• Energy Research

Significance Changes in both quantity and speciation of nitrogen in coastal waters impact phytoplankton communities, contributing to eutrophication and harmful algal blooms. Multidisciplinary oceanographic time series of high resolution are rare but crucial for identifying complex mechanisms that underlie such anthropogenic impacts. Analysis and modeling of such a time series from a seasonally stratified fjord showed that dilution of nitrifier biomass by variable winter mixing altered the timing and rates of nitrification, which converts ammonia to nitrite and nitrate. This reveals a link among climate-sensitive physical dynamics, nitrifier abundance, and diversity, with controls on phytoplankton ecology. The findings imply that explicit measurement and modeling of microbial communities will be required to project impacts of climate change on coastal ecosystems.

Significance Changes in both quantity and speciation of nitrogen in coastal waters impact phytoplankton communities, contributing to eutrophication and harmful algal blooms. Multidisciplinary oceanographic time series of high resolution are rare but crucial for identifying complex mechanisms that underlie such anthropogenic impacts. Analysis and modeling of such a time series from a seasonally stratified fjord showed that dilution of nitrifier biomass by variable winter mixing altered the timing and rates of nitrification, which converts ammonia to nitrite and nitrate. This reveals a link among climate-sensitive physical dynamics, nitrifier abundance, and diversity, with controls on phytoplankton ecology. The findings imply that explicit measurement and modeling of microbial communities will be required to project impacts of climate change on coastal ecosystems.

This collection contains data from continuous SUNA nitrate (SUNA_nitrate_profiles.xlsx) and CTD sensor profiles (CTD_BIO_1999-2018.xlsx) as well as data from discrete depths (5 meters and 60 meters; BB_Dal_data_discrete_depths_surface_bottom.xlsx; BB_BIO_data_discrete_depths_surface_bottom.xlsx). All data are from Compass Buoy Station, Bedford Basin, Nova Scotia, Canada.BB_annual averages (annual_averages.xlsx) were calculated from part of the above data or, in the case of Sackville River discharge, from the Environment Canada database.CTD data as well as the long-term data from discrete water samples were provided by the Bedford Institute of Oceanography (BIO), Dartmouth, Nova Scotia, Canada. They were partly modified, e.g. through unit conversion, and the original data can be found on the BIO website: http://www.bio.gc.ca/science/monitoring-monitorage/bbmp-pobb/bbmp-pobb-en.phpThe oxygen sensor data as of March 2015 in the 4-year dataset (2014-2017) were cross-calibrated against oxygen concentrations from discrete samples analyzed by Winkler titration. All oxygen sensor data in the long-term dataset (1994-2018) rely on the factory settings of the sensor.The R script contains code for a biogeochemical model of Bedford Basin bottom water and instructions on how to run it. It requires input from the data sheet BB_2014to17_nutrients_umol_kg.xlsx (included in doi:10.1594/PANGAEA.914695).Descriptions on methodology can be found in Haas et al. (PNAS, 2021).

This collection contains data from continuous SUNA nitrate (SUNA_nitrate_profiles.xlsx) and CTD sensor profiles (CTD_BIO_1999-2018.xlsx) as well as data from discrete depths (5 meters and 60 meters; BB_Dal_data_discrete_depths_surface_bottom.xlsx; BB_BIO_data_discrete_depths_surface_bottom.xlsx). All data are from Compass Buoy Station, Bedford Basin, Nova Scotia, Canada.BB_annual averages (annual_averages.xlsx) were calculated from part of the above data or, in the case of Sackville River discharge, from the Environment Canada database.CTD data as well as the long-term data from discrete water samples were provided by the Bedford Institute of Oceanography (BIO), Dartmouth, Nova Scotia, Canada. They were partly modified, e.g. through unit conversion, and the original data can be found on the BIO website: http://www.bio.gc.ca/science/monitoring-monitorage/bbmp-pobb/bbmp-pobb-en.phpThe oxygen sensor data as of March 2015 in the 4-year dataset (2014-2017) were cross-calibrated against oxygen concentrations from discrete samples analyzed by Winkler titration. All oxygen sensor data in the long-term dataset (1994-2018) rely on the factory settings of the sensor.The R script contains code for a biogeochemical model of Bedford Basin bottom water and instructions on how to run it. It requires input from the data sheet BB_2014to17_nutrients_umol_kg.xlsx (included in doi:10.1594/PANGAEA.914695).Descriptions on methodology can be found in Haas et al. (PNAS, 2021).