Aim: The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean).Location: Worldwide, 12,343 sites, all ecosystem types.Time Period: 1991 to present.Major Taxa Studied: All soniferous taxa.Methods: We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms.Results: Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm²) than aquatic sampling (0.3 and 1.8 sites/Mkm² in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds.Main Conclusions: PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio‐temporal replication in freshwater habitats. Overall, this worldwide PAM network holds promise to support cross‐realm biodiversity research and monitoring efforts.

Ecoacoustics, the study of environmental sound, is a rapidly growing discipline offering ecological insights at scales ranging from individual organisms to whole ecosystems. Substantial methodological developments over the last 15 years have streamlined extraction of ecological information from audio recordings. One widely used set of methods are acoustic indices, which offer numerical summaries of the spectral, temporal and amplitude patterns in audio recordings. Currently, the specifics of each index's background, methodology and the soundscape patterns they are designed to summarise, are spread across multiple sources. Critically, details of index calculation are sometimes scarce, making it challenging for users to understand how index values are generated. Discrepancies in understanding can lead to misuse of acoustic indices or reporting of spurious results. This hinders ecological inference, replicability and discourages adoption of these tools for conservation and ecosystem monitoring, where they might otherwise provide useful insight. Here we present the Acoustic Index User's Guide—an interactive RShiny web app that defines and deconstructs eight of the most commonly used acoustic indices to facilitate consistent application across the discipline. We break the acoustic indices calculations down into easy‐to‐follow steps to better enable practical application and critical interpretation of acoustic indices. We demonstrate typical soundscape patterns using a suite of 91 example audio recordings: 66 real‐world soundscapes from terrestrial, aquatic and subterranean systems around the world, and 25 synthetic files demonstrating archetypal soundscape patterns. Our interpretation figures signpost specific soundscape patterns likely to be reflected in acoustic indices' values. This RShiny app is a living resource; additional acoustic indices will be added in the future through collaboration with authors of pre‐existing and new indices. The app also serves as a best‐practice template for the information required when publishing new acoustic indices, so that authors can facilitate the widest possible understanding and uptake of their indices. In turn, improved understanding of acoustic indices will aid effective hypothesis generation, application and interpretation in ecological research, ecosystem monitoring and conservation management.

Camera traps are increasingly one of the fundamental pillars of environmental monitoring and management. Even outside the scientific community, thousands of camera traps in the hands of citizens may offer valuable data on terrestrial vertebrate fauna, bycatch data in particular, when guided according to already employed standards. This provides a promising setting for Citizen Science initiatives. Here, we suggest a possible pathway for isolated observations to be aggregated into a single database that respects the existing standards (with a proposed extension). Our approach aims to show a new perspective and to update the recent progress in engaging the enthusiasm of citizen scientists and in including machine learning processes into image classification in camera trap research. This approach (combining machine learning and the input from citizen scientists) may significantly assist in streamlining the processing of camera trap data while simultaneously raising public environmental awareness. We have thus developed a conceptual framework and analytical concept for a web-based camera trap database, incorporating the above-mentioned aspects that respect a combination of the roles of experts’ and citizens’ evaluations, the way of training a neural network and adding a taxon complexity index. This initiative could well serve scientists and the general public, as well as assisting public authorities to efficiently set spatially and temporarily well-targeted conservation policies.

The urgency for remote, reliable, and scalable biodiversity monitoring amidst mounting human pressures on climate and ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), but there has been no comprehensive overview of its coverage across realms. We present metadata from 358 datasets recorded since 1991 in and above land and water constituting the first global synthesis of sampling coverage across spatial, temporal, and ecological scales. We compiled summary statistics (sampling locations, deployment schedules, focal taxa, and recording parameters) and used eleven case studies to assess trends in biological, anthropogenic, and geophysical sounds. Terrestrial sampling is spatially denser (42 sites/M·km ² ) than aquatic sampling (0.2 and 1.3 sites/M·km ² in oceans and freshwater) with only one subterranean dataset. Although diel and lunar cycles are well-covered in all realms, only marine datasets (65%) comprehensively sample all seasons. Across realms, biological sounds show contrasting diel activity, while declining with distance from the equator and anthropogenic activity. PAM can thus inform phenology, macroecology, and conservation studies, but representation can be improved by widening terrestrial taxonomic breadth, expanding coverage in the high seas, and increasing spatio-temporal replication in freshwater habitats. Overall, PAM shows considerable promise to support global biodiversity monitoring efforts.

Oxbow lakes and their riparian habitats are dynamic but vulnerable ecosystems in intensively modified European landscapes, yet their acoustic ecology remains poorly explored. This study presents their first year-round ecoacoustic monitoring. We surveyed seven man-made oxbow lakes in the Lower Morava River Basin (Czechia) from March 2022 to February 2023 to examine seasonal and diurnal patterns of the soundscape, the effects of conservation status, and the role of surrounding land use. We compared two acoustic indices—the Acoustic Complexity Index (ACI) and the Mid-Frequency Cover (MFC)—as proxies for avian biophony, anchored by manual aural inspection of 1,380 stratified one-minute recordings. Generalized additive models revealed clear intra-annual and diurnal structuring of the soundscape, with a dominant morning peak—and a weaker secondary afternoon rise in some months—most pronounced from late winter through spring. October stood out with an increase in species richness and MFC, reflecting intensified stopover use during autumn migration. MFC tracked avian species richness more closely than ACI, while ACI remained more sensitive to local acoustic context. Protected oxbow lakes consistently exhibited higher acoustic index values and greater cumulative species richness than unprotected sites, while landscape gradients, particularly road proximity and urban cover, strongly shaped the assemblages. Year-round passive acoustic monitoring captured both natural phenological shifts and anthropogenic pressures. We recommend MFC as the more reliable biophony-tracking index for these soundscapes, with ACI as a complementary context-sensitive metric, offering a scalable tool for biodiversity assessment and restoration of small wetland systems in human-dominated landscapes.