country possesses, rather than just a species head-count, since some countries may contain just a handful of animals while another houses the bulk of the world’s population. They used mammals as a proxy for biodiversity because more information tends to be available for mammals than for other types of animals or plants, and because conservation dollars oftentimes favor the cute and furry over the scaly or slimy.
Upper income countries, as defined by the World Bank, distributed 94 percent of conservation funding, the team found, while countries in the lowest income bracket supplied just 0.5 percent. The U.S. and Germany topped the list of countries that provide aid to promote conservation; non-nation donors that contribute the most aid are the Global Environment Facility and the World Bank . The report also listed the 40 countries that receive the least funding given what would be expected based upon their size, biodiversity and GDP. From those, the top ten are:
- Solomon Islands
When the team plugged all of their data into a statistical model to try and figure out what’s driving these disparities, the results, published in the journal Proceedings of the National Academy of Sciences, explained 86 percent of the variation in how conservation money is spent each year. The most important factors for determining how funding is invested, they found, were the number of species, a country’s size (larger countries were favored for receiving funding over smaller ones) and the country’s GDP (higher GDPs were favored for receiving funding over smaller ones).
To see how conservation spending related to biodiversity, they compared funding data to the proportion of threatened biodiversity nations house. Significantly, they write, 40 of the most highly underfunded countries contain 32 percent of the world’s threatened species. The most strikingly disparate examples included Chile, Malaysia, the Solomon Islands and Venezuela. Highly underfunded countries also tended to occur in geographical groups, such as Central Asia, Northern Africa, the Middle East and parts of Oceania, meaning some species may miss out on protection across their entire range.
How did those 40 countries slip through the cracks? Some of the variation, they found, reflected political and historical biases. For example, predominantly Islamic countries receive less than half the funding as other countries that are equally biodiverse but follow a different religious and political scheme.
Other poorly funded countries, like Sudan and the Ivory Coast, suffered recent or ongoing conflicts, suggesting that donors may be hesitant to invest in conservation efforts in areas they perceive as being threatened by human strife. The researchers did not have enough data to include Somalia in the study, though they guess that it most likely falls within the severely underfunded category. “Globally, countries in conflict have high levels of both biodiversity and threat,” the authors write. “Donor reticence therefore deserves careful consideration because removal of funding may make a bad situation even worse.”
They do not address, however, whether or not nations in strife would be able to effectively manage conservation projects, though that likely depends on a case-by-case basis. Afghanistan, for example, declared its first national park in 2009, and long-term conservation efforts in the Central African Republic were threatened but still managed to prevail when violence broke out earlier this year.
Targeting underfunded areas that contain high levels of biodiversity, the authors think, could make a greater impact for protecting species than investing that money elsewhere, where ample resources already exist. Strengthening conservation efforts in the places with the highest biodiversity but least funding support “may therefore reduce short-term biodiversity losses with appreciably greater efficiency than would current spending patterns,” they write.
Because the most underfunded countries tend to be developing nations, they continue, a relatively small investment on the part of the international community could make a significant difference for wildlife there. They add, “Our results therefore suggest that international conservation donors have the opportunity to act now, in a swift and coordinated fashion, to reduce an immediate wave of further biodiversity declines at relatively little cost.”
June 19, 2013
For nearly two centuries, biologists have been struck by a mystery of geography and biodiversity peculiar to Europe. As Edward Forbes pointed out as far back as 1846, there are a number of life forms (including the Kerry slug, a particular species of strawberry tree and the Pyrenean glass snail) that are found in two specific distant places—Ireland and the Iberian Peninsula—but few areas in between.
Recently, Adele Grindon and Angus Davidson, a pair of scientists at the University of Nottingham in the UK, decided to come at the question with one of the tools of modern biology: DNA sequencing. By closely examining the genetic diversity of one of the species shared by these two locales, the grove snail, they thought they’d be able to trace the migratory history of the creatures and better understand their present-day distribution.
When they sequenced the mitochondrial DNA of hundreds of these snails scattered across Europe, the data pointed them towards an unexpected explanation for the snails’ unusual range. As they suggest in a paper published today in PLOS ONE, the snails likely hitched a boat ride from Spain to Ireland some 8,000 years ago along with migrating bands of Stone Age humans.
Grove snails as a whole are distributed all over Europe, but a specific variety of the snail, with a distinctive white-lipped shell, is found exclusively in Ireland and in the Pyrenees mountains that lie on the border between France and Spain. The researchers sampled a total of 423 snail specimens from 36 sites distributed across Europe, with an emphasis on gathering large numbers of the white-lipped variety.
When they sequenced genes from the mitochondrial DNA of each of these snails and used algorithms to analyze the genetic diversity between them, they found that the snails fell into one of 7 different evolutionary lineages. And as indicated by the snails’ outward appearance, a distinct lineage (the snails with the white-lipped shells) was indeed endemic to the two very specific and distant places in question:
Explaining this is tricky. Previously, some had speculated that the strange distributions of creatures such as the white-lipped grove snails could be explained by convergent evolution—in which two populations evolve the same trait by coincidence—but the underlying genetic similarities between the two groups rules that out. Alternately, some scientists had suggested that the white-lipped variety had simply spread over the whole continent, then been wiped out everywhere besides Ireland and the Pyrenees, but the researchers say their sampling and subsequent DNA analysis eliminate that possibility too.
