Electric vehicles (EVs) use high-powered rechargeable batteries to store electrical energy that powers their motors. Most EV batteries use a specific mixture of metals to hold the battery’s charge, called nanoscale lithiated nickel manganese cobalt oxides, aka NMC materials. NMC materials can store a large amount of energy, making them ideal for batteries, but the metals themselves can be toxic in soil and water if the batteries are not properly recycled.  

NMC batteries leave behind a specific combination of toxic metals, so researchers can study how bacteria respond to this waste when it enters the environment. By understanding how bacteria adapt to toxic metals, scientists hope to predict how new technologies like EV batteries will affect the environment. 

In 2019, a team of researchers from the University of Minnesota reported that a common soil bacterium called Shewanella oneidensis can rapidly evolve resistance to NMC materials, but they didn’t know how. True beneficial gene evolution usually develops over thousands of generations, but Shewanella oneidensis adapted much more quickly. The team recently conducted a follow-up study to determine the genetic mechanisms behind this bacterium’s unique metal resistance.

They started by analyzing the complete DNA sequences of 33 Shewanella colonies that were resistant to either pure NMC waste or individual metals like nickel and cobalt, using a process called whole-genome sequencing. They expected to find DNA mutations in genes that control how bacteria take up and expel toxic metals that would give the resistant Shewanella an advantage in surviving NMC metal toxicity. However, they didn’t find any DNA mutations in metal-related genes. Instead, they found that a specific DNA region was duplicated 10 to 15 times in all 33 NMC-resistant or metal-resistant colonies. This result suggested to the researchers that a temporary adaptation, not true gene evolution, was responsible for the bacteria’s metal resistance. 

The researchers wanted to know more about the duplicated DNA region in metal-resistant Shewanella, so they focused on 2 gene duplication mechanisms that might serve as temporary adaptations in Shewanella. The first mechanism directly duplicates DNA regions to create extra copies of a gene, via a process called gene duplication amplification. The second mechanism moves DNA segments, called mobile genetic elements, into different DNA sections to create extra copies of a gene. 

To test whether either of these mechanisms was causing the gene duplication in Shewanella, the team grew the bacteria in different liquid media containing only cobalt, only nickel, or cobalt and nickel together. The researchers used a technique that measures the exact amount of DNA in a given sequence, called quantitative polymerase chain reaction (qPCR), to calculate how many duplicated DNA regions were in each genome sequence. They found that neither cobalt nor nickel alone triggered DNA duplication. But the bacteria grown in the media with cobalt and nickel together had 8 additional copies of the DNA region. This suggested that something about the combination of nickel and cobalt triggered gene duplication. 

To determine whether this gene duplication only occurred with nickel and cobalt, the team substituted copper and zinc for nickel in the growth media. They exposed Shewanella cells to different combinations of these metals using a grid pattern in a 96-well plate, a technique called a checkerboard assay. Then they repeated the qPCR process to measure the duplicated DNA region. They found that no other combinations of metals triggered gene duplication, suggesting that nickel and cobalt are especially toxic together, and that the gene duplication responds to this combined toxicity.

Finally, the researchers identified the function of the duplicated DNA region. They found that one of the genes within the region turned on a special protein that pushes metal ions and other toxins out of the cell, called an efflux pump protein. The team hypothesized that this efflux pump protein was responsible for removing cobalt and nickel from the bacteria’s cells. To test this theory, they grew modified Shewanella cells without the efflux pump gene in media containing cobalt, nickel, or cobalt and nickel together. They found that the modified bacteria grew well in the nickel-only treatment, but didn’t grow in the cobalt-only or combined treatments, which indicated that the efflux pump protein is necessary for Shewanella to resist cobalt toxicity. 

Based on their experiments, the team concluded that nickel and cobalt together trigger a gene duplication event in Shewanella oneidensis. This gene duplication event increases the number of efflux proteins in Shewanella that can expel cobalt and underlies its unique NMC resistance. They suggested that future researchers study temporary adaptations similar to gene duplication in bacteria to track how they respond to electronic waste and other new technologies.

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In March 2023, Southern California experienced heavy storms after almost 20 years of drought. Sixteen of these came from long, narrow regions of concentrated water vapor moving through the air, known as atmospheric rivers. During droughts, groundwater supplies up to 60% of the total water to the Greater Los Angeles area, so researchers think that stormwater from the 2023 events could have recharged groundwater reserves for future use. However, scientists lack adequate tools to assess how much groundwater is in the subsurface.

To overcome this challenge, researchers from California and Texas developed a new method to study deep groundwater. California gets a large number of earthquakes, so it has over 450 stations around the state to monitor the associated vibrations, referred to as seismic waves. To study how much the 2023 storm recharged the groundwater in the Greater Los Angeles area, the team used a tool called seismic-DI, which employs these seismic waves to estimate how much groundwater is stored at different depths. 

Seismic-DI works by measuring how fast seismic waves move through the Earth, known as their seismic velocity. The researchers explained that the Greater Los Angeles area is a very crowded city, so it produces an abundance of seismic waves. They measured changes in the seismic velocities of these underground waves to see where the groundwater is full of water, or saturated, versus partially full, or unsaturated. They used 20 years of these data to produce graphs that record changes in groundwater storage at different depths, known as hydrographs.

To validate the accuracy of seismic-DI, the researchers also compared their seismic hydrographs with groundwater thickness data from a NASA mission known as the Gravity Recovery and Climate Experiment, or GRACE. GRACE consists of 2 satellites that measure changes in Earth’s gravity, which reflect surface water and groundwater storage. Both their seismic hydrographs and the GRACE groundwater data demonstrated that the Greater Los Angeles region experienced an increase in groundwater storage from stormwater in the winter, and a reduction in groundwater storage during the dry summer. 

The team’s hydrographs showed that the 2023 storms saturated the Greater Los Angeles area groundwater down to 35 meters (114 feet), but it was abnormally dry at 200 meters (656 feet), and at a severe drought at 450 meters (1,476 feet). The GRACE data similarly demonstrated that the subsurface is in severe drought. Combined, these data indicated the 2023 storms helped the Greater Los Angeles area recover up to 90% of its groundwater at depths of 50 meters (164 feet) since the 2006-2022 drought. However, it recovered only about 40% at 75 meters (246 feet) and 20% to 30% at 100 meters (328 feet).

They concluded that the 2023 atmospheric river events recharged only shallow groundwater reserves in the Los Angeles area. As a result, they suggested future researchers consider deep groundwater reserves for a more complete assessment of total regional water deficits. While this study demonstrated that seismic-DI can successfully reveal spatial changes in groundwater and drought at depth, the team acknowledged that contributing factors like the amount, rate, and spatial variability of groundwater reserves remain targets of future work. 

