Competing for high status speeds up aging in male baboons

Study suggests that high social status contributes to accelerated aging in baboons, despite its other advantages

ELIFE

Research News

 

IMAGE: MALE BABOONS IN AMBOSELI NATIONAL PARK, KENYA, ENGAGE IN PHYSICAL COMPETITION FOR HIGH RANK, DEMONSTRATING THE POTENTIAL COSTS OF ATTAINING HIGH STATUS view more 

CREDIT: BETH ARCHIE (CC BY 4.0)

Battling other male baboons to achieve high social status comes with physiological costs that accelerate aging, according to study published today in eLife.

The findings suggest that current life circumstances may be more important contributors to premature aging than early life hardship, at least in baboons.

Chemical changes to DNA, also called epigenetic changes, can be used as a kind of 'clock' to measure aging. While these epigenetic changes usually correspond with age, they can also be used to detect signs of premature aging.

"Environmental stressors can make the clock tick faster, so that some individuals appear biologically older than their actual age and experience a higher risk of age-related disease," explains co-first author Jordan Anderson, a PhD student in Evolutionary Anthropology at Duke University, Durham, North Carolina, US. "We sought to answer what social or early life experiences contribute to accelerated aging in baboons."

The team measured aging in 245 wild baboons from a well-studied population in Kenya using the epigenetic clock and other methods. They found that the epigenetic clock was a good predictor of chronological age overall. But contrary to what they expected, early life adversity was not a good predictor of accelerated aging in the animals.

Instead, they found that the highest-ranking males showed signs of accelerated aging. Higher body mass index, which is associated with having more lean muscle mass in baboons, was also associated with accelerated aging, likely because of the physical demands of maintaining high status. The team was also able to show that the epigenetic clock sped up as the animals climbed the social ladder and slowed down as they moved down it.

"Our results argue that achieving high rank for male baboons - the best predictor of reproductive success in these animals - imposes costs that are consistent with a 'live fast, die young,' life history strategy," says co-first author Rachel Johnston, Postdoctoral Associate in Evolutionary Anthropology at Duke University.

"While the findings reveal how social pressures can influence aging for males, we don't see the same effect of rank in female baboons, who are born into their social rank rather than having to fight for it," adds senior author Jenny Tung, Associate Professor in the Departments of Evolutionary Anthropology and Biology at Duke University, and a Faculty Associate of the Duke University Population Research Institute.

"Our results have important implications for research on the social determinants of health in humans and other animals because they show that 'high status' can mean very different things in different contexts. They also highlight the importance of examining the effects of both early life and current life environments on biological aging," Tung concludes.

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This study will be published as part of 'Evolutionary Medicine: A Special Issue' from eLife. For more information, visit https://elifesciences.org/inside-elife/bb34a238/special-issue-call-for-papers-in-evolutionary-medicine.

About eLife

eLife is a non-profit organisation created by funders and led by researchers. Our mission is to accelerate discovery by operating a platform for research communication that encourages and recognises the most responsible behaviours. We aim to publish work of the highest standards and importance in all areas of biology and medicine, including Evolutionary Biology, and Genetics and Genomics, while exploring creative new ways to improve how research is assessed and published. eLife receives financial support and strategic guidance from the Howard Hughes Medical Institute, the Knut and Alice Wallenberg Foundation, the Max Planck Society and Wellcome. Learn more at https://elifesciences.org/about.

To read the latest Evolutionary Biology research published in eLife, visit https://elifesciences.org/subjects/evolutionary-biology.

And for the latest in Genetics and Genomics, see https://elifesciences.org/subjects/genetics-genomics.

CAPTION

Teeth-baring, glaring confrontations are a normal part of being the boss male baboon. A new study shows that the guys at the top will age faster as a result of constantly having to defend their higher status.

CREDIT

Courtney L. Fitzpatrick

Being top baboon costs males their longevity

Struggle for dominance leaves a mark on genes and speeds up aging

DUKE UNIVERSITY

Research News

IMAGE: MALE BABOONS MAINTAIN THEIR PECKING ORDER IN THE TROOP WITH PHYSICAL DISPLAYS OF AGGRESSION. A NEW STUDY SHOWS THAT THE GUYS AT THE TOP WILL AGE FASTER AS A RESULT... view more 

CREDIT: ELIZABETH ARCHIE

DURHAM, N.C. -- Some guys have it all: the muscle, the power, the high social status, the accelerated aging.

But wait. Faster aging? Who wants that? For male baboons, it's the price they pay to be at the top.

