Saturday, January 24, 2026

 

Study finds fisheries management—not predator recovery—drives catch levels in the North Sea



Analysis supports fisheries policies that balance economic and conservation goals




University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science

Marine mammal and seabird population changes have contrasting but limited impacts on fisheries catches in the North Sea 

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Harbour seals (Phoca vitulina) basking on a rocky shore. Recent data shows these charismatic marine mammals have surged in the past few decades. However, new research suggests this increased population size remains compatible with sustainable fisheries.

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Credit: Jeremy Kiszka, Ph.D.,Florida International University.





A new research study found that well-managed fisheries can support the recovery of large marine predators such as seals and porpoises, showing that conservation and sustainable seafood production can go hand in hand.

While the impacts of protected species are often debated, the study led by researchers at University of Miami-based Cooperative Institute for Marine and Atmospheric Studies (CIMAS) showed that fishing effort—not predator recovery—is the main driver of fishery yields in the North Sea.

“Our findings offer an important takeaway: fisheries management goals can be achieved without sacrificing conservation goals,” said the study’s lead author Matthew Woodstock, Ph.D., an assistant scientist at CIMAS. “This new evidence can help reframe the conversation around how conservation and economic activity can coexist.”

To conduct the study, the researchers developed a comprehensive ecosystem model of the southern North Sea and eastern English Channel to capture the full marine food web—from microscopic plankton to top predators such as gray seals, harbor porpoises, and seabirds—alongside 12 commercial fishing fleets. The model was grounded in real-world data, drawing on diet studies, fish stock assessments, and fisheries catch records to reflect conditions as accurately as possible.

The analysis found that the recovery of large marine predators does not automatically lead to declines in fishery yields. Although seals and porpoises consumed more fish as their populations increased, these impacts were outweighed by the effects of fisheries management decisions. In these regions, the data suggest that sustainable fisheries and recovering predator populations can coexist when fishing effort is managed effectively.

This study adds new, data-driven insights from one of the world’s most heavily fished regions, showing that increasing seal populations in the southern North Sea have not curtailed fisheries operations.

The findings support ecosystem-based fisheries management—an approach that looks at the entire food web and environment—by demonstrating that predator consumption is often less impactful than human fishing pressure, helping managers balance conservation goals with sustainable seafood production and fishing livelihoods.

The study, titled “Marine mammal and seabird population changes have contrasting but limited impacts on fisheries catches in the North Sea,” was published in the Canadian Journal of Fisheries and Aquatic Sciences.

About the University of Miami and Rosenstiel School of Marine, Atmospheric and Earth Science

 The University of Miami is a private research university and academic health system with a distinct geographic capacity to connect institutions, individuals, and ideas across the hemisphere and around the world. The University’s vibrant academic community comprises 12 schools and colleges serving more than 19,000 undergraduate and graduate students in more than 180 majors and programs. Located within one of the most dynamic and multicultural cities in the world, the University is building new bridges across geographic, cultural, and intellectual borders, bringing a passion for scholarly excellence, a spirit of innovation, and a commitment to tackling the challenges facing our world. The University of Miami is a member of the prestigious Association of American Universities (AAU).

 Founded in 1943, the Rosenstiel School of Marine, Atmospheric, and Earth Science is one of the world’s premier research institutions in the continental United States. The School’s basic and applied research programs seek to improve understanding and prediction of Earth’s geological, oceanic, and atmospheric systems by focusing on four key pillars:

*Saving lives through better forecasting of extreme weather and seismic events. 

*Feeding the world by developing sustainable wild fisheries and aquaculture programs. 

*Unlocking ocean secrets through research on climate, weather, energy and medicine. 

*Preserving marine species, including endangered sharks and other fish, as well as protecting and restoring threatened coral reefs. www.earth.miami.edu.

 

 

SCI-FI-TEK 70 YRS IN THE MAKING

PPPL launches STELLAR-AI platform to accelerate fusion energy research



A new computing platform that pairs artificial intelligence (AI) with high performance computing aims to end the bottleneck holding back fusion energy research by speeding the simulations needed to advance the field.



Princeton University

A colorized photograph of the inside of NSTX-U. 

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A colorized photograph of the inside of NSTX-U.

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Credit: PPPL Communications Department




A new computing platform that pairs artificial intelligence (AI) with high performance computing aims to end the bottleneck holding back fusion energy research by speeding the simulations needed to advance the field. 

The project — known as the Simulation, Technology, and Experiment Leveraging Learning-Accelerated Research enabled by AI (STELLAR-AI ) — will be led by the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL). STELLAR-AI will expand far beyond the Lab’s walls, however, bringing together national laboratories, universities, technology companies and industry partners to build the computational foundation the fusion community needs.

