Scientists reveal how regional species pools shape tree diversity and rarity in subtropical and tropical forests

KeAi Communications Co., Ltd.

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Typical forest landscapes of subtropical and tropical regions in china, and the distribution map of the forest plots used in this study.

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Credit: Jian Zhang

Why do some natural forests hold so many different kinds of rare trees? It's a question that has long puzzled and intrigued ecologists. While early studies focused on local environmental conditions, scientists now know that broader regional forces, like historical climates and species dispersal, also shape local forest diversity.

In new research published in the KeAi journal Plant Diversity, an international research team led by Dr. Jian Zhang from Sun Yat-sen University explored what drives tree diversity and rarity in China's subtropical and tropical forests—some of the most biodiverse forests on Earth at the same latitudes.

Using a large database (VAST-China) of 3,923 forest plots covering 3,307 tree species, the team defined the regional species pool as all the species that could potentially occur in a given area. They then quantified how regional and local factors together determine species richness (how many species live there) and rarity (how uncommon they are).

"We found that the size of the regional species pool is the strongest driver of local tree diversity," shares Zhang. "Forests with larger species pools, supported by favorable climates, varied habitats, and stable historical climates, have far more species locally."

In particular, warmth, complex topography, and steady paleoclimates help species accumulate over time at regional scales, boosting both total diversity and the number of rare trees.

"Large species pools support both high diversity and many rare species," explains lead author Dr. Houjuan Song from Shanxi Agricultural University. "Rare trees thrive where regional species pools are rich and human pressure is low. This tells us we must prioritize rare and endemic species protection in highly damaged and fragmented forests."

"To preserve forest diversity and save rare and endemic species as the accelerating environment changes, we need to protect the areas with large species pools and minimize human disturbance, especially in biodiversity hotspots," Zhang adds.

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Contact the author: Jian Zhang, zhangjian6@mail.sysu.edu.cn

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

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Journal

Plant Diversity

DOI

10.1016/j.pld.2026.02.002 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

The relative role of regional species pools in determining tree species richness and rarity in Chinese subtropical and tropical forests

COI Statement

The author Jian Zhang is an Editor for Plant Diversity and was not involved in the editorial review or the decision to publish this article. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

 

New research brings machine‑learning‑based physics a step closer to solving real engineering challenges

University of Manchester

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A mathematics professor at The University of Manchester has developed a novel machine-learning method to detect sudden changes in fluid behaviour, improving speed and cost of identifying these instabilities and overcoming one of the major obstacles faced when using machine learning to simulate physical systems.

Computational simulations of mathematical models of fluid flow are essential for everyday applications ranging from predicting the weather to the assessment of nuclear reactor safety. The advent of this simulation capability over the past 50 year has revolutionised the development of fuel-efficient aeroplanes and sail configurations on racing yachts can now be optimised in real time, providing the marginal gains needed to win races in the Americas Cup.

Optimised aerodynamics means that modern day cyclists can ride faster, golf balls fly further and Olympic swimmers consistently set world records. Computational fluid dynamics also enables the modelling of the flow of blood in the human heart, making the provision of patient-specific surgery possible.

Scientists and engineers rely on computer-based simulations to understand, predict, and design these systems that they can’t easily test in real life. But traditional fluid‑simulation methods often require hours or even days of computation, and struggle when the flow becomes fast or highly complex.  

Machine‑learning‑based simulations, once trained, can make these assessments almost instantly. Instant feedback would allow rapid design testing, real‑time adjustments, and rapid testing variation without the usual computational burden.

The findings were published in the Journal of Computational Physics.

Professor David Silvester, Professor of Applied Mathematics at The University of Manchester said: "Solving reliability issues that machine‑learning models encounter would offer major benefits for scientific research and engineering. The issue to be faced is that naive AI predictions of flows generated solely from data are highly likely to feature impossible scenarios. This is a serious concern when predicting extreme events like tornados and tsunamis.”

The study uses the stability of fluid motion as the foundation for a new method that predicts how complex systems behave. Instead of relying on costly laboratory experiments, solutions to the fundamental equations of fluid motion are generated numerically. This allows the machine-learning model to be trained on accurate, high-quality data drawn directly from physics, demonstrating that the model can accurately handle challenging simulations.

A key focus of the work is identifying bifurcation points –the moments when a smooth, steady flow (laminar flow) suddenly begins to change – similar to calm, evenly flowing river as it hits an obstruction, or splits and fluids start to mix and form eddies. Laminar flow is when a liquid behaves in a smooth and orderly way, like pouring honey, the flow is consistent and steady.

By successfully using a machine‑learning model to identify the points at which a system changes behaviour or in this case bifurcates, the study suggests that, with further refinement, machine‑learning‑based models could become a practical alternative to traditional fluid‑modelling techniques in the future.

Professor Silvester added: "This marriage of old and new approaches holds the promise of efficient computation of physically realistic fluid flows in a myriad of practical situations. The development of refined mathematical models of complex fluids is likely to be critically important if the promise of AI is to be effectively realised in the future.”

