Here’s a good idea for the next presidential candidate debate: They can insult each other about their ignorance of statistics.
Actually, it’s a pertinent topic for political office seekers, as public opinion polls use statistical methods to measure the electorate’s support (or lack thereof) for a particular candidate. But such polls are notoriously unreliable, as Hillary Clinton found out in Michigan.
It probably wouldn’t be a very informative debate, of course — just imagine how Donald Trump would respond to a question asking what he thought about P values. Sadly, though, he and the other candidates might actually understand P values just about as well as many practicing scientists — which is to say, not very well at all. In recent years criticism about P values — statistical measures widely used to analyze experimental data in most scientific disciplines — has finally reverberated loudly enough for the scientific community to listen. A watershed acknowledgment of P value problems appeared this week when the American Statistical Association issued a statement warning the rest of the world about the limitations of P values and their widespread misuse.
“While the p-value can be a useful statistical measure, it is commonly misused and misinterpreted,” the statistical association report stated. “This has led to some scientific journals discouraging the use of p-values, and some scientists and statisticians recommending their abandonment.”
In light of these issues, the association convened a group of experts to formulate a document listing six “principles” regarding P values for the guidance of “researchers, practitioners and science writers who are not primarily statisticians.” Of those six principles, the most pertinent for people in general (and science journalists in particular) is No. 5: “A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.”
What, then, does it measure? That’s principle No. 1: “… how incompatible the data are with a specified statistical model.” But note well principle No. 2: “P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.” And therefore, always remember principle No. 3: “Scientific conclusions … or policy decisions should not be based only on whether a p-value passes a specific threshold.”
In other words, the common convention of judging a P value less than .05 to be “statistically significant” is not really a proper basis for assigning significance at all. Except that scientific journals still regularly use that criterion for deciding whether a paper gets published. Which in turn drives researchers to finagle their data to get a P value of less than .05. As a result, the scientific process is tarnished and the published scientific literature is often unreliable. As the statistical association statement points out, this situation is far from merely of academic concern.
“The issues touched on here affect not only research, but research funding, journal practices, career advancement, scientific education, public policy, journalism, and law,” the authors point out in the report, published online March 7 in The American Statistician.
Many of the experts who participated in the process wrote commentaries on the document, some stressing that it did not go far enough in condemning P values’ pernicious influences on science.
“Viewed alone, p-values calculated from a set of numbers and assuming a statistical model are of limited value and frequently are meaningless,” wrote biostatistician Donald Berry of MD Anderson Cancer Center in Houston. He cited the serious negative impact that misuse and misinterpretation of P values has had not only on science, but also on society. “Patients with serious diseases have been harmed. Researchers have chased wild geese, finding too often that statistically significant conclusions could not be reproduced. The economic impacts of faulty statistical conclusions are great.”
Echoing Berry’s concerns was Boston University epidemiologist Kenneth Rothman. “It is a safe bet that people have suffered or died because scientists (and editors, regulators, journalists and others) have used significance tests to interpret results,” Rothman wrote. “The correspondence between results that are statistically significant and those that are truly important is far too low to be useful. Consequently, scientists have embraced and even avidly pursued meaningless differences solely because they are statistically significant, and have ignored important effects because they failed to pass the screen of statistical significance.”
Stanford University epidemiologist John Ioannidis compared the scientific community’s attachment to P values with drug addiction, fueled by the institutional rewards that accompany the publication process.
“Misleading use of P-values is so easy and automated that, especially when rewarded with publication and funding, it can become addictive,” Ioannidis commented. “Investigators generating these torrents of P-values should be seen with sympathy as drug addicts in need of rehabilitation that will help them live a better, more meaningful scientific life in the future.”
Although a handful of P value defenders can still be found among the participants in this discussion, it should be clear by now that P values, as currently used in science, do more harm than good. They may be valid and useful under certain specific circumstances, but those circumstances are rarely relevant in most experimental contexts. As Berry notes, statisticians can correctly define P values in a technical sense, but “most statisticians do not really understand the issues in applied settings.”
