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Abstract Model‐based acoustic localisation estimates the locations of underwater objects by comparing sensor measurements with model predictions. To obtain high quality predictions, propagation models need to be run for a large set of environmental parameters. However, real‐time Model‐based acoustic localisation estimations using onboard computational resources are often limited. To address this, the authors propose a Quantum annealing (QA) algorithm for enhancing underwater acoustic localisation. A restricted Boltzmann machine (RBM) is trained to predict the probability distribution of underwater targets. Advantage of this approach is that part of the computation is moved to offline‐training. Moreover, the probability distribution can potentially be sampled efficiently using a quantum annealer possibly enabling real‐time accurate target estimations being made onboard.The RBM is applied to a simplified multi‐sensor horizontal localisation problem where a constant and linear acoustic propagation is assumed. Using simulated annealing the authors show that the RBM is able to learn probability distributions that resemble target locations. Preliminary results show that training and sampling the RBM can be done using QA hardware by D‐Wave Systems.However, there remains room for improvement especially in ranging predictions. Further research into possible benefits of QA RBMs is needed to provide theoretical and practical results of a speed‐up.

Model‐based acoustic localisation estimates the locations of underwater objects by comparing sensor measurements with model predictions. To obtain high quality predictions, propagation models need to be run for a large set of environmental parameters. However, real‐time Model‐based acoustic localisation estimations using onboard computational resources are often limited. To address this, the authors propose a Quantum annealing (QA) algorithm for enhancing underwater acoustic localisation. A restricted Boltzmann machine (RBM) is trained to predict the probability distribution of underwater targets. Advantage of this approach is that part of the computation is moved to offline‐training. Moreover, the probability distribution can potentially be sampled efficiently using a quantum annealer possibly enabling real‐time accurate target estimations being made onboard.The RBM is applied to a simplified multi‐sensor horizontal localisation problem where a constant and linear acoustic propagation is assumed. Using simulated annealing the authors show that the RBM is able to learn probability distributions that resemble target locations. Preliminary results show that training and sampling the RBM can be done using QA hardware by D‐Wave Systems.However, there remains room for improvement especially in ranging predictions. Further research into possible benefits of QA RBMs is needed to provide theoretical and practical results of a speed‐up. [ABSTRACT FROM AUTHOR]

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.

Abstract Background Habitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ. Methods Here, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats’ positions and vegetation structures. Results Our results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light. Conclusion By combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction.

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. [ABSTRACT FROM AUTHOR]

Background: Habitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ. Methods: Here, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats' positions and vegetation structures. Results: Our results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light. Conclusion: By combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction. [ABSTRACT FROM AUTHOR]

Entanglement in net fisheries (static and drift) is the largest known cause of direct anthropogenic mortality to many small cetacean species, including harbour porpoise (Phocoena phocoena), in UK waters. Despite this, little is known about the behaviour of small cetaceans in proximity to nets.We have developed a passive acoustic monitoring (PAM) system for tracking the fine‐scale three‐dimensional (3D) movements of echolocating cetaceans around actively fishing nets by localising their acoustic clicks. The system consists of two compact four‐channel acoustic recorders with sample‐synchronised sensor packages that use 3D motion tracking technology to accurately log orientation, depth, water temperature and ambient light level. Two recorders were used in tandem, with each one attached to and floating above the net floatline. The system can be deployed during normal fishing operations by a trained researcher or experienced fisheries observer. Recordings were analysed in PAMGuard software and the 3D positions of echolocating animals in the vicinity of the system were calculated using an acoustic particle filter‐based localisation method.We present findings from four deployments in UK waters (each 1–2 days in duration) in which 12 distinct harbour porpoise encounters yielded a sufficient number of detected clicks to track their movements around the net. The tracks show a variety of behaviours, including multiple instances of animals actively foraging in close proximity to the fishing net.We show that a relatively inexpensive PAM system, which is practical to deploy from active fishing vessels, is capable of providing highly detailed data on harbour porpoise behaviour around nets. As harbour porpoises are the one of the most difficult species to localise, this methodology is likely to be suitable for elucidating the behaviour of many other toothed whale species in a variety of situations. [ABSTRACT FROM AUTHOR]

