A thousand people voluntarily buried their cotton underpants for two months—not to keep the Underpants Gnomes from stealing them, but as part of a citizen science project to map out soil health in 25 countries around the world. The results of this unique experiment were reported in a new paper published in the journal Plants People Planet.

“Our results show that how soil is managed can significantly affect both soil life and soil quality,” said co-author Marcel van der Heijden, an agroecologist at the University of Zurich. “Healthy, biologically active soil is crucial for fertility, nutrient cycling and many other ecosystem services.”

Soil health is critical for agriculture and, by extension, food security, not to mention a healthy global ecosystem, but it has been declining worldwide, per the authors. One key indicator of soil health is decomposition rates of complex organic matter, breaking down stuff like leaves, wood, even cadavers into simpler organic and inorganic compounds, releasing C0₂ and nutrients in the process. There have been relatively few large-scale national decomposition studies involving different land use types, although smaller studies have shown that how humans use a site can significantly affect the soil's biological diversity.

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After ten years of fieldwork in Taiwan’s remote mountain regions, researchers have identified an 84.1-meter (276-foot) Taiwania fir, now recognized as the tallest tree documented in East Asia. The team also found dense forests containing dozens of giant Taiwania firs, suggesting that other giant trees may remain hidden in Taiwan’s rugged interior.

The study, published in Frontiers in Forests and Global Change, was conducted by a team led by Rebecca Chia-Chun Hsu of the Institute of Taiwan Forestry Research, in collaboration with researchers Chi-Kuei Wang and Chung-Cheng Lee of National Cheng Kung University. Starting in 2014, the group, known as the Taiwan tree seekers, located and measured trees that had previously gone unrecorded by combining traditional field surveys with laser-based mapping and the help of citizen scientists.

The researchers found that the giant trees generally grow at elevations of about 2,087 meters and on slopes with an average inclination of 40 degrees. This combination of elevation and rugged terrain makes conventional tree surveys extremely difficult.

A Decade of Ground-Truthing

In August 2014, a systematic search began when scientists from the Taiwan Forestry Research Institute visited the Cilan conservation area to measure the ‘Chilan Three Sisters’, a group of Taiwania firs known locally but not formally recorded. The tallest tree was 69.3 meters high and had a trunk almost three meters wide. The Tree Project, a group of professional climbers from Australia, photographed the trees in 2017 and helped draw greater attention to Taiwan’s giant forests.

During a subsequent expedition near Mt. Benya and Great Ghost Lake, the team hiked for four days with their equipment to reach the study area. This experience showed how difficult it is to estimate the height of giant trees from the ground, since visual estimates can be unreliable in densely wooded, multi-layered old-growth forests. Although the team measured one tree at 71.7 meters, they concluded that ground-based surveys alone would not be sufficient for such a large and complex landscape.

LiDAR and a Problem With the Landscape

To improve their search, Hsu’s team collaborated with Wang and Lee’s remote sensing group at National Cheng Kung University and adopted LiDAR technology. LiDAR uses laser pulses from aircraft to create three-dimensional models of the forest, including estimates of tree height. However, Taiwan’s rugged topography caused the automated software to misinterpret cliffs and abrupt elevation changes as extremely tall trees.

Human reviewers proved more effective than the automated software at identifying these geological false positives. In 2020, hundreds of citizen scientists began examining the LiDAR profile images manually to distinguish real cases from artifacts. By the end of 2022, the Taiwan Giant Tree Map had identified 941 trees taller than 65 meters.

The researchers concluded that combining airborne laser scanning with crowdsourced image reviews and expert analysis can greatly reduce terrain-related errors in estimating tree heights.

Finding the “Heaven Sword”

In 2023, the mapping project led the team to a candidate for Taiwan’s tallest tree, which could be reached after traveling 20 kilometers down a river and then spending two days climbing. A direct measurement established the tree’s height at 84.1 meters, making it the tallest tree ever documented in Taiwan and East Asia. The team called it the ‘Heaven Sword of the Da’an River’. The Indigenous Rukai people have a name for trees of this kind and describe them as ones that ‘hit the moon’. By early 2026, researchers had documented 10 Taiwania firs taller than 70 meters, including two that exceeded 80 meters.

The mapping effort also brought the team back to the Great Ghost Lake region, nearly ten years after their initial visit. There, they identified about 30 large Taiwania firs growing together in an ancient stand, which the team began calling ‘temples of giants.’ In a hectare of forest near Mt. Benya, they recorded 11 individual trees exceeding 65 meters in height.

