The datasets contain land surface evapotranspiration (ET, in mm of H2O) for Canada's landmass at a spatial resolution of 5-km and temporal intervals of a month and a year over a 24-year period of 2000-2023. The ET was produced by the Land Surface Model EALCO (Ecological Assimilation of Land and Climate Observations) developed at Natural Resources Canada. The EALCO model was run at a 30-minute time step. The monthly (or annual) ET in the datasets is the sum of the 30-minute ET values in a month (or a year). Dew and frost formations simulated by EALCO are included in the ET as negative values, so the ET represents the net water flux between land surface and the atmosphere. Details of the datasets and the EALCO ET modelling algorithms can be found in Wang (2007, Simulation of Evapotranspiration and Its Response to Plant Water and CO2 Transfer Dynamics. J. Hydrometeorology, 9, 426-443, doi: 10.1175/2007JHM918.1) and Wang et al. (2013, Spatial and seasonal variations in evapotranspiration over Canada’s landmass. Hydrol. Earth Syst. Sci., 17, 3561–3575, doi:10.5194/hess-17-3561-2013).

The purpose of the cartographic data resulting from analyses of the ecological connectivity of natural environments in the St. Lawrence lowlands is to equip users by making it possible to integrate the concepts of ecological connectivity and the quality of the habitat of natural terrestrial environments into conservation issues. It is a knowledge tool for recognizing natural environments of importance for ecological connectivity in the St. Lawrence Lowlands region. This data is the result of research conducted by McGill University and its partners (Apex Resource Management Solutions, Quebec Center for Biodiversity Science and Habitat) on behalf of the Ministry of the Environment, the Fight against Climate Change, and its partners (Apex Resource Management Solutions, Wildlife and Parks) (MELCCFP).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Bivalve aquaculture has direct and indirect effects on plankton communities, which are highly sensitive to short-term (seasonal, interannual) and long-term climate changes, although how these dynamics alter aquaculture ecosystem interactions is poorly understood. Here, we investigate seasonal patterns in plankton abundance and community structure spanning several size fractions from 0.2 µm up to 5 mm, in a deep aquaculture embayment in northeast Newfoundland, Canada. Using flow cytometry and FlowCam imaging, we observed a clear seasonal relationship between fraction sizes driven by water column stratification (freshwater input, nutrient availability, light availability, water temperature). Plankton abundance decreased proportionally with increasing size fraction, aligning with size spectra theory. Within the bay, greater mesozooplankton abundance, and a greater relative abundance of copepods, was observed closest to the aquaculture lease. No significant spatial effect was observed for phytoplankton composition. While the months of August to October showed statistically similar plankton composition and size spectra slopes (i.e., food chain efficiency) and could be used for interannual variability comparisons of plankton composition, sampling for longer periods could capture long-term phenological shifts in plankton abundance and composition related to various processes, including climate change. Conclusions provide guidance on optimal sampling to monitor and assess aquaculture pathways of effects.Cite this data as: Sharpe H, Lacoursière-Roussel A, Gallardi D (2024). Ecological insight of seasonal plankton succession to monitor shellfish aquaculture ecosystem interactions. Version 3.2. Fisheries and Oceans Canada. Sampling event dataset. https://doi.org/10.25607/2ujdvh

Parks Canada’s National Program for Ecological Corridors was initiated to strengthen the network of protected areas across Canada through the creation of ecological corridors. To achieve this goal, Parks Canada sought out to develop tools for a common approach on the scientific and governance aspects of corridor creation and management. The National Priority Areas for Ecological Corridors (NPAECs) were developed using a scientific framework for national-scale prioritization of where ecological corridors are most urgently needed. Improving or maintaining ecological connectivity in these areas will greatly benefit biodiversity conservation and climate change adaptation. The NPAECs were identified based on a methodology that is multivariate, data driven, national in scale, and spatially explicit at a coarse resolution. The Criteria for Ecological Corridors in Canada provide a common approach to ensure ecological corridors are managed and stewarded to maintain or restore effective ecological connectivity, while upholding Indigenous stewardship values. They are derived from the internationally recognized International Union for Conservation of Nature’s Guidelines on Connectivity and adapted to the Canadian context. The NPAECs geographic data layer, the list of datasets used to identify them, the Criteria and their accompanying guidance can be found below. More details and context about both program elements are available on the Program’s webpage (https://parks.canada.ca/nature/science/conservation/corridors-ecologiques-ecological-corridors).

