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URL: https://open.canada.ca/data/en/dataset/0173af88-dc57-4328-9cd6-c7ee97bda045
This dataset is derived from analyses of photo samples obtained by deploying drop camera photo (DCP) systems conducted during various research surveys in coastal areas of the north shore of the St. Lawrence Estuary and the Gulf between Portneuf-sur-Mer and Sept-Îles between June and October of 2019 to 2022. It contains 4866 species occurrence data of 109 different taxa for epibenthic invertebrates and submerged aquatic vegetation (including algae) at depths ranging from 0 to more than 50 meters. Additional information about this dataset is available in the “Method step description” section.
The research surveys were undertaken by the Department of Fisheries and Oceans Canada as part of the baseline program of the Ocean Protection Plan. This initiative aims to acquire environmental baseline data contributing to the characterization of important coastal areas and to support evidence-based assessments and management decisions for preserving marine ecosystems.
Data acquired during the research surveys additionally include: 1) fish and invertebrate species occurrence data derived from analyses of video samples collected using a stereoscopic baited remote underwater camera video systems (stereo-BRUVs) 2) fish and invertebrates catch data from beam trawl sampling (occurrence and catch weights by species), 3) substrate classification based on drop camera samples, 4) oceanographic measurements of the water column from Seabird 19plus V2 profiling CTD (conductivity, temperature, depth, photosynthetic active radiation, pH, dissolved oxygen), 5) nutrients (NO2, NO3, NH4, PO4, SiO3) and dissolve organic carbon (DOC) concentrations, and 6) current speed and direction from tilt meters. The datasets of the first two elements will also be available as independent datasets on the OBIS/GBIF portal. To obtain data from items 3-6 and/or biological data collected on fish and invertebrate taxa, please contact David Lévesque or Marie-Julie Roux.
The elaboration of conservation objectives based on an ecosystem assessment approach for fishery stock assessment requires the development of sampling methods to maximize the data collection on the ecosystem, while minimizing the impact on organisms and the marine environment. This project aims at characterising the coastal ecosystem of the St. Lawrence Estuary and Gulf between Portneuf-sur-Mer and Sept-Îles (QC), including the physico-chemistry of water, phytoplankton, zooplankton, submerged vegetation, benthic habitats as well as assemblages of fish and invertebrates. Sampling was performed by combining conventional methods such as CTD profiling, zooplankton nets, and beam trawl, with non-extractive methods such as drop camera photo (DCP) and stereoscopic baited remote underwater camera video systems (stereo-BRUVs). The data collected will help define baseline ecosystem conditions in the study area; explore the links between environmental conditions, habitat structure and biological assemblages; identify important habitats for marine species; as well as the evaluation of the performance of visual sampling methods compared to conventional methods. The results will make it possible to optimize the seasonal or annual monitoring in order to better understand the direct and indirect effects of human activities in coastal environments.
Method Step Description:
- Acquisition of photo samples in sequence: The drop camera photo (DCP) system used to sample underwater pictures is a stainless steel frame in the shape of a triangular prism of 50 cm wide, 100 cm long and 76 cm high at the level of the central eyelet. The sampling area is a quadrat of 0.25 m2 (interior dimensions of 50 cm by 50 cm). The system consists of two GoPro Hero 5 cameras (4000 × 3000 pixels) and two 8000 lumens dive lights (Big Blue VL8000). The first camera captures the elements located in the quadrat when viewed from above. The second camera offers an oblique view facilitating the evaluation of the elements present in the quadrat. At all sampling stations, five to nine system deployments (replicas) capturing photos every 10 seconds for 60 to 120 seconds were performed.
Surveys took place between :
June 28th to July 5th 2019
July 13th to July 20th 2019
September 30th to October 9th 2019
August 10th to August 20th 2020
October 1st to October 10th 2020
April 22nd to May 5th 2021
July 27th to August 10th 2021
October 15th to October 24th 2021
June 24th to Jully 5th 2022
August 15th to August 26th 2022
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Image analysis: A photo image analysis method with sequence (moving images) was used for the occurrence data extraction and organism counts; measurements were taken to obtain vegetation cover percentages and substrate analyzes were also carried out. Analyzes were performed with the open-source Fiji software from ImageJ. A quality/visibility rating was assigned to the analyzed image sequences.
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Taxonomic approach: Epibenthic organisms were identified at the lowest possible taxonomic rank. A morphotype approach has been systematically used (during annotations) for the identification of sponges, hydrozoans and bryozoans, and occasionally for other organisms such as algae. Species codes were also used to distinguish certain species that could not be identified at the time of the annotations (see verbatim Identification). To eliminate observer bias, the same person analyzed all images used in this database. The organisms were identified from underwater images using a combination of identification guides and scientific papers.
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Open nomenclature: The concept of open nomenclature has been integrated into occurrence data to support taxonomic identifications with their level of certainty, as recommended by Horton et al., 2021. The abbreviation stet. (stetit) was used when the decision not to go lower was made but an identification might be possible, whereas indet (Indeterminabilis) was used when a lower level identification was considered uncertain or impossible (see identificationqualifier). In addition, the abbreviation Confer (cf.) was used and integrated into the data tables (see occurrenceRemarks) in order to link identifications that could potentially and/or possibly be associated.
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Remarks: Several remarks have also been incorporated (see organismRemarks, identificationRemarks and taxonRemarks), and are intended to provide additional information that may be useful to some data users; Please note that these sections could be modified or improved.
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Quality control: The taxonomic identifications were verified through a validation process, in collaboration with various expert taxonomists. All scientific names have been checked against the World Register of Marine Species (WoRMS) to match currently recognized standards. The WoRMS match was placed in the taxonID field of the instance file. Data quality control was performed using Robistools and worms packages. All sample locations were plotted on a map for visual verification that the latitude and longitude coordinates were within the described sample area.
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Data sharing: Only metadata and biodiversity occurrence data are shared in this dataset. The two files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event" file includes generic event information, including date and location. The "occurrence" file includes the original identifiers of the observed organisms, identification comments and their taxonomy. A data dictionary is also provided to explain the fields used. For access to other data or images, contact David Lévesque.
For more details about the project and the methodology, a technical report (Scallon-Chouinard et al., 2022) including sampling methods with drop camera photo systems (DCP) and stereoscopic baited remote underwater camera video systems (stereo-BRUVs) is currently available online (https://waves-vagues.dfo-mpo.gc.ca/library-bibliotheque/41081225.pdf); another technical report detailing photo and video image analysis methods will also be available.
This project was funded by the Department of Fisheries and Oceans Canada as part of the baseline program of the Ocean Protection Plan.
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Additional Information
| Field | Value |
|---|---|
| Data last updated | January 16, 2026 |
| Metadata last updated | January 16, 2026 |
| Created | January 16, 2026 |
| Format | CSV |
| License | License not specified |
| Datastore active | False |
| Has views | False |
| Id | 9b59a521-de70-46c8-842d-bc9141e7b419 |
| Package id | e566686e-5128-4120-ac37-9f69c2c4393c |
| Position | 0 |
| State | active |