Young children are terrible at hiding – psychologists have a new theory why

By Henrike Moll, University of Southern California – Dornsife College of Letters, Arts and Sciences and Allie Khalulyan, University of Southern California – Dornsife College of Letters, Arts and Sciences.

Young children across the globe enjoy playing games of hide and seek. There’s something highly exciting for children about escaping someone else’s glance and making oneself “invisible.”

However, developmental psychologists and parents alike continue to witness that before school age, children are remarkably bad at hiding. Curiously, they often cover only their face or eyes with their hands, leaving the rest of their bodies visibly exposed.

For a long time, this ineffective hiding strategy was interpreted as evidence that young children are hopelessly “egocentric” creatures. Psychologists theorized that preschool children cannot distinguish their own perspective from someone else’s. Conventional wisdom held that, unable to transcend their own viewpoint, children falsely assume that others see the world the same way they themselves do. So psychologists assumed children “hide” by covering their eyes because they conflate their own lack of vision with that of those around them.

But research in cognitive developmental psychology is starting to cast doubt on this notion of childhood egocentrism. We brought young children between the ages of two and four into our Minds in Development Lab at USC so we could investigate this assumption. Our surprising results contradict the idea that children’s poor hiding skills reflect their allegedly egocentric nature.

Who can see whom?

Each child in our study sat down with an adult who covered her own eyes or ears with her hands. We then asked the child whether or not she could see or hear the adult, respectively. Surprisingly, children denied that they could. The same thing happened when the adult covered her own mouth: Now children denied that they could speak to her.

A number of control experiments ruled out that the children were confused or misunderstood what they were being asked. The results were clear: Our young subjects comprehended the questions and knew exactly what was asked of them. Their negative responses reflected their genuine belief that the other person could not be seen, heard, or spoken to when her eyes, ears, or mouth were obstructed. Despite the fact that the person in front of them was in plain view, they flatout denied being able to perceive her. So what was going on?

It seems like young children consider mutual eye contact a requirement for one person to be able to see another. Their thinking appears to run along the lines of “I can see you only if you can see me, too” and vice versa. Our findings suggest that when a child “hides” by putting a blanket over her head, this strategy is not a result of egocentrism. In fact, children deem this strategy effective when others use it.

Built into their notion of visibility, then, is the idea of bidirectionality: Unless two people make eye contact, it is impossible for one to see the other. Contrary to egocentrism, young children simply insist on mutual recognition and regard.

An expectation of mutual engagement

Children’s demand of reciprocity demonstrates that they are not at all egocentric. Not only can preschoolers imagine the world as seen from another’s point of view; they even apply this capacity in situations where it’s unnecessary or leads to wrong judgments, such as when they are asked to report their own perception. These faulty judgments – saying that others whose eyes are covered cannot be seen – reveal just how much children’s perception of the world is colored by others.

The seemingly irrational way in which children try to hide from others and the negative answers they gave in our experiment show that children feel unable to relate to a person unless the communication flows both ways – not only from me to you but also from you to me, so we can communicate with each other as equals.

We are planning to investigate children’s hiding behavior directly in the lab and test if kids who are bad at hiding show more reciprocity in play and conversation than those who hide more skillfully. We would also like to conduct these experiments with children who show an atypical trajectory in their early development.

Children want to interact with the people around them.
Eye contact image via

Our findings underscore children’s natural desire and preference for reciprocity and mutual engagement between individuals. Children expect and strive to create situations in which they can be reciprocally involved with others. They want to encounter people who are not only looked at but who can return another’s gaze; people who not only listen but are also heard; and people who are not just spoken to but who can reply and thus enter a mutual dialogue.

At least in this respect, young children understand and treat other human beings in a manner that is not at all egocentric. On the contrary, their insistence on mutual regard is remarkably mature and can be considered inspirational. Adults may want to turn to these preschoolers as role models when it comes to perceiving and relating to other humans. These young children seem exquisitely aware that we all share a common nature as people who are in constant interaction with others.

The ConversationHenrike Moll, Assistant Professor in Developmental Psychology, University of Southern California – Dornsife College of Letters, Arts and Sciences and Allie Khalulyan, Ph.D. Student in Developmental Psychology, University of Southern California – Dornsife College of Letters, Arts and Sciences

This article was originally published on The Conversation. Read the original article.

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Eat Lots of Fiber or Microbes Will Eat Your Colon

It sounds like the plot of a 1950s science fiction movie: normal, helpful bacteria begin to eat their host from within, because they don’t get what they want.

But that’s exactly what happens when microbes inside the digestive system don’t get the natural fiber that they rely on for food.

Starved, they begin to munch on the natural layer of mucus that lines the gut, eroding it to the point where dangerous invading bacteria can infect the colon wall.

For a new study, researchers looked at the impact of fiber deprivation on the guts of specially raised mice. The mice were born and raised with no gut microbes of their own, then received a transplant of 14 bacteria that normally grow in the human gut. They knew the full genetic signature of each one, so were able to track activity over time.

Fiber, fiber, fiber

The findings, published in the journal Cell, have implications for understanding not only the role of fiber in a normal diet, but also the potential of using fiber to counter the effects of digestive tract disorders.

“The lesson we’re learning from studying the interaction of fiber, gut microbes, and the intestinal barrier system is that if you don’t feed them, they can eat you,” says Eric Martens, associate professor of microbiology at the University of Michigan Medical School.

Researchers used the gnotobiotic, or germ-free, mouse facility and advanced genetic techniques to determine which bacteria were present and active under different conditions. They studied the impact of diets with different fiber content—and those with no fiber. They also infected some of the mice with a bacterial strain that does to mice what certain strains of Escherichia coli can do to humans—cause gut infections that lead to irritation, inflammation, diarrhea, and more.

The result: the mucus layer stayed thick, and the infection didn’t take full hold in mice that received a diet that was about 15 percent fiber from minimally processed grains and plants. But when the researchers substituted a diet with no fiber in it, even for a few days, some of the microbes in their guts began to munch on the mucus.

