September 26, 2013
Some 46 Martian days after landing on Mars in August 2012, after traveling nearly 1,000 feet from its landing site, Curiosity came upon a pyramid-shaped rock, roughly 20 inches tall. Researchers had been looking for a rock to use for calibrating a number of the rover’s high-tech instruments, and as principal investigator Roger Wiens said at a press conference at the time, “It was the first good-size rock that we found along the way.”
For the first time, scientists used the rover’s Hand Lens Imager (which takes ultra-high resolution photos of a rock’s surface) and the Alpha Particle X-ray Spectrometer (which bombards a rock with alpha particles and X-rays, kicking off electrons in patterns that allow scientists to identify the elements locked within it). They also used the ChemCam, a device that fires a laser at a rock and measures the abundances of elements vaporized.
Curiosity, for its part, commemorated the event with a pithy tweet:
I did a science! 1st contact science on rock target Jake. Here’s an action shot pic.twitter.com/pzcgH6Bk
— Curiosity Rover (@MarsCuriosity) September 22, 2012
A year later, the Curiosity team’s analysis of the data collected by these instruments, published today in Science, shows that they made a pretty lucky choice in finding a rock to start with. The rock, dubbed “Jake_M” (after engineer Jake Matijevic, who died a few days after Curiosity touched down), is unlike any rock previously found on Mars—and its composition intriguingly suggests that it formed after molten rock cooled quickly in the presence of underground water.
The new discovery was published as part of a special series of papers in Science that describe the initial geologic data collected by Curiosity’s full suite of scientific instrumentation. One of the other significant findings is a chemical analysis of a scoop of Martian soil—heated to 835 degrees Celsius inside the Sample Analysis at Mars instrument mechanism—showing that it contains between 1.5 and 3 percent water by weight, a level higher than scientists expected.
But what’s most exciting about the series of findings is the surprising chemical analysis of Jake_M. The researchers determined that it is likely igneous (formed by the solidification of magma) and, unlike any other igneous rocks previously found on Mars, has a mineral composition most similar to a class of basaltic rocks on Earth called mugearites.
“On Earth, we have a pretty good idea how mugearites and rocks like them are formed,” Martin Fisk, an Oregon State University geologist and co-author of the paper, said in a press statement. “It starts with magma deep within the Earth that crystallizes in the presence of one to two percent water. The crystals settle out of the magma, and what doesn’t crystallize is the mugearite magma, which can eventually make its way to the surface as a volcanic eruption.” This happens most frequently in underground areas where molten rock comes into contact with water—places like mid-ocean rifts and volcanic islands.
The fact that Jake_M closely resembles mugearites indicates that it likely took the same path, forming after other minerals crystallized in the presence of underground water and the remaining minerals were sent to the surface. This would suggest that, at least at some time in the past, Mars contained reserves of underground water.
The analysis is part of a growing body of evidence that Mars was once home to liquid water. Last September, images taken by Curiosity showed geologic features that suggested the one-time presence of flowing water at the surface. Here on Earth, analyses of several meteorites that originated on Mars have also indicated that, at some point long ago, the planet held reserves of liquid water deep underground.
This has scientists and members of the public excited, of course, because (at least as far as we know) water is a necessity for the evolution of life. If Mars was once a water-rich planet, as Curiosity’s findings increasingly suggest, it’s possible that life may have once evolved there long ago—and there may even be organic compounds or other remnants of life waiting to be found by the rover in the future.
September 25, 2013
Sure, it’s not much to look at. But stare long enough, and you’ll see a jaw (jutting out towards the right), a pair of nostrils (small perforations directly above the mouth cavity) and even a tiny eye socket (just above the mouth, to the left of the nostrils, staring out sideways).
This admittedly homely fish fossil, the 419-million-year old Entelognathus primordialis, was recently discovered in China and described for the first time in an article published today in Nature. What makes it remarkable is everything that’s come after it: It’s the oldest known creature with a face, and may have given rise to virtually all the faces that have followed in the hundreds of millions of years since, including our own.
The uncommonly well-preserved, three-dimensional fossil, analyzed by a group of researchers from the Chinese Academy of Sciences, was excavated near Xiaoxiang Reservoir in Southeast China, in a layer of sediment that dates to the Silurian period, which ranged from roughly 419 to 443 million years ago. All other fish specimens from this era are jawless fish (a group of more primitive creatures that still live on today as lampreys and hagfish), so this is the first one that has what we might call a face: a mouth, nose and two eyes.
It’s difficult to conclude very much about the behavior or lifestyle of the ancient creature, but we do know that it swam in water (land animals didn’t begin to evolve until the Devonian period, which spanned 359 to 419 million years ago) and was likely a top-level predator of the early ocean ecosystem.
What has scientists so excited, though, is that the particular anatomical features of this fossil could upend our understanding of how vertebrates evolved over time. “When I first saw this, I was completely blown away,” says Matt Friedman a paleobiologist at the University of Oxford that reviewed the paper and wrote an accompanying article in Nature. “It’s the kind of fossil you might see once or twice in your lifetime, as a research scientist.”
