Even we *almost* missed them.Globally, about 1 billion people live near glaciers and depend on glacial water. As temperatures continue to climb due to global warming, glaciers can become unstable and hazardous to nearby communities. These communities risk exposure to a series of linked processes, like ice collapse, flooding, and landslides, which could have life-threatening consequences. Scientists call these events catastrophic glacier hazard chains, or CGHC. So, a team of scientists in China sought to understand the driving forces behind these dangerous phenomena.
The team focused on a CGHC event that occurred in 2018 at the Sedongpu Glacier, located in the Eastern Himalayas. During this event, the glacier obstructed the flow of 30 million square meters (more than 300 million square feet) of the Yarlung Tsanpo River, one of the main rivers within the region.
To reconstruct the event, the team observed satellite images of the Sedongpu glacier over time, using a process called remote sensing. They used the images to calculate ice volumes, glacier velocities, and glacier movement between 1961 and 2018, to determine the glacier’s long-term physical properties and how they changed over time. They found that from 1968 to 2013, the Sedongpu Glacier shrank, or retreated, by over 6 football fields. It also lost ice and water volume and increased its velocity.
The scientists also measured earthquake waves on the Earth’s surface, or seismicity, to characterize any downslope rock or debris movements caused by the glacial retreat. They measured seismicity across 11 different seismic stations within 580 kilometers (about 350 miles) of the glacier. Then, they used this data to identify 2 distinct phases of the CGHC event on Sedongpu Glacier: the ice-rock avalanche and the debris flow.
When the ice-rock avalanche occurred, the team detected noticeable increases in seismicity, suggesting it lasted over 30 seconds. During those 30 seconds, over 8 million cubic meters (more than 280 million cubic feet) of ice fell onto the main glacier. The team recorded even more increases in seismicity during the debris flow, which lasted nearly 5 minutes. The debris from the avalanche hit the main valley glacier at nearly 300 kilometers (200 miles) per hour.
The researchers combined these data to describe how the 2018 CGHC event unfolded. First, the backward movement and shrinkage of the glacier produced an unstable sheet of ice hanging over a cliff face. The steep slope and large cracks in the ice caused an ice-rock avalanche. Debris from the avalanche then accumulated on the main glacier, causing it to move faster. As the main valley glacier moved downslope, it picked up even more debris from the bottom and sides of the glacial valley. The melting ice also increased the glacier’s velocity, and its movement eventually transitioned into a debris flow, which obstructed the Yarlung Tsangpo River.
In total, the CGHC event in the Sedongpu Glacier travelled over 10 kilometers (6 miles) and moved down in elevation for over 3 kilometers (about 2 miles) at a velocity of about 100 kilometers (about 70 miles) per hour. The glacier accumulated enough debris and ice to fill thousands of Olympic-sized swimming pools. Imagine what this could do to a nearby village!
It’s difficult for scientists to estimate when CGHC events will occur, but this team proposed several factors that could help predict them. These factors included the size of the ice-rock avalanche and the glacier velocity, as well as the occurrence of earthquakes, temperature changes, and the melting of the glacier’s underside. They stated that these factors can combine to create a domino effect that drives the CGHC process. They recommended consistent glacial monitoring as temperatures continue to climb, in the Himalayas and globally.
The team concluded that understanding CGHC events can help scientists mitigate glacial hazards and keep people safe, especially in mountainous areas such as the Eastern Himalayas. They stated that their findings illustrate the dangerous processes behind glacial melting, but further research is needed to accurately assess those processes and ensure nearby communities remain safe.
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In the Bolivian Altiplano, fewer Aymara farmers are relying on local weather knowledge to schedule farming tasks, like choosing the best dates to plant crops and predicting a frost or drought. At the same time, there is growing doubt about whether these traditional indicators remain relevant today in the context of rapid social, economic, and climatic changes.
Farmers are struggling because the National Weather Service of Bolivia (SEHNAMI) fails to provide useful weather forecasts due to the geographical complexity of the Andes and a scarcity of weather stations. In addition, many farmers can’t access this information because SEHNAMI lacks local outreach programs to deliver reports directly to them. In a region with a weak technical forecasting system, local knowledge is a vital resource, but its use is declining.
Scientists in the past focused on preserving local weather knowledge or discussing how it might help communities adapt to climate change. However, very few researchers have scientifically validated local weather indicators, since doing so requires many years of historical meteorological data to establish statistical “skill” in local forecasting.