They also tried a diet that was rich in prebiotic fiber—purified forms of soluble fiber similar to what some processed foods and supplements currently contain. This diet resulted in a similar erosion of the mucus layer as observed in the lack of fiber.

The researchers also saw that the mix of bacteria changed depending on what the mice were being fed, even day by day. Some species of bacteria in the transplanted microbiome were more common—meaning they had reproduced more—in low-fiber conditions, others in high-fiber conditions.

And the four bacteria strains that flourished most in low-fiber and no-fiber conditions were the only ones that make enzymes that are capable of breaking down the long molecules called glycoproteins that make up the mucus layer.

In addition to looking at the of bacteria based on genetic information, the researchers could see which fiber-digesting enzymes the bacteria were making. They detected more than 1,600 different enzymes capable of degrading carbohydrates—similar to the complexity in the normal human gut.

Mucus layer

Just like the mix of bacteria, the mix of enzymes changed depending on what the mice were being fed, with even occasional fiber deprivation leading to more production of mucus-degrading enzymes.

Images of the mucus layer, and the “goblet” cells of the colon wall that produce the mucus constantly, showed the layer was thinner the less fiber the mice received. While mucus is constantly being produced and degraded in a normal gut, the change in bacteria activity under the lowest-fiber conditions meant that the pace of eating was faster than the pace of production—almost like an overzealous harvesting of trees outpacing the planting of new ones.

When the researchers infected the mice with Citrobacter rodentium—the E. coli-like bacteria—they observed that these dangerous bacteria flourished more in the guts of mice fed a fiber-free diet. Many of those mice began to show severe signs of illness and lost weight.

When the scientists looked at samples of their gut tissue, they saw not only a much thinner or even patchy mucus later—they also saw inflammation across a wide area. Mice that had received a fiber-rich diet before being infected also had some inflammation but across a much smaller area.

“To make it simple, the ‘holes’ created by our microbiota while eroding the mucus serve as wide open doors for pathogenic micro-organisms to invade,” says former postdoctoral fellow Mahesh Desai, now a principle investigator at the Luxembourg Institute of Health.

The researchers will next look at the impact of different prebiotic fiber mixes, and of diets with more intermitted natural fiber content over a longer period. They also want to look for biomarkers that could tell them about the status of the mucus layer in human guts—such as the abundance of mucus-digesting bacteria strains, and the effect of low fiber on chronic disease such as inflammatory bowel disease.

“While this work was in mice, the take-home message from this work for humans amplifies everything that doctors and nutritionists have been telling us for decades: Eat a lot of fiber from diverse natural sources,” says Martens.

“Your diet directly influences your microbiota, and from there it may influence the status of your gut’s mucus layer and tendency toward disease. But it’s an open question of whether we can cure our cultural lack of fiber with something more purified and easy to ingest than a lot of broccoli.”

Source: Republished from as a derivative work under the Attribution 4.0 International license. Original article posted to Futurity by .

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Confirmation bias: A psychological phenomenon that helps explain why pundits got it wrong

By Ray Nickerson, Tufts University.

As post mortems of the 2016 presidential election began to roll in, fingers started pointing to what psychologists call the confirmation bias as one reason many of the polls and pundits were wrong in their predictions of which candidate would end up victorious.

Confirmation bias is usually described as a tendency to notice or search out information that confirms what one already believes, or would like to believe, and to avoid or discount information that’s contrary to one’s beliefs or preferences. It could help explain why many election-watchers got it wrong: in the runup to the election, they saw only what they expected, or wanted, to see.

Psychologists put considerable effort into discovering how and why people sometimes reason in less than totally rational ways. The confirmation bias is one of the better-known of the biases that have been identified and studied over the past few decades. A large body of psychological literature reports how confirmation bias works and how widespread it is.

The role of motivation

Confirmation bias can appear in many forms, but for present purposes, we may divide them into two major types. One is the tendency, when trying to determine whether to believe something is true or false, to look for evidence that it is true while failing to look for evidence that it is false.

Imagine four cards on a table, each one showing either a letter or number on its visible side. Let’s say the cards show A, B, 1 and 2. Suppose you are asked to indicate which card or cards you would have to turn over in order to determine whether the following statement is true or false: If a card has A on its visible side, it has 1 on its other side. The correct answer is the card showing A and the one showing 2. But when people are given this task, a large majority choose to turn either the card showing A or both the card showing A and the one showing 1. Relatively few see the card showing 2 as relevant, but finding A on its other side would prove the statement to be false. One possible explanation for people’s poor performance of this task is that they look for evidence that the statement is true and fail to look for evidence that it is false.

Another type of confirmation bias is the tendency to seek information that supports one’s existing beliefs or preferences or to interpret data so as to support them, while ignoring or discounting data that argue against them. It may involve what is best described as case building, in which one collects data to lend as much credence as possible to a conclusion one wishes to confirm.

At the risk of oversimplifying, we might call the first type of bias unmotivated, inasmuch as it doesn’t involve the assumption that people are driven to preserve or defend their existing beliefs. The second type of confirmation bias may be described as motivated, because it does involve that assumption. It may go a step further than just focusing on details that support one’s existing beliefs; it may involve intentionally compiling evidence to confirm some claim.

It seems likely that both types played a role in shaping people’s election expectations.

A proper venue for leaving out conflicting evidence.
Clyde Robinson, CC BY

Case building versus unbiased analysis

An example of case building and the motivated type of confirmation bias is clearly seen in the behavior of attorneys arguing a case in court. They present only evidence that they hope will increase the probability of a desired outcome. Unless obligated by law to do so, they don’t volunteer evidence that’s likely to harm their client’s chances of a favorable verdict.

Another example is a formal debate. One debater attempts to convince an audience that a proposition should be accepted, while another attempts to show that it should be rejected. Neither wittingly introduces evidence or ideas that will bolster one’s adversary’s position.