New research appearing April 6 in eLife by Jenny Tung, associate professor of evolutionary anthropology and biology at Duke University, and her colleagues shows that male baboons that climb the social ladder age faster than males with lower social standing. If a male drops in social status, his estimated rate of aging drops as well.

Using blood samples from 245 wild baboons in the Amboseli ecosystem in Kenya, the team analyzed chemical modifications to DNA known as DNA methylation marks.

"These marks change with age in a clock-like fashion," Tung said. "However, environmental stressors can make the clock tick faster." This would make an individual appear older than they really are, and, research in humans suggests, can put them at a higher risk of aging-related disease.

Since this cohort of baboons is one of the most intensively studied wild mammal populations in the world, the researchers already knew not only each baboon's age, but also the environment in which they grew up, their exposure to early life adversity, and a great deal about their adult environment, especially the aspects that predict how long they live and how many offspring they leave behind.

"We used DNA methylation to compare the baboons known ages to their 'biological ages,'" said Jordan Anderson, a graduate student in the Tung lab who co-led the work. These methylation markers are found across the genome, so the team first needed to measure a large number of these sites - about 400,000 of them - and then, through statistical methods and models, whittle the number of sites down to about 500 that best predicted age.

Interestingly, for males, early life adversity didn't affect how fast their biological clocks tick.

Adult social status was the strongest factor that affected aging. "Male baboons who compete successfully for high social status appear to age faster," Tung said. "We repeatedly sampled some of these males and were able to show that the clock can speed up or slow down as males move up or down the social ladder."

This is contrary to what we see in humans. Typically, high social status in humans predicts better health, not worse. The most wealthy and powerful humans have access to and can afford the best houses, schools, healthcare and more. Those who live in poverty and have lower socioeconomic status are at increased risk and have higher rates of disease, cancer and all-cause mortality.

Male baboons, though, have to fight for their social status. Because of this, it's common to see male-male competition on a regular basis, where baboon observers can see a clear winner and a clear loser.

To maintain their social status, males at the top regularly have to hold their ground and defend themselves physically. Because of this, male baboons at the top tend to have more muscle mass and better body condition than lower ranking baboons. But as their physicality starts to diminish with age, a new, younger, stronger male may overcome them for the top spot.

High ranking males also spend a lot of time mate-guarding females. Around ovulation, they follow females closely and ward off other males. Mate-guarding constrains a male's other activities, and Tung and her team think it is likely to be energetically costly -- perhaps helping to explain their accelerated aging result.

So why do these males work so hard to achieve a high stress social status? It's simple: to have offspring.

"If male baboons are going to have babies, they need to achieve high rank," Tung said. "They will have very little chance to leave offspring if they don't achieve high rank, which creates a powerful evolutionary motivation."

This study highlights one way that the social environment can influence aging. "Our research shows that the manner in which social status is attained and maintained is crucial to understanding its consequences," Tung said.

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This research was supported by the US National Science Foundation and the US National Institutes of Health, Canadian Institute of Advanced Research, North Carolina Biotechnology Center, and the Center for Population Health and Aging. (2018264636, IOS1456832, R01AG053308, R01AG053330, R01HD088558, P01AG031719, F32HD095616, 2016-IDG-1013, P30AG034424)

CITATION: "High Social Status Males Experience Accelerated Epigenetic Aging in Wild Baboons," Jordan A. Anderson, Rachel A. Johnston, Amanda J. Lea, Fernando A. Campos, Tawni N. Voyles, Mercy Y. Akinyi, Susan C. Alberts, Elizabeth A. Archie, Jenny Tung. eLife, April 6, 2021. DOI: 10.7554/eLife.661

Deep learning networks prefer the human voice -- just like us

Columbia engineers demonstrate that AI systems might reach higher performance if programmed with sound files of human language rather than with binary data labels

COLUMBIA UNIVERSITY SCHOOL OF ENGINEERING AND APPLIED SCIENCE

Research News

 

IMAGE: A DEEP NEURAL NETWORK THAT IS TAUGHT TO SPEAK OUT THE ANSWER DEMONSTRATES HIGHER PERFORMANCES OF LEARNING ROBUST AND EFFICIENT FEATURES. THIS STUDY OPENS UP NEW RESEARCH QUESTIONS ON THE... view more 

CREDIT: CREATIVE MACHINES LAB/COLUMBIA ENGINEERING

New York, NY--April 6, 2021--The digital revolution is built on a foundation of invisible 1s and 0s called bits. As decades pass, and more and more of the world's information and knowledge morph into streams of 1s and 0s, the notion that computers prefer to "speak" in binary numbers is rarely questioned. According to new research from Columbia Engineering, this could be about to change.