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More than 90,000 tons of radioactive waste are stored on-site at nuclear power plants awaiting permanent disposal. Nuclear waste releases unstable forms of uranium and other elements, called radionuclides. Long-term exposure to radionuclides can cause cancer, so environmental safety regulations require that nuclear waste be made inaccessible for at least 1 million years. Thus, researchers are evaluating possible ways to store this waste permanently. 

One proposal is to store nuclear waste thousands of feet below ground in rock formations called deep geological repositories, or DGRs, to seal in radionuclides. Scientists can’t see into the future, so instead they look to the past to determine how well DRGs could work. A team of researchers from Canada recently analyzed uranium deposits that have existed for billions of years as an analog for how nuclear waste behaves deep underground.

Past researchers found that uranium deposits in ideal DGR conditions, with their protective casings intact, will stay contained for more than a billion years. However, these conditions require a stable geological environment. This team instead focused on uranium deposits in non-ideal conditions. They studied the Canadian Kiggavik uranium deposits, which occur in cracked bedrock approximately 500 meters (1,600 feet) below the surface. The team claimed these conditions are similar to DRGs with broken protective casings.

The team knew from previous work that most of the uranium minerals in the Kiggavik deposits are more than 274 million years old, but about 10% are less than 1 million years old. They found that these younger uranium minerals occurred alongside clay minerals surrounding the older uranium minerals and within open fractures. The researchers interpreted the older uranium as the source material of the younger uranium because of their proximity. They concluded that water must have infiltrated the deposits and interacted with older minerals, transporting uranium and forming new, younger minerals.

To determine the source of water infiltration, the researchers compared the chemistry of clay minerals formed alongside the younger uranium with modern snow in the Kiggavik region. They looked at the number of neutrons, or isotopes, in hydrogen atoms within the clay minerals, and found heavy values matching those of fresh water. These values indicated that snow or glacial meltwater was the source, rather than underlying bedrock or seawater. 

To determine when the water infiltrated over the last million years, the researchers used uranium-thorium dating to find when the radionuclides were mobilized. This dating method shows when uranium was removed from the original minerals by measuring how much its concentration was reduced by water infiltration. They found that the dates of these radionuclide mobilizations clustered into 5 separate water-infiltration events that occurred from 36 to 470 thousand years ago. 

Comparing these dates with existing climate records, the researchers found that the water infiltration events overlapped with periods when glaciers covered the ground surface. Normally, water can’t travel thousands of feet underground. But, like a hydraulic press, a glacier pushes water at its base downward. If the water encountered cracks in the ground, the pressure could inject it to the depth of the uranium deposit.

Next, the researchers wanted to measure how quickly radionuclides left the deposit. They measured the maximum distance between younger and older uranium minerals and used the age of the most recent water-infiltration event to estimate the fastest speed at which the radionuclides could have moved. They determined this maximum speed to be 0.0014 millimeters (0.000055 inches) per year. At that rate, they calculated it would take 360 million years for uranium radionuclides to travel 500 meters from a DGR at the same depth as the Kiggavik uranium deposits to the surface. This timescale far exceeds the necessary 1 million years for nuclear waste containment.

The team concluded that if glaciers were to return to Kiggavik, the uranium deposits wouldn’t experience substantial radionuclide movement, even in contact with glacial meltwater. Similarly, if a DGR has broken protective casings, radionuclides would be moving too slowly to reach the surface even if water leaked into it. They suggested their findings reduce constraints on viable DGR sites and could help ensure the long-term secure storage of nuclear waste.

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Astronomers and astrobiologists use a wide range of tools in their search for extraterrestrial life. Probes, satellites, and rovers take photographs and collect data on the geology and chemistry of planets and moons in the solar system. Radio telescopes sweep the sky, looking for signals from intelligent civilizations. And telescopes like the JWST can observe the atmospheres of distant exoplanets, potentially finding ones similar to Earth’s. 

A limitation of these methods is that they assume life on other worlds is fundamentally similar to life on Earth, requiring the same balance of molecules in the atmosphere, producing the same chemicals for their metabolic processes, or developing technologies analogous to those humans make. While researchers use these assumptions as baseline markers to look for, assuming such similarities might lead them astray. Scientists looking for life using too narrow a set of parameters might miss life unlike Earthlings. Or they might mistake coincidentally similar geological, chemical, or astronomical processes for having been made by living things.

So, to complement techniques that assume alien life resembles life on Earth, scientists also consider approaches that detect signs of life while making as few assumptions as possible about what it could look like, known as agnostic biosignatures. One of these life signs with few assumptions is complexity. Though it can be difficult to quantify precisely, living things tend to create more complex systems than nonliving things. A team of researchers recently tested whether living and nonliving worlds could be distinguished solely by examining the complexity of the light-reflection patterns from their surfaces.

For this, the team treated Earth and Mars, similar planets but one with life and one without, as stand-ins for exoplanets around other stars. They collected data on the light reflected off both planets’ surfaces from a space station monitoring Earth, known as the Deep Space Climate Observatory or DSCOVR, and an orbiter around Mars observing its atmosphere, known as the Hope Probe. 

The team identified 3 nearly identical wavelengths of light that these machines observe from their respective planets. From the machine databases, they collected measurements of how much light each planet reflected, called its reflectance, at these 3 wavelengths over 486 local days. By using only individual wavelengths of light rather than full photographs, the team claimed that, if their test worked, researchers could apply it to distant exoplanets that cannot be photographed in detail.

Without looking for any particular chemical or signal, the team compared these measured reflectances using a series of statistical tests to determine the variation and randomness in their reflected light, which they considered representative of the complexity of the planets’ surfaces. The researchers found that of the 27 complexity metrics they tested, Earth scored higher than Mars on 25, Mars scored higher than Earth on 1, and the planets scored the same on 1. So, even though both planets have rocky surfaces with atmospheres, weather, and cyclical changes, the one with life is measurably more intricate and dynamic, and the researchers found this difference detectable even with a small stream of light at great distances.

The researchers claimed their test demonstrated that complexity could be a valid agnostic biosignature, with caveats. One is that Earth is quite distinct from Mars, in that it has oceans and a relatively thick atmosphere. This means that processes like making and dispersing clouds and the sharp contrast between land and water could add complexity to Earth’s measurements without direct ties to living things. 

They suggested 2 possible explanations for how these complicating factors relate to life on Earth. Either life allows Earth to have these factors, or Earth is inherently complex regardless of life, and therefore it can’t be used to assess how life leads to measurable complexity. To say which is the case, one would have to replicate this test with a lifeless copy of Earth. However, the team argued that Earth’s biosphere is unlikely to be wholly unrelated to its water cycle.

