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We have found 67 datasets for the keyword "macroalgues". You can continue exploring the search results in the list below.
Datasets: 103,466
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67 Datasets, Page 1 of 7
Inventory of macroalgae and benthic macroinvertebrates on the north shore of the Saint-Lawrence Estuary (2019)
This inventory, conducted from September 26th to October 3th, 2019, aimed to describe the community structure of macroalgae and benthic macroinvertebrates of five small estuaries of the Upper North Shore of Quebec, namely Barthélemy Bay and the Colombier, Mistassini, Franquelin and Saint-Nicolas rivers. This inventory is part of a doctoral study of Valentine Loiseau on the global changes in the St. Lawrence system, mainly the study of marine benthic communities in response to changes of salinity, to ensure proper management of the environment in the face of future changes. The main objective is to describe the structure and the levels of specific diversities of mediolittoral communities of benthic macroinvertebrates and macroalgae along a salinity gradient. These five small estuaries were selected because of their similar size, hard substrates and easy access. Three levels of hypoosmotic stress (low, medium, high) and one control level (seawater) were used for each of the selected estuaries, with eight quadrats per stress level. Quadrat positions were randomly selected but had to meet two criteria: (1) regular height in the foreshore to control the influence of other stresses (temperature, exposure); and (2) presence of at least one macroalga to maintain homogeneity. A percentage cover by macroalgal and macroinvertebrate species was estimated, and then all organisms were weighed by species and size group. The salinity of the nearest water point was measured at mid-tide with a portable refractometer and a Castaway-type CTD (Conductivity-Temperature-Density) probe. The inventory was done using a stratified random sampling design and the sampling unit was a quadrat measuring 25 x 25 cm. The three files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes the generic information of the quadrat, including date and location. The "additional_information_event_and_occurrence" file includes salinity and substrate type of the quadrat, as well as the total weight of all individuals of the same species caught in the quadrat extrapolated to one square metre of surface. For nudibranchs and barnacles, weight was estimated from the size of the individuals so that they were not removed from the environment. The "taxon_occurrence" file includes the taxonomic inventory of macroalgal and benthic macroinvertebrate species observed in the quadrat, identified to the lowest possible species or taxonomic level and biomass by identified species.For quality control, organisms were identified on the field using the following guide: Chabot, Robert et Anne Rossignol. 2003. Algues et faune du littoral du Saint-Laurent maritime : Guide d'identification. Institut des Sciences de la mer de Rimouski, Rimouski ; Pêches et Océans Canada (Institut Maurice-Lamontagne), Mont-Joli. 113 pages. The taxonomy was checked against the World Register of Marine Species (WoRMS) to match recognized standards and using the R obistools and worrms libraries. The WoRMS match was placed in the "scientificNameID" field of the occurrence file. All sample locations were spatially validated. This project was funded by DFO Coastal Environmental Baseline Program under Canada’s Oceans 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.
Quantitative PCR (qPCR) of key macroalgal non-indigenous species in Nova Scotia and New Brunswick waters
To support the surveillance of key macroalgae and non-indigenous species in Nova Scotia and New Brunswick, five quantitative PCR (qPCR) assays were designed and tested at 111 sites in 2022-2023 targeting the following non-indigenous macroalgal species: Antithamnion sparsum, Bonnemaisonia hamifera, Codium fragile, Dasysiphonia japonica, Fucus serratus. All assays were developed in 2022 by the Center for Environmental Genomics Applications (CEGA, Newfoundland, Canada) except Antithamnion sparsum, for which an assay was developed in 2023 by the Aquatic Biotechnology Laboratory (ABL) at the Bedford Institute of Oceanography. All amplification was performed by the ABL in 2022-2023. The assay developed for Fucus serratus was later determined to be non-specific, and amplifies both F. serratus and Fucus distichus.Cite this data as: Krumhansl K, Brooks C, Lowen B, DiBacco C, (2025). Quantitative PCR (qPCR) of Key Macroalgal Non-Indigenous Species in Nova Scotia and New Brunswick Waters. Version 1.5. Fisheries and Oceans Canada. Samplingevent dataset. https://ipt.iobis.org/obiscanada/resource?r=quantitative_qpcr_macroalgal_nonindigenous_species_novascotia_newbrunswick_2022_2023&v=1.5For additional information please see:LeBlanc F., Belliveau V., Watson E., Coomber C., Simard N., DiBacco C., Bernier R., Gagné N. 2020. Environment DNA (eDNA) detection of marine aquatic invasive species (AIS) in Eastern Canada using a targeted species-specific qPCR approach. Management of Biological Invasions 11(2):201-217Krumhansl K.A., Brooks C.M., Lowen B., O’Brien J., Wong M., DiBacco C. Loss, resilience and recovery of kelp forests in a region of rapid ocean warming. Annals of Botany 2024 Mar 8; 133(1):73-92Brooks C.M., Krumhansl K.A. 2023. First record of the Asian Antithamnion sparsum Tokida, 1932 (Ceramiales, Rhodophyta) in Nova Scotia, Canada. BioInvasions Records 12(3):745-725.