In these contexts, it is proper for protagonists to behave in this fashion. We generally understand the rules of engagement. Lawyers and debaters are in the business of case building. No one should be surprised if they omit information likely to weaken their own argument. But case building occurs in contexts other than courtrooms and debating halls. And often it masquerades as unbiased data collection and analysis.

Where confirmation bias becomes problematic

One sees the motivated confirmation bias in stark relief in commentary by partisans on controversial events or issues. Television and other media remind us daily that events evoke different responses from commentators depending on the positions they’ve taken on politically or socially significant issues. Politically liberal and conservative commentators often interpret the same event and its implications in diametrically opposite ways.

Anyone who followed the daily news reports and commentaries regarding the election should be keenly aware of this fact and of the importance of political orientation as a determinant of one’s interpretation of events. In this context, the operation of the motivated confirmation bias makes it easy to predict how different commentators will spin the news. It’s often possible to anticipate, before a word is spoken, what specific commentators will have to say regarding particular events.

Here the situation differs from that of the courtroom or the debating hall in one very important way: Partisan commentators attempt to convince their audience that they’re presenting a balanced factual – unbiased – view. Presumably, most commentators truly believe they are unbiased and responding to events as any reasonable person would. But the fact that different commentators present such disparate views of the same reality makes it clear that they cannot all be correct.

Reporters in the media center watched a presidential debate, but might have seen something different.
AP Photo/John Locher

Selective attention

Motivated confirmation bias expresses itself in selectivity: selectivity in the data one pays attention to and selectivity with respect to how one processes those data.

When one selectively listens only to radio stations, or watches only TV channels, that express opinions consistent with one’s own, one is demonstrating the motivated confirmation bias. When one interacts only with people of like mind, one is exercising the motivated confirmation bias. When one asks for critiques of one’s opinion on some issue of interest, but is careful to ask only people who are likely to give a positive assessment, one is doing so as well.

This presidential election was undoubtedly the most contentious of any in the memory of most voters, including most pollsters and pundits. Extravagant claims and counterclaims were made. Hurtful things were said. Emotions were much in evidence. Civility was hard to find. Sadly, “fallings out” within families and among friends have been reported.

The atmosphere was one in which the motivated confirmation bias would find fertile soil. There is little doubt that it did just that and little evidence that arguments among partisans changed many minds. That most pollsters and pundits predicted that Clinton would win the election suggests that they were seeing in the data what they had come to expect to see – a Clinton win.

None of this is to suggest that the confirmation bias is unique to people of a particular partisan orientation. It is pervasive. I believe it to be active independently of one’s age, gender, ethnicity, level of intelligence, education, political persuasion or general outlook on life. If you think you’re immune to it, it is very likely that you’ve neglected to consider the evidence that you’re not.

The ConversationRay Nickerson, Research Professor of Psychology, Tufts University

This article was originally published on The Conversation. Read the original article.

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Understanding the four types of AI, from reactive robots to self-aware beings

By Arend Hintze, Michigan State University.

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us?

The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won’t see machines “exhibit broadly-applicable intelligence comparable to or exceeding that of humans,” though it does go on to say that in the coming years, “machines will reach and exceed human performance on more and more tasks.” But its assumptions about how those capabilities will develop missed some important points.

As an AI researcher, I’ll admit it was nice to have my own field highlighted at the highest level of American government, but the report focused almost exclusively on what I call “the boring kind of AI.” It dismissed in half a sentence my branch of AI research, into how evolution can help develop ever-improving AI systems, and how computational models can help us understand how our human intelligence evolved.

The report focuses on what might be called mainstream AI tools: machine learning and deep learning. These are the sorts of technologies that have been able to play “Jeopardy!” well, and beat human Go masters at the most complicated game ever invented. These current intelligent systems are able to handle huge amounts of data and make complex calculations very quickly. But they lack an element that will be key to building the sentient machines we picture having in the future.

We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

Type I AI: Reactive machines

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn’t have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn’t rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a “representation” of the world.

The current intelligent machines we marvel at either have no such concept of the world, or have a very limited and specialized one for its particular duties. The innovation in Deep Blue’s design was not to broaden the range of possible movies the computer considered. Rather, the developers found a way to narrow its view, to stop pursuing some potential future moves, based on how it rated their outcome. Without this ability, Deep Blue would have needed to be an even more powerful computer to actually beat Kasparov.

Similarly, Google’s AlphaGo, which has beaten top human Go experts, can’t evaluate all potential future moves either. Its analysis method is more sophisticated than Deep Blue’s, using a neural network to evaluate game developments.

These methods do improve the ability of AI systems to play specific games better, but they can’t be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can’t function beyond the specific tasks they’re assigned and are easily fooled.

They can’t interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver. But it’s bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won’t ever be bored, or interested, or sad.

Type II AI: Limited memory

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this. My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.

Type III AI: Theory of mind

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

Type IV AI: Self-awareness

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial intelligences. Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

The ConversationArend Hintze, Assistant Professor of Integrative Biology & Computer Science and Engineering, Michigan State University

This article was originally published on The Conversation. Read the original article.

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World set for hottest year on record: World Meteorological Organization

By Blair Trewin, World Meteorological Organization.

2016 is set to be the world’s hottest year on record. According to the World Meteorological Organization’s preliminary statement on the global climate for 2016, global temperatures for January to September were 0.88°C above the long-term (1961-90) average, 0.11°C above the record set last year, and about 1.2°C above pre-industrial levels.

While the year is not yet over, the final weeks of 2016 would need to be the coldest of the 21st century for 2016’s final number to drop below last year’s.

Record-setting temperatures in 2016 came as no real surprise. Global temperatures continue to rise at a rate of 0.10-0.15°C per decade, and over the five years from 2011 to 2015 they averaged 0.59°C above the 1961-1990 average.

