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Abstract The rise of passive acoustic monitoring and the rapid growth in large audio datasets is driving the development of analysis methods that allow ecological inferences to be drawn from acoustic data. Acoustic indices are currently one of the most widely applied tools in ecoacoustics. These numerical summaries of the sound energy contained in digital audio recordings are relatively straightforward and fast to calculate but can be challenging to interpret. Misapplication and misinterpretation have produced conflicting results and led some to question their value. To encourage better use of acoustic indices, we provide nine points of guidance to support good study design, analysis and interpretation. We offer practical recommendations for the use of acoustic indices in the study of both whole soundscapes and individual taxa and species, and point to emerging trends in ecoacoustic analysis. In particular, we highlight the critical importance of understanding the links between soundscape patterns and acoustic indices. Acoustic indices can offer insights into the state of organisms, populations, and ecosystems, complementing other ecological research techniques. Judicious selection, appropriate application and thorough interpretation of existing indices is vital to bolster robust developments in ecoacoustics for biodiversity monitoring, conservation and future research.

Abstract Associating fish sounds to specific species and behaviours is important for making passive acoustics a viable tool for monitoring fish. While recording fish sounds in tanks can sometimes be performed, many fish do not produce sounds in captivity. Consequently, there is a need to identify fish sounds in situ and characterise these sounds under a wide variety of behaviours and habitats. We designed three portable audio‐video platforms capable of identifying species‐specific fish sounds in the wild: a large array, a mini array and a mobile array. The large and mini arrays are static autonomous platforms than can be deployed on the seafloor and record audio and video for one to two weeks. They use multichannel acoustic recorders and low‐cost video cameras mounted on PVC frames. The mobile array also uses a multichannel acoustic recorder, but mounted on a remotely operated vehicle with built‐in video, which allows remote control and real‐time positioning in response to observed fish presence. For all arrays, fish sounds were localised in three dimensions and matched to the fish positions in the video data. We deployed these three platforms at four locations off British Columbia, Canada. The large array provided the best localisation accuracy and, with its larger footprint, was well suited to habitats with a flat seafloor. The mini and mobile arrays had lower localisation accuracy but were easier to deploy, and well suited to rough/uneven seafloors. Using these arrays, we identified, for the first time, sounds from quillback rockfish Sebastes maliger, copper rockfish Sebastes caurinus and lingcod Ophiodon elongatus. In addition to measuring temporal and spectral characteristics of sounds for each species, we estimated mean source levels for lingcod and quillback rockfish sounds (115.4 and 113.5 dB re 1 μPa, respectively) and maximum detection ranges at two sites (between 10.5 and 33 m). All proposed array designs successfully identified fish sounds in the wild and were adapted to various budget, logistical and habitat constraints. We include here building instructions and processing scripts to help users replicate this methodology, identify more fish sounds around the world and make passive acoustics a more viable way to monitor fish.

Passive acoustic monitoring is an effective technique for long-term monitoring of the soundscape in marine protected areas. Ocean noise is a key concern for the U.S. Office of National Marine Sanctuaries and has been identified as a research priority. The Sanctuary Soundscape Monitoring Project (“SanctSound”) was implemented to support efforts to address ocean noise across seven U.S. sanctuaries using a comprehensive and standardized approach. In this study, acoustic recordings were collected in the Hawaiian Islands Humpback Whale National Marine Sanctuary during the humpback whale seasons (November-May) from 2018-2022. Data encompassed 14 deployments across four sites in the main Hawaiian Islands: Hawaiʻi, Maui, O’ahu, and Kauaʻi. The soundscape was dominated by biological sources, most prominently the seasonal detection of humpback whale song. Third octave level monthly medians ranged from 70.4-105 dB re 1 µPa across sites with distinct peaks from January to April particularly at both Hawaiʻi and Maui sites. Overall, we reported relatively low vessel detection rates, with Maui having the highest daily average of vessel detections (x = 19.16). No COVID-19 impact could be observed acoustically using soundscape metrics which was likely due to the dominance of humpback whale chorusing. However, vessel detections and AIS data revealed a reduction in vessel activity after the onset of the pandemic at the Maui and Hawaiʻi sites. This study demonstrates that standardized metrics are a useful tool for obtaining long-term, baseline soundscape levels to understand the various contributions to the underwater soundscape and potential changes within marine protected areas in Hawaiʻi.

Urban-natural environments, proximal to rapidly urbanizing cities, provide essential ecosystem functions that benefit both city residents and ecological communities. With escalating urbanization, the resilience of these ecosystems is being progressively challenged, highlighting the need for robust monitoring mechanisms. Acoustic monitoring has emerged as an unobtrusive method to evaluate the status of these environments, capitalizing on indicators that reflect both landscape features and specific acoustic events. Despite potentially offering significant insights, this approach generates a large volume of acoustic data, introducing complexities in subsequent analyses. To mitigate this, we propose integrating artificial neural networks with acoustic indices to enhance data analysis. Our approach emphasizes the usefulness of short-time acoustic indices, computed over finite-duration analysis windows, to enhance polyphonic sound event detection accuracy. Empirical results support the performance of our approach, registering both an F1-Score and an error rate of 0.614. Overall, this study delineates a novel paradigm geared towards enhancing or preserving the biological diversity of urban-natural environments in areas with population growth and urban development.

Abstract Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine‐learning techniques to the analysis of two PAM datasets. We combine pre‐trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine‐learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low‐ and high‐frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large‐scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.

Passive acoustic monitoring serves as a minimally invasive and effective method for biodiversity assessment, particularly in bird monitoring through the application of acoustic indices. However, the use of different recording devices and different types of environmental noise (e.g., rain, wind, stream, traffic noise) lead to signal distortions that affect the application of ecoacoustics indices. Currently, there are no established guidelines specifying the technical requirements for recording devices and the signal-to-noise ratio (SNR) threshold for accurate calculation of acoustic indices. To enhance the accuracy of acoustic indices in bird biodiversity assessments, this study investigated the impact of different recording devices and environmental noise (rain, wind, stream, traffic noise) on acoustic indices. In this study, we selected six acoustic indices: Acoustic Complexity Index, Acoustic Diversity Index, Acoustic Evenness Index, Bioacoustic Index, Acoustic Entropy Index, and Normalized Difference Soundscape Index, and we used four different recording devices to simultaneously record 104 h of bird-sound data at the same location. In addition, 44 h of noisy bird signals with different noise types and intensities were artificially synthesized for comparison. The sound data were then used to analyze the effects of the recording devices and environmental noise on the acoustic indices used for bird biodiversity assessment. Our results showed that (a) all six acoustic indices were affected by the recording device used; (b) each acoustic index had different sensitivities to different noise types; and (c) there was a SNR threshold above which the effect of noise on the acoustic index was negligible. This study provides recommendations for recording device selection and determines SNR thresholds for noisy bird signals, contributing to the refinement of protocols for acquiring and preprocessing signals in the application of acoustic indices for bird biodiversity assessment. These findings aim to establish standardized protocols for signal acquisition and preprocessing in future application of acoustic indices.