In its statement, the statistical association goes a long way toward validating the concerns about P values that have been expressed for decades by many critical observers. This validation may succeed in initiating change where previous efforts have failed. But that won’t happen without identifying some alternative to the P value system, and while many have been proposed, no candidate has emerged as an acceptable nominee for a majority of the scientific world’s electorate. So the next debate should not be about P values — it should be about what to replace them with.
NEW YORK — Lip-readers’ minds seem to “hear” the words their eyes see being formed. And the better a person is at lipreading, the more neural activity there is in the brain’s auditory cortex, scientists reported April 4 at the annual meeting of the Cognitive Neuroscience Society.
Earlier studies have found that auditory brain areas are active during lipreading. But most of those studies focused on small bits of language — simple sentences or even single words, said study coauthor Satu Saalasti of Aalto University in Finland. In contrast, Saalasti and colleagues studied lipreading in more natural situations. Twenty-nine people read the silent lips of a person who spoke Finnish for eight minutes in a video. “We can all lip-read to some extent,” Saalasti said, and the participants, who had no lipreading experience, varied widely in their comprehension of the eight-minute story.
In the best lip-readers, activity in the auditory cortex was quite similar to that evoked when the story was read aloud, brain scans revealed. The results suggest that lipreading success depends on a person’s ability to “hear” the words formed by moving lips, Saalasti said.
To rewrite an Alanis Morissette song, the brain has a funny way of waking you up (and putting you to sleep). Isn’t it ionic? Some scientists think so.
Changes in ion concentrations, not nerve cell activity, switch the brain from asleep to awake and back again, researchers report in the April 29 Science. Scientists knew that levels of potassium, calcium and magnesium ions bathing brain cells changed during sleep and wakefulness. But they thought neurons — electrically active cells responsible for most of the brain’s processing power — drove those changes. Instead, the study suggests, neurons aren’t the only sandmen or roosters in the brain. “Neuromodulator” brain chemicals, which pace neuron activity, can bypass neurons altogether to directly wake the brain or lull it to sleep by changing ion concentrations.
Scientists hadn’t found this direct connection between ions and sleep and wake before because they were mostly focused on what neurons were doing, says neuroscientist Maiken Nedergaard, who led the study. She got interested in sleep after her lab at the University of Rochester in New York found a drainage system that washes the brain during sleep (SN: 11/16/13, p. 7).When measuring changes in the fluid between brain cells, Nedergaard and colleagues realized that ion changes followed predictable patterns: Potassium ion levels are high when mice (and presumably people) are awake, and drop during sleep. Calcium and magnesium ions follow the opposite pattern; they are higher during sleep and lower when mice are awake. In the study, Nedergaard’s group administered a “wake cocktail” of neuromodulator chemicals to mouse brains. Levels of potassium ions floating between brain cells increased rapidly after the treatment, the researchers found. That ion change happened even when the researchers added tetrodotoxin to stop neuron activity. The results suggest that the brain chemicals — norepinephrine, acetylcholine, dopamine, orexin and histamine — directly affect ion levels with no help from neurons. Exactly how the chemicals manage ion levels still isn’t known. Similar changes happen under anesthesia. When awake mice were anesthetized, potassium ion levels in their brains dropped sharply, while levels of calcium and magnesium rose, the researchers found. As mice awoke from anesthesia, potassium ion levels rose quickly. But calcium and magnesium levels took longer to drop. As a result, the mice “are totally confused,” says Nedergaard. “They bump into their cages, they run around and they don’t know what they are doing.”
Those results may help explain why people are groggy after waking up from anesthesia; their ion levels haven’t returned to “awake” levels yet, says Amita Sehgal, a sleep researcher at the University of Pennsylvania School of Medicine.
Learning more about how ions affect wake and sleep may eventually lead to a better understanding of sleep, consciousness and coma, Nedergaard says.
But, says neuroscientist Chiara Cirelli of the University of Wisconsin‒Madison, practical implications of the work, such as improved sleep drugs, are probably far in the future. “How they make use of it will take some time, but just knowing this is certainly very eye-opening.” It would be interesting to find out what happens to ion concentrations during REM sleep, when neurons are as active as they are when a person is awake, she says.
You wouldn’t expect wardrobe classics like leather jackets or denim jeans at an exhibit celebrating fashion at its most forward. But “#techstyle” at the Museum of Fine Arts in Boston features those sartorial mainstays and others, each with a technological twist.
