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Mesocarnivores play important ecological roles and are valued by diverse stakeholders. These species are often the focus of conservation efforts or are managed for sustainable harvest. Management actions require accurate population monitoring, but such monitoring is challenging because mesocarnivores are elusive and persist at low densities. We addressed this challenge by evaluating 2 monitoring methods (scent stations and motion‐sensitive cameras) using multi‐method modeling. We estimated occurrence probabilities and habitat relationships for 8 mesocarnivore species by fitting occupancy models to data collected at 75 sites from October to December 2021 across a 3,200‐km2 system in New Hampshire, USA. We assessed the relative estimated precision of the methodological approaches and their costs. We also evaluated tradeoffs in occurrence estimation and uncertainty among study designs by analyzing simulations run across various numbers of study sites and 2 study durations. Cameras cost roughly 10 times more than scent stations but strongly outperformed them in terms of species' detectability and parameter estimate precision. Multi‐method models yielded the most precise estimates of occurrence probability and habitat relationships. Parameter estimates were on average twice as precise for camera and multi‐method models compared to scent stations. Additionally, the estimated precision and direction (positive or negative) of habitat relationships varied with the method employed. Longer camera deployments, additional study sites, and multi‐method approaches nearly always reduced uncertainty, but these reductions were species‐specific and generally most pronounced for more rarely detected species. Overall, our results demonstrate the utility of motion‐sensitive cameras traps for monitoring mesocarnivores while revealing the additional benefits of multi‐method modeling. Our results also provide guidance for designing monitoring programs for mesocarnivores while navigating tradeoffs between study design, cost, and uncertainty. Despite its benefits, multi‐method modeling remains uncommon as a general monitoring approach. We suggest managers consider this approach in light of existing datasets and design monitoring programs that integrate traditional methods with emergent technologies. [ABSTRACT FROM AUTHOR]