A new study from Mechanical Engineering Professor Hod Lipson and his PhD student Boyuan Chen proves that artificial intelligence systems might actually reach higher levels of performance if they are programmed with sound files of human language rather than with numerical data labels. The researchers discovered that in a side-by-side comparison, a neural network whose "training labels" consisted of sound files reached higher levels of performance in identifying objects in images, compared to another network that had been programmed in a more traditional manner, using simple binary inputs.

VIDEO: https://youtu.be/Iq2YjHCAPRQ

PROJECT WEBSITE: https://www.creativemachineslab.com/label-representation.html https://engineering.columbia.edu/faculty/hod-lipson

"To understand why this finding is significant," said Lipson, James and Sally Scapa Professor of Innovation and a member of Columbia's Data Science Institute, "It's useful to understand how neural networks are usually programmed, and why using the sound of the human voice is a radical experiment."

When used to convey information, the language of binary numbers is compact and precise. In contrast, spoken human language is more tonal and analog, and, when captured in a digital file, non-binary. Because numbers are such an efficient way to digitize data, programmers rarely deviate from a numbers-driven process when they develop a neural network.

Lipson, a highly regarded roboticist, and Chen, a former concert pianist, had a hunch that neural networks might not be reaching their full potential. They speculated that neural networks might learn faster and better if the systems were "trained" to recognize animals, for instance, by using the power of one of the world's most highly evolved sounds--the human voice uttering specific words.

One of the more common exercises AI researchers use to test out the merits of a new machine learning technique is to train a neural network to recognize specific objects and animals in a collection of different photographs. To check their hypothesis, Chen, Lipson and two students, Yu Li and Sunand Raghupathi, set up a controlled experiment. They created two new neural networks with the goal of training both of them to recognize 10 different types of objects in a collection of 50,000 photographs known as "training images."

One AI system was trained the traditional way, by uploading a giant data table containing thousands of rows, each row corresponding to a single training photo. The first column was an image file containing a photo of a particular object or animal; the next 10 columns corresponded to 10 possible object types: cats, dogs, airplanes, etc. A "1" in any column indicates the correct answer, and nine 0s indicate the incorrect answers.

The team set up the experimental neural network in a radically novel way. They fed it a data table whose rows contained a photograph of an animal or object, and the second column contained an audio file of a recorded human voice actually voicing the word for the depicted animal or object out loud. There were no 1s and 0s.

Once both neural networks were ready, Chen, Li, and Raghupathi trained both AI systems for a total of 15 hours and then compared their respective performance. When presented with an image, the original network spat out the answer as a series of ten 1s and 0s--just as it was trained to do. The experimental neural network, however, produced a clearly discernible voice trying to "say" what the object in the image was. Initially the sound was just a garble. Sometimes it was a confusion of multiple categories, like "cog" for cat and dog. Eventually, the voice was mostly correct, albeit with an eerie alien tone (see example on website).

At first, the researchers were somewhat surprised to discover that their hunch had been correct--there was no apparent advantage to 1s and 0s. Both the control neural network and the experimental one performed equally well, correctly identifying the animal or object depicted in a photograph about 92% of the time. To double-check their results, the researchers ran the experiment again and got the same outcome.

What they discovered next, however, was even more surprising. To further explore the limits of using sound as a training tool, the researchers set up another side-by-side comparison, this time using far fewer photographs during the training process. While the first round of training involved feeding both neural networks data tables containing 50,000 training images, both systems in the second experiment were fed far fewer training photographs, just 2,500 apiece.

It is well known in AI research that most neural networks perform poorly when training data is sparse, and in this experiment, the traditional, numerically trained network was no exception. Its ability to identify individual animals that appeared in the photographs plummeted to about 35% accuracy. In contrast, although the experimental neural network was also trained with the same number of photographs, its performance did twice as well, dropping only to 70% accuracy.

Intrigued, Lipson and his students decided to test their voice-driven training method on another classic AI image recognition challenge, that of image ambiguity. This time they set up yet another side-by-side comparison but raised the game a notch by using more difficult photographs that were harder for an AI system to "understand." For example, one training photo depicted a slightly corrupted image of a dog, or a cat with odd colors. When they compared results, even with more challenging photographs, the voice-trained neural network was still correct about 50% of the time, outperforming the numerically-trained network that floundered, achieving only 20% accuracy.

Ironically, the fact their results went directly against the status quo became challenging when the researchers first tried to share their findings with their colleagues in computer science. "Our findings run directly counter to how many experts have been trained to think about computers and numbers; it's a common assumption that binary inputs are a more efficient way to convey information to a machine than audio streams of similar information 'richness,'" explained Boyuan Chen, the lead researcher on the study. "In fact, when we submitted this research to a big AI conference, one anonymous reviewer rejected our paper simply because they felt our results were just 'too surprising and un-intuitive.'"

