PURPOSE:This Archer fiord data is associated with a larger program ArcticCORE, which was created to fulfill knowledge gaps and develop long term protection in the extremely remote Tuvaijuittuq region. The main objectives of this expedition were to improve our comprehension of the key drivers for productive capacity, diversity and ecosystem structure in areas connected to Baffin Bay and Tuvaijuittuq, including Archer fiord.DESCRIPTION:ArcticCORE is a 5-year broader program aiming to characterize Tuvaijuittuq’s unique ecosystem and its influence and connectivity with the adjacent ecosystems to inform sustainable management and conservation initiatives in Tuvaijuittuq and the eastern Arctic. In an Arctic Ocean with rapidly declining sea ice, Tuvaijuittuq area retains the oldest and thickest sea ice, and can act as a refuge for ice-dependent species. This program aims to characterize the Arctic marine ecosystem and establish baseline measurements for future comparisons in the region. From 2023, water collection was carried out at four stations throughout Archer Fiord and analyzed for primary productivity, chlorophyll a, phytoplankton flow cytometry and phytoplankton taxonomy down to the lowest identifiable level. These data will contribute to a better understanding of the key drivers for productive capacity, diversity and ecosystem structure in Archer fiord. Characterization of these upstream areas are relevant for an ecosystem-based approach to fisheries management in Baffin Bay, a priority for DFO and an intrinsic part of mandated activities, as they influence the ecosystem and fisheries resources downstream.

Phytoplankton pigments, determined by high-performance liquid chromatography (HPLC) are measured on DFO cruises three times a year in February, June, and August/September along Line P in the northeast subarctic Pacific. Sampling for phytoplankton pigments started in 2006 at the five main Line P stations and was expanded to sample at all twenty seven stations along the transect in June 2010.

Description:Seasonal mean total phytoplankton at the surface from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period to create seasonal mean surface climatology of the Canadian Pacific Exclusive Economic Zone.Methods:Total phytoplankton is the sum of diatoms and flagellates concentration. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal surface phytoplankton climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

Description:Seasonal mean total phytoplankton at the surface from the British Columbia continental margin model (BCCM) were averaged over the 1993 to 2020 period to create seasonal mean surface climatology of the Canadian Pacific Exclusive Economic Zone.Methods:Total phytoplankton is the sum of diatoms and flagellates concentration. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal surface phytoplankton climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

Phytoplankton pigments, determined by high-performance liquid chromatography (HPLC) have been measured on DFO La Perouse cruises since 2011. Surface samples are taken along a series of transects off the west coast of Vancouver Island twice a year usually in May/June and early September.

Phytoplankton counts (cell/L)) at the 3 fixed stations and some of the 46 stations grouped into Atlantic Zone Monitoring Program (AZMP) transects under Quebec region responsibility.Phytoplankton data counts at AZMP stations in June 2014, 2018 and 2019 are displayed as 5 layers: Diatoms, Dinoflagellates, Flagellates, Protozoans and Total Phytoplankton. Another layer displays the fixed stations Rimouski, Anticosti Gyre and Gaspe Current and the attached files contain the phytoplankton data acquired at those stations: a .png file for each one, showing time series of counts for the 5 groups, and a .csv file containing the data themselves (columns : Latitude,Longitude, Date(UTC), Depth_min/Profondeur_min(m), Depth_max/Profondeur_max(m), Diatoms/Diatomées(cells/L), Dinoflagellates/Dinoflagellés(cells/L), Flagellates/Flagellés(cells/L), Protozoans/Protozoaires(cells/L), Phytoplankton/Phytoplancton(cells/L)).PurposeThe Atlantic Zone Monitoring Program (AZMP) was implemented in 1998 with the aim of increasing the Department of Fisheries and Oceans Canada’s (DFO) capacity to detect, track and predict changes in the state and productivity of the marine environment.The AZMP collects data from a network of stations composed of high-frequency monitoring sites and cross-shelf sections in each following DFO region: Québec, Gulf, Maritimes and Newfoundland. The sampling design provides basic information on the natural variability in physical, chemical, and biological properties of the Northwest Atlantic continental shelf. Cross-shelf sections sampling provides detailed geographic information but is limited in a seasonal coverage while critically placed high-frequency monitoring sites complement the geography-based sampling by providing more detailed information on temporal changes in ecosystem properties.In Quebec region, two surveys (46 stations grouped into transects) are conducted every year, one in June and the other in autumn in the Estuary and Gulf of St. Lawrence. Historically, 3 fixed stations were sampled more frequently. One of these is the Rimouski station that still takes part of the program and is sampled about weekly throughout the summer and occasionally in the winter period.Annual reports (physical, biological and a Zonal Scientific Advice) are available from the Canadian Science Advisory Secretariat (CSAS), (http://www.dfo-mpo.gc.ca/csas-sccs/index-eng.htm).Devine, L., Scarratt, M., Plourde, S., Galbraith, P.S., Michaud, S., and Lehoux, C. 2017. Chemical and Biological Oceanographic Conditions in the Estuary and Gulf of St. Lawrence during 2015. DFO Can. Sci. Advis. Sec. Res. Doc. 2017/034. v + 48 pp.Supplemental InformationPhytoplankton samples are collected using Niskin bottles, preserved with acid Lugol solution and analysed according to AZMP sampling protocol:Mitchell, M. R., Harrison, G., Pauley, K., Gagné, A., Maillet, G., and Strain, P. 2002. Atlantic Zonal Monitoring Program sampling protocol. Can. Tech. Rep. Hydrogr. Ocean Sci. 223: iv + 23 pp.