Macroalgae in the coastal zone of maritime Quebec
This dataset was designed for Environment and Climate Change Canada's (ECCC) National Environmental Emergencies Center (NEEC) for oil spill preparedness and response. The polygons of this layer were selected from the surface geodatabase of coastal ecosystems from the UQAR-MPO project Mapping coastal ecosystems of the Estuary and Gulf of St. Lawrence. Are represented in this dataset exclusively the polygons whose plant dominance corresponds to a class of macroalgae and presenting a semi-vegetated (25-75%) or vegetated (75-100%) cover. The study area includes all of the estuarine and maritime coasts of Quebec, with the exception of certain sectors, including most of the Lower North Shore and Anticosti Island, with the exception of villages of Kegaska, la Romaine, Chevery, Blanc-Sablon and Port-Menier. Some islands off the estuary and gulf coasts are part of the region covered, such as Île d'Orléans, Isle-aux-Coudres, Île Verte and Île Bonaventure.The mapping of coastal ecosystems was carried out jointly by the Laboratory for Dynamics and Integrated Coastal Zone Management (LDGIZC) of the University of Quebec at Rimouski as part of the Coastal Resilience Project (https: //ldgizc.uqar.ca/Web/projets/projet-resilience-cotiere) funded by the MELCC; and by the Fisheries and Oceans Canada team, as part of its Integrated marine response planning (IMRP) component of the Oceans Protection Plan (OPP),with the objective of updating the Marine Oil Spill Preparedness and Response Regime of Canada. The master geodatabase of coastal ecosystems is hosted and distributed by UQAR on their SIGEC-Web mapping platform: https://ldgizc.uqar.ca/Web/sigecwebThe macroalgae characterization was mainly carried out from the photo-interpretation of RGBI aerial photos acquired by the DFO (2015-2020) and oblique helicopter photos acquired by UQAR in 2017. Data from 2959 sampling stations, conducted aboard small boats during DFO field campaigns (2017-2021) were used to detail the nature of algal communities and validate the photo-interpretation.Credits © UQAR-MPO (2023, Laboratoire de dynamique et de gestion intégrée des zones côtières, Pêches et Océans Canada)Provencher-Nolet, L., Paquette, L., Pitre, L.D., Grégoire, B. and Desjardins, C. 2024. Cartographie des macrophytes estuariens et marins du Québec. Rapp. Tech. Can. Sci. halieut. Aquat. 3617 : v + 99 p.Grégoire, B., Pitre, L.D., Provencher-Nolet, L., Paquette, L. and Desjardins, C. 2024. Distribution d’organismes marins de la zone côtière peu profonde du Québec recensés par imagerie sous-marine de 2017 à 2021. Rapp. tech. can. sci. halieut. aquat. 3616 : v + 78 p.Grégoire, B. 2022. Biodiversité du relevé côtier Planification pour une intervention environnementale intégrée dans l’estuaire et le golfe du Saint-Laurent (2017–2021). Observatoire global du Saint-Laurent. [Jeu de données]Jobin, A., Marquis, G., Provencher-Nolet, L., Gabaj Castrillo. M. J., Trubiano C., Drouet, M., Eustache-Létourneau, D., Drejza, S. Fraser, C. Marie, G. et P. Bernatchez (2021) Cartographie des écosystèmes côtiers du Québec maritime — Rapport méthodologique. Chaire de recherche en géoscience côtière, Laboratoire de dynamique et de gestion intégrée des zones côtières, Université du Québec à Rimouski. Rapport remis au ministère de l’Environnement et de la Lutte contre les changements climatiques, septembre 2021, 98 p.
