Degree Name

Master of Science (MS)

Semester of Degree Completion


Thesis Director

Jeffrey R. Laursen


In the past different sampling strategies have been used to relate macroinvertebrate assemblages with habitat quality in the Sangamon River, above and below the sanitary district effluent discharge in Decatur, IL. The standard 20 jab method of proportional sampling in multiple microhabitats, based on QHEI physical habitat score, sampled allowed for comparison between sites based on overall community composition. However, it oversampled fine sediments, which dominate the Sangamon, therefore potentially missing sensitive taxa in isolated quality habitats. In the fall of 2016 I tested an enhanced qualitative approach to better gauge the importance of microhabitat types to macroinvertebrate assemblages in the river. We sampled five different natural microhabitats (riffles, fine sediments, root wads, snags, leaf packs) and 2 artificial substrates (Hester Dendy samplers, artificial leaf packs) at seven different sites. Sampling a subset of specific microhabitats allow for comparisons between sites, capture of sensitive taxa, and identification of specific habitats important in reclamation efforts. Non-metric multidimensional scaling (NMDS) in conjunction with a PERMANOVA and two-factorial MANOVA tests showed there were significant differences in assemblages between microhabitat types and between upstream and downstream sampling sites. Results indicate that root wad microhabitats are distinct from other microhabitat's assemblage structure because they harbor more sensitive taxa than any other microhabitat thus making it an ideal habitat to sample in this system. However, microhabitat assemblage structure was found to be heavily influenced by physical factors (QHEI and flow) overshadowing any potential effects of water quality alteration provided by the effluent. Ultimately, changing the flow patterns of the Sangamon to replicate a more temporal-based/natural regime, rather than the current altered one, would in turn minimize variation in physical factors between sampling sites.