Soundscape ecology and ecoacoustics study the spatiotemporal dynamics of a soundscape and how the dynamics reflect and influence ecological processes in the environment. Soundscape analysis methods employ acoustic recording units (ARUs) that collect acoustic data in study areas over time. Analyzing these data includes computation of several acoustic diversity indices developed to quantify species abundance, richness, or habitat condition through digital audio processing and algorithm adaptations for within-group populations. However, the success of specific indices is often dependent on habitat type and biota richness present and analyzing these data can be challenging due to temporal pseudo-replication. Time-series analytical methods address the inherent problems of temporal autocorrelation for soundscape analyses challenges. Animal population dynamics fluctuate in a variety of ways due to changes in habitat or natural patterns of a landscape and chronic noise exposure, with soundscape phenology patterns evident in terrestrial and aquatic environments. Historical phenological soundscape patterns have been used to predict expected soundscape patterns in long-term studies but limited work has explored how forecasting can quantify changes in short-term studies. We evaluate how forecasting from an acoustic index can be used to quantify change in an acoustic community response to a loud, acute noise. We found that the acoustic community of a Midwestern restored prairie had decreased acoustic community activity after a loud sound event (LSE), a Civil War Reenactment, mainly driven by observed changes in the bird community and quantified using two methods: an automated acoustic index and species richness. Time-series forecasting maybe considered an underutilized tool in analyzing acoustic data whose experimental design can be flawed with temporal autocorrelation. Forecasting using auto ARIMA with acoustic indices could benefit decision makers who consider ecological questions at different time scales. 

https://doi.org/10.1016/j.ecoinf.2023.102100