Eelgrass (Zostera marina) is important to waterfowl such as Atlantic Brant (Branta bernicla hrota), Canada Goose (Branta canadensis), American Black Duck (Anas rubripes), Common Goldeneye (Bucephala clangula) and Barrow's Goldeneye (Bucephala islandica). In New Brunswick eelgrass can be found along the Gulf of St. Lawrence, in protected harbours. Within this dataset are the results of eelgrass land-cover classifications using either satellite or aerial photography for seven harbours: Bouctouche (46 30’N, 64 39’W); Miscou (47.90 N, -64.55 W); Neguac (47.25 N, -65.03 W); Richibucto (46.70 N, -64.80 W); Saint-Simon (47.77 N, -64.76 W); Tracadie (47.55 N, -64.88 W); and Cocagne (46.370 N, -64.600 W). Information on each dataset is provided: 1. Bouctouche This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 83.7% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837]. 2. Miscou True colour aerial photography at 57 centimetre resolution was collected on August 20th and 24th, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 103 sites. From these sites 70% were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 96.7% of the time (30/31 = 0.967). 3. Neguac This dataset contains results from an eelgrass classification for Neguac Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 126 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 81% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 39 sites that were within the classification area, 27 were correct, 9 were "one-off", and 3 were incorrect [(27 + (9/2))/ 39 = 0.81]. 4. Richibucto Eelgrass classification in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 5. Saint-Simon An eelgrass distribution map was classified from remotely sensed imagery in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 6. Tracadie This dataset contains results from an eelgrass classification for Tracadie Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 101 sites. Approximately two-thirds of these sites were used to assist in image classification, while the remainder was used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 79.3% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 29 sites that were within the classification area, 18 were correct, 10 were "one-off", and 1 was incorrect [(18 + (10/2))/ 29 = 0.793]. 7. Cocagne Visible orthorectified aerial photography was used to classify polygons containing eelgrass in Cocagne Harbour. Field data for image training and validation were collected along transects in summer 2008 using a dGPS positioned towfish holding sidescan sonar and a video camera that was later transcribed as XY geographic points to describe eelgrass presence and a qualitative description of density. The area was flown for photography on September 24, 2008. eCognition Developer 8 software was used to segment the imagery, essentially polygons. Polygons were then classified manually for the presence of eelgrass. Using field data revealed eelgrass presence to be mapped correctly 87.2% of the time.