Faced with this gap, researchers in the US and Bolivia recently set out to validate indigenous indicators in 2 Bolivian municipalities: Umala in the Central Altiplano and Ancoraimes in the Northern Altiplano. To do this, they paired each traditional weather sign with a measurable variable, like temperature, that could approximate it. This approach allowed them to directly compare traditional observations with scientific data.
The researchers wanted to determine whether traditional weather knowledge is still accurate and relevant under current conditions, since validating these methods would strengthen local knowledge and benefit the schools and policies that promote it. To test this, the researchers worked with 6 Aymara communities. First, they conducted workshops with local leaders and experts to compile a set of weather-forecast indicators. Then, they surveyed 95 farmers to rank the reliability of these indicators.
Through discussions with these communities, the team identified 13 weather indicators in the Central region and 20 in the Northern region. These indicators included specific plants, animals, and physical factors like constellations or winds that the farmers used to decide when and where to plant crops, and to predict weather conditions for the next season. The researchers focused on 2 key indicators that the farmers ranked as most reliable: the presence or absence of moisture under rocks on the Fiesta de San Juan on June 24th, and the flowering patterns of specific plants, called Thola and Sank’ayu. The farmers used the San Juan indicator to predict whether the upcoming year would be rainy or dry, and the plants to determine the most favorable dates for planting to avoid agricultural risks like frosts.
Finally, the researchers compared forecasts based on these 2 signs with over 30 years of meteorological data from nearby weather stations. In both cases, they used the minimum daily temperature as the measurable way to represent these local signs scientifically. Their results showed that the minimum daily temperature on the Fiesta de San Juan could explain more than half (55.5%) of the changes in rainfall during the growing season. They also confirmed that severe frosts that “burned” Thola and Sank’ayuthe flowers were followed by periods without rain, validating the farmers’ strategy of delayed planting.
The researchers concluded that local weather indicators used by Aymara farmers are most reliable for seasonal planning, especially in predicting the start of the rainy season. They stated that this result helps legitimize traditional knowledge and empower the community. For example, these communities now hold regular meetings to discuss forecasts and share this information with their neighbors via WhatsApp groups.
However, the team acknowledged that local knowledge was limited to short-term forecasting, as it only provided warnings a few hours in advance. Farmers need alerts several days in advance to effectively reduce their vulnerability to events such as frosts or hail. For this reason, the researchers suggested that the next step will be to co-produce forecasts by combining meteorological science with traditional knowledge.
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The spots on a leopard, the stripes on a zebra, and the mask on a raccoon are all types of coloration in mammal fur, referred to as pelage. Scientists have struggled for a long time to understand how these colorations evolved. A team of researchers recently discovered how to reconstruct the colors of ancient mammal fur using cell structures that contain the chemical pigment melanin, known as melanosomes. They hope to use this understanding of ancient mammal pelage to determine how they lived and what their environments were like.
To decipher the color of ancient mammal fur, the researchers constructed a statistical computer model to predict their fur colors based on the shape of their melanosomes. First, they needed to understand how the shapes of melanosomes control fur color in modern mammals. To measure the length and width of the modern melanosomes, the scientists used a beam of electrons like a flashlight to map them in great detail, a technique called scanning electron microscopy.
They measured the length and width of 2,615 melanosomes from the fur of 116 different living mammal species, including a Red Panda, a German Shepard, and one or more species of most types in the mammalian classification. Then they recorded the wavelengths of light reflected off each melanosome using a microspectrophotometer. These wavelengths represented the exact color of hair from each melanosome they measured. This means they now had a quantitative measurement of the color of each hair to compare with its corresponding melanosome length and width.
When they examined their data, the researchers found that spherical melanosomes produced brighter fur colors, like reds and oranges, while elongated melanosomes produced darker fur colors, like browns and grays. The researchers plotted the microspectrophotometric measurements for each melanosome as a graph showing how much light it absorbed across each possible wavelength, creating reflectance curves. Based on these data, the researchers used computer software to create a statistical model that could predict reflectance curves, and therefore color, from melanosome shape alone.
Once the researchers created a model, they needed to find fossilized mammal melanosomes to predict their fur color. Mammal fossils are rare, and pieces of cells are only preserved in the most immaculate fossil deposits. In fact, these researchers only found 6 fossils of this caliber, from Mesozoic rocks deposited around 250 million years ago, soon after mammals evolved.