Friedman and others find the fossil so remarkable because it combines a series of characteristics from two different groups: placoderms, an ancient class of armored fish that went extinct millions of years ago, and bony fish, a lineage that gave rise to all modern fish with jaws and bone skeletons. Previously, it was assumed that placoderms died out completely (and that the other, more recent types of fish with similar armor plating had independently re-evolved it much later), while a different, shark-like group of fish called acanthodians led to the bony fishes.
“What a fossil like this shows is that maybe that’s not the case,” Friedman says. “Because if you look at just the top of the skull and the body, it looks like a placoderm. But when you look at the side, and the front, you see it has jaws that, bone for bone, closely resemble the jaws of bony fish.”
This is significant because of what happened next: bony fish gave rise to all modern vertebrate fish, along with all amphibians, reptiles, birds and mammals, including ourselves. In other words, this fossil might mean that the placoderms didn’t go extinct, but rather evolved into the tremendous diversity of animals that live on both land and sea—and that this ancient, strange-looking face belongs to one of your oldest ancestors.
Scientists won’t immediately jump to reorganize their evolutionary family trees overnight, but the new finding will prompt a period of renewed scrutiny of the previous model. “It’s going to take a while for people to digest it and figure out what it all means,” Friedman says. “From a fossil like this, you’ve got a cascade of implications, and this is just the first paper to deal with them.”
Eventually, though, this finding could help transform our understanding of just how evolution occurred in our planet’s ancient oceans—and how the primitive creatures that swam in them eventually gave rise to the faces we see everyday.
September 24, 2013
When it comes to the calculating the likelihood of catastrophic weather, one group has an obvious and immediate financial stake in the game: the insurance industry. And in recent years, the industry researchers who attempt to determine the annual odds of catastrophic weather-related disasters—including floods and wind storms—say they’re seeing something new.
“Our business depends on us being neutral. We simply try to make the best possible assessment of risk today, with no vested interest,” says Robert Muir-Wood, the chief scientist of Risk Management Solutions (RMS), a company that creates software models to allow insurance companies to calculate risk. “In the past, when making these assessments, we looked to history. But in fact, we’ve now realized that that’s no longer a safe assumption—we can see, with certain phenomena in certain parts of the world, that the activity today is not simply the average of history.”
This pronounced shift can be seen in extreme rainfall events, heat waves and wind storms. The underlying reason, he says, is climate change, driven by rising greenhouse gas emissions. Muir-Wood’s company is responsible for figuring out just how much more risk the world’s insurance companies face as a result of climate change when homeowners buy policies to protect their property.
First, a brief primer on the concept of insurance: Essentially, it’s a tool for spreading risk—say, the chance your house will be washed away by a hurricane—among a larger group of people, so that the cost of rebuilding the destroyed house is shared by everyone who pays insurance. To accomplish this, insurance companies sell flood policies to thousands of homeowners and collect enough in payments from all of them so that they have enough to pay for the inevitable disaster, plus keep some extra revenue as profit afterward. To protect themselves, these insurance companies even buy their own policies from reinsurance companies, who make the same sorts of calculations, just on another level upward.
The tricky part, though, is determining just how much these companies need to charge to make sure they have enough to pay for disasters and to stay in business—and that’s where Muir-Wood’s work comes in. “If you think about it, it’s actually quite a difficult problem,” he says. “You’ve got to think about all the bad things that can happen, and then figure out how likely all those bad things are, and then work out ‘How much do I need to set aside per year to pay for all the catastrophic losses that can happen?’”
With natural disasters like floods, he notes, you can have many years in a row with no damage in one particular area, then have tens of thousands of houses destroyed at once. The fact that the frequency of some catastrophic weather events may be changing due to climate change makes the problem even more complex.
The best strategy for solving it is the use of computer models, which simulate thousands of the most extreme weather disasters—say, a record-setting hurricane slamming into the East Coast just when the power grid is overloaded due to a heat wave—to tell insurance companies the worst-case scenario, so they know just how much risk they’re taking on, and how likely it is they’ll have to pay out.
“Catastrophes are complex, and the kinds of things that happen during them are complex, so we are constantly trying to improve our modeling to capture the full range of extreme events,” Muir-Wood says, noting that RMS employs more than 100 scientists and mathematicians towards this goal. “When Hurricane Sandy happened, for instance, we already had events like Sandy in our models—we had anticipated the complexity of having a really big storm driving an enormous storm surge, even with wind speeds that were relatively modest.”
These models are not unlike those used by scientists to estimate the long-term changes our climate will undergo as it warms over the next century, but there’s one important difference: Insurance companies care mainly about the next year, not the next 100 years, because they mostly sell policies one year at a time.
But even in the short term, Muir-Wood’s team has determined, the risk of a variety of disasters seems to have already shifted. “The first model in which we changed our perspective is on U.S. Atlantic hurricanes. Basically, after the 2004 and 2005 seasons, we determined that it was unsafe to simply assume that historical averages still applied,” he says. “We’ve since seen that today’s activity has changed in other particular areas as well—with extreme rainfall events, such as the recent flooding in Boulder, Colorado, and with heat waves in certain parts of the world.”
