Although sediment is a natural component of stream ecosystems, excess sediment presents a threat to natural freshwater ecosystems. Sediment management is complicated because sediment can be dissolved in the water column, suspended as particles in the water column, or rest on the bottom of the stream bed, and can move between these forms (e.g. bedded sediment can be resuspended). Each form of sediment affects aquatic life in a specific way. To manage stream sediment in a way that protects aquatic life, we need to understand the ways different forms of sediment affect living things, and we need to be able to predict how sediment changes form under different stream conditions (for example, during high water events). To improve our understanding of these things, the studies in this dissertation set out to: (1) identify how often sediment is specifically mentioned as the primary pollutant “stressor” of the benthic macroinvertebrate community (primarily aquatic insects); (2) determine which forms of sediment have the largest negative impacts on aquatic insects in Virginia and what levels of sediment may cause harm; and (3) measure the changes of sediment between suspended and bedded forms in a small stream to provide information needed to restore the health of stream ecosystems. An inventory of published US Clean Water Act Total Maximum Daily Load (TMDL) reports, which states write to identify their impaired waters and their plans to improve those waters, revealed that sediment is an important stressor in over 70% of waters that have altered aquatic insect communities. If the language used to describe how waters are evaluated and what is causing the impairments were standardized among states, data collected under the Clean Water Act could be more broadly used to help understand water quality issues and ways to address them. Analysis of 10 years of Virginia Department of Environmental Quality sediment and aquatic insect community data collected within 5 ecoregions of the state indicates that a combination of 9 sediment parameters reflecting dissolved, suspended, and bedded forms explains between 20.2% and 76.4% of the variability in the health of the aquatic insect community within these regions. Embeddedness, which measures how much larger particles such as gravel and cobble are buried by finer particles like sand; and conductivity, which is a measure of dissolved salts in the water column, both have substantial impacts on the aquatic insect community. Sensitivity thresholds for embeddedness and conductivity indicate the levels of these parameters above which 5% of insect families are absent from a stream; therefore, these levels are considered protective of 95% of the insect community. Thresholds for embeddedness are 68% for the 5 combined ecoregions, 65% for the Mountain bioregion (comprised of Central Appalachian, Ridge and Valley, and Blue Ridge ecoregions), and 88% for the Piedmont bioregion (comprised of Northern Piedmont and Piedmont ecoregions). Thresholds for conductivity are 366 µS/cm for combined ecoregions, 391 µS/cm for the Mountain bioregion, and 136 µS/cm for the Piedmont bioregion. These thresholds can be used by water quality professionals to identify waters with sediment impairments and can be used to help identify appropriate stream restoration goals. A study of sediment movement within the channel of a small stream indicated average transport speeds of ~ 0.21 m/s during floods with peak flows of ~ 55 L/s. The use of rare earth elements (REE) to trace sediment particles revealed individual particle transport distances ranging from 0 m to >850 m. Deposition on a unit area basis was greater in the stream channel than on the floodplain, and the movement of sediment from the stream bed to the water column and back again during sequential floods was evident. Approximately 80% of the tracer was deposited within the first 66 m of the reach. This information can aid the development of models that predict the impact of stream restoration practices on in-stream habitat and improve predictions on the time it will take between the initiation of stream restoration projects and when we see improvements in the biological community. / PHD

Bedload transport studies are essential in the understanding of river forms, functions and processes. These studies have been done using various methods over the past century. In recent years Radio Frequency Identification Technology (RFID) has become popular with researchers to track bedload particles. However, no standard operating procedures are used in the implementation of this technology. Methods used for tagging, seeding and tracking RFID tracers (RFID transponders inserted into a bedload particle) can introduce variability in their detection. In this study, RFID tracers were used to study four sites in Laurel Creek in Waterloo, Ontario. Two hundred RFID tracers were seeded in each of the four sites. Following three major storm events, the tracers were tracked with an antenna and their locations surveyed. The tracers were able to be detected to a precision of 1 m as a transponder used can be detected at a maximum of this distance. Practical tracking in the field highlighted the need for the understanding of how precisely the tag location can be identified. Laboratory experiments were designed and carried out to determine the effects of factors (tracer orientation, antenna orientation, tracer size, clustering of multiple tracers, burial depth, saturation and submergence of the soil matrix) that possibly confounded detection. Of these factors, tracer orientation, clustering and burial depths were determined to be the ones that affected detection distances the most. A transponder in a vertical orientation was found to have as much as 40% larger range of detection than a transponder in a horizontal orientation (i.e., they could be detected from further away). Additionally, “skip zones” were identified during laboratory and field experiments. These are zones of gaps in the electromagnetic field of the transponder that occur directly over the transponder. These zones were experimentally determined to extend to approximately 10 cm on each side of the transponder. Therefore, by identifying the skip zones, the tracers can be located to a precision of 10 cm; this is an order of magnitude smaller than the published detection limit of the transponder. The precision of detection can also be improved by the reduction of the effects of confounding factors. However, the improvement in the precision of detection is a tradeoff with the ease of detection. A tagging, seeding and tracking protocol is recommended to counter the effects of confounding factors.