Wednesday, December 10, 2025

 

Ancient manatee relative reveals that sea cows have engineered the Arabian Gulf’s seagrass ecosystems for over 20 million years




Smithsonian researchers and collaborators at Qatar museums describe a sea cow bonebed in Qatar that is among the richest deposits of marine mammal fossils in the world




Smithsonian

Artistic reconstruction of a herd of ancient sea cows 

image: 

An artistic reconstruction of a herd of ancient sea cows foraging on the seafloor.

In southwest Qatar, fossils of a new species of ancient sea cow, Salwasiren qatarensis, were found in 21-million-year-old rocks along with evidence of extinct sharks, barracuda-like fish, prehistoric dolphins and sea turtles. In a paper published today in the journal PeerJ, researchers at the Smithsonian’s National Museum of Natural History worked with collaborators at Qatar Museums to name and describe the new species of sea cow.  

Salwasiren was a miniature version of living dugongs. Dugongs today inhabit coastal waters from western Africa through the Indo-Pacific and into northern Australia. The Arabian Gulf is home to the largest herd of dugongs in the world, where the sea cows serve as important ecosystem engineers.

At an estimated 250 pounds, Salwasiren would have weighed as much as an adult panda or a heavyweight boxer. But it was still among the smaller sea cow species ever discovered. Some modern dugongs are nearly eight times heavier than Salwasiren.

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Credit: Alex Boersma

            




Today, the Arabian Gulf is home to manatee-like marine mammals called dugongs that shape the seafloor as they graze on seagrasses. A newly described fossil site in Qatar reveals that ancient sea cows engineered aquatic ecosystems in a similar way more than 20 million years ago.

            In a paper published today in the journal PeerJ, researchers at the Smithsonian’s National Museum of Natural History worked with collaborators at Qatar Museums to also name a new species of sea cow that was a miniature version of living dugongs.  

            “We discovered a distant relative of dugongs in rocks less than 10 miles away from a bay with seagrass meadows that make up their prime habitat today,” said Nicholas Pyenson, the curator of fossil marine mammals at the National Museum of Natural History who helped lead the new study. “This part of the world has been prime sea cow habitat for the past 21 million years—it’s just that the sea cow role has been occupied by different species over time.”

            With a burly build and a downturned snout lined with sensitive bristles, dugongs (Dugong dugon) today resemble their relatives, manatees. The one key difference between these aquatic herbivores, which are often called sea cows, is their tails: a manatee’s tail is rounded like a paddle while a dugong’s fluked tail is more similar to that of a dolphin (however, dugongs and manatees are more closely related to elephants than they are to dolphins, whales and porpoises). 

            Dugongs inhabit coastal waters from western Africa through the Indo-Pacific and into northern Australia. The Arabian Gulf is home to the largest individual herd of dugongs in the world, where the sea cows serve as important ecosystem engineers. As they munch on seagrass, dugongs reshape the seafloor by creating feeding trails that release buried nutrients into the surrounding water for other aquatic animals and plants to use.

            Fossils reveal that ancient dugong relatives have grazed on aquatic vegetation around the world for some 50 million years. However, their future in the Gulf is tenuous. The marine mammals are accidentally caught by local fishermen, and the coastal waters where they reside are impacted by pollution and development. The Gulf’s seagrass meadows that dugongs rely on are also affected by rising temperatures and salinity levels.

            According to Ferhan Sakal, an archaeologist who is the head of excavation and site management at Qatar Museums and a coauthor of the new paper, key insights into the fate of dugongs and seagrasses in the Gulf are preserved in the region’s rocks. 

            “If we can learn from past records how the seagrass communities survived climate stress or other major disturbances like sea-level changes and salinity shifts, we might set goals for a better future of the Arabian Gulf,” he said. 

            Because delicate blades of seagrass rarely fossilize, researchers must glean insights into past marine ecosystems from the sturdier bones of ancient herbivores. 

            Few places preserve as many of these bones as Al Maszhabiya [AL mahz-HA-bee-yah], a fossil site in southwestern Qatar. The bonebed was initially discovered when geologists conducted mining and petroleum surveys in the 1970s and noted abundant “reptile” bones scattered across the desert. In the early 2000s, paleontologists returned to the area and quickly realized that the fossils were not from ancient reptiles but sea cows. 

            “The area was called ‘dugong cemetery’ among the members of our authority,” Sakal said. “But at the time, we had no idea just how rich and vast the bonebed actually was.”