To bolster the validity of their methods, the team plans to expand their sample of planets to include Venus, Saturn’s moon Titan, and simulated exoplanets with modeled biochemistry and atmospheres. If the model holds up, their methodology could represent a new category of mission to find alien life, with long searches for variations in reflected light uncovering properties of distant worlds that would otherwise go unnoticed.

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Waterfalls play an essential role in river development and landscape evolution. Researchers are particularly interested in how abrupt changes in the steepness of a river channel, also called knickpoints, develop. Knickpoints vary in size and can be as small as your index finger or as large as Niagara Falls. Researchers from Virginia hypothesized that waterfalls, a type of knickpoint, can influence erosion when a river channel is cut into solid rock, called bedrock. 

The researchers wondered whether fractures and other pre-existing weaknesses can influence how water removes rock slabs from a river channel, via an erosional process called plucking. Fractures can form in many different orientations, but the researchers focused on 3. Imagine a flat tabletop that represents a riverbed. Fractures that are parallel to the flat tabletop are called horizontal fractures. If you tip the table so the low end points downstream, that’s the direction of downstream-dipping fractures. If you tip the table so the low end points upstream, that’s the direction of upstream-dipping fractures. 

First, the researchers tested how fracture orientation changed erosion before an established knickpoint. They used a man-made channel, called a flume, to simulate a natural river system. To set up the experiment, they glued layers of small tiles about the size of an adult’s thumbnail to a plastic frame and put it in the flume. Then, they placed a layer of unglued tiles upstream of the knickpoint. 

To begin the experiment, the researchers incrementally increased the amount of water flowing through the flume every 5 minutes until they reached a maximum flow or all the loose tiles were “eroded.” They considered a tile to be “eroded” once it was transported over the knickpoint and downstream. The researchers measured the water depth at a fixed point upstream of the knickpoint, the number of tiles eroded through plucking, and the volume of water. They repeated this 8 times for each fracture orientation.

The team found differences in the amount and timing of erosion between the 3 orientations. In particular, they saw that erosion began at lower water speeds for horizontal beds than for tilted beds. Based on the observed erosion rates, they concluded that horizontal beds are the most susceptible to erosion, while upstream-dipping beds are the most resistant, and downstream-dipping beds fall between the 2. 

The researchers then tested how a knickpoint would develop and influence water flow if all of the tiles were unglued and free to move. They individually stacked almost 5,000 unglued tiles across the channel. Before starting the experiment, they filled the flume slowly with water to submerge all of the tiles without moving them. Then, they increased the water’s speed to start erosion. Instead of collecting measurements as they did in the first experiment, the researchers filmed this experiment and observed how the knickpoint shape changed and how plucking occurred. 

The researchers observed that the plucked tiles in this experiment formed piles downstream of the knickpoint in a process known as armoring. This armor of tiles, which represents loose rocks near a waterfall in a natural setting, prevents erosion of the underlying river bed. Only when the water transports the armor downstream can it erode the bedrock and develop a knickpoint.

Unlike the first experiment, researchers found no differences in erosion between fracture orientations in this experiment, but did observe differences in armoring. They found that upstream-dipping beds had the least armoring because water quickly transported piles of tiles near the waterfall downstream. Downstream-dipping beds had the most armoring, and their eroded tiles took longer to move downstream. 

The team concluded that waterfalls will erode at different rates based on their fracture orientation and armoring processes. The orientation of bedrock fractures also impacts the shape of the waterfall and the river channel around it. So the next time you see a large waterfall, consider that it may have started as a small fracture the size of your thumbnail.

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Drilling for electricity or geothermal energy is a common human-caused source of earthquakes. Drillers or engineers inject water into the earth to fracture underground rock through a process known as fracking. This allows scientists to produce clean energy almost anywhere on Earth by heating up water in the cracks using facilities called enhanced geothermal systems or EGS plants. However, fracking causes earthquakes, which are a major concern for people’s safety globally. Therefore, scientists are looking for new fracking methods that cause fewer earthquakes.

Fracking for EGS plants can trigger earthquakes by changing the pressure underground and adding stress to the surrounding rock. However, these earthquakes happen at pre-existing, naturally occurring cracks called faults, which are different from the newly formed, man-made cracks. Faults are naturally under pressure from the overlying rock and the surrounding earth since they reach deep underground. As pressure builds up over time, it eventually causes the rocks to shift along these faults and release energy in the form of earthquakes. 

During fracking, engineers use 4 different shut-in strategies to finish off water pumping. The standard industry strategy is called instant shut-in, where the injection pump is turned off immediately, but the water stays underground, causing the pressure to lower naturally. In a similar strategy, known as the tapered shut-in, engineers gradually reduce the water injection rate to zero instead of immediately turning it off. For the high-rate and low-rate extraction strategies, they pump the water back out after injection is complete, at different pump rates.

Recently, scientists aimed to mitigate the risk of earthquakes by testing these 4 shut-in strategies to determine which is most effective. They compared the shut-in strategies using crystalline basement rock with low porosity and permeability, meaning water couldn’t easily flow through it. Engineers use this same type of rock for EGS plants so they can control where the water flows. The scientists pumped water into the rock, then measured the pressure and stress levels where rocks began to move along faults, determining the point at which earthquakes would occur. 

The scientists applied high-rate and low-rate extraction methods to the rock and found that pressure and fault movements decreased. However, when they applied instant and tapered shut-in methods to the same rock, they increased the pressure and caused continuous fault slipping. They measured stress in the rock during these methods and found that high-rate extraction stabilized the fastest, while low-rate extraction stabilized more slowly. In contrast, instant shut-in continuously increased the pressure and slip, and tapered shut-in caused the highest stress of all.

The scientists also used a computer model to simulate what happened at an EGS site in South Korea, where earthquakes occurred after injections using the instant shut-in method. They set up the model using a rock with the same characteristics, injection wells, and faults as that site. Then, they simulated periods of injection based on the previous real-world events and modeled the effects of pressure and stress to determine if the earthquakes could have been prevented. 

The team used the model to compare how a site like this would respond to the instant shut-in method versus the other shut-in methods. They found that the tapered shut-in method increased pressure and stress in the rock, while the high-rate and low-rate methods both decreased pressure and stress. The high-rate method decreased pressure and stress more than the low-rate method. 

The scientists concluded that high-rate extraction is the most effective method for reducing the risks of post-injection earthquakes during EGS fracking in crystalline basement rock. Additionally, the scientists determined that the earthquakes in South Korea could have been prevented if engineers had used either the high-rate or low-rate extraction methods. They recommend using high-rate extraction to reduce earthquakes during EGS fracking, which would allow EGS plants to generate clean energy more safely.