It can take months to run a single high-fidelity computer simulation or to train an artificially intelligent (AI) system capable of designing an ideal fusion system using existing infrastructure. STELLAR-AI is designed to reduce that timeline by using artificial intelligence. The platform connects computing resources directly to experimental devices, including PPPL's National Spherical Torus Experiment-Upgrade (NSTX-U), which is scheduled to go live this year, allowing researchers to analyze data as experiments occur.

Building the Computational Foundation for Fusion

Jonathan Menard, deputy director for research at PPPL, sees STELLAR-AI as a cornerstone of the U.S. fusion ecosystem: a dedicated, AI-driven research environment built specifically for the fusion energy mission. STELLAR-AI will pair speed with precision, accelerating the path to commercially viable fusion power.

“Fusion is a complex system of systems. We need AI and high performance computing to really optimize the design for economic construction and operation,” said Menard. "We want to link simulation technology and experiments — in particular, NSTX-U — with AI and partnerships to get to accelerated fusion.”

STELLAR-AI will achieve this goal by integrating CPUs, GPUs and QPUs in an ideal  configuration of hardware for tackling the challenges facing private fusion companies as they race to bring a solution to market. CPUs, or central processing units, are standard computer chips that handle everyday computing tasks. GPUs, or graphics processing units, are specialized chips that excel at the parallel calculations needed for artificial intelligence. QPUs, or quantum processing units, use the principles of quantum physics to solve certain complex problems that would take traditional computers far longer to complete.

A critical part of the Genesis Mission

STELLAR-AI is part of the Genesis Mission, a national effort launched by executive order in November 2025 to use AI to speed up scientific discovery across DOE laboratories. 

"The Genesis platform is an integrated, ambitious system that will bring together the various unique DOE assets: experimental and user facilities, the supercomputers, data archives and, importantly, the AI models," said Shantenu Jha, head of PPPL's Computational Sciences Department. While Genesis provides that broad infrastructure, STELLAR-AI contributes fusion-specific computer codes, data and scientific models back into the national system. The project also aligns with the DOE's Fusion Science and Technology Roadmap, which calls for building an AI-Fusion Digital Convergence platform to accelerate commercialization of a fusion power plant, achieve U.S. energy dominance, and provide the abundant power needed to drive the next generation of AI and computing.

Researchers plan to use STELLAR-AI for projects that span simulation, design and real-time experiment support. One effort will create a digital twin of NSTX-U: a computer model that mirrors the physical machine so closely that scientists can test ideas virtually before running actual experiments. Another project, called StellFoundry, uses AI to speed the design of stellarators, a type of fusion device with a twisted, pretzel-like shape that some scientists believe could offer advantages over other designs. Stellarator design requires sifting through enormous amounts of data to find the best configurations, a process that traditionally takes months or years and will greatly benefit from the STELLAR-AI platform. 

A Network of Public and Private Partners

The strength of STELLAR-AI lies in PPPL’s partnerships with DOE National Laboratories, AI and HPC companies, academic institutions, as well as fusion and engineering companies. The team includes world-leading capabilities from national laboratories, including PPPL and UKAEA as well as top universities such as Massachusetts Institute of Technology and University of Wisconsin-MadisonPrinceton University, which manages the laboratory for the U.S. DOE's Office of Science, is also a key partner.  Princeton will support operations, research software engineering, and user training for the STELLAR-AI infrastructure. Crucial technical support comes from tech giants like NVIDIA which is providing expertise to improve the performance of several critical fusion codes, and Microsoft, which will federate Azure’s leading cloud capabilities. We also have direct collaboration with the fusion industry, including Commonwealth Fusion SystemsGeneral AtomicsType One Energy and Realta Fusion. This unique combination of partners will deliver proven AI models and key tools for the U.S. fusion industry.

STELLAR-AI is just one of several initiatives that position PPPL as a hub for public-private collaboration in fusion energy. The laboratory's seven decades of plasma research, combined with experimental facilities like NSTX-U and computational expertise, have made it a destination for companies and research institutions seeking to accelerate fusion development. 


PPPL is mastering the art of using plasma — the fourth state of matter — to solve some of the world’s toughest science and technology challenges. Nestled on Princeton University’s Forrestal Campus in Plainsboro, New Jersey, our research ignites innovation in a range of applications including fusion energy, nanoscale fabrication, quantum materials and devices, and sustainability science. The University manages the Laboratory for the U.S. Department of Energy’s Office of Science, which is the nation’s single largest supporter of basic research in the physical sciences. Feel the heat at https://energy.gov/science and https://www.pppl.gov.  

