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Biology subjects

Sintes, T.

Publications and source records attributed to Sintes, T..

2 recordsLinked to original sources

Thermal stress drives seagrass fragmentation in the Mediterranean Sea

Posidonia oceanica meadows, which underpin Mediterranean coastal ecosystems, are undergoing accelerated decline, partly driven by thermal stress. While previous quantitative studies have identified temperature thresholds beyond which seagrass mortality increases sharply, we show the cumulative and sublethal impacts of prolonged warming under fluctuating subthreshold conditions. To capture these effects, we introduce Stress Degree Days (SDD), a physiologically grounded index derived from an experimentally validated mortality rate function. Using sea surface temperature (SST) data, we quantified the cumulative thermal exposure across the Mediterranean Basin from 2000 to 2020. Leveraging high-resolution satellite imagery and deep learning-based habitat mapping, we linked SDD-derived thermal exposure to meadow fragmentation, which is a proxy for seagrass health. Our results show that high thermal stress (> 50%) is concentrated along the southern and eastern Mediterranean, where meadows exhibit more than 40% cover loss and elevated fragmentation, even though maximum SSTs remained below lethal limits (LT50 = 28.9 {degrees}C). This finding highlights the critical role of chronic sublethal thermal stress in driving structural degradation. Future projections under the RCP8.5, business as usual, and the more moderate RCP4.5 climatic scenarios indicate basin-wide regression, with expected cover losses of approximately 80% and 40%, respectively, by 2100, and near-total habitat suitability collapse in the southern regions. Consequently, fragmentation indices are projected to double or triple, further disrupting clonal connectivity, sediment retention, and oxygen export. In summary, by integrating physiological mechanisms, large-scale remote sensing, and climate modeling, the SDD framework identifies thermal hotspots, reveals emergent vulnerability patterns, and offers a predictive tool to guide conservation strategies in warming oceans.

ecology↗

Mapping the distribution of seagrass meadows from space with deep convolutional neural networks

Seagrass meadows play a vital role in supporting coastal communities by promoting biodiversity, mitigating coastal erosion and contributing to local economies. These ecosystems face significant threats, including habitat loss and degradation or climate change. United Nations has recognized the urgency of conserving marine ecosystems, highlighting the need for evidence-based conservation strategies and high-quality monitoring. However, traditional monitoring approaches are often time-consuming, labor-intensive, and costly, limiting their scalability and effectiveness. The growing availability of remote sensing data coupled to the rise of machine learning technologies offer an unprecedented opportunity to develop autonomous, efficient and scalable monitoring systems. Despite many efforts, the development of such systems for seagrass meadows remains a challenge, with recent attempts presenting several limitations such as limited satellite imagery, inadequate metrics for evaluating model performance or insufficient ground truth data, leading to simple proof of concepts rather than useful solutions. Here, we overcome these limitations by developing a comprehensive framework to map Posidonia oceanica meadows in the Mediterranean Sea using an extensive georeferenced habitat dataset and diverse satellite imagery for model training. We successfully evaluate the model generalization capability across different regions and provide the trained model for broader application in biodiversity monitoring and management.

ecology↗