A feast for the eyes, the diversity of pieces is matched by the diversity of artists and approaches. Yet a single theme unites: The fusion of technology and fashion will increasingly influence both. Visitors are introduced to this theme via a room featuring works by prominent designers already known for merging fashion and tech: A digitally printed silk dress by Alexander McQueen hangs next to a fiberglass “airplane dress” by Hussein Chalayan that has flaps that open and shut via remote control. The largest part of the exhibit focuses on how technology is changing design and construction strategies. In addition to clothes made with mainstream techniques like laser-cutting, several 3-D printed garments are on display. These include a kinematic dress made of more than 1,600 interlocking pieces that can be customized to a wearer’s body via a 3-D scan. The dress comes off the printer fully assembled. Other pieces are made with technologies still being developed, such as the laser-welded fabrics from sustainable textile researcher Kate Goldsworthy. The real standouts are in the “Performance” section, which displays attire that uses data from the immediate environment to generate some visible aspect of the garment. These interactive pieces “reveal something to the eye that you wouldn’t see normally, something that science often captures with graphs and charts,” says Pamela Parmal, a curator of the exhibit. For instance, the interactive dress “Incertitudes” is adorned with pins that flex in response to nearby voices, creating waves in the fabric; a dress embedded with thousands of tiny LEDs can display tweeted messages or other illuminated patterns. And there are two leather jackets that, at first glance, look like their innovation is merely a stylish cut. But the jackets are coated in reactive inks that shimmer with iridescent colors in response to the wind and heat generated by heat guns in the display case. (These creations were born after designer and trained chemist Lauren Bowker used the reactive compounds to reveal the aerodynamics of race cars in a wind tunnel in a project for Formula One.)
Visitors seeking in-depth explanations of the science behind the fashions will have to look elsewhere. But “#techstyle” still has something for everyone, whether fashionista or engineer. And while the fashions represented are all cutting edge, the show harks back to an era when clothes were custom-made. Technology might have brought us mass-produced cookie-cutter clothing, but it can also enable clothing tailored to the individual.
From within the dark confines of the skull, the brain builds its own version of reality. By weaving together expectations and information gleaned from the senses, the brain creates a story about the outside world. For most of us, the brain is a skilled storyteller, but to spin a sensible yarn, it has to fill in some details itself.
“The brain is a guessing machine, trying at each moment of time to guess what is out there,” says computational neuroscientist Peggy Seriès. Guesses just slightly off — like mistaking a smile for a smirk — rarely cause harm. But guessing gone seriously awry may play a part in mental illnesses such as schizophrenia, autism and even anxiety disorders, Seriès and other neuroscientists suspect. They say that a mathematical expression known as Bayes’ theorem — which quantifies how prior expectations can be combined with current evidence — may provide novel insights into pernicious mental problems that have so far defied explanation. Bayes’ theorem “offers a new vocabulary, new tools and a new way to look at things,” says Seriès, of the University of Edinburgh.
Experiments guided by Bayesian math reveal that the guessing process differs in people with some disorders. People with schizophrenia, for instance, can have trouble tying together their expectations with what their senses detect. And people with autism and high anxiety don’t flexibly update their expectations about the world, some lab experiments suggest. That missed step can muddy their decision-making abilities. Given the complexity of mental disorders such as schizophrenia and autism, it is no surprise that many theories of how the brain works have fallen short, says psychiatrist and neuroscientist Rick Adams of University College London. Current explanations for the disorders are often vague and untestable. Against that frustrating backdrop, Adams sees great promise in a strong mathematical theory, one that can be used to make predictions and actually test them.
“It’s really a step up from the old-style cognitive psychology approach, where you had flowcharts with boxes and labels on them with things like ‘attention’ or ‘reading,’ but nobody having any idea about what was going on in [any] box,” Adams says.
Applying math to mental disorders “is a very young field,” he adds, pointing to Computational Psychiatry, which plans to publish its first issue this summer. “You know a field is young when it gets its first journal.”
