Paired sampling standardizes point count data from humans and acoustic recorders

Abstract

Acoustic recordings are increasingly used to quantify occupancy and abundance in avian monitoring and research. The recent development of relatively inexpensive programmable autonomous recording units (ARUs) has further increased the utility of acoustic recording technologies. Despite their potential advantages, persistent questions remain as to how comparable data are between ARUs and traditional (human observer) point counts. We suggest that differences in counts obtained from ARUs versus human observers primarily stem from differences in the effective detection radius of humans (EDRH) versus ARUs (EDRA). We describe how paired sampling can be used in conjunction with generalized linear (GLM) or generalized linear mixed models (GLMM) to estimate correction factors (δ) to remove biases between ARUs and traditional point counts. Furthermore, if human observers conduct distance estimation, we show that density estimates can be derived from single ARUs by estimating EDRA as a function of EDRH and δ, thus providing alternatives to more complicated and expensive approaches. We demonstrate our approach using data from 363 point count stations in 105 unique boreal study sites at which field staff conducted point count surveys that were simultaneously recorded by an ARU and later transcribed in the lab. Finally, we used repeated random subsampling of the data to split the data into model creation (70%) and validation (30%) subsets to iteratively estimate δ and validate density estimates from ARUs against densities calculated from human observers at the same independent validation locations. We modeled density of 35 species of boreal forest birds and show that incorporating δ in statistical offsets successfully removes systematic biases in estimated avian counts and/or density between human and ARU derived surveys. Our method is therefore easily implemented and will facilitate the integration of ARU and human observer point count data, facilitating expanded monitoring efforts and meta-analyses with historic point count data.

Keywords

• ARU
• autonomous recording unit
• avian monitoring
• bioacoustics
• detection probability
• detection radius
• point count
• survey bias