Biodiversity of the Planning for Integrated Environmental Response Coastal Survey in the St. Lawrence Estuary and Gulf (2017-2021)
The Planning for an Environmental Response (PIER) initiative falls under the umbrella of the Government of Canada’s Oceans Protection Plan (OPP), whose goal is preserving marine ecosystems vulnerable to increased transportation and the development of the marine industry. The PIERs’ main mandate is to acquire and update biological sensitivity data under its jurisdiction for preparation and response purposes in the event of an oil spill.This dataset contains all observations of marine organisms noted during the analysis of 2959 underwater images sampled over a large extent of the coastal zone (≤10 m) of the Estuary and the Gulf of St. Lawrence (Quebec region). The dataset includes 21 490 occurrences of 150 taxa and informal categories including macroalgae, invertebrates and fish. Underwater images were collected between 2017 and 2021 according to a directed sampling protocol whose primary goal was to map large seaweed and eelgrass beds. Images were normally recorded as videos using a GoPro Hero camera installed on a pole and placed near the seabed from a small boat. The collected data served primarily as ground-truth data to validate coasting zone mapping based on aerial photographs within the framework of the PIER's initiative.The two files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes generic event information, including date and location. The "taxon_occurrence" file includes the original identifiers of the observed organisms (verbatimIdentification field), identification remarks and their taxonomy.Taxonomic names were verified on the World Register of Marine Species (WoRMS) to match recognized standards. The WoRMS match has been put in the scientificNameID field in the occurrence file. Data quality control was performed using the R packages obistools and worrms. All sampling locations were plotted on a map to perform a visual check confirming that the latitude and longitude coordinates were within the described sampling area.A visual dictionary was developed as an identification aid and accompanies this dataset (unilingual french only, the English version will be published soon). More data, including a visibility index, estimated macroalgae and eelgrass cover, substrate type and dominant macroalgae and animals were recorded but not included in this dataset. These data may be made available upon request.CreditsProvencher-Nolet, L., Paquette, L., Pitre, L.D., Grégoire, B. and Desjardins, C. 2024. Cartographie des macrophytes estuariens et marins du Québec. Rapp. Tech. Can. Sci. halieut. Aquat. 3617 : v + 99 p.Grégoire, B., Pitre, L.D., Provencher-Nolet, L., Paquette, L. and Desjardins, C. 2024. Distribution d’organismes marins de la zone côtière peu profonde du Québec recensés par imagerie sous-marine de 2017 à 2021. Rapp. tech. can. sci. halieut. aquat. 3616 : v + 78 p.
Epifauna Diversity on Dockside Surface Perimeters in Burrard Inlet and Fraser River Delta, British Columbia
These data sets provide information pertaining to epifauna and substrate estimates collected at dockside perimeters of floating docks located in Burrard Inlet and Fraser River Delta, British Columbia, between August and November, 2020. Data sets were compiled and formatted by Meagan Mak.Epifauna diversity was examined along surface perimeters of floating docks in Burrard Inlet and Fraser River Delta in southwestern British Columbia. Diversity estimates were obtained from video surveys collected over three depth-intervals: 1) Splash zone (SZ): depth-interval directly 15-cm above air-water interface; 2) Subsurface zone (SSZ): depth-interval (0-21 cm) below air-water interface; and 3) Deep-water zone (DZ): depth-interval below the SSZ (21-41 cm). Dock substrate consisted of combinations of wood, concrete, tires, plastic-floats, and metal, while epifauna and epiflora included anemones, tunicates, sponge, tube-worms, sea stars, bivalves, crabs, nudibranchs, urchins, barnacles, limpets, chitons, isopods, macroalgae and seagrass. Mussels ranged between 46% and 95% coverage across docks (median: 93%), while frequency of occurrence ranged between 85% and 100% (median: 99%), providing a biological-based substrate for other epifauna. The splash-zone consisted of outcropped mussels, encroached macroalgae from the waterline, and invertebrates above the waterline (limpets, chiton). If present, Ulva spp. typically formed a consistent narrow band (2-3 cm) above the waterline across all docks. Benthic (pipefish, sculpin) and pelagic (perch) fish were associated with epifaunal coverage and pelagic (open-water medium) settings. The Coast Guard Sea Island dock may experience episodic low-salinity intrusions supporting marine organisms at this site (ochre star, sculpin, limpet).
Ski Resorts
Ski Resorts is a point dataset identifying the location of ski resorts in British Columbia.
CMIP5 Multi-Model Ensembles of Snow Depth projections
Multi-model ensembles of snow depth based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of snow depth (m) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.
Areas potentially exposed to landslides
Delineation of areas potentially exposed to landslides and protective bands.attributs:ID - Unique IdentifierType - Entity type**This third party metadata element was translated using an automated translation tool (Amazon Translate).**
Projected streets
Planned routes of new streets or street extensions.attribut:ID - Unique identifier**This third party metadata element was translated using an automated translation tool (Amazon Translate).**
Projected Snow Depth change based on CMIP5 multi-model ensembles
Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in snow depth based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in snow depth is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of snow depth change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in snow depth (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.
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