Giving temperatures a further boost this year was the very strong El Niño event of 2015−16. As we saw in 1998, global temperatures in years where the year starts with a strong El Niño are typically 0.1-0.2°C warmer than the years either side of them, and 2016 is following the same script.

Global temperature anomalies (difference from 1961-90 average) for 1950 to 2016, showing strong El Niño and La Niña years, and years when climate was affected by volcanoes.
World Meteorological Organization

Almost everywhere was warm

Warmth covered almost the entire world in 2016, but was most significant in high latitudes of the Northern Hemisphere. Some parts of the Russian Arctic have been a remarkable 6-7°C above average for the year, while Alaska is having its warmest year on record by more than a degree.

Almost the whole Northern Hemisphere north of the tropics has been at least 1°C above average. North America and Asia are both having their warmest year on record, with Africa, Europe and Oceania close to record levels. The only significant land areas which are having a cooler-than-normal year are northern and central Argentina, and parts of southern Western Australia.

The warmth did not just happen on land; ocean temperatures were also at record high levels in many parts of the world, and many tropical coral reefs were affected by bleaching, including the Great Barrier Reef off Australia.

Global temperatures for January to September 2016.
UK Meteorological Office Hadley Centre

Greenhouse gas levels continued to rise this year. After global carbon dioxide concentrations reached 400 parts per million for the first time in 2015, they reached new record levels during 2016 at both Mauna Loa in Hawaii and Cape Grim in Australia.

On the positive side, the Antarctic ozone hole in 2016 was one of the smallest of the last decade; while there is not yet a clear downward trend in its size, it is at least not growing any more.

Global sea levels continue to show a consistent upward trend, although they have temporarily levelled off in the last few months after rising steeply during the El Niño.

Droughts and flooding rains

El Niño was over by May 2016 – but many of its effects are still ongoing.

Worst affected was southern Africa, which gets most of its rain during the Southern Hemisphere summer. Rainfall over most of the region was well below average in both 2014-15 and 2015-16.

With two successive years of drought, many parts are suffering badly with crop failures and food shortages. With the next harvests due early in 2017, the next couple of months will be crucial in prospects for recovery.

Drought is also strengthening its grip in parts of eastern Africa, especially Kenya and Somalia, and continues in parts of Brazil.

On the positive side, the end of El Niño saw the breaking of droughts in some other parts of the world. Good mid-year rains made their presence felt in places as diverse as northwest South America and the Caribbean, northern Ethiopia, India, Vietnam, some islands of the western tropical Pacific, and eastern Australia, all of which had been suffering from drought at the start of the year.

The world has also had its share of floods during 2016. The Yangtze River basin in China had its wettest April to July period this century, with rainfall more than 30% above average. Destructive flooding affected many parts of the region, with more than 300 deaths and billions of dollars in damage.

Europe was hard hit by flooding in early June, with Paris having its worst floods for more than 30 years.

In western Africa, the Niger River reached its highest levels for more than 50 years in places, although the wet conditions also had many benefits for the chronically drought-affected Sahel, and eastern Australia also had numerous floods from June onwards as drought turned to heavy rain.

Tropical cyclones are among nature’s most destructive phenomena, and 2016 was no exception. The worst weather related natural disaster of 2016 was Hurricane Matthew. Matthew reached category five intensity south of Haiti, the strongest Atlantic storm since 2007. It hit Haiti as a category 4 hurricane, causing at least 546 deaths, with 1.4 million people needing humanitarian assistance. The hurricane then went on to cause major damage in Cuba, the Bahamas and the United States.

Other destructive tropical cyclones in 2016 included Typhoon Lionrock, responsible for flooding in the Democratic People’s Republic of Korea which claimed at least 133 lives, and Cyclone Winston, which killed 44 people and caused an estimated US$1.4 billion damage in Fiji’s worst recorded natural disaster.

Arctic sea ice extent was well-below average all year. It reached a minimum in September of 4.14 million square kilometres, the equal second smallest on record, and a very slow autumn freeze-up so far means that its extent is now the lowest on record for this time of year.

In the Antarctic, sea ice extent was fairly close to normal through the first part of the year but has also dropped well below normal over the last couple of months, as the summer melt has started unusually early.

It remains to be seen what impact the summer of 2016 has had on the mountain glaciers of the Northern Hemisphere.

While 2016 has been an exceptional year by current standards, the long-term warming trends mean there will be more years like it to come. Recent research has shown that global average temperatures which are record-breaking now are likely to become the norm within the next couple of decades.

The ConversationBlair Trewin, Lead author, 2016 WMO Global Statement on the Status of the Global Climate, World Meteorological Organization

This article was originally published on The Conversation. Read the original article.

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NASA Finds Unusual Origins of High-Energy Electrons

High above the surface, Earth’s magnetic field constantly deflects incoming supersonic particles from the sun. These particles are disturbed in regions just outside of Earth’s magnetic field – and some are reflected into a turbulent region called the foreshock. New observations from NASA’s THEMIS – short for Time History of Events and Macroscale Interactions during Substorms – mission show that this turbulent region can accelerate electrons up to speeds approaching the speed of light. Such extremely fast particles have been observed in near-Earth space and many other places in the universe, but the mechanisms that accelerate them have not yet been concretely understood.

The new results provide the first steps towards an answer, while opening up more questions. The research finds electrons can be accelerated to extremely high speeds in a near-Earth region farther from Earth than previously thought possible – leading to new inquiries about what causes the acceleration. These findings may change the accepted theories on how electrons can be accelerated not only in shocks near Earth, but also throughout the universe. Having a better understanding of how particles are energized will help scientists and engineers better equip spacecraft and astronauts to deal with these particles, which can cause equipment to malfunction and affect space travelers.

“This affects pretty much every field that deals with high-energy particles, from studies of cosmic rays to solar flares and coronal mass ejections, which have the potential to damage satellites and affect astronauts on expeditions to Mars,” said Lynn Wilson, lead author of the paper on these results at NASA’s Goddard Space Flight Center in Greenbelt, Maryland.

