Microphone variability and degradation: implications for monitoring programs employing autonomous recording units

Abstract

Autonomous recording units (ARUs) are emerging as an effective tool for avian population monitoring and research. Although ARU technology is being rapidly adopted, there is a need to establish whether variation in ARU components and their degradation with use might introduce detection biases that would affect long-term monitoring and research projects. We assessed whether microphone sensitivity impacted the probability of detecting bird vocalizations by broadcasting a sequence of 12 calls toward an array of commercially available ARUs equipped with microphones of varying sensitivities under three levels (32 dBA, 42 dBA, and 50 dBA) of experimentally induced noise conditions selected to reflect the range of noise levels commonly encountered during avian surveys. We used binomial regression to examine factors influencing probability of detection for each species and used these to examine the impact of microphone sensitivity on the effective detection area (ha) for each species. Microphone sensitivity loss reduced detection probability for all species examined, but the magnitude of the effect varied between species and often interacted with distance. Microphone sensitivity loss reduced the effective detection area by an average of 25% for microphones just beyond manufacturer specifications (-5 dBV) and by an average of 66% for severely compromised microphones (-20 dBV). Microphone sensitivity loss appeared to be more problematic for low frequency calls where reduction in the effective detection area occurred most rapidly. Microphone degradation poses a source of variation in avian surveys made with ARUs that will require regular measurement of microphone sensitivity and criteria for microphone replacement to ensure scientifically reproducible results. We recommend that research and monitoring projects employing ARUs test their microphones regularly, replace microphones with declining sensitivity, and record sensitivity as a potential covariate in statistical analyses of acoustic data.

Keywords

• acoustic surveys
• autonomous recording units
• bird monitoring
• detection probability
• effective detection radius
• environmental noise
• microphone sensitivity