            After receiving the necessary permits in 2023, Pyenson, Sakal and their colleagues conducted a survey of Al Maszhabiya’s fossils. Based on the surrounding rocks, the team dated the bonebed to the Early Miocene epoch around 21 million years ago. The team uncovered fossils that revealed that this area was once a shallow marine environment inhabited by sharks, barracuda-like fish, prehistoric dolphins and sea turtles.  

            These waters were also home to sea cows. The team identified more than 170 different locations containing sea cow fossils throughout the Al Maszhabiya site. According to Pyenson, this makes the bonebed the richest assemblage of fossilized sea cow bones in the world. Al Maszhabiya even rivals famed marine mammal deposits like Cerro Ballena, a site in Chile’s Atacama Desert where Pyenson and other researchers uncovered an ancient graveyard of stranded whales. 

            The fossilized bones at Al Maszhabiya resembled the skeletons of living dugongs. However, the ancient sea cows still possessed hind limb bones, which modern dugongs and manatees have lost through their evolution. The site’s prehistoric sea cows also had a straighter snout and smaller tusks than their living relatives.

            The researchers described Al Maszhabiya’s fossil sea cows as a new species, Salwasiren qatarensis. The genus name “Salwasiren” references the Bay of Salwa, a nearby area of the Gulf where dugongs live. While the Bay of Salwa spans the waters of multiple countries, the team specifically honored the State of Qatar as the site where the new sea cow was found with the species name “qatarensis.”  

            “It seemed only fitting to use the country’s name for the species as it clearly points to where the fossils were discovered,” Sakal said. 

            At an estimated 250 pounds, Salwasiren would have weighed as much as an adult panda or a heavyweight boxer, according to Pyenson. But it was still among the smaller sea cow species ever discovered. Some modern dugongs are nearly eight times heavier than Salwasiren.

            Based on the fossils, the researchers posit that this region contained plentiful seagrass beds more than 20 million years ago, during a time in Earth’s history when the Gulf was a hotspot for biodiversity. Tending to these aquatic pastures were sea cows.

            “The density of the Al Maszhabiya bonebed gives us a big clue that Salwasiren played the role of a seagrass ecosystem engineer in the Early Miocene the way that dugongs do today,” Pyenson said. “There’s been a full replacement of the evolutionary actors but not their ecological roles.”

            And it is possible that Salwasiren was not the only species filling this role. According to Pyenson, sea cow fossils are often found grouped together among different species, making it plausible that the Al Maszhabiya bonebed could yield additional species of dugong relatives. 

            Sakal hopes that the ongoing collaboration between Qatar Museums and the Smithsonian will help lead to future discoveries at Al Maszhabiya and nearby sites. But the first step is protecting the area’s rich fossil heritage. Sakal and his colleagues are planning to nominate the area for protection as a UNESCO World Heritage site. 

            “The most important part of our collaboration is ensuring that we provide the best possible protection and management for these sites, so we can preserve them for future generations,” Sakal said.

            “Dugongs are an integral part of our heritage, not only as a living presence in our waters today, but also in the archaeological record that connects us to generations past,” said Faisal Al Naimi, study coauthor and director of the Archaeology Department at Qatar Museums, referencing the abundant zooarchaeological sites with dugong bones throughout the Gulf. “The findings at Al Maszhabiya remind us that this heritage is not confined to memory or tradition alone, but extends deep into geologic time, reinforcing the timeless relationship between our people and the natural world. In preserving and studying these remarkable creatures, we are also safeguarding a narrative that speaks to our nation’s identity, resilience and enduring connection to the sea.”

            To preserve and make data from their research widely accessible, Pyenson and Sakal, in collaboration with the Smithsonian’s Digitization Program Office, digitally scanned several of the fossil sites they studied as well as the fossil skull, vertebrae, a tooth and other skeletal elements of the newly described ancient dugong species. Digital 3D models of the scans are available for the public to view and explore via the open-source Smithsonian Voyager platform, including interactive educational experiences about the research team’s findings and a 3D tour showing the fossil excavation process.

            In addition to Pyenson and Sakal, the study includes authors affiliated with the Smithsonian’s Digitization Program Office; the Stone Ridge School of the Sacred Heart; Texas A&M University at Galveston; Texas A&M University, College Station; and the Natural History Museum of Los Angeles County.

            This research was supported by a collaborative agreement between the Smithsonian Institution and Qatar Museums and by funding from the National Museum of Natural History and the Qatar National Research Fund.

