ECO - Business and economic (economy) Economic activities or employment. For example, resources describing labor; revenue; commerce; industry; tourism and ecotourism; forestry; fisheries; commercial or subsistence hunting; and exploration and exploitation of resources, such as minerals, oil, and gas.

Coastal vegetated ecosystems are recognized for their role in cycling and storing carbon in the world’s oceans (i.e., blue carbon); however, high uncertainty in carbon sequestration rates is partly due to an absence of studies estimating carbon export to the deep sea. We modeled export from nearshore kelp forests and seagrass beds, showing variability by orders of magnitude across spatial scales (3 to hundreds of kilometers), kelp and seagrass species, seasons, and carbon forms, raising caution in using generalized export rates in blue carbon accounting. Our results also show rapid (20 to 30 days) and extensive export of neutrally buoyant dissolved organic carbon particles to the shelf break (up to 44% within 90 days), contrasting sinking particulate organic carbon particles that largely remained within 100-meter water depth in the nearshore. These results improve estimates of carbon sequestration by blue carbon ecosystems and reveal contrasting patterns of export relative to other regions of the globe. Cite this data as: Kira A. Krumhansl et al., Pathways of blue carbon export from kelp and seagrass beds along the Atlantic coast of Nova Scotia.Sci. Adv.11,eadw1952(2025).DOI: 10.1126/sciadv.adw1952

Many communities in Canada depend to some extent on forestry and the forest sector. The importance of the forest industry to the regional economy can be assessed using the CanEcumene GIS Database. “Ecumene” is a term used by geographers, meaning “inhabited lands.” A forest ecumene refers to areas where human settlement coincides with forested areas, including locations where people depend on the forest for their livelihood. Populated places in the ecumene database are referenced using natural boundaries, as opposed to administrative or census boundaries, and provide a more suitable means for integrating socio-economic data with ecological and environmental data in a region.An analysis of ecumene labour force data and location of mill facilities resulted in a generalized rendering showing regional dependency of the forest industry. The location of mill facilities layer includes information on mill type (i.e., pulp and saw) and ownership. The sensitivity to forest industry layer shows which forest communities and regions are more sensitive to economic impacts in the forest industry.Two layers are provided: the sensitivity of forest communities and regions to forest industry, and the location of mill facilities.

Sightings data were collected by the Mingan Island Cetacean Study (MICS) from 1980 to 2008 with annual surveys realised in the Gulf of St. Lawrence between the end of may and early november. Surveys were conducted using inflatable boats enabling the close approaches necessary to photograph and biopsy blue whales.The aim of this project was to provide additional information for designating blue whale critical habitat as required under the Canadian Species at Risk Act.For more details consult the following report:Ramp, C. and Sears, R. 2013. Distribution, densities, and annual occurrence of individual blue whales (Balaenoptera musculus) in the Gulf of St. Lawrence, Canada from 1980-2008. DFO Can. Sci. Advis. Sec. Res. Doc. 2012/157. vii + 37 p.http://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2012/2012_157-eng.htmlData of blue whale sightings, collected by the MICS, have been analysed per km of effort in 3 x 3 km grid cells in the Gulf of St. Lawrence for the 2000-2008 period.

The St. Lawrence Estuary is known as a summer foraging area for several species of marine mammals, including several species of rorquals. Among these is the blue whale, which feeds almost exclusively on euphausiids. Therefore, the abundance, distribution and local density of krill should logically be a strong explanatory variable for the distribution of blue whales. However little is known about the spatial association of blue whales with the aggregation dynamics of krill in eastern Canada. Six years of acoustic surveys, conducted in August from 2009 to 2014, were undertaken to study the medium- and small-scale distribution of krill within the northwestern Gulf of St. Lawrence and estuary. The data shows a mosaic of the maximum annual density of arctic krill (T. raschii) made from these surveys.McQuinn, I.H., Gosselin, J.-F., Bourassa, M.-N., Mosnier, A., St-Pierre, J.-F., Plourde, S., Lesage, V., Raymond, A. 2016. The spatial association of blue whales (Balaenoptera musculus) with krill patches (Thysanoessa spp. and Meganyctiphanes norvegica) in the estuary and northwestern Gulf of St. Lawrence. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/104. iv + 19 p.

