An interdisciplinary study of the Kuroshio nutrient stream The surface water of the Kuroshio, a western boundary current in the North Pacific Ocean, is nutrient-depleted and has relatively low primary productivity, yet abundant fish populations are supported in the region. This is called the “Kuroshio Paradox”. Kuroshio Current: Physical, Biogeochemical and Ecosystem Dynamics presents research from a multidisciplinary team that conducted observational and modeling studies to investigate this contradiction. This timely and important contribution to the ocean sciences literature provides a comprehensive analysis of the Kuroshio. Volume highlights include: New insights into the role of the Kuroshio as a nutrient stream The first interdisciplinary examination of the Kuroshio Paradox Reflections on the influence of the Kuroshio on Japanese culture Research results on both the lower and higher trophic levels in the Kuroshio ecosystem Comparisons of nutrient dynamics in the Kuroshio and Gulf Stream Predictions of ecosystem responses to future climate variability

An interdisciplinary study of the Kuroshio nutrient stream The surface water of the Kuroshio, a western boundary current in the North Pacific Ocean, is nutrient-depleted and has relatively low primary productivity, yet abundant fish populations are supported in the region. This is called the “Kuroshio Paradox”. Kuroshio Current: Physical, Biogeochemical and Ecosystem Dynamics presents research from a multidisciplinary team that conducted observational and modeling studies to investigate this contradiction. This timely and important contribution to the ocean sciences literature provides a comprehensive analysis of the Kuroshio. Volume highlights include: New insights into the role of the Kuroshio as a nutrient stream The first interdisciplinary examination of the Kuroshio Paradox Reflections on the influence of the Kuroshio on Japanese culture Research results on both the lower and higher trophic levels in the Kuroshio ecosystem Comparisons of nutrient dynamics in the Kuroshio and Gulf Stream Predictions of ecosystem responses to future climate variability

This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation equipment and technology, there has been an explosive growth of ocean data, propelling marine science into the era of big data. As effective tools for processing and analyzing large-scale ocean data, DL techniques have great potential and broad application prospects in marine science. Applying DL to intelligent analysis and exploration of research data in marine science can provide crucial support for various domains, including meteorology and climate, environment and ecology, biology, energy, as well as physical and chemical interactions. Despite the significant progress in DL, its application to the aforementioned marine science domains is still in its early stages, necessitating the full utilization and continuous exploration of representative applications and best practices.

Submersibles and Their Use in Oceanography and Ocean Engineering