Monitoring bay-scale bivalve aquaculture ecosystem interactions using flow cytometry
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
Datasets available for download
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Event data for the monitoring bay-scale...CSV
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Monitoring bay-scale bivalve aquaculture...CSV
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Occurrence data for the monitoring bay-scale...CSV
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Event data for the monitoring bay-scale...FGDB/GDB
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Data dictionaryCSV
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Monitoring bay-scale bivalve aquaculture...Esri REST
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Monitoring bay-scale bivalve aquaculture...Esri REST
Additional Info
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| Last Updated | October 20, 2025, 02:57 (UTC) |
| Created | October 20, 2025, 02:57 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Biota, Environment, Oceans |
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Title
Title for the Dataset.
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Monitoring bay-scale bivalve aquaculture ecosystem interactions using flow cytometry |
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Description
A description of the dataset.
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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 |
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Format (CSV, XLS, TXT, PDF, etc)
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2024-10-16 |
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Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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License
License used to access the dataset.
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Open Government Licence - Canada |
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Limits on use
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Location
Location of the dataset.
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https://open.canada.ca/data/en/dataset/94496446-dff8-4e14-b6b1-ea8d06e989d0 |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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Fisheries and Oceans Canada | Pêches et Océans Canada |
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Contact Point
Who to contact regarding access?
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Government of Canada; Fisheries and Oceans Canada; Coastal Ecosystem Science Division (CESD), [email protected] |
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Contact Point Email
The email to contact regarding access?
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Publisher
Publisher of the dataset.
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Publisher Email
Email of the publisher.
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[email protected] |
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Author
Author of the dataset.
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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Identifier
Unique identifier for the dataset.
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Language(s) of the dataset
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Link to dataset description
A URL to an external document describing the dataset.
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Portal Type
Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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Number of data columns
If tabular dataset, total number of unique columns.
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Number of data cells
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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