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Journal

Journal of Computational Physics

DOI

10.1016/j.jcp.2026.114743 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Machine learning for hydrodynamic stability

Reliable material databases bridge AI- and experimental-led material discovery

Materials databases lie at the heart of future data-driven discovery in energy-related fields, say researchers from Tohoku University.

Advanced Institute for Materials Research (AIMR), Tohoku University

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The evolution of materials science paradigms.

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Credit: Li et al.

Materials databases lie at the heart of future data-driven discovery in energy-related fields, say researchers from Tohoku University.

In a new article published in the journal Precision Chemistry, they examined how different types of databases, both computational and experimental, work together to support modern artificial intelligence (AI) tools used in materials science.

The study found that materials databases are no longer just places to store information. Instead, they play a central role in determining how well AI models perform. The way data is collected, organized, and shared - known as database architecture - can directly affect whether AI systems produce reliable and useful results.

"In a library, if books are poorly labeled, have missing pages, or are difficult to access, even the most skilled reader will struggle to find accurate information," stresses Hao Li, lead author of the paper and Distinguished Professor at Tohoku University's Advanced Institute for Materials Research (AIMR). "In the same way, AI models depend on well-structured and carefully curated data to make sound predictions."

Li and his team categorized computational databases into two main groups: those that focus on bulk material properties and those that focus on surfaces and interfaces. They also reviewed experimental databases that cover areas such as crystal structures, catalysis, energy storage, and materials characterization.

Further analysis revealed the growing importance of integrated platforms. These systems connect computational predictions with detailed experimental data, allowing scientists to test ideas, refine models, and validate results in a continuous cycle. This approach supports more efficient and reliable materials discovery.

Moreover, the researchers introduced a roadmap for combining databases, AI models, and experimental workflows. This includes the use of graph neural networks, machine learning interatomic potentials, and large language model-based AI agents to accelerate the discovery process while maintaining scientific rigor.

However, the researchers identified several challenges that must be addressed. These include the need for standardized data practices aligned with FAIR principles (Findable, Accessible, Interoperable, Reusable), better tracking of data origins, and improved reporting of negative results, which are often missing but are important for reducing bias.

"Materials databases are the foundation of trustworthy AI in science," adds Li. "If we want AI to guide discovery in a reliable way, we must first ensure that the data it learns from is complete, transparent, and well-structured. Without reliable data, AI-led discovery will itself become unreliable."

Looking ahead, the team plans to improve database quality and connectivity across fragmented data sources. They also aim to develop new AI systems that can learn from multiple types of data simultaneously and work alongside experiments and human researchers. These efforts are expected to support more dependable and efficient discovery of materials for energy, sustainability, and everyday applications.

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Journal

Precision Chemistry

DOI

10.1021/prechem.5c00449 

Article Title

Materials Databases: Foundations of Modern Digital Materials

Computational and integrated platform. 

Database-to-model-to-experiment roadmap for domain models and AI Agents.

Credit

Li et al.

One DNA letter can trigger complete sex reversal, Bar-Ilan University study finds

Researchers show that a tiny mutation outside a gene caused XX mice to develop as males instead of females, revealing the powerful role of non-coding DNA

Bar-Ilan University

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Dr. Nitzan Gonen, Goodman Faculty of Life Sciences and Institute of Nanotechnology and Advanced Materials, Bar-Ilan University

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Credit: Courtesy Bar-Ilan University

Researchers at Bar-Ilan University have discovered that changing just one letter in DNA can completely alter sex development in mice. In the new study, published in Nature Communications, a single-letter insertion in a non-coding regulatory region caused XX mice, which would normally develop as females, to develop instead as males with testis and male genitalia.

The finding is especially striking because the mutation was not made in a gene itself, but in a distant stretch of DNA that helps control a key developmental gene. The study highlights the major role of the non-coding genome -- the 98 percent of DNA that does not make proteins but helps regulate when and how genes are turned on and off.

“This is a remarkable finding because such a tiny change -- just one DNA letter out of ~2.8 billion -- was enough to produce a dramatic developmental outcome,” said Dr. Nitzan Gonen, from the Goodman Faculty of Life Sciences and Institute of Nanotechnology and Advanced Materials at Bar-Ilan University. “It shows that non-coding DNA can have a profound effect on development and disease.”

The mutation was introduced into a regulatory element known as Enh13, which controls the activity of Sox9, a gene essential for testis development. For ovaries to develop normally, Sox9 must be kept turned off. The researchers found that Enh13 acts as a kind of molecular battle site/switch: in males, factors that promote testis development bind to it and activate Sox9 whereas in females, factors that promote ovary development bind to it and repress Sox9.

When the researchers introduced the mutation using CRISPR genome editing, that female repression failed. As a result, Sox9 was activated in XX mice, and testis developed, leading to complete internal and external male development.

The team created several mouse models with very small mutations in Enh13, including a one-base-pair insertion and a three-base-pair deletion. Both mutations caused XX mice to develop testis. The researchers then used cell-line reporter assays to understand how the mutation disrupted the normal regulatory mechanism.

The study builds on earlier work by the same group, published in 2024, which showed that other small mutations in the same regulatory element could have the opposite effect, causing XY mice to develop as females. Together, the findings suggest that Enh13 has a dual role: it acts as an enhancer in male development, but must also be repressed in female development.