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Since the formation of the Earth 4.2 billion years ago, hot rock from the interior of the planet has cooled off and developed crystals that accumulated over millions of years, forming surface rocks with a layered composition, referred to as continental crust. Geologic evidence of how continental crust formed comes from large, ancient crustal blocks, called cratons. Cratons have existed for billions of years and form the cores of continents. 

Scientists have shown that the upper 10 to 15 kilometers (6 to 9 miles) of continental crust contains high concentrations of elements like uranium and thorium that decay and release heat over time, referred to as radiogenic elements. However, the lower 20 kilometers (12 miles) of crust has very few radiogenic elements. The cause of this planetary-scale differentiation is highly questioned, as geoscientists don’t know what formation processes could produce this unique characteristic. 

Earlier researchers suggested that changes in the concentration of radiogenic elements could occur when tectonic plates collide. Plate collision induces temperatures in the lower crust high enough to melt minerals that contain uranium and thorium, like zircon and monazite. The newly melted minerals form magma, which then rises and crystallizes in the upper crust. To test whether this process formed the layered continental crust, researchers Andrew J. Smye and Peter B. Keleman incorporated geochemical data from studies worldwide into a mathematical model comparing how concentrations of radiogenic elements vary under different temperatures. 

The team separated their global analyses into 2 categories of rocks that were buried deep in the crust and subjected to intense pressures and temperatures, via a process known as metamorphism. The first category included rocks formed from the accumulation of eroded grains buried in the deep crust that are subjected to high pressures and temperatures, called metasedimentary rocks. The second category included rocks formed when magma rises from the deep Earth and solidifies before experiencing high pressures and temperatures, called metaigneous rocks.

The researchers analyzed 3 subcategories from each of the 2 types of deep-crustal rocks. The first was rocks that experienced no intense pressure or temperature, the second was rocks subjected to 650 to 900°C (about 1,200 to 1,650°F), and the third was rocks subjected to 900 to 1,100°C (about 1,650 to 2,000°F). The researchers modeled the geochemistry of the 3 rock types at different temperatures to determine whether ultra-high-temperature metamorphism could cause crustal differentiation of radiogenic elements.

Smye and Keleman found that monazite doesn’t melt in either metasedimentary or metaigneous rocks at temperatures below 900°C, as indicated by the relatively high thorium content in these deep-crustal rocks. Alternatively, they found that above 900°C, uranium and thorium concentrations decreased in metasedimentary rocks, suggesting that minerals like zircon and monazite were melting. The melted minerals enter magma and rise into the upper crust, leaving behind metasedimentary rocks depleted in radiogenic elements by around 0.10 ppm.  

The team concluded that the enrichment of uranium and thorium in the upper continental crust requires temperatures greater than 900°C to initiate the melting of zircon and monazite in the lower crust. Temperatures above 900°C are linked to the movement and collision of continental plates. This result suggests that evidence of Earth’s differentiation has been preserved for more than 3 billion years, spanning most of the planet’s geological history. Since stable continental crust formed, uranium and thorium enrichment in the upper crust has consistently recorded layering produced by ultra-high temperature metamorphism.

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Since the 1850s, plowing and clear-cutting in modern agriculture have increased how fast soil erodes in the Upper Mississippi River Valley. Across the globe, farming strips soil up to 100 times faster than natural processes. It also removes farmable land and clogs waterways, threatening long-term environmental sustainability. However, scientists in the past have struggled to separate the effects of land-use changes from the transition out of the last ice age over the past 20,000 years. 

To determine whether current farming practices are sustainable, researchers from Minnesota recently measured the speed at which soil eroded during the ice age transition and compared it with modern agricultural erosion. They focused on the Trout Creek tributary of the Mississippi River. This tributary has 2 step-like formations, or terraces, on its bank that represent past floodplains before the river eroded to its current level. 

The oldest, upper terrace is about 14 meters (50 feet) above the lower terrace, which is about 8 meters (26 feet) above the modern floodplain. Scientists studying nearby areas have suggested that the terraces contain sediment from the last ice age, 20,000 years ago, referred to as the Last Glacial Maximum, to today. 

In the upper terrace, the researchers pushed an empty cylinder into the ground, like a straw into a layer cake, to collect a cylindrical sample of sediments about 4 centimeters (1.5 inches) wide and 6 meters (20 feet) deep, called a sediment core. They collected slices of this core from 2 meters (6 feet) and 6 meters (20 feet) to find the terrace’s age. 

Some grains in the sediment trap electrons while buried and release them when exposed to light. So the team shone light on the sediment slices to stimulate these electrons and measure the intensity of light emitted, known as their optically stimulated luminescence. Brighter light emissions indicate the sediment is older and was buried for a longer period of time. Using this method, they confirmed that the deep sediment slice was 22,000 years old, and the shallow sediment slice was 20,000 years old, dating the terrace back to the Last Glacial Maximum.

Next, the team drilled into the upper terrace with an auger to collect 25-centimeter (10-inch) wide plastic tubes of sediment. They pulled 4 samples from 0 to 2 meters (0 to 6 feet), and 1 sample from 6 meters (20 feet) deep. They explained that minerals in the sediment called quartz produce the radioactive form of the element beryllium, known as ¹⁰Be, when exposed to the Sun, so low ¹⁰Be means the sediment was buried quickly and had little sun exposure. The team measured the concentration of ¹⁰Be in each sediment sample and used an online calculator to estimate how quickly it eroded. The researchers calculated that during the Last Glacial Maximum, 0.073 millimeters (0.003 inches) of surface sediment were removed per year.

The researchers collected similar samples from the lower terrace and used optically stimulated luminescence to determine that it was between 22,000 and 14,000 years old. This terrace was the floodplain during the transition out of the Last Glacial Maximum, or deglacial period, where temperatures warmed and permafrost retreated. They measured the concentration of ¹⁰Be in samples from this terrace and determined that 0.049 millimeters (0.002 inches) of surface sediment were removed per year. 

Finally, the researchers measured ¹⁰Be in a sediment sample from the modern floodplain, deposited between 14,00 years ago and today. They found that the sediment had eroded 0.053 millimeters (0.002 inches) per year during that interval, at a rate slower than it had during the glacial period, but similar to the deglacial period. 

The researchers compared their findings to the average soil loss caused by modern agriculture, as reported by 4 previous scientists studying the area. They saw that modern farming removes an average of 0.60 millimeters (0.02 inches) of soil per year, which is 8 to 12 times greater than all the natural erosion rates they calculated. 

They concluded that the recent spike in soil loss from agriculture greatly exceeds that caused by the largest natural climate transition in recent Earth history. Human-driven erosion shows an upward “hockey stick” trend like the one seen in global temperatures, suggesting that similar trends could exist throughout Earth systems. The team concluded that modern agricultural practices aren’t currently sustainable. While measures like native plant reintroduction can help curb this trend, more research is required to determine how best to limit long-term erosion.

