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.

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.

Objective: Seed mortality in geoduck aquaculture is a major bottleneck for production and is often attributed to predation. In the cultivation of the Pacific geoduck Panopea generosa, predator exclusion devices significantly reduce seed mortality, but farmers still report low postplanting survival, and the factors that influence this mortality are largely unknown. We conducted a laboratory study to assess the effect of siphon orientation and clam size (wet weight and shell length) on the postplanting survival of hatchery-reared juvenile Pacific geoducks. Methods: Juvenile geoducks (N = 540) were individually marked, and shell length (mm) and wet weight (g) were measured and used to calculate a condition factor (g/cm). The experimental system consisted of thirty 20-L containers in a flow-through seawater system. Individuals were randomly assigned to a container based on size, and each container was randomly assigned a treatment factor representing the siphon angle with respect to the sediment surface (siphon oriented upright, horizontal, or inverted) at the time of planting. Geoducks were planted at 15 cm depth in each container, and survival was assessed after 14 d. Results: We found that juvenile geoduck survivorship was dependent on planting orientation and that this relationship was highly size-dependent. Mean survivorship (±SD) was highest for geoducks planted upright (68.5 ± 7.6%), followed by geoducks planted horizontally (50.7 ± 8.7%) and those that were inverted (34.1 ± 11.7%). Across all planting orientations, juvenile survivorship was positively correlated with shell length, condition, and wet weight, with the latter two metrics best predicting survival. Orientation did not affect the survivorship of geoducks that had a condition of 1.6 g/cm or a wet weight of at least 2.5 g. Conclusions: Our results show that planting Pacific geoducks upright increases juvenile survivorship, possibly leading to increased outplanting production. If controlling planting orientation is impossible, we recommend selecting juvenile geoducks of the highest possible condition to maximize survival. To our knowledge, this is the first analysis demonstrating that planting orientation affects mortality in juvenile Pacific geoducks.

The world's oceans are undergoing rapid changes due to climate change and other anthropogenic impacts, affecting marine species' distribution, abundance, and behavior. Traditional ecological monitoring methods struggle to keep pace with these transformations, especially in underwater habitats. Recently, computer vision techniques have emerged to enhance the efficiency of video and image-based underwater monitoring. These methods facilitate the detection and classification of objects in visual data, potentially streamlining the process of counting and classifying marine organisms. However, the adoption of computer vision in marine ecology has been slow, partly due to its inaccessibility to ecologists and the lack of easily adaptable tools for ecological monitoring.This study investigates the application and validation of computer vision techniques for monitoring underwater pelagic macrofaunal diversity, using a case study from coastal British Columbia. Over 9000 hours of underwater video were collected from four sites over 18 months, using mounted cameras programmed to record five minutes of video every hour. Due to the infrequency of organisms present in the videos and challenges with underwater visibility, we created a stepwise iterative screening process to sequentially refine video data and aid the image annotation process. This involved using unsupervised classifiers (e.g. ResNet-18) to assist in reducing the number of background (i.e. 'empty water') images shown to annotators. To address the scarcity of annotations for certain taxa, an ‘adjacency filter’ was employed to increase the number of annotated frames for rare species. A rigorous QAQC process ensured standardization and minimized inter-annotator bias. Finally, a supervised computer vision model (YOLOv8) was trained with approximately 240,000 annotations to assess the presence and abundance of marine species over 18 months in the area. This approach provided high-resolution temporal data on the diversity and abundance of pelagic fish and gelatinous zooplankton at these sites.Here, we detail the process of employing these computer vision techniques for long-term underwater ecological monitoring, emphasizing accessibility for ecologists. A stepwise method for adapting computer vision techniques to achieve biodiversity monitoring objectives is presented, highlighting the strengths and limitations of our approach. We address our work in the context of the key barriers facing computer vision in underwater ecology, and provide tools for researchers seeking to incorporate AI in image or video-based marine research. Finally, we propose future directions for integrating these technologies into new and existing monitoring programs, and suggest priority areas for future research to advance the use of computer vision in underwater ecological monitoring.