Sound advice: A calibration framework for defining detection space in passive acoustic monitoring

Passive Acoustic Monitoring (PAM) has emerged as a transformative tool for biodiversity assessment in recent years. Despite widespread acceptance and application for conservation-related outcomes, the interactive effects of hardware limitations, signal propagation, and environmental conditions on how far a signal can be reliably detected remain critically understudied. Such studies in regions of high biodiversity such as the Western Ghats of India, can bolster ongoing monitoring of conservation efforts. We quantified changes in signal detectability using Autonomous Recording Units (ARUs) in a tropical agroecosystem in the Western Ghats. We played back standardized pure tone (1–8 kHz) signals in fallow rice paddy fields over 50–300 m distance gradient. We measured detection using a four-ARU array and modeled operative detectability of signals. We found the ‘detection space’ of an ARU to be highly frequency-dependent and environmentally modulated. Detection probability for low-frequency signals (1 kHz) decreased rapidly (50% threshold at ~100 m), whereas mid-range frequencies (4–6 kHz) occupied an acoustic window that remained reliably detectable up to 250 m. Higher relative humidity significantly enhanced overall detection, while increasing temperatures disproportionately reduced low-frequency detectability. The orientation of the ARU to the signal source was critical, as the detection probability declined from 81% for recorders facing the source (0°) to 14% for rear-facing units (180°). We propose a ‘Decision Support Framework’ that provides a pathway for researchers to integrate focal taxa traits with technical constraints to determine detection space and optimize study designs when using PAM for monitoring biodiversity and assessing conservation action.