The fossils came from 2 world-renowned deposits in Northeastern China, the Yanliao and Jehol biotas. They selected 5 previously studied fossils from these locations, including 1 specimen of Megaconus mammaliaformis, 1 specimen of Vilevolodon diplomylos, 2 specimens from the group Docodonta, and 1 specimen from the group Eutheria. All of these animals were small, rodent-like mammals. The researchers also discovered a new species of tree-dwelling mammal, Arboroharamiya fuscus.
The scientists used scanning electron microscopy to measure a total of 2,159 fossilized melanosomes to refine their color predictions. The fossilized melanosomes were all slightly elongated spheres and had far fewer differences in shape than in modern mammals. The researchers used their model to predict reflectance curves based on the shapes of the fossil melanosomes to determine the ancient mammals’ colors. They found that all the extinct mammals they examined likely had dark brown fur without any color patterns.
Based on these findings, the researchers suggested that most, if not all, early mammals were nocturnal, like many dark-colored mammals today. They suggested that this nocturnal lifestyle probably helped them survive the meteorite impact that wiped out the dinosaurs 100 million years later. While dinosaurs were unable to survive in the post-impact wasteland, these smaller, dark-colored mammals were more resilient and thrived in the aftermath. As mammals filled the ecological roles left by the dinosaurs, they evolved new color patterns, some based solely on camouflage, and others on new factors, like sexual selection and visual communication.
The researchers concluded that their model can predict fossilized mammal colors and help determine how these ancient animals lived. Notably, their data suggest that mammals likely stayed small and nocturnal throughout the Mesozoic until the dinosaurs were gone. Future researchers could use similar methods to uncover exactly how mammals evolved the colors we see today. For example, studying mammals closer to the impact event could reveal if it took a long time to develop diverse fur patterns, or if they began before the dinosaurs’ demise. Ultimately, similar studies could help determine whether the extinction of dinosaurs was necessary for the rise of mammals.
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Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.
One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.
Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.
To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.
The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes.
They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios.
After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR).
They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model.
They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.
Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions.
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Around 4.6 billion years ago, an immense cloud of gas and dust in space collapsed in on itself. As it collapsed, gravity pulled most of the material toward several small points, one of which would eventually become our Sun. The other points formed other stars in what scientists call an open cluster.
During this early stage of the Sun’s life, it was surrounded by a swirling disk of leftover material aligned with its equator, known as a protoplanetary disk. Eventually, this disk condensed to form the planets that make up our solar system. This process normally results in a system with a star at its center, and the planets orbiting the plane of the star’s equator.
However, scientists have found that this scenario doesn’t quite hold for our solar system. The planets in our solar system orbit the Sun in a single plane offset from the Sun’s equator by approximately 6°. The fact that the planets are in the same orbital plane suggests that the protoplanetary disk model of solar system formation is correct, since the odds of the Sun capturing 8 rogue planets in this arrangement are astronomically low. So, what threw the planets in our solar system off-kilter?
The plane in which the solar system’s planets orbit the Sun is offset from the Sun’s equator by approximately 6°. Li simulated how the solar system tilted based on its 4 largest planets, from left to right: Neptune, Saturn, Jupiter, and Uranus. Illustration by Andrew Bizal.
To determine what caused the solar system to tilt, Daohai Li at Queen Mary University of London recently used the computer program NBODY6++ to model different versions of the open cluster from which the Sun came. In these simulations, he varied the number of stars in the cluster, from 500 to 2,000 to 8,000. He also varied the radius of the cluster, from 0.25 to 1 to 4 parsecs (8 to 30 to 100 trillion kilometers or 5 to 20 to 70 trillion miles). Finally, he varied the percentage of stars with a binary companion from 10% to 50% to 90%. Li assigned each star a randomly generated initial mass, starting position, and velocity, and simulated their interactions for 400 million years.
First, Li examined the simulations to identify stars with masses approximately equal to the Sun’s. From this group of solar-massed stars, Li found ones that left the cluster after 400 million years as a single star, after either forming alone or having lost a former companion star. He proposed that these stars were the most similar to the Sun and could thus host a simulated solar system.
Li then simulated the planets and their orbits as they would have formed on these potential host stars. To simplify this process, Li only simulated the 4 giant planets, Jupiter, Saturn, Uranus, and Neptune, which dominate planetary behavior in our solar system. He modeled these solar systems independently for 200 million years to ensure they would remain intact and used those that did for statistical analysis of the solar system’s past.
