ASU researchers found that genetics alone cannot explain the origins of eusociality. Inside an ant colony, queens reproduce while workers care for young and maintain a society built around shared labor. For more than six decades, evolutionary biologists have debated whether this extraordinary arrangement, known as eusociality, arose partly because ants, bees, and wasps share [...]

The Mauritian dodo—a global symbol of human-caused extinction and one of the world's largest pigeons—may not have deserved its reputation as a silly simpleton and may have been the only member of its group to live in the dark.

The Mauritian dodo—a global symbol of human-caused extinction and one of the world's largest pigeons—may not have deserved its reputation as a silly simpleton and may have been the only member of its group to live in the dark.

A tiny greenish, nocturnal frog that thrives in coffee plantations has been discovered in Costa Rica's central Pacific region, where it also faces risks from agrochemicals used in farming.

A tiny greenish, nocturnal frog that thrives in coffee plantations has been discovered in Costa Rica's central Pacific region, where it also faces risks from agrochemicals used in farming.

Scientists and citizen volunteers discovered two new snail species in northwestern Montenegro, revealing the region’s hidden biodiversity and strengthening calls to protect Komarnica Canyon within the Durmitor World Heritage Site rather than build a hydropower plant. Two previously unknown snails have been added to Europe’s documented wildlife. Named Cochlostoma komarnica and Cochlostoma dobrido, the species [...]

Weather extremes may be contributing to an unusually quiet summer for Britain’s ecologically important social wasps. A summer picnic often feels complete only when a wasp arrives to investigate the food. Common social wasps are familiar warm-weather visitors, whether they are circling outdoor meals or building an unwanted nest in the loft. Summer 2026, however, [...]

What can Earth's magnetic field teach us about attracting people to a more connected environmental movement?

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.

Strict regulations have prevented continental U.S. beekeepers from participating in the lucrative organic honey market, but new research finds that bees' actual habits make certain rules too extreme. The findings potentially provide evidence for federal agencies to ease restrictions and open organic markets to U.S. producers.

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.

Authors: María-Luisa Chas-Amil, Catedrática de Economía Aplicada, Universidade de Santiago de Compostela and Julia María Touza, Professor of Environmental Economics, University of York

The post Eight lifesaving ways to improve wildfire risk management appeared first on Mapping Ignorance.

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.

Nature is full of paradoxes. It is a balance of stability and chaos, individual yet relational, both brutal and beautiful.

In the era of information overload, what can we learn from owls about filtering knowledge in favor of wisdom?

Nature has never demanded perfection of us, nor has evolution ever rewarded it.

If you look up invasive species in a textbook, there's a good chance you'll find a picture of a brown tree snake. Native to Australia and the South Pacific, the brown tree snake arrived on Guam sometime after World War II, possibly by stowing away aboard cargo planes.

Thousands of native milkweeds are transforming North Florida roadsides into experimental pollinator corridors. Along rural two-lane highways in North Florida, newly installed signs now identify grassy roadside slopes as wildflower areas. At this time of year, the labels may seem unnecessary. Thousands of milkweed plants are already producing dense clusters of five-sided buds and open [...]

If you’ve ever swum in the ocean and been touched by seaweed, there’s a good chance it was Sargassum! This free-floating seaweed can have population explosions in coastal waters, degrading water quality, ecosystems, fisheries, and tourism. However, despite its drawbacks, scientists think that Sargassum could be useful for extracting valuable metals, known as rare earth elements (REEs). 

REEs are classified by their average masses and atomic weights. Light REEs include cerium, praseodymium, and neodymium, middle REEs include gadolinium, terbium, and dysprosium, and heavy REEs include erbium, thulium, and ytterbium. These metals are used in magnets, electronic motors, wind turbines, spacecraft, and more. Our current methods of extracting REEs, open-pit mining and chemical processing, are invasive, causing habitat loss and producing toxic waste. Additionally, REEs are difficult to import due to restricted access worldwide. But what if these metals could be extracted using natural methods? 

Scientists from Woods Hole and UCLA recently tested whether Sargassum could accumulate REEs, creating a sustainable solution to these problems. They harvested Sargassum at Waterfront Park in Woods Hole, rinsed the wet samples, and stored them in sterilized, filtered seawater, which they aerated with 12-hour cycles of light and dark to keep the Sargassum alive. They dried another set of samples in the sun under controlled outdoor conditions, stored them in sealed containers, and later rehydrated them in seawater for 3 hours before the experiments.

For each experiment, they added 5 grams (about 0.2 ounces) of Sargassum to solutions containing a mixture of 9 REEs – cerium, praseodymium, neodymium, gadolinium, terbium, dysprosium, erbium, thulium, and ytterbium – in concentrations totalling 0.1 micromolar (μM) REEs, 11μM REEs, 100 to 110μM REEs, and 575 to 600μM REEs. They performed each experiment 4 times: twice with fresh seaweed and twice with rehydrated seaweed. 

The researchers removed 1 gram (about 0.04 ounces) of seaweed from each solution after 1, 3, and 7 days, and dissolved it in acid to release the REEs it had absorbed. They analyzed the concentrations of REEs in each seaweed sample and the leftover solutions using 2 different plasma-based methods. They used these REE concentrations to calculate the ratio of REEs in the Sargassum versus in the solution, known as the bioconcentration factor, to determine how effectively the seaweed absorbed REEs. 

They found that after 7 days at lower REE concentrations (0.1μM), the fresh seaweed had bioconcentration factors of over 1,000. At higher REE concentrations (11μM), their bioconcentration factors ranged from about 400 to 700. However, these levels of REEs were eventually toxic to the fresh Sargassum, so they died after 2 weeks. In addition, the higher REE concentrations of 100 to 600μM were immediately toxic to the fresh Sargassum, which died within 1 day in solution. 

The researchers determined that once the seaweed died, it could no longer actively take up REEs into its tissues but could only accumulate them on its surface or adsorb them. Adsorption can be thought of as a powdered donut – when the surface is covered with powdered sugar, no more powder fits on the donut. Similarly, there was a limit to how many REEs could stick to the Sargassum. Due to this limit, the fresh seaweed samples had bioconcentration factors below 250 in the highest REE concentrations. In contrast, the rehydrated Sargassum consumed less REEs at lower concentrations, but didn’t experience toxicity and continued to accumulate REEs at higher concentrations.

Finally, the team compared Sargassum to a common industrial method of accumulating REEs, known as activated carbon. REEs bond easily to activated carbon because of its abundant surface area and high porosity. Both materials accumulated a similar amount of REEs in solutions of 0.1μM REEs. However, at 575 to 600μM REEs, Sargassum accumulated about twice as many REEs as activated carbon, especially the heavier REEs. 

​The researchers concluded that Sargassum is a natural hyper-accumulator, meaning it successfully concentrates REEs. However, they recommended that future researchers test whether Sargassum performs the same in the natural environment as it does in the lab. If so, Sargassum could be used to accumulate REEs from wastewater, like mine drainage, or other water sources with higher concentrations of REEs. Regardless, they suggested that using Sargassum to obtain REEs could simultaneously expand global access to these metals and mitigate toxic blooms.

The post Using seaweed to harvest valuable metals appeared first on Sciworthy.

A research team found reproducing populations of Manila clams in Cape Cod and Boston Harbor. A stretch of Atlantic shoreline that had remained free of Manila clams now appears to support reproducing populations of the invasive shellfish. Biologists led by the University of Massachusetts Amherst, MIT Sea Grant at the Massachusetts Institute of Technology and [...]