Beyond its significance for basic biology, the study may have important implications for people with Differences of Sex Development (DSD), a group of conditions that affect about 1 in 4,000 births worldwide. More than half of DSD cases still lack a genetic diagnosis, even after sequencing the protein-coding parts of the genome.

“Our findings show that it is not enough to look only at genes,” said Elisheva Abberbock, the PhD student leading the research. “Important disease-causing mutations may also lie in the non-coding genome, in DNA regions that control gene activity rather than encode proteins.”

The researchers believe Enh13 may be just the beginning, and that many more regulatory regions in non-coding DNA may be involved in sex determination and other developmental disorders. They are now working to identify these regions systematically and test their function.

The study was led by Elisheva Abberbock together with other researchers from Bar-Ilan University. Collaborators included Dr. Ariel Afek of the Weizmann Institute and Dr. Francis Poulat of the University of Montpellier. The research was funded by the Israel Science Foundation and an ERC Starting Grant.

 

 

The figure depicts the Enh13 regulatory region alongside the Sox9 gene as a “battle site” between the sexes. Pro-female factors act to repress Sox9 via binding to Enh13, while pro-male factors activate it. The balance between these opposing forces ultimately determines whether male or female development occurs.

Credit

Neta Varsano

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Journal

Nature Communications

DOI

10.1038/s41467-026-71328-9 

Article Publication Date

9-Apr-2026

New study reveals great apes mirror facial expressions with surprising precision

Research suggests that orangutans and chimpanzees replicate happy facial expressions in ways similar to humans

University of Portsmouth

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New research from the University of Portsmouth has found that great apes exhibit exactness in mimicking one another’s facial expressions in social contexts. 

The study, published by Nature’s Scientific Reports, explored how orangutans and chimpanzees mirror expressions during social interactions, particularly laugh faces, drawing comparisons with human behaviours such as the Duchenne smile - a genuine smile that engages both the mouth and eyes. 

The research, supported by a Royal Society Research Grant and funded by the Department of Science, Innovation and Technology, Leakey Foundation and the FEELIX GROWING project, reviewed behaviours of 96 great apes and investigated whether the animals mirrored each other's laugh faces within three seconds, examining both with and without their upper teeth exposed. 

It analysed the behaviours of 39 orangutans and 57 chimpanzees living in eight separate groups, comprising a mix of ages and genders. 

The study showed that the animals were most likely to mirror each other's expressions when the upper teeth were not exposed, as this is a gentler expression. These accurate ways of matching can help people better understand others’ emotions and predict how they might behave.  

Measuring this mimicry provides scientists with unique insights into face-to-face exchanges, allowing them to test how exact the responses of primates really are.  While humans are known for having precise and versatile language, this study suggests that great apes 10-16 million years ago already matched the level of facial communication exactness seen in humans - an ability that gave them important advantages. 

The research also explored play duration and play intensity, analysing whether the length of a play session was linked to how likely the animals were to mirror their playmate's expression.  

When orangutans engaged in longer play sessions, they showed a greater precision in mirroring their playmate's expression.   

Lead author Diane Austry, who completed her Masters at the University of Portsmouth, said: “What we found was remarkable - these animals don't just replicate a general expression, they copy the exact same facial movement pattern their social partner uses, much like when a human mirrors another person's natural Duchenne smile. 

“The apes showed the same pattern for both automatic rapid responses as well as more delayed responses.” 

Dr Marina Davila Ross, co-author and Associate Professor in Comparative Psychology at the University of Portsmouth, said: “This level of explicit replication wasn't previously known across the great apes. What's more, these expressions are shared with humans – previous research measuring muscle activity shows that chimpanzees and humans use the same muscles to produce laugh faces. This points to a real complexity in positive communication across great apes.” 

While both orangutans and chimpanzees showed such level of exactness in their facial responses, they seemed to differ in how this was done. The chimpanzees appeared to avoid mimicking the upper tooth rows of their social partners in order not to signal risky play, which might be disadvantageous to match, at least when interacting with older partners or males. 

Dr Davila-Ross from the University of Portsmouth’s School of Psychology, Sport and Health Sciences and Centre for Comparative and Evolutionary Psychology added: “In the future, we are interested in exploring how these expressions might be used beyond play. 

“The concept of evolutionary continuity is fascinating - in humans, smiles serve many purposes, from expressing happiness to conveying mockery, so it would be interesting to examine these expressions outside of a play context.” 

The orangutans involved in this study lived at the Sepilok Orangutan Rehabilitation Centre (SORC) near to Kabili-Sepilok Forest Reserve in Malaysia. The chimpanzees lived in large outdoor enclosures in the Chimfunshi Wildlife Orphanage (CWO) in Zambia. 

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Journal

Nature

DOI

10.1038/s41598-026-43992-w 

Method of Research

Observational study

Subject of Research

Animals

Article Title

Towards the complexity of laugh communication in great apes: exact facial replications in laugh faces of orangutans and chimpanzees

Article Publication Date

9-Apr-2026