Overall, Li discovered 2 scenarios that could explain the solar system’s tilt. In the first, the primordial solar system was jostled by many distant neighboring stars in its cluster. This jostling tilts the planets’ orbits, but it does so randomly. Therefore, this scenario would make it unlikely for the planets to remain in the same orbital plane. It would also cause them to orbit the Sun faster and more chaotically than in our solar system.
In the second scenario, the Sun had a temporary companion star that it either formed with or briefly encountered before separating. When this occurred, the simulated planets’ orbits all tilted together. Stars that formed with a binary companion had a 10% chance of tilting their planets’ orbits by 6° or more, while stars that formed alone had a less than 0.1% chance.
Li concluded that the solar system could have tilted as a result of interactions between the Sun and a temporary companion star within its parent cluster. However, he acknowledged that the model strongly depends on how closely a star passes by the Sun, since the closer a star passes, the more it disturbs the solar system. Despite this uncertainty, it’s possible that somewhere in space is the Sun’s twin or former neighbor that we haven’t seen in eons, and that’s what made our solar system crooked!
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If you’ve ever swum in the ocean and been touched by seaweed, there’s a good chance it was Sargassum! This free-floating seaweed can have population explosions in coastal waters, degrading water quality, ecosystems, fisheries, and tourism. However, despite its drawbacks, scientists think that Sargassum could be useful for extracting valuable metals, known as rare earth elements (REEs).
REEs are classified by their average masses and atomic weights. Light REEs include cerium, praseodymium, and neodymium, middle REEs include gadolinium, terbium, and dysprosium, and heavy REEs include erbium, thulium, and ytterbium. These metals are used in magnets, electronic motors, wind turbines, spacecraft, and more. Our current methods of extracting REEs, open-pit mining and chemical processing, are invasive, causing habitat loss and producing toxic waste. Additionally, REEs are difficult to import due to restricted access worldwide. But what if these metals could be extracted using natural methods?
Scientists from Woods Hole and UCLA recently tested whether Sargassum could accumulate REEs, creating a sustainable solution to these problems. They harvested Sargassum at Waterfront Park in Woods Hole, rinsed the wet samples, and stored them in sterilized, filtered seawater, which they aerated with 12-hour cycles of light and dark to keep the Sargassum alive. They dried another set of samples in the sun under controlled outdoor conditions, stored them in sealed containers, and later rehydrated them in seawater for 3 hours before the experiments.
For each experiment, they added 5 grams (about 0.2 ounces) of Sargassum to solutions containing a mixture of 9 REEs – cerium, praseodymium, neodymium, gadolinium, terbium, dysprosium, erbium, thulium, and ytterbium – in concentrations totalling 0.1 micromolar (μM) REEs, 11μM REEs, 100 to 110μM REEs, and 575 to 600μM REEs. They performed each experiment 4 times: twice with fresh seaweed and twice with rehydrated seaweed.
The researchers removed 1 gram (about 0.04 ounces) of seaweed from each solution after 1, 3, and 7 days, and dissolved it in acid to release the REEs it had absorbed. They analyzed the concentrations of REEs in each seaweed sample and the leftover solutions using 2 different plasma-based methods. They used these REE concentrations to calculate the ratio of REEs in the Sargassum versus in the solution, known as the bioconcentration factor, to determine how effectively the seaweed absorbed REEs.
They found that after 7 days at lower REE concentrations (0.1μM), the fresh seaweed had bioconcentration factors of over 1,000. At higher REE concentrations (11μM), their bioconcentration factors ranged from about 400 to 700. However, these levels of REEs were eventually toxic to the fresh Sargassum, so they died after 2 weeks. In addition, the higher REE concentrations of 100 to 600μM were immediately toxic to the fresh Sargassum, which died within 1 day in solution.
The researchers determined that once the seaweed died, it could no longer actively take up REEs into its tissues but could only accumulate them on its surface or adsorb them. Adsorption can be thought of as a powdered donut – when the surface is covered with powdered sugar, no more powder fits on the donut. Similarly, there was a limit to how many REEs could stick to the Sargassum. Due to this limit, the fresh seaweed samples had bioconcentration factors below 250 in the highest REE concentrations. In contrast, the rehydrated Sargassum consumed less REEs at lower concentrations, but didn’t experience toxicity and continued to accumulate REEs at higher concentrations.
