Biodiversity monitoring is crucial in the Anthropocene era, yet traditional methods lack spatial and temporal coverage. Passive acoustic monitoring (PAM) offers a solution by enabling continuous, non-invasive data collection across ecosystems and taxa. Ecoacoustics uses PAM to assess biodiversity through acoustic metrics, requiring robust methodologies for data collection, analysis and sharing. Essential Biodiversity Variables (EBVs) provide a standardized monitoring framework, but integrating PAM data as EBVs remains challenging due to imperfect detectability. This challenge arises from various biases, including variations in signal emission, propagation, and reception influenced by habitat structure, background noise, and equipment sensitivity. These factors affect the accuracy and reliability of PAM-based assessments. Here, we assess how PAM data contribute to EBVs across biological scales and explore the role of spatio-temporal detectability in shaping data reliability. To address these issues, we propose a novel conceptual framework that combines physical and statistical approaches to characterize in situ detectability using a minimum set of variables. This framework lays the foundations for the standardization of PAM protocols and facilitates its integration into EBVs. By overcoming these challenges, we enhance PAM-based biodiversity monitoring, ensuring more robust data for conservation strategies and promoting interoperability across national and global scales.