The system, referred to as the “Living Shoreline Mosaic™,” is a groundbreaking integration of engineered porous concrete reef modules with natural marine habitats including oysters, marshes, and seagrass beds. This modular construction approach creates a dynamic wave attenuation mechanism that absorbs and reduces energy from incoming waves by over 90%, far surpassing many traditional coastal defenses. Beyond simple wave dampening, the design encourages natural colonization and succession processes, fostering a resilient and self-repairing biological reef system that enhances habitat complexity and biodiversity.

At the heart of this innovation is the Reefense Modules™, developed with interdisciplinary expertise combining coastal engineering, materials science, and marine ecology. These concrete units are carefully shaped and strategically arranged offshore to disrupt wave energy effectively while providing a substrate for oyster larvae and other marine organisms to settle and proliferate. Over time, the biogenic growth further strengthens the structural integrity of the reef, creating a feedback loop that enhances coastal protection while simultaneously fostering ecosystem services such as shoreline stabilization, sediment capture, and nursery habitat formation.

This hybrid approach contrasts sharply with conventional hard infrastructure solutions like seawalls and breakwaters, which often resist natural processes and degrade over time due to lack of ecological integration. By embracing natural regenerative mechanisms, living shorelines offer the prospect of long-term sustainability and adaptability in the face of climate change-driven stressors. The evolving reef system at Tyndall Air Force Base in Florida, heavily damaged by Hurricane Michael in 2018, stands as a testbed validating this concept through rigorous field measurement, numerical modeling, and ongoing ecological monitoring.

Wave energy reduction is the lynchpin of coastal erosion mitigation, translating directly to diminished storm surge impacts and reduced risk to critical infrastructure. The modular reefs function as engineered breakwaters with enhanced ecological benefits. As the reef matrix becomes colonized by oysters and other filter feeders, water quality improvement and sediment stabilization effects further contribute to shoreline resilience. This multifaceted protective mechanism embodies the fusion of engineering precision with ecological wisdom, presenting a paradigm shift in coastal defense philosophy.

Installation of the living reef system took place between late 2024 and early 2025, supported by the Defense Advanced Research Projects Agency’s (DARPA) Reefense program. This initiative catalyzed collaboration among an international consortium of academic institutions and industry partners, including notable contributions from Rutgers University and several Australian and U.S. universities. These efforts underscore the importance of cross-disciplinary and cross-sector cooperation in addressing complex environmental challenges through innovative design and long-term scientific inquiry.

One of the most striking features of the living shoreline mosaic is its capacity for self-repair and natural growth. Unlike static structures, the reef modules provide a living framework that oyster populations quickly colonize. As oysters grow and produce calcium carbonate shells, the reef accretes material, strengthening itself against wave forces and elevating the reef structure relative to sea level. This bioengineering process not only amplifies protection efficacy but also reverses degradation trends by building natural habitat complexity—a critical refuge for numerous marine species.

Quantitative assessments conducted on-site utilized an integrated approach combining direct wave energy measurements, sediment transport analysis, and ecological surveys. This comprehensive dataset allowed the researchers to quantify reductions in wave power exceeding 90% on average across the reef array, demonstrating exceptional performance in dissipating storm surge forces. Concurrently, monitoring documented rapid settlement and survival of oysters and associated marine fauna, indicating healthy ecosystem functioning and promising prospects for reef maturation as a living coastal buffer.

The ecological-engineering approach embodied by Reefense modules distinguishes itself by harnessing the synergistic effects of material science, hydrodynamics, and marine biology. Porous concrete formulations were carefully optimized to balance durability with biological colonization potential, ensuring the modules withstand harsh oceanic conditions while promoting oyster larval attachment. Structural design considerations took into account hydrodynamic flow regimes to maximize wave energy disruption while facilitating sediment deposition and habitat connectivity with adjacent marsh and seagrass systems.

Looking ahead, the researchers envision broad application of this technology in coastal regions where oysters naturally form reefs and where wave energy mitigation is vital. By supporting nature-based solutions that complement engineered interventions, living shoreline mosaics can contribute significantly to climate adaptation strategies, safeguarding ecosystems and human communities alike. This vision aligns with emerging global priorities in coastal resilience, sustainable infrastructure, and biodiversity conservation under accelerating environmental change.

