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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.

The post The long-term fate of Earth’s biosphere appeared first on Sciworthy.

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Even we *almost* missed them.

Globally, about 1 billion people live near glaciers and depend on glacial water. As temperatures continue to climb due to global warming, glaciers can become unstable and hazardous to nearby communities. These communities risk exposure to a series of linked processes, like ice collapse, flooding, and landslides, which could have life-threatening consequences. Scientists call these events catastrophic glacier hazard chains, or CGHC. So, a team of scientists in China sought to understand the driving forces behind these dangerous phenomena. 

The team focused on a CGHC event that occurred in 2018 at the Sedongpu Glacier, located in the Eastern Himalayas. During this event, the glacier obstructed the flow of 30 million square meters (more than 300 million square feet) of the Yarlung Tsanpo River, one of the main rivers within the region. 

To reconstruct the event, the team observed satellite images of the Sedongpu glacier over time, using a process called remote sensing. They used the images to calculate ice volumes, glacier velocities, and glacier movement between 1961 and 2018, to determine the glacier’s long-term physical properties and how they changed over time. They found that from 1968 to 2013, the Sedongpu Glacier shrank, or retreated, by over 6 football fields. It also lost ice and water volume and increased its velocity.

The scientists also measured earthquake waves on the Earth’s surface, or seismicity, to characterize any downslope rock or debris movements caused by the glacial retreat. They measured seismicity across 11 different seismic stations within 580 kilometers (about 350 miles) of the glacier. Then, they used this data to identify 2 distinct phases of the CGHC event on Sedongpu Glacier: the ice-rock avalanche and the debris flow.

When the ice-rock avalanche occurred, the team detected noticeable increases in seismicity, suggesting it lasted over 30 seconds. During those 30 seconds, over 8 million cubic meters (more than 280 million cubic feet) of ice fell onto the main glacier. The team recorded even more increases in seismicity during the debris flow, which lasted nearly 5 minutes. The debris from the avalanche hit the main valley glacier at nearly 300 kilometers (200 miles) per hour.

The researchers combined these data to describe how the 2018 CGHC event unfolded. First, the backward movement and shrinkage of the glacier produced an unstable sheet of ice hanging over a cliff face. The steep slope and large cracks in the ice caused an ice-rock avalanche. Debris from the avalanche then accumulated on the main glacier, causing it to move faster. As the main valley glacier moved downslope, it picked up even more debris from the bottom and sides of the glacial valley. The melting ice also increased the glacier’s velocity, and its movement eventually transitioned into a debris flow, which obstructed the Yarlung Tsangpo River. 

In total, the CGHC event in the Sedongpu Glacier travelled over 10 kilometers (6 miles) and moved down in elevation for over 3 kilometers (about 2 miles) at a velocity of about 100 kilometers (about 70 miles) per hour. The glacier accumulated enough debris and ice to fill thousands of Olympic-sized swimming pools. Imagine what this could do to a nearby village!

It’s difficult for scientists to estimate when CGHC events will occur, but this team proposed several factors that could help predict them. These factors included the size of the ice-rock avalanche and the glacier velocity, as well as the occurrence of earthquakes, temperature changes, and the melting of the glacier’s underside. They stated that these factors can combine to create a domino effect that drives the CGHC process. They recommended consistent glacial monitoring as temperatures continue to climb, in the Himalayas and globally. 

The team concluded that understanding CGHC events can help scientists mitigate glacial hazards and keep people safe, especially in mountainous areas such as the Eastern Himalayas. They stated that their findings illustrate the dangerous processes behind glacial melting, but further research is needed to accurately assess those processes and ensure nearby communities remain safe.

The post Can melting ice increase glacial hazards? appeared first on Sciworthy.

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.

The post Testing local weather knowledge in the Bolivian Altiplano appeared first on Sciworthy.

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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.

The post What colors were prehistoric mammals? appeared first on Sciworthy.

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. 

The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.

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