Monitoring data from DFO invasive species monitoring programs, along with occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict present-day and project future distributions of 24 non-indigenous species (NIS) on North America`s east coast, and 31 NIS on its west coast. Future distributions were predicted for 2100, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) have been estimated by summing the occurrence probabilities of NIS. This data set includes the present-day and year 2100 species distribution modeling results for each species, and the estimated species richness.Cite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Updated species distribution models for marine invasive species hotspot identification. Published: November 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1439dcb3-82a6-40fd-a9a4-8f045b20ff5b

A spatial representation of the general distribution of wild mountain sheep (bighorn and thinhorn sheep) in British Columbia. Populations that extend into neighbouring provinces and states are also included. The distribution polygons are divided by species into bighorn and thinhorn sheep.

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

Since 2005, Fisheries and Oceans Canada has been collecting monitoring data for aquatic invasive species (e.g. https://open.canada.ca/data/en/dataset/8d87f574-0661-40a0-822f-e9eabc35780d, https://open.canada.ca/data/en/dataset/503a957e-7d6b-11e9-aef3-f48c505b2a29, https://open.canada.ca/data/en/dataset/8661edcf-f525-4758-a051-cb3fc8c74423). This monitoring data, as well additional occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict the present-day and future potential distributions of 12 moderate to high risk invasive species on Canada’s east and west coasts. Future distributions were predicted for 2075, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) has also been estimated by summing their occurrence probabilities. This data set includes the occurrence locations of each species, the present-day and future species distribution modeling results for each species, and the estimated species richness. This research has been published in the scientific literature(Lyons et al. 2020).Lyons DA, Lowen JB, Therriault TW, Brickman D, Guo L, Moore AM, Peña MA, Wang Z, DiBacco C. (In Press) Identifying Marine Invasion Hotspots Using Stacked Species Distribution Models. Biological InvasionsCite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Species distribution models and occurrence data for marine invasive species hotspot identification. Published: November 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1bbd5131-8b34-4245-b999-3b4c4259d74f

Description:This dataset contains layers of predicted occurrence for 65 groundfish species as well as overall species richness (i.e., the total number of species present) in Canadian Pacific waters, and the median standard error per grid cell across all species. They cover all seafloor habitat depths between 10 and 1400 m that have a mean summer salinity above 28 PSU. Two layers are provided for each species: 1) predicted species occurrence (prob_occur) and 2) the probability that a grid cell is an occurrence hotspot for that species (hotspot_prob; defined as being in the lower of: 1) 0.8, or 2) the 80th percentile of the predicted probability of occurrence values across all grid cells that had a probability of occurrence greater than 0.05.). The first measure provides an overall prediction of the distribution of the species while the second metric identifies areas where that species is most likely to be found, accounting for uncertainty within our model. All layers are provided at a 1 km resolution.Methods:These layers were developed using a species distribution model described in Thompson et al. 2023. This model integrates data from three fisheries-independent surveys: the Fisheries and Oceans Canada (DFO) Groundfish Synoptic Bottom Trawl Surveys (Sinclair et al. 2003; Anderson et al. 2019), the DFO Groundfish Hard Bottom Longline Surveys (Lochead and Yamanaka 2006, 2007; Doherty et al. 2019), and the International Pacific Halibut Commission Fisheries Independent Setline Survey (IPHC 2021). Further details on the methods are found in the metadata PDF available with the dataset.Abstract from Thompson et al. 2023:Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fisheries-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of habitats that are occupied by groundfish. Bottom trawl gear is used to sample soft bottom habitat, predominantly on the continental shelf and slope, whereas longline gear often focuses on nearshore and hardbottom habitats where trawling is not possible. Because data from these two gear types are not directly comparable, previous species distribution models in this region have been limited to using data from one survey at a time, restricting their spatial extent and usefulness at a regional scale. Here we demonstrate a method for integrating presence-absence data across surveys and gear types that allows us to predict the coastwide distributions of 66 groundfish species in British Columbia. Our model leverages the use of available data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in catchability by the different surveys. Overall, we find that this integrated method has two main benefits: 1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on the accuracy when data are already sufficient to make predictions, 2) it reduces uncertainty, resulting in tighter confidence intervals on predicted species occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish research surveys.Data Sources:Research data was provided by Pacific Science’s Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2020 for all species which had at least 150 observations, across all gear type and survey datasets available.Uncertainties:These are modeled results based on species observations at sea and their related environmental covariate predictions that may not always accurately reflect real-world groundfish distributions though methods that integrate different data types/sources have been demonstrated to improve model inference by increasing the accuracy of the predictions and reducing uncertainty.

Effective fisheries and habitat management processes require knowledge of the distribution of areas of high ecological or biological significance. On the Scotian Shelf and Slope, a number of benthic ecologically or biologically significant areas consisting of habitat-forming species such as sponges and deep-water corals have been identified. However, knowledge of their spatial distribution is largely based on targeted surveys that are limited in their spatial extent. We used a species distribution modelling approach called random forest (RF) to predict the probability of occurrence and biomass of sponges, sea pens, and large and small gorgonian corals across the entire spatial extent of Fisheries and Oceans Canada’s (DFO) Maritimes Region. We also modelled the rare sponge Vazella pourtalesi, which forms the largest known aggregation of its kind on the Scotian Shelf. We utilized a number of data sources including DFO multispecies trawl catch data and in situ benthic imagery observations. Most models had excellent predictive capacity with cross-validated Area Under the Receiver Operating Characteristic Curve (AUC) values ranging from 0.760 to 0.977. Areas of suitable habitat were identified for each taxon and were contrasted against their known distribution and when applicable, the location of closure areas designated for their protection. Generalized additive models (GAMs) were developed to predict the biomass distribution of each taxonomic group and serve as a comparison to the RF models. The RF and GAM models provided comparable results, although GAMs provided superior predictions of biomass along the continental slope for some taxonomic groups. In the absence of data observations, the results of this study could be used to identify the potential distribution of sensitive benthic taxa for use in fisheries and habitat management applications. These results could also be used to refine significant concentrations of these taxa as identified through the kernel density analyses.Cite this data as: Beazley, Lindsay; Kenchington, Ellen; Murillo-Perez, Javier; Lirette, Camille; Guijarro-Sabaniel, Javier; McMillan, Andrew; Knudby, Anders (2019). Species Distribution Modelling of Corals and Sponges in the Maritimes Region for Use in the Identification of Significant Benthic Areas. Published July 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/356e92f3-5bf3-4810-98b1-3e10cd7742aa

