A small dinosaur bone discovered in Fukushima Prefecture more than 25 years ago is giving scientists new clues about the variety of plant-eating dinosaurs that once lived along the ancient coast of Japan. The fossil belonged to an iguanodontian, a large and diverse group of herbivorous dinosaurs that lived from the Late Jurassic through the […]

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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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Scientists have uncovered new evidence that modern humans inherited DNA from two previously unknown ancient human relatives, revealing another surprising chapter in our evolutionary history. The discovery was made using a new genetic analysis method that can identify traces of long-extinct human groups even when no ancient DNA from them has ever been found. The […]

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Mars is slowly losing parts of its atmosphere to space, and scientists have discovered that the process may be driven by giant waves created where the solar wind meets the planet’s upper atmosphere. The new research, led by Boston University, shows that these waves act much like wind blowing across the surface of a lake, […]

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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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For as long as readers have pored over Homer’s Odyssey they have pictured Ithaca, the homeland Odysseus fights for ten years to reach, as an island unto itself.

The post Was Odysseus’ Ithaca Never an Island? Scholars Challenge Centuries-Old Assumption appeared first on Sci.News: Breaking Science News.

Researchers have developed a new artificial intelligence (AI) system that can predict robberies and other crimes more accurately than several existing methods. By studying where crimes happen, when they occur and broader social patterns, the system achieved an accuracy rate of 86.3% when forecasting robberies in several U.S. cities. Predicting crime is extremely challenging because […]

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Tiny traces of material from a giant cosmic explosion are still falling to Earth more than 100 million years after the event occurred, according to a new international study published in Nature Astronomy. The evidence comes from an unusual source: a slow-growing chunk of ferromanganese crust collected from the floor of the Pacific Ocean. Hidden […]

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When a whale dies, a very special natural phenomenon can come alive. The carcass might float at the surface for some time, attracting sharks and other predators. As it becomes weathered, it may start to sink, falling through the water until it eventually settles on the seafloor, where deep-sea scavengers feast upon it. The scientific […]

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Our search for technosignatures – clear signs of advanced civilizations beyond Earth – takes many forms. Many are driven by the famous Drake equation, which attempts to estimate how many technological civilizations there are in the Milky Way. However, there’s a big fat question mark at the end of that equation in the form of […]

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As our world is rapidly reshaped by climate change, glaciers have retreated and revealed new swaths of bare land. Throughout the coming decades to centuries, this rocky ground will become dappled with lichens and scrub, eventually giving rise to newborn forests in a process known as ecological succession. 

Ecologists have mapped out the stages of ecological succession for plant communities, observing which species are the first to colonize land and how these pioneer species promote secondary growth. Yet before any plants sink their roots into the soil, it is populated by thriving communities of single-celled microbes that prepare it for further habitation. Scientists study how these microbial communities arise to understand how healthy ecosystems develop.

Newly exposed land is nutrient-poor and subject to large temperature fluctuations, so the first species to settle there must overcome these challenges. Pioneer plant species are often habitat generalists, meaning they can thrive under a variety of environmental conditions. However, all plants convert water and sunlight into carbon and energy. Microbes can use many different energy sources, and often contain genes for multiple metabolisms in their genomes. Therefore, researchers wondered whether pioneer microbes could also be defined by their metabolic flexibility. 

A team of scientists based at Monash University in Australia tested this hypothesis by studying land left in the wake of 2 retreating glaciers: one on an island off the coast of Antarctica and one in the Swiss Alps. The researchers sampled soils along a path from the tip of the glacier outward, which had been exposed to the air for different amounts of time. By comparing the microbial communities within these soils, the researchers could track the different stages of ecological succession after glacial retreat.

The researchers extracted DNA from the soils and analyzed it using 2 sequencing methods. First, they sequenced a gene called 16S rRNA, which is like a microbial fingerprint for the different species present. They used this method to understand the diversity of these communities and track their species overlap, helping them to identify habitat generalists that thrived under different soil conditions. 

To understand the metabolic flexibility of these microbes, the researchers used a second method, known as metagenomics, which sequences all of the DNA in a sample rather than just a single gene. They used these data to reconstruct the entire genomes of microbes present in the soils, providing information on what kinds of metabolic activities they were capable of. The team also measured chemicals like ammonium and sulfide in the soils, as well as atmospheric gases like methane and carbon monoxide, to assess how the microbes used these compounds for growth.

The researchers found microbes inhabiting even the youngest soils, demonstrating the speed at which life takes hold in new environments. The abundance of microbes increased by approximately 8-fold in older soils, alongside a similar rise in species diversity, indicating that complex communities persisted over time. They also found the metabolic capabilities of microbes inhabiting Antarctic and Swiss glacial soils to be remarkably similar, suggesting common selective pressures drove the establishment of these new ecosystems.

They were surprised to discover that the most abundant microbes in younger soils were habitat specialists that were rare in older soils. These pioneer microbes, while still metabolically flexible, had adapted to utilize meager energy sources like atmospheric trace gases, including hydrogen, methane, and carbon monoxide. Many of these microbes could also derive energy from chemicals that dissolved out of rocks, like inorganic sulfur compounds. The researchers proposed that pioneer microbes could quickly take advantage of newly opened ecological niches, like soils exposed during a glacier’s retreat, because of their efficiency at using scant resources. 