 

Why wetland restoration needs citizens on the ground




Chinese Society for Environmental Sciences
Roadmap for scaling citizen science in wetland restoration. 

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Roadmap for scaling citizen science in wetland restoration. The figure illustrates the progression from current approaches, where citizen science remains fragmented and marginal, to emerging capacities enabled by technological, social, and institutional advances, and finally toward integration into formal monitoring and adaptive management frameworks. This pathway highlights how citizen science can be scaled into credible and enduring infrastructure, closing spatial, temporal, and institutional gaps that constrain wetland restoration monitoring and long-term success.

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Credit: Environmental Science and Ecotechnology





Wetland restoration is expanding worldwide, but long-term success often remains uncertain. Most projects rely on short-term, expert monitoring that ends long before restored wetlands stabilize, leaving major gaps in understanding how restored wetlands actually evolve over time. One increasingly discussed way to close these gaps is to extend monitoring beyond professional teams by engaging local communities and citizens in long-term observation.

In a Perspective published (DOI: 10.1016/j.ese.2026.100656) in Environmental Science and Ecotechnology in January 2026, researchers from Aarhus University and Wetlands International examined how citizen science is currently used in wetland restoration worldwide. By reviewing 120 restoration sites, the team found that fewer than 20% formally integrate citizen science, even in regions with strong restoration policies. The authors argue that recent technological and institutional shifts now make it possible to move citizen science from the margins into the core of restoration monitoring, where it can directly inform adaptive management and long-term decision-making.

The study highlights a clear mismatch between the potential of citizen science and how it is currently used in wetland restoration. Most initiatives remain small, fragmented, and focused on education rather than long-term ecological monitoring. Citizen science projects are heavily concentrated in high-income regions, while wetlands in low- and middle-income countries—often under the greatest pressure—receive little participatory monitoring support.

The authors show that this situation is rapidly changing. Affordable satellites and drones now allow volunteers to track vegetation patterns and water dynamics across entire landscapes. Low-cost sensors enable citizens to monitor water quality, soil conditions, biodiversity, and even greenhouse gas fluxes. Smartphones and mobile platforms make it possible to collect large volumes of georeferenced data over long periods.

Crucially, the study emphasizes that data quality concerns, while still important, are increasingly manageable. Standardized protocols, automated checks, and expert-supported validation systems can substantially improve the reliability and transparency of citizen-generated data. The remain challenge lies largely in institutional practice. Restoration programs still treat citizen science as an add-on rather than a source of decision-relevant information. Integrating these data into formal monitoring systems would greatly improve spatial coverage, temporal continuity, and the ability to detect early signs of ecological success—or failure.

"Wetland restoration does not follow project timelines—it follows ecological ones," the authors note. They stress that relying solely on short-term expert assessments limits the ability to understand long-term outcomes. Citizen science offers a way to extend monitoring far beyond the lifespan of individual projects. When properly designed and validated, public observations can complement professional assessments rather than compete with them. The researchers argue that treating citizen science as monitoring infrastructure, instead of outreach activity, is essential for improving how restoration success is evaluated and managed over time.

Embedding citizen science into wetland restoration could reshape how restoration success is measured worldwide. It provides a scalable, cost-effective way to expand monitoring while strengthening public engagement with ecosystems. For practitioners, continuous local observations support adaptive management and faster responses to unexpected change. For policymakers, citizen-generated data can contribute to national reporting systems and global biodiversity and restoration targets. The authors suggest that future restoration guidelines should explicitly include citizen science, supported by clear protocols, training, and feedback. If widely adopted, this approach could help ensure that restored wetlands remain resilient, functional, and sustainable under increasing climate and land-use pressures.

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References

DOI

10.1016/j.ese.2026.100656

Original Source URL

https://doi.org/10.1016/j.ese.2026.100656

Funding information

This work was supported by the European Union's Horizon Europe programmes WET HORIZONS (Grant Agreement 101056848), NBS4Drought (Grant Agreement 101181351), and PATTERN (Grant Agreement 101094416).

About Environmental Science and Ecotechnology

Environmental Science and Ecotechnology (ISSN 2666-4984) is an international, peer-reviewed, and open-access journal published by Elsevier. The journal publishes significant views and research across the full spectrum of ecology and environmental sciences, such as climate change, sustainability, biodiversity conservation, environment & health, green catalysis/processing for pollution control, and AI-driven environmental engineering. The latest impact factor of ESE is 14.3, according to the Journal Citation ReportsTM 2024.