Passive acoustic monitoring has been an effective tool for bird sound analysis. However, bird sounds often include cicada noise, which is an obstacle for investigating bird sounds. For example, cicada noise can result in large deviations of acoustic index, which will lead to the mismonitoring of species richness trends. Therefore, there is a critical need to filter cicada noise for helping bird sound analysis. We develop a novel end-to-end deep learning model, named CicadaNet for filtering cicada chorus from recordings containing bird sound. CicadaNet utilizes a convolutional encoder-decoder network to encode and decode acoustic features and a conformer module for global and local sequence modeling. We build a clean bird sound dataset and collect a large amount of real cicada noise data for model evaluation. We compare CicadaNet with current state-of-the-art deep denoising models and traditional denoising algorithms. Experimental results show that CicadaNet achieves the best denoising performance (SegSNR is improved by 9.59 dB and SI-SNR is improved by 20.08 dB when the noisy SNR = 0 dB). Meanwhile, CicadaNet achieves good performance for the real-time denoising of cicada noise. Furthermore, CicadaNet achieves bird species-independent noise reduction. We evaluate the effectiveness of CicadaNet for bird diversity survey. CicadaNet achieves the best performance, which can effectively eliminate the deviation caused by cicada noise to the acoustic index. CicadaNet can be easily extended to the cancellation of other environmental noise, and we propose it for the acoustic denoising of other vocalizing animals.

Background From passive acoustic monitoring (PAM) recordings, the vocal activity rate (VAR), vocalizations per unit of time, can be calculated and is essential for assessing bird population abundance. However, VAR is subject to influences from a range of factors, including species and environmental conditions. Identifying the optimal sampling design to obtain representative acoustic data for VAR estimation is crucial for research objectives. PAM commonly uses temporal sampling strategies to decrease the volume of recordings and the resources needed for audio data management. Yet, the comprehensive impact of this sampling approach on VAR estimation remains insufficiently explored. Methods In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: short-term periodic, diel, and hourly. For short-term periodic sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis. Results We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes. Conclusions While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.

Acoustic surveys for sperm whales (Physeter macrocephalus) were conducted in the Mediterranean Sea in summer 2018 as part of the vessel-based component of the ACCOBAMS Survey Initiative (ASI). Equal-spaced zigzag transects provided uniform coverage of key sperm whale habitats and were surveyed using a towed hydrophone array deployed from a research vessel at speeds of 5-8 knots. A total of 14,039 km of tracklines were surveyed in the western basin, Hellenic Trench and Libyan waters, with an acoustic coverage of 10% realised for sperm whales. During these surveys, 254 individual sperm whales were detected on the trackline, with an additional 66 individuals off-track. Sperm whales were only seen ten times on-track, with an additional 16 off-track sightings. Estimates of slant range to echolocating whales were used to derive density estimates through both design- and model-based distance sampling methodologies. An acoustic availability of 0.912 (sd = 0.036) was derived from via published models. When correcting for availability bias, a design-based abundance estimates of 2,673 individuals (95% CI 1,739-4,105; CV = 0.21) was derived for the surveyed blocks, which incorporated most known sperm whale habitat in the Mediterranean Sea. The equivalent model-based estimate was 2,825 whales (2,053-3,888; CV = 0.16). Over 97% of detected whales were in the western basin, with highest densities in the Algerian and Liguro-Provencal Basins between Algeria and Spain/France. In the eastern basin, detections were sparse and concentrated along the Hellenic Trench. A density surface modelling (DSM) exercise identified location and benthic aspect as being the most instructive covariates for predicting whale abundance. Distance sampling results were used in a power analysis to quantify the survey effort required to identify population trends. In the most extreme scenario modelled (10% per annum decline with decennial surveys), the population could have dropped by 90% before the decline was identified with high statistical power. Increasing the regularity of surveys would allow population trends to be detected more expediently. Mediterranean sperm whales are listed as Endangered on the IUCN’s Red List and the need for urgent conservation measures to reduce injury and mortality remains paramount for this unique sub-population.

Increased knowledge about marine mammal seasonal distribution and species assemblage from the South Orkney Islands waters is needed for the development of management regulations of the commercial fishery for Antarctic krill (Euphausia superba) in this region. Passive acoustic monitoring (PAM) data were collected during the autumn and winter seasons in two consecutive years (2016, 2017), which represented highly contrasting environmental conditions due to the 2016 El Niño event. We explored differences in seasonal patterns in marine mammal acoustic presence between the two years in context of environmental cues and climate variability. Acoustic signals from five baleen whale species, two pinniped species and odontocete species were detected and separated into guilds. Although species diversity remained stable over time, the ice-avoiding and ice-affiliated species dominated before and after the onset of winter, respectively, and thus demonstrating a shift in guild composition related to season. Herein, we provide novel information about local marine mammal species diversity, community structure and residency times in a krill hotspot. Our study also demonstrates the utility of PAM data and its usefulness in providing new insights into the marine mammal habitat use and responses to environmental conditions, which are essential knowledge for the future development of a sustainable fishery management in a changing ecosystem.

The increasing conversion of natural areas for anthropic land use has been a major cause of habitat loss, destabilizing ecosystems and leading to a biodiversity crisis. Passive acoustic sensors open the possibility of remotely sensing fauna on large spatial and temporal scales, improving our understanding of the current state of biodiversity and the effects of human influences. Acoustic indices have been widely used and tested in recent years, with an aim towards understanding the relationship between indices and the acoustic activity of several taxa in different types of environments. However, studies have shown divergent relationships between acoustic indices and the vocal activity of most soniferous taxa. A combination of indices has, in turn, been reported as a promising tool for representing biodiversity in different contexts. We used uni- and bivariate models to test different combinations of 8 common indices in relation to bird assemblage metrics. We recorded twenty-two study sites in Brazil's Atlantic Forest and three different types of environments in each site (forest, pasture, and swamp). Our results showed that 1) the best acoustic indices for explaining bird richness, abundance, and diversity were Bioacoustic and Acoustic Complexity; 2) the type of environment (forest, pasture, and swamp) influenced the performance of acoustic indices in explaining bird biodiversity, with the highest score model (biggest R² value) being a combination between Acoustic Diversity and Bioacoustic indices. Our results do support the use of acoustic indices in monitoring the acoustic activity of birds, but combining indices is encouraged since it provided the best results. However, given the divergence we found across environments, we recommend that sets of indices are tested to determine which of them best describe the biodiversity pattern models for a specific habitat. Based on our results, we propose that biodiversity patterns can be predicted through acoustic patterns. However, the level of confidence will depend on the acoustic index used and on focal taxa of interest (i.e., birds, amphibians, insects, and mammals). [ABSTRACT FROM AUTHOR]

