Trophic interactions are critical drivers of ecosystem change and stability, yet are often excluded from fishery assessment models. For fisheries that are primarily dependent on a single prey species, replacing single species assessment models with multispecies models may improve population estimates while quantifying the impact of species interactions on fishery dynamics. In Lake Ontario, recreational salmonine fisheries, including Chinook Salmon (Oncorhynchus tshawytscha) and Lake Trout (Salvelinus namaycush), are heavily dependent on a single prey species, Alewife (Alosa pseudoharengus). My dissertation research focuses on assessing these predator-prey dynamics and then exploring how novel data streams could improve the assessment model. First, I developed a multispecies stock assessment model that jointly estimates the dynamics of Chinook Salmon, Lake Trout, and Alewife. I found that a risk assessment of future predator-prey dynamics indicated that recruitment of naturally reproduced Chinook Salmon could be a key driver of future fishery sustainability and additional data on the relative abundance of naturally reproduced fish could improve model estimates. Second, to address this data gap, I piloted a project to explore if parentage-based tagging could be an accurate monitoring program to provide data on the relative abundance of hatchery-origin and naturally reproduced Chinook Salmon. Third, I compared the resource-efficiency of parentage-based tagging to a suite of other mass marking techniques that have been used to differentiate hatchery-origin and naturally reproduced Chinook Salmon in Lake Ontario. Finally, I assessed the impact of a parentage-based tagging monitoring program on improving model estimates relative to expanding existing monitoring programs.