Summer, Winter Alpine, and Winter Forest-Dwelling habitat model for caribou in the Itcha, Ilgachuz, and Rainbow Mountains of West-Central BC. This habitat model was developed using telemetry from the Itcha-Ilgachuz, Rainbow, and Charlotte Alplands Herds. [Season] field should be used to split the data out into separate summer, winter alpine, and winter forest-dwelling habitat models. Model development is detailed in _Apps, C. D., T. A. Kinley, and J. A. Young. 2001. Multi-scale habitat modeling for woodland caribou in the Itcha, Ilgachuz, and Rainbow mountains of west-central British Columbia.Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada_. See also: https://catalogue.data.gov.bc.ca/dataset/caribou-habitat-model-for-the-western-cariboo-region-2017-. __Note: The 2017 habitat model covers a similar area, but does not supersede the 2001 habitat model.__

The Scotian Shelf population of northern bottlenose whales (Hyperoodon ampullatus) is listed as Endangered under Canada’s Species at Risk Act. Partial critical habitat was identified for this population in the Recovery Strategy first published in 2010 (Fisheries and Oceans Canada 2016), and three critical habitat areas were designated along the eastern Scotian Shelf, encompassing the Gully, Shortland Canyon, and Haldimand Canyon (shapefile available online: https://open.canada.ca/data/en/dataset/db177a8c-5d7d-49eb-8290-31e6a45d786c). However, the Recovery Strategy recognized that additional areas may constitute critical habitat for the population and recommended further studies based on acoustic and visual monitoring to assess the importance of inter-canyon areas as foraging habitat and transit corridors for northern bottlenose whales.In a subsequent study of the distribution, movements, and habitat use of northern bottlenose whales on the eastern Scotian Shelf (Stanistreet et al. in press), several sources of data were assessed and additional important habitat was identified in the inter-canyon areas located between the Gully, Shortland Canyon, and Haldimand Canyon (DFO 2020). A summary of the data inputs, analyses, and limitations is provided below.Year-round passive acoustic monitoring conducted with bottom-mounted recorders at two inter-canyon sites from 2012-2014 revealed the presence and foraging activity of northern bottlenose whales in these areas throughout much of the year, with a seasonal peak in acoustic detections during the spring. Detections from acoustic recordings collected during vessel-based surveys provided additional evidence of species occurrence in inter-canyon areas during the summer months. Photo-identification data collected in the Gully, Shortland, and Haldimand canyons between 2001 and 2017 were used to model the residency and movement patterns of northern bottlenose whales within and between the canyons, and demonstrated that individuals regularly moved between the three canyons as well as to and from outside areas. Together, these results indicated a strong degree of connectivity between the Gully, Shortland, and Haldimand canyons, and provided evidence that the inter-canyon areas function as important foraging habitat and movement corridors for Scotian Shelf northern bottlenose whales. The inter-canyon habitat area polygon was delineated using the 500 m depth contour and straight lines connecting the southeast corners of the existing critical habitat areas, but these boundaries are based on limited spatial information on the presence of northern bottlenose whales in deeper waters. More data are needed to determine whether this area fully encompasses important inter-canyon habitat, particularly in regard to the deeper southeastern boundary. Similarly, the full extent of important habitat for Scotian Shelf northern bottlenose whales remains unknown, and potential critical habitat areas outside the canyons and inter-canyon areas on the eastern Scotian Shelf have not been fully assessed. See DFO (2020) for further information.References:DFO. 2020. Assessment of the Distribution, Movements, and Habitat Use of Northern Bottlenose Whales on the Scotian Shelf to Support the Identification of Important Habitat. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2020/008. https://www.dfo-mpo.gc.ca/csas-sccs/Publications/SAR-AS/2020/2020_008-eng.html Fisheries and Oceans Canada. 2016. Recovery Strategy for the Northern Bottlenose Whale, (Hyperoodan ampullatus), Scotian Shelf population, in Atlantic Canadian Waters [Final]. Species at Risk Act Recovery Strategy Series. Fisheries and Oceans Canada, Ottawa. vii + 70 pp. https://www.canada.ca/en/environment-climate-change/services/species-risk-public-registry/recovery-strategies/northern-bottlenose-whale-scotian-shelf.html Stanistreet, J.E., Feyrer, L.J., and Moors-Murphy, H.B. In press. Distribution, movements, and habitat use of northern bottlenose whales (Hyperoodon ampullatus) on the Scotian Shelf. DFO Can. Sci. Advis. Sec. Res. Doc. [https://publications.gc.ca/collections/collection_2022/mpo-dfo/fs70-5/Fs70-5-2021-074-eng.pdf]Cite this data as: Stanistreet, J.E., Feyrer, L.J., and Moors-Murphy, H.B. Data of: Northern bottlenose whale important habitat in inter-canyon areas on the eastern Scotian Shelf. Published: June 2021. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/9fd7d004-970c-11eb-a2f3-1860247f53e3