Data Sources: Banque informatisée des oiseaux de mer au Québec (BIOMQ: ECCC-CWS Quebec Region) Atlantic Colonial Waterbird Database (ACWD: ECCC-CWS Atlantic Region).. Both the BIOMQ and ACWD contain records of individual colony counts, by species, for known colonies located in Eastern Canada. Although some colonies are censused annually, most are visited much less frequently. Methods used to derive colony population estimates vary markedly among colonies and among species. For example, census methods devised for burrow-nesting alcids typically rely on ground survey techniques. As such, they tend to be restricted to relatively few colonies. In contrast, censuses of large gull or tern colonies, which are geographically widespread, more appropriately rely on a combination of broad-scale aerial surveys, and ground surveys at a subset of these colonies. In some instances, ground surveys of certain species are not available throughout the study area. In such cases, consideration of other sources, including aerial surveys, may be appropriate. For example,data stemming from a 2006 aerial survey of Common Eiders during nesting, conducted by ECCC-CWS in Labrador, though not yet incorporated in the ACWD, were used in this report. It is important to note that colony data for some species, such as herons, are not well represented in these ECCC-CWS databases at present. Analysis of ACWD and BIOMQ data (ECCC-CWS Quebec and Atlantic Regions): Data were merged as temporal coverage, survey methods and geospatial information were comparable. Only in cases where total counts of individuals were not explicitly presented was it necessary to calculate proxies of total counts of breeding individuals (e.g., by doubling numbers of breeding pairs or of active nests). Though these approaches may underestimate the true number of total individuals associated with a given site by failing to include some proportion of the non-breeding population (i.e., visiting adult non-breeders, sub-adults and failed breeders), tracking numbers of breeding individuals (or pairs) is considered to be the primary focus of these colony monitoring programs.In order to represent the potential number of individuals of a given species that realistically could be and may historically have been present at a given colony location (see section 1.1), the maximum total count obtained per species per site since 1960 was used in the analyses. In the case of certain species,especially coastal piscivores (Wires et al. 2001; Cotter et al. 2012), maxima reached in the 1970s or 1980s likely resulted from considerable anthropogenic sources of food, and these levels may never be seen again. The effect may have been more pronounced in certain geographic areas. Certain sites once used as colonies may no longer be suitable for breeding due to natural and/or human causes, but others similarly may become suitable and thus merit consideration in long-term habitat conservation planning. A colony importance index (CII) was derived by dividing the latter maximum total count by the potential total Eastern Canadian breeding population of that species (the sum of maximum total counts within a species, across all known colony sites in Eastern Canada). The CII approximates the proportion of the total potential Eastern Canadian breeding population (sum of maxima) reached at each colony location and allowed for an objective comparison among colonies both within and across species. In some less-frequently visited colonies, birds (cormorants, gulls, murres and terns, in particular) were not identified to species. Due to potential biases and issues pertaining to inclusion of these data, they were not considered when calculating species’ maximum counts by colony for the CII. The IBA approach whereby maximum colony counts are divided by the size of the corresponding actual estimated population for each species (see Table 3.1.2; approximate 1% continental threshold presented) was not used because in some instances individuals were not identified to species at some sites, or population estimates were unavailable.Use of both maxima and proportions of populations (or an index thereof) presents contrasting, but complementary, approaches to identifying important colonial congregations. By examining results derived from both approaches, attention can be directed at areas that not only host large numbers of individuals, but also important proportions of populations. This dual approach avoids attributing disproportionate attention to species that by their very nature occur in very large colonies (e.g., Leach’s Storm Petrel) or conversely to colonies that host important large proportions of less-abundant species (Roseate Tern, Caspian Tern, Black-Headed Gull, etc.), but in smaller overall numbers. Point Density Analysis (ArcGIS Spatial Analyst) with kernel estimation, and a 10-km search radius,was used to generate maps illustrating the density of colony measures (i.e., maximum count by species,CII by species), modelled as a continuous field (Gatrell et al. 1996). Actual colony locations were subsequently overlaid on the resulting cluster map. Sites not identified as important should not be assumed to be unimportant.

The national wetland layer contains wetland data compiled from the best available data from each region, classified by wetland type. Wetlands are mapped as polygons in geographic layers, which are integrated into a master geodatabase at the national scale.Information from each contributing dataset was classified based on the Canadian Wetland Classification System, which contains five main wetland classes (Bog, Fen, Marsh, Swamp, and Shallow Water) that represent the types of wetlands encountered in Canada. An additional category, “partially classified” was used to preserve boundary information for wetlands that could not be classified into the main categories with existing information.

This file includes the spatial boundaries for the Pacific Great Blue Heron Potential Area of Occupancy for its entire Canadian range. The Potential Area of Occupancy is a simple model that highlights the heron's preferred forest habitat at a high level. Potential Area of Occupancy is defined as terrestrial areas within the Coastal Douglas Fir and Coastal Western Hemlock Biogeoclimatic zones that are less than 10 km from a potential foraging area and west of the Cascades mountain range. Potential foraging areas are defined as the entire coastline and major river systems less than 1000 m in elevation. Refer to the "Management Plan for the Great Blue Heron fannini subspecies (Ardea herodias fannini) in Canada" on the SARA Regristry for more information.

