Fortified corrals are one strategy used in Tanzania to protect both livestock and vulnerable carnivore species. But then where do lions, leopards and hyenas go for dinner? Do they feed on the next herd over?

A new study led by Colorado State University has found that good fences truly do make good neighbors because fortified enclosures also benefit livestock keepers who live nearby. Instead of dining on easier meals next-door and negatively impacting neighbors who don’t have fortified enclosures, predators seem to completely avoid neighborhoods when some corrals are built from chain-link fencing, which is more effective than traditional African boma fences made of thorny bushes.

These surprising results are the first to demonstrate a beneficial spillover effect from a strategy to reduce conflict with large carnivores, which play an important role in ecosystems. Losing apex predators can cause ripple effects that disrupt the food web and impact environmental health.

“Coexistence between humans and carnivores is a global challenge, and conflict resulting from carnivores attacking livestock is among the most important coexistence threats globally, including here in the Rocky Mountain West and Colorado specifically,” said Kevin Crooks, co-author of the study and director of the CSU Center for Human-Carnivore Coexistence. “Our results provide important evidence of the effectiveness of proactive, non-lethal tools to prevent livestock predation by carnivores, benefiting not just the target household but potentially neighboring households as well.”

Lead author Jonathan Salerno, an associate professor in CSU’s Department of Human Dimensions of Natural Resources, said that while the intervention method studied is only applicable in limited contexts in the U.S. West, the need to understand the complex interactions among predators, people and conflict interventions is universal.

“Understanding these dynamics can help guide effective use of conservation resources and support better outcomes for people, livestock and threatened species,” he added.

Chain-link linked to safety, savings

In a previous study published in January, Salerno and his collaborators showed that chain-link corrals reduced predation on cattle, goats and sheep in an area surrounding Ruaha National Park in southern Tanzania, a critical landscape for large carnivore conservation. In this agropastoralist system, livestock are kept in fenced compounds at night, when predators are active, and are herded to community grazing lands during the day.

The park and surrounding conservation areas protect 10% of the world’s African lions, among other carnivores, but each household bordering the park has about a 30% chance of losing one or more of its animals to predation each year, a significant economic loss for these small-scale farmers.

Conservation organization Lion Landscapes subsidized 75% of the cost of fortified enclosures for livestock keepers near the park who chose to implement the intervention and cover the remaining 25% of construction costs. A cost-benefit analysis published in the paper showed that after just five years, the benefits from preventing livestock deaths were three to seven times greater than the amounts paid by livestock owners.

“The break-even point is anywhere from three months to two years, given that the loss of one cow is a substantial amount of wealth,” Salerno said. “So, you reduce the risk enough that the fortified enclosure actually pays for itself relatively quickly.”

Using monthly data from 758 livestock-keeping households from 2010 to 2016, the first study also found that the chain-link corrals were 94% effective at reducing the risk of predation in the short term and 60% effective in the long term.

Beneficial spillover effect

The new study, published March 6 in Conservation Letters, examined 25,000 monthly reports from livestock keepers and found that households neighboring those with chain-link corrals also reported fewer attacks on their livestock, the first time a beneficial spillover effect has been demonstrated. The study used data collected by Lion Landscapes and was funded by CSU’s School of Global Environmental Sustainability.

“This research provides scientific evidence about the effectiveness of antipredation interventions, which not only reduce livestock losses but also have positive spillover effects, fostering coexistence between humans and carnivores,” said co-author Joseph Francis Kaduma, a research manager with Lion Landscapes. “By demonstrating how non-lethal methods can benefit both people and wildlife, the study offers practical conservation solutions that can be scaled to other regions facing similar conflicts worldwide.”

Why are the carnivores staying away?

While the study does not answer this question, Salerno said that it’s possible the neighborhoods with enclosures are just too much work for predators.

“The neighborhood with three or four enclosures is going to represent more risk or more effort for the carnivore, because they know they can’t pull livestock out of the fortified enclosures, though a few leopards will try with a goat or sheep,” he said. “It reduces the availability; the night-time livestock buffet is simply less accessible and attractive.”

Why not fence the park?