About the National Museum of Natural History

The National Museum of Natural History is connecting people everywhere with Earth’s unfolding story. It is one of the most visited natural history museums in the world. Opened in 1910, the museum is dedicated to maintaining and preserving the world’s most extensive collection of natural history specimens and human artifacts. The museum is open daily, except Dec. 25, from 10 a.m. to 5:30 p.m. Admission is free. For more information, visit the museum on its websiteblogFacebookLinkedIn and Instagram.

About Qatar Museums

Now marking its 20th anniversary, Qatar Museums (QM), the nation’s preeminent institution for art and culture, provides authentic and inspiring cultural experiences through a growing network of museums, heritage sites, festivals, public art installations and programs. QM preserves and expands the nation’s cultural offerings, sharing art and culture from Qatar, the Middle East, North Africa and South Asia (MENASA) with the world and enriching the lives of citizens, residents and visitors.


Fossils of Salwasiren qatarensis, a newly described 21-million-year-old ancient sea cow species found in Al Maszhabiya [AL mahz-HA-bee-yah], a fossil site in southwestern Qatar.

Few places preserve as many ancient sea cow fossils as the site in Al Maszhabiya. The bonebed was initially discovered when geologists conducted mining and petroleum surveys in the 1970s and noted abundant “reptile” bones scattered across the desert. In the early 2000s, paleontologists returned to the area and quickly realized that the fossils were not from ancient reptiles but sea cows.

Researchers have identified more than 170 different locations containing sea cow fossils throughout the Al Maszhabiya site, making the bonebed the richest assemblage of fossilized sea cow bones in the world.

In a paper published today in the journal PeerJ, researchers at the Smithsonian’s National Museum of Natural History worked with collaborators at Qatar Museums to name and describe the new species of sea cow.

Credit

ARC.2023.23.008, Qatar Museums, Doha, State of Qatar. Photo by James Di Loreto, Smithsonian.


Nicholas Pyenson, the curator of fossil marine mammals at the Smithsonian’s National Museum of Natural History, and Ferhan Sakal, an archaeologist who is the head of excavation and site management at Qatar Museums, survey Al Maszhabiya with the fossil ribs of a 21-million-year-old sea cow in the foreground.

In a paper published today in the journal PeerJ, researchers at the Smithsonian’s National Museum of Natural History worked with collaborators at Qatar Museums to name and describe a new species of sea cow.

Sakal hopes that the ongoing collaboration between Qatar Museums and the Smithsonian will help lead to future discoveries at Al Maszhabiya and nearby sites. But the first step is protecting the area’s rich fossil heritage. Sakal and his colleagues are planning to nominate the area for protection as a UNESCO World Heritage site.

Credit

Clare Fieseler

 

U.S. debt ceiling disputes show measurable impact on global crude oil markets





Shanghai Jiao Tong University Journal Center

Influence mechanism of debt ceiling uncertainty on crude oil market 

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Debt ceiling uncertainty may affect crude oil market price through multiple channels simultaneously, e.g. enterprise production, investor sentiment (SENT), and government shutdown. More specifically, first, debt ceiling uncertainty will change the macro-economy environment and real business activities. Furthermore, the changes will affect the actual demand for crude oil in the future, which is finally reflected in the crude oil price (Guo et al., 2022b; Zhang et al., 2023b). Second, the increasing debt ceiling uncertainty will heighten investors’ concerns about the future economy. At the same time, their pessimistic sentiment toward expected oil demand can be easily reflected in crude oil price dynamics (Maghyereh et al., 2020; Wang et al., 2021b; Jiang et al., 2022). Finally, the differences in political positions and interest distributions between the two parties in the United States may make it difficult to reach an agreement on debt ceiling-related issues in a short time and may even lead to government shutdown. Once the government shuts down due to debt problems, the formulation and implementation of policies will be seriously hindered, and those announced projects relying on crude oil may be forced to suspend (Cappelli et al., 2023).

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Credit: Kun Yang (Yunnan University of Finance and Economics, China) Xining Yang and Wenhua Yu (Chengdu University of Technology, China) Yu Wei (Yunnan University of Finance and Economics, China)






Background and Motivation

The United States debt ceiling—the legal limit on federal borrowing—has been a recurring source of political and economic uncertainty, especially as U.S. national debt has nearly doubled over the past decade. While existing research has explored how broad economic policy uncertainty affects financial markets, little attention has been paid to the specific impact of debt ceiling uncertainty on commodity markets, particularly crude oil. Given oil’s central role in the global economy, understanding how U.S. fiscal policy risks transmit to energy markets is crucial for investors, policymakers, and industry stakeholders.

