Passive acoustic monitoring using autonomous recording units (ARUs) has become essential for avian biodiversity research, with tools like BirdNET enabling rapid processing of massive audio datasets. However, extracting reliable species occurrence data from algorithm outputs requires validation, and protocols vary widely in time investment and data retention. We compare how two validation approaches affect estimates of species richness and occupancy using recordings from 270 sites across California over three years. The first protocol uses intensive manual validation of only the highest-confidence BirdNET detections, ensuring accuracy but requiring substantial effort and potentially discarding valuable data. The second relies on validation of a relatively small but representative subset of detections and applies ecoregion-specific, logistic regression-based confidence thresholds to site-level recordings. This automated approach is less time-intensive and retains more data, potentially making it a highly valuable approach for many research/management applications, but its performance relative to manual validation at site scales remains untested. By comparing occupancy and richness estimates derived using each approach, we identify the conditions under which the threshold approach performs as well as manual validation. These results will help researchers select appropriate validation approaches based on study scale, resources, and conservation objectives, advancing standardization in acoustic monitoring workflows.