What the Giants Are Storing

The importance of these forests goes beyond their height. In 2024, Hsu’s team, in collaboration with 15 citizen scientists, surveyed Tao Tree Valley, home to Taiwan’s third-tallest tree, to determine how much carbon the forest stores. They found a carbon density of 1,384.5 megagrams per hectare, without including the root systems. The estimate is comparable to carbon-storage densities reported for some of the world’s most carbon-rich forests, including giant eucalypt forests in Tasmania. The researchers noted that their estimate did not include root systems and may have underestimated the forest’s true carbon storage.

The researchers still do not know how many other giant trees remain hidden in Taiwan’s mountains. However, the combination of LiDAR mapping, citizen science, and field verification gives them a way to search terrain that would otherwise be nearly impossible to survey systematically.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

During the 8 April 2024 solar eclipse, the path of totality swept across North America, dazzling millions of skygazers. Scattered along this path were 82 viewing sites of the Dynamic Eclipse Broadcast Initiative citizen science project, which aimed to observe the dynamics of the tenuous solar corona. Though poor weather dashed many observers’ hopes, the project managed to collect data at 11 sites spanning 2,700 kilometers, capturing 49 minutes of coronal behavior. The image above shows the solar corona as seen during totality from one of the DEB Initiative sites. In a recent research article, Matthew J. Penn (Southern Illinois University Carbondale; Tucson Amateur Astronomy Association) and collaborators described the results of the Initiative’s viewing efforts, including identification of downflows and outflows, plumes, coronal loops, and apparent motion of background stars. To learn more about these observations of the solar corona, be sure to check out the full article linked below.

Citation

“Structure and Dynamics of the Inner Corona Measured from the DEB Initiative 2024 Eclipse Image Sequence,” Matthew J Penn et al 2026 ApJL 1006 L25. doi:10.3847/2041-8213/ae8797

The post Featured Image: Citizen Science and Solar Structure appeared first on AAS Nova.

Paternal care is uncommon among several wild animal species, even among arthropods—the invertebrates that include insects, arachnids, and crustaceans.

Now, in a recent study in the Zoological Journal of the Linnean Society, harvestmen—also known as daddy longlegs—account for more than half of all independently evolved cases of paternal care currently documented among arthropods.

The news insights were made possible by a recent citizen science effort that significantly increased the available data about these paternal behaviors in arthropods, all within days.

Built for Fatherhood

Harvestmen are one of the most diverse arachnid groups, with more than 6,900 described species. Despite making up just 0.6% of arthropod diversity, they show more independent origins of paternal care or egg guarding than any other arthropod lineage. This concentration of paternal care makes harvestmen a key group for studying the evolution of this behavior.

Led by Glauco Machado at the University of São Paulo, the research team combined almost thirty years of fieldwork with a review of iNaturalist, a public website for sharing nature sightings. From 1936 to 2025, published studies had found parental guarding in only 80 harvestman species. However, the iNaturalist search added 85 new records: 78 showing parental care and seven showing no care, more than doubling the number of harvestman species with available behavioral data.

Machado was surprised by how quickly the platform produced results. “It’s a tremendous source of information that can improve the velocity with which we accumulate biological information,” he said. “I would never be able to do this by visiting museums around the world. It would be very expensive, very time-consuming, but here we conducted the search in only one week.”

The Evolution of Fatherhood

With the expanded dataset, the researchers were able to map parental behaviors onto a newly constructed evolutionary tree encompassing 165 species across nine families. This represented the first reconstruction of how both maternal and paternal care evolved within the superfamily Gonyleptoidea. The findings challenge the assumption that caregiving behaviors follow a single evolutionary pathway.

The analysis found that maternal care evolved only from ancestors that did not provide care, a pattern also seen in insects. Paternal care, however, appeared in two ways: once from ancestors with no care, and once from species where females already guarded eggs. This difference suggests that separate evolutionary pressures shaped the development of parental care in harvestmen.

Machado suggests that sexual selection may have driven the evolution of paternal care from maternal care. In this scenario, which the researchers describe as enhanced fecundity, females favor males that are already tending eggs.

“It’s very rare in nature, paternal care, and this behavior evolved many times independently,” Machado said. “In many species where males care for the offspring alone, the caring activity is a sexually selected behavior, which means that females prefer males that are caring for the eggs.”

The evolutionary tree also showed that parental care does not always persist across lineages. Several groups appear to have lost caregiving behavior over evolutionary time. Some harvestmen now use easier methods, like hiding their eggs or burying them under debris, to protect them without having to stay and guard them.