The purpose of the survey is to document and record habitat types and associated algae and marine invertebrate species in a variety of habitat types. Transect locations are randomly selected throughout the study area, which rotates between the north and south coasts of British Columbia on a biannual basis. Transects are laid perpendicular to the shoreline. A team of two divers swim the transect with data sheets to collect habitat, algae and marine invertebrate data as detailed below in the methods section. Data is keypunched in an MS Access database that can be queried for species observations and environmental information.This dataset includes three tables pulled from the original database containing observations by species, observations by quadrat, and additional header information for each observation. All three tables can be linked by the field HKey. Three lookup tables are included as well, one for algae, one for invertebrates, and one for substrates.

Landscape Units (Subdivisions) were developed as a part of the 2002 Northern Caribou Strategy and are used for landscape level planning within Wildlife Habitat Area 5-086. For details please see: [Apps, C. D., T. A. Kinley, and J. A. Young. 2001. Multi-scale habitat modeling for woodland caribou in the Itcha, Ilgachuz, and Rainbow mountains of west-central British Columbia. Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada.](http://www.env.gov.bc.ca/cariboo/env_stewardship/wildlife/inventory/caribou/northcar/hmi/hsi06-01.pdf)

The data in this layer represents habitat suitability of soft-shelled clams (Mya arenaria) in the DFO Maritimes region, and was developed using an interdepartmental approach. Substrate classification data as well as bathymetric data for the Region were used to identify potential habitat for soft-shelled clams. Substrates identified as suitable included: sand, mud, sand and mud (Greenlaw, 2022). Contours (0m and 70m) from GEBCO bathymetric data were used to isolate depths between which soft-shelled clams are present. At this stage, a polygon reflecting soft substrates from 0-70m was created as "Suitable". A "Not Suitable" layer was similarly created using the substrates: boulders, continuous bedrock, discontinuous bedrock, gravel, mixed sediment, sand and gravel.To digitally validate the model, the Regional shoreline was divided into subsectors (developed by Environment and Climate Change Canada for the Canadian Shellfish Sanitation Program). Data from DFO (clam harvesting intensity) as well as Conservation and Protection (clam harvesting infraction locations) were used to established species presence within each sub-sector. If there had been any harvesting activity, legal or illegal, in an individual subsector, it was considered "Suitable and Validated". Merged into one final product, the model includes areas that are "Not Suitable", "Suitable", as well as "Suitable and Validated" for soft-shelled clam habitat.Cite this data as: Harvey, C., Vincent, M., Greyson, P., Hamer, A. (2024) Data of: A Soft-Shelled Clam (Mya arenaria) Habitat Suitability Model For The DFO Maritimes Region.Published: January 2024. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/c76f7813-d802-4b31-8ebe-476f8a7cacf2

Speckled Dace (Rhinichthys osculus) are listed under the Species at Risk Act (SARA) as endangered. These fresh water fish, of the family Cyprinidae, are found in Canada only in the Kettle Valley of British Columbia. Proposed critical habitat was based on minimum viable population analysis and assumed densities of fish. From October 19th to the 22nd of 2015, night time pole seining surveys were conducted to enumerate Speckled Dace in proposed critical habitat on the West Kettle River; one of three rivers containing Speckled Dace. The estimated population abundance of Speckled Dace within the survey area was 8,978 (6,143-11,814), however only 1,014 of these are estimated to be adults.