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Temperatures in the Arctic are increasing at a rate 4 times faster than the average global temperature. Scientists have documented the global effects of climate change, but there are still regions where these effects are less well known. This is the case for freshwater ecosystems in the Arctic.  

When the ground has been permanently frozen for 2 years, scientists call it permafrost. Permafrost contains soil, sand, rocks, and organic matter held together by ice. As temperatures rise, permafrost thaws, which can change groundwater flow paths. It can also alter a stream’s temperature, flow patterns, and nutrient loads by introducing new material from the permafrost.  

To determine how thawing permafrost affects Arctic ecosystems, scientists from the U.S. Geological Survey recently studied 10 headwater streams within the Brooks Range in the Noatak National Preserve of Alaska. The scientists studied streams with different amounts of permafrost and ice to test whether connections exist between permafrost, food webs, and populations of fish called the Dolly Varden and Arctic Graylings. They focused on these 2 species of fish because their migrations often indicate ecological shifts in the area.    

The scientists collected fish from the streams by temporarily stunning them, using a method called electrofishing, or by catching them in minnow traps. They manually identified each species they collected, then froze the fish and shipped them to a lab. Once in the lab, the scientists measured the length of each fish and used documented growth patterns to estimate its biomass. To calculate fish abundances, they divided their total biomass by the time spent electrofishing or trapping. Finally, they measured the energy density of each fish by burning it in a device called a bomb calorimeter. 

At each stream, the scientists took manual temperature measurements and deployed loggers that recorded the temperature every 15 minutes. They also deployed equipment at the bottom of each stream to record water pressure in 15-minute intervals and converted the data to depth, since pressure increases with depth. Then, they used wading rods to measure how much water was flowing through each stream at a given spot. 

They combined the depth and wading rod data to determine the flow rate of water in the streams over time. They used these data to identify unstable streams where water rose and fell rapidly. They also collected water samples from each stream and measured their nutrient concentrations, including dissolved organic carbon, nitrogen, and phosphorus, to determine water quality.   

Finally, they measured 2 types of smaller organisms that allow energy to flow through the food web to the fish, called microbial biofilms and macroinvertebrates. They used a hand-held device to estimate the types and amounts of biofilms, and nets to collect macroinvertebrates.

Once they had compiled these data, the team conducted a statistical analysis to compare the studied parameters and fish populations to the extent of permafrost in each area. They found that streams with more permafrost in the area had warmer temperatures, because the permafrost prevented water from infiltrating the soil to cool off. The streams with warmer temperatures, in turn, had lower abundances and biomass of Dolly Varden fish and Arctic Grayling with lower energy densities. They suggested that these results meant that the high temperatures had pushed the fish above their optimum temperature range. 

They also learned that the limited infiltration in permafrost areas caused rainfall to flow straight into the stream. This process created instability as the stream levels rose and fell rapidly. Fish in these unstable streams had lower energy densities, likely due to the harsher environment. 

Areas with greater permafrost also had higher concentrations of dissolved organic carbon and phosphorus, which stimulated the formation of biofilms at the bottom of the food chain. They noted that areas with more biofilms also had more macroinvertebrates and more Dolly Varden biomass. Since the Dolly Varden eats macroinvertebrates, this finding suggested that the lowest levels of the food web shape the health of species higher up. This is called the bottom-up effect, and means that areas with more biofilms and macroinvertebrates will likely have healthier and larger fish populations.  

The scientists explained that knowing how these factors influence Arctic fish populations can help researchers understand how thawing permafrost might affect Arctic ecosystems in the future. They anticipated that their data could help future researchers predict how these and other ecosystems will be impacted by changing landscape dynamics caused by global warming. 

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Have you ever experienced a severe weather event, like a hurricane or a tornado? Researchers have shown that climate change is causing such weather events to become more extreme and more frequent, destroying communities and infrastructure. To determine which areas are most at risk, researchers from Canada studied the environmental effects of a powerful flooding event that occurred in southwestern British Columbia (BC) in November 2021. 

They focused on a type of severe flooding event that is characteristic of the North American Pacific coastal region, called an atmospheric river. An atmospheric river occurs when a long, narrow vapor cloud flows toward land from the Pacific Ocean, causing heavy rainstorms. In November 2021, an atmospheric river in southwestern BC destroyed numerous regional highways, pipelines, power lines, and railways within its path. Over 18,000 people were evacuated due to flooding, and 6 people died. 

The team explained that before the storm, BC experienced a severe drought and an unprecedented heatwave, followed by widespread thunderstorm activity. In 2021, lightning and human activity caused nearly 1,000 wildfires, which led to the third-worst wildfire season ever recorded in BC, partially because trees grow back slowly in forests that have been cut down or harvested. Poor vegetation recovery makes harvested forests more vulnerable to wildfires than old-growth forests. The November atmospheric river occurred shortly after this wildfire season. 

To determine how the atmospheric river impacted southwestern BC, the researchers analyzed approximately 70,000 square kilometers (27,027 square miles) of affected area. They compared satellite images of southwestern BC from Google Earth, Sentinel, and Landsat taken before and after the event to detect changes in river channels and identify geohazards, like landslides and debris flows. They also used satellite images and Canadian government data to identify which forests had been harvested within the study area. The researchers then traversed the area to confirm their remote analyses. 

The researchers used a type of 3D remote sensing, called light detection and ranging, or lidar, to measure how the 2021 atmospheric river redistributed sediment in the Coquihalla and Coldwater River valleys. With the help of lidar, the researchers discovered that both rivers had severely unstable banks due to erosion from the flooding event. Along the Coquihalla River, approximately 53,000 cubic meters (1,871,677 cubic feet) of sediment were eroded, while nearly 50,000 cubic meters (1,765,733 cubic feet) of sediment were eroded along the Coldwater River. 

The researchers also found that hillslope geohazards increased the volume of sediment in both rivers. And in some cases, upstream areas with a history of forest harvesting and wildfires also affected stable banks downstream. For example, they identified debris flows on an unstable hillslope that triggered a landslide on a riverbank downstream. They observed that the river and stream channels shifted due to a sudden addition of sediment from hillslope geohazards and floodwater during the event.

In total, the researchers documented over 1,300 geohazards along 47 kilometers (about 30 miles) of the Coquihalla River and 19 kilometers (about 12 miles) of the Coldwater River and nearby creeks. Nearly half of the geohazards originated in an area with a history of wildfires or forest harvesting. Of the 155 geohazards at the Coquilhalla River, 43% occurred below forest harvesting resource roads, and of the 117 geohazards observed at the Coldwater River, 29% originated in areas with past wildfires.