Context: Synchronised acoustic recorders can be used as a non-invasive tool to detect and localise sounds of interest, including vocal wildlife and anthropogenic sounds. Due to the high cost of commercial synchronised recorders, acoustic localisation has typically been restricted to small or well funded surveys. Recently, low-cost acoustic recorders have been developed, but until now their efficacy has not been compared with higher specification recorders. Aims: The present study aimed to compare the efficacy of a newly developed low-cost recorder, the Conservation at Range through Audio Classification and Localisation (CARACAL), with an established, high-end recorder, the Wildlife Acoustics Song Meter (SM). Methods: Four recorders of each type were deployed in a paired set-up across five nights in Wisconsin, USA. The recordings allowed for manual identification of domestic dog (Canis familiaris), grey wolf (Canis lupus), coyote (Canis latrans) and barred owl (Strix varia) calls, and then the ability of each recorder type to detect and localise the vocalising animals was compared. Key results: The CARACALs were less sensitive, detecting only 47.5% of wolf, 55% of coyote, 65% of barred owl and 82.5% of dog vocalisations detected by the paired SMs. However, when the same vocalisations were detected on both recorders, localisation was comparable, with no significant difference in the precision or maximum detection ranges. Conclusions: Low-cost recording equipment can be used effectively for acoustic localisation of both wild and domestic animals. However, the lower sensitivity of the CARACALs means that a denser network of these recorders would be needed to achieve the same efficacy as the SMs. Deploying a greater number of cheaper recorders increases the labour time in the field and the quantity of data to process and store. Thus, there is a trade-off between cost and time to be considered. Implications: The ability to use low-cost recorders for acoustic localisation provides new avenues for tracking, managing and researching a wide range of wildlife species. Presently, CARACALs are more suited to monitoring species that have small home ranges and high amplitude vocalisations, and for when a large time investment for in situ equipment checks and data processing is feasible. Synchronised acoustic recorders can be used as a non-invasive tool to detect and localise sounds of interest, including vocal wildlife. This study compared the efficacy of a commercial, high-end acoustic recorder with a newly developed low-cost recorder, finding that although the commercial recorder was more sensitive, the acoustic localisation capabilities of the two were comparable. The ability to use low-cost recorders for acoustic localisation provides new avenues for tracking, managing and researching a wide range of wildlife species. Image by Bethany R. Smith. [ABSTRACT FROM AUTHOR]

Eastern Indian Ocean pygmy blue (EIOPB) whales were localised and tracked through the Perth Canyon area utilising a passive acoustic tracking array. Vocalising animals were tracked using the Type II unit of the EIOPB whale song across recordings for an average of 109.3 min (95% CI ± 27 min), suggesting that animals move slowly through the array area. Comparisons of the distribution of animals between sample years revealed non-uniform distribution of animals suggesting that environmental variables may drive pygmy blue whale spatial distribution within foraging areas. Analysis of the movement patterns of acoustically tracked animals found that animals tracked between January and early March generally exhibited directional movement while singing animals tracked between March and May exhibited directional and milling movement patterns, indicating that they may be foraging, and thus that animals may sing between feeding bouts. Comparisons of visual and acoustic localisations found animals appeared to utilise similar areas around the passive acoustic receiver array on days where survey effort overlapped, though acoustic survey methods returned consistently higher numbers of detections. Further, acoustic and visual detections of animals within the same day were not within the same location highlighting the need for holistic studies of species habitat utilisation. The inclusion of in-situ environmental data collection in the design of future acoustic tracking studies would be valuable to understand whether there is a link between environmental factors at the local scale and the localised movement of whales around the listening area. • Whales were tracked around a passive acoustic array by localising their signals. • Early in the season animals exhibited more directional movement patterns. • Later in the season animals spent more time milling. • Animals may balance behavioural trade-offs by singing between foraging bouts. [ABSTRACT FROM AUTHOR]