Morgan A. Ziegenhorn

1. Passive acoustic monitoring (PAM) enables non-invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Participants also proposed practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.

Passive acoustic monitoring (PAM) enables non-invasive sampling of wildlife across broad spatial, temporal and taxonomic scales. Its ongoing and widespread use has generated unprecedented volumes of acoustic data, shifting the primary bottleneck from data collection to the storage, processing, integration, and interpretation of PAM outputs. Although many software tools exist to address these challenges, differences in their design, scope, and usability often create fragmented and complex analytical workflows. To identify the key barriers and opportunities shaping the implementation of PAM surveys, we conducted a structured expert solicitation involving 30 international practitioners working across terrestrial and aquatic ecosystems. Experts identified and ranked their most critical pain points in current PAM workflows, spanning data storage, processing, and interpretation. The top challenge identified related to accurate species identification using deep learning and artificial intelligence (AI) models, especially in noisy soundscapes or for underrepresented taxa. Eight additional priority challenges included workflow fragmentation, limited availability of user-friendly analytical and visualisation tools, uneven access to software, manual validation bottlenecks, computational constraints, and difficulties in data handling, standardisation, and sharing. Participants also proposed practical mitigation strategies for these priority challenges, supported by step-by-step guidance to help overcome key barriers. Together, these insights provide a roadmap toward more scalable, open-access, and collaborative software systems, which are increasingly essential to realise the full potential of PAM in global biodiversity monitoring.

The rapid decline of global bird populations demands effective and efficient monitoring approaches, particularly for shorebirds, which are experiencing significant declines in abundance. Most shorebird monitoring programmes rely on visual surveys, but such surveys face significant challenges in remote locations such as the Arctic where high costs and logistical constraints limit survey effort and duration. We compared data collected from visual area search surveys and acoustic autonomous recording units (ARUs) for monitoring shorebird species in two large regions of Alaska's Arctic Coastal Plain in 2022–2023. We deployed ARUs at 130 sites (55 sites in Arctic National Wildlife Refuge [ANWR]; 75 in Teshekpuk Lake Special Area [TLSA]) where visual surveys were also conducted and examined differences in species richness, encounter rates and predicted distributions for 15 shorebird species. ARU surveys resulted in 50% higher species richness than visual surveys in ANWR and 33% higher at TLSA. Species accumulation curves demonstrated that ARUs required fewer plots to detect the maximum number of species. ARU data enabled species distribution modelling for additional species compared to visual data due to higher encounter rates, resulting in differences in predicted high‐use areas. Visual encounter rates were slightly higher for phalaropes than with ARUs in both regions. Additionally, several rare species were detected in the visual surveys but not in the ARU surveys (e.g. Wilson's Snipe, Western Sandpipers). Synthesis and applications . Our findings demonstrate that ARUs can effectively monitor shorebird species presence, offering advantages in temporal coverage and synchronous data collection across large spatial extents. This may be particularly important in remote regions where traditional monitoring approaches are challenging to implement and sustain.

Amplitude, or intensity, of sound is a fundamental characteristic of acoustic communication, with relevance in many scientific fields. The amplitude of an animal's acoustic signal at its source (‘source amplitude’) may be particularly relevant in the field of acoustic allometry, where relationships between species' physical and acoustic features (e.g. dominant frequency) have been well‐established across taxa. However, despite their potential scientific value, records and studies of source amplitude remain remarkably scarce for avian species. Here we present novel estimates of source amplitude (range and median) for 17 species of Arctic‐breeding birds, derived from measurements made in Utqiaġvik, Alaska, during June 2024. We found a strong positive correlation between body‐mass and source amplitude in these data via Markov chain Monte Carlo multivariate generalized linear mixed models (MCMCglmms). This relationship was influenced by both phylogenetic and individual identity. In contrast, effects from environmental factors and measurement characteristics were minimal. Our work represents one of few studies that explicitly model an interspecific relationship between source amplitude and body mass across avian genera. We hope that this study will spur further investigations into avian source amplitude and its relationship to morphological and life‐history features for species in the Arctic and elsewhere.

Amplitude, or intensity, of sound is a fundamental characteristic of inter acoustic communication, with relevance in many scientific fields. The amplitude of an animal’s acoustic signal at its source (“source amplitude”) may be particularly relevant in the field of acoustic allometry, where relationships between species’ physical and acoustic features (e.g., dominant frequency) have been well-established across taxa. However, despite their potential scientific value, records and studies of source amplitudes remain remarkably scarce for avian species. Here we present novel estimates of source amplitude (range, mean, and median) for 17 species of Arctic-breeding birds, derived from measurements made in Utqiaġvik, Alaska during June 2024. We found a strong positive correlation between body mass and source amplitude in this data via a Markov Chain Monte Carlo multivariate generalized linear mixed model (MCMCglmm). Both phylogeny and individual identity were important random effects in this model. In contrast, random effects from environmental factors and measurement characteristics were minimal. Our work represents the first model describing the relationship between source amplitude and allometry across avian genera. We hope that this study will spur other investigations into avian source amplitude and its relationship to morphological and life history features for species in the Arctic and elsewhere.

