Responses of biological and chemical components in North East Atlantic coastal water to experimental nitrogen and phosphorus addition--a full scale ecosystem study and its relevance for management

Water quality within the greater Suva urban marine environment through spatial analysis of nutrients and water properties

Coastal ecosystems in Pacific Island Countries and Territories are vital to local livelihoods, yet increasingly face pressures from urbanization. In Fiji, the Greater Suva Urban Area, where one-third of the nation's population live, exemplifies these challenges. This study examines spatial and temporal water quality variations in the coastal zone, focusing on physicochemical, nutrients, and clarity parameters. Using a Seabird Scientific SBE19 CTD and Thermo Scientific Orion™ AQUAfast™ colorimeter, coupled with hierarchical clustering and principal component analysis, six water quality clusters were identified, influenced by oceanic processes, river inputs, and anthropogenic activities. Key findings highlight nutrient enrichment near urban centers particularly at the Kinoya Sewage Treatment Plant outfall, where ammonia exceeded 17.8 mg/L, and significant variation observed in nitrate (up to 0.24 ± 0.06 mg/L) and nitrite (up to 0.24 ± 0.06 mg/L) concentrations near river mouths. Seasonal runoff contributed to elevated turbidity (up to 3.5 NTU) and total suspended solids (up to 14.7 mg/L) levels during wet months. Salinity, and temperature exhibited strong spatial and seasonal variability, reflecting land-ocean interactions and restricted water exchange. These findings emphasize the need for targeted action to mitigate nutrient pollution, urban runoff, and wastewater impacts. This study provides a cost-effective monitoring framework for water quality management, offering insights for sustainable coastal resource management in Fiji and other Pacific regions amidst urbanization and climate change.

Shallow-Water Habitat Mapping using Underwater Hyperspectral Imaging from an Unmanned Surface Vehicle: A Pilot Study

The impacts of human activity on coastal ecosystems are becoming increasingly evident across the world. Consequently, there is a growing need to map, monitor, and manage these regions in a sustainable manner. In this pilot study, we present what we believe to be a novel mapping technique for shallow-water seafloor habitats: Underwater hyperspectral imaging (UHI) from an unmanned surface vehicle (USV). A USV-based UHI survey was carried out in a sheltered bay close to Trondheim, Norway. In the survey, an area of 176 m2 was covered, and the depth of the surveyed area was approximately 1.5 m. UHI data were initially recorded at a 1-nm spectral resolution within the range of 380–800 nm, but this was reduced to 86 spectral bands between 400-700 nm (3.5-nm spectral resolution) during post-processing. The hyperspectral image acquisition was synchronized with navigation data from the USV, which permitted georeferencing and mosaicking of the imagery at a 0.5-cm spatial resolution. Six spectral classes, including coralline algae, the wrack Fucus serratus, green algal films, and invertebrates, were identified in the georeferenced imagery, and chosen as targets for support vector machine (SVM) classification. Based on confusion matrix analyses, the overall classification accuracy was estimated to be 89%–91%, which suggests that USV-based UHI may serve as a useful tool for high-resolution mapping of shallow-water habitats in the future.