A mind for math Bayesian reasoning may be new to the mental illness scene, but the math itself has been around for centuries. First described by the Rev. Thomas Bayes in the 18th century, this computational approach truly embraces history: Evidence based on previous experience, known as a “prior,” is essential to arriving at a good answer, Bayes argued. He may have been surprised to see his math meticulously applied to people with mental illness, but the logic holds. To make a solid guess about what’s happening in the world, the brain must not rely just on current input from occasionally unreliable senses. The brain must also use its knowledge about what has happened before. Merging these two streams of information correctly is at the heart of perceiving the world as accurately as possible.
Bayes figured out a way to put numbers to this process. By combining probabilities that come from prior evidence and current observations, Bayes’ formula can be used to calculate an overall estimate of the likelihood that a given suspicion is true. A properly functioning brain seems to do this calculation intuitively, behaving in many cases like a skilled Bayesian statistician, some studies show (SN: 10/8/11, p. 18).
This reckoning requires the brain to give the right amount of weight to prior expectations and current information. Depending on the circumstances, those weights change. When the senses falter, for instance, the brain should lean more heavily on prior expectations. Say the mail carrier comes each day at 4 p.m. On a stormy afternoon when visual cues are bad, we rely less on sight and more on prior knowledge to guess that the late-afternoon noise on the front porch is probably the mail carrier delivering letters. In certain mental illnesses, this flexible balancing act may falter.
People with schizophrenia often suffer from hallucinations and delusions, debilitating symptoms that arise when lines between reality and imagination blur. That confusion can lead to hearing voices that aren’t there and believing things that can’t possibly be true. These departures from reality could arise from differences in how people integrate new evidence with previous beliefs. There’s evidence for such distorted calculations. People with schizophrenia don’t fall for certain visual illusions that trick most people, for instance. When shown a picture of the inside of a hollowed-out face mask, most people’s brains mistakenly convert the image to a face that pops outward off the page. People with schizophrenia, however, are more likely to see the face as it actually is — a concave mask. In that instance, people with schizophrenia give more weight to information that’s coming from their eyes than to their expectation that noses protrude from the rest of the face. To complicate matters, the opposite can be true, too, says neuropsychologist Chris Frith of the Wellcome Trust Centre for Neuroimaging at University College London. “In this case, their prior is too weak, but in other cases, their prior is too strong,” he says.
In a recent study, healthy people and those who recently began experiencing psychosis, a symptom of schizophrenia, were shown confusing shadowy black-and-white images. Participants then saw color versions of the images that were easier to interpret. When shown the black-and-white images again, people with early psychosis were better at identifying the images, suggesting that they used their prior knowledge — the color pictures — to truly “see” the images. For people without psychosis, the color images weren’t as much help. That difference suggests that the way people with schizophrenia balance past knowledge and present observations is distinct from the behavior of people without the disorder. Sometimes the balance tips too far — in either direction.
In a talk at the annual Computational and Systems Neuroscience meeting in February in Salt Lake City, Seriès described the results of a different visual test: A small group of people with schizophrenia had to describe which way a series of dots were moving on a screen. The dots moved in some directions more frequently than others — a statistical feature that let the scientists see how well people could learn to predict the dots’ directions. The 11 people with schizophrenia seemed just as good at learning which way the dots were likely to move as the 10 people without, Seriès said. In this situation, people with schizophrenia seemed able to learn priors just fine.
But when another trick was added, a split between the two groups emerged. Sometimes, the dots were almost impossible to see, and sometimes, there were no dots at all. People with schizophrenia were less likely to claim that they saw dots when the screen was blank. Perhaps they didn’t hallucinate dots because of the medication they were on, Seriès says. In fact, very early results from unmedicated people with schizophrenia suggest that they actually see dots that aren’t there more than healthy volunteers. Preliminary results so far on schizophrenia are sparse and occasionally conflicting, Seriès admits. “It’s the beginning,” she says. “We don’t understand much.”
The research is so early that no straightforward story exists yet. But that’s not unexpected. “If 100 years of schizophrenia research have taught us anything, it’s that there’s not going to be a nice, simple explanation,” Adams says. But using math to describe how people perceive the world may lead to new hunches about how that process goes wrong in mental illnesses, he argues.