They found that habitat generalists, on the other hand, tended to dominate older soils.  This result suggested that the habitat specialists were eventually outcompeted by the slow and steady growth of the habitat generalists in a real-life turtle-and-hare race.

The team concluded that using multiple growth strategies helps microbes adapt to new environments. However, they acknowledged that ecological succession could occur differently in other landscapes, such as in the aftermath of volcanic eruptions, meteorite impacts, or forest fires. They suggested that future researchers work to uncover how microbial communities lay the groundwork across these diverse ecosystems. 

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Scientists have discovered that a major earthquake zone in Southern California may be carrying more built-up stress than at any time in the past 1,000 years. While this does not mean a large earthquake is about to happen, researchers say the findings highlight the importance of being prepared. Earthquakes occur when stress builds up along […]

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A rare meteorite found in the Sahara Desert has revealed evidence of a giant ancient world that once existed in our solar system but later disappeared. Scientists believe this lost world formed about 4.5 billion years ago, during the early days of the solar system. It may have been as large as the Moon or […]

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A remarkable archaeological discovery in Slovakia is changing what scientists thought they knew about life and death in some of Europe’s earliest farming communities. Since 2022, researchers have been excavating a 7,000-year-old settlement near the modern town of Vráble. What they found was startling: dozens of human skeletons piled together in and around a ditch, […]

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From the air, you see it only through the constant jolt, tilt, and shudder of the low-flying Cessna aircraft. The landscape of the Llanos de Moxos, northern Bolivia, appears as a disconnected patchwork of open grassland savannahs, forest islands, and lakes. It feels random, almost unreadable. Only gradually does the pattern resolve itself: raised causeways […]

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Massive curriculum changes are required for UK school Geography after the Met Office's climate projections were ruled "implausible" by the IPCC. But don't expect the revisions any time soon, says Chris Morrison.

The post Massive Curriculum Changes Required for UK School Geography After Met Office Climate Projections Ruled “Implausible” appeared first on The Daily Sceptic.

Does the Spring Equinox Begin on March 19 or 20?

© Westend61 / Getty Images

The evolution of land plants about 450 million years ago altered many of Earth’s geologic processes, like weathering and erosion. Due to the lack of evidence for meandering rivers before then, past scientists hypothesized that plants could have caused straight rivers to meander. However, in recent decades, researchers have challenged this idea. They’ve suggested that plants could have changed rivers without causing them to meander.

To understand how vegetation changed rivers in the past, researchers recently studied 49 modern meandering rivers. They sorted these rivers into 3 categories – vegetated, unvegetated, and semi-vegetated – by analyzing color images taken of them from the air. They identified 18 vegetated rivers located in South America, 24 unvegetated rivers in the western United States, and 7 semi-vegetated rivers in China and the Eastern United States. 

To examine the impact of plants on these rivers, the researchers quantified how much each river channel curves, known as its sinuosity. They used opposite banks of each river bend to find its center point, then, using digital maps, drew a line along the river’s trajectory at an equal distance between the bend center points. They used this line to calculate the angle between the river’s curve and the center point. This angle, called the migration angle, shows how a river bend relates to the river’s downstream direction. By measuring it, researchers can tell whether a river is developing more vertically or horizontally, and how sharp its bends are, either of which could be influenced by plants. 

The researchers compared migration angles across each river system to determine how river bends varied between vegetated and unvegetated rivers. They found that vegetated rivers tend to deposit sediments in the river bend, leading to curvier bends that develop horizontally and widen over time. In contrast, unvegetated rivers deposit sediment downstream, which means the rivers bend less and have greater variability in bend width.

The question remained whether plants were the primary cause of these differences or whether other factors were at play. To resolve this, the researchers investigated 3 additional factors. The first was the natural fluctuations in water flow across a river system, called its flow variability. They found that during storms, flow variability caused river bends to move downstream in unvegetated rivers, but not in vegetated rivers. This result suggested that flow variability alone didn’t drive downstream migration, although it can directly impact vegetation. 

The second variable the researchers analyzed was the amount of sediment a river can carry, or its sediment flux. They found that rivers carrying more sediment can erode more banks, also shifting river bends. However, rivers with more sediment but the same level of plant coverage had statistically similar bend angles. Thus, the researchers concluded that sediment flux alone can’t drive bend development, and that the changes were instead dependent on vegetation cover. 

The third variable they analyzed was riverbank strength. The researchers observed rivers with strong banks, made of rock or compacted sediment, and weak banks, made of loose sediment. They observed no difference in river bends with the same vegetation cover but different bank strengths. The researchers concluded that bank strength is also not the primary driver of bend migration in vegetated or unvegetated rivers. 

Of the 4 variables the researchers examined – flow variability, sediment flux, bank strength, and vegetation cover – vegetation cover consistently had the greatest impact on the appearance of meandering rivers. They concluded that meandering rivers could have existed before plants, but would have looked different. Like modern unvegetated rivers, ancient meandering rivers likely had lower-angle bends. As plants evolved and grew on river banks, the bends would have developed differently, becoming curvier like modern vegetated rivers. They suggested that understanding this process provides insight into life on Earth before plants evolved 450 million years ago. 

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