Species Distribution Models (SDM) were used to predict and identify priority areas for enhanced monitoring of cetaceans in eastern Canadian waters off Nova Scotia, Newfoundland and Labrador. This data set represents information presented in Gomez et al. (2020) and includes sighting records and SDM outputs for ten cetacean species with sufficient records (n > 450) and sightings only for an additional six species. For more information about sighting records including which were included in each SDM, please see Gomez et al. 2020. This study used a compilation of aerial- and vessel-based cetacean sightings data from 1975-2015 assembled in Gomez et al. (2017) from variety of sources. Note that sightings data from many of these sources are not effort-corrected and apparent distribution patterns based on these opportunistic sightings data are biased by when and where survey activities were conducted. Unfavorable weather and reduced visual effort in winter, spring, and autumn likely account for the fewer sighting records in these seasons compared to summer. The dataset does not include dead animal, stranding, entanglement or entrapment data. While some of the databases include records obtained during the whaling period (catches or sightings recorded prior to 1975), for all analyses/modelling conducted in this study, only sightings of free-swimming whales obtained during the post-whaling period (1975-2015) were used. Quality control checks included discarding all records outside of our study area and removing redundant records (identical species, day, month, latitude and longitude).The data used do not reflect any updates or corrections to the databases that have occurred since the data were compiled in 2016. Sightings are not available for download here, please contact the original data sources listed below to obtain raw sightings data. This study represents an important initiative in eastern Canada to highlight key areas for cetacean monitoring in waters off Nova Scotia, Newfoundland and Labrador. Habitats with high suitability are interpreted as areas where cetacean monitoring efforts may be prioritized, and results can help direct future survey efforts. These model outputs used cetacean sightings from several decades and dynamic environmental predictors that used seasonal averages across multiple years. As proxies for prey availability, five predictor environmental variables were selected for the SDM: ocean depth, compound topographic index, sea surface temperature, areas of persistently high chlorophyll-a concentration, and regional chlorophyll-a magnitude. See Gomez et al. (2020) for further details on modelling methods. Persistent patterns over time (between 1975-2015) are the main patterns expected to be captured by these models. Further, SDM results presented here are not the same as species density maps; rather, they portray predicted suitable habitat based on environmental characteristics and sightings data that were not always derived from effort-based surveys. Consequently, the use of these models in marine spatial planning processes should be accompanied by complimentary approaches such as acoustic and visual validation of the SDM results as well as additional monitoring and modeling efforts. Please refer to Gomez et al. (2020) for examples on how to best use these data outputs. Future efforts will focus on using more recent data and improving these models to facilitate the inclusion of cetaceans in marine spatial planning processes that are currently underway. Data sources: Fisheries and Oceans Canada Maritimes region and Newfoundland and Labrador region (Whale Sightings Database, Ocean and Ecosystem Sciences Division, Dartmouth, NS; http://www.inter.dfo-mpo.gc.ca/Maritimes/SABS/popec/sara/Database, MacDonald et. al. 2017) Ocean Biogeographic Information System (OBIS; http://www.iobis.org/), North Atlantic Right Whale Consortium (NARWC; http://www.narwc.org/) Whitehead Lab at Dalhousie University (http://whitelab.biology.dal.ca/) Environment and Climate Change Canada’s (Canadian Wildlife Service) Eastern Canada Seabirds at Sea (ECSAS) program (Gjerdrum et al. 2012). References: Gomez, C., Konrad, C.M., Vanderlaan, A., Moors-Murphy, H.B., Marotte, E., Lawson, J., Kouwenberg, A-L., Fuentes-Yaco, C., Buren, A. 2020. Identifying priority areas to enhance monitoring of cetaceans in the Northwest Atlantic Ocean. Can. Tech. Rep. Fish. Aquat. Sci. 3370: vi + 103 p. http://waves-vagues.dfo-mpo.gc.ca/Library/40869155.pdf Gomez C, Lawson J, Kouwenberg A, Moors-Murphy H, Buren A, Fuentes-Yaco C, Marotte E, Wiersma YF, Wimmer T. 2017. Predicted distribution of whales at risk: identifying priority areas to enhance cetacean monitoring in the Northwest Atlantic Ocean. Endangered Species Research 32:437-458 https://www.int-res.com/abstracts/esr/v32/p437-458/ Gjerdrum, C., D.A. Fifield, and S.I. Wilhelm. 2012. Eastern Canada Seabirds at Sea (ECSAS) standardized protocol for pelagic seabird surveys from moving and stationary platforms. 31 Canadian Wildlife Service Technical Report Series No. 515. Atlantic Region. vi + 37 p. MacDonald, D., Emery, P., Themelis, D., Smedbol, R.K., Harris, L.E., and McCurdy, Q. 2017. Marine mammal and pelagic animal sightings (Whalesightings) database: a user’s guide. Can. Tech. Rep. Fish. Aquat. Sci. 3244: v + 44 p.