“You can instill expectations in subjects in many different ways, and you can control what evidence they see,” Adams says. Bayesian theory “tells you what they should conclude from those prior beliefs and that evidence.” If their conclusions diverge from predictions, scientists can take the next step. Brain scans, for instance, may reveal how the wrong answers arise. With a clear description of these differences, he says, “we might be able to measure people’s cognition in a new way, and diagnose their disorders in a new way.”
Now vs. then The way the brain combines incoming sensory information with existing knowledge may also be different in autism, some researchers argue. In some cases, people with autism might put excess weight on what their senses take in about the world and rely less on their expectations. Old observations fit with this idea. In the 1960s, psychologists had discovered that children with autism were just as good at remembering nonsense sentences (“By is go tree stroke lets”) as meaningful ones (“The fish swims in the pond”). Children without autism struggled to remember the non sequiturs. But the children with autism weren’t thrown by the random string of words, suggesting that their expectations of sentence meaning weren’t as strong as their ability to home in on each word in the series.
Another study supports the notion that sensory information takes priority in people with autism. People with and without autism were asked to judge whether a sight and a sound happened at the same time. They saw a white ring on a screen, and a tone played before, after or at the same time. Adults without autism were influenced by previous trials in which the ring and tone were slightly off. But adults with autism were not swayed by earlier trials, researchers reported in February in Scientific Reports.
This literal perception might get in the way of speech perception, Marco Turi of the University of Pisa in Italy and colleagues suggest. Comprehending speech requires a listener to mentally stitch together sights and sounds that may not arrive at the eyes and ears at the same time. Losing that flexibility could make speech harder to understand.
A different study found that children with autism perceive moving dots more clearly than children without autism (SN Online: 5/5/15). The brains of people with autism seem to prioritize incoming sensory information over expectations about how things ought to work. Elizabeth Pellicano of University College London and David Burr of the University of Western Australia in Perth described the concept in 2012 in an opinion paper in Trends in Cognitive Sciences. Intensely attuned to information streaming in from the senses, people with autism experience the world as “too real,” Pellicano and Perth wrote.
New data, however, caution against a too-simple explanation. In an experiment presented in New York City in April at the annual meeting of the Cognitive Neuroscience Society, 20 adults with and without autism had to quickly hit a certain key on a keyboard when they saw its associated target on a screen. Their job was made easier because the targets came in a certain sequence. All of the participants improved as they learned which keys to expect. But when the sequence changed to a new one, people with autism faltered. This result suggests that they learned prior expectations just fine, but had trouble updating them as conditions changed, said cognitive neuroscientist Owen Parsons of the University of Cambridge. Distorted calculations — and the altered versions of the world they create — may also play a role in depression and anxiety, some researchers think. While suffering from depression, people may hold on to distorted priors — believing that good things are out of reach, for instance. And people with high anxiety can have trouble making good choices in a volatile environment, neuroscientist Sonia Bishop of the University of California, Berkeley and colleagues reported in 2015 in Nature Neuroscience.
In their experiment, people had to choose a shape, which sometimes came with a shock. People with low anxiety quickly learned to avoid the shock, even when the relationship between shape and shock changed. But people with high anxiety performed worse when those relationships changed, the researchers found. “High-anxious individuals didn’t seem able to adjust their learning to handle how volatile or how stable the environment was,” Bishop says. Scientists can’t yet say what causes this difficulty adjusting to a new environment in anxious people and in people with autism. It could be that once some rule is learned (a sequence of computer keys, or the link between a shape and a shock), these two groups struggle to update that prior with newer information.
This rigidity might actually contribute to anxiety in the first place, Bishop speculates. “When something unexpected happens that is bad, you wouldn’t know how to respond,” and that floundering “is likely to be a huge source of anxiety and stress.”
Recalculating “There’s been a lot of frustration with a failure to make progress” on psychiatric disorders, Bishop says. Fitting mathematical theories to the brain may be a way to move forward. Researchers “are very excited about computational psychiatry in general,” she says.