Nightjars (Family Caprimulgidae) are insectivorous birds with crepuscular and nocturnal habits. Their coloration and cryptic behavior make this group extremely difficult to register, being more easily heard than sighted. The rufous nightjar (Antrostomus rufus) is a widely distributed species from Southern Central America to Northern Argentina, but most aspects of its life history remain unknown. By the employment of an acoustic remote unit, we evaluated the vocal activity and environmental variables of vocalizations by rufous nightjars during a complete annual cycle at one site in the Yungas Andean Forests of Northwestern Argentina. The results, based on one sampled site, indicate that the vocal activity of this species is performed during the breeding season (between September and November). During these months, this species showed a peak of vocal activity at midnight (between 00:00 and 01:00 hs) and at dawn (06:00 hs). In addition, most vocalizations were recorded during full moon nights, and temperature was negatively related to the vocal activity. We highlight the importance of using passive acoustic monitoring as a tool for understanding the life history of nocturnal neotropical birds. [ABSTRACT FROM AUTHOR]

Some nonhuman primate species, whose original habitats have been reclaimed by artificial activities, have acquired boldness toward humans which is evident based on the diminished frequency of escape behaviors. Eventually, such species have become regular users of human settlements, and are referred to as "urban primates." Considering this, we developed a noninvasive technique based on bioacoustics to provide a transparent assessment of troop addiction levels in anthropogenic environments, which are determined by the dependence on agricultural crops and human living sphere for their diets and daily ranging, respectively. We attempted to quantify the addiction levels based on the boldness of troops when raiding settlements, characterized by a "landscape of fear" because of the presence of humans as predators. We hypothesized that the boldness of troops could be measured using two indices: the frequency of raiding events on settlements and the amount of time spent there. For hypothesis testing, we devised an efficient method to measure these two indices using sound cues (i.e., spontaneous calls) for tracing troop movements that are obtainable throughout the day from most primate species (e.g., contact calls). We conducted a feasibility study of this assessment procedure, targeting troops of Japanese macaques (Macaca fuscata). For this study, we collected 346 recording weeks of data using autonomous recorders from 24 troops with different addiction levels during the nonsnowy seasons. The results demonstrated that troops that reached the threshold level, at which radical interventions including mass culling of troop members is officially permitted, could be readily identified based on the following behavioral characteristics: troop members raiding settlements two or three times per week and mean time spent in settlements per raiding event exceeding 0.4 h. Thus, bioacoustic monitoring could become a valid option to ensure the objectivity of policy judgment in urban primate management. Research highlights: To cope with urban primate issues, we developed a transparent procedure to determine the addiction levels of troops to the human sphere using a bioacoustic approach, which has several advantages.We conducted a feasibility study of the present procedure by collecting 346 recording weeks of data targeting different addiction levels of Japanese macaque troops (n = 24) in eastern Japan.The frequency and duration of settlement raiding events by troops could readily be measured using the present procedure and serve as effective indices to identify problem troops, which may need to be approved for mass culling as a practical option. [ABSTRACT FROM AUTHOR]

Processing methods that maximize species richness from acoustic recordings obtained from regional monitoring programs can increase detections of uncommon, rare, and cryptic species and provide key information on species status and distribution. Using data from regional bird monitoring in Yukon, Canada, we (1) compared the number of bird species detected (species richness) and the cost associated with four acoustic processing methods (Listening, Visual Scanning, Recognizer, and Recognizer with Validation) and (2) combined Listening and Recognizer with Validation information to increase detections of all bird species at the ecoregion scale. We used comprehensive Visual Scanning to detect all bird species on the recordings. We processed ∼1% of the recordings using Listening and detected 56% of the bird community with 71.5 h of human effort. We used Recognizer (multispecies recognizer BirdNET) with Validation and detected 89% of the bird community with ∼22% of the effort required for Visual Scanning (56 and 257 h, respectively). As an application of our approach, we combined Listening and Recognizer with Validation to process recordings from five northern ecoregions and found a 23%–63% increase in the number of bird species detected with little additional effort. Combining Listening and Recognizer with Validation can maximize species detections from large passive acoustic monitoring data sets. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) is one of the most effective and widely used methods for marine mammal detection and population density estimation. In most cases, it is associated with an inevitably large volume of raw acoustic data, making analysis exclusively by human experts challenging and extremely time-demanding. We present a neural network algorithm for detection of tonal signals produced by toothed cetaceans which allocates time intervals with high likelihood of marine mammal signal presence in underwater sound recordings. The proposed model is based on a convolutional neural network (CNN) of the ResNet152 architecture used as a backbone with additional linear layers. The input data for the model are spectrograms of short segments of underwater sound recordings. Training of the network was performed on a benchmark dataset of bottlenose dolphin whistles labeled by human experts. Analysis of the performance metrics (Precision, Recall, and F1-score) showed the proposed model's superiority in comparison to widely used acoustic energy-based approaches. In virtue of the model's versatility, we believe that it can be successfully used for detection of tonal signals of other species characterized by producing tonal acoustic signals. This assumption was confirmed by preliminary results of the application of the developed CNN to a large archive of the White Sea beluga whale recordings. [ABSTRACT FROM AUTHOR]

Context. The ecology of cryptic animals is difficult to study without invasive tagging approaches or labour-intensive field surveys. Acoustic localisation provides an effective way to locate vocalising animals using acoustic recorders. Combining this with land cover classification gives new insight into wild animal behaviour using non-invasive tools. Aims. This study aims to demonstrate how acoustic localisation – combined with high-resolution land cover classification – permits the study of the ecology of vocalising animals in the wild. We illustrate this technique by investigating the effect of land cover and distances to anthropogenic features on coyote and wolf vocal behaviour. Methods. We collected recordings over 13 days in Wisconsin, USA, and triangulated vocalising animals’ locations using acoustic localisation. We then mapped these locations onto land cover using a high-resolution land cover map we produced for the area. Key results. Neither coyotes nor wolves vocalised more in one habitat type over another. Coyotes vocalised significantly closer to all human features than expected by chance, whereas wolves vocalised significantly further away. When vocalising closer to human features, coyotes selected forests but wolves showed no habitat preference. Conclusions. This novel combination of two sophisticated, autonomous sensing-driven tools permits us to examine animal land use and behavioural ecology using passive sensors, with the aim of drawing ecologically important conclusions. Implications. We envisage that this method can be used at larger scales to aid monitoring of vocally active animals across landscapes. Firstly, it permits us to characterise habitat use while vocalising, which is an essential behaviour for many species. Furthermore, if combined with additional knowledge of how a species’ habitat selection while vocalising relates to its general habitat use, this method could permit the derivation of future conclusions on prevailing landscape use. In summary, this study demonstrates the potential of integrating acoustic localisation with land cover classification in ecological research. [ABSTRACT FROM AUTHOR]