The data represent the distribution of species of amphibians, reptiles, reptiles, terrestrial mammals and freshwater and migratory fish in Quebec.The files represent:amphibians: 21 speciesterrestrial mammals: 69 speciesfreshwater and migratory fish: 118 speciesreptiles: 17 speciesThe ranges were established on the basis of various sources of information and validated by the Main Directorate of expertise on terrestrial fauna (DPEFT), the Main Directorate for Threatened or Vulnerable Species (DPEMV) and the Main Directorate of Expertise on Aquatic Wildlife (DPEFA) of the Ministry of the Environment, the Fight against Climate Change, Climate Change, Wildlife and Parks (MELCCFP).The ranges of species of _freshwater and migratory fish_ are also illustrated in the [“Freshwater Fish of Quebec”] poster (https://cdn-contenu.quebec.ca/cdn-contenu/faune/documents/animaux/affiche-poissons-eau-douce.pdf). Some ranges have been slightly modified since they were included in the poster.__There may be differences between the ranges of the species shown in the files and the current spatial distribution of the species. __The distribution areas were produced on a small scale; they provide indicative information on the presence of the species in Quebec.The cards are the property of MELCCFP.__Atten:__ The ranges of marine mammals that frequent the coasts of the province of Quebec are not included in this dataset.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset displays the Canadian geographic ranges of the priority species identified under the Pan-Canadian Approach for Transforming Species at Risk Conservation in Canada (“Pan-Canadian Approach”). These species include Barren-ground Caribou (including the Dolphin and Union population); Greater Sage-Grouse; Peary Caribou; Wood Bison; Caribou, Boreal population (“Boreal Caribou”); and Woodland Caribou, Southern Mountain population (“Southern Mountain Caribou”). The priority species were chosen following a number of criteria and considerations in collaboration with federal, provincial, and territorial partners. These include, but were not limited to, the species' ecological role on a regional or national scale, their conservation status and achievability of conservation outcomes, their social and cultural value (particularly to Indigenous peoples), and the leadership/partnership opportunities that they present. Delivering conservation outcomes for targeted priority species can have significant co-benefits for other species at risk, and wildlife in general. For more information on the Pan-Canadian Approach and the priority species, see https://www.canada.ca/en/services/environment/wildlife-plants-species/species-risk/pan-canadian-approach.html.This dataset includes: 1) the range for the Boreal Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/2253); 2) the local populations for the Southern Mountain Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/1309); 3) the range for the Greater Sage-Grouse (see https://species-registry.canada.ca/index-en.html#/consultations/1458); 4) local populations for the Peary Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/3657); 5) range for the Barren-ground Caribou (see https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 6) range for the Barren-ground Caribou, Dolphin and Union population (https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 7) range for the Wood Bison (see https://species-registry.canada.ca/index-en.html#/consultations/2914).

A derivative of DFO’s benthic species survey for the Strategic Program for Ecosystem-based Research and Advice (SPERA) (open data record ID: e736c0f0-b19e-4842-903d-28bfc756d48a), this benthic survey funded through the Canadian Healthy Oceans Network (CHONeII) looks at the presence/absence and abundance of two biogenic habitat-forming species that are listed as vulnerable to disturbance in a subset of 50 drift camera transects in the ‘Head Harbour/West Isles Archipelago/The Passages’ Ecologically and Biologically Significant Area (EBSA) in the Bay of Fundy, New Brunswick, Canada (~113km2). Presence/absence and abundance data of the stalked sea squirt (Boltenia ovifera) and horse mussel (Modiolus modiolus) were derived from the use of high-resolution Nikon D800 36.1 megapixel still images (n=2576, see link to parent record for more descriptive survey information) to be used in species distribution modelling. Image field of view (FOV) was estimated using a 10 cm-wide trigger weight for scale,and standardized across images using the average FOV estimate (0.75 x 0.5 m) across a subset of 200 images. Species counts were then converted to abundance estimates (number of individuals per square-meter) by dividing counts by 0.375m2. Boltenia ovifera was observed at densities reaching 456 ind./m2, while Modiolus modiolus density reached a maximum of 240 ind./m2.Cite this data as: Mireault C.A., Lawton P., Devillers R. and Teed L. Presence/absence and abundance of vulnerable marine ecosystem species Boltenia ovifera and Modiolus modiolus in the lower Bay of Fundy derived from high resolution still imagery. Published September 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/152ae3f1-d2b9-43d9-a7b4-d769d9e9fc41

The distribution of nesting areas for bird colonies in coastal British Columbia showing relative abundance (RA) by season and overall relative importance (RI). RI is based on project region and not on the province as a whole. Number counts for various species in the colony location are provided. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.