The researchers suggested that their findings could help scientists predict where severe geohazards will occur during extreme weather events, since both watersheds had geohazards originating in harvested forests. Before 2000, clear-cutting was a widespread practice in BC, and the team’s results demonstrate that unsustainable harvesting practices like this can have lasting environmental impacts.

The researchers concluded that both forest harvesting and wildfires contributed to the severe impacts of the November 2021 atmospheric river, causing catastrophic landslides and debris flows to occur in traditionally dry regions. They suggested that more accurate risk assessments can better equip communities for severe weather events like this. While the next atmospheric river that lands in the Pacific coastal region may be worse than the last, this study can inform scientists and communities about which areas it might affect most.

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The economist John Maynard Keynes once remarked, “In the long run we are all dead.” From an astronomical standpoint, this is true for the Earth itself. In roughly 5 billion years, the Sun will exhaust the hydrogen at its core and enter the next phase of its life as a Red Giant, where it will swell up large enough to engulf and destroy the Earth.

Currently, the Sun is still in its main-sequence phase, where it fuses hydrogen into helium in its core. Although it remains relatively stable during this phase, it will gradually grow brighter with time. As the Sun shines brighter, the increased heat and radiation will raise Earth’s surface temperature, potentially pushing it past the threshold for life to survive. 

One of Earth’s geological processes that helps regulate its surface temperature is the carbon cycle. Over millions of years, carbon moves from the Earth’s interior into the atmosphere as carbon dioxide or CO₂, where it functions as a greenhouse gas that traps heat. Plants take in this CO₂ through photosynthesis and move carbon through the biosphere. 

Meanwhile, rain and rock weathering draw carbon into the oceans and rocks. By pulling CO₂ out of the atmosphere, the carbon cycle could mitigate Earth’s warming as the Sun brightens over time. Today, human activities are increasing atmospheric CO₂ and warming the planet. However, over billions of years, the carbon cycle could gradually remove enough CO₂ from the atmosphere that eventually too little would remain to support photosynthesis. 

With these challenges to life on Earth in mind, the researchers Jacob Haqq‐Misra and Eric Wolf investigated how long Earth’s biosphere could last in the distant future. The team used computer models to calculate the balance of energy that Earth would receive and emit over time, accounting for the effects of increasing sunlight and changing atmospheric CO₂ concentrations. They followed this up with calculations using the ExoCAM model to determine the temperature and atmospheric conditions of future Earth across different latitudes. They related these conditions to a habitability metric to determine their impact on the biosphere.

To estimate the biosphere’s maximum possible lifespan, the team conducted 29 simulations representing the planet’s conditions over the next 2 billion years. In one set of simulations, the Earth’s carbon cycle contributed little to regulating surface temperature, with the atmosphere’s CO₂ concentration fixed at approximately modern levels of 400 parts per million (ppm) and surface temperatures continuing to increase. In another, the carbon cycle worked to keep Earth’s average surface temperature fixed at the modern average of approximately 59℉ (15℃), no matter how much it had to reduce CO₂ in the atmosphere. 

In the scenario with a fixed CO₂ concentration, the team found that in 1.7 billion years, Earth’s surface reached an average of 122°F (50°C), which is too hot for most land plants. After 1.9 billion years, it reached 149°F (65°), becoming too hot for all land plants to survive as temperatures continued to rise. 

They also found that in the fixed average surface temperature scenario, atmospheric CO₂ dropped below the 150 ppm level that most plants require for photosynthesis within 500 million years. After 1.4 billion years, CO₂ levels fell below 10 ppm, leaving only a few land photosynthesizers and aquatic plants alive. After about 1.8 billion years, atmospheric CO₂ dropped to 1 ppm, leaving only microbes capable of photosynthesis.

The researchers’ findings generally extend the estimated lifetime of Earth’s future biosphere compared with earlier, simpler models that suggested maximum lifespans of 100 million to 1.5 billion years. However, they clarified that other scientists should verify their results by running tests with other models, especially under extreme conditions of high incoming solar radiation and low atmospheric CO₂. 

Lastly, Haqq‐Misra and Wolf noted that their model did not account for major evolutionary changes that could help life adapt to future conditions. Over time, plants could evolve to tolerate higher temperatures or lower atmospheric CO₂ levels. Future humans or another intelligent species might also engage in large-scale geoengineering projects to prevent the end of life on Earth. If life on Earth proves adaptable enough, the biosphere may endure until a Red Giant fills the daytime sky.

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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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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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Nitrogen is an essential building block of proteins, amino acids, and DNA. Without nitrogen, cells can’t function or reproduce. Nitrogen is abundant in the atmosphere as dinitrogen gas, but plants and animals can’t access atmospheric nitrogen. Instead, we rely on biologically available forms like ammonia and nitrate. 

Specialized microbes can transform atmospheric nitrogen into ammonia via a process called biological nitrogen fixation. Nitrogen-fixing soil bacteria have a unique protein complex, the nitrogenase complex, that facilitates this nitrogen transformation. The nitrogenase complex is activated by a group of genes known as nif genes. 

Legume plants like peanuts, peas, and beans have nitrogen-fixing bacteria in the soil zone around their roots, called the rhizosphere. The symbiotic relationship between nitrogen-fixing soil bacteria and legume plants supplies most of the biologically available nitrogen to life on Earth.

Bacteria attached to plant roots “feed” nitrogen to plants through the soil, and in turn receive organic acids for energy from plants performing photosynthesis. Source: Wikimedia Commons

However, one piece of the puzzle remains unexplored: the role of soil viruses in these nitrogen transformations, referred to as the nitrogen cycle. Viruses are microscopic particles made up of genetic material and proteins, but they can’t reproduce without a host cell. Scientists consider viruses to be biological entities that are halfway between living and non-living.

To reproduce, viruses contact a host cell and inject their genetic material into it. The virus’s genetic material takes over the host cell’s genome and forces the cell to produce proteins that assemble into new viruses. As new viruses are created, they can incorporate genes from the host cell into their own genetic material. Genes that are transferred from a host cell to a virus are known as auxiliary metabolic genes (AMGs). Viruses can carry AMGs and pass them to other hosts or use them to increase their own ecological fitness. 

Scientists from China, Spain, and the Czech Republic set out to investigate whether viruses from legume roots could increase local soil nitrogen fixation by expressing nif AMGs or by transferring nif genes to other bacteria. First, they determined the global distribution of viral nitrogen-fixing genes by analyzing about 8.6 million viral genomes from the Integrated Microbial Genomes/Virus database. They looked for the DNA sequences of known nitrogen-fixing genes and the locations where the viruses were sampled. They found that only 0.003% of the viruses in the database had at least 1 nitrogen-fixing gene, but they clustered in the same locations as nitrogen-fixing bacteria. 

