Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic-index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random-forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random-forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.
Acoustic recordings have the potential to address a suite of important conservation questions, from assessing phenology shifts due to climate change, to examining the impact of anthropogenic noise on wildlife, to monitoring biodiversity at enormous spatio-temporal scales. However, consistent methods are required to extract meaningful information from these large datasets. Here we apply a method of calibrating recordings to standardize acoustic data collected at over 50 unique sites in a diversity of habitats across the continental U.S. using a variety of recording units and parameters. The calibration method results in a coarser data resolution, decreasing storage space and computation time of further analysis. We then apply recently developed acoustic indices to evaluate biodiversity in our recordings. A review of existing acoustic indices and degree of correlation with bioacoustic activity, species richness, functional diversity, landscape attributes, and anthropogenic influence guided our decisions about what indices to implement. Resulting indices were compared with the diversity of birds from observer point counts and from animal vocalizations observed in the recording spectrograms and to anthropogenic sounds observed in the recordings. The results provide important insight on the utility of each index, or group of indices, to investigate dynamics of ecological communities across large scales.
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