Since 2006, white-nose syndrome has caused drastic declines in populations of several hibernating bat species throughout eastern North America. Thus, there is a growing need to establish long-term monitoring programs to assess changes in bat populations over time. Information on the seasonal timing of species occurrence and the sampling effort required to acoustically detect individual bat species and obtain complete inventories will enable researchers to design and implement more effective monitoring programs. From April to October 2018 to 2021, we passively sampled for bats using full-spectrum detectors at eight permanent sites at Fort Indiantown Gap National Guard Training Center, Pennsylvania. We examined seasonal activity patterns and estimated bat species richness among sites and seasons using species accumulation curves. We also estimated probability of detection (p) and site occupancy (Ψ) using single-season occupancy models in PRESENCE software and then determined the minimum number of sampling nights needed to reliably infer the absence of each species. We identified 286,131 bat passes of eight species in 4,107 detector-nights. Seasonal patterns of activity varied among species. We needed approximately 20 sampling nights to detect 90% of the total bat species richness among sites, and we needed 4 to 10 nights to detect 90% of species richness among seasons. We needed relatively few nights (≤12 nights) to detect most species during summer; however, we needed many more nights to detect acoustically rare species. Our results indicate that the acoustic sampling effort currently required to determine the presence or probable absence of Indiana myotis Myotis sodalis, northern long-eared myotis M. septentrionalis, and tricolored bats Perimyotis subflavus during summer may not be adequate for these species in some areas and that a considerable level of effort (>40 nights) is needed to detect little brown myotis M. lucifugus. Monitoring programs that incorporate efficient sampling methodologies will be critical for future conservation efforts as populations of several bat species continue to decline. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring has proven effective for broad-scale population surveys of acoustically active species, making it a valuable tool for conserving threatened species. However, successful automated classification of anuran vocalisations in large audio datasets has been limited. We deployed five autonomous recording units at three known breeding areas of the Yosemite toad (Anaxyrus canorus), which is threatened and relatively uncommon, and the sympatric Pacific chorus frog (Pseudacris regilla), which is widespread and more common, to test the viability of bioacoustics as a means of supplementing ongoing, human survey efforts. We analysed the audio data with the BirdNET algorithm, which was originally developed for birds but has been expanded to include both species. We achieved efficient and accurate identification of both species in 2,756 h of audio, which yielded high-resolution phenological data about seasonal and daily vocal activity as well as daily detection counts. These findings demonstrate that a newly expanded machine learning detector, BirdNET, can effectively process passive acoustic surveys for these species. Further exploration of how passive acoustic monitoring may complement existing survey techniques for these and other Anurans is warranted. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) has become increasingly popular in ecological studies, but its efficacy for assessing overall terrestrial vertebrate biodiversity is unclear. To quantify this, its performance for species detection must be directly compared to that obtained using traditional observer-based monitoring (OBM). Here, we review such comparisons across all major terrestrial vertebrate classes and identify factors impacting PAM performance. From 41 studies, we found that while PAM-OBM comparisons have been made for all major terrestrial vertebrate classes, most comparisons have focused on birds (65%) in North America (52%). PAM performed equally well or better (61%) compared to OBM in general. We found no statistical difference between the methods for total number of species detected across all vertebrate classes (excluding reptiles); however, recording period and region of study influenced the relative performance of PAM, while acoustic analysis method and which method sampled for longer overall showed no impact. Further studies comparing PAM performance in non-avian vertebrates using standardised methods are needed to investigate in more detail the factors that may influence PAM performance. While PAM is a valuable tool for vertebrate surveys, a combined approach with targeted OBM for non-vocal species should achieve the most comprehensive assessment of terrestrial vertebrate communities. [ABSTRACT FROM AUTHOR]

Vocalisations are a key component of communication for species that employ crypsis. The Eastern Ground Parrot (Pezoporus wallicus wallicus) is one such species that lives within dense heathland habitats in eastern Australia. Passive acoustic monitoring is now a common method used for avian monitoring and conservation programmes. For implementation of this method to be successful, we require a minimum level of understanding of the environmental and behavioural factors that may influence vocal activity. We tested how Eastern Ground Parrot call rate and chorus duration fluctuated across key temporal, environmental and meteorological variations over one year using passive acoustic monitoring from one site. Season, moon phase and days since rainfall predicted vocal activity of the birds at this site. Our results demonstrate the importance of considering the behavioural response of species to environmental conditions to account for potential bias in the interpretation of passive acoustic monitoring data. [ABSTRACT FROM AUTHOR]

Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real‐time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual‐level ecological metrics, such as presence, detection‐weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community‐level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species‐level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods. [ABSTRACT FROM AUTHOR]

Acoustic techniques are rapidly becoming powerful tools for species monitoring and biodiversity assessment. These methods can be particularly appropriate for forest-dwelling crocodiles which are difficult to survey visually. However, basic vocal-repertoire data is lacking for many of the poorly known species. Here, we used passive acoustic recorders to capture 97 spontaneous vocal signals from a pair of captive adult African dwarf crocodiles (Osteolaemus tetraspis). We catalogued their acoustic repertoire and compared the calls recorded in captivity with 201 suspected wild O. tetraspis calls recorded in Gabon to determine whether the wild calls belonged to the same species. Captive and wild crocodiles produced the same four types of calls, not previously identified in other crocodylids. Short, low-frequency "drums" (31±12 Hz), longer, low-frequency "rumbles" (40 ± 14 Hz), as well as higher frequency "moos" (299 ± 133 Hz) and "gusts" (219 ± 108 Hz). Our results provide reference for species identification and support implementation of acoustic-based methods for African dwarf crocodile monitoring and conservation assessment. The data can further contribute to landscape-wide biodiversity monitoring and counter-poaching activities, as well as improving our understanding of crocodilian ecology and behaviour. [ABSTRACT FROM AUTHOR]

Killer whales (Orcinus orca) have group-specific call repertoires that can be used to track groups and populations using passive acoustic monitoring. To provide a detailed description of the Icelandic killer whale repertoire and its variation, we analyzed acoustic data collected in five locations between 1985 and 2016. Calls were classified manually, and CART and random forest analyses were employed to validate the manual classification. A total of 91 call categories (including call types and subtypes) were defined. Most call categories were recorded in more than one location, with the highest proportion shared between herring grounds in Vestmannaeyjar (South) and Breiðafjörður (West). However, both locations included call categories that were not recorded elsewhere in Iceland. Recordings from past herring wintering grounds in eastern Iceland included few call categories that matched other locations. Sample sizes from Reykjanes (Southwest) and Skjálfandi (North) were small and did not include unique call categories. The relative occurrence of call categories in Vestmannaeyjar changed little over a 14-year period (2002-2016), although shorter-term changes between years were observed that appeared to correlate to changes in individuals identified. This comparison of acoustic repertoires provides valuable information on the social structure and movement patterns of herring-eating killer whales around Iceland. [ABSTRACT FROM AUTHOR]