14 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

This record contains a comprehensive synthesis of previously published highly branched isoprenoid (HBI) results, providing a quantitative spatial and temporal assessment of carbon partitioning within the Arctic marine ecosystem and validating estimates of sea-ice particulate organic carbon (iPOC) values as quantitative predictors of ice algal carbon in Arctic food webs.This publication was a collaborative effort with the following contributors: David Yurkowski (Fisheries and Oceans Canada), Lisa Loseto (Fisheries and Oceans Canada), Steve Ferguson (Fisheries and Oceans Canada), Bruno Rosenberg (Fisheries and Oceans Canada), C.W. Koch (Natural History Museum, London, UK; University of Maryland Center for Environmental Science, Maryland, US); T.A. Brown (Scottish Association for Marine Science, Oban, Scotland); R. Amiraux (Centre for Earth Observation Science, University of Manitoba, Canada); C. Ruiz-Gonzalez (Scottish Association for Marine Science, Oban, Scotland); M. Maccorquodale (Scottish Association for Marine Science, Oban, Scotland); G. Yunda-Guarin (Québec-Océan and Takuvik, Biology Department, Laval University, Canada); D. Kohlbach (Norwegian Polar Institute, Fram Centre, Tromsø, Norway); N.E. Hussey (Integrative Biology, University of Windsor, Ontario, Canada).

100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

40 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

Zebra Mussel (Dreissena polymorpha) and Quagga Mussel (Dreissena rostriformis bugensis) have a long history of invasion in European and North American freshwater ecosystems, with significant ecological and economic impacts. An ecological risk assessment for these two invasive species for freshwater ecosystems in Canada was completed in April 2022 with the aim to provide science-based guidance to inform management decisions and actions. These include early detection, response planning, and/or regulatory and policy measures aimed at mitigating the potential spread and risk posed by Zebra and Quagga Mussels to Canadian freshwater ecosystems (DFO 2023). The Potential for Introduction (propagule pressure and connectivity), the Potential for Establishment (habitat suitability, including a Calcium-based and Maximum Entropy (MaxEnt)-based model), the Potential for Invasion, and the Ecological Impacts were used to derive Ecological Risk for Zebra and Quagga Mussels in Canada. This assessment did not evaluate the risk to individual waterbodies but rather was conducted at a 9,260 m x 9,260 m grid cell resolution. These high resolution maps are provided here. Maps of Ecological Risk at the sub-drainage level are also provided. Fisheries and Oceans Canada is not responsible for any omissions or errors that may be contained in this dataset and shall not be liable for any losses, financial or otherwise, due to the use of these data. Please credit Wilcox et al. 2024 as the source of the data in any maps, reports, or articles that are printed or published on paper or the Internet.

Water quality and ecosystem health data used to conduct a cumulative effects assessment of Canadian Great Lakes nearshore waters in support of the Great Lakes Water Quality Agreement are included in this dataset. The data was collected by various government and non-government agencies and organizations and integrated into this dataset to allow the assessment to be conducted. By conducting a regular, systematic assessment of cumulative effects in the nearshore waters of the Great Lakes Environment and Climate Change Canada (ECCC) is able to identify areas of high quality and areas under stress. Knowledge of ecological thresholds, other Great Lakes assessments, stressor information, indicators and local and traditional ecological knowledge will be used to aid in: 1) the identification and mapping of high quality nearshore areas and areas that are or may become subject to high stress and; 2) the determination of factors and cumulative effects that are causing stress or threats. Cumulative effects impacting the nearshore and future threats to areas of high ecological value will be better understood and the knowledge shared will assist in priority setting for science and management at a meaningful and practical spatial scale within each Great Lake and connecting channel.