The results, published in Physical Review Letters, on Nov. 14, 2016, describe how such particles may get accelerated in specific regions just beyond Earth’s magnetic field. Typically, a particle streaming toward Earth first encounters a boundary region known as the bow shock, which forms a protective barrier between the solar wind, the continuous and varying stream of charged particles flowing from the sun, and Earth. The magnetic field in the bow shock slows the particles, causing most to be deflected away from Earth, though some are reflected back towards the sun. These reflected particles form a region of electrons and ions called the foreshock region.

Some of those particles in the foreshock region are highly energetic, fast moving electrons and ions. Historically, scientists have thought one way these particles get to such high energies is by bouncing back and forth across the bow shock, gaining a little extra energy from each collision. However, the new observations suggest the particles can also gain energy through electromagnetic activity in the foreshock region itself.

The observations that led to this discovery were taken from one of the THEMIS – short for Time History of Events and Macroscale Interactions during Substorms – mission satellites. The five THEMIS satellites circled Earth to study how the planet’s magnetosphere captured and released solar wind energy, in order to understand what initiates the geomagnetic substorms that cause aurora. The THEMIS orbits took the spacecraft across the foreshock boundary regions. The primary THEMIS mission concluded successfully in 2010 and now two of the satellites collect data in orbit around the moon.

Operating between the sun and Earth, the spacecraft found electrons accelerated to extremely high energies. The accelerated observations lasted less than a minute, but were much higher than the average energy of particles in the region, and much higher than can be explained by collisions alone. Simultaneous observations from the additional Heliophysics spacecraft, Wind and STEREO, showed no solar radio bursts or interplanetary shocks, so the high-energy electrons did not originate from solar activity.

“This is a puzzling case because we’re seeing energetic electrons where we don’t think they should be, and no model fits them,” said David Sibeck, co-author and THEMIS project scientist at NASA Goddard. “There is a gap in our knowledge, something basic is missing.”

The electrons also could not have originated from the bow shock, as had been previously thought. If the electrons were accelerated in the bow shock, they would have a preferred movement direction and location – in line with the magnetic field and moving away from the bow shock in a small, specific region. However, the observed electrons were moving in all directions, not just along magnetic field lines. Additionally, the bow shock can only produce energies at roughly one tenth of the observed electrons’ energies. Instead, the cause of the electrons’ acceleration was found to be within the foreshock region itself.

“It seems to suggest that incredibly small scale things are doing this because the large scale stuff can’t explain it,” Wilson said.

This visualization represents one of the traditional proposed mechanisms for accelerating particles across a shock, called a shock drift acceleration. The electrons (yellow) and protons (blue) can be seen moving in the collision area where two hot plasma bubbles collide (red vertical line). The cyan arrows represent the magnetic field and the light green arrows, the electric field.
Credits: NASA Goddard’s Scientific Visualization Studio/Tom Bridgman, data visualizer

High-energy particles have been observed in the foreshock region for more than 50 years, but until now, no one had seen the high-energy electrons originate from within the foreshock region. This is partially due to the short timescale on which the electrons are accelerated, as previous observations had averaged over several minutes, which may have hidden any event. THEMIS gathers observations much more quickly, making it uniquely able to see the particles.

Next, the researchers intend to gather more observations from THEMIS to determine the specific mechanism behind the electrons’ acceleration.

Source: news release reused under public domain rights and in accordance with the NASA media guidelines.

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This Newly-Identified Human Antibody May Lead to Zika Vaccination

Researchers have identified a human antibody that prevents Zika from infecting the fetus and damaging the placenta in pregnant mice. The antibody also protects adult mice from Zika disease.

The most devastating consequence of Zika virus infection is the development of microcephaly, or an abnormally small head, in fetuses infected in utero.

“This is the first antiviral that has been shown to work in pregnancy to protect developing fetuses from Zika virus,” says Michael Diamond, professor of medicine at Washington University School of Medicine in St. Louis and the study’s co-senior author. “This is proof of principle that Zika virus during pregnancy is treatable, and we already have a human antibody that treats it, at least in mice.”


Diamond, co-senior author James Crowe Jr. of Vanderbilt, and colleagues screened 29 anti-Zika antibodies from people who had recovered from Zika infection. They found one, called ZIKV-117, that efficiently neutralized in the lab five Zika strains—representing the worldwide diversity of the virus.

To test whether the antibody also protects living animals, the researchers gave the antibody to pregnant mice either one day before or one day after they were infected with the virus. In both cases, antibody treatment markedly reduced the levels of virus in pregnant females and their fetuses, as well as in the placentas, compared with pregnant mice that did not get the antibody.

“These naturally occurring antibodies isolated from humans represent the first medical intervention that prevents Zika infection and damage to fetuses,” Crowe says.

The placentas from the treated females appeared normal and healthy, unlike those from the untreated females, which showed destruction of the placental structure. Damage to the placenta can cause slow fetal growth and even can cause fetal death, both of which are associated with Zika infection in humans.

“We did not see any damage to the fetal blood vessels, thinning of the placenta, or any growth restriction in the fetuses of the antibody-treated mice,” says coauthor Indira Mysorekar, an associate professor of obstetrics and gynecology, and of pathology and immunology at Washington University, and co-director of the university’s Center for Reproductive Sciences.

“The anti-Zika antibodies are able to keep the fetus safe from harm by blocking the virus from crossing the placenta.”

The antibody also protected adult male mice against a lethal dose of Zika virus, even when given five days after initial infection. Zika is rarely lethal in humans, so using a lethal dose allowed the scientists to see how well the antibody works under the most stringent conditions.

“We stacked the deck against ourselves by using a highly pathogenic strain of Zika, and even in that case, the antibody protected the mice,” says Diamond, who is also a professor of pathology and immunology, and of molecular microbiology.

