Item talk:Q144925

Evaluating autonomous acoustic surveying techniques for rails in tidal marshes

There is a growing interest toward the use of autonomous recording units (ARUs) for acoustic surveying of secretive marsh bird populations. However, there is little information on how ARUs compare to human surveyors or how best to use ARU data that can be collected continuously throughout the day. We used ARUs to conduct 2 acoustic surveys for king (Rallus elegans) and clapper rails (R. crepitans) within a tidal marsh complex along the Pamunkey River, Virginia, USA, during May–July 2015. To determine the effectiveness of an ARU in replacing human personnel, we compared results of callback point‐count surveys with concurrent acoustic recordings and calculated estimates of detection probability for both rail species combined. The success of ARUs at detecting rails that human observers recorded decreased with distance (P ≤ 0.001), such that at <25 m, 90.3% of human‐recorded rails also were detected by the ARU, but at >75 m, only 34.0% of human‐detected rails were detected by the ARU. To determine a subsampling scheme for continuous ARU data that allows for effective surveying of presence and call rates of rails, we used ARUs to conduct 15 continuous 48‐hr passive surveys, generating 720 hr of recordings. We established 5 subsampling periods of 5, 10, 15, 30, and 45 min to evaluate ARU‐based presence and vocalization detections of rails compared with each of the full 60‐min sampling of ARU‐based detection of rails. All subsampling periods resulted in different (P ≤ 0.001) detection rates and unstandardized vocalization rates compared with the hourly sampling period. However, standardized vocalization counts from the 30‐min subsampling period were not different from vocalization counts of the full hourly sampling period. When surveying rail species in estuarine environments, species‐, habitat‐, and ARU‐specific limitations to ARU sampling should be considered when making inferences about abundances and distributions from ARU data.