When considered in the broader context of information theory however, Lipson and Chen's hypothesis actually supports a much older, landmark hypothesis first proposed by the legendary Claude Shannon, the father of information theory. According to Shannon's theory, the most effective communication "signals" are characterized by an optimal number of bits, paired with an optimal amount of useful information, or "surprise."

"If you think about the fact that human language has been going through an optimization process for tens of thousands of years, then it makes perfect sense, that our spoken words have found a good balance between noise and signal;" Lipson observed. "Therefore, when viewed through the lens of Shannon Entropy, it makes sense that a neural network trained with human language would outperform a neural network trained by simple 1s and 0s."

The study, to be presented at the International Conference on Learning Representations conference on May 3, 2021, is part of a broader effort at Lipson's Columbia Creative Machines Lab to create robots that can understand the world around them by interacting with other machines and humans, rather than by being programed directly with carefully preprocessed data.

"We should think about using novel and better ways to train AI systems instead of collecting larger datasets," said Chen. "If we rethink how we present training data to the machine, we could do a better job as teachers."

One of the more refreshing results of computer science research on artificial intelligence has been an unexpected side effect: by probing how machines learn, sometimes researchers stumble upon fresh insight into the grand challenges of other, well-established fields.

"One of the biggest mysteries of human evolution is how our ancestors acquired language, and how children learn to speak so effortlessly," Lipson said. "If human toddlers learn best with repetitive spoken instruction, then perhaps AI systems can, too."

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About the Study

The study is titled "BEYOND CATEGORICAL LABEL REPRESENTATIONS FOR IMAGE CLASSIFICATION"

Authors are: Boyuan Chen, Yu Li, Sunand Raghupathi, Hod Lipson, Mechanical Engineering and Computer Science, Columbia Engineering.

The study was supported by NSF NRI 1925157 and DARPA MTO grant L2M Program HR0011-18-2-0020.

The authors declare no financial or other conflicts of interest.

LINKS:

Paper: https://openreview.net/pdf?id=MyHwDabUHZm

VIDEO: https://youtu.be/Iq2YjHCAPRQ

PROJECT WEBSITE: https://www.creativemachineslab.com/label-representation.html

password cml1234

https://engineering.columbia.edu/faculty/hod-lipson

http://www.cs.columbia.edu/~bchen/

https://me.columbia.edu/

https://www.cs.columbia.edu/

http://engineering.columbia.edu/

Columbia Engineering

Columbia Engineering, based in New York City, is one of the top engineering schools in the U.S. and one of the oldest in the nation. Also known as The Fu Foundation School of Engineering and Applied Science, the School expands knowledge and advances technology through the pioneering research of its more than 220 faculty, while educating undergraduate and graduate students in a collaborative environment to become leaders informed by a firm foundation in engineering. The School's faculty are at the center of the University's cross-disciplinary research, contributing to the Data Science Institute, Earth Institute, Zuckerman Mind Brain Behavior Institute, Precision Medicine Initiative, and the Columbia Nano Initiative. Guided by its strategic vision, "Columbia Engineering for Humanity," the School aims to translate ideas into innovations that foster a sustainable, healthy, secure, connected, and creative humanity.

Understanding fruit fly behavior may be next step toward autonomous vehicles

Could the way drosophila use antennae to sense heat help us teach self-driving cars make decisions?

THE UBIQUITOUS DRSOPHILIA IN SCIENCE

NORTHWESTERN UNIVERSITY

Research News

 

IMAGE: THE WAY FRUIT FLIES ESCAPE HEAT CAN INFORM MODELS FOR SELF DRIVING VEHICLES. view more 

CREDIT: GALLIO LAB

With over 70% of respondents to a AAA annual survey on autonomous driving reporting they would fear being in a fully self-driving car, makers like Tesla may be back to the drawing board before rolling out fully autonomous self-driving systems. But new research from Northwestern University shows us we may be better off putting fruit flies behind the wheel instead of robots.

Drosophila have been subjects of science as long as humans have been running experiments in labs. But given their size, it's easy to wonder what can be learned by observing them. Research published today in the journal Nature Communications demonstrates that fruit flies use decision-making, learning and memory to perform simple functions like escaping heat. And researchers are using this understanding to challenge the way we think about self-driving cars.

"The discovery that flexible decision-making, learning and memory are used by flies during such a simple navigational task is both novel and surprising," said Marco Gallio, the corresponding author on the study. "It may make us rethink what we need to do to program safe and flexible self-driving vehicles."