Locating colonies of rare bats can be a time consuming process, as it is often difficult to know where to focus survey effort. However, identifying peaks of bat activity via acoustic monitoring may provide insights into whether a colony is locally present, and help screen out sites with low potential. Using a triage approach, we developed a survey methodology for locating colonies of the woodland-specialist barbastelle bat (Barbastella barbastellus). We investigated whether woodland occupancy by a colony could be predicted by acoustic data, and assessed the influence of survey effort (number of acoustic detectors deployed) on detectability. The methodology was then trialled in citizen science surveys of 77 woodlands, with follow-up radio-tracking surveys by specialists being used to confirm presence or absence. Using Receiver Operating Characteristic (ROC) curve analysis, we found that a threshold of four barbastelle passes recorded by at least one detector within one hour of sunset optimised the balance between the true- and false-positive rates. Subsequently, we found that a minimum survey effort of one detector per 6.25 hectares of woodland was needed to ensure a colony would be detected using this threshold, based on a survey sensitivity of 90%. Radio-tracking surveys in a subset of the woodlands, identified as having a high probability of being occupied by a colony based on acoustic monitoring, confirmed the presence of five previously unknown barbastelle maternity colonies. These results demonstrate that a triage system, in which high probability woodland sites are identified based on acoustic survey data, can be used to prioritise sites for future specialist surveys and conservation action. [ABSTRACT FROM AUTHOR]

Red hind (Epinephelus guttatus) is a commercially important grouper that forms transient spawning aggregations following a lunar cycle. Reproductive displays coincide with courtship-associated sounds (CAS), with calling during diel and daily maxima increasing to form choruses. Choruses may be key to detecting patterns of reproductive activity, especially spawning times, at varying temporal scales, which could provide a better understanding of reproductive behaviors and help predict when peak reproductive activity occurs. Yet, choruses have only been briefly described acoustically and temporally during a single aggregation event. Using passive acoustic monitoring, we fully describe the acoustical characteristics of choruses, quantify how choruses compare to non-chorusing periods, examine the interannual consistency of chorusing relative to spawning times and temperature variations, and determine if months of peak chorusing follow a model predicting red hind aggregation times. Choruses were readily distinguished, with a lower dominant frequency (207.36 ± 32.30 Hz) and at least 10 dB higher than the ambient soundscape at dusk without chorus. Choruses generally occurred during expected aggregation times, although this varied across years and mostly followed the predictive model if differences in expected aggregation times between sites were accounted for. Choruses may be better predictors of aggregation times than quantifying individual CAS, and their acoustic characterization can lead to reliable auto-detection using existing methods to rapidly search for aggregations and define aggregation times at other sites. [ABSTRACT FROM AUTHOR]

Abstract Passive acoustic monitoring is usually presented as a complementary approach to monitoring wildlife communities and assessing ecosystem conditions. Automatic species detection methods support biodiversity monitoring and analysis by providing information on the presence–absence of species, which allows understanding the ecosystem structure. Therefore, different alternatives have been proposed to identify species. However, the algorithms are parameterized to identify specific species. Analysing multiple species would help to monitor and quantify biodiversity, as it includes the different taxonomic groups present in the soundscape. We present an unsupervised methodology for multi‐species call recognition from ecological soundscapes. The proposal is based on a clustering algorithm, specifically the learning algorithm for multivariate data analysis (LAMDA) 3pi algorithm, which automatically suggests the number of clusters associated with the sonotypes. Emphasis was made on improving the segmentation of the audio to analyse the whole soundscape without parameterizing the algorithm according to each taxonomic group. To estimate the performance of our proposal, we used four datasets from different locations, years and habitats. These datasets contain sounds from the four major taxonomic groups that dominate terrestrial soundscapes (birds, amphibians, mammals and insects) in audible and ultrasonic spectra. The methodology presents performances between 75% and 96% in presence–absence species recognition. Using the clusters proposed by our methodology, the whole soundscape biodiversity was measured and compared with the estimate of four acoustic indices (ACI, NP, SO and BI). Our approach performs biodiversity assessments similar to acoustic indices with the advantage of providing information about acoustic communities without the need for prior knowledge of the species present in the audio recordings.

Background: Passive acoustic monitoring (PAM) has become a popular tool for bird monitoring, with vocal activity rate (VAR) being a key metric to gauge bird populations. However, the effective temporal sampling design at the community level for representative VAR data remains underexplored. Methods: In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: seasonal, diel, and hourly. For seasonal sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis. Results: We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes. Conclusions: While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.

Species distribution modeling is important for predicting species responses to environmental change, but model accuracy can be limited by a lack of data in remote areas. Hierarchically stratified surveys (cluster sampling) offer an efficient approach to sampling in remote areas, but an appropriate balance is needed between cost efficiency and statistical independence. The cost-effectiveness of cluster sampling likely varies with temporal sampling intensity (e.g., single vs. multiple repeat samples) due to differences in spatial autocorrelation. Our aim was to assess the trade-offs between spatial and temporal replication and optimize sampling in which temporal replication occurs. We used bootstrap resampling to create alternative designs from multi-species avian point-count and autonomous recording unit data. We varied the number of primary sample units (PSUs), secondary sampling units per PSU (SSUs), and temporal repeat samples (i.e., visits) at each SSU. We fit species accumulation curves to examine how spatial and temporal replication influenced species accumulation. We split data into spatially independent model training and validation datasets and fit species distribution models (SDM) for 47 species using generalized linear models and examined how prediction accuracy changed with sampling intensity to examine the cost-benefit trade-offs between spatial versus temporal replication within PSUs. We found that spatial and temporal replication were partially redundant and adding more visits had less influence on predictive accuracy when there were more SSUs and vice versa. The cost-benefit of increasing spatial replication within PSUs varied with the costs of accessing SSUs. The optimal number of SSUs per PSU varied with both temporal replication and the number of unique PSUs sampled. In general, using ≤ 3 SSUs per PSU produced the most accurate SDM predictions when the number of PSUs was high. When the number of PSUs was low and/or SSU costs were low, increasing clustering within PSUs can optimize sampling.