The team also found 3 types of viruses that most commonly carried nitrogen-fixing genes: Kyanoviridae, Nudiviridae, and Bronfenbrennervirinae. Kyanoviridae viruses carried a nif gene called nifU. The researchers used a computer program called DRAM-v to confirm that nifU was a fully functional gene and a real tool that viruses could use. 

Next, the team collected soil samples from a cowpea field in Nanjing, China, to test whether the cowpea roots influenced the abundance of viruses using nitrogen-fixing genes. They collected soil from the cowpea rhizospheres and soil away from the plants. They analyzed both viral and bacterial RNA in the soil samples to identify active genes, using a sequencing process called metatranscriptomics. They found that the viral nifU gene was actively expressed in the cowpea rhizosphere samples more than in the non-crop soil. Although about 96% of nifU genes were being expressed by bacteria, viruses contributed about 4%. 

The researchers then assessed whether rhizospheric viruses changed the amount of nitrogen being fixed in these soils. They set up small, enclosed containers of sterile soil and added either bacteria or a mixture of bacteria and viruses extracted from the cowpea rhizospheres. They found that soil with viruses had higher total nitrogen (36 milligrams per kilogram, or mg/kg) than soil without viruses (17 mg/kg). 

Finally, they adjusted the air in each container to have a different form of dinitrogen gas. Atoms of the same element that have different atomic masses are known as isotopes. Nitrogen has 2 stable isotopes: lighter nitrogen-14 and heavier nitrogen-15. Bacteria or viruses that fix nitrogen at natural isotope abundances will take up nitrogen-14. But scientists can track who in the population fixes nitrogen by adding nitrogen-15 to the air, a process called nitrogen-15 stable isotope probing. Nitrogen-fixers will incorporate the heavy nitrogen-15 into their biomass over time and increase their weight, allowing researchers to separate and identify them.

The team grew the bacteria and viruses with nitrogen-15 for 35 days, then used stable isotope probing to separate the nitrogen fixers. They detected viral nifU AMGs among the heavier biomass, confirming that rhizospheric viruses were fixing nitrogen. 

The team concluded that rhizospheric viruses and the viral nifU AMG can increase nitrogen fixation in cowpea soils. The team suggested that although viruses with nitrogen-fixing genes are rare, they can influence the soil nitrogen cycle. They recommended that future researchers further test the contribution of viral nifU genes to plant nitrogen fixation with controlled infection experiments.

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In March 2021, the carcasses of at least 260 marine organisms from 81 species, including large animals called megafauna, washed ashore in South Africa. Scientists questioned whether this event was the result of an extreme temperature drop that can happen when deep, cold water rises to the surface along coastlines. Scientists refer to these rapid cooling events as coastal upwelling. 

Climate change is making oceans warmer, but it’s also making coastal upwelling stronger, with unknown consequences for marine megafauna. Scientists recently studied 2 boundary currents that are rapidly warming and creating strong upwelling near the edges of sharks’ geographic ranges: the Agulhas Current in southern Africa and the East Australian Current in eastern Australia. They wanted to determine whether climate-driven changes in upwelling zones are making cold-shock events more frequent and intense, and how these events are pushing sharks beyond their temperature limits. 

First, the researchers analyzed sea surface temperature data from satellites to see if rapid cooling from upwelling caused the mass marine die-off in 2021. They also obtained hourly temperature recordings from temperature loggers to measure subsurface temperature changes during the event. They found that during the March 2021 event, sea surface temperatures along the coast dropped by up to 7°C (12°F) in less than 48 hours, from 24°C (75°F) to below 17°C (63°F). Subsurface temperatures dropped faster, by more than 9°C (16°F) in 24 hours, from 21°C (70°F) to 12°C (54°F). 

Bullsharks experience physiological stress at temperatures below 19°C (66°F), which is near their lower thermal limit, when migrating through rapid cooling zones. Therefore, the researchers deduced that during the 2021 cold-shock event, the sharks were exposed to temperatures several degrees below their thermal limit, possibly more than 5°C (or 10°F) colder. 

The researchers also tagged 66 bull sharks with acoustic and satellite transmitters – including 41 at the Agulhas Current in South Africa and 25 at the East Australian Current in eastern Australia – to determine how they responded near their lower thermal limits in these zones. The acoustic tags record when sharks pass underwater receivers, providing information on their movements, locations, and water depths at those specific locations. In contrast, satellite tags continuously record the water temperatures and depths the sharks experience over time. 

The researchers used these data to compare sharks living in warm subtropical waters with those living in cooler waters at the edge of their temperature range. The researchers found that low temperatures that persisted for long periods prevented the sharks and other megafauna from physiologically recovering. They saw that some sharks try to swim to shallower depths and look for warmer water, but cooling sometimes happened faster than they could respond. 

The researchers also obtained wind records from the South African Weather Service to determine if upwelling-favorable winds happened before the 2021 event. They reported strong easterly winds pushing cold water on the surface along the coastline immediately before the event occurred. Because the cooling, strong winds, and animal deaths happened at the same time and place, the researchers inferred that upwelling was the main reason for the die-off.

Finally, the researchers examined 41 years of sea surface temperature data and used mathematical models to compute changes in the frequency and intensity of these events. Their analysis showed that rapid cooling events linked to upwelling can last a lot longer than the usual 1 to 3 days. For example, the March 2021 event lasted for 7 days. 

They found that these events have increased in both frequency and intensity in the past decades, and produce more extreme temperature drops than typically happen within 24 hours. Their results suggested that the 2021 die-off was part of an increasing trend, where warming oceans allow species to move into different areas, but intense upwelling can suddenly expose them to fatal cold events. This pattern is what scientists call a climate bait-and-switch. 

The researchers concluded that climate change is making coastal upwelling stronger, which can push marine megafauna beyond their thermal limits, even when initial warming appears to be beneficial. Their findings highlight an understudied aspect of climate change in the context of how temperature and upwelling cause shifts in species survival. They concluded their findings could help predict how marine ecosystems respond to future changing ocean conditions.

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When you think of an asteroid impact, you’re probably imagining something like the cataclysmic event that nearly wiped out the dinosaurs. In an astonishingly rare event, a rock the size of the island of Manhattan could come hurtling from deep space towards Earth and destroy the world.

On the contrary, asteroids are generally leftover debris from the primordial solar system that didn’t become part of a larger body, like a planet or moon, and they’re not particularly rare. Scientists estimate that there are about 1 billion asteroids and comets classified as near-Earth objects, or NEOs. NEOs are generally much smaller than the asteroid that killed the dinosaurs. They range from weakly held-together piles of rubble 200 meters (700 feet) across, to the minuscule 2024 BX1, approximately 44 centimeters (17 inches) across. 

