Acoustic communication structures interactions among individuals and species throughout the avian life cycle. The intraspecific diversity of bird vocalisations reflects adaptive strategies to cope with variations in the acoustic environment over time and space. Additionally, the composition of species assemblages can influence how acoustic functions are spatially distributed across the landscape. Using data from 2,427 bird assemblages across France, we tested whether the diversity of acoustic traits in local bird assemblages varies along climate and landscape gradients. We quantified 16 acoustic traits from field recordings expressing the frequency, complexity, rhythm, and duration of vocalisations for 117 species. We employed a five-table ordination analysis to investigate links between acoustic traits and bioclimatic and land-use gradients, accounting for spatial and phylogenetic dependencies. We analysed how the average acoustic trait composition of bird assemblages shifted along environmental gradients for three key acoustic traits. We found that acoustic traits were clustered along environmental gradients, with a phylogenetic signal, supporting the prediction that acoustic strategies are phylogenetically conserved. Our results indicated that bird species share similar acoustic traits in assemblages under the same climatic envelopes (e.g., lower complexity and isochronous rhythms under higher precipitation and temperature seasonality). We found mixed support for the hypothesis that habitat selection shapes the acoustic composition of species assemblages, such that vocalisations are adapted to sound propagation properties of any given habitat. For instance, urbanisation was correlated with complex vocalisations featuring large spectral bandwidths. These wide bandwidths support the prediction that urban assemblages consist of species capable of avoiding acoustic masking caused by noise pollution. However, their complexity may hinder their transmission due to the same noise interference. Overall, our study highlights how bioclimatic and landscape-scale habitat features, alongside niche conservatism, collectively shape acoustic trait assemblages across large spatial extents.

The Acoustic Complexity Index (ACI) is one of the most used metrics in ecoacoustics, demonstrating reliability across a broad range of environments and ecological conditions. However, this index requires specific procedures to be applied in the correct way. Based on the “Canberra metric,” the ACI is an unsupervised metric formulated to extract information from fast Fourier transform (FFT) sonic matrices. The ACI measures contiguous differences in acoustic energy of each frequency bin i along temporal steps (ACItf) and of a temporal interval j along the frequency bins (ACIft). The aggregation of data after an FFT using a clumping procedure allows for better scaling of the sonic signals before the computation of the ACI. The application of a filter to reduce the effects of non-environmental signals produced by microphone electrical noise is mandatory to avoid masking effects. Due to a singularity of the index for values equal to 0, ACIs require ad hoc procedures to exclude from the comparisons pairs of elements of which one is equal to 0. The spectral sonic signature and temporal sonic signature are vectors obtained from the sequence of ACItf and ACIft values, respectively. The comparison between sonic signatures using the chord distance index returns spectral and temporal sonic dissimilarities that allow the evaluation of sonic patterns emerging at different temporal and spatial resolutions. The number of frequency bins, sonic variability, and sonic evenness are further derivative metrics that help to interpret sonic heterogeneity by distinguishing the temporal and spatial heterogeneity of sonoscapes. A change of the name of the “Acoustic Complexity Index” to the “Sonic Heterogeneity Index” is recommended.