The Barents Sea is considered a warming hotspot in the Arctic; elevated sea surface temperatures have been accompanied with increased inflow of Atlantic water onto the shelf sea. Such hydrodynamic changes and a concomitant reduction of sea ice coverage enables a prolonged phytoplankton growing season, which will inevitably affect nutrient stoichiometry and the controls on primary production. During the summer of 2018, we investigated the role of phosphorus in mediating primary production in the Barents Sea. Dissolved inorganic phosphorus (DIP), its most bioavailable form, had an average net turnover time of 9.4 AE 4.8 d. The most southern Atlantic influenced station accounted for both the highest rates of primary production (655 mg C m 2 d −1) and shortest net DIP turnover (2.8 AE 0.5 d). The fraction of assimilated DIP released as dissolved organic phosphorus (DOP) at this station was < 4% compared to an average of 21% at all other stations. We observed significant differences between phytoplankton communities in Arctic and Atlantic waters within the Barents Sea. Slower DIP turnover and greater release of DOP was associated with Phaeocystis pouchetii dominated communities in Arctic waters. Faster turnover rates and greater phosphorus retention occurred among the Atlantic phytoplankton communities dominated by Emiliania huxleyi. These findings provide baseline measurements of P utilization in the Barents Sea, and suggest increased Atlantic intrusion of this region could be accompanied by more rapid DIP turnover, possibly leading to future P limitation (rather than N limitation) on primary production.

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.

Abstract. The recycling of scarce nutrient resources in the sunlit open ocean is crucial to ecosystem function. Nitrification directs ammonium (NH4+) derived from organic matter decomposition towards the regeneration of nitrate (NO3-), an important resource for photosynthetic primary producers. However, the technical challenge of making nitrification rate measurements in oligotrophic conditions combined with the remote nature of these environments means that data availability, and the understanding that provides, is limited. This study reports nitrite (NO2-) regeneration rate (RNO2 – the first product of nitrification derived from NH4+ oxidation) over a 13 000 km transect within the photic zone of the Atlantic Ocean. These measurements, at relatively high resolution (order 300 km), permit the examination of interactions between RNO2 and environmental conditions that may warrant explicit development in model descriptions. At all locations we report measurable RNO2 with significant variability between and within Atlantic provinces. Statistical analysis indicated significant correlative structure between RNO2 and ecosystem variables, explaining ∼65 % of the data variability. Differences between sampling depths were of the same magnitude as or greater than horizontally resolved differences, identifying distinct biogeochemical niches between depth horizons. The best overall match between RNO2 and environmental variables combined chlorophyll-a concentration, light-phase duration, and silicate concentration (representing a short-term tracer of water column physical instability). On this basis we hypothesize that RNO2 is related to the short-term autotrophic production and heterotrophic decomposition of dissolved organic nitrogen (DON), which regenerates NH4+ and supports NH4+ oxidation. However, this did not explain the observation that RNO2 in the deep euphotic zone was significantly greater in the Southern Hemisphere compared to the Northern Hemisphere. We present the complimentary hypothesis that observations reflect the difference in DON concentration supplied by lateral transport into the gyre interior from the Atlantic's eastern boundary upwelling ecosystems.

Different techniques exist for determining chlorophyll-a concentration as a proxy of phytoplankton abundance. In this study, a novel method based on the spectral particulate beam-attenuation coefficient (c p) was developed to estimate chlorophyll-a concentrations in oceanic waters. A multi-layer perceptron deep neural network was trained to exploit the spectral features present in c p around the chlorophyll-a absorption peak in the red spectral region. Results show that the model was successful at accurately retrieving chlorophyll-a concentrations using c p in three red spectral bands, irrespective of time or location and over a wide range of chlorophyll-a concentrations.