According to Gallio, an associate professor of neurobiology in the Weinberg College of Arts and Sciences, the questions behind this study are similar to those vexing engineers building cars that move on their own. How does a fruit fly (or a car) cope with novelty? How can we build a car that is flexibly able to adapt to new conditions?

This discovery reveals brain functions in the household pest that are typically associated with more complex brains like those of mice and humans.

"Animal behavior, especially that of insects, is often considered largely fixed and hard-wired -- like machines," Gallio said. "Most people have a hard time imagining that animals as different from us as a fruit fly may possess complex brain functions, such as the ability to learn, remember or make decisions."

To study how fruit flies tend to escape heat, the Gallio lab built a tiny plastic chamber with four floor tiles whose temperatures could be independently controlled and confined flies inside. They then used high-resolution video recordings to map how a fly reacted when it encountered a boundary between a warm tile and a cool tile. They found flies were remarkably good at treating heat boundaries as invisible barriers to avoid pain or harm.

Using real measurements, the team created a 3D model to estimate the exact temperature of each part of the fly's tiny body throughout the experiment. During other trials, they opened a window in the fly's head and recorded brain activity in neurons that process external temperature signals.

Miguel Simões, a postdoctoral fellow in the Gallio lab and co-first author of the study, said flies are able to determine with remarkable accuracy if the best path to thermal safety is to the left or right. Mapping the direction of escape, Simões said flies "nearly always" escape left when they approach from the right, "like a tennis ball bouncing off a wall."

"When flies encounter heat, they have to make a rapid decision," Simões said. "Is it safe to continue, or should it turn back? This decision is highly dependent on how dangerous the temperature is on the other side."

Observing the simple response reminded the scientists of one of the classic concepts in early robotics.

"In his famous book, the cyberneticist Valentino Braitenberg imagined simple models made of sensors and motors that could come close to reproducing animal behavior," said Josh Levy, an applied math graduate student and a member of the labs of Gallio and applied math professor William Kath. "The vehicles are a combination of simple wires, but the resulting behavior appears complex and even intelligent."

Braitenberg argued that much of animal behavior could be explained by the same principles. But does that mean fly behavior is as predictable as that of one of Braitenberg's imagined robots?

The Northwestern team built a vehicle using a computer simulation of fly behavior with the same wiring and algorithm as a Braitenberg vehicle to see how closely they could replicate animal behavior. After running model race simulations, the team ran a natural selection process of sorts, choosing the cars that did best and mutating them slightly before recombining them with other high-performing vehicles. Levy ran 500 generations of evolution in the powerful NU computing cluster, building cars they ultimately hoped would do as well as flies at escaping the virtual heat.

This simulation demonstrated that "hard-wired" vehicles eventually evolved to perform nearly as well as flies. But while real flies continued to improve performance over time and learn to adopt better strategies to become more efficient, the vehicles remain "dumb" and inflexible. The researchers also discovered that even as flies performed the simple task of escaping the heat, fly behavior remains somewhat unpredictable, leaving space for individual decisions. Finally, the scientists observed that while flies missing an antenna adapt and figure out new strategies to escape heat, vehicles "damaged" in the same way are unable to cope with the new situation and turn in the direction of the missing part, eventually getting trapped in a spin like a dog chasing its tail.

Gallio said the idea that simple navigation contains such complexity provides fodder for future work in this area.

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Work in the Gallio lab is supported by the NIH (Award No. R01NS086859 and R21EY031849), a Pew Scholars Program in the Biomedical Sciences and a McKnight Technological Innovation in Neuroscience Awards.

The paper is titled "Robustness and plasticity in Drosophila heat avoidance." In addition to Gallio, Simões, Kath and Levy, authors on the paper include Emanuela Zaharieva, Leah Vinson, Peixiong Zhao, Michael Alpert and Alessia Para.

Great tits change their traditions for the better

Immigration helps populations shift to more efficient behaviors

UNIVERSITY OF KONSTANZ

Research News

VIDEO: AUTOMATED PUZZLE BOXES, WHICH GAVE A FOOD REWARD IF SOLVED, WERE USED TO TEST IF GROUPS COULD CHANGE A SOCIALLY LEARNED FEEDING TRADITION. WHEN A BIRD SOLVED THE PUZZLE, THE... view more 

CREDIT: MICHAEL CHIMENTO

Researchers at the University of Konstanz and Max Planck Institute for Animal Behavior in Germany have found that birds are able to change their culture to become more efficient. Populations of great tits were able to switch from one behavior to a better alternative when their group members were slowly replaced with new birds. Published today as open access in the journal Current Biology, this research reveals immigration as a powerful driver of cultural change in animal groups that could help them to adapt to rapidly changing environments.

