The use of environmental DNA (eDNA) to quantify aquatic biodiversity is a rapidly emerging technique used for academic, government, and private purposes. The ease and relative low costs of taking environmental samples versus other methods of sampling makes eDNA methods an attractive choice, however, the broad variability in natural environments and a lack of standardization in laboratory analysis methods means that the robustness and replicability of these methods remain difficult to assess while the uncertainty around eDNA estimates requires validation. To address these deficiencies, I designed novel metabarcodes to detect and distinguish between closely related Salmonid species. I then conducted a controlled winter experiment using 12 naturalized experimental streams and stocked caged Brook trout (Salvelinus fontinalis), Rainbow Trout (Oncorhynchus mykiss), and Cutthroat Trout (Oncorhynchus clarkii) in each stream at varying biomasses. Triplicate water samples were collected from the start, middle, and end of each stream and filtered to collect DNA. I analyzed these samples using the novel, narrow-target DNA metabarcodes, broad-target metabarcodes, and species-specific qPCR to compare the detection rate and quantification reliability of each method. My thesis revealed that both assay choice and bioinformatic choices affect species detection and taxon resolution of eDNA. DNA quantities varied with biomass and distance downstream, consistent with previous research, but also with an ecological gradient present in the naturalized streams. My results indicate that stream ecology is crucial to take into consideration when making inferences from eDNA methods. I also discuss the importance of accounting for overdispersion and non-detects that frequently arise in eDNA data due to the physical state of eDNA in aquatic environments. To my knowledge, this is the first eDNA experiment to directly examine eDNA quantification under a controlled, replicated design within a semi-natural system. This work highlights the need for development of process-based models when using eDNA application to infer species abundances.