Ultimately, the Reefense living reef system offers a rare confluence of engineering rigor and ecological functionality. Its pioneering success showcases how interdisciplinary research and innovative material and design strategies can yield coastal defenses that not only endure but flourish as living, adaptive systems. As such, it heralds a promising future for science-based, nature-integrated solutions to the pressing challenges of storm impact mitigation and shoreline preservation.

Subject of Research: Animals

Article Title: Reefense: Living shoreline mosaics can achieve ecological and engineering outcomes with interdisciplinary design

News Publication Date: 3-Apr-2026

Web References: http://dx.doi.org/10.1073/pnas.2516197123

References: Published in Proceedings of the National Academy of Sciences

Image Credits: Eric Sparks/Mississippi State University

Keywords

Ocean engineering, living shorelines, coastal resilience, wave energy reduction, ecological restoration, modular reef systems, nature-based infrastructure, climate adaptation, oyster reef colonization, coastal habitat stabilization

Sea level anomaly (SLA) emerges as a pivotal metric in this realm, representing deviations in sea surface height relative to a long-term average. SLA embodies the fluctuations of geostrophic ocean currents, denoting discrepancies between present ocean surface states and their climatological baseline. This measure, rendered accessible through satellite altimetry, offers a global vantage point of sea surface dynamics, capturing subtle but consequential changes that precede coastal impacts. However, translating these data into accurate forecasts remains a formidable task.

Numerical models have traditionally driven short-term SLA prediction efforts. By integrating satellite altimetry data and physical oceanographic principles, these models simulate ocean circulation and sea level dynamics to forecast SLA trends. Yet, numerical approaches often grapple with persistent biases, limited resolution, and high computational demands that restrict their applicability—and accuracy—particularly in regional or near-coast contexts where high precision is paramount. The quest for improvement has catalyzed a shift toward data-driven methodologies.

Artificial intelligence (AI) has revolutionized many scientific domains by leveraging vast datasets to unveil complex, nonlinear patterns—capabilities that classical numerical models occasionally struggle to capture. In marine sciences, AI-driven ocean forecasting systems have begun to surpass traditional approaches in skill, especially across 10-day prediction horizons. These large-scale Global Ocean Forecast Systems (GOFSs), while effective, are tailored for global operations and require substantial computational resources, impeding their deployment for localized, resource-limited applications.

Recognizing these constraints, a cross-institutional team of researchers from Sun Yat-Sen University, Zhejiang Institute of Marine Planning and Design, and Pusan National University pursued an alternative route. Their aim was to enhance regional sea level prediction capacity in the North Pacific by refining AI training protocols without escalating the complexity of model architectures. This strategic pivot underscores a paradigm shift from model intricacy to model training efficacy.

The researchers’ work was recently published in Ocean-Land-Atmosphere Research, framing a novel approach centered on optimizing the training methodologies of AI models for SLA forecasting. Rather than constructing more intricate neural networks, they revisited the assumptions underlying prediction targets and temporal training frameworks to diminish errors that typically accumulate over extended forecasting intervals. The emphasis on improved training decoration rather than architectural modification holds promise for broad applicability.

One breakthrough involved redefining the forecasting focus from absolute SLA values toward the temporal tendency of SLA—that is, the day-to-day change in sea level anomalies. This subtle reframing captures slower-evolving dynamics more effectively, aligning prediction goals with the physical behavior of ocean currents and mitigating noise introduced by rapid, smaller-scale fluctuations. Another innovation dealt with the critical challenge of the “training-forecast gap.”

The “training-forecast gap” refers to the mismatch arising when models are trained on short-term forecast horizons yet deployed for longer-range predictions, leading to error propagation and reduced reliability over time. The team addressed this by employing a multi-step training paradigm and rolling forecast technique. This strategy entailed training the model primarily for daily SLA tendencies but enabling it to generate reliable forecasts over broader horizons through sequential, stepwise updates—akin to effectively chaining short-term predictions into robust medium-range outlooks.

