Incorporating Industrial and Climatic Covariates into Analyses of Fish Health Indicators Measured in a Stream in Canada’s Oil Sands Region

Abstract

Industrial and other human activities in Canada’s oil sands region (OSR) influence the environment. However, these impacts can be challenging to separate from natural stresses in flowing waters by comparing upstream reference sites to downstream exposure locations. For example, health indicators of lake chub (Couesius plumbeus) compared between locations in the Ells River (Upper and Lower) in 2013 to 2015 and 2018 demonstrated statistical differences. To further examine the potential sources of variation in fish, we also analyzed data at sites over time. When fish captured in 2018 were compared to pooled reference years (2013–2015), results indicated multiple differences in fish, but most of the differences disappeared when environmental covariates were included in the Elastic Net (EN) regularized regression models. However, when industrial covariates were included separately in the EN, the large differences in 2018 also disappeared, also suggesting the potential influence of these covariables on the health of fish. Further ENs incorporating both environmental and industrial covariates along with other variables which may describe industrial and natural influences, such as spring or summer precipitation and summer wind speeds and distance-based penalty factors, also support some of the suspected and potential mechanisms of impact. Further exploratory analyses simulating changes from zero and the mean (industrial) activity levels using the regression equations respectively suggest effects exceeding established critical effect sizes (CES) for fish measurements may already be present or effects may occur with small future changes in some industrial activities. Additional simulations also suggest that changing regional hydrological and thermal regimes in the future may also cause changes in fish measurements exceeding the CESs. The results of this study suggest the wide applicability of the approach for monitoring the health of fish in the OSR and beyond. The results also suggest follow-up work required to further evaluate the veracity of the suggested relationships identified in this analysis.

A Review of Control Charts and Exploring Their Utility for Regional Environmental Monitoring Programs

“…Control charts have been used in many environmental studies, including studies of water quality, air quality, sediment quality, and many others [21,30,37,41,72,[100][101][102][103][104][105][106][107][108][109][110][111]. Control charts have also been suggested and used as tools in conservation science [31,112], and the concept has been used in other work, e.g., [96,113].…”

“…However, a run of four d b t values from 2016 to 2019 at the ELBN exceeded the UCL computed for the first two years from the MAKR data (Figures 1 and 5). These exceedances could be related to the changes in the Ells Basin after development of the Joslyn North mine was halted in May 2014 [96].…”

“…However, a standardized and simplified presentation can also obscure the details embedded in the analyses, including the validity of any assumptions made. The approach also requires reference data, which may be challenging to obtain, especially in the OSR, where multiple stressors, such as physical disturbances of habitats, may often be accompanied by atmospheric deposition of contaminants of concern [96].…”

Examination of recent hydroelectric dam projects in Canada for alignment of baseline studies, predictive modeling, and postdevelopment monitoring phases of aquatic environmental impact assessments

“…For further discussion of sustainable flows, the Ecological Limits of Hydrologic Alteration (ELOHA) framework provides guidance on ecologically relevant hydrological modeling (Poff et al, 2010). The main drivers of fish growth are discharge (or water level) and water temperature (e.g., Arciszewski et al, 2022; Jonsson & Jonsson, 2009; Zarri & Palkovacs, 2019). Although these were the services with good baseline and quantitative modeling at many facilities, more useful would be to evaluate how flow and temperature differences drive performance of the system.…”

“…The main drivers of fish growth are discharge (or water level) and water temperature (e.g., Arciszewski et al, 2022; Jonsson & Jonsson, 2009; Zarri & Palkovacs, 2019). Although these were the services with good baseline and quantitative modeling at many facilities, more useful would be to evaluate how flow and temperature differences drive performance of the system.…”