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Passive acoustic monitoring (PAM) is a powerful tool for ecological research, but recordings can be compromised by background noise such as wind. Addressing wind noise (e.g., clipping and masking) in bioacoustic data remains a challenge, especially as climate change is predicted to increase wind speeds, particularly near the poles. Adélie penguins (Pygoscelis adeliae), key indicators of the Antarctic ecosystem, are well-suited for PAM, where large-scale monitoring could assess climate-driven population changes—if wind noise is managed effectively. In this study, the convolutional neural network, BirdNET, inversely identifies unwanted sounds in Adélie penguin colony recordings. Multiple custom models were developed in which the background nontarget noise was Adélie vocalizations, and wind conditions (low, medium, and high) were the target classes. The best-performing model achieved an F-score of 0.43 and accuracy of 0.53. The high wind class within this model had a precision of 0.76 and recall of 0.94. A six-step workflow is presented for creating custom BirdNET models, evaluating their performance and determining an optimal confidence threshold prior to model application on an entire dataset. By automating unwanted sound detection, this approach enables researchers to efficiently identify and remove affected files, streamline data cleaning, and focus on recordings of interest for further analysis. 

A southern giant petrel was satellite tracked during a long foraging trip. While presumably at rest on the sea surface, the giant petrel drifted in a counterclockwise corkscrew pattern that is characteristic of an inertial oscillation in the Southern Ocean. This note demonstrates that tracking data from resting seabirds, like giant petrels, can be used as passive drifters to estimate ocean surface currents in a notoriously stormy environment where data near the air-sea interface is difficult to obtain.   

Adélie penguins (Pygoscelis adeliae) are bioindicators for the rapidly changing Antarctic environment, making understanding their population dynamics and behavior of high research priority. However, collecting detailed population data throughout the breeding season on many colonies is difficult due to Antarctica’s harsh conditions and remote location. The colonial breeding ecology of Adélie penguins has led to the evolution of a highly vocal species with individualized calls, making them well-suited for passive acoustic monitoring (PAM) with autonomous recording. PAM units can potentially provide an easily deployable and scalable way to collect fine-scale data on population estimates and breeding phenology. Here I present a framework for using acoustic indices to monitor phenology of dense penguin colonies even under high wind conditions. I evaluate the relationship between acoustic indices such as RMS amplitude and penguin colony size between distinct breeding stages (incubation, guard, crèche, and fledge) on Torgersen and Humble Islands in the West Antarctic Peninsula with an automated pipeline implemented in R. Using PAM to interpret penguin vocalizations for population size and breeding phenology estimates could lead to the development of a real-time remote monitoring system over a large spatial footprint, revealing Adélie penguin responses to climate change. 

Acoustic indices are an efficient method for monitoring dense aggregations of vocal animals but require understanding the acoustic ecology of the species under examination. The present understanding of avian behavior and vocal development is primarily derived from the research of songbirds (Passeriformes). However, given that behavior and environment can differ greatly among bird orders, passerine birdsong may be insufficient to define the vocal ontogeny of non-passerine birds. Like many colonial nesting seabirds, the Adélie penguin (Pygoscelis adeliae) is adapted to loud and congested environments with limited cues to identify kinship within aggregations of conspecifics. In addition to physical or geographical cues to identify offspring, adult P. adeliae rely on vocal modulation. Numerous studies have been conducted on mutual vocal modulations in mature P. adeliae, but limited research has explored the vocal repertoire of the chicks and how their vocalizations evolve over time. Using the deep learning-based system, DeepSqueak, this study characterized the vocal ontogeny of P. adeliae chicks in the West Antarctic Peninsula to aid in autonomously tracking their age. Understanding the phenological communication patterns of vocal-dependent seabirds can help measure the impact of climate change on this indicator species through non-invasive methods. 

Abstract Human activities and climate change threaten seabirds globally, and many species are declining from already small breeding populations. Monitoring of breeding colonies can identify population trends and important conservation concerns, but it is a persistent challenge to achieve adequate coverage of remote and sensitive breeding sites. Southern giant petrels (Macronectes giganteus) exemplify this challenge: as polar, pelagic marine predators they are subject to a variety of anthropogenic threats, but they often breed in remote colonies that are highly sensitive to disturbance. Aerial remote sensing can overcome some of these difficulties to census breeding sites and explore how local environmental factors influence important characteristics such as nest-site selection and chick survival. To this end, we used drone photography to map giant petrel nests, repeatedly evaluate chick survival and quantify-associated physical and biological characteristics of the landscape at two neighboring breeding sites on Humble Island and Elephant Rocks, along the western Antarctic Peninsula in January–March 2020. Nest sites occurred in areas with relatively high elevations, gentle slopes, and high wind exposure, and statistical models predicted suitable nest-site locations based on local spatial characteristics, explaining 72.8% of deviance at these sites. These findings demonstrate the efficacy of drones as a tool to identify, map, and monitor seabird nests, and to quantify important habitat associations that may constitute species preferences or sensitivities. These may, in turn, contextualize some of the diverse population trajectories observed for this species throughout the changing Antarctic environment. 

Abstract The management and conservation of tuna and other transboundary marine species have to date been limited by an incomplete understanding of the oceanographic, ecological and socioeconomic factors mediating fishery overlap and interactions, and how these factors vary across expansive, open ocean habitats. Despite advances in fisheries monitoring and biologging technology, few attempts have been made to conduct integrated ecological analyses at basin scales relevant to pelagic fisheries and the highly migratory species they target. Here, we use vessel tracking data, archival tags, observer records, and machine learning to examine inter‐ and intra‐annual variability in fisheries overlap (2013–2020) of five pelagic longline fishing fleets with North Pacific albacore tuna (Thunnus alalunga, Scombridae). Although progressive declines in catch and biomass have been observed over the past several decades, the North Pacific albacore is one of the only Pacific tuna stocks primarily targeted by pelagic longlines not currently listed as overfished or experiencing overfishing. We find that fishery overlap varies significantly across time and space as mediated by (1) differences in habitat preferences between juvenile and adult albacore; (2) variation of oceanographic features known to aggregate pelagic biomass; and (3) the different spatial niches targeted by shallow‐set and deep‐set longline fishing gear. These findings may have significant implications for stock assessment in this and other transboundary fishery systems, particularly the reliance on fishery‐dependent data to index abundance. Indeed, we argue that additional consideration of how overlap, catchability, and size selectivity parameters vary over time and space may be required to ensure the development of robust, equitable, and climate‐resilient harvest control rules.