Forests with a greater diversity of trees are more productive—potentially leading to greater efficiency in capturing planet-warming carbon dioxide from the atmosphere, a new study reveals.

Researchers found that trees that grow quickly, and capture carbon faster, tend to be smaller and have shorter lifespans, leading to lower carbon storage and faster release back into the atmosphere.

Slower growing species live longer and grow larger, tending to capture more atmospheric carbon—particularly in the setting of more diverse forests.

Analyzing 3.2 million measurements from 1,127 species of trees across the Americas—from southern Brazil to northern Canada—an international team of experts mapped life expectancies for trees ranging from 1.3 to 3,195 years.

In an article published Oct. 3 in Science, an international group including researchers from South America, Central America, Europe and North America—led by ETH Zurich, Switzerland—further identify four main types of tree life-cycles: Fast-growing species with shorter lifespans and low maximum sizes, plus three clusters of slow-growing species.

At one end of the three conservative growth clusters are tree species with high life-expectancies and small maximum sizes, while at the other end are species with low life expectancies and large maximum sizes.

Co-author Dr. Adriane Esquivel-Muelbert, Brazilian researcher based at the University of Birmingham, commented, “Tree growth and lifespan trade-offs are crucial for the planet’s carbon balance. The positive relationship between trait diversity and productivity suggests that maintaining diverse forests is crucial for ecosystem health and climate change mitigation.

“Forests with diverse tree species can capture carbon more effectively, meaning that promoting forest biodiversity in forests can help capture more carbon. Understanding how these factors are linked can guide restoration and conservation projects.

“By selecting the right mix of tree species, we may be able to maximize carbon storage and develop strategies that enhance forest resilience to climate change.”

The researchers mapped life history traits of tree species across the Americas, finding a strong connection between temperature and tree growth with colder climates associated with slower growth. Tropical forests exhibit higher diversity compared to temperate and boreal forests.

Lead author Dr. Lalasia Murphy, from ETH Zurich, commented, “This study provides a broad-scale assessment of tree life expectancy for species across the Americas, with estimates ranging from tens to thousands of years.

“We also find that more diverse forests, which include a wider range of fast-growing, short-lived, and slow-growing, long-lived species, tend to sequester more carbon. These findings provide new insights that can be used to inform biodiversity conservation and climate change mitigation.”

They found that tropical regions retain the full range of four demographic functional types, while tree species found in non-tropical areas fall into two clusters of predominantly slow-growing species. Mean life expectancy tends to be higher outside the tropics.

Experts used the largest dataset of dynamic tree information to date—calculating the mean life expectancy and maximal lifespan for a wide range of trees from Northern Canada to Southern Brazil.

“Such large-scale analysis is only possible with large collaborative efforts involving hundreds of researchers monitoring these forests for many years,” commented Dr. Thomas Pugh, co-author of the study from the University of Birmingham and Lund University.

Data also included long-term records from an international network of researchers, including members of the Global Forest Dynamics, ForestPlots, and ForestGeo networks, as well as the United States and Canadian forest inventory programs.

Researchers reporting in the journal Current Biology on October 7 have made the surprising discovery that one species of comb jelly (Mnemiopsis leidyi) can fuse, such that two individuals readily turn into one following an injury. Afterwards, they rapidly synchronize their muscle contractions and merge digestive tracts to share food.

“Our findings suggest that ctenophores may lack a system for allorecognition, which is the ability to distinguish between self and others,” says Kei Jokura of the University of Exeter, UK, and National Institutes of Natural Sciences in Okazaki, Japan. “Additionally, the data imply that two separate individuals can rapidly merge their nervous systems and share action potentials.”

Jokura and colleagues made the observation after keeping a population of the comb jellies in a seawater tank in the lab. They noticed an unusually large individual that seemed to have two backends and two sensory structures known as apical organs instead of one. They wondered if this unusual individual arose from the fusion of two injured jellies.

To find out, they removed partial lobes from other individuals and placed them close together in pairs. It turned out that, nine out of 10 times, it worked. The injured individuals became one, surviving for at least three weeks.

Further study showed that after a single night, the two original individuals seamlessly became one with no apparent separation between them. When the researchers poked at one lobe, the whole fused body reacted with a prominent startle response, suggesting that their nervous systems were also fully fused.

“We were astonished to observe that mechanical stimulation applied to one side of the fused ctenophore resulted in a synchronized muscle contraction on the other side,” Jokura said.

More detailed observations showed that the fused comb jellies had spontaneous movements for the first hour. After that, the timing of contractions on each lobe started to synch up more. After just two hours, 95% of the fused animal’s muscle contractions were completely synchronous, they report.

They also looked closely at the digestive tract to find that it also had fused. When one of the mouths ingested fluorescently labeled brine shrimp, the food particles worked their way through the fused canal. Eventually, the comb jelly expelled waste products from both anuses, although not at the same time.