Understanding environmental drivers of species’ behavior is key for successful conservation. Within cetacean research, studies focused on understanding such drivers often consider local conditions (e.g., sea surface temperature), but rarely include large-scale, long-term parameters such as climate indices. Here we make use of long-term passive acoustic monitoring data to examine relationships between eight classes of toothed whales and climate indices, specifically El Niño Southern Oscillation, Pacific Decadal Oscillation, and North Pacific Gyre Oscillation, as well as local surface conditions (temperature, salinity, sea surface height) at two sites in the Hawaiian Archipelago. We find that El Niño Southern Oscillation most influenced cetacean detections at monitored sites. In many cases, detection patterns matched well with combinations of one or more climate indices and surface conditions. Our results highlight the importance of considering climate indices in efforts to understand relationships between marine top predators and environmental conditions.

Successful conservation and management of marine top predators rely on detailed documentation of spatiotemporal behavior. For cetacean species, this information is key to defining stocks, habitat use, and mitigating harmful interactions. Research focused on this goal is employing methodologies such as visual observations, tag data, and passive acoustic monitoring (PAM) data. However, many studies are temporally limited or focus on only one or few species. In this study, we make use of an existing long‐term (2009–2019), labeled PAM data set to examine spatiotemporal patterning of at least 10 odontocete (toothed whale) species in the Hawaiian Islands using compositional analyses and modeling techniques. Species composition differs among considered sites, and this difference is robust to seasonal movement patterns. Temporally, hour of day was the most significant predictor of detection across species and sites, followed by season, though patterns differed among species. We describe long‐term trends in species detection at one site and note that they are markedly similar for many species. These trends may be related to long‐term, underlying oceanographic cycles that will be the focus of future study. We demonstrate the variability of temporal patterns even at relatively close sites, which may imply that wide‐ranging models of species presence are missing key fine‐scale movement patterns. Documented seasonal differences in detection also highlights the importance of considering season in survey design both regionally and elsewhere. We emphasize the utility of long‐term, continuous monitoring in highlighting temporal patterns that may relate to underlying climatic states and help us predict responses to climate change. We conclude that long‐term PAM records are a valuable resource for documenting spatiotemporal patterns and can contribute many insights into the lives of top predators, even in highly studied regions such as the Hawaiian Islands.

Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. From 2008–2019, a set of PAM recordings covering the frequency band of most toothed whale (odontocete) echolocation clicks were collected at sites off the islands of Hawaiʻi, Kauaʻi, and Pearl and Hermes Reef. However, due to the size of this dataset and the complexity of species-level acoustic classification, multi-year, multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was possible to distill ten unique echolocation click ‘types’ attributable to regional odontocetes at the genus or species level. In one case, auxiliary sightings and recordings were used to attribute a new click type to the rough-toothed dolphin,Steno bredanensis. Types defined by clustering were then used as input classes in a neural-network based classifier, which was trained, tested, and evaluated on 5-minute binned data segments. Network precision was variable, with lower precision occurring most notably for false killer whales,Pseudorca crassidens, across all sites (35–76%). However, accuracy and recall were high (>96% and >75%, respectively) in all cases except for one type of short-finned pilot whale,Globicephala macrorhynchus, call class at Kauaʻi and Pearl and Hermes Reef (recall >66%). These results emphasize the utility of machine learning in analysis of large PAM datasets. The classifier and timeseries developed here will facilitate further analyses of spatiotemporal patterns of included toothed whales. Broader application of these methods may improve the efficiency of global multi-species PAM data processing for echolocation clicks, which is needed as these datasets continue to grow.

Many animals use sound for communication, navigation, and foraging, particularly in deep water or at night when light is limited, so describing the soundscape is essential for understanding, protecting, and managing these species and their environments. The nearshore deep-water acoustic environment off the coast of Kona, Hawai’i, is not well documented but is expected to be strongly influenced by anthropogenic activities such as fishing, tourism, and other vessel activity. To characterize the deep-water soundscape in this area we used High-frequency Acoustic Recording Packages (HARPs) to record acoustic data year-round at a 200 or 320 kHz sampling rate. We analyzed data spanning more than 10 years (2007-2018) by producing measurements of frequency-specific energy and using a suite of detectors and classifiers for general and specific sound sources. This provided a time series for sounds coming from biological, anthropogenic and physical sources. The soundscape in this location is dominated by signals generated by humans and odontocete cetaceans (mostly delphinids), generally alternating on a diel cycle. During daylight hours the dominant sound sources are vessels and echosounders, with strong signals ranging from 10 Hz to 80 kHz and above, while during the night the clicks from odontocetes dominate the soundscape in mid-to-high frequencies, generally between 10 and 90 kHz. Winter-resident humpback whales are present seasonally and produce calls in lower frequencies (200-2,000 Hz). Overall, seasonal variability is relatively subtle, which is unsurprising given the tropical latitude and deep-water environment. These results, and particularly the inclusion of sounds from frequencies above 2 kHz, represent the first long-term analysis of a marine soundscape in the North Pacific, and the first assessment of the intense, daily presence of manmade noise at this site. The decadal time series allows us to characterize the dynamic nature of this location, and to begin to identify changes in the soundscape over time. This type of analysis facilitates protection of natural resources and effective management of human activities in an ecologically important area.