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From 1 - 10 / 201
  • Data will be created primarily using hydrodynamic-ice models, such as the Massachusetts Institute of Technology General Circulation Model (MITgcm) and the Los Alamos Sea Ice model (CICE). Input data for both models will be collected online via US Environmental Protection Agency and Environment and Climate Change Canada (ECCC) databases. The MITgcm will likely provide the hydrodynamic core for CICE, thus allowing us to output velocity, temperature, surface elevation, heat flux, and ice diagnostic data for Lake Erie. Post-processing will be performed using proprietary software developed using MATLAB and Python. All codes will be made available on a private GitHub. Interested individuals may contact the group for access to these codes and data.

  • A review of the Muskoka Watershed Council reporting, as well as literature that also reviewed other programs, demonstrated a need for a standard, more objective, approach for identifying, and where budgetary limitations require, prioritizing indicators or reducing their numbers. The Criteria-based Ranking (CBR) process was developed, inspired by tools used in Environmental Assessment. A workshop with members and guests of the Muskoka Watershed Council was carried out on August 5, 2016, at which the CBR process was tested to see if using the process would yield different results than the conventional approach. The outcome of the workshop was that using this standard process generated a very different outcome than what emerged from the conventional approach.

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    These data were collected to address the need for a hydroecological monitoring program, with focus on metals of concern, in the Peace-Athabasca Delta (PAD). To do so, we deployed artificial substrate samplers in ~60 lakes for the duration of the ice-free seasons of 2017 and 2018. We assessed the accrued biofilm-sediment mixtures for enrichment of metals of concern above pre-industrial levels determined from analyses of sediment cores in the PAD. We also related metals enrichment to periphytic algae community composition, inferred from diagnostic algal pigments, to explore taxa-specific rates of active biological uptake of metals of concern.

  • Indigenous Self-governance to some is seen as the new way forward for Indigenous communities to govern their people. The Délįne Got’ine Government (DGG) is the first community in the Northwest Territories (NWT) to self-govern. Despite the power bestowed upon this Indigenous government, there are still challenges to effectively govern their people, as the community is still bound under the policies of the Land Claim agreement. While the Land Claim agreement set the foundation for self-governance, it is a policy that was co-created with the communities in the settlement area, the territorial government, and the federal government. In the summer of 2019, a country food processing training was operated out of a food processing facility to expose the community to new food processing techniques, increase food safety, and introduce muskox as a viable country food alternative. The people of Délįne are caribou people, but with declining herd populations, the harvesting rate has drastically declined, and traditional food consumption has thus reduced. As part of this study, an evaluation was conducted on both the country food training and the food processing facility to understand the community’s perceptions on these new foods and methods. The evaluation utilized a mixed method approach, with a pre and post survey, participant observation, and semi structured interviews. This need for this evaluation is the result of prior community based participatory research conducted with the community.

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    Intestinal samples were collected from fathead minnows. Fish collected from the field were collected in minnow traps set overnight. All samples were collected with sterile dissection tools, and samples were immediately placed on ice prior to being transferred to a -80C freezer for long-term storage. 16s amplicon sequencing Total genomic DNA was extracted from guts using the DNeasy PowerSoil Kit (Qiagen Inc., Mississauga, ON). Concentrations were measured using a Qubit 4 Fluorometer and dsDNA HS assay kit (ThermoFisher Scientific, Waltham, MA). The V3-V4 variable region of the 16S rRNA gene was amplified using the Bact-0341 forward primer (CCTACGGGNGGCWGCAG) (Klindworth et al., 2013) and the Bact-806 reverse primer (GGACTACNVGGGTWTCTAAT) (Apprill et al., 2015). Samples were dual indexed to increase throughput of sequencing (Fadrosh et al., 2014). Samples were amplified with a 50 μL PCR reaction including Phusion green polymerase (ThermoFisher Scientific) using a SimpliAmp thermal cycler (ThermoFisher Scientific) under the following conditions: initial denaturation at 98°C for 30s, followed by 25 cycles of 98°C for 30s, 58°C for 30s, and 72°C for 30s, with a final extension at 72°C for 10 min. PCR products were assessed for size and specificity using electrophoresis on a 1.2% w/v agarose gel and purified using the Qiagen QIAquick PCR Purification Kit (Qiagen Inc.). All purified products were quantified with the Qubit dsDNA HS assay kit and concentrations were adjusted to 1 ng/ μL with molecular-grade water. Purified products were pooled, and libraries were constructed using the NEBNext® DNA Library Prep Master Mix Set for Illumina® (New England BioLabs Inc., Whitby, ON). Libraries were quantified prior to sequencing using the NEBNext® Library Quant Kit for Illumina®. Sequencing was performed on an Illumina® MiSeq instrument (Illumina, San Diego, CA) using a 2x300 base pair kit. See also: www.gwfnet.net/Metadata/Record/T-2020-11-30-81jA3aJ82ZSUGNzmMj9aYhgw