In animals, "culture" is considered to be any behavior that is learned from others, shared by members of the group, and persistent over generations. Cultural traditions are known to exist in many animal groups, including primates, dolphins and whales, rodents, and birds.

Great tits provide a classic example of animal culture. In the 1920s, birds in a town in Great Britain were observed to open the foil tops of milk bottles to steal cream. This behavior spread over 20 years, until birds throughout the country were doing the same.

In 2015 scientists experimentally confirmed that great tits were able to maintain cultural traditions. A new way of feeding--what scientists refer to as an innovation--could be taught to a single bird, and that solution would be learned by other birds and gradually spread throughout populations.

But for great tits, and other animals with cultural traditions, it was still not known if groups can change. Once a tradition has taken root, are animals condemned to repeating the same behaviors or can they pivot to more efficient ones?

Now, the new study has demonstrated that more efficient behaviors can outcompete an established inefficient behavior. It pinpoints a fundamental process--population turnover--as crucial for the ability of animals to change their traditions. The study, which involved teaching wild-caught birds to solve puzzles and fine-scale tracking of their behavior, provides quantitative support for the evolution of culture.

"Experimental evidence of cultural change in animals is pretty rare, so we were surprised and excited by the outcome," says first author Michael Chimento, a doctoral student in the Research Group of Cognitive and Cultural Ecology at the Max Planck Institute of Animal Behavior.

The research team led by senior author Lucy Aplin, who is a Max Planck Research Group Leader and also a principal investigator at the Custer of Excellence 'Centre for the Advanced Study of Collective Behavior' at the University of Konstanz, studied populations of great tits caught from forests around Konstanz. Because wild great tits form changeable social groups during winter, when conditions are harshest, the scientists thought that immigration could play a part in cultural evolution.

"These fluid groups could influence how their culture changes, as new group members might see solutions to problems with clearer eyes, because of their lack of experience," says Chimento.

The researchers used captive populations of wild-caught great tits to ask how fluid social groups might change a socially learned feeding tradition. They created 18 groups of birds, each with an automated puzzle-box that gave a reward. When a bird solved the puzzle, the type of solution, time of solution, and identity were recorded using RFID, infrared, and computer vision technology. Each group had a tutor that was trained on a relatively inefficient puzzle solution, which then spread through the group. Then, half of the groups were kept static, and in the other half, group members were gradually replaced with new birds from wild over the course of 4 weeks.

Despite both types of groups innovating a more efficient solution, fluid groups were much more likely to adopt it as their preferred behavior. The original residents, who were experienced with the puzzle, were generally the ones who innovated the efficient solution, but didn't adopt it as their preferred behavior. The inexperienced immigrants, on the other hand, picked up on this innovation and did adopt it, amplifying the available social information. Birds in fluid groups were able to solve the puzzle-box faster than in static groups, despite having less overall experience.

"Great tits seem to do well in and among human-made habitats, compared to other species," says Chimento. "Our study shows how their fluid social dynamics might be part of their secret to success and contribute to their flexibility.

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Facts:

-Study shows rare experimental evidence that animals are able to change their culture to become more efficient.

-Using populations of great tits caught in forests around the Konstanz region, the study highlights the crucial role of immigrant birds in enabling populations to adopt more efficient cultures.

-Populations that replaced birds with naïve individuals were able to change from an inefficient culture to a more efficient culture, while populations that had static membership could not.

-Original publication: Population turnover facilitates cultural selection for efficiency in birds. Michael Chimento, Gustavo Alarcón-Nieto and Lucy Aplin. Current Biology (2021) https://doi.org/10.1016/j.cub.2021.03.057

- Michael Chimento and Gustavo Alarcón-Nieto are researchers in the lab of Lucy Aplin who leads the Research Group of Cognitive and Cultural Ecology at the Max Planck Institute of Animal Behavior. Aplin is also a Principal Investigator at the Custer of Excellence 'Centre for the Advanced Study of Collective Behavior' at the University of Konstanz

- Funding was provided by the DFG Centre of Excellence 2117 ''Centre for the Advanced Study of Collective Behaviour'' under Germany's Excellence Strategy-- EXC2117--4220379

CAPTION

Automated puzzle boxes, which gave a food reward if solved, were used to test if groups could change a socially learned feeding tradition. When a bird solved the puzzle, the type of solution, time of solution and bird identity were recorded. Individual identities of birds were obtained using an antenna in the perch which read transponders attached to the birds' legs, as well as a bar code attached to a leg harness.