“If the snails naturally colonized Ireland, you would expect to find some of the same genetic type in other areas of Europe, especially Britain. We just don’t find them,” Davidson, the lead author, said in a press statement.
Moreover, if they’d gradually spread across the continent, there would be some genetic variation within the white-lipped type, because evolution would introduce variety over the thousands of years it would have taken them to spread from the Pyrenees to Ireland. That variation doesn’t exist, at least in the genes sampled. This means that rather than the organism gradually expanding its range, large populations instead were somehow moved en mass to the other location within the space of a few dozen generations, ensuring a lack of genetic variety.
“There is a very clear pattern, which is difficult to explain except by involving humans,” Davidson said. Humans, after all, colonized Ireland roughly 9,000 years ago, and the oldest fossil evidence of grove snails in Ireland dates to roughly the same era. Additionally, there is archaeological evidence of early sea trade between the ancient peoples of Spain and Ireland via the Atlantic and even evidence that humans routinely ate these types of snails (pdf) before the advent of agriculture, as their burnt shells have been found in Stone Age trash heaps.
The simplest explanation, then? Boats. These snails may have inadvertently traveled on the floor of the small, coast-hugging skiffs these early humans used for travel, or they may have been intentionally carried to Ireland by the seafarers as a food source. “The highways of the past were rivers and the ocean–as the river that flanks the Pyrenees was an ancient trade route to the Atlantic, what we’re actually seeing might be the long lasting legacy of snails that hitched a ride…as humans travelled from the South of France to Ireland 8,000 years ago,” Davidson said.
All this analysis might help biologists solve the bigger mystery: why so many other species share this strange distribution pattern. More research could reveal that the Kerry slug, strawberry tree and others were carried from Iberia to Ireland by prehistoric humans too—and that, as a species, we were impacting the Earth’s biodiversity long before we could’ve possibly realized it.
June 12, 2013
You likely don’t give a ton of thought to the sounds and patterns that make up the language you speak everyday. But the human voice is capable making of a tremendous variety of noises, and no language includes all of them.
About 20 percent of the world’s languages, for example, make use of a type of sound called an ejective consonant, in which an intense burst of air is released suddenly. (Listen to all the ejectives here.) English, however—along with most European languages—does not include this noise.
Linguists have long assumed that the incorporation of different sounds into various languages is an entirely random process—that the fact that English includes no ejectives, for instance, is an accident of history, simply a result of the sounds arbitrarily incorporated into the language that would evolve into German, English and most other European languages. But recently, Caleb Everett, a linguist at the University of Miami, made a surprising discovery that suggests the assortment of sounds in human languages is not so random after all.
When Everett analyzed hundreds of different languages from around the world, as part of a study published today in PLOS ONE, he found that those that originally developed at higher elevations are significantly more likely to include ejective consonants. Moreover, he suggests an explanation that, at least intuitively, makes a lot of sense: The lower air pressure present at higher elevations enables speakers to make these ejective sounds with much less effort.
The finding—if it holds up when all languages are analyzed—would be the first instance in which geography is found to influence the sound patterns present in spoken words. It could open up many new avenues of inquiry for researchers seeking to understand the evolution of language throughout human history.
Everett started out by pulling a geographically diverse sampling of 567 languages from the pool of an estimated 6,909 that are currently spoken worldwide. For each language, he used one location that most accurately represented its point of origin, according to the World Atlas of Linguistic Structures. English, for example, was plotted as originating in England, even though it’s spread widely in the years since. But for most of the languages, making this determination is much less difficult than for English, since they’re typically pretty restricted in terms of geographic scope (the average number of speakers of each languageanalyzedis just 7,000).
He then compared the traits of the 475 languages that do not contain ejective consonants with the 92 that do. The ejective languages were clustered in eight geographic groups that roughly corresponded with five regions of high elevation—the North American Cordillera (which include the Cascades and the Sierra Nevadas), the Andes and the Andean altiplano, the southern African plateau, the plateau of the east African rift and the Caucasus range.
When Everett broke things down statistically, he found that 87 percent of the languages with ejectives were located in or near high altitude regions (defined as places with elevations 1500 meters or greater), compared to just 43 precent of the languages without the sound. Of all languages located far from regions with high elevation, just 4 percent contained ejectives. And when he sliced the elevation criteria more finely—rather than just high altitude versus. low altitude—he found that the odds of a given language containing ejectives kept increasing as the elevation of its origin point also increased:
Everett’s explanation for this phenomenon is fairly simple: Making ejective sounds requires effort, but slightly less effort when the air is thinner, as is the case at high altitudes. This is because the sound depends upon the speaker compressing a breath of air and releasing it in a sudden burst that accompanies the sound, and compressing air is easier when it’s less dense to begin with. As a result, over the thousands of years and countless random events that shape the evolution of a language, those that developed at high altitudes became gradually more and more likely to incorporate and retain ejectives. Noticeably absent, however, are ejectives in languages that originate close to the Tibetean and Iranian plateaus, a region known colloquially as the roof of the world.
The finding could prompt linguists to look for other geographically-driven trends in the languages spoken around the world. For instance, there might be sounds that are easier to make at lower elevations, or perhaps drier air could make certain sounds trip off the tongue more readily.