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Copyright of Journal of Applied Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Scientists are using acoustic monitoring to assess the impact of altered soundscapes on wildlife communities and human systems. In the soundscape ecology field, monitoring and analyses approaches rely on the interdisciplinary intersection of ecology, acoustics, and computer science. Combining theory and practice of each field in the context of Knowledge Discovery in Databases (KDD), soundscape ecologists provide innovative monitoring solutions for ecologically-driven research questions. We propose a soundscape content analysis framework for improved knowledge outcome with assistance of the new multi-label (ML) concept. Here, we investigated the effectiveness of a ML k -nearest neighbor algorithm (ML- k NN) for labeling concurrent soundscape components within a single recording. We manually labeled 1200 field recordings for the presence of soundscape components and extracted ecological acoustic features, audio profile features, and Gaussian-mixture model features for each recording. Then, we tested the ML- k NN algorithm accuracy with well-established metrics adapted to ML learning. We found that seventeen unique acoustic features could predict a set of biophonic, geophonic, and anthrophonic labels for a single field recording with average precision of 0.767. However, certain labels were predicted incorrectly depending on the time of day and co-occurrence of that label with another label, suggesting further refinement is needed to improve the accuracy of predicted labels. Overall, this ML classification approach could enable researchers to label field recordings more quickly and generate an "alert" system for monitoring changes in a specific sound class. Ultimately, the adaptation of the ML algorithm may provide soundscape ecologists with new metadata labels that are searchable in large databases of soundscape field recordings. Highlights • Acoustic features with multi-label (ML) k- nearest neighbor algorithm could predict a set of labels for a single field recording. • ML algorithm could provide output of metadata labels that are searchable in large databases of soundscape field recordings. • ML algorithm uses training data to assist in faster multi-label classification of field recordings. [ABSTRACT FROM AUTHOR]

Youth are introduced to STEM topics through informal settings like science camps, aquaria, and zoos. In these interactive and sensory-rich environments, a well-designed programme can help participants to acquire knowledge and cultivate interest through experiential learning. Given the importance of informal activities in environmental education, it is crucial to identify which contextual components lead to successful learning outcomes. Thus far, research in environmental STEM education has focused on brief experiences, such as one-time visits to curated environments likeaquaria. Investigating the impact of multi-day/longer experiences in natural settings is critical because the level of engagement with the STEM topic and the interaction with the learning environment in such experiences have different cognitive and affective impacts. To address these current limitations, we explored whether there is evidence that a four-day, immersive outdoor soundscape ecology camp contributed to situational interest and conceptual understanding of middle-school youth. During the soundscape camp, a variety of evidence was collected through different instruments including drawing activities, questionnaires, an interview, and field-observations. Through a qualitative analysis and open coding, we identified three core principles of informal outdoor curricular design that positively contribute to participants’ learning experiences, including direct experience with nature, the use of authentic technology, and exercises that promote collaborative teamwork. We argue that activities that promote scientific practices and engagement with authentic tools in a real-world context creates a learning environment in which participants collaboratively construct deep conceptual understanding of different aspects of environmental STEM topics and foster interest in the context of science inquiry. [ABSTRACT FROM AUTHOR]

Soundscape ecology and ecoacoustics study the spatiotemporal dynamics of a soundscape and how the dynamics reflect and influence ecological processes in the environment. Soundscape analysis methods employ acoustic recording units (ARUs) that collect acoustic data in study areas over time. Analyzing these data includes computation of several acoustic diversity indices developed to quantify species abundance, richness, or habitat condition through digital audio processing and algorithm adaptations for within-group populations. However, the success of specific indices is often dependent on habitat type and biota richness present and analyzing these data can be challenging due to temporal pseudo-replication. Time-series analytical methods address the inherent problems of temporal autocorrelation for soundscape analyses challenges. Animal population dynamics fluctuate in a variety of ways due to changes in habitat or natural patterns of a landscape and chronic noise exposure, with soundscape phenology patterns evident in terrestrial and aquatic environments. Historical phenological soundscape patterns have been used to predict expected soundscape patterns in long-term studies but limited work has explored how forecasting can quantify changes in short-term studies. We evaluate how forecasting from an acoustic index can be used to quantify change in an acoustic community response to a loud, acute noise. We found that the acoustic community of a Midwestern restored prairie had decreased acoustic community activity after a loud sound event (LSE), a Civil War Reenactment, mainly driven by observed changes in the bird community and quantified using two methods: an automated acoustic index and species richness. Time-series forecasting maybe considered an underutilized tool in analyzing acoustic data whose experimental design can be flawed with temporal autocorrelation. Forecasting using auto ARIMA with acoustic indices could benefit decision makers who consider ecological questions at different time scales. • Forecasting using auto ARIMA with acoustic index quantifies changes in the acoustic community in a Midwestern restored prairie. • Short-term pre and post study indicates loud sound event is disruptive to acoustic community patterns. • Forecasting with spectral entropy index is supported by species richness curves. [ABSTRACT FROM AUTHOR]

Modern biogeography now encompasses an impressive diversity of patterns and phenomena of the geography of nature, providing insights fundamental to understanding the forces influencing the spatial and temporal dynamics of biological diversity. However, rather than praise our discipline for its great breadth of visions, our purpose here is to point out our glaring oversight of a potentially transformative frontier in the geography of nature. A new, emerging area called soundscape ecology, if guided by the principles of biogeography, holds the promise of 'opening the ears' of our field and providing fresh perspectives on fundamental problems being addressed by biogeographers. [ABSTRACT FROM AUTHOR]

