Date of Award

2018

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Earth and Environmental Sciences

Keywords

Athabasca River; Bioassessment; Biodiversity; Macroinvertebrates; Metabarcoding; Oil Sands

Supervisor

MacIsaac, Hugh

Supervisor

Heath, Daniel

Rights

info:eu-repo/semantics/openAccess

Abstract

Currently, a need exists to assess the biological significance of distinct stressors related to groundwater inputs in the Lower Athabasca River (LAR). I used traditional taxonomy supplemented with metabarcoding to conduct a bioassessment of the LAR benthic macroinvertebrate (BMI) communities. Using traditional taxonomy, I identified BMIs at the family level (or lower) at sites exhibiting either low or high conductivity in both upstream, downstream and industry adjacent loci. I used metabarcoding as a complementary approach to traditional taxonomy and established a criterion to provide an efficient methodology for incorporating the two techniques. However, due to the quality of DNA in the pooled barcoded samples, I could not sequence any samples to generate a second, complementary, community data set. Results from traditional taxonomy alone found no relationship between diversity and conductivity or location. I observed that conductivity was associated with the evenness of taxa present at sites represented by reference sites upstream and industry adjacent oil sands sites as well as between reference sites upstream and saline downstream sites. Rank abundance distributions of BMI families did not fit generally accepted theoretical models of reference and stressed conditions for low and high conductivity sites, respectively. Lastly, I did not observe a difference in the composition of taxa between communities across all site types. Results of my study suggest that groundwater inputs from natural, municipal or industrial sources may influence the composition of taxa in different ways, but do not affect overall diversity or evenness of benthic macroinvertebrate communities. This research did not detect a difference in biodiversity between reference sites and sites impacted by municipal, industrial or natural inputs.

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