Like many national parks, Ruaha National Park is vast, and it’s not feasible to enclose it within a chain-link fence. Fencing the park also would have negative ecological consequences by isolating wildlife, and shutting people out would create even greater conflict between nearby communities and conservation interests, Salerno said.

Case study for a global issue

Lion Landscapes has long-term relationships with local livestock keepers and diligently tracked the data that supported these studies. Salerno said that having this kind of data from other places would help conservation organizations and wildlife managers find solutions to similar conflicts.

“If we gather these data, we can understand what factors are contributing to predation events on a particular ranch, and by accounting for the complexity of the larger system, we can start to understand what methods are going to be effective,” he added.

A paper describing the research appeared today March 17 in the Astronomical Journal. The findings offer clues about the properties of sub-Neptunes and help address long-standing questions about their origins, the team said.

“The majority of the 5,500 or so exoplanets discovered to date have a very close orbit to their stars, closer than Mercury to our sun, which we call ‘close-in’ planets,” said Rachel Fernandes, President’s Postdoctoral Fellow in the Department of Astronomy and Astrophysics at Penn State and leader of the research team. “Many of these are gaseous sub-Neptunes, a type of planet absent from our own solar system. While our gas giants, like Jupiter and Saturn, formed farther from the sun, it’s unclear how so many close-in sub-Neptunes managed to survive near their stars, where they are bombarded by intense stellar radiation.”

To better understand how sub-Neptunes form and evolve, the researchers turned to planets around young stars, which only recently became observable thanks to TESS.

“Comparing the frequency of exoplanets of certain sizes around stars of different ages can tell us a lot about the processes that shape planet formation,” Fernandes said. “If planets commonly form at specific sizes and locations, we should see a similar frequency of those sizes across different ages. If we don’t, it suggests that certain processes are changing these planets over time.”

Observing planets around young stars, however, has traditionally been difficult. Young stars emit bursts of intense radiation, rotate quickly and are highly active, creating high levels of “noise” that make it challenging to observe planets around them.

“Young stars in their first billion years of life throw tantrums, emitting a ton of radiation,” Fernandes explained. “These stellar tantrums cause a lot of noise in the data, so we spent the last six years developing a computational tool called Pterodactyls to see through that noise and actually detect young planets in TESS data.”

The research team used Pterodactyls to evaluate TESS data and identify planets with orbital periods of 12 days or less — for reference, much less than Mercury’s 88-day orbit — with the goal of examining the planet sizes, as well as how the planets were shaped by the radiation from their host stars. Because the team’s survey window was 27 days, this allowed them to see two full orbits from potential planets. They focused on planets between a radius of 1.8 and 10 times the size of Earth, allowing the team to see if the frequency of sub-Neptunes is similar or different in young systems versus older systems previously observed with TESS and NASA’s retired Kepler Space Telescope.

The researchers found that the frequency of close-in sub-Neptunes changes over time, with fewer sub-Neptunes around stars between 10 and 100 million years of age compared to those between 100 million and 1 billion years of age. However, the frequency of close-in sub-Neptunes is much less in older, more stable systems.

“We believe a variety of processes are shaping the patterns we see in close-in stars of this size,” Fernandes said. “It’s possible that many sub-Neptunes originally formed further away from their stars and slowly migrated inward over time, so we see more of them at this orbital period in the intermediate age. In later years, it’s possible that planets are more commonly shrinking when radiation from the star essentially blows away its atmosphere, a process called atmospheric mass loss that could explain the lower frequency of sub-Neptunes. But it’s likely a combination of cosmic processes shaping these patterns over time rather than one dominant force.”

The researchers said they would like to expand their observation window with TESS to observe planets with longer orbital periods. Future missions like the European Space Agency’s PLATO may also allow the research team to observe planets of smaller sizes, similar to that of Mercury, Venus, Earth and Mars. Expanding their analysis to smaller and more distant planets could help the researchers refine their tool and provide additional information about how and where planets form.

Additionally, NASA’s James Webb Space Telescope could permit the characterization of the density and composition of individual planets, which Fernandes said could give additional hints to where they formed.

