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Biota

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    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.

  • 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.

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    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

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    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.

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    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

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    Towfish (sidescan and video) and echo sounder surveys were utilized to examine bottom type and macrophyte cover within the area of two coastal marine finfish aquaculture sites, one in New Brunswick (Welch Cove) and one in Nova Scotia (Jordan Bay). Both towfish and echo sounder data could be used independently of one another. However, the towfish data were very useful for ground truthing echo sounder based classifications. All survey data were placed into a GIS which could be used to answer management questions such as the placement of cages at sites, benthic impacts and baseline conditions to determine long term changes. Cite this data as: Vandermeulen H. Data of: Exploratory Video-Sidescan and Echosounder Survey of Jordan Bay. Published: March 2019. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/752d277f-8b3e-40c7-b99d-cfa67e69d975

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    The “Terrestrial Ecoregions of Canada” dataset provides representations of ecoregions. An ecoregion is a subdivision of an ecoprovince and is characterized by distinctive regional ecological factors, including climate, physiography, vegetation, soil, water, and fauna. For example, the Maritime Barrens ecoregion (no. 114) is one of nine ecoregions within the Newfoundland ecoprovince.

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    The National Ecological Framework for Canada's "Soil Texture by Ecoprovince” dataset contains tables that provide soil texture information within the ecoprovince framework polygon. It provides soil texture codes and their English and French language descriptions as well as the percentage of the polygon that the component occupies. Soil texture indicates the relative proportions of the various soil separates (sand, silt, clay) as described by classes of texture. Soil separates are mineral particles, 2.0 mm in diameter and include: gravel 0.2 -7.5 cm and cobbles 7.5-25.0 cm. There are 12 texture group classes definitions and one class definition for Not Applicable (which indicates, for example, water, ice or urban areas).

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    The National Ecological Framework for Canada's "Surface Form by Ecodistrict" dataset contains tables that provide surface form information for components within the ecodistrict framework polygon. It provides surface form codes and their English and French-language descriptions as well as information about the percentage of the polygon that the component occupies. Surface form descriptions describe assemblages of slopes or recurring patterns of forms that occur at the earth's surface. When applied to consolidated materials (material that has been transformed to hard rock), it refers to the form produced after modification by geological processes. The mineral soil surface forms are: dissected; hummocky (irregular); inclined; level, rolling; ridged; steep; terraced; undulating. The wetland surface forms are: bog; fen; marsh; swamp.

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    A benthic imagery survey was conducted along the Eastern Scotian Slope in June 2018 to collect data in support of a Strategic Program for Ecosystem-Based Research and Advice project to evaluate the effectiveness of the Lophelia Coral Conservation Area and identify new areas of importance for benthic species that may qualify for protection under Fisheries and Oceans Canada’s 2009 Policy for Managing the Impact of Fishing on Sensitive Benthic Areas. Linear video and photographic transects from ~200 to 1000 m depth were collected at 10 stations between the Gully Marine Protected Area and the Lophelia Coral Conservation Area using the video and photographic camera system Campod and the ‘4K Camera’ drop camera system. Here we present a quantitative assessment of the corals and sponges observed at each of these 10 stations. Patterns in distribution by transect and depth are presented, as well as the relationship between coral distribution and groundfish fishing effort. We highlight the importance of the slope outside the canyons for the distribution of corals and sponges, where nearly 25 taxa were recorded between 167 – 970 m depth. Diversity and abundance appeared to show a west-to-east gradient across the study area, being highest on those stations adjacent to the Lophelia Coral Conservation Area. Groundfish fishing activity overlapped the distribution of corals and sponges in some parts of the study area, particularly between 200 and 500 m where the large branching corals Paragorgia arborea and Primnoa resedaeformis were observed, and also suggested that fishing may have taken place within the boundaries of the Lophelia Coral Conservation Area since its implementation in 2004. An extension of the boundaries of this closure may ensure its continued effectiveness and provide protection for the diverse and abundant coral and sponge communities that reside beyond its boundaries. Cite this data as: Beazley, Lindsay; Lirette, Camille; Guijarro, Javier (2019). Characterization of the Corals and Sponges of the Eastern Scotian Slope from a Benthic Imagery Survey. Published July 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b6567b77-4fda-4fcf-b059-fcfeb4dcc2fb