Limitations of Citizen Science

Machado emphasizes that experts remain essential to this process. It takes specialized knowledge to correctly identify species, tell the sex of the guarding animal, and tell real parental care apart from similar behaviors such as mate-guarding. “I think taxonomists’ role in modern science is more important than ever,” he said. “We cannot preserve a species that doesn’t have a name. And names are provided by taxonomists.”

Additionally, animals that are actively guarding eggs are more likely to be photographed than those not showing any behavior, which could make active parental care seem more common than it really is.

What Comes Next

Machado views this approach as a model for future research. More than half of the new records came from individuals without formal training in arachnology, and he anticipates similar results as researchers apply iNaturalist to other animal groups for which parental care remains poorly documented.

“I think it’s a very broad contribution for people that are working with insects, frogs, and all kinds of groups, animal groups, in which we have both maternal care and paternal care,” he said. The next mystery solved through citizen science may not involve arachnids at all.

Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.

“I didn’t sign up to try to measure a new record or anything”, said Matt Carnicle, a volunteer for the NASA-sponsored Community Collaborative Rain, Hail, and Snow Network, or CoCoRaHS, project. Carnicle measured a whopping 29.06 inches of rainfall on June 18th, 2026, breaking an all-time 24-hour record for the state of Louisiana of 22.00 inches. “I’m just a regular guy who likes to track the weather, and I report what I get in my gauge whether it’s zero, two hundredths, or whatever is in there when I read it.” 

CoCoRaHS  (pronounced KO-ko-rozz) is a network of volunteer weather observers of all ages working together to measure and map rain, hail, and snow by measuring precipitation in their backyards. Together, these thousands of daily precipitation reports – openly available on the project website – are used by scientists and citizens for a wide variety of purposes, to include improving weather forecasting, informing water and land management, driving atmospheric models, and triggering flash flood and severe weather warnings. 

Matt joined through a storm-spotter class where he learned how CoCoRaHS is part of a NASA hail research project focused on Gulf States in the Southeast United States. CoCoRaHS reports (and photos) of hail are used for researching the “melt rate” between when the satellite estimates the stone sizes in the clouds and what volunteers measure on the ground. Matt took it a step further and purchased a standardized rain gauge in order to participate with CoCoRaHS by measuring rainfall.

Matt’s June 18, 2026 rain measurement shatters Louisiana’s 1962 state record of 22.00 inches of rain in 24-hours (Hawaii holds the national record with 49.69 inches in 24-hours). Even more remarkably, the 29.06 inches he measured fell in less than 12 hours! According to Louisiana State Climatologist Jay Grymes, who validated Matt’s measurement along with National Weather Service representatives, an event of this magnitude in this area is expected to happen less than once in a thousand years. A National Oceanic and Atmospheric Administration (NOAA) committee will convene in the coming months to verify and document the new record.

You can join Matt and other CoCoRaHS volunteers and submit official rainfall reports to the National Weather Service. They’re also on the lookout for hail in the southeast, where CoCoRaHS and NASA are doing research on how hail melts as it moves from the clouds to the ground. The only requirement for participation is that volunteers use the correct manual gauge, which is precise to the nearest 1/100th of an inch and is approved by the National Weather Service (measurements from automated rain gauges are not accepted). Sign up here, and you might measure the next record precipitation event: https://science.nasa.gov/citizen-science/community-collaborative-rain-hail-and-snow-network/

Some stars have planets. Others are orbited by brown dwarfs, balls of gas too massive to be planets, but too low-mass to be stars. Astronomers love these brown dwarf-star pairs because being paired with a star helps reveal a brown dwarf’s age. Ages of astronomical objects are often hard to measure, but essential for understanding how they form. 

Now, you can join NASA’s new Backyard Worlds: Binaries project and help astronomers discover these rare and interesting pairs. As a volunteer, you’ll inspect images from NASA’s Wide-field Infrared Survey Explorer (WISE) space telescope. Brown dwarfs may appear as small dots moving across a field of otherwise static stars. 

“We need your help to gain critical insights into these enigmatic cosmic objects,” said project lead Aaron Meisner.

Brown dwarfs are common but mysterious because they are so faint. There’s one for every three or four stars in our corner of the Milky Way galaxy. They are important laboratories for understanding giant planets like Jupiter.