Finally, the team compared Sargassum to a common industrial method of accumulating REEs, known as activated carbon. REEs bond easily to activated carbon because of its abundant surface area and high porosity. Both materials accumulated a similar amount of REEs in solutions of 0.1μM REEs. However, at 575 to 600μM REEs, Sargassum accumulated about twice as many REEs as activated carbon, especially the heavier REEs.
The researchers concluded that Sargassum is a natural hyper-accumulator, meaning it successfully concentrates REEs. However, they recommended that future researchers test whether Sargassum performs the same in the natural environment as it does in the lab. If so, Sargassum could be used to accumulate REEs from wastewater, like mine drainage, or other water sources with higher concentrations of REEs. Regardless, they suggested that using Sargassum to obtain REEs could simultaneously expand global access to these metals and mitigate toxic blooms.
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Humans have been around for only about 0.01% of Earth’s history, yet we’ve altered natural landscapes and processes that have remained stable for millions of years. A team of researchers from the University of Glasgow recently studied a formerly sandy beach in the UK that highlights how fast these changes can occur.
Industrial steel and iron furnaces operated extensively in the UK from 1856 until the 1980s. During these decades, workers dumped roughly 335 million cubic meters (90 billion gallons) of molten furnace waste, called slag, across the country. Of that, roughly 27 million cubic meters (6 million gallons) now sits on the coast of Derwent Howe in a massive 30-meter (100-foot), solidified cliff.
The researchers analyzed this region to understand how man-made, or anthropogenic, materials influence the way rocks form. Rocks naturally form and change into different types of rocks over million-year timescales, through a continuous process called the rock cycle. When the team uncovered a soda can tab manufactured in 1989 within the hardened grains, or clasts, of slag, they concluded that the beach had solidified within the last 35 years. Based on this discovery, they proposed a new term for rocks formed from anthropogenic sediments: the anthropoclastic rock cycle.
The team collected 1,300 grains of sediment from 13 sites in the study area and found that 83% were slag. Natural beach sediments, like quartz sand, comprised only about 17% of the grains. As wind and waves broke down the cliff, slag clasts fell off and became rounded by friction as tides transported them from the cliff to the beach.
At the boundary where the slag touched the beach, the researchers also saw evidence that the slag had seeped into the underlying wet sediment. The rocks there resembled a type of rock called peperite, which forms when lava flows over wet, loose sediments. They concluded that this peperite-like rock formed as the molten slag flowed over the still sandy beach. Slag is much thicker than wet sand, so the resulting rock looked like blobs of slag encased in sandy rock.
The researchers then visually observed how the slag grains were arranged in the solidified rock to reconstruct the tidewater speeds at different points in the coast’s history. The different ways sediments are arranged within a rock are called sedimentary features, and they reflect the processes that formed the rock. For example, as water flows over sand, it forms small, dune-like ripples. When the sand beds harden into rock, the ripples are preserved as features known as trough cross-bedding.
Faster water erases any ripple formations and instead forms horizontal layers in the sediment, known as planar lamination. The researchers observed both trough cross-bedding and planar laminations preserved in the slag beach rocks. These sedimentary features indicate that the beach at Derwent Howe turned from loose sediment into rock in a generation.
To investigate why this beach rapidly solidified, the researchers scanned the surfaces of the beach rock with electron beams and X-rays using a scanning electron microscope, or SEM, equipped with an energy dispersive X-ray, or EDX. Every chemical element emits a unique set of X-rays with different wavelengths when hit with EDX, so the researchers could compare the X-rays from their samples to databases of known mineral X-ray signatures. They identified the primary binding agents as calcium- and iron-rich minerals like calcite, goethite, and brucite. These minerals are called cements because they bind grains of sediment together.
The researchers expected the slag to produce these cement compositions, since extracting metal ores from rock enriches the resultant slag in calcium, magnesium, manganese, and iron, which readily dissolve in water. When water flowing between closely packed slag grains can no longer hold additional dissolved elements, those elements precipitate as solid mineral cements. Since cements only form when the water has very high concentrations of dissolved elements, the introduction of massive amounts of slag caused the cement to form faster than it would in waters with lower concentrations of elements.
The researchers suspected that the anthropoclastic rock cycle they observed at Derwent Howe is not uncommon. Since slag contains all the elements necessary to form rocks when in contact with seawater and air, slag deposits all over the world could be rapidly influencing their local environments. With this in mind, the team suggested that future researchers update geological models to account for human contributions to the natural rock cycle.
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