At the heart of the AI architecture lies Earthformer, a cutting-edge deep learning model designed to process spatiotemporal data in parallel rather than sequentially. This parallelism enables efficient handling of complex oceanographic datasets while capturing crucial temporal correlations. Tailoring Earthformer to the extrinsic characteristics of the North Pacific’s altimetry data and imbuing it with optimized training schemata, the researchers crafted the Multistep-Earthformer forecasting system.

Comparative evaluations underscored the superiority of this approach. The Multistep-Earthformer significantly outperformed conventional approaches, including persistence forecasts—which naively assume future states will mirror the present—and even benchmarked numerical models such as GLO12v4. These improvements did not necessitate complicated model modifications but rather stemmed from intelligent training adjustments, suggesting a scalable pathway for other geophysical forecasting challenges.

Beyond sea level prediction, the conceptual advancements introduced—the focus on temporal tendencies and training regimen refinement—may invigorate broader applications in geosciences. AI modelers working on atmospheric, terrestrial, or coupled Earth system predictions could potentially adapt these strategies to mitigate forecast drift and error accumulation, thereby elevating forecast reliability across domains.

With the promising results in the North Pacific region as a foundation, the research collective now envisages scaling their approach globally. This expansion will demand careful tailoring of training strategies to accommodate regional oceanographic peculiarities and data availability. Their ultimate vision is the realization of an AI-driven, globally comprehensive sea level forecasting platform capable of supporting diverse stakeholders, from environmental managers to maritime industries.

This breakthrough emerges in the context of a rapidly evolving scientific landscape where interdisciplinary collaboration—spanning oceanography, AI, and computational modeling—fuels progress. The study received support from the Guangdong Basic and Applied Basic Research Foundation and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), exemplifying the vital role of sustained funding and institutional synergy in addressing pressing environmental challenges.

Co-authors include Yong Liu of the Zhejiang Institute of Hydraulics & Estuary, Guangyu Yang of Sun Yat-Sen University, Young-Heon Jo of Pusan National University, and Zhigang Lai of Sun Yat-Sen University, reflecting a collaborative network bridging China and South Korea in marine sciences innovation.

As coastal regions worldwide confront intensifying climate-related hazards, tools that enable anticipatory action through advanced sea level forecasting will be indispensable. The Multistep-Earthformer model and its training-focused innovations provide a beacon of progress, illustrating how methodical refinement rather than mere complexity can unlock new horizons in ocean state prediction. This blend of sophisticated AI design and pragmatic training strategy exemplifies the next wave of environmental modeling poised to better safeguard coastal futures.

Subject of Research: Not applicable

Article Title: Optimized Training Strategies for AI-Based Sea Level Anomaly Forecasting in the North Pacific Ocean

News Publication Date: 23-Jan-2026

Web References: 10.34133/olar.0128

Image Credits: Jiangnan He et al. / Ocean-Land-Atmosphere Research

Keywords: Ocean circulation, Ocean physics, Oceanography, Air-sea interactions

During the intensive two-day summit, representatives from the diverse COAST-SCAPES consortium introduced their institutions’ respective roles in the project’s collective vision. Each partner elaborated on their unique expertise and contributions, forming an integrated knowledge network vital for constructing scalable coastal adaptation frameworks. The project’s coordinator, Professor Manel Grifoll, steered comprehensive dialogues focused on setting critical milestones and deliverables, while identifying future technical and environmental challenges. Central to these conversations was the delineation of Core and Replicating Pilots, which constitute the strategic backbone enabling the development of replicable, locally tailored resilience solutions that can be scaled and exported beyond pilot locations.

Initiated officially on September 1, 2025, the COAST-SCAPES project embodies an interdisciplinary approach to restore and fortify coastal ecosystems that are increasingly vulnerable to accelerating climatic stressors. The project’s scientific agenda includes the co-design of integrated resilience indicators and the development of advanced climate early-warning systems. These innovations will support proactive management and strategic decision-making in coastal environments by incorporating cutting-edge monitoring technologies and predictive models. By leveraging these tools, COAST-SCAPES aspires to reduce climate-induced risks and improve the understanding of land-sea interactions critical for sustainable coastal management.