CREDIT

Michael Chimento

Seismic coda used to locate and define damage from explosions

SEISMOLOGICAL SOCIETY OF AMERICA

Research News

 

Comparison of coda waves, the scattered waves that arrive after the direct waves of a seismic event, can be used to determine the relative locations of two underground explosions, according to a new study published in the open-access journal The Seismic Record.

The technique, called coda wave interferometry, was tested on explosions conducted as part of the Source Physics Experiment (SPE). Lawrence Livermore National Laboratory researchers Sean Ford and Bill Walter report that coda wave interferometry can also put a limit on the extent of damage caused by an explosion.

The findings suggest the technique could be used to improve the estimates of the relative locations of larger explosions, such as the series of announced nuclear tests conducted by the Democratic People's Republic of Korea over the past two decades.

"Based on the size and frequency scaling that we were able to employ in the paper and successes at SPE," said Ford, "a conclusion point is that this technique could be used for larger explosions at larger separations recorded at more distant stations" such as those used to monitor North Korean testing.

Unlike the direct and strong P- and S-seismic waves produced by an earthquake or explosion event, coda waves arrive later and are more sensitive to scattering by the rock that they pass through. Any changes in the scattering structure--from rocks pushed or crushed by an explosion, for example--"will show up in how these later arriving waves have bounced around in that medium over a greater duration," Ford explained.

In the new study, Ford and Walter used data from the SPE, an ongoing multi-institutional project involving Lawrence Livermore, Los Alamos and Sandia National Laboratories at the former Nevada nuclear test site. The SPE conducts chemical explosions to better understand the seismic waves they produce and to refine explosion detection techniques, using the analyses to improve monitoring of global nuclear explosions.

The researchers used coda wave interferometry to determine the known relative location of two chemical explosions that took place during Phase I of the SPE. The first explosion, SPE-1, was equivalent to 87.9 kilograms of TNT. The second explosion, SPE-2, was equivalent to 997 kilograms of TNT.

Their analysis concluded that the two explosions were located between 6 and 18 meters apart, and most likely 9.2 meters apart. The known separation between the two explosions is about 9.4 meters apart.

Previous research by seismologists David Robinson and coworkers, showed that coda wave interferometry could precisely locate earthquakes separated by hundreds of meters. "We were confident that the approach would work for chemical explosions, but the question for us was whether it could work for such small and closely located explosions," said Ford.

Ford and Walter also used the technique to better characterize the underground damage caused by SPE-2, comparing its coda waves with those produced by the 905-kilogram TNT equivalent SPE-3 that was later detonated in the same spot as SPE-2.

The details of the damage "can't be seen from the direct waves arriving at the [1-kilometer or more] distant stations that we're used to, so we thought perhaps we could see it in these more sensitive scattered waves, the coda waves," Ford explained

Based on the analysis, the damage caused by SPE-2 must have been confined to a spherical region with a radius less than 10 meters, the researchers concluded.

"We thought there would be much more damage, or at least more of an effect on the outgoing waves, but now there is evidence against that hypothesis, so this points us in other directions to explain the observed P- and S-waves," Ford said.

The study is the first research paper published in The Seismic Record, a new short-form, open-access journal from the Seismological Society of America.

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Mapping North Carolina's ghost forests from 430 miles up

Rising seas and inland-surging seawater are leaving behind the debris of dying forests. Now, 35 years of satellite images capture the changes from space.

DUKE UNIVERSITY

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IMAGE: EMILY URY MEASURES SOIL SALINITY IN A GHOST FOREST. view more 

CREDIT: PHOTO BY EMILY BERNHARDT, DUKE UNIVERSITY

DURHAM, N.C. -- Emily Ury remembers the first time she saw them. She was heading east from Columbia, North Carolina, on the flat, low-lying stretch of U.S. Highway 64 toward the Outer Banks. Sticking out of the marsh on one side of the road were not one but hundreds dead trees and stumps, the relic of a once-healthy forest that had been overrun by the inland creep of seawater.

"I was like, 'Whoa.' No leaves; no branches. The trees were literally just trunks. As far as the eye could see," said Ury, who recently earned a biology Ph.D. at Duke University working with professors Emily Bernhardt and Justin Wright.

In bottomlands throughout the U.S. East Coast, trees are dying off as rising seas and higher storm surges push saltwater farther inland, poisoning soils far from shore.

While these "ghost forests" are becoming a more common sight in North Carolina's coastal plain, scientists had only a rough idea of their extent. Now, satellite images are providing new answers.