The large, habitat-forming bubblegum coral, Paragorgia arborea, is a vulnerable marine ecosystem indicator with an antitropical distribution. Dense aggregations of the species have been protected from bottom-contact fishing in the Scotian Shelf bioregion off Nova Scotia, Canada in the northwest Atlantic Ocean. Recently, basin-scale habitat suitability ensemble modeling has projected an alarming loss of 99% of suitable habitat for this species across the North Atlantic by 2100. Here, a regional reassessment of the predicted distribution of this species in the bioregion, using both machine learning (random forest) and generalized additive model (GAM) frameworks, including projection to 2046−2065, was undertaken. Extrapolation diagnostics were applied to determine the degree to which the models projected into novel covariate space (i.e., extrapolation) in order to avoid erroneous inferences. The best predictors of the species’ distribution were a suite of temporally-invariant terrain variables that identified suitable habitat along the upper continental slope. Additional predictors, projected to vary with future ocean climatologies, identified areas of the upper slope in the eastern portion of the study area that will remain within suitable ranges for P. arborea at least through to the mid-century. Additionally, 3-D Lagrangian particle tracking simulations indicated potential for both connectivity among known occurrence sites and existing protected areas, and for colonization of unsurveyed areas predicted to have suitable habitat, from locations of known occurrence. These results showed that extirpation of this iconic species from the Scotian Shelf bioregion is unlikely over the next decades. Potential climate refugia were identified and results presented in the context of protected area network design properties of representativity, connectivity, adequacy, viability and resilience. Here we provide the interpolated environmental predictor variables used in present day and future species distribution models. Predicted surfaces from random forest (RF) and generalized additive models (GAM) are provided for present day and projected (RCP4.5 and RCP8.5 2045-2065) time frames. Outputs after applying extrapolation diagnostic tools (calculated using the R package dsmextra) are provided for each model. Climate change refugia for this species under the RCP4.5 2045-2065 projections showing analogue conditions are also provided. Explanations for the surfaces can be found in Wang, Murillo & Kenchington (2022) and in the associated supplementary files. Reference:Wang S, Murillo FJ and Kenchington E (2022) Climate-Change Refugia for the Bubblegum Coral Paragorgia Arborea in the Northwest Atlantic. Front. Mar. Sci. 9:863693. doi: 10.3389/fmars.2022.863693

Description:Biophysical Units: Under the Pacific Marine Ecological Classification System (PMECS; DFO 2016; Rubidge et al. 2016), biophysical units are areas of distinct physiographic and oceanographic conditions and processes that shape species composition at spatial extents of 1000s of km. Geomorphic units:Geomorphic units or geozones are discrete geomorphological structures at the scale of 100s of km that are assumed to have distinctive biological assemblages (e.g., plateaus, ridges, seamounts, canyons). Although the spatial scale of geomorphic units is nested within biophysical units, a single geomorphic unit such as a trough may span more than one biophysical unit. The following 5 layers are included in this geodatabase:1. Biophysical_Units_L4A - Predicted PMECS Biophysical Units (Level 4A) output from the random forest analysis2. Biophysical_Units_L4B - Predicted PMECS Biophysical Units (Level 4B) output from the random forest analysis3. Biophysical_Units_ProbAssign_L4AB - Layer showing the probability that a grid cell was assigned to a given biophysical unit in the final random forest predictive modelling step4. Cluster_L4AB - Layer showing the output of species assemblage cluster analysis5. Geomorphic_Units - Geomorphic units for the BC coast that combines geomorphic units produced by Rubidge et al. 2016) and Proudfoot and Robb (2022).Methods:Biophysical Units:Rubidge et al. (2016) used a two-step process to identify biophysical units in British Columbia. First, a cluster analysis based on the similarity of species composition was used to group sites with similar species into distinct biological assemblages. Second, a random forest analysis was used to identify environmental correlates of the biological assemblages identified by the cluster analysis and to predict and assign the biological assemblage present in areas with too few biological data. Two different similarity thresholds were used to identify two levels (4A, 4B) of biophysical units; see Rubidge et al. (2016) for details. Indicator species for each assemblage (biophysical unit) were also identified.Geomorphic units:Rubidge et al. (2016) used the benthic terrain modeller (BTM) tool with broad and fine-scale benthic positioning index (BPI) parameters to define geomorphic units on the continental shelf in the Northern Shelf Bioregion and the continental slope in both the Northern Shelf Bioregion and Southern Shelf Bioregion. In 2022, geomorphic units were produced for the Strait of Georgia and Southern Shelf Bioregions following the same methods as Rubidge et al. (2016) (Proudfoot and Robb 2022). The geomorphic units produced as part of the PMECS process were merged with the geomorphic units produced for the Strait of Georgia and Southern Shelf bioregions to produce a continuous spatial data product representing geomorphic units for the Canadian Pacific continental shelf and slope. After merging, the geomorphic units produced in 2016 were unchanged (i.e., they are consistent with the original geomorphic units described in Rubidge et al. 2016).Data Sources:From Rubidge et al. (2016): Species data was taken from Fisheries and Oceans Canada (DFO) standardized fisheries-independent research surveys: groundfish trawl and long-line (2003-2013), Tanner Crab trawl and trap (2000–2006), and Dungeness Crab trap (2000–2014). Environmental data came from NASA, the Canadian Hydrographic Service, Fisheries and Oceans Canada, Bio-ORACLE, and elsewhere (details in Rubidge et al. 2016). From Proudfoot and Robb (2022): bathymetry data came from Natural Resources Canada (details in Proudfoot and Robb 2022).Uncertainties:The data is intended for use at the bioregional scale, and caution should be used for finer-scale analyses.