At its core, COAST-SCAPES emphasizes the importance of nature-based solutions (NbS) to foster biodiversity conservation and reduce the environmental footprint of human activities under conditions of natural resource scarcity. NbS approaches prioritize the restoration and sustainable management of coastal habitats such as wetlands, dunes, and seagrass meadows, which serve as natural buffers against extreme weather events and contribute to carbon sequestration. This nature-centric paradigm challenges traditional engineered coastal defenses by investing in ecosystem services that enhance resilience while maintaining ecological integrity.

The consortium represents a diverse coalition of research institutions, universities, and technology organizations spanning Europe, Africa, and Latin America, forming a global alliance poised to address coastal vulnerabilities through a systems thinking perspective. Human intervention has significantly disrupted natural coastal evolution, rendering these ecosystems fragile and functionally impaired. By prioritizing low-carbon adaptation strategies, COAST-SCAPES harnesses ecological processes that mitigate climate risks while simultaneously safeguarding biodiversity and ecosystem services vital for human well-being.

Professor Manel Grifoll articulated the project’s transformative vision: “COAST-SCAPES is a collective effort to rethink how we coexist with coastal systems. By integrating natural processes, technology, and community knowledge, we aim to create adaptive landscapes that safeguard biodiversity and support sustainable livelihoods.” His statement underscores the project’s holistic framework, which blends ecological science with socio-economic and cultural dimensions to construct resilient coastal systems capable of thriving amid escalating climate pressures.

A central element of COAST-SCAPES is its Core Pilots, including the Mar Menor lagoon in the Iberian Peninsula, which will serve as real-world testbeds for implementing comprehensive resilience plans. These pilots will provide empirical evidence for large-scale adaptation, balancing ecological preservation with infrastructural modernization. Addressing existing challenges such as degraded water quality, habitat loss, and anthropogenic infrastructure constraints requires innovative engineering solutions combined with robust governance models that promote participatory stakeholder engagement.

The project’s strategy hinges on social and technical innovation, advocating a governance shift that facilitates cross-sectoral collaboration among scientists, policymakers, industry leaders, environmental activists, and local communities. This participatory model fosters knowledge exchange and ensures that resilience interventions align with local realities and socio-economic needs. By bridging these diverse groups, COAST-SCAPES promotes integrative stewardship of coastal landscapes that accommodates both ecological functions and human activities.

Fundamentally, COAST-SCAPES reinforces the critical role of coastal restoration as a pivotal climate adaptation strategy. Coastal ecosystems, long undervalued and threatened by unsustainable practices, offer multiple co-benefits including carbon storage, storm protection, and habitat provision. The project’s integrated approach acknowledges the interconnectedness of land and sea, with restoration efforts targeting systemic enhancements of landscape connectivity and ecological resilience.

Innovations developed through COAST-SCAPES will incorporate advanced sensor networks, remote sensing technologies, and data analytics to monitor environmental changes in real time. Proactive climate warning systems aim to provide early alerts for extreme events such as storms, sea-level rise, and flooding, enabling timely responses and adaptive management. These technological advancements synergize with ecosystem-based approaches, creating a robust and adaptive framework for climate-resilient coastal landscapes.

In summary, COAST-SCAPES represents a cutting-edge interdisciplinary initiative that leverages collaborative science, technological innovation, and community-driven governance to redefine the future of coastal resilience. The project’s vision extends beyond mere survival—aiming to cultivate thriving coastal ecosystems that support biodiversity, sustain livelihoods, and offer scalable solutions to global climate challenges. With rigorous science and participatory methods at its core, COAST-SCAPES exemplifies a forward-looking paradigm essential for confronting the unprecedented threats facing vulnerable coastal regions worldwide.

Subject of Research: Climate-resilient coastal landscapes and systemic land-to-sea climate adaptation solutions.

Article Title: COAST-SCAPES: Pioneering Interdisciplinary Resilience Strategies for Climate-Adapted Coastal Systems.

News Publication Date: Information not provided.

Web References: Information not provided.

References: Information not provided.

Image Credits: COAST-SCAPES

Keywords: Climate change, Climate sensitivity, Climate change mitigation, Climate change adaptation, Climatology