A modelling analysis conducted by Fisheries and Oceans Canada (DFO) identified these areas as the most suitable habitat for Blue whales: Gulf of St. Lawrence, waters off the southern coast of Newfoundland, the region of Mecatina Trough, the Esquiman Channel and the continental shelf margin off Nova Scotia. They represent important areas for foraging, feeding and socializing for Blue whales. The sources of data used to determine these important areas (by the enclosing boxes method) and the annual and seasonal cycles of Blue whale travel patterns include, but are not limited to, radio and satellite telemetry, passive acoustic monitoring, line-transect aerial surveys, anecdotal reports of observations and modelling.This layer does not represent the general distribution of the Blue whale. Important areas have been identified by reviewing several sources of information and to the best of researchers' knowledge. Several information about Blue whales, their behaviour and habitat use are still unknown. Data is scarce in some areas during winter periods. Observation efforts mostly occur during the summer period, however, data sources can validate their presence during seasons when the observation effort is lower. The Mecatina trough region represents an important area based on historical and non-current data. The presence data per month refers strictly to the information available in the cited research document, and does not express the absence of the species outside the months when a presence was validated. The presented information is valid until the following research survey.Reference:Lesage, V., J.-F. Gosselin, J. W. Lawson, I. McQuinn, H. Moors-Murphy, S. Plourde, R. Sears. and Y. Simard. 2018. Habitats important to blue whales (Balaenoptera musculus) in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/080: iv + 50 p.

11 tagged Blue whales (Balaenoptera musculus) were tracked during the daytime movements as well as the feeding behaviour in the St. Lawrence River estuary. Kernel density was applied to derminate the high density feeding areas of all individuals combined (30, 40, 50, 60, 75, 95 %).Doniol-Valcroze T, Lesage V, Giard J, Michaud R, 2012. Challenges in marine mammal habitat modelling: evidence of multiple foraging habitats from the identification of feeding events in blue whales. Endang Species Res, Vol. 17 : 255–268, doi : 10.3354/esr00427(English version only)

The blue whale (Balaenopterus musculus) is a wide-ranging cetacean that can be found in all oceans, inhabiting coastal and oceanic habitats. In the North Atlantic, little is known about blue whale distribution and genetic structure, and if whether animals found in Icelandic waters, the Azores, or Northwest Africa are part of the same population as those from the Northwest Atlantic. In the Northwest Atlantic, seasonal movements of blue whales and habitat use, including the location of breeding and wintering areas, are poorly understood.The behaviour of remotely-monitored animals can be inferred from a time series of location data. This is because animals tend to demonstrate stochasticity in their movement paths as a result of spatial variation in environmental characteristics, such as topography or prey density (Curio 1976; Gardner et al. 1989; Turchin 1991; Wiens et al. 1993). Predators are expected to decrease travel speed and/or increase turning frequency and turning angle when a suitable resource, e.g., food patch, is encountered (Turchin 1991), otherwise known as area-restricted search (ARS). In contrast, animals in transit or travelling tend to move at faster and more regular speeds, with infrequent and smaller turning angles (Kareiva and Odell 1987; Turchin 1998).Based on satellite telemetry to track the seasonal movements of 24 blue whales from eastern Canada in 2002 and from 2010 to 2015, it was possible to estimate trajectories and locations where ARS behaviour of blue whales was inferred at a 4h time interval.To assess blue whale movements and behavior, a Bayesian switching statespace model (SSSM) was applied to Argos-derived telemetry data (Jonsen et al. 2005; Jonsen et al. 2013). An SSSM essentially estimates animal location at fixed time intervals, movement parameters and behavioral patterns.Two important sources of uncertainty can be measured separately: estimation error resulting from inaccurate observations (Argos location error) and process variability linked to the stochasticity of the movement process (behavior mode estimation) (Jonsen et al. 2003; Patterson et al. 2008).The points visible on land are the result of errors in the Argos geographic position calculation. They have been deliberately left unchanged to assess the performance of the model, which was able to clean up some positions, but not all.Lesage, V., Gavrilchuk, K., Andrews, R.D., and Sears, R. 2016. Wintering areas, fall movements and foraging sites of blue whales satellite-tracked in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/078. v + 38 p.

The consolidation and enhancement of the commercial centers indicated in __map 4 — Concentrations of commercial establishments __, as well as the commercial activity of streets, axes and shopping centers should be preferred.In addition, the economic clusters represented on __map 8 — Economic clusters of the agglomeration of Montreal__ is a particularity of the Montreal region. These clusters total important employment pools and have the potential for growth and wealth creation.The data available in this set come from sections 2.1 and 2.2 of the Land Use and Development Plan of the Agglomération de Montréal.This urban planning and development plan for the agglomeration of Montreal outlines the main parameters that will guide the Montreal agglomeration council in decisions relating to land use planning in the coming years. From a perspective of sustainable development, this document guides decisions that shape the territory in order to promote compact and greener neighborhoods, increase public and active transportation, support the economic dynamism of the agglomeration and highlight areas of interest.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This theme presents observations of invasive exotic species (IAS)transmitted and validated using the Sentinelle tool, an EEE detection system.An invasive exotic species is a plant, animal or microorganism (virus,bacteria or fungi) that are introduced outside of their natural range. Sonestablishment or its spread may pose a threat to the environment,the economy or society. The species listed are species of fauna and floraconcerning (or potentially worrying) for Quebec's biodiversity. Ellesinclude EEE present in Quebec and EEE not listed in Quebec atmonitor.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**