High-pass filters do not consistently strengthen associations between acoustic indices and avian species richness

• The effects of high-pass filters on acoustic index values derived from PAM recordings are not well documented. • High-pass filtering treatments did not consistently strengthen associations between acoustic indices and species richness. • ACI, AR, and CB demonstrated greater resistance to modifications caused by high-pass filters. • CityBioNet (CB) had a notably high correlation with species richness compared to the other examined acoustic indices. High-pass sound filters are a common form of audio manipulation that attenuates or removes low frequency sounds from audio recordings. While high-pass filters are used to reduce anthropogenic noise, there is limited guidance on their optimal application and their effects on acoustic indices—numerical values derived from passive acoustic monitoring (PAM) recordings to summarize acoustic information. Here, we investigated the effects of three high-pass filter treatments (482 Hz, 1 kHz, and 2 kHz) on eight commonly implemented acoustic indices and one less-commonly used convolutional neural network metric. Specifically, we used simulated soundscapes with three levels of traffic noise interference and then applied the filter treatments to field recordings collected throughout Illinois, USA during May 2022–July 2023 and derived acoustic indices to further understand these effects. Our analysis revealed that interactions between acoustic filtering and vehicular noise pollution have diverse effects on the nine acoustic indices, both in simulated soundscapes and empirical PAM recordings. In general, a 1 kHz or 2 kHz filter was necessary in order to produce significant changes in acoustic index values. However, none of the applied filtering treatments consistently strengthened correlations between the examined acoustic indices and avian species richness. The Acoustic Complexity Index (ACI), Acoustic Richness Index (AR), and CityBioNet (CB) demonstrated greater resistance to biologically non-informative changes caused by filter implementation, with CB showing a notably higher correlation with species richness compared to the other examined indices. Our findings suggest that ACI, AR, and CB may be better suited to studies of avian species richness in soundscapes with high levels of anthropogenic noise. Future research is needed to establish best practices for acoustic filtering, understand the behavior of acoustic indices under various environmental contexts, and explore alternative methodologies for avian monitoring in human-modified contexts.