A workflow to optimize spatial sampling in ecoacoustic studies

Context Landscape monitoring through sounds relies on spatial sampling to characterize soundscape variation across a heterogeneous space. However, the effectiveness of different sampling strategies remains poorly understood. Objectives We examined how spatial sampling design affects the representativeness of soundscape information across heterogeneous ecosystems. Our goal was to identify efficient acoustic sub sampling strategies that integrate landscape heterogeneity while balancing effort and performance. Specifically, we (1) assessed how sampling structure and effort affect interpolated soundscape representativeness; (2) compared random, thematic, and tessellation-based designs; (3) examined interactions among landscape, acoustic, and temporal variables; (4) evaluated the influence of landscape heterogeneity on sampling performance; and (5) developed a workflow to guide ecoacoustic sub sampling. Methods Our study was conducted across three Colombian ecosystems. We constructed discrete and continuous landscape proxies using Sentinel-2 imagery, calculated acoustic indices from field recordings, and then implemented various spatial sampling designs from sub samples based on image tessellation techniques. Using kriging, a geostatistical method for interpolation,we compared sub samples to full spatial scenarios and assessed their performance using structural and distributional metrics. Results We found that tessellation-based methods, especially those based on watershed segmentation, significantly outperformed random and thematic designs, showing higher sample performance, stronger correlations with sample size, and greater representativeness of landscape heterogeneity. Multivariate analyses confirmed their structural coherence across variables. Our results demonstrate that spatial structure, not just sample size, critically determines sampling performance. We propose a landscape-based workflow to support the design of efficient ecoacoustic sampling strategies. This approach advances scalable and spatially informed biodiversity monitoring using ecoacoustics methods.