Matrix of functional traits of species sampled in the annual survey of the estuary and the northern Gulf of St. Lawrence
This dataset contains functional traits for fish and invertebrate taxa sampled in the annual ecosystem bottom trawl survey in the Estuary and northern Gulf of St. Lawrence. The purpose of this functional trait matrix is to contribute to the development and monitoring of community indicators to support the implementation of ecosystem-based management. In a context of climate change, this information will better inform and facilitate sustainable management processes and the adaptation of human activities that directly depend on the state of marine species and communities.
The functional trait matrix was established based on a literature review, the knowledge of experts who have worked for over twenty years to develop knowledge on marine species in the St. Lawrence, and empirical data from the ecosystem bottom trawl survey conducted by DFO Quebec Region in the Estuary and Northern Gulf of St. Lawrence (NGSL) since 1990. It includes a total of 103 fish taxa and 178 invertebrate taxa captured in the survey. Each trait is compiled for all or a subset of the selected taxa. The trait matrix includes a grouping of taxa into trophic guilds based on fuzzy coding, a categorization of taxa based on their mobility, a qualitative assessment of the presence of traits related to habitat provision/physical structures and bioturbation, as well as a review of available knowledge on the longevity and size at maturity of selected taxa, i.e. species of commercial value. The matrix also includes an empirical assessment of the average occurrence, relative density, maximum size and condition of taxa sampled representatively over the last ten years (2014 to 2023). The files were created for an Excel format but are distributed in .txt format to ensure consistency.
For details on taxa selection, literature review and trait calculation from empirical data, see the following report:
Isabel, L., Scallon-Chouinard, P.-M., Roux, M.-J. et Nozères, N. 2024. Matrice des traits fonctionnels des taxons échantillonnés dans le relevé annuel de l'estuaire et le nord du golfe du Saint-Laurent. Rapp. stat. can. sci. halieut. aquat. 1421 : vii + 65 p.
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Additional Info
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| Last Updated | October 20, 2025, 02:39 (UTC) |
| Created | October 20, 2025, 02:39 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Biota, Environment |
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Title
Title for the Dataset.
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Matrix of functional traits of species sampled in the annual survey of the estuary and the northern Gulf of St. Lawrence |
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Description
A description of the dataset.
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This dataset contains functional traits for fish and invertebrate taxa sampled in the annual ecosystem bottom trawl survey in the Estuary and northern Gulf of St. Lawrence. The purpose of this functional trait matrix is to contribute to the development and monitoring of community indicators to support the implementation of ecosystem-based management. In a context of climate change, this information will better inform and facilitate sustainable management processes and the adaptation of human activities that directly depend on the state of marine species and communities. The functional trait matrix was established based on a literature review, the knowledge of experts who have worked for over twenty years to develop knowledge on marine species in the St. Lawrence, and empirical data from the ecosystem bottom trawl survey conducted by DFO Quebec Region in the Estuary and Northern Gulf of St. Lawrence (NGSL) since 1990. It includes a total of 103 fish taxa and 178 invertebrate taxa captured in the survey. Each trait is compiled for all or a subset of the selected taxa. The trait matrix includes a grouping of taxa into trophic guilds based on fuzzy coding, a categorization of taxa based on their mobility, a qualitative assessment of the presence of traits related to habitat provision/physical structures and bioturbation, as well as a review of available knowledge on the longevity and size at maturity of selected taxa, i.e. species of commercial value. The matrix also includes an empirical assessment of the average occurrence, relative density, maximum size and condition of taxa sampled representatively over the last ten years (2014 to 2023). The files were created for an Excel format but are distributed in .txt format to ensure consistency. For details on taxa selection, literature review and trait calculation from empirical data, see the following report: Isabel, L., Scallon-Chouinard, P.-M., Roux, M.-J. et Nozères, N. 2024. Matrice des traits fonctionnels des taxons échantillonnés dans le relevé annuel de l'estuaire et le nord du golfe du Saint-Laurent. Rapp. stat. can. sci. halieut. aquat. 1421 : vii + 65 p. |
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Keywords/tags categorizing the dataset.
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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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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2025-02-01 |
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Time Period Data Span (start date)
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
Limits on use of data.
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Location
Location of the dataset.
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https://open.canada.ca/data/en/dataset/83af5394-ed06-4faa-b9d4-bc94712e4442 |
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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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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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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
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
If tabular dataset, total number of cells with data.
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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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