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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. 

Passive acoustic monitoring for bats has become a common method to determine species presence and activity levels. However, current acoustic methods are ineffective for monitoring species abundance at large summer colonies. We used synchronized acoustic and thermal-imaging data collected at 6 colonies of Myotis grisescens (Gray Bats) and found a significant positive relationship between acoustic energy and number of emerging bats. Our findings reinforce that acoustics have the potential to estimate population sizes of summer bat colonies. Additionally, we examined ultrasonic amplitude variance across 19 AudioMoth devices at 5 different gain settings and found significant differences among devices and settings. Further exploration into device variability and bat behavior are necessary to develop a robust model of population estimates using acoustic energy. 

Socio-ecological models combine ecological systems with human social dynamics in order to better understand human interactions with the environment. To model human behavior, replicator dynamics can be used to model how societal influence and financial costs can change opinions about resource extraction. Previous research on replicator dynamics has shown how evolving opinions on conservation can change how humans interact with their environment and therefore change population dynamics of the harvested species. However, social-ecological models often assume that human societies are homogeneous with no social structure. Building on previous work on social-ecological models, we develop a two-patch socio-ecological model with social hierarchy in order to study the interactions between spatial dynamics and social inequity. We found that fish movement between patches is a major driver of model dynamics, especially when the two patches exhibit different social equality and fishing practices. Further, we found that the societal influence between groups of harvesters was essential to ensuring stable fishery dynamics. Next, we developed a case study of two independently managed fisheries that were connected by fish movement where one human group fishes sustainably while another was over-harvests, resulting in a fishery collapse of both patches. We also found that because in this model, the influence of one human patch on another only communicates the amount of each catch and no fishing strategies were employed, increased social influence decreased the sustainability of the fishery. The findings of this study indicate the importance of including spatial components to socio- ecological models and highlights the importance of understanding species’ movements when making conservation decisions. Further, we demonstrate how incorporating fishing methods from outside sources can result in higher stability of the harvested population, demonstrating the need for effective communication across management regimes. 

Rare, but potentially impactful, extreme events in socio-ecological systems (SES) can trigger significant consequences. The scarcity of theoretical frameworks for such events in SES is due to data limitations and difficulty in parameterizing coupled SES models. We explore the effect of extreme events on coupled socio- ecological systems using two stylized case studies: harvesting of old-growth forests and coral reef fisheries. We found that extreme events alter the long-term and transient dynamics of the systems. We identify counter- intuitive situations where the degradation of forests or coral habitat can prevent extinction through social dynamics feedback. Management outcomes show maximum variability at intermediate disturbance frequencies, complicating predictions of ecological recovery. We also found that initial conditions significantly influence system responses to shocks. Our work lays a foundation for future studies on extreme events in socio-ecological dynamics. Future work could explore more detailed models rooted in the literature, especially related to the modeling of the social dynamics. 

The blue octopus (Octopus cyanea) fishery off the southwest coast of Madagascar is important for coastal com- munities. This fishery is a key economic resource for the local community as blue octopus catch is sold by local fishers to international and local export markets. Thus, it is important to monitor and evaluate the status of octopus to ensure its sustainability. One common octopus management approach is through the use of temporary spatial closures. Models can be a useful support tool to evaluate the status of a population and assess different possible management strategies. To better understand the biology and assess the sustainability of blue octopus, we parameterize a Levkovitch population matrix model using existing catch data. We found that the octopus population was experiencing a 1.8% decline per month at the time of data collection in 2006. However, since 2006, a number of management practices, including temporary closures lasting several weeks to several months have been implemented successfully. In line with these efforts, our model indicates that the fishery has likely been sustained since 2006 due to these annual closures. Our model provides support to the idea that temporary closures have restored this population and that temporary closures provide flexibility in management strategies that local communities can tailor to their economic and social needs. In addition, we were able to estimate several important life history metrics, such as time in each stage, stable stage distribution, reproductive value, and per stage survivability, that can be used in future work. Collectively, our study provides insight into the biology of blue octopus as well as demonstrate how temporary closures can be an effective conservation strategy due to the wide range of implementation options. 

Conservation planning is the process of locating, implementing, and maintaining areas that are managed to promote the persistence of biodiversity, ecosystem function, and human use. In this review, we analyze the ways in which social processes have been integrated into Marxan, a spatially explicit conservation planning tool used as one step in a broader process to select the location and size of protected areas. Drawing on 89 peer-reviewed articles published between 2005 and 2020, we analyzed the ways in which human activity, values, and processes are spatialized in the environment, something we call socialscape ecology. A socialscape ecology approach to conservation planning considers not only the spatial configuration of human activity in a land or seascape but also the underlying drivers of these activities, how resource use rights and access operate in an area, and how resource users contribute to data collection and decision making. Our results show that there has been a small but statistically significant increase in the total number of cost variables into Marxan analysis over time, with uneven performance across seven of the nine categories assessed. One notable area of improvement has been the increase over time in number of studies integrating socio-environmental change (e.g., climate change) in their analysis. Including accurate, context-specific, and detailed accounts of social features and processes within land and seascapes is essential for developing conservation plans that are cost-effective, ecologically sound, socially desirable, and just. 

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. 

Migratory seabirds are vulnerable to decline due to climate change and anthropogenic disturbances. Common terns (Sterna hirundo) are highly vocal colonial seabirds that serve as bioindicators of their foraging grounds throughout their migratory range. Historically, monitoring colonial seabirds is invasive and time-consuming, and traditional acoustic approaches are complicated by high amounts of call overlap. Monitoring the behavioral ramifications of disturbance, as well as overall colony size and health, is crucial to implementing effective management decisions. However, methods are needed to do so efficiently and with minimal disturbance. In this study, we demonstrate that population size, demographics, and behavior can be assessed acoustically through changes in acoustic energy across varying temporal scales. To do this, we compared acoustic energy to in-person observations of nest density, chick-hatching, and investigator disturbance. We found that trends in acoustic energy align with observations of nest density, and the distribution of acoustic energy across frequency bands is indicative of colony demographics. Furthermore, we found a significant relationship between acoustic energy and investigator disturbance within 20 meters of an acoustic recorder. Overall, our findings suggest that colony-wide trends in population size, demographics, and behavior can be monitored via acoustic energy without the time-consuming analysis of individual calls. 

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.