Get data about the water quality for Ontario's inland lakes. The data is collected through volunteer monitoring efforts – citizen science. The [Lake Partner environment map](https://www.ontario.ca/page/map-lake-partner) The Lake Partner Program (LPP) measures water quality in inland lakes across Ontario. This dataset provides water quality and water clarity data, as well as data on the concentrations of: * total phosphorus * calcium * chloride * sulphate Spatial information for lake monitoring locations across Ontario are also available. Keywords: Lake, Water, Citizen Science, Community Science, Volunteer, Phosphorus, Calcium, Chloride, Water Clarity

In 2012, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada (except Newfoundland), in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (DMC, SPOT) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues.

In 2013, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

In 2014, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

This record contains a comprehensive synthesis of previously published highly branched isoprenoid (HBI) results, providing a quantitative spatial and temporal assessment of carbon partitioning within the Arctic marine ecosystem and validating estimates of sea-ice particulate organic carbon (iPOC) values as quantitative predictors of ice algal carbon in Arctic food webs.This publication was a collaborative effort with the following contributors: David Yurkowski (Fisheries and Oceans Canada), Lisa Loseto (Fisheries and Oceans Canada), Steve Ferguson (Fisheries and Oceans Canada), Bruno Rosenberg (Fisheries and Oceans Canada), C.W. Koch (Natural History Museum, London, UK; University of Maryland Center for Environmental Science, Maryland, US); T.A. Brown (Scottish Association for Marine Science, Oban, Scotland); R. Amiraux (Centre for Earth Observation Science, University of Manitoba, Canada); C. Ruiz-Gonzalez (Scottish Association for Marine Science, Oban, Scotland); M. Maccorquodale (Scottish Association for Marine Science, Oban, Scotland); G. Yunda-Guarin (Québec-Océan and Takuvik, Biology Department, Laval University, Canada); D. Kohlbach (Norwegian Polar Institute, Fram Centre, Tromsø, Norway); N.E. Hussey (Integrative Biology, University of Windsor, Ontario, Canada).

Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).

The Fieldnotes dataset represents anticipated locations of science field research and monitoring to be conducted throughout the year by Fisheries and Oceans Canadas' Pacific Science team and collaborators in the Northeast Pacific and Arctic oceans, and in the coastal and interior waters of British Columbia and Yukon.

The Fieldnotes dataset represents anticipated locations of science field research and monitoring to be conducted throughout the year by Fisheries and Oceans Canadas' Pacific Science team and collaborators in the Northeast Pacific and Arctic oceans, and in the coastal and interior waters of British Columbia and Yukon.

PURPOSE:These data have been updated following a Canadian Science Advice Secretariat (CSAS) Regional Science Advisory Process. Associated publications are available in the citation section below or will be posted on the Fisheries and Oceans Canada (DFO) Science Advisory Schedule as they become available.Estimate the abundance of Striped bass spawners in the Northwest Miramichi estuary.DESCRIPTION:Spawner abundance estimates of Striped Bass in the Northwest Miramichi estuary based on Catch per unit effort (CPUE) analysis in the commercial gaspereau fishery.USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Species distribution models (SDMs) are tools that combine species observations of occurrence, abundance, or biomass with environmental variables to predict the distribution of a species in unsampled locations. To produce accurate predictions of occurrence, abundance or biomass distribution, a wide range of physical and/or biological variables is desirable. Such data is often collected over limited or irregular spatial scales, and require the application of geospatial techniques to produce continuous environmental surfaces that can be used for modelling at all spatial scales. Here we provide a review of 102 environmental data layers that were compiled for the entire spatial extent of Fisheries and Oceans Canada’s (DFO) Maritimes Region. Variables were obtained from a broad range of physical and biological data sources and spatially interpolated using geostatistical methods. For each variable we document the underlying data distribution, provide relevant diagnostics of the interpolation models and an assessment of model performance, and present the final standard error and interpolation surfaces. These layers have been archived in a common (raster) format at the Bedford Institute of Oceanography to facilitate future use. Based on the diagnostic summaries in this report, a subset of these variables has subsequently been used in species distribution models to predict the distribution of deep-water corals, sponges, and other significant benthic taxa in the Maritimes Region.Cite this data as: Beazley, Lindsay; Guijarro, Javier, Lirette; Camille; Wang, Zeliang; Kenchington, Ellen (2020). Characteristics of Environmental Data Layers for Use in Species Distribution Modelling in the Maritimes Region. Published July 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/34a917cb-a0e3-403c-91c7-af3dc20628b1