The Coastal Oceanography and Ecosystem Research section (DFO Science) reviewed the presence of Lobster in the Population Ecology Division (DFO Science) Ecosystem Survey trawls to describe the likelihood of presence. The survey consists of a stratified random design using a bottom trawl. Lobsters are found in few trawls in the Bay of Fundy and Port Hawkesbury Area Response Plan regions, however Lobsters are landed in nearshore areas. Therefore, lobsters are described as being likely present throughout the ARP. This layer was created for consideration in oil spill response planning. A version of this dataset was created for the National Environmental Emergency Center (NEEC) following their data model and is available for download in the Resources section. Cite this data as: Lazin, G., Hamer, A.,Corrigan, S., Bower, B., and Harvey, C. Data of: Likelihood of presence of American Lobster in Area Response Planning pilot areas. Published: June 2018. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/47bf4555-ce3c-492f-a367-a6eab1862970

The objective of the study was to describe the spatial distribution of krill in eastern Canadian waters using a statistical modelling approach in support of the identification of important habitat for the western North Atlantic (WNA) blue whale (Balaenoptera musculus). Generalized Additive Models (GAMs) were used to predict ‘Significant Aggregations of Krill’ (SAK), i.e., areas where dense krill aggregations would have a greater probability of occurring. SAK cover less than 2% of the entire spatial domain and their location varied among krill categories and seasons. These SAK are interpreted as areas where environmental conditions promote krill aggregation on a regular basis and therefore are potentially important for WNA blue whale foraging in eastern Canadian waters. Plourde, S., Lehoux, C., McQuinn, I.H., and Lesage, V. 2016. Describing krill distribution in the western North Atlantic using statistical habitat models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/111. v + 34 p.

Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of Saint Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of these taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.

Deep arctic sponge aggregations. This habitat can be described as a type of deep-sea sponge aggregation (sensu OSPAR 2010) occurring only in the deeper, colder water (Arctic modified, and Norwegian Sea Deep Water), where glass sponges (class Hexactinellida) are typical and other strictly deep-sea sponges are common. One of the most common species of glass sponge is the Caulophacus arcticus, which is generally found on hard sea bottoms on the lower part of the continental slope.

Likelihood of Presence of Bottlenose Whales in the Bay of Fundy and the Port Hawkesbury Area Response Plan. The Coastal Oceanography and Ecosystem Research section (DFO Science) reviewed reported opportunistic whale sightings and local knowledge sources to estimate areas where Northern Bottlenose Whales are seasonally present and delineate these areas. A version of this dataset was created for the National Environmental Emergency Center (NEEC) following their data model and is available for download in the Resources section. Cite this data as: Lazin, G., Hamer, A.,Corrigan, S., Bower, B., and Harvey, C. Data of: Likelihood of presence of Bottlenose Whale in Area Response Planning pilot areas. Published: June 2018. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/29dd835b-7c96-4c62-b558-275dfe13cbe9

A layer showing all polygon features that have not been mapped using the Physical shorezone Mapping system