Nightjars are a family of nocturnal and aerial insectivorous birds that have experienced long-term declines driven primarily by loss of habitat and prey populations. In Illinois, structured survey efforts documented the presence of Eastern Whip-poor-will ( Antrostomus vociferus ) and Chuck-will’s-widow ( Antrostomus carolinensis ) over 30 years ago, but their current distribution in the state is not well known. We deployed autonomous recording units at 142 locations during May–July 2022 to resurvey a uniform sampling grid developed by the Illinois Breeding Bird Atlas during 1986–1991 surveys, with the objective of estimating the current distribution of the species within the state and understanding how their distribution has changed over 30 years. We used a bird call identification algorithm, BirdNet, to detect Nightjars in 4-hour recordings and then manually verified detections where species were reported. We used single-season, single-species occupancy models and a suite of remotely sensed, ecological variables to identify key drivers of their current distribution relating to landcover, forest patch configuration, and forest disturbances. BirdNet was highly accurate at detecting Nightjars and reduced the time spent manually annotating recordings. Eastern Whip-poor-will were positively associated with large core forests and proportion of pastureland within a sampling block. Additionally, Eastern Whip-poor-will were more likely to occupy areas that had experienced low to moderate disturbance in forests. Covariates used to model Chuck-will’s-widow occupancy explained little variation in detection or occupancy. However, examining non-significant trends suggest some similar relationships as documented with Eastern Whip-poor-will. Compared to the 1986–1991 surveys, Chuck-will’s-widow populations remained constant while Eastern Whip-poor-will declined although the spatial distribution of occurrences for both species changed. Our results provide updated knowledge of the current distributions of Nightjars in our region and highlight the need for further studies examining the drivers of distribution particularly in the case of Chuck-will’s-widow.

Recent advances in acoustic recording equipment enable autonomous monitoring with extended spatial and temporal scales, which may allow for the censusing of species with individually distinct vocalizations, such as owls. We assessed the potential for identifying individual Barred Owls ( Strix varia ) through detections of their vocalizations using passive acoustic monitoring. We placed autonomous recording units throughout the John Prince Research Forest (54°27' N, 124°10' W, 700 m ASL) and surrounding area, in northern British Columbia, Canada, from February to April 2021. The study area was 357 km 2 with a minimum of 2 km between the 66 recording stations. During this period, we collected 454 Barred Owl calls, specifically the two-phrase hoot , from 10 recording stations, which were of sufficient quality for spectrographic analysis. From each call, we measured 30 features: 12 temporal and 18 frequency features. Using forward stepwise discriminant function analysis, the model correctly categorized 83.2% of the calls to their true recording location based on a 5-fold cross validation. The model showed substantial agreement between the recording station that the call was classified to originate from, and where the call was actually recorded. The most important features of the calls that enabled discrimination were call length, interval between the 4th and the 5th note, interval between the 6th and 7th note, and duration of the 8th note. Our results suggest that passive acoustic monitoring can be used not only to detect presence/absence of species but also, where vocalizations have individually distinct features, for population censusing.

Invasive predators can alter ecosystem function and be detrimental to native wildlife by direct predation and through exploitative and interference competition. Barred owls (Strix varia), a native species in eastern North America, have expanded their range to Pacific Northwest forests, threatening native owls and community dynamics. The western screech-owl (Megascops kennicottii) is a species of conservation concern, and the apparent population decline coincides with the arrival of barred owls. We collected data in 2021 under a broad scale passive acoustic monitoring program at 2482 stations in nine study areas to evaluate western screech-owl distribution and potential threats to population persistence in the Pacific Northwest. We fitted single-species occupancy models to estimate barred owl and western screech-owl landscape use and identify important features of habitat. We quantified effects of barred owls on western screech-owl occupancy and detection probability using co-occurrence models. Barred owls used approximately 0.79 (95 % confidence interval: 0.72, 0.85) of our sampling locations. Western screech-owl occupancy and weekly detection varied by study area from 0.24 (0.15, 0.34) to 0.92 (0.89, 0.95) and 0.12 (0.08, 0.16) to 0.71 (0.69, 0.74), respectively. Western screech-owl detection dropped slightly from 0.36 (0.31, 0.41) to 0.29 (0.26, 0.33) where barred owls were present. Western screech-owls frequently occupied sites used by barred owls but occurred less in older forests where barred owls were present, suggesting they may experience higher predation risk in older forests. Western screech-owls may be unable to avoid habitat used by ubiquitous barred owls, but they may adapt behaviorally to avoid this invasive predator.

Mesophotic coral ecosystems (MCEs) are the deepest part of tropical coral reefs, ranging from depths of 30 to over 170 m. Despite their significance, MCEs remain largely unexplored due to the challenges associated with accessing these depths. However, the application of passive acoustic monitoring methods (PAM) is a suitable approach for studying fish communities within these unique habitats. In French Polynesia, recent PAM studies have unveiled a higher occurrence of frequency-modulated fish sounds in MCEs than in shallower reef environments. This study aims to further enhance our understanding of fish sounds in MCEs by examining their diel patterns, focusing specifically on the two most abundant frequency-modulated fish sounds that were recorded at depths of 60 and 120 m at six Polynesian islands. Both sound types occurred predominantly during the beginning and the end of nocturnal periods. The presence and abundance of these sounds exhibited variation between the islands, highlighting potential regional disparities in vocal activity or the bathymetric distribution of the sound-producing species. By characterizing the diel cycles and bathymetric differences in relation to their geographical distribution, this study offers preliminary insights into identifying the potential sound-producing species.

The increasing conversion of natural areas for anthropic land use has been a major cause of habitat loss, destabilizing ecosystems and leading to a biodiversity crisis. Passive acoustic sensors open the possibility of remotely sensing fauna on large spatial and temporal scales, improving our understanding of the current state of biodiversity and the effects of human influences. Acoustic indices have been widely used and tested in recent years, with an aim towards understanding the relationship between indices and the acoustic activity of several taxa in different types of environments. However, studies have shown divergent relationships between acoustic indices and the vocal activity of most soniferous taxa. A combination of indices has, in turn, been reported as a promising tool for representing biodiversity in different contexts. We used uni- and bivariate models to test different combinations of 8 common indices in relation to bird assemblage metrics. We recorded twenty-two study sites in Brazil’s Atlantic Forest and three different types of environments in each site (forest, pasture, and swamp). Our results showed that 1) the best acoustic indices for explaining bird richness, abundance, and diversity were Bioacoustic and Acoustic Complexity; 2) the type of environment (forest, pasture, and swamp) influenced the performance of acoustic indices in explaining bird biodiversity, with the highest score model (biggest R2 value) being a combination between Acoustic Diversity and Bioacoustic indices. Our results do support the use of acoustic indices in monitoring the acoustic activity of birds, but combining indices is encouraged since it provided the best results. However, given the divergence we found across environments, we recommend that sets of indices are tested to determine which of them best describe the biodiversity pattern models for a specific habitat. Based on our results, we propose that biodiversity patterns can be predicted through acoustic patterns. However, the level of confidence will depend on the acoustic index used and on focal taxa of interest (i.e., birds, amphibians, insects, and mammals).