Support for a vaccine

These findings provide evidence that antibodies alone can protect adults and fetuses from Zika. Further, they suggest that a vaccine that elicits protective antibodies in women also may protect their fetuses in current and future pregnancies. A vaccine is already in human trials, but it was never tested in pregnant animals, so this new study represents strong evidence that a vaccine that elicits protective antibodies in adults is likely to protect fetuses as well.

A Zika vaccine is likely to be the cheapest and simplest method of preventing Zika-related birth defects. However, there is an outside possibility that a Zika vaccine could worsen symptoms in people who encounter the virus later. This is known to occur with dengue virus, a close relative of Zika. People who have antibodies against one strain of dengue virus get sicker when infected with a second strain than those who do not have such antibodies.

The phenomenon, known as antibody-dependent enhancement, has been observed with Zika in a petri dish but never in living animals or in epidemiologic surveys of people in Zika-endemic regions.

Nonetheless, the researchers tested whether they could eliminate the possibility of antibody-dependent enhancement of Zika infection by modifying the antibody so it could not participate in the process. The modified antibody, they showed, was just as effective as the original at protecting the placenta and fetus.

Treatment during pregnancy?

Until a human vaccine is available, it may be possible to protect fetuses by administering antibodies to pregnant women in an attempt to prevent transmission from mother to fetus. Under this scenario, a woman living in a Zika-endemic area would receive the antibodies throughout her pregnancy, starting when she first learns she is pregnant, regardless of whether she is diagnosed with Zika. Alternatively, pregnant women or their partners with acute infection could be treated with antibodies.

Crowe is continuing the process of developing the antibody as a potential therapeutic, ramping up production and laying the groundwork for human studies. Meanwhile, Diamond is focusing on determining whether antibodies could be used to clear persistent Zika infection. Together, they are working with others to gain a higher-resolution understanding of how ZIKV-117 binds the virus and inhibits infection.

“We know that Zika can persist in certain parts of the body, such as the eyes and the testes, where it can cause long-term damage, at least in mice,” Diamond says. “We showed that the antibody can prevent disease, and now we want to know whether it can clear persistent infection from those parts of the body.”

The study appears online in Nature.

Source: Republished from as a derivative work under the Attribution 4.0 International license. Original article posted to Futurity by .

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Strange Depression on Mars Could Harbor Life

A strangely shaped depression on Mars could be a new place to look for signs of life on the Red Planet, researchers say.

The depression was probably formed by a volcano beneath a glacier and could have been a warm, chemical-rich environment well suited for microbial life.

“We were drawn to this site because it looked like it could host some of the key ingredients for habitability—water, heat, and nutrients,” says lead author Joseph Levy, a research associate at the University of Texas Institute for Geophysics, a research unit of the Jackson School of Geosciences.

(Left) A graph charting the depth of the Hellas depression at different points, and a topographic map of the depression. (Right) A graph charting the depth of the Galaxias Fossae depression at different points, and a topographic map of the depression. (Credit: Joseph Levy/NASA)
(Left) A graph charting the depth of the Hellas depression at different points, and a topographic map of the depression. (Right) A graph charting the depth of the Galaxias Fossae depression at different points, and a topographic map of the depression. (Credit: Joseph Levy/NASA)

The depression is inside a crater perched on the rim of the Hellas basin on Mars and surrounded by ancient glacial deposits. It first caught Levy’s attention in 2009, when he noticed crack-like features on pictures of depressions taken by the Mars Reconnaissance Orbiter that looked similar to “ice cauldrons” on Earth, formations found in Iceland and Greenland made by volcanos erupting under an ice sheet. Another depression in the Galaxias Fossae region of Mars had a similar appearance.

A depression located inside a crater on the edge of the Hellas basin region of Mars. New research suggests that the depression was formed by volcanic activity beneath an ice sheet—an environment that could be suitable for microbial life. View larger. (Credit: Joseph Levy/NASA)
A depression located inside a crater on the edge of the Hellas basin region of Mars. New research suggests that the depression was formed by volcanic activity beneath an ice sheet—an environment that could be suitable for microbial life. (Credit: Joseph Levy/NASA)

“These landforms caught our eye because they’re weird looking. They’re concentrically fractured so they look like a bull’s-eye. That can be a very diagnostic pattern you see in Earth materials,” says Levy, who was a postdoctoral researcher at Portland State University when he first saw the photos of the depressions.

But it wasn’t until this year that researchers were able to more thoroughly analyze the depressions using stereoscopic images to investigate whether the depressions were made by underground volcanic activity that melted away surface ice or by an impact from an asteroid.

Study collaborator Timothy Goudge, a postdoctoral fellow at the institute, used pairs of high-resolution images to create digital elevation models of the depressions that enabled in-depth analysis of their shape and structure in 3D.

Lava and ice

“The big contribution of the study was that we were able to measure not just their shape and appearance, but also how much material was lost to form the depressions. That 3D view lets us test this idea of volcanic or impact,” Levy says.

The analysis revealed that both depressions shared an unusual funnel shape, with a broad perimeter that gradually narrowed with depth.

“That surprised us and led to a lot of thinking about whether it meant there was melting concentrated in the center that removed ice and allowed stuff to pour in from the sides. Or if you had an impact crater, did you start with a much smaller crater in the past, and by sublimating away ice, you’ve expanded the apparent size of the crater,” Levy says.

After testing formation scenarios for the two depressions, researchers found that they probably formed in different ways. The debris spread around the Galaxias Fossae depression suggests that it was the result of an impact—but the known volcanic history of the area still doesn’t rule out volcanic origins, Levy said. In contrast, the Hellas depression has many signs of volcanic origins. It lacks the surrounding debris of an impact and has a fracture pattern associated with concentrated removal of ice by melting or sublimation.