Bay-scale empirical demonstrations of how bivalve aquaculture alters plankton composition, and subsequently ecological functioning and higher trophic levels, are lacking. Temporal, inter- and within-bay variation in hydrodynamic, environmental, and aquaculture pressure limit efficient plankton monitoring design to detect bay-scale changes and inform aquaculture ecosystem interactions. Here, we used flow cytometry to investigate spatio-temporal variations in bacteria and phytoplankton (< 20 µm) composition in four bivalve aquaculture embayments. We observed higher abundances of bacteria and phytoplankton in shallow embayments that experienced greater freshwater and nutrient inputs. Depleted nutrient conditions may have led to the dominance of picophytoplankton cells, which showed strong within-bay variation as a function of riverine vs freshwater influence and nutrient availability. Although environmental forcings appeared to be a strong driver of spatio-temporal trends, results showed that bivalve aquaculture may reduce near-lease phytoplankton abundance and favor bacterial growth. We discuss aquaculture pathways of effects such as grazing, benthic-pelagic coupling processes, and microbial biogeochemical cycling. Conclusions provide guidance on optimal sampling considerations using flow cytometry in aquaculture sites based on embayment geomorphology and hydrodynamics.Cite this data as: Sharpe H, Lacoursière-Roussel A, Barrell J (2024). Monitoring bay-scale bivalve aquaculture ecosystem interactions using flow cytometry. Version 1.2. Fisheries and Oceans Canada. Samplingevent dataset. https://ipt.iobis.org/obiscanada/resource?r=monitoring_bay-scale_bivalve_aquaculture_ecosystem_interactions_using_flow_cytometry&v=1.2

This layer represents areas where primary production is considered to be high. Primary production includes microscopic algal blooms, named phytoplankton, a food resource at the base of the food web of marine ecosystems. The knowledge of these zones can serve as a proxy to identify areas of the St. Lawrence where productivity is higher at different times of the year. Impacting his component may influence the rest of the life cycle in the affected area. Data were generated from the Gulf of St. Lawrence Biogeochemical Model (GSBM) developed by Dr. Diane Lavoie. This model makes it possible to calculate, using 10 variables, the primary production in each cell of the grid of the model. This calculation was done at a monthly resolution and a threshold was then applied to the data to keep only those cells where the estimated concentrations exceeded 20 mg C / m-2. This level of primary production is considered high.Additional InformationMonthly mean primary production (mg C m-2) in the first 50 meters of the simulated surface with the three-dimensional CANOPA-GSBM numerical model over a period of 13 years (1998-2010).The Gulf of St. Lawrence Biogeochemical Model (GSBM) simulates biogeochemical cycles of oxygen, carbon and nitrogen, and the biological components that determine the dynamics of the planktonic ecosystem. The model has 10 state variables. The NPZD (nutrients, primary production, zooplankton, detritus) model includes both simplified herbivorous and microbial food chains typical of bloom and post-bloom conditions. The export of biogenic matter at depth is mediated by the herbivorous food web (nitrate, large phytoplankton (diatoms), mesozooplankton, particulate organic matter), while the microbial food web (ammonium, small phytoplankton, microzooplankton, dissolved organic matter) is mainly responsible for nutrient recycling in the euphotic zone. Nitrate is also supplied by rivers. The tight coupling between small phytoplankton growth and microzooplankton grazing, autochtonous nitrogen release and (dissolved organic nitrogen) DON remineralization to ammonium (NH4+) is used to represent the dynamic of the microbial food chain. Biological transfer functions are derived from bulk formulations using mean parameters found in the literature. Biological variables are calculated in nitrogen units and algal biomass and production converted to Chl a and carbon units using fixed stoichiometric ratios. Detrital particulate organic nitrogen (PON) gets fragmented to dissolved organic nitrogen (DON) as it sinks toward the bottom. The phytoplankton growth rate is a function of light and nutrient availability. The available light for phytoplankton growth is a function of sea-ice cover, Chl a and colored dissolved organic matter (CDOM). The GSBM biogeochemical model, coupled with the CANOPA regional circulation model, was used to produce the Chl a layer. The grid of the model is 1/12° horizontally (about 6 x 8 km), 46 layers vertical and covers the Gulf of St. Lawrence, Scotian Shelf and Gulf of Maine regions. The vertical resolution is variable (between 6 m close to the surface to 90 m at depths of about 500 m). This model includes tidal forcing and the freshwater supply of the St. Lawrence River and the many rivers in the region, as well as atmospheric forcing (temperature, wind, etc.) produced by an independent model (National Center for Environmental Prediction (NCEP) Climate Forecast System Version 2). In addition, the circulation model is coupled with a model of sea ice that reproduces the seasonality of the ice cover in the region. The temperature and salinity fields are produced freely by the model and only constrained by monthly climatologies of these conditions at the boundaries of the model domain. The simulation was carried out over a part of the period covering the Zonal Monitoring Program (AZMP) from 1998 to 2010.

Phytoplankton pigments, determined by high-performance liquid chromatography (HPLC) are measured seasonally along a 20-station transect in the Juan de Fuca / Strait of Georgia Basin. Sampling was initiated in 2004, discontinued in 2012 and restarted in 2015. In addition, occasional sampling is carried out at several locations and times in inland waters.For data inquiries please contact: Angelica Pena for data from 2004 to 2015 and Nina Nemcek for data from 2015 to present.

The objective of this project was to gather data to develop a model of the food web of the lower trophic levels of the nearshore area of the Beaufort Sea. Sampling took place from 2005 to 2008 using the CCGS Nahidik. The multidisciplinary character of the Nahidik program produced measurements of biology/ecology (primary production, phytoplankton, zooplankton, benthos, fish), chemical and physical oceanography, contaminants, geology and hydro acoustics. The data were collected in July and August of each year. The Nahidik program provided data to provide a baseline for future studies as well as an information source for environmental assessment.