Computational psychiatrist Quentin Huys of the University of Zurich is one of those people. Math can help clarify mental illnesses in a way that existing approaches can’t, he says. In the March issue of Nature Neuroscience, Huys and colleagues argued that math can demystify psychiatric disorders, and that thinking of the brain as a Bayesian number cruncher might lead to a more rigorous understanding of mental illness. Huys says that a computational approach is essential. “We can’t get away without it.” If people with high anxiety perform differently on a perceptual test, then that test could be used to both diagnose people and monitor how well a treatment works, for instance.
Scientists hope that a deeper description of mental illnesses may lead to clearer ways to identify a disorder, chart how well treatments work and even improve therapies. Bishop raises the possibility of developing apps to help people with high anxiety evaluate situations — outsourcing the decision making for people who have trouble. Frith points out that cognitive behavioral therapy could help depressed people recalculate their experiences by putting less weight on negative experiences and perhaps breaking out of cycles of despondence.
Beyond these potential interventions, simply explaining to people how their brains are working might ease distress, Adams says. “If you can give people an explanation that makes sense of some of the experiences they’ve had, that can be a profoundly helpful thing,” he says. “It destigmatizes the experience.”
Our home planet is young at heart. According to new calculations, Earth’s center is more than two years younger than its surface.
In Einstein’s general theory of relativity, massive objects warp the fabric of spacetime, creating a gravitational pull and slowing time nearby. So a clock placed at Earth’s center will tick ever-so-slightly slower than a clock at its surface. Such time shifts are determined by the gravitational potential, a measure of the amount of work it would take to move an object from one place to another. Since climbing up from Earth’s center would be a struggle against gravity, clocks down deep would run slow relative to surface timepieces. Over the 4.5 billion years of Earth’s history, the gradual shaving off of fractions of a second adds up to a core that’s 2.5 years younger than the planet’s crust, researchers estimate in the May European Journal of Physics. Theoretical physicist Richard Feynman had suggested in the 1960s that the core was younger, but only by a few days. The new calculation neglects geological processes, which have a larger impact on the planet’s age. For example, Earth’s core probably formed earlier than its crust. Instead, says study author Ulrik Uggerhøj of Aarhus University in Denmark, the calculation serves as an illustration of gravity’s influence on time — very close to home.
The green hairstreak butterfly (Callophrys rubi) gets its blue-green hue from complex nanoscale structures on its wings. The structures, called gyroids, are repeating patterns of spiral-shaped curls. Light waves bouncing off the patterned surface (top inset above) interfere with one another, amplifying green colors while washing out other shades (SN: 6/7/08, p. 26).
Scientists led by Min Gu of the Royal Melbourne Institute of Technology in Australia have now painstakingly re-created the gyroid structure by sculpting the shapes out of a special resin that solidifies when hit with laser light. The technique, called optical two-beam lithography, uses a pair of lasers to set the material in just the right pattern. Afterward, the remaining resin can be washed away, leaving only the gyroid structure. The fabricated version repeats its pattern every 360 nanometers, or billionths of a meter.
The gyroid structures determine more than just color. They also divvy up light that is circularly polarized — its electric fields spiral either clockwise or counterclockwise. In the butterfly, this effect is weak because of irregularities in the structure. But the artificial version sorts the light according to polarization, reflecting one type much more than the other, the researchers report May 13 in Science Advances.
The ability to control circular polarization of light with structures like these could allow scientists to increase the bandwidth of optical communications, the researchers say. The two polarizations of light could each carry different information, which could then be separated and decoded down the line.
In Colorado’s Rocky Mountains, male and female valerian plants have responded differently to hotter, drier conditions, a new study shows. Rapidly changing ratios of the sexes could be a quick sign of climate change, the researchers say.
Valerian (Valeriana edulis) plants range from hot, scrubby lowlands to cold alpine slopes. In each patch of plants, some are male and some are female. The exact proportion of each sex varies with elevation. High on the mountain, females are much more common than males; they can make up 80 percent of some populations. Four decades ago, in patches of valerian growing in the middle of the plant’s elevation range, 33.4 percent of the plants were males. Those patches grew in the Rockies at elevations around 3,000 meters. Today, you would have to hike considerably higher to find the same proportion of male plants. Males, now 5.5 percent more common on average, are reaching higher elevations than in the past, researchers report in the July 1 Science.