In recent years, passive acoustic monitoring (PAM) has been widely applied in the field of soundscape ecology. Meanwhile, acoustic indices, as an important tool for rapid biodiversity assessment (RBA), have also attracted increasing attention. However, considerable evidence demonstrates that existing acoustic indices are susceptible to noise, which poses a challenge for wider applications of acoustic indices. In this work, we firstly provide insights into how noise affects the widely used acoustic diversity index (ADI), revealing that the global threshold scheme (i.e., −50 dBFS for all time–frequency bins) to generate the binary spectrogram (spectrogram containing time–frequency bins denoted as ‘1′ with power above the threshold and ‘0′ below) is the key factor that makes ADI sensitive to noise. To address this problem, we propose a revised ADI version named frequency-dependent acoustic diversity index (FADI), which employs a new threshold scheme to reduce noise impacts on its values. Specifically, considering that the noise floor along frequency axis usually varies in practice, i.e., a colored noise rather than a white noise exists in field recordings, floating thresholds adapted to the noise level at each frequency bin were employed and further incorporated with the global threshold of ADI. Controlled computational experiments using field recordings were conducted where manipulated noise levels were introduced as different signal-to-noise ratios (SNR). It was shown that the proposed FADI was significantly robust to noise within a much wider SNR range as compared with ADI. Furthermore, a singular example using a real-world recording also verified its effectiveness and highlighted the promising potential of FADI in applications of PAM.

The extraction of species-specific calls from passive acoustic recordings is a common preliminary step in ecological analysis. But for many species, especially those occupying noisy, acoustically variable habitats, the call extraction process remains largely manual, a time-consuming, and increasingly unsustainable process. Deep neural networks have been shown to provide excellent performance in a range of acoustic classification applications. We take as an example the recognition of four songs of one of the rarest mammals in the world, the western black-crested gibbon (Nomascus concolors).The process of recognition in this paper which includes distributed BIC ambient sound segmentation based on the OpenPAI platform; DNN-based western black-crested gibbon song enhancement processing; data pre-processing, labeling samples; proposed DNN + ResNet34 + CBAM + GRU + Attention recognition model; comparing other classical neural network models.Our best model converts acoustic recordings into spectrogram images on the mel frequency scale and uses these images to train convolutional neural networks.Our proposed model proved to be very accurate in predicting the segmented western black-crested gibbon songs with an accuracy of 99.8%, and almost a few western black-crested gibbon songs were incorrectly identified when all segmented data were recognized. In the four consecutive years of the acoustic monitoring system deployed in the Ailao Mountain National Nature Reserve, the western black-crested gibbon was most active near the monitoring site from March to August each year, and least active in January and February. Based on call sound intensity analysis, we monitored a total of two different western black-crested gibbon groups (G1 and G2) during the monitoring cycle. We demonstrate that passive acoustic monitoring combined with CNN classifiers is an effective tool for the remote detection of one of the rarest and most threatened species in the world.

The Mediterranean sperm whale population, Physeter macrocephalus, is listed as endangered due to population decline caused by human activities. To mitigate the impact of these activities, accurate knowledge of their distribution and abundance is crucial. During their long dives, sperm whales are not available to visual observation, but since they produce sounds when they dive, they are available to acoustic detection. Therefore, we aimed to use towed acoustic data to model their habitat and fill the knowledge gap on Mediterranean sperm whale distribution. Generalised additive models were used to link the number of sperm whales detected acoustically during the ACCOBAMS Survey Initiative in 2018 with different environmental variables integrated over different depth classes, encompassing the depth range used by the species for foraging. Sperm whale distribution was influenced by water temperature at the bottom, eddy kinetic energy between 200 and 600 m, as well as gradients of sea surface temperature and chlorophyll-a concentrations. The abundance of sperm whales was estimated at 2,959 individuals [2,077 - 4,265] in the sampled areas of the Mediterranean Sea. We predicted that sperm whales were mainly distributed in summer along the continental slope of the north-western Mediterranean basin from the Balearic Islands to the Ligurian Sea and off the Algerian coast. They were present throughout the western Mediterranean Sea and in the northern Ionian Sea. In contrast, predicted densities were low in the eastern part of the Mediterranean Sea. The use of acoustic data compensated for the main difficulty in studying sperm whales, the unavailability of animals at the surface during visual observation and the paucity of visual data. We thus encourage more systematic use of passive acoustics to study sperm whale distribution. The model highlighted a higher concentration of sperm whales in the western Mediterranean basin than in the eastern basin in summer, opening up avenues to improve the conservation of this endangered Mediterranean sub-population.

Estimating the detection probability of small cetaceans using either visual or acoustic surveys is difficult because they do not surface or vocalise continuously and can be imperceptible to an observer or hydrophone. Animals seen at the surface may have lower vocalisation rates, while submerged individuals may be more vocally active. This study aims to estimate visual, acoustic and combined detection probability by using Mark-Recapture Distance Sampling (MRDS) methodology. We used vessel-based visual sightings and acoustic data (based on click identification) collected simultaneously during the ACCOBAMS Survey Initiative in summer 2018 onboard the R/V Song of the Whale. This study focused on small cetaceans in the Mediterranean Sea, including the most commonly-encountered species, the striped dolphin (Stenella coeruleoalba). We identified duplicate events between visual and acoustic platforms using a decision tree based on time and distance thresholds to estimate g(0) (the detection probability on the trackline) for small cetaceans. A total of 30 duplicate events were identified from 107 and 109 events identified by the visual and acoustic platforms respectively. We tested the models with two key functions. With a hazard-rate key function, the g(0) was estimated at 0.52 (CV=21.0%) for both platforms combined, 0.29 (CV=25.6%) for the visual platform and 0.32 (CV=25.1%) for the acoustic platform. With a half-normal key function, g(0) was estimated at 0.51 (CV = 21.7%) for both platforms combined, 0.29 (CV = 25.6%) for the visual platform and 0.33 (CV = 23.2%) for the acoustic platform. Our results illustrate that passive acoustic monitoring can be used as an independent platform in MRDS to estimate the detection probability. Our estimate of g(0) was well below 1, far from the perfect detection commonly assumed for abundance estimation. Without correction for detection biases, total abundance would be underestimated by a factor of two when using both acoustic and visual data. This highlights the importance of using dual-platform surveys to estimate detection probability in order to improve abundance estimates and conservation efforts.

Locating colonies of rare bats can be a time consuming process, as it is often difficult to know where to focus survey effort. However, identifying peaks of bat activity via acoustic monitoring may provide insights into whether a colony is locally present, and help screen out sites with low potential. Using a triage approach, we developed a survey methodology for locating colonies of the woodland-specialist barbastelle bat (Barbastella barbastellus). We investigated whether woodland occupancy by a colony could be predicted by acoustic data, and assessed the influence of survey effort (number of acoustic detectors deployed) on detectability. The methodology was then trialled in citizen science surveys of 77 woodlands, with follow-up radio-tracking surveys by specialists being used to confirm presence or absence. Using Receiver Operating Characteristic (ROC) curve analysis, we found that a threshold of four barbastelle passes recorded by at least one detector within one hour of sunset optimised the balance between the true- and false-positive rates. Subsequently, we found that a minimum survey effort of one detector per 6.25 hectares of woodland was needed to ensure a colony would be detected using this threshold, based on a survey sensitivity of 90%. Radio-tracking surveys in a subset of the woodlands, identified as having a high probability of being occupied by a colony based on acoustic monitoring, confirmed the presence of five previously unknown barbastelle maternity colonies. These results demonstrate that a triage system, in which high probability woodland sites are identified based on acoustic survey data, can be used to prioritise sites for future specialist surveys and conservation action.

