Letting ecosystems speak for themselves: An unsupervised methodology for mapping landscape acoustic heterogeneity

Abstract

Passive Sonic Monitoring (PSM) refers to the analysis of patterns and structures shaped by sound, offering a complementary approach to traditional landscape analysis methods, such as satellite imagery. In particular, satellite-based methods alone may overlook specific dynamics of the organism at multiple taxonomic levels and local abiotic interactions. This paper introduces a novel unsupervised methodology for mapping similarities between soundscapes. Using Gaussian Mixture Models (GMM), this approach generates soundscape maps that reveal ecological processes throughout the day. We applied our methodology to data from 94 sites within a heterogeneous Colombian Orinoquia ecosystem. We found correlations between the cluster maps, satellite images, and biotic presences (bird and amphibian sonotypes). Our results align with established remote-sensing data and uncover previously unrecognized sonic patterns, offering new ecological insights that complement traditional landscape assessments. Our approach bridges the gap between image satellite-based assessments and ecological sonic processes, paving the way for comprehensive long-term biodiversity monitoring.