For several decades, astronomical surveys of the sky have looked for these asteroids. NASA’s Center for Near-Earth Object Studies (CNEOS) Fireball and Bolide Database has records of over 1,000 incidents of objects colliding with our planet since the mid-1990s. Among these is a set of 11 objects that scientists detected before they hit Earth, known as imminent impactors. Finding imminent impactors allows scientists unique opportunities to study the smallest building blocks of the solar system, enabling them to learn more about asteroid composition, structure, and history.

A team of scientists recently conducted a study on how the newly built Vera Rubin Observatory could detect more imminent impactors before they strike Earth. They examined the CNEOS Fireball and Bolide Database impact records spanning February 1, 1994, to January 1, 2026, for records of the asteroids’ coordinates, velocities, and the energy they released at the time of collision. 

They found that 343 NEOs met this criterion, including 5 known imminent impactors. Using these recorded data, they reconstructed the objects’ pre-impact characteristics. The team predicted the asteroids’ orbital paths from the recorded coordinates and velocity. Assuming the objects were spherical, they estimated the size from velocity and impact energy. From this information about the asteroids’ state before impact, the team could then simulate whether the Rubin Observatory would have detected them. 

When small asteroids hit Earth, they mostly burn up in the atmosphere, producing a bright flash that is relatively easy to spot compared to when they are in space. The Rubin Observatory can see objects up to 10 billion times dimmer than the brightest star in the night sky, Sirius. To predict if the Rubin Observatory can detect dim NEOs, the team used the solar system simulator SORCHA. SORCHA takes an observatory’s specifications and the physical properties of the NEOs and uses these to determine whether the observatory could detect them. It also estimates the measured sizes and speeds of the NEOs, and predicts when the observatory would have seen them. 

At the Rubin Observatory, an imminent impactor is considered discovered if it’s observed at least 3 separate times over 15 days before hitting Earth. So this team applied this same criterion to their simulation. They found that of their 343 impactors, SORCHA estimated that the Rubin Observatory would have discovered 14 of them before impact. 

They also found that SORCHA predicted 4 additional impactors that could have been observed at least once, potentially allowing scientists to search other telescope data for evidence of their existence in pre-collision images. Based on these results, the team estimated that the Rubin Observatory could find 4% of all objects longer than 1 meter (3 feet) before they hit Earth. This percentage of discovery translates to 1 to 2 imminent impactors detected every year, at least double their current discovery rate.

The ability to more consistently find imminent impactors would allow researchers to study the smallest pieces of the solar system in detail and to draw general conclusions about the properties of objects that could potentially collide with Earth. This would also allow scientists to more accurately calculate the orbits of imminent impactors, determine where more meteorites land to recover them, and keep tabs on the extremely rare but more hazardous large impactors for planetary defense initiatives. So, humanity can learn more about the ancient solar system, and hopefully avoid going the way of the dinosaurs while doing so!

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Nearly 170 billion tons of ice melt off the Greenland Ice Sheet every year, but scientists don’t fully understand the process by which this melting occurs. Researchers in the past suggested that liquid water forms at the glacier’s surface, where warm air and sunlight melt the ice. This meltwater travels down through cracks in the glacier and along the bedrock beneath it and flows out to sea. 

However, some scientists view this model as too simple because it assumes water only flows downward and ignores how temperature affects water movement. They argue that an oversimplified melting model is unreliable for predicting how future ice sheets will behave.

One group of researchers tested this by investigating how water movement across Greenland is controlled by lakes beneath the ice sheet, called subglacial lakes. They monitored a newly discovered subglacial lake using high-resolution satellite images taken from 2012 to 2019 and 3D surface maps generated from the images. They focused on ice sheet changes from July 22 to August 1, 2014, during a suspected subglacial lake drainage event. A drainage event is like a water balloon that’s too full, bursting from excess pressure. 

They examined images of the Greenland ice sheet taken from Landsat-8, 3D surface maps from the Polar Geospatial Center, and data from ICESat and ICESat-2 taken in the summer of 2014. Based on these data, the researchers identified a 2-square-kilometer (0.8-square-mile) region of ice rising 10 to 15 meters (about 30 to 50 feet), forming a dome on the ice sheet’s surface. They suggested that this dome formed when a large, meltwater-fed lake developed on the bedrock beneath the ice sheet, pushing the ice upward. 

They also saw that on July 22, 2014, the dome began to collapse. Over the next 10 days, it dropped by 85 meters (about 280 feet), forming a basin. Using the dome’s dimensions, the researchers estimated that 90 million cubic meters, or about 3 billion cubic feet, of water drained from the lake at an average rate of 100 cubic meters, or about 3500 cubic feet, per second over the 10 days. This is roughly equivalent to 36,000 Olympic swimming pools draining at a rate of 1 pool every 25 seconds! 

The researchers also found that during the collapse, 40-meter-high (130-foot) ice blocks were uprooted about 1 kilometer (0.6 mile) downstream from the basin, followed by 6 square kilometers (about 2 square miles) of smooth ice. The researchers hypothesized that these features formed when water burst through the ice, flowed along its surface, and then reentered the ice sheet.

Next, they used data from Landsat 5, Landsat 9, the National Snow and Ice Data Center, and the U.S. Geological Survey to demonstrate that the drainage event also affected the surrounding environment. When the water reentered the ice sheet, it flowed beneath the Harding Glacier downstream. This influx of fast-moving water reduced pressure at the glacier’s base, slowing its movement. Simultaneously, the increased drainage beneath the Harding Glacier caused 500 to 600 meters (about 1,600 to 2,000 feet) of ice to break off its end. 

In response to these changes, the researchers hypothesized that subglacial waters rose to the surface rather than sinking to the bedrock because the ice sheet was frozen to it. To test this, they simulated temperatures at the base of the ice sheet using computer-generated thermal models. They input a range of likely bedrock temperatures and ice thicknesses to simulate possible conditions under the ice sheet. They found that all the simulations predicted the glacier’s base would remain below -5°C (23°F). At such low temperatures, the ice would be frozen to the bedrock before subglacial water could drain out. Since water couldn’t flow under the glacier, it was forced to travel upward. 

The researchers used these findings to develop a new conceptual model of how glacial meltwater moves. First, surface ice melts and flows into a subglacial lake. As meltwater accumulates, pressure builds at the base of the ice sheet, forming a dome on the surface. A drainage event causes the dome to collapse. As water moves downward through the glacier, it is blocked from reaching the base because the ice is frozen to the bedrock. The water then moves upward, bursts through the surface, travels across it, and eventually reenters the glacier before draining to the ocean.

The team concluded that water movement above, through, and below glaciers is interconnected. Destructive upward flow of meltwater, followed by downward flow beneath glaciers, can weaken ice sheet structure and alter downstream glacier dynamics. Their work highlights the need for more scientists to consider the processes driving glacial ice loss.

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