Mixed-species forest stands may provide suitable habitats for more bird species than tree monocultures by increasing the number of niches and resources available. However, assessing the effects of forest diversity on birds over time and across multiple sites using traditional bird survey methods can be expensive, time-consuming and impractical. Alternatively, we used passive acoustic monitoring (PAM) and acoustic indices to investigate how tree species diversity, relative density, and other plot characteristics affect the species richness of avian communities in temperate forests. Acoustic monitoring was conducted in Nationalpark Hainich, Germany, using plots which differed in tree species diversity and relative species density. Using a subset of our data, five acoustic indices, the normalised difference soundscape index (NDSI), acoustic complexity index (ACI), bioacoustic index (BI), the acoustic evenness index (AEI) and temporal entropy (Ht), were initially assessed using audio recordings of different soundscape elements to determine the degree to which they were influenced by non-biophony soundscape elements. Of these, NDSI, ACI and BI were considered least biased and displayed significant positive relationships with bird species richness assessed using audio recordings. However, NDSI values plateaued at around 5 bird species when measuring bird species richness and ACI values for dawn choruses were lower than expected, potentially due to the high density of birdsong saturating the soundscape. Plot tree species diversity had significant positive effects on ACI and BI during dawn, dusk and daytime periods. Increased basal area of sycamore (Acer pseudoplatanus) and, to a lesser degree, beech (Fagus sylvatica), had a negative effect on acoustic indices. These results could potentially be due to the earlier leaf out timings of these tree species in relation to the dates when surveys were performed. While the use of PAM and acoustic indices enabled surveys across multiple sites over longer time periods than would previously have been achievable, our results show that care should be taken when selecting indices and interpreting acoustic index results.

Simple Summary: Most seabird species are in need of effective conservation, with 43% being near to globally threatened. Passive acoustic monitoring could serve as a cost-effective, noninvasive population monitoring tool essential for informing future conservation efforts. As such, we set out to investigate whether passive acoustic monitoring could successfully predict the African penguin density at a remote colony in Betty's Bay, South Africa. We first automated the detection and counting of penguins' vocalisations in our recordings to facilitate the handling of large datasets. Then, we investigated whether temperature, humidity, and wind speed affected the calling rate of penguins, which would be essential for an accurate census. Finally, taking into account the variations with weather conditions, we showed that passive acoustic monitoring could successfully predict the number of callers within a 10.5 m radius around our devices, indicating that it can be used for cost-effective, noninvasive censuses of African penguin colonies. Global biodiversity is in rapid decline, and many seabird species have disproportionally poorer conservation statuses than terrestrial birds. A good understanding of population dynamics is necessary for successful conservation efforts, making noninvasive, cost-effective monitoring tools essential. Here, we set out to investigate whether passive acoustic monitoring (PAM) could be used to estimate the number of animals within a set area of an African penguin (Spheniscus demersus) colony in South Africa. We were able to automate the detection of ecstatic display songs (EDSs) in our recordings, thus facilitating the handling of large datasets. This allowed us to show that calling rate increased with wind speed and humidity but decreased with temperature, and to highlight apparent abundance variations between nesting habitat types. We then showed that the number of EDSs in our recordings positively correlated with the number of callers counted during visual observations, indicating that the density could be estimated based on calling rate. Our observations suggest that increasing temperatures may adversely impact penguin calling behaviour, with potential negative consequences for population dynamics, suggesting the importance of effective conservation measures. Crucially, this study shows that PAM could be successfully used to monitor this endangered species' populations with minimal disturbance. [ABSTRACT FROM AUTHOR]

Aquatic ecosystems are constantly changing due to anthropic stressors, which can lead to biodiversity loss. Ocean sound is considered an essential ocean variable, with the potential to improve our understanding of its impact on marine life. Fish produce a variety of sounds and their choruses often dominate underwater soundscapes. These sounds have been used to assess communication, behaviour, spawning location, and biodiversity. Artificial intelligence can provide a robust solution to detect and classify fish sounds. However, the main challenge in applying artificial intelligence to recognize fish sounds is the lack of validated sound data for individual species. This review provides an overview of recent publications on the use of machine learning, including deep learning, for fish sound detection, classification, and identification. Key challenges and limitations are discussed, and some points to guide future studies are also provided. [ABSTRACT FROM AUTHOR]

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The rise of passive acoustic monitoring and the rapid growth in large audio datasets is driving the development of analysis methods that allow ecological inferences to be drawn from acoustic data.Acoustic indices are currently one of the most widely applied tools in ecoacoustics. These numerical summaries of the sound energy contained in digital audio recordings are relatively straightforward and fast to calculate but can be challenging to interpret. Misapplication and misinterpretation have produced conflicting results and led some to question their value.To encourage better use of acoustic indices, we provide nine points of guidance to support good study design, analysis and interpretation. We offer practical recommendations for the use of acoustic indices in the study of both whole soundscapes and individual taxa and species, and point to emerging trends in ecoacoustic analysis. In particular, we highlight the critical importance of understanding the links between soundscape patterns and acoustic indices.Acoustic indices can offer insights into the state of organisms, populations, and ecosystems, complementing other ecological research techniques. Judicious selection, appropriate application and thorough interpretation of existing indices is vital to bolster robust developments in ecoacoustics for biodiversity monitoring, conservation and future research. [ABSTRACT FROM AUTHOR]

Bare board AudioMoth recorders offer a low-cost, open-source solution to passive acoustic monitoring (PAM) but need protecting in an enclosure. We were concerned that the choice of enclosure may alter the spectral characteristics of recordings. We focus on polythene bags as the simplest enclosure and assess how their use affects acoustic metrics. Using an anechoic chamber, a series of pure sinusoidal tones from 100 Hz to 20 kHz were recorded on 10 AudioMoth devices and a calibrated Class 1 sound level meter. The recordings were made on bare board AudioMoth devices, as well as after covering them with different bags. Linear phase finite impulse response filters were designed to replicate the frequency response functions between the incident pressure wave and the recorded signals. We applied these filters to ~1000 sound recordings to assess the effects of the AudioMoth and the bags on 19 acoustic metrics. While bare board AudioMoth showed very consistent spectral responses with accentuation in the higher frequencies, bag enclosures led to significant and erratic attenuation inconsistent between frequencies. Few acoustic metrics were insensitive to this uncertainty, rendering index comparisons unreliable. Biases due to enclosures on PAM devices may need to be considered when choosing appropriate acoustic indices for ecological studies. Archived recordings without adequate metadata may potentially produce biased acoustic index values and should be treated cautiously. [ABSTRACT FROM AUTHOR]