Can light detection and ranging predict acoustic detection distance in heterogeneous forest environments?

The estimation of the distance at which an animal can be detected is important information in wildlife monitoring. To account for the imperfect detection of individuals, frameworks like distance sampling or spatial capture-recapture build a detection function based on the distance between the source and the receiver. In acoustic monitoring, this detection distance is influenced by sound attenuation. In forest environments, vegetation significantly contributes to sound attenuation, and its effect varies with vegetation structure. Light Detection and Ranging (LiDAR) data provide fine-scale information on both horizontal and vertical structure of vegetation. These data are becoming increasingly available for large areas through public online geographic information libraries. In this study, we conducted broadcasting tests along linear transects to model the sound attenuation due to vegetation as a function of frequency and distance. The objective of this work is to estimate the attenuation coefficient based on fine-scale vegetation data to predict detection distance in an array of recorders.