• Continuous soundscape recordings were made in eight ecosystems on four continents. • Temporal variability of soundscapes was quantified using two distinct measures. • Daytime exhibited high temporal variability relative to dusk and nighttime. • Increasing evenness of subsampling distribution reduces error in representation. • Marginal precision gains diminish substantially beyond 1/3 recording time. Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems. Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables. Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes. We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions drastically increased the representativeness of a sampling scheme, while increases in subsample duration yielded fairly minimal gains in representativeness between 33 and 67% of the full time one wishes to represent. Generally, our results show that for a long-term, fine temporal resolution monitoring program, one should record in evenly distributed durations at least as short as 1 min while only recording up to a third of the time one wishes to represent. While more continuous monitoring can be advantageous and necessary in many cases, current economic and logistical limitations in power, data storage, and analysis capabilities will often warrant optimized subsampling designs. [ABSTRACT FROM AUTHOR]

• Continuous soundscape recordings were made in eight ecosystems on four continents. • Temporal variability of soundscapes was quantified using two distinct measures. • Daytime exhibited high temporal variability relative to dusk and nighttime. • Increasing evenness of subsampling distribution reduces error in representation. • Marginal precision gains diminish substantially beyond 1/3 recording time. Unprecedented rates of biodiversity loss and intensifying human attempts to rectify the biodiversity crisis have heightened the need for standardized, large-scale, long-duration biodiversity monitoring at fine temporal resolution. While some innovative technologies such as passive acoustic monitoring are well suited for such monitoring challenges, many questions remain as to how they should be scaled out and optimally implemented across ecosystems. Our research questions center on temporal sampling regimes—how frequently and how long one should collect data to represent biodiversity conditions over a given timeframe. Addressing this concern in the context of passive acoustic monitoring, we investigated whether temporal soundscape variability—the characteristic short-term acoustic change in an environment—is consistent across ecosystems and times of day, and we considered how various temporal subsampling schemes affect the representativeness of resultant acoustic index values, relative to continuous sampling. We quantified soundscape variability at eight sites across four continents based on temporal autocorrelation ranges and standard deviations of acoustic index values, and we created a heuristic model to classify types of soundscape variability based on those two variables. Drawing on values derived from three distinct acoustic indices, we found that the characteristic temporal variability of soundscapes varied between sites and times of day (dawn, daytime, dusk, and nighttime). Some sites exhibited little difference in variability between times of day whereas other sites exhibited greater within-site differences between times of day than many inter-site differences. Daytime soundscapes generally tended to exhibit more temporal variability than nighttime soundscapes. We also compared potential subsampling schemes that could be advantageous in terms of power, data storage, and data analysis costs by modeling subsample error as a function of total analysis time and number of subsamples within a larger block of time. Greater numbers of evenly distributed subdivisions drastically increased the representativeness of a sampling scheme, while increases in subsample duration yielded fairly minimal gains in representativeness between 33 and 67% of the full time one wishes to represent. Generally, our results show that for a long-term, fine temporal resolution monitoring program, one should record in evenly distributed durations at least as short as 1 min while only recording up to a third of the time one wishes to represent. While more continuous monitoring can be advantageous and necessary in many cases, current economic and logistical limitations in power, data storage, and analysis capabilities will often warrant optimized subsampling designs. [ABSTRACT FROM AUTHOR]

• Soundscapes can help to measure dimensions of ecological resilience. • Degree of soundscape alteration correlated with disturbance magnitude. • Degree of soundscape alteration corresponded with distance to shoreline. • Soundscapes became more variable after Hurricane Maria. • Maria impacted animal communities at dry forest sites more than at coral reefs sites. Disturbance regimes and biodiversity—two factors that govern the stability of ecosystems—are changing rapidly due to anthropogenic forces including climate change. Determining whether ecosystems retain their structure and function through intensifying disturbance regimes is an urgent task. However, quantitatively assessing the resilience of natural systems is a complex and challenging endeavor, especially for animal communities, for which datasets around disturbance events are scarce. Here, we apply an emerging remote sensing technology—the recording and analysis of soundscapes—to quantify the resilience of Puerto Rican coral reef and dry forest animal communities in relation to Hurricane Maria, which struck the island in September 2017. Using recordings collected between March 2017 and January 2018 at three terrestrial and three marine sites, we measured three dimensions of resilience—the magnitude of the impacts (resistance), the spatial pattern of the impacts (heterogeneity), and the diversity and timeline of functional responses (recovery)—across eight sound types representing different broad taxonomic groups. While the coral reef communities exhibited high resistance to the storm, all sound types within the dry forest were significantly impacted, with two of the three insect choruses and bird vocalizations at dawn declining approximately 50% in the weeks following Hurricane Maria. The mid-frequency insect sound type returned to pre-storm levels after 56 days, while bird vocalizations returned after 67 days, though seasonal and lunar patterns underscored the importance of long-term data for accurately measuring trajectories of recovery. This study demonstrates that soundscape methodologies can help to quantify elusive dimensions of animal community resilience in order to better understand how biodiversity and ecosystem functioning will change under novel disturbance regimes. [ABSTRACT FROM AUTHOR]