The Great Lakes Migrant Waterfowl Surveys provide periodic data on waterfowl abundance, spatial and temporal distributions, and use along the shorelines of major water bodies and river systems in Ontario during mostly during spring and fall, and to a lesser extent during summer and winter, seasons. The primary survey area covers the shoreline and nearshore (~1km) waters of the Lower Great Lakes region of Ontario, specifically including the St. Lawrence River, Lake Ontario, Niagara River, Lake Erie, Detroit River and Lake St. Clair and associated major marshes and embayments. Aerial surveys, typically flown several times within spring (March –May: 1969, 1971, 1972, 1975 –1979, 1981, 1982, 1984 –1988, 1991 –1996, 1998 –2003 & 2009 –2011) and fall (September –December: 1968, 1970, 1971, 1974 –2003 & 2009 –2011) survey periods, have been conducted periodically on a relatively regular basis (approx. 5-10 years) along the Lower Great Lakes shorelines between 1968 and 2011. Smaller-scale surveys also have been conducted periodically during summer (June –August: 1968 –1970, 1972, 1974, 1975, 1977, 1982, 1984, 1986, 1989, 1999 & 2002) in this region. This survey often has been conducted in conjunction with the Midwinter Survey, so its data (up to 2004) also are included in the CWS Migrant Waterfowl Surveys database (Year ≥2004 & Month = January & February).Data from several aerial surveys conducted periodically during the non-breeding period outside the Lower Great Lakes region also are included in this database. Spring and fall surveys have been conducted along the shorelines and nearshore waters of the Upper Great Lakes region of Ontario, specifically at St. Clair River (Fall 2012 & 2013), Lake Huron (Fall 1973, 1996; Spring 1974) / Georgian Bay (Fall 1973, 1996, 2012 & 2013) & Lake Superior (Fall 2000). Aerial surveys also have been conducted inland in southeastern Ontario along the Rideau River (Fall 1998 & Spring 1999).

This spatial file includes the polygon boundaries of the four Pacific Great Blue Heron Conservation Regions in British Columbia, as described in the management plan. These Conservation Regions are recognized based on degree of isolation, population sizes, and differences in trends and threats. The management objective for Pacific Great Blue Heron is to ensure that all four Conservation Regions have stable or locally increasing numbers of herons. Refer to the "Management Plan for the Great Blue Heron fannini subspecies (Ardea herodias fannini) in Canada" on the SARA Regristry for more information.

The atlas provides printable maps, Web Services and downloadable data files representing seabirds at-sea densities in eastern Canada. The information provided on the open data web site can be used to identify areas where seabirds at sea are found in eastern Canada. However, low survey effort or high variation in some areas introduces uncertainty in the density estimates provided. The data and maps found on the open data web site should therefore be interpreted with an understanding of this uncertainty. Data were collected using ships of opportunity surveys and therefore spatial and seasonal coverage varies considerably. Densities are computed using distance sampling to adjust for variation in detection rates among observers and survey conditions. Depending on conditions, seabirds can be difficult to identify to species level. Therefore, densities at higher taxonomic levels are provided. more details in the document: Atlas_SeabirdsAtSea-OiseauxMarinsEnMer.pdf. By clicking on "View on Map" you will visualize a example of the density measured for all species combined from April to July - 2006-2020. ESRI REST or WMS map services can be added to your web maps or opened directly in your desktop mapping applications. These are alternatives to downloading and provide densities for all taxonomical groups and species as well as survey effort.

The Canadian Breeding Bird Census (BBC) Database contains data for 928 breeding bird plot censuses representing all known censuses of breeding birds carried out in Canada during the period 1929–1993. The 928 records in the database represent 640 unique census plots located in all provinces and territories, except Prince Edward Island. The BBC, which was replaced by the current Breeding Bird Survey, is one of the longest-running surveys of bird populations in North America, and was designed to help determine abundance and distribution patterns of bird species. An important feature of the BBC Database is the habitat data associated with each census plot. The most prevalent vegetation species in different layers (canopy, shrub and ground cover) were recorded to reflect the assumption that birds respond principally to vegetative structure.

Survey transects is a line feature class containing transects completed in 2011.

Environment and Climate Change Canada’s monitoring program for species at risk, rare and difficult-to-monitor species uses targeted sampling designs to assess the population status and trend of species that are not readily sampled by other programs. A formal analysis was used to prioritize landbird species for monitoring under this program. Old-forest songbirds were determined to be the highest priority for monitoring because they can be vulnerable to habitat disturbance, and their habitats are less common overall and difficult to restore once disturbed. An old-forest landbird monitoring program was initiated in 2014. A separate focused study is assessing the potential impacts of oil sands mining on Whooping Cranes, which migrate through the oil sands region twice annually and sometimes stop over during migration. Data from this study are not currently available.