The researchers say it remains unclear how the fusion of two individuals into one functions as a survival strategy. They suggest that future studies will help to fill the gaps in understanding, with potential implications for regenerative research.

“The allorecognition mechanisms are related to the immune system, and the fusion of nervous systems is closely linked to research on regeneration,” Jokura says. “Unraveling the molecular mechanisms underlying this fusion could advance these crucial research areas.”

Scientists from the University of Sharjah believe they have created an artificial intelligence system that can automatically identify which Arabic dialect someone is speaking. The work is published in IEEE Xplore.

They say their system unravels the rich and complex tapestry of Arabic dialects which hitherto conventional speech systems fall short of accurately interpreting and identifying.

“Arabic is a rich language with many regional dialects, and each one has its own unique vocabulary, expressions, and pronunciation. This diversity makes it challenging for technology to accurately understand and differentiate between them,” said Ashraf Elnagar, Professor of Computer Science and Intelligence Systems.

“To address this, we developed a system that can automatically identify which Arabic dialect someone is speaking.”

The official language in 22 countries spanning the Middle East, North Africa and the Arabian Peninsula, Arabic is one of the most spoken languages globally with more than 370 million people having it as their mother tongue. It is also one of the world’s most immersed languages in culture and those having it as a mother tongue or learning it as a second or foreign language find themselves learning about Islam and its culture as well.

With a totally different alphabet than English, the language has numerous sounds that are specific to its phonology. The charm of its sounds and characters bewilders countless foreign learners who aspire to speak it fluently. Though most learning of the Arabic language occurs in the standard formal variety, many foreign learners opt for colloquial or daily versions, particularly the spoken forms in currency in Egypt and Syria.

The authors say they didn’t face an easy task in their attempt when teaching computers to recognize different Arabic dialects just by listening to spoken words. They write, “The primary challenge is the development of a machine learning model capable of accurately identifying a wide range of Arabic dialects from audio recordings.

“This task is compounded by the inherent diversity and complexity of Arabic dialects, coupled with the technical challenges of audio processing and machine learning model optimization.”

The authors utilized datasets comprising more than 3,000 hours of audio segments collected from YouTube. The data includes 19 different dialects spoken in Algeria, Egypt, Iraq, Jordan, Saudi Arabia, Kuwait, Lebanon, Libya, Mauritania, Tunisia, Morocco, Oman, Palestine, Qatar, Sudan, Syria, the United Arab Emirates (U.A.E.), Bahrain and Yemen.

The results were impressive, said Prof. Elnagar, underscoring the model’s high accuracy in Arabic dialect identification regionally and at country levels. “Our model correctly identified regional dialects 97.29% of the time and specific country dialects 94.92% of the time.

“What is remarkable is that we achieved this using only 29% of the training data typically required by other researchers. We have made our models publicly available so that other researchers and developers can use them to create better speech-related technologies for Arabic speakers.”

The project has the potential to enhance communication and accessibility for millions of Arabic speakers worldwide. Prof. Elnagar said the model’s ability to correctly identify a dialect can “improve voice-activated technologies like virtual assistants, translation services, and automated customer support systems.

“This not only bridges communication gaps between different Arabic-speaking regions but also contributes to making technology more inclusive and user-friendly for Arabic speakers.”

Despite the astounding results, Prof. Elnagar noted, the project can still be improved. For this purpose, the authors have made their system publicly available “online on a platform called HuggingFace, so others can access and build upon our work to improve Arabic language technologies.”

The research is the outcome of collaboration between Prof. Elnagar and three of his undergraduate students as part of a project to build a deep learning model for Arabic dialect identification from speech. The initial research results were first presented at the 15th Annual Undergraduate Research Conference on Applied Computing (URC) in 2024.

“Developed by our dedicated students, the technology behind our system integrates cutting-edge methodologies and deep learning techniques. Expanding its functionality from text to audio signals sets it apart, providing a multi-modal approach to understanding and processing the Arabic language,” Prof. Elnagar said.

For student researcher Amr Barakat, the project “bridges a critical gap in language technology, enabling more inclusive and accurate communication for Arabic speakers worldwide. By leveraging advanced machine learning, we have created a model that not only excels in performance but also paves the way for future innovations in speech recognition.”

Another student researcher, Abdulla Aldhaheri, reported wide interest from the industry in the project, as it “holds the potential for widespread adoption, offering numerous benefits and improvements to various AI-driven language applications and services.”

Besides its high accuracy, the tool the authors have developed, unlike currently available models, requires less data and computational resources, rendering it accessible for wider use. This feature, according to the authors, was behind the industry’s interest in their work. They cited tech corporations like Microsoft and governmental bodies in Sharjah in the U.A.E. as being particularly enthusiastic about their work.