Full dataset used in the study.

The analysis of audio recordings through acoustic indices has been proposed as an efficient way to measure and monitor biodiversity, given the assumption that greater biodiversity makes more rapidly-changing and complex sound. However, in previous work in south China, we have found only moderate correlations between the acoustic indices and bird diversity, when analyzing recordings that were made at the same time as conducting point counts of birds. Here, we extended that work in three study regions in Guangxi Province, making observations both inside relatively undisturbed forest reserves and in the surrounding agricultural lands. We found that of 42 correlations between bird species richness and the acoustic indices (seven commonly used acoustic indices [ACI, ADI, AEI, AR, BIO, H, NDSI] each calculated in six habitat/region combinations) only seven had even a moderate relationship (i.e., |r| ≥ 0.20). To understand this paucity of relationships, we listened to a subsample of the recordings, scoring them for various kinds of biophony (abundance of bird and insect sounds), anthropophony (amount of observer-produced sounds, sounds made by other humans, and sounds made by machines) and geophony (primarily wind, occasionally water). These analyses found that insects were positively related to the acoustic indices, especially in forests. Moreover, anthrophony and geophony, were, in general, negatively correlated to the indices. The abundance of bird vocalizations also had consistent and strong correlations to the residuals of the models relating the acoustic indices to bird diversity, demonstrating that the indices are clearly sensitive to the amount of bird sounds, but that is not necessarily strongly correlated to bird species richness. Multivariate models confirmed that the amounts of different sound categories were more influential on the acoustic indices than bird species richness. Our results demonstrate how competing noises, in both relatively undisturbed environments and heavily modified ones, can confound relationships between the acoustic indices and biodiversity. We discuss possible remedial steps for using acoustic indices in noisy soundscapes, including tailoring the selection of indices and frequency bands for the objectives of the project, or alternatively using a big-data approach that combines multiple indices.