Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest

We determined the relationship between acoustic diversity and metrics of vertical forest structure derived from light detection and ranging (LIDAR) data in a neotropical rainforest in Costa Rica. We then used the LIDAR-derived metrics to predict acoustic diversity across the forest landscape. Sound recordings were obtained from 14 sites for six consecutive days during dusk chorus (6?pm). Acoustic diversity was calculated for each day as the total intensity across acoustic frequency bands using the Shannon index and then averaged over the 6?days at each site. A 10?m radius around each site was used to obtain several LIDAR-derived metrics describing the vertical structural attributes of the forest canopy. Multiple linear regression (MLR) with Akaike information criterion was used to determine a top-ranked model with acoustic diversity as the dependent variable and the LIDAR metrics as independent variables. Acoustic diversity was modeled for forested areas (where canopy height was?>20?m) at 20?m resolution using coefficients obtained from the MLR, and a hotspot analysis was conducted on the resulting layer. Acoustic diversity was strongly correlated (R ² ?=?0.75) with the LIDAR metrics suggesting that LIDAR-derived metrics can be used to determine canopy structural attributes important to vocal fauna species. The hotspot analysis revealed that the spatial distribution of these canopy structural attributes across the La Selva forest is not random. Our approach can be used to identify forest patches of potentially high acoustic diversity for conservation or management purposes.