Search

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at “being held” (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at “being held” exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have resulted in the intensified use of water resources. For example, open pit bitumen extraction by Canada’s oil sands operations uses an estimated volume of three barrels of water for every barrel of oil produced. The waste tailings–oil sands process water (OSPW)–are stored in holding ponds, and present an environmental concern as they are comprised of residual hydrocarbons and metals. Following the hypothesis that endogenous OSPW microbial communities have an enhanced tolerance to heavy metals, we tested the capacity of planktonic and biofilm populations from OSPW to withstand metal ion challenges, using Cupriavidus metallidurans, a known metal-resistant organism, for comparison. The toxicity of the metals toward biofilm and planktonic bacterial populations was determined by measuring the minimum biofilm inhibitory concentrations (MBICs) and planktonic minimum inhibitory concentrations (MICs) using the MBEC ™ assay. We observed that the OSPW community and C. metallidurans had similar tolerances to 22 different metals. While thiophillic elements (Te, Ag, Cd, Ni) were found to be most toxic, the OSPW consortia demonstrated higher tolerance to metals reported in tailings ponds (Al, Fe, Mo, Pb). Metal toxicity correlated with a number of physicochemical characteristics of the metals. Parameters reflecting metal-ligand affinities showed fewer and weaker correlations for the community compared to C. metallidurans, suggesting that the OSPW consortia may have developed tolerance mechanisms toward metals present in their environment. [ABSTRACT FROM AUTHOR]

Purpose: To examine a process for evaluating physiotherapy (PT) students' knowledge of and adherence to the Ambassador Low Back Pain (LBP) guideline using vignettes. Methods: The study used a cross-sectional survey design. Participants were PT students who had received information related to the guideline as part of their curriculum. Primary measures were responses to questions about the management of four clinical vignettes. Adherence to guideline recommendations was measured by comparing participant scores to a 'guideline-based' set of responses from a physiotherapist involved in developing the Ambassador guideline, which was considered a criterion standard. Results: A total of 74 respondents provided complete data, for a response rate of 89%; 65 (88%) reported no knowledge of the guideline. Overall consistency with the criterion standard was high (>70%). Respondents demonstrated high adherence when identifying red flags and deciding whether to refer to another provider. Conclusion: Despite known exposure, knowledge of the guideline was low in this sample of Canadian PT students. Nevertheless, in several key areas, unconscious adherence was high relative to the guideline-based criterion standard. With minor modifications, the vignettes are suitable for evaluating the Ambassador LBP guidelines in a larger study. [ABSTRACT FROM AUTHOR]