3D intrusions transport active surface microbial assemblages to the dark ocean.

Subtropical oceans contribute significantly to global primary production, but the fate of the picophytoplankton that dominate in these low-nutrient regions is poorly understood. Working in the subtropical Mediterranean, we demonstrate that subduction of water at ocean fronts generates 3D intrusions with uncharacteristically high carbon, chlorophyll, and oxygen that extend below the sunlit photic zone into the dark ocean. These contain fresh picophytoplankton assemblages that resemble the photic-zone regions where the water originated. Intrusions propagate depth-dependent seasonal variations in microbial assemblages into the ocean interior. Strikingly, the intrusions included dominant biomass contributions from nonphotosynthetic bacteria and enrichment of enigmatic heterotrophic bacterial lineages. Thus, the intrusions not only deliver material that differs in composition and nutritional character from sinking detrital particles, but also drive shifts in bacterial community composition, organic matter processing, and interactions between surface and deep communities. Modeling efforts paired with global observations demonstrate that subduction can flux similar magnitudes of particulate organic carbon as sinking export, but is not accounted for in current export estimates and carbon cycle models. Intrusions formed by subduction are a particularly important mechanism for enhancing connectivity between surface and upper mesopelagic ecosystems in stratified subtropical ocean environments that are expanding due to the warming climate.

Spatial and temporal variation of methane emissions in the coastal Balearic Sea, Western Mediterranean.

Methane (CH4) gas is the most important GHG after carbon dioxide, with open ocean areas acting as discreet CH4 sources and coastal regions as intense but variable CH4 sources to the atmosphere. Here, we report CH4 concentrations and air-sea fluxes in the coastal area of the Balearic Islands Archipelago (Western Mediterranean Basin). CH4 levels and related biogeochemical variables were measured in three coastal sampling sites between 2018 and 2021, with two located close to the densely populated island of Mallorca and one in a pristine area in the Cabrera Archipelago National Park. CH4 concentrations in seawater during the study period ranged from 2.7 to 10.9 nM, without significant differences between the sampling sites. Averaged estimated CH4 fluxes during the sampling period for the three stations oscillated between 0.2 and 9.7 µmol m-2 d-1 according to a seasonal pattern and in general all sites behaved as weak CH4 sources throughout the sampling period.

pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning.

The decreasing seawater pH trend associated with increasing atmospheric carbon dioxide levels is an issue of concern due to possible negative consequences for marine organisms, especially calcifiers. Globally, coastal areas represent important transitional land-ocean zones with complex interactions between biological, physical and chemical processes. Here, we evaluated the pH variability at two sites in the coastal area of the Balearic Sea (Western Mediterranean). High resolution pH data along with temperature, salinity, and also dissolved oxygen were obtained with autonomous sensors from 2018 to 2021 in order to determine the temporal pH variability and the principal drivers involved. By using environmental datasets of temperature, salinity and dissolved oxygen, Recurrent Neural Networks were trained to predict pH and fill data gaps. Longer environmental time series (2012-2021) were used to obtain the pH trend using reconstructed data. The best predictions show a rate of [Formula: see text] pH units year[Formula: see text], which is in good agreement with other observations of pH rates in coastal areas. The methodology presented here opens the possibility to obtain pH trends when only limited pH observations are available, if other variables are accessible. Potentially, this could be a way to reliably fill the unavoidable gaps present in time series data provided by sensors.