 

Methodology and Scope

To investigate this relationship, researchers constructed a monthly Debt Ceiling Uncertainty Index based on the frequency of related terms in U.S. newspaper coverage from 1998 to 2023. They then examined six major crude oil markets: WTI and Brent futures and spots, as well as Oman and Tapis spot prices. The study employed two advanced econometric approaches: a nonparametric causality-in-quantiles test to capture nonlinear and asymmetric effects across different market conditions (e.g., bull, normal, and bear markets), and a time-varying parameter vector autoregressive (TVP-VAR) model to trace dynamic impacts over time and during specific debt ceiling events.

 

Key Findings and Contributions

  • Nonlinear and asymmetric effects: Debt ceiling uncertainty significantly influences crude oil markets, with stronger impacts during normal market conditions than during extreme highs or lows.
  • Dynamic and event-driven responses: Shocks from debt ceiling uncertainty are generally negative but become weakly positive after about three months and fully dissipate within six months. Major debt ceiling events—such as the 2011 credit rating downgrade or the 2023 limit breach—amplify these effects.
  • Transmission channels: The study identifies three key mechanisms through which uncertainty spreads: changes in enterprise production plans, shifts in investor sentiment, and risks of government shutdown delaying oil-dependent projects.

 

Why It Matters

As the world’s largest economy, the U.S. fiscal policy uncertainty carries global ripple effects. Crude oil markets are particularly sensitive to changes in economic outlook and investor confidence. This study provides empirical evidence that debt ceiling impasses are not just political theatre; they have measurable, temporally nuanced consequences for energy prices and market stability. Recognising these patterns helps in distinguishing short-term noise from sustained risk, which is essential for accurate forecasting and risk management.

 

Practical Applications

  • Investors can better time their entries and exits in oil markets by monitoring debt ceiling developments and anticipating the typical 3–6 month absorption period.
  • Producers may adjust their extraction and inventory strategies in response to anticipated negative price pressures resulting from debt ceiling tensions.
  • Policy makers and regulators can design early-warning systems and confidence-building measures, such as transparent communication and shutdown contingency plans, to mitigate uncertainty spillovers into energy markets.
  • Portfolio managers may consider incorporating debt ceiling uncertainty indicators into their asset allocation models, particularly for commodities and energy-linked equities.

 

Discover high-quality academic insights in finance from this article published in China Finance Review International. Click the DOI below to read the full-text!

 

The hidden toll of substance use disorder: annual cost of lost productivity to US economy nearly $93 billion



A study in the American Journal of Preventive Medicine quantifies the hidden economic and societal burden of productivity losses beyond healthcare costs



Elsevier





A new study shows that in 2023, substance use disorders led to nearly $93 billion in lost productivity in the United States from missed work, reduced job performance, inability to work, and lost household productivity. The novel analysis appearing in the American Journal of Preventive Medicine, published by Elsevier, highlights the need for prevention and treatment strategies to reduce harm and costs.

“Substance use disorders can impair cognitive and behavioral functioning, resulting in productivity losses,” said the team of investigators from the Centers for Disease Control and Prevention. “While medical costs and premature deaths associated with substance use disorders have been well documented, the impact of productivity losses on workers, families, and employers due to illness remains less visible and are often underestimated.”

The researchers used recent national survey data—primarily from the National Survey on Drug Use and Health (NSDUH)—on adults 18 years of age or older with substance use disorders to estimate costs related to absenteeism and inability to work as well as two often-overlooked factors: presenteeism (being present but not productive at work), and household production (e.g., cooking, cleaning), thereby offering a more complete picture of the societal burden.

The analyses show estimated productivity losses of $92.65 billion in total, or $3,703 per adult with a substance use disorder, in 2023 (analyzed in 2025). The breakdown of these costs is as follows:

  • Inability to work: $45.25 billion
  • Absenteeism: $25.65 billion
  • Presenteeism: $12:06 billion
  • Household productivity loss: $9.68 billion

Of this total cost, males accounted for $61.19 billion and females for $31.45 billion.

The study’s authors note, “These costs are comparable to direct healthcare spending on substance use disorders, showing the wide economic impact of these disorders. The largest shares came from the inability to work, followed by absenteeism, highlighting that economic harms extend well beyond medical bills.”

The investigators also point out the striking magnitude of the shares of costs associated with presenteeism (13%) and household productivity losses (10%), which indicate that interventions aimed at improving workers’ functioning, not just their attendance, could yield substantial economic benefits.

The researchers conclude, “Effective prevention and treatment of substance use disorders require integrated, ethically grounded approaches that address both individual risk factors and broader social determinants through coordinated efforts across health, social, and justice systems. At a time when employers and policymakers are weighing investments in treatment access, workplace supports, and prevention, our results highlight the potential returns on such investments beyond healthcare savings.”