“Combining studies of individual planets with the population studies like we conducted here would give us a much better picture of planet formation around young stars,” Fernandes said. “The more solar systems and planets we discover, the more we realize that our solar system isn’t really the template; it’s an exception. Future missions might enable us to find smaller planets around young stars and give us a better picture of how planetary systems form and evolve with time, helping us better understand how our solar system, as we know it today, came to be.”

In addition to Fernandes, the research team at Penn State includes Rebekah Dawson, Shaffer Career Development Professor in Science and professor of astronomy and astrophysics at the time of the research and now a physical scientist at NASA. The research team also includes Galen J. Bergsten, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Tommi T. Koskinen and Katia Cunha at the University of Arizona; Gijs Mulders at Pontifical Catholic University of Chile; Steven Giacalone, Eric Mamajek, Kyle Pearson, David Ciardi, Preethi Karpoor, Jessie Christiansen and Jon Zink at the California Institute of Technology; James Rogers at the University of Cambridge, Los Angeles; Akash Gupta at Princeton University; Kiersten Boley at the Carnegie Institution for Science; Jason Curtis at Columbia University; Sabina Sagynbayeva at Stony Brook University; Sakhee Bhure at the University of Southern Queensland in Australia; and Gregory Feiden at the University of North Georgia.

Funding from NASA, including through support of the “Alien Earths” grant; Chile’s National Fund for Scientific and Technological Development; and the U.S. National Science Foundation supported this research. Additional support was provided by the Penn State Center for Exoplanets and Habitable Worlds and the Penn State Extraterrestrial Intelligence Center. Computations for this research were performed with Penn State’s University’s Institute for Computational and Data Sciences’ Roar supercomputer.

The model, called patch foundational transformer for sleep (PFTSleep), analyzes brain waves, muscle activity, heart rate, and breathing patterns to classify sleep stages more effectively than traditional methods, streamlining sleep analysis, reducing variability, and supporting future clinical tools to detect sleep disorders and other health risks.

Current sleep analysis often relies on human experts manually scoring short segments of sleep data or using AI models that are not capable of analyzing a patient’s entire night of sleep. This new approach, developed using thousands of sleep recordings, takes a more comprehensive view. By training on full-length sleep data, the model can recognize sleep patterns throughout the night and across different populations and settings, offering a standardized and scalable method for sleep research and clinical use, say the investigators.

“This is a step forward in AI-assisted sleep analysis and interpretation,” says first author Benjamin Fox, a PhD candidate at the Icahn School of Medicine at Mount Sinai in the Artificial Intelligence and Emerging Technologies Training Area. “By leveraging AI in this way, we can learn relevant clinical features directly from sleep study signal data and use them for sleep scoring and, in the future, other clinical applications such as detecting sleep apnea or assessing health risks linked to sleep quality.”

The model was built using a large dataset of sleep studies (polysomnograms) that measure key physiological signals, including brain activity, muscle tone, heart rate, and breathing patterns. Unlike traditional AI models, which analyze only short, 30-second segments, this new model considers the entire night of sleep, capturing more detailed and nuanced patterns. Further, the model is trained via a method known as self-supervision, which helps learn relevant clinical features from physiological signals without using human labeled outcomes.

“Our findings suggest that AI could transform how we study and understand sleep,” says co-senior corresponding author Ankit Parekh, PhD, Assistant Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine) at the Icahn School of Medicine at Mount Sinai, and Director of the Sleep and Circadian Analysis Group at Mount Sinai. “Our next goal is to refine the technology for clinical applications, such as identifying sleep-related health risks more efficiently.”

The researchers emphasize that this AI tool, while promising, would not replace clinical expertise. Instead, it would serve as a powerful aid for sleep specialists, helping to speed up and standardize sleep analysis. Next, the team’s research aims to expand its capabilities beyond sleep-stage classification to detecting sleep disorders and predicting health outcomes.

“This AI-driven approach has the potential to revolutionize sleep research,” says co-senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine, Director of the Hasso Plattner Institute for Digital Health, and the Irene and Dr. Arthur M. Fishberg Professor of Medicine. Dr. Nadkarni is also the inaugural Chief of the Division of Data-Driven and Digital Medicine and Co-Director of the Mount Sinai Clinical Intelligence Center. “By analyzing entire nights of sleep with greater consistency, we can uncover deeper insights into sleep health and its connection to overall well-being.”