Impact of parameterization in multiple acoustic index comparisons: practical cases in terrestrial and underwater soundscapes

Acoustic indices are increasingly used to characterize soundscapes and infer biodiversity patterns in terrestrial and marine environments. However, methodological choices during data collection and signal processing—particularly the selection of sampling frequency, Fourier transform number of points and window overlap—can influence the output of acoustic indices, multivariate analysis and their ecological interpretations. Here, we evaluated the effects of these parameters on multivariate soundscape separation with two example environment comparisons: terrestrial (Bushland vs. Urban) and underwater (Pocillopora dominated vs. Non-Pocillopora dominated). We assessed the influence of parameterization by computing 432 spectrogram configurations per recording across five commonly used acoustic indices. Using non-metric multidimensional scaling, multivariate descriptors and Bayesian models, we found that parameter selection influenced soundscape separation in each environment example with data-specific interactions. For instance, greater NFFT values increased centroid distance between habitats in terrestrial soundscapes but decreased it in underwater soundscapes. Our results confirm earlier findings that acoustic indices can be sensitive to spectrogram parameterization, and extend these by demonstrating, with a systematic multivariate framework, how interactions among sampling frequency, NFFT and window overlap affect soundscape separation across environments. This approach emphasizes the need for parameter sensitivity testing, transparent reporting and careful interpretation when comparing soundscapes.

Code: https://github.com/juancarlosazofeifasolano/acousticindices_parametrisation.git.