Greenland Sharks (Somniosus microcephalus) are estimated to have the highest longevity of any invertebrate (392 ± 120 years), making bycatch a significant concern (Nielsen et al. 2016). However, in the Newfoundland and Labrador (NL) region, accurate estimates of bycatch are not often available for the species (Simpson et al. 2021). To address this, species distribution models (SDMs) were generated to delineate habitat suitability for the species throughout the NL region in order to identify areas where a higher rate of bycatch is expected to occur.Observations of Greenland shark bycatch recorded by At-Sea Observers (ASOs) in NL (1983 – 2019), Spain (1999 – 2017), and by the Northwest Atlantic Fisheries Organization (NAFO) Secretariat (2014 – 2019) were compiled to generate a presence-only dataset. Multiple environmental variables were assessed for collinearity, and non-collinear variables (Bathymetry and mean monthly bottom temperature for March and November (1990 – 2015)) were retained for use in the SDM. MaxEnt (maximum entropy) software was used to model habitat suitability because it is a presence-only modelling program that is able to account for a lack of absence data by comparing the environmental conditions at occurrence locations to those at randomly selected background points. Overall, the results indicated that habitat suitability for Greenland Shark was highest in deeper waters along the shelf edge in NAFO Divisions 3OP, and the Laurentian Channel, but also extended along the edge of the Labrador shelf, the Grand Banks, and deeper areas along the continental shelf such as the Hawke Channel, Funk Island Deep, and the slopes of Saglek, Nain, and Hamilton Banks. Beyond the Economic Exclusive Zone (EEZ) and within the NAFO regulatory area (NRA), suitable habitat was also present within the Flemish Pass, and along the slope of the Flemish Cap and shelf edge in NAFO Divisions 3NO (Simpson et al. 2021). More detailed information can be found in Simpson et al. 2021.References:Nielsen, J., R. B. Hedeholm, J. Heinemeier, P. G. Bushnell, J. S. Christiansen, J. Olsen, C. B. Ramsey, R. W. Brill, M. Simon, K. F. Steffensen, J. F. Steffensen. 2016. Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science 353 (6300):702-704Simpson, M. R., Gullage, L., Konecny, C., Ollerhead, N., Treble, M.A., Nogueira, A., González-Costas, F. 2021. Spatial-temporal variation in Greenland shark (Somniosus microcephalus) bycatch in the NAFO Regulatory Area. NAFO SCR Doc. 21/028

This project was completed by the Productive Capacity group (Coastal and Freshwater Ecology Section) in the Newfoundland and Labrador Science Branch of Fisheries and Oceans Canada (DFO). American lobster (Homarus americanus) is a commercially important decapod crustacean species along the east coast of North America, ranging from the Labrador coast south to Cape Hatteras. Juvenile lobster < 40 mm CL (carapace length) recruitment has been studied extensively in the southern portions of their range. However, investigations of settlement habitat association and recruitment of juvenile lobster in the northern extremes along the Newfoundland coast have been largely unsuccessful. We investigated juvenile lobster density, habitat, and depth associations in three areas of Newfoundland, using scuba – Port Saunders area, 8 dives conducted 28 September - 2 October 2017, and Burin Peninsula 10 dives (7 Placentia Bay, 3 Fortune Bay) conducted 30 September - 4 October 2018 over a total seabed area of 9,138 m2, within 200 meters of shore. Port Saunders and Fortune Bay had relatively higher lobster density (0.09 and 0.40 m-2, respectively); >65% were juveniles, in contrast to Placentia Bay where lobster densities of all size groups were low (mean 0.01 m-2) and no juvenile lobsters were observed at all. Where observed all juvenile lobster were significantly associated with shallow (<6 m) habitat and showed no overlap with distribution of adults (>82.5 mm CL) which we observed at depths 6 to 17 m. Our sites were dominated by varying mixes of cobble and pebble (77%); rock/bedrock (12%) and mud/sand/small pebble (11%) substrates interspersed with overlying kelp (32%) and eelgrass (11%) vegetation. We observed no significant associations with substrate or vegetation. This record contains the geographic locations of the 7 Placentia Bay sites surveyed, and information on the timing and type of data collected at each site, which was one component supported by Coastal Environmental Baseline Program of a larger collaborative project.