NWT Species and Habitat Viewer

The Atlantic Colonial Waterbird Monitoring database captures geo-referenced data collected under the Atlantic Region’s Canadian Wildlife Service (CWS) Colonial Seabird Monitoring program, and data collected under CWS’ waterfowl and protected areas programs. These programs collectively monitor 22 species of colonial waterbirds breeding in the Atlantic Region of the following groups: fulmars, shearwaters, storm-petrels, gannets, gulls, terns, alcids, eiders, herons and cormorants, with the primary outcome to update information on the distribution, status, and trends of waterbirds breeding in all four Atlantic provinces. Surveys are conducted using one of three platforms: aerial, boat, or ground. Aerial surveys provide the most cost-effective way of conducting a comprehensive population census of diurnal species visible from air, namely gulls (Herring, Great Black-backed, Ring-billed, Glaucous), Black-legged Kittiwakes, Northern Gannets, terns (Arctic, Common, Roseate, Caspian), cormorants (Greater, Double-crested), Black Guillemots, and Common Eiders. Aerial surveys are also effective in identifying new colonies and provide additional information on how to most effectively conduct boat and ground surveys. Boat and ground surveys are conducted at smaller geographic scales and are necessary to obtain complementary information on the species composition of colonies and correction factors to finalize gull, tern and eider estimates obtained from the aerial surveys. Ground and/or boat surveys are also necessary to conduct colony counts of cliff-nesting birds (murres and fulmars), crevice nesting birds (razorbills and guillemots) and burrow-nesting birds (puffins, storm-petrels, shearwaters). Raw data are captured using various survey methods, including:1) visual estimates, 2) direct counts of number of individuals or apparently occupied sites and/or photo counts, 3) sub-sampling the colony through plot counts and extrapolating the estimated occupied densities over the occupied area which in turn is estimated using a variety of geographic information system approaches (e.g., Wilhelm et al. 2015). Important outcomes for how data are used include informing marine spatial planning, emergency preparedness, impact assessment of various anthropogenic threats, and COSEWIC status assessment (e.g., COSEWIC 2020). Further, these data are regularly used internally or in collaboration with partners to form the basis of peer-reviewed publications on regional, national, and/or global population trends for target species (e.g., Langlois Lopez et al. 2022, Iles et al. 2025). This dataset represents a subset of data collected from surveys led by CWS spanning from 1910 to 2024 and summarized to present, at a given colony, the year and count of birds from the most recent survey, the year and maximum count of birds within the last 20 years (if available), and the year and maximum count of birds ever recorded. In addition, CWS works closely with a variety of partners (e.g., provincial, other federal departments, academic, not-for profit organizations) to supplement colonial waterbird data collected in the Atlantic Region. To request additional census or survey data, please contact CWS. COSEWIC. 2020. COSEWIC assessment and status report on the Leach’s Storm-Petrel (Atlantic population) Oceanodroma leucorhoa in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xii + 70 pp. (https://www.canada.ca/en/environment-climate-change/services/species-risk-public-registry.html). Iles, D.T., S.E. Gutowsky, A.M. Calvert, S.I. Wilhelm, J.-F. Rail, A. Hedd, H.L. Major, A.C. Smith, and G.J. Robertson. 2025. Estimating regional trajectories and trends of seabirds from sparse and inconsistent colony counts: case studies from eastern Canada with Leach’s Storm-Petrel and Atlantic Puffin. Avian Conservation and Ecology 20(2):16. https://doi.org/10.5751/ACE-02931-200216 Langlois Lopez, S., Bond, A.L., O’Hanlon, N.J., Wilson, J.M., Vitz, A., Mostello, C.S., Hamilton, F., Rail, J.-F., Welch, L., Boettcher, R., Wilhelm, S.I., Anker-Nilssen, T., Daunt, F., and Masden, E. 2022. Global population and conservation status of the Great Black-backed Gull Larus marinus. Bird Conservation International, 1–11. https://doi.org/10.1017/S0959270922000181 Wilhelm, S.I., Mailhiot, J., Arany, J., Chardine, J.W., Robertson, G.J., and Ryan, P.C. 2015. Update and trends of three important seabird populations in the western North Atlantic using a geographic information system approach. Marine Ornithology 43: 211-222.

The National Ecological Framework for Canada's "Surface Material by Ecodistrict” dataset provides surface material information within the ecodistrict framework polygon. It provides surface material codes and their English and French language descriptions as well as information about the percentage of the polygon that the component occupies. Surface material includes the abiotic material at the earth's surface. The materials can be: ICE and SNOW - Glacial ice and permanent snow ORGANIC SOIL - Contains more than 30% organic matter as measured by weight ROCK - Rock undifferentiated MINERAL SOIL - Predominantly mineral particles: contains less than 30% organic matter as measured by weight URBAN - Urban areas. Note that only a few major urban area polygons are included on SLC source maps, therefore, do not use for tabulating total urban coverage.