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 These layers have been archived in a common (raster) format at the Bedford Institute of Oceanography to facilitate future use.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

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The Air pollutant emissions indicators track emissions from human activities of 6 key air pollutants: sulphur oxides (SOX), nitrogen oxides (NOX), volatile organic compounds (VOCs), ammonia (NH3), carbon monoxide (CO) and fine particulate matter (PM2.5). Black carbon, which is a component of PM2.5, produced by combustion, is also reported. Sectoral indicators on air pollutant emissions from the oil and gas industry, transportation, off-road vehicles and mobile equipment and electric utilities provide additional analysis on the largest sources of Canada's air pollutant emissions.For each air pollutant, the indicators are provided at the national and provincial/territorial levels. They also identify the major sources of emissions and provide links to detailed information on air pollutant emissions from facilities. The Air pollutant emissions indicators are intended to inform Canadians and decision makers about progress made towards reducing emissions from human-related sources of air pollutants and about the effectiveness of emission reduction measures in reducing emissions to improve ambient air quality in Canada. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.Canadian Environmental Sustainability Indicators: https://www.canada.ca/environmental-indicators

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The Air pollutant emissions indicators track emissions from human activities of 6 key air pollutants: sulphur oxides (SOX), nitrogen oxides (NOX), volatile organic compounds (VOCs), ammonia (NH3), carbon monoxide (CO) and fine particulate matter (PM2.5). Black carbon, which is a component of PM2.5, produced by combustion, is also reported. Sectoral indicators on air pollutant emissions from the oil and gas industry, transportation, off-road vehicles and mobile equipment and electric utilities provide additional analysis on the largest sources of Canada's air pollutant emissions.For each air pollutant, the indicators are provided at the national and provincial/territorial levels. They also identify the major sources of emissions and provide links to detailed information on air pollutant emissions from facilities. The Air pollutant emissions indicators are intended to inform Canadians and decision makers about progress made towards reducing emissions from human-related sources of air pollutants and about the effectiveness of emission reduction measures in reducing emissions to improve ambient air quality in Canada. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.Canadian Environmental Sustainability Indicators: https://www.canada.ca/environmental-indicators

This dataset contains the abundance (per m²) and the biomass (mg dry per m²) of macrofauna (≥ 500µm) in eelgrass and adjacent bare soft sediments, collected at sites in the Atlantic of Nova Scotia from 2009 to 2013.Cite this data as: Wong M.C. Data of Benthic invertebrates in seagrass and bare soft sediments in Atlantic Nova Scotia Published May 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/05d5f46a-7f19-11ea-8a4e-1860247f53e3Publications: Wong, M. C., & Dowd, M. (2021). Functional trait complementarity and dominance both determine benthic secondary production in temperate seagrass beds. Ecosphere. 12(11), e03794. https://doi.org/10.1002/ecs2.3794Wong, M. C. (2018). Secondary Production of Macrobenthic Communities in Seagrass (Zostera marina, Eelgrass) Beds and Bare Soft Sediments Across Differing Environmental Conditions in Atlantic Canada. Estuaries and Coasts, 41, 536–548. https://doi.org/10.1007/s12237-017-0286-2

Environmental Monitoring Program data for aquaculture.

ENV - Environment and conservation (environment)Environmental resources, protection, and conservation. For example, resources describing pollution, waste storage and treatment, environmental impact assessment, environmental risk, and nature reserves. )

The Canadian Weathership Program collected meteorological data at Station Papa (50N, 145W) in the North Pacific Ocean between 1949 and 1981. In 2014, researchers at the University of Washington (UW) Applied Physics Laboratory (APL) and the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) analyzed this historic data to determine its efficacy as a scientific tool. The data available here are the Government of Canada data files that were utilized for this analysis. The "OWSP Full Data (1949-1981)" file contains the entire Canadian Weathership Program record of data collected from Station Papa and the "OWSP Daily Averaged Wind Speed and Wave Height Data (1949-1981)" file contains daily averaged values of wind speed and wave height generated by the UW APL and NOAA PMEL researchers. The Data Dictionary for each data file contains notes on any quality controls that were applied to the data by the UW APL and NOAA PMEL researchers. The UW documents titled, "Data Documentation for Dataset 1170 (DSI-1170), Surface Marine Data, National Climatic Data Center" (https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/25570/td1170.pdf?sequence=6&isAllowed=y) and "Table detailing units of data values in each file" (https://digital.lib.washington.edu/researchworks/handle/1773/25570), provide further information on the key values, point scales, and other units that were used in these datasets.

Environmental Monitoring Stations (EMS) spatial points coverage for the Province by LOCATION TYPES. The following spatial layers reference this as a data source: 1. Environmental Monitoring - All Stations 2. Environmental Monitoring Stations - Air Monitoring (Ambient Air Site) 3. Environmental Monitoring Stations - Air Monitoring (Air Permit) 4. Environmental Monitoring Stations - Water Sites (Water Monitoring) 5. Environmental Monitoring Stations - Water Sites (Water Permits) 6. Environmental Monitoring Stations - Water Sites (Well) 7. Environmental Monitoring Stations - Water Sites (Observation Well) 8. Environmental Monitoring Stations - Water Sites (Spring)

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices.This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada.This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

14 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).