The interaction of lava and ice to form a depression would be an exciting find, Levy says, because it could create an environment with liquid water and chemical nutrients, both ingredients required for life on Earth. The Hellas depression and, to a lesser extent, the Galaxias Fossae depression, should be kept in mind when looking for habitats on Mars.

“These features do really resemble ice cauldrons known from Earth, and just from that perspective they should be of great interest,” says Gro Pedersen, a volcanologist at the University of Iceland who agrees that the depressions are promising sites for future research. He was not involved with the study,

“Both because their existence may provide information on the properties of subsurface material—the potential existence of ice—and because of the potential for revealing ice-volcano interactions.”

Researchers from Brown University and Mount Holyoke College are coauthors of the study that was supported by a NASA Mars Data Analysis Program award and was published in the journal Icarus.

Source: Republished from as a derivative work under the Attribution 4.0 International license. Original article posted to Futurity by .

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How Twitter bots affected the US presidential campaign

By Emilio Ferrara, University of Southern California.

Key to democracy is public engagement – when people discuss the issues of the day with each other openly, honestly and without outside influence. But what happens when large numbers of participants in that conversation are biased robots created by unseen groups with unknown agendas? As my research has found, that’s what has happened this election season.

Since 2012, I have been studying how people discuss social, political, ideological and policy issues online. In particular, I have looked at how social media are abused for manipulative purposes.

It turns out that much of the political content Americans see on social media every day is not produced by human users. Rather, about one in every five election-related tweets from Sept. 16 to Oct. 21 was generated by computer software programs called “social bots.”

These artificial intelligence systems can be rather simple or very sophisticated, but they share a common trait: They are set to automatically produce content following a specific political agenda determined by their controllers, who are nearly impossible to identify. These bots have affected the online discussion around the presidential election, including leading topics and how online activity was perceived by the media and the public.

How active are they?

The operators of these systems could be political parties, foreign governments, third-party organizations, or even individuals with vested interests in a particular election outcome. Their work amounts to at least four million election-related tweets during the period we studied, posted by more than 400,000 social bots.

That’s at least 15 percent of all the users discussing election-related issues. It’s more than twice the overall concentration of bots on Twitter – which the company estimates at 5 to 8.5 percent of all accounts.

To determine which accounts are bots and which are humans, we use Bot Or Not, a publicly available bot-detection service that I developed in collaboration with colleagues at Indiana University. Bot Or Not uses advanced machine learning algorithms to analyze multiple cues, including Twitter profile metadata, the content and topics posted by the account under inspection, the structure of its social network, the timeline of activity and much more. After considering more than 1,000 factors, Bot Or Not generates a likelihood score that the account under scrutiny is a bot. Our tool is 95 percent accurate at this determination.

There are many examples of bot-generated tweets, supporting their candidates, or attacking the opponents. Here is just one:

@u_edilberto: RT @WeNeedHillary: Polls Are All Over the Place. Keep Calm & Hillary On! #p2 #ctl #ImWithHer #TNTweeters https://t …

How effective are they?

The effectiveness of social bots depends on the reactions of actual people. We learned, distressingly, that people were not able to ignore, or develop a sort of immunity toward, the bots’ presence and activity. Instead, we found that most human users can’t tell whether a tweet is posted by another real user or by a bot. We know this because bots are being retweeted at the same rate as humans. Retweeting bots’ content without first verifying its accuracy can have real consequences, including spreading rumors, conspiracy theories or misinformation.

Some of these bots are very simple, and just retweet content produced by human supporters. Other bots, however, produce new tweets, jumping in the conversation by using existing popular hashtags (for instance, #NeverHillary or #NeverTrump). Real users who follow these Twitter hashtags will be exposed to bot-generated content seamlessly blended with the tweets produced by other actual people.

Bots produce content automatically, and therefore at a very fast and continuous rate. That means they form consistent and pervasive parts of the online discussion throughout the campaign. As a result, they were able to build significant influence, collecting large numbers of followers and having their tweets retweeted by thousands of humans.

A deeper understanding of bots

Our investigation into these politically active social bots also uncovered information that can lead us to more nuanced understanding of them. One such lesson was that bots are biased, by design. For example, Trump-supporting bots systematically produced overwhelmingly positive tweets in support of their candidate. Previous studies showed that this systematic bias alters public perception. Specifically, it creates the false impression that there is grassroots, positive, sustained support for a certain candidate.

Location provided another lesson. Twitter provides metadata about the physical location of the device used to post a certain tweet. By aggregating and analyzing their digital footprints, we discovered that bots are not uniformly distributed across the United States: They are significantly overrepresented in some states, in particular southern states like Georgia and Mississippi. This suggests that some bot operations may be based in those states.

Also, we discovered that bots can operate in multiple ways: For example, when they are not engaged in producing content supporting their respective candidates, bots can target their opponents. We discovered that bots pollute certain hashtags, like #NeverHillary or #NeverTrump, where they smear the opposing candidate.

These strategies leverage known human biases: for example, the fact that negative content travels faster on social media, as one of our recent studies demonstrated. We found that, in general, negative tweets are retweeted at a pace 2.5 times higher than positive ones. This, in conjunction with the fact that people are naturally more inclined to retweet content that aligns with their preexisting political views, results in the spreading of content that is often defamatory or based on unsupported, or even false, claims.

It is hard to quantify the effects of bots on the actual election outcome, but it’s plausible to think that they could affect voter turnout in some places. For example, some people may think there is so much local support for their candidate (or the opponent) that they don’t need to vote – even if what they’re seeing is actually artificial support provided by bots.

Our study hit the limits of what can be done today by using computational methods to fight the issue of bots: Our ability to identify the bot masters is bound by technical constraints on recognizing patterns in their behavior. Social media is acquiring increasing importance in shaping political beliefs and influencing people’s online and offline behavior. The research community will need to continue to explore, to make these platforms as safe from abuse as possible.

The ConversationEmilio Ferrara, Research Assistant Professor of Computer Science, University of Southern California

This article was originally published on The Conversation. Read the original article.

