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 These layers have been archived in a common (raster) format at the Bedford Institute of Oceanography to facilitate future use.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

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. )

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)

Description:Seasonal mean dissolved inorganic carbon concentration from the British Columbia continental margin model (BCCM) were averaged over the 1993 to 2020 period to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone.Methods:Dissolved inorganic carbon concentrations at up to forty-six linearly interpolated vertical levels from surface to 2400 m and at the sea bottom are included. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal dissolved inorganic carbon concentration climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution and 47 vertical levels.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

Description:Seasonal mean dissolved inorganic carbon concentration from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone.Methods:Dissolved inorganic carbon concentrations at up to forty-six linearly interpolated vertical levels from surface to 2400 m and at the sea bottom are included. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal dissolved inorganic carbon concentration climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution and 47 vertical levels.Uncertainties:Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6).CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset).Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale.Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

Environmental Monitoring Program data for aquaculture.

This polygon layer displays sub-basin-level average precipitation derived from the ECMWF (European Centre for Medium-Range Weather Forecasts) model. This layer helps hydrologists, forecasters, and planners see how much rainfall/snowfall is predicted or has occurred in each sub-basin, supporting medium-range water resource and flood management. We are intersested in the forecast period of 7 days.This layer aggregates ECMWF forecast precipitation over polygonal sub-basins. Each feature includes attributes for average accumulated precipitation, forecast run/valid times, and sub-basin identifiers. ECMWF is a leading global model offering medium-range (up to 10 days) forecasts at a high skill level. By focusing on sub-basins, this layer aids in local-scale decision-making—enabling more precise flood risk assessments, reservoir inflow estimates, and water resource planning across the region of interest.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in mean temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Downscaled daily mean temperature was calculated by averaging downscaled daily minimum and maximum temperature. Daily minimum and maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). Historical gridded minimum and maximum temperature datasets of Canada (ANUSPLIN) were used as the respective downscaling targets. Projected change in mean temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected mean temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of mean temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled minimum mean temperature (°C) 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 downscaled 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.

Area Based Management Plans (ABMP) are designated areas for the purposes of environmental protection. It must consider the impact of point and non-point source of waste, cumulative impact of the wastes, environmental management objectives and outcomes for the designated area, and ongoing monitoring and reporting.