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    To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.

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    These data explored how plant community composition and traits change across the Scotty Creek Forest Dynamics plot in response to environmental variation, including active layer thickness, organic layer thickness, and forest structure (i.e., canopy cover and tree basal area). To do this, we used a random stratified design to select ten 20 m by 20 m grid cells belonging to each of four aboveground tree biomass categories for a total of 40 grid cells across the Scotty Creek Forest Dynamics Plot. Within each grid cell, we randomly placed two 1 m by 1m quadrats to assess community composition of vascular plants via stem counts and measure canopy cover, active and organic layer thickness. These data were averaged to provide an estimate at the grid cell level. Basal area was calculated at the level of the grid cell. Plant functional traits were collected from 3 replicate individuals per species within 2-3 grid cells per aboveground tree biomass category.

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    The dataset is comprised of inputs to and outputs from the Cold Regions Hydrological Model (CRHM) when it was run as a virtual model of the High Elevation Grasslands class, as defined by Wolfe et al. (2019). These watersheds represented typified prairie watersheds based on physiogeography and coherent response to environmental change. Model parameters were informed by the results of Wolfe et al. (2019). The .prj files necessary to run the virtual models are included in the dataset. Climate forcing data are from the Adjusted and Homogenized Canadian Climate Dataset from a cohort of stations contained within each watershed class and cover a period from 1960-2006. There are a series of climate sensitivity scenarios that include applying a delta method to the original climate data (i.e., 1°C increments of warming, and -20%, +10%, +20% and +30% of precipitation). Model output includes hourly catchment outflow, rainfall, snowfall, snow sublimation and snow water equivalent for the baseline and each scenario. See also: www.gwfnet.net/Metadata/Record/T-2021-11-25-B1tYZfnY3r0y7es3nd8M7Ew

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    The extraction of gold from arsenopyrite at Giant Mine, near Yellowknife, generated arsenic trioxide between 1940 and 2004. This contamination went beyond the immediate mining sites via emission to the atmosphere and subsequent deposition on soils and lakes. At present, the extent of this legacy is poorly known. Yellowknife is in the subarctic area, one of the most rapidly warming areas in the world. As climate warms, the permafrost melts and the decomposition rates for organic matter accelerates. This increases the load of dissolved organic matter, promotes greenhouse gas emissions and increases the mobility of contaminants in the water. The water content (porewater) of sediment samples was analysed by inductively coupled plasma mass spectrometry (ICP-MS) for metals and metalloids. Acid volatile sulfides (AVS) were analysed by ultraviolet-visible spectroscopy after extractions. Comparison of the concentration profile obtained for different elements allows speculating on the reactions that occur in the sediment. Finally, inverse diagenetic modelling was performed to determine arsenic reaction rates and the fluxes. This information was then used to discriminate between natural and anthropogenic arsenic sources, and to quantify its mobility in sediments and its probabilities of remobilization to lake water.

  • The Soil Landscapes of Canada were downloaded from Agriculture and Agri-Food Canada website (http://sis.agr.gc.ca/cansis/nsdb/slc/v3.2/index.html). Agriculture and Agri-Food Canada developed the Soil Landscapes of Canada (SLC) version 3.2 to provide information about the country's agricultural soils. For this project data was cropped to Great Lakes and then converted into the NetCDF format. Data are then made available to the project collaborators on a private GitHub. Researchers interested in finding more about the data can email Juliane Mai (University of Waterloo; juliane.mai@uwaterloo.ca)