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The oceans are full of plastic, but why do seabirds eat it?

By Matthew Savoca, University of California, Davis.

Imagine that you are constantly eating, but slowly starving to death. Hundreds of species of marine mammals, fish, birds, and sea turtles face this risk every day when they mistake plastic debris for food.

Plastic debris can be found in oceans around the world. Scientists have estimated that there are over five trillion pieces of plastic weighing more than a quarter of a million tons floating at sea globally. Most of this plastic debris comes from sources on land and ends up in oceans and bays due largely to poor waste management.

Plastic does not biodegrade, but at sea large pieces of plastic break down into increasingly smaller fragments that are easy for animals to consume. Nothing good comes to animals that mistake plastic for a meal. They may suffer from malnutrition, intestinal blockage, or slow poisoning from chemicals in or attached to the plastic.

Many tube-nosed seabirds, like this Tristram’s storm petrel (Oceanodroma tristrami), eat plastic particles at sea because they mistake them for food.
Sarah Youngren, Hawaii Pacific University/USFWS, Author provided

Despite the pervasiveness and severity of this problem, scientists still do not fully understand why so many marine animals make this mistake in the first place. It has been commonly assumed, but rarely tested, that seabirds eat plastic debris because it looks like the birds’ natural prey. However, in a study that my coauthors and I just published in Science Advances, we propose a new explanation: For many imperiled species, marine plastic debris also produces an odor that the birds associate with food.

A nose for sulfur

Perhaps the most severely impacted animals are tube-nosed seabirds, a group that includes albatrosses, shearwaters and petrels. These birds are pelagic: they often remain at sea for years at a time, searching for food over hundreds or thousands of square kilometers of open ocean, visiting land only to breed and rear their young. Many are also at risk of extinction. According to the International Union for the Conservation of Nature, nearly half of the approximately 120 species of tube-nosed seabirds are either threatened, endangered or critically endangered.

Although there are many fish in the sea, areas that reliably contain food are very patchy. In other words, tube-nosed seabirds are searching for a “needle in a haystack” when they forage. They may be searching for fish, squid, krill or other items, and it is possible that plastic debris visually resembles these prey. But we believe that tells only part of a more complex story.

A sooty shearwater (Puffinus griseus) takes off from the ocean’s surface in Morro Bay, California.
Mike Baird/Flickr, CC BY

Pioneering research by Dr. Thomas Grubb Jr. in the early 1970s showed that tube-nosed seabirds use their powerful sense of smell, or olfaction, to find food effectively, even when heavy fog obscures their vision. Two decades later, Dr. Gabrielle Nevitt and colleagues found that certain species of tube-nosed seabirds are attracted to dimethyl sulfide (DMS), a natural scented sulfur compound. DMS comes from marine algae, which produce a related chemical called DMSP inside their cells. When those cells are damaged – for example, when algae die, or when marine grazers like krill eat it – DMSP breaks down, producing DMS. The smell of DMS alerts seabirds that food is nearby – not the algae, but the krill that are consuming the algae.

Dr. Nevitt and I wondered whether these seabirds were being tricked into consuming marine plastic debris because of the way it smelled. To test this idea, my coauthors and I created a database collecting every study we could find that recorded plastic ingestion by tube-nosed seabirds over the past 50 years. This database contained information from over 20,000 birds of more than 70 species. It showed that species of birds that use DMS as a foraging cue eat plastic nearly six times as frequently as species that are not attracted to the smell of DMS while foraging.

To further test our theory, we needed to analyze how marine plastic debris smells. To do so, I took beads of the three most common types of floating plastic – polypropylene and low- and high-density polyethylene – and sewed them inside custom mesh bags, which we attached to two buoys off of California’s central coast. We hypothesized that algae would coat the plastic at sea, a process known as biofouling, and produce DMS.

Author Matthew Savoca deploys experimental plastic debris at a buoy in Monterey Bay, California.
Author provided

After the plastic had been immersed for about a month at sea, I retrieved it and brought it to a lab that is not usually a stop for marine scientists: the Robert Mondavi Institute for Food and Wine Science at UC Davis. There we used a gas chromatograph, specifically built to detect sulfur odors in wine, beer and other food products, to measure the chemical signature of our experimental marine debris. Sulfur compounds have a very distinct odor; to humans they smell like rotten eggs or decaying seaweed on the beach, but to some species of seabirds DMS smells delicious!

Sure enough, every sample of plastic we collected was coated with algae and had substantial amounts of DMS associated with it. We found levels of DMS that were higher than normal background concentrations in the environment, and well above levels that tube-nosed seabirds can detect and use to find food. These results provide the first evidence that, in addition to looking like food, plastic debris may also confuse seabirds that hunt by smell.

When trash becomes bait

Our findings have important implications. First, they suggest that plastic debris may be a more insidious threat to marine life than we previously believed. If plastic looks and smells like food, it is more likely to be mistaken for prey than if it just looks like food.

Second, we found through data analysis that small, secretive burrow-nesting seabirds, such as prions, storm petrels, and shearwaters, are more likely to confuse plastic for food than their more charismatic, surface-nesting relatives such as albatrosses. This difference matters because populations of hard-to-observe burrow-nesting seabirds are more difficult to count than surface-nesting species, so they often are not surveyed as closely. Therefore, we recommend increased monitoring of these less charismatic species that may be at greater risk of plastic ingestion.

Finally, our results provide a deeper understanding for why certain marine organisms are inexorably trapped into mistaking plastic for food. The patterns we found in birds should also be investigated in other groups of species, like fish or sea turtles. Reducing marine plastic pollution is a long-term, large-scale challenge, but figuring out why some species continue to mistake plastic for food is the first step toward finding ways to protect them.

The ConversationMatthew Savoca, Ph.D. Candidate, University of California, Davis

This article was originally published on The Conversation. Read the original article.

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