Metallic glass discovery using AI-guided graph learning from Wikipedia data




Songshan Lake Materials Laboratory
Graph-based recommendation system for MGs. 

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The input elemental Wikipedia embeddings are mapped to node representations by MLP layers. By embeddings post-processing (e.g. by Transformer) and advanced message passing, different GNN models  are designed to generated the final embeddings. A recommendation system is thus built to score the appearance of a link (binary system) or a triangle (ternary system) in the material networks. (PD: inner product; HDM: Hadamard product)

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Credit: Yuan-Chao Hu from Songshan Lake Material Laboratory.




A research team from the Songshan Lake Materials Laboratory has developed an AI-guided "Recommendation System" to discover new metallic glasses (MG). By combining element embeddings learned from Wikipedia by a language model with graph neural networks analyzing hidden material relationships. This approach addresses longstanding challenges related to the vast chemical space and limited experimental datasets, opening new horizons for materials design and accelerating the development of next-generation MGs.

Metallic glasses are a class of amorphous alloys valued for their unique mechanical, chemical, and physical properties, widely used in the industry from aerospace to biomedicine. However, discovering new MGs remains a formidable challenge. Traditional trial-and-error methods require long time and resources to explore the vast compositional landscape, as the formation of MGs critically depends on complex multi-element interactions that are difficult to predict. Since the 1960s, researchers have explored only several thousand compositions from the vast elemental combination space avilable. While machine learning has brought new opportunities, its application faces fundamental challenges: scarce experimental data and limitations in how materials are represented computationally. Traditional methods encode materials using predefined physical properties, which may miss non-traditional glass-forming mechanisms and limit model generalizability. In this context, the recent study by Ouyang et al. introduces a novel framework that leverages the rich, unstructured knowledge stored in Wikipedia. 

First, the authors generated element embeddings using AI models trained to process Wikipedia entries on chemical elements, enabling the extraction of rich, context-dependent information without human bias. Second, they constructed material networks in which elements serve as nodes and known metallic-glass compositions define the connections. On these networks, they applied graph neural networks (GNNs) that integrate Wikipedia-derived element features with the underlying network topology to predict new glass-forming systems.

The core innovation of this work lies in transforming unstructured textual knowledge into structured, learnable representations that inform the search for amorphous alloys. The team processed Wikipedia articles from multiple languages to generate robust, semantic element embeddings, which encode chemical behaviours and relationships without relying solely on experimental data.

Using these embeddings as input, they trained versatile graph neural network architectures, including graph convolutional networks (GCN), neural graph collaborative filtering (NGCF), and Transformer-based GNNs (TransGNN), to model the complex interactions among elements in metallic glasses. These models serve as recommendation systems, suggesting element pairs for binary MGs and complexes for ternary systems, thus guiding experimental efforts more intelligently.

Results demonstrated that the models could reliably identify promising compositions with high glass-forming ability, some of which had not been previously explored. The TransGNN architecture, enhanced with attention mechanisms, stood out as the most accurate, effectively capturing the long-range and multi-element relationships essential for MG formation. This approach confirmed that high-quality predictive performance could be achieved even when relying on knowledge from diverse natural language sources.

This paradigm shift from experimental trial-and-error to knowledge-driven computational prediction. Accelerating the discovery process, reduces costs, and broadens the scope of feasible alloys.

The Future: This work showcases the potential of integrating natural language processing, graph learning, and materials science to revolutionize materials discovery. Future efforts will focus on expanding the knowledge base with further multilingual data, refining models to incorporate thermodynamic and kinetic information, and experimentally validating top predictions. Additionally, the framework can be extended beyond metallic glasses to other complex materials such as high-entropy alloys, composites, and functional ceramics.

By bridging unstructured textual data with sophisticated graph models, this approach paves the way for a new paradigm: knowledge-powered, data-efficient materials design that accelerates innovation while conserving resources. As the methodology matures, it promises to significantly shorten the developmental cycle of advanced materials, fostering rapid progress toward sustainable, high-performance technologies.

The research has been recently published in the online edition of AI for Science.

Reference: Kaichen Ouyang, Shiyun Zhang, Song-Ling Liu, Jiachuan Tian, Yuanhao Li, Hua Tong, Hai-Yang Bai, Wei-Hua Wang and Yuan-Chao Hu.Graph learning metallic glass discovery from Wikipedia[J]. AI Sci. DOI: 10.1088/3050-287X/ae1b20