Adding Saito paper

src/data/papers-citing-parcels.ts

@@ -2519,4 +2519,13 @@ export const papersCitingParcels: Paper[] = [
     abstract:
       'The quality of coastal waters, particularly aquaculture zones, is crucial to sustainable development and human health. Traditional monitoring methods based on scheduled in-situ sampling are often too slow, costly, and limited in spatial and temporal coverage to meet the needs of large-scale aquaculture management. To overcome these constraints, we introduce the Artificial Intelligence-based Water QUAlity Plus Plus model (AIQUAM++), an AI-based modeling framework designed to predict E. coli contamination directly within farmed mussels. We evaluated and compared a suite of recent and high-performing machine learning architectures, such as K-Nearest Neighbors (KNN), and several Transformer-based architectures (Transformer, Informer, Reformer, TimesNet), to address the complex temporal dependencies within this time series classification (TSC) task. AIQUAM++ was trained with historical microbiological measures of E. coli levels in the mussels provided by the local authorities involved in food safety monitoring. The system architecture, featuring an inference engine completely written in C++ for high performance, leverages hierarchical parallelism to ensure scalability and computational efficiency, incorporating Message Passing Interface (MPI) for inter-process communication on multi-core architectures, OpenMP for multithreaded processing, and CUDA-based acceleration for GPU-optimized computations. This design enables high-throughput inference that is suitable for operational deployment in aquaculture monitoring networks. A test case application of AIQUAM++ was conducted in the Gulf of Naples (Campania, Italy). Empirical results demonstrated that the proposed system achieves classification accuracies that exceed 90%, supporting its efficacy as a real-time data-driven decision support tool for aquaculture water quality management, minimizing health risks and contributing to sustainable marine resource governance.',
   },
+  {
+    title:
+      'Connectivity of Coral Reefs Along the Kuroshio Current Calls for Transboundary Conservation Strategies',
+    published_info: 'Ecology and Evolution, 15, e72203',
+    authors: 'Saito, N, A Iguchi (2025)',
+    doi: 'https://doi.org/10.1002/ece3.72203',
+    abstract:
+      'Environmental conservation becomes more effective when ecological connectivity between patchy habitats is maintained. The coral reef ecosystems of the Yaeyama and Miyako Islands (YAE) in Japan are highly biodiverse and culturally significant but have deteriorated over recent decades. Although coral larvae are expected to be supplied to YAE via the Kuroshio Current from regions outside Japan, previous population genetic and biophysical studies have focused exclusively on connectivity among Japanese coral populations. In this study, we conducted biophysical modelling of 30 years of larval dispersal across the Northwest Pacific using Lagrangian particle tracking, aiming to identify major sources of coral larvae to YAE. The model showed that 86% of virtual larvae reaching YAE represented self-recruitment. Of the externally sourced virtual larvae, ~70% came from the Philippines, ~20% from Taiwan and only a few percent from Japan. The Kuroshio Current acted as a corridor facilitating dispersal from the northeast Philippines and eastern Taiwan, while simultaneously serving as a barrier to retrograde or transverse dispersal from northern Taiwan and Japan. These findings suggest that most externally supplied larvae to YAE originate from regions outside Japan, upstream of the Kuroshio Current. This study highlights that transboundary collaboration is crucial to understanding and maintaining connectivity between coral reef ecosystems along ocean currents.',
+  },
 ]