In a study published April 4 in the journal Ecological Applications, a Duke-led team mined 35 years of satellite images of a 245,000-acre area in the state's Albemarle-Pamlico Peninsula.

The images show that, between 1985 and 2019, 11% of the area's tree cover was taken over by ghost forests. Instead of mirroring the gradual pace of sea level rise, most of this spread occurred abruptly in the wake of extreme weather events such as hurricanes and droughts, which can concentrate salts or send them surging into the region's interior.

The study focused on the Alligator River National Wildlife Refuge, which was established in 1984 to protect the area's unique forested wetlands and the endangered red wolves, red-cockaded woodpeckers and other wildlife that live there.

Here, the Duke team is monitoring what Bernhardt and other researchers call "the leading edge of climate change."

From 1900 to 2000, the sea rose about a foot in this part of coastal North Carolina, faster than the global average. By the end of this century, it could rise two to five feet more.

Shrinking shorelines dominate most discussions of sea-level rise, as oceans submerge coastlines and chew away at beachfront property. Yet less talked about is what's happening farther inland.

Long before beaches shrink and disappear under the rising sea, seawater starts creeping into low-lying regions.

Most of the Alligator River National Wildlife refuge sits less than two feet above sea level, "which makes it all the more vulnerable to sea level rise," Ury said.

Add to that the hundreds of miles of ditches and canals that crisscross the region. Built during the mid-1900s to drain water out, they now act as a conduit for seawater -- which is about 400 times saltier than freshwater -- to flow in.

With no barriers in the way, seawater gets pushed inland through these channels, leaving its salty fingerprints on the soils. As the salt moves in, it draws water out of plant cells and strips seeds of their moisture, making it harder for new tree seedlings to sprout. Salt-sensitive tree species first fail to reproduce and eventually die off, as freshwater forest turns to salt marsh.

Using pictures taken by 430-mile-high Landsat satellites, the team was able to map the spread of ghost forests in the refuge over time.

Each pixel in the satellite images represents the wavelengths of light bouncing off the Earth below, in an area on the ground roughly the size of a baseball diamond.

The team fed the satellite images to a computer algorithm, which in turn analyzed each pixel and determined whether it was dominated by dominated by pines, hardwoods, shrubs, grassy marsh, open water or dead trees. Any pixel with as many as 20 to 40 visibly dead trees present at once was labeled as ghost forest.

The view from space changed over the 35 years of the study.

More than three-fourths of the study area was covered in trees in 1985. Since then, even without any logging or development, the refuge has lost more than 46,950 acres of forest, or a quarter of its 1985 tree cover.

More than half of these losses occurred in the interior of the refuge, more than a kilometer from any coast, the study revealed.

"It's not just the fringe that's getting wetter," Ury said.

Of the more than 21,000 acres of ghost forest that formed between 1985 and 2019, the most noticeable die-off was in 2012. The area had just endured a five-year drought and then a potent strike by Hurricane Irene in 2011, when a 6-foot wall of seawater was pushed ashore. The storm surge swept across the refuge, cresting over Highway 264, more than 1.2 miles inland from the coast. Within months, entire stands of dying and downed trees were visible from space.

What is happening in eastern North Carolina is happening elsewhere, too, the researchers say. In coastal regions across the globe, saltwater is starting to reach areas that haven't seen it before, even reducing crop yields and jeopardizing freshwater aquifers that people rely on for drinking water.

The Duke team is collaborating with other researchers to expand their study to other parts of the Atlantic and Gulf coastal plains, from Cape Cod to Texas.

"Because of its geological location, North Carolina is just ahead of other coastal areas in terms of how far sea level rise has progressed," Ury said. "Lessons learned here could help manage similar transitions in other places," or pinpoint areas that are likely to be vulnerable in the future.

CAPTION

This animation uses satellite images to show changes in tree cover across 245,000 acres in the Albemarle-Pamlico Peninsula over 35 years, from 1985 to 2019. Green areas represent healthy forests. Brown areas are dominated by shrubs. Red areas have a high density of dead trees.

CREDIT

Source: Satellite images by NASA/USGS Landsat

Funding for this research was provided by a NASA Earth and Space Science Fellowship (80NSSC17K0355), the North Carolina Sea Grant/Space Grant (R/MG -1806), the National Science Foundation Coastal SEES program (1426802), and by the Duke University Data+ Program through the Rhodes Information Initiative.

CITATION: "Rapid Deforestation of a Coastal Landscape Driven by Sea Level Rise and Extreme Events," Emily A. Ury, Xi Yang, Justin P. Wright, Emily S. Bernhardt. Ecological Applications, April 4, 2021. DOI: 10.1002/eap.2339