RMS isn’t alone. In June, the Geneva Association, an insurance industry research group, released a report (PDF) outlining evidence of climate change and describing the new challenges insurance companies will face as it progresses. “In the non-stationary environment caused by ocean warming, traditional approaches, which are solely based on analyzing historical data, increasingly fail to estimate today’s hazard probabilities,” it stated. “A paradigm shift from historic to predictive risk assessment methods is necessary.”
Moving forward, Muir-Wood’s group will attempt to keep gauging the shifting likelihood of a range of extreme weather events, so that insurers can figure out how much to charge so that they can compete with others, but not be wiped out when disaster strikes. In particular, they’ll be closely looking at changing the model for flooding rates in higher latitudes, such as Canada and Russia—where climate is shifting more quickly—as well as wildfires around the planet.
On the whole, it seems likely that insurance premiums for houses and buildings in flood-prone coastal regions will go up to account for the shifts Muir-Wood is seeing. On the other hand, because of the complex impacts of climate change, we might see risks—and premiums—go down in other areas. There’s evidence, for example, that snowmelt-driven springtime floods in Britain will become less frequent in the future.
For his own part, Muir-Wood puts his money where his mouth is. “I personally wouldn’t invest in beachfront property anymore,” he says, noting the steady increase in sea level we’re expecting to see worldwide in the coming century, on top of more extreme storms. “And if you’re thinking about it, I’d calculate quite carefully how far back you’d have to be in the event of a hurricane.”
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.”
September 20, 2013
In a species of tiger moth native to the Arizona desert, scientists have discovered a new weapon in the endless evolutionary arms race between predator and prey. New research shows that the moths, Bertholdia trigona, have the ability to detect and jam bats’ biological sonar—the technique that allows bats to “see” through echolocation. The moths’ remarkable ability, which as far as scientists know is unique in the animal kingdom, allows the insect to evade hungry bats and fly away.
Evidence of this ability was first uncovered in 2009, by a group led by Aaron Corcoran, a wildlife biologist who was then a PhD student at Wake Forest University. “It started with a question has been out there for a while, since the 1960s—why do some moths produce clicking sounds when bats attack them?” Corcoran explains.
Scientists knew that most species of tiger moths that emitted ultrasonic clicking sounds did so to signal their toxicity to bats—similar to how, for example, poison dart frogs are brightly colored so that predators can easily associate their striking hues with toxic substances and learn to look elsewhere for food. This particular species, though, emitted about ten times as much sound as most moths, indicating that it might be serving a different purpose entirely.
To learn more, he and colleagues collected trigona moths, put them in a mesh cage, attached them to ultra-thin filaments to keep track of their survival, and introduced brown bats. “If the sounds are for warning purposes, it’s well-documented that the bats have to learn to associate the clicks with toxic prey over time,” he says. “So if that were the case, at first, they’d ignore the clicks and capture the moth, but eventually they’d learn that it’s toxic, and avoid it.”
But that wasn’t what happened. The bats didn’t have to learn to avoid the moths—rather, Corcoran says, “they couldn’t catch them right from the beginning.” The reason for this, they determined, was that the moths were using the clicks to jam the bats’ sonar.
A bat’s sonar works like this: Normally—because they hunt at night and their eyesight is so poorly developed—bats send out ultrasonic noises and analyze the path they take as they bounce back to “see” their environment. But when approached by the bats, the moths produced their own ultrasonic clicking sounds at a rate of 4,500 times per second, blanketing the surrounding environment and cloaking themselves from sonar detection.
“This effectively blurs the acoustic image the bat has of the moth,” Corcoran says. “It knows there’s a moth out there, but can’t quite figure out where it is.”
But the experiment left a remaining question: How did the moths know when to activate their anti-bat signal? The team’s latest work, published this summer in PLOS ONE, shows that the trigona moths are equipped with a built-in sonar detection system.
As the bats approach, they increase the frequency of their calls to paint a more detailed picture of their prey. Corcoran’s team hypothesized that the moths listen to this frequency, along with the raw volume of the bats’ calls, to determine when they’re in danger of attack.
To test this idea, he attached tiny microphones to moths to record the exact sounds they heard when attacked by bats. He also stationed microphones a few feet away. The mics near the moths heard a slightly different sound profile of approaching bats. Then, he played each of these sounds to an entirely different group of moths to see their responses.
The moths that heard the recordings only began emitting their own ultrasonic noises when the researchers played the sounds heard by the moths actually in peril—and not the sounds that would be heard by moths a few feet away from the one in danger. By analyzing the two acoustic variables (volume and frequency), the moths could effectively differentiate between the two.
The moths click “only when they can confidently determine that they’re getting attacked,” Corcoran says. This makes sense, because the ability to figure out exactly when they’re in danger is particularly crucial for this species of tiger moths—unlike other, toxic species, these ones taste good to bats.