“We think climate is acting almost like a filter on males and females,” says Will Petry of ETH Zurich, who led the study while at the University of California, Irvine. “The settings on this filter are controlling the sex ratio.” Those settings are sweeping up the mountainside like a rising tide at a rate of 175 meters per decade, Petry and colleagues found. Ecologists already knew that the ratio of male to female plants can vary with altitude or water availability, says ecologist Spencer Barrett of the University of Toronto, who was not involved in this study. But “the idea that a sex ratio is moving upslope — nobody’s ever done that before.”
Those moving sex ratios have kept pace with climate change since the late 1970s. Today, winter snows are melting earlier and summers are hotter, with less rain. As a result, the same amount of precipitation that would have fallen at one elevation in 1978 now falls at higher elevations instead; it has moved upslope by 133 meters per decade. Soil moisture has moved up the mountain, too, by 195 meters per decade.
The parallel shifts mean that changing sex ratios could be a marker of climate change, says population biologist Tom Miller of Rice University in Houston, a coauthor of the study. Today, movements of whole species — often up in latitude or altitude — are a hallmark of climate change. But proportions of males and females are changing “substantially faster than species are moving,” Miller says. They “might be a much more rapid fingerprint of climate change than where species are migrating to.” Petry’s team found that fingerprint while hiking around the Rocky Mountain Biological Laboratory in Crested Butte, Colo. As the scientists walked through the mountains in Chaffee and Gunnison counties, they counted flowering males and females at 31 sites in 2011, then compared their modern data with historical counts from nine of the same populations, made by coauthor Judy Soule from 1978 to 1980. When Petry saw that the percentage of males and females had changed, “we also started thinking about the consequences,” he says.
If one sex vastly outnumbers the other, populations could die out. “Imagine if it became an Amazonia situation,” says Kailen Mooney, whose lab at UC Irvine led the new study. A 100 percent female population wouldn’t be pollinated, and would disappear once the mature females died, he says.
If those female-only populations grew above a certain altitude and died out because males couldn’t reach them, then male plants would set the upper boundary for the whole species. Sex ratios “add nuance” to the way scientists think about climate-driven migration, Mooney says, because one sex could determine geographic limits for whole species.
NASA’s Juno spacecraft has sent back its first picture of Jupiter since arriving at the planet July 4 (SN: 7/23/16, p. 14). The image, taken July 10 when the spacecraft was 4.3 million kilometers from Jupiter, shows off the planet’s clouds, its Great Red Spot (a storm a bit wider than Earth) and three of its moons (Io, Europa and Ganymede).
Juno is on the outbound leg of its first of two 53.5-day orbits of the gas giant (Juno will then settle into 14-day orbits). During orbit insertion, all of Juno’s scientific instruments were turned off while the spacecraft made its first dive through the harsh radiation belts that encircle the planet. This first image indicates that Juno is in good health and ready to study the largest planet in the solar system.
The probe is the ninth to visit Jupiter and the second to stay in orbit (SN: 6/25/2016, p. 32). For the next 20 months, Juno will investigate what lurks beneath the opaque clouds that enshroud the planet (SN: 6/25/2016, p. 16). The spacecraft won’t take its first intimate pictures of Jupiter until August 27, when it flies within 5,000 kilometers of the cloud tops.
U.S. drivers love to hit the road. The problem is doing so safely.
In 2013, 32,894 people in the United States died in motor vehicle crashes. Although down since 2000, the overall death rate — 10.3 per 100,000 people — tops 19 other high-income countries, the U.S. Centers for Disease Control and Prevention reported July 8. Belgium is a distant second with 6.5 deaths per 100,000. Researchers reviewed World Health Organization and other data on vehicle crash deaths, seat belt use and alcohol-impaired driving in 2000 and 2013. Canada had the highest percentage of fatal crashes caused by drunk drivers: 33.6 percent. New Zealand and the United States tied for second at 31 percent. But Canada and 16 other countries outperformed the United States on seat belt use — even though, in 2013, 87 percent of people in the United States reported wearing safety belts while riding in the front seat.
Spain saw the biggest drop — 75 percent — in its crash death rate. That country improved nearly all aspects of road safety, including decreasing alcohol-impaired driving and increasing seat belt use, the researchers say.