Join the Backyard Worlds: Binaries project today and help astronomers understand where and when brown dwarfs form! You can also try one of our other brown dwarf-related projects: Backyard Worlds: Cool Neighbors! Anyone with a laptop or cell phone can participate. Participation does not require citizenship in any particular country.

The post Search for Hidden Cosmic Companions in Sun’s Backyard appeared first on NASA Science.

Some stars have planets. Others are orbited by brown dwarfs, balls of gas too massive to be planets, but too low-mass to be stars. Astronomers love these brown dwarf-star pairs because being paired with a star helps reveal a brown dwarf’s age. Ages of astronomical objects are often hard to measure, but essential for understanding how they form. 

Now, you can join NASA’s new Backyard Worlds: Binaries project and help astronomers discover these rare and interesting pairs. As a volunteer, you’ll inspect images from NASA’s Wide-field Infrared Survey Explorer (WISE) space telescope. Brown dwarfs may appear as small dots moving across a field of otherwise static stars. 

“We need your help to gain critical insights into these enigmatic cosmic objects,” said project lead Aaron Meisner.

Brown dwarfs are common but mysterious because they are so faint. There’s one for every three or four stars in our corner of the Milky Way galaxy. They are important laboratories for understanding giant planets like Jupiter.

Join the Backyard Worlds: Binaries project today and help astronomers understand where and when brown dwarfs form! You can also try one of our other brown dwarf-related projects: Backyard Worlds: Cool Neighbors! Anyone with a laptop or cell phone can participate. Participation does not require citizenship in any particular country.

The post Backyard Worlds: Binaries appeared first on NASA Science.

Motivated by a desire to support community members financially during the coronavirus pandemic, researchers employed 30 local citizen scientists in the Hubble Image Similarity Project. This project quantified the similarities between astronomical images, providing a way to test the results of image-search algorithms.

Seeking Similarities

Say you have an image of a star-forming region, featuring eye-catching gas clouds, dense and dusty knots, and newborn stars. How would you go about finding other images that resemble yours?

You might start your search with an astronomical image database, using filters for object type or instrument to sift through thousands and thousands of options. But even filtering out everything but star-forming regions might yield vastly different results, given the widely varying shapes, colors, and sizes of these regions.

Or maybe you’ll feed your image into a neural network that has been trained to spot similar images. The results may seem promising, but how can you tell whether the algorithm has found the images that are the most similar? Would another algorithm do better?

The Hubble Image Similarity Project

Astronomical image collections rarely contain information about similarities between images in their metadata, and while neural networks appear to excel at gathering similar images, the results of these models are generally unverified. The Hubble Image Similarity Project, led by Richard White (Space Telescope Science Institute) and Josh Peek (Space Telescope Science Institute and Johns Hopkins University), addressed these issues with a team of citizen scientists who generated similarity information for astronomical images, providing a quantitative means to test the results of neural networks.

White and Peek began by amassing a sample of images from the Hubble Legacy Archive. This sample included many different object types, such as galaxies, planetary nebulae, star-forming regions, and star clusters. After trimming and binning the images, converting them to 8-bit grayscale, filtering out low-contrast images, and eliminating satellite trails, image artifacts, and repeated observations of the same patch of sky, 2,098 images of 666 objects remained.

Citizen Scientists, Assemble

White and Peek recruited 30 members of the community within walking distance of the Space Telescope Science Institute to identify similar astronomical images, and the reviewers were paid for their work. In the three phases of the project, reviewers considered test images one at a time and 1) selected all similar images from a set of 15 comparison images, 2) selected the most similar image from a narrowed-down set of 6 comparison images, and finally 3) selected the most similar image from a set of 3 comparison images.

The citizen science team ultimately compared 5.4 million pairs of images, and White and Peek used these comparisons to produce an image similarity matrix. The matrix describes the metaphorical “distance” between the images, with the most similar images being the smallest distance apart.

Similar images resemble one another in terms of structure, texture, and other factors that White and Peek say are “difficult even to describe in words” — for example, the diffuse glow of a galaxy interrupted by a bright star with diffraction spikes, or a nebula speckled with stars and dense dusty clumps. The similarity data from this study are available online and can be used to test the performance of image-search algorithms. In future work, the authors plan to carry out a similar project using images of the Martian landscape.

Citation

“The Hubble Image Similarity Project,” Richard L. White and J. E. G. Peek 2025 AJ 169 306. doi:10.3847/1538-3881/adcb43

The post Citizen Science Born in the Pandemic: The Hubble Image Similarity Project appeared first on AAS Nova.