@article {57893, title = {A roadmap for survey designs in terrestrial acoustic monitoring}, year = {2019}, abstract = {

Passive acoustic monitoring (PAM) is increasingly popular in ecological research and conservation programs, with high-volume and long-term data collection provided by automatized acoustic sensors offering unprecedented opportunities for faunal and ecosystem surveys. Practitioners and newcomers interested in PAM can easily find technical specifications for acoustic sensors and microphones, but guidelines on how to plan survey designs are largely scattered over the literature. Here, we (i) review spatial and temporal sampling designs used in passive acoustic monitoring, (ii) provide a synthesis of the crucial aspects of PAM survey design and (iii) propose a workflow to optimize recording autonomy and recording schedules. From 1992 to 2018, most of the 460 studies applying PAM in terrestrial environments have used a single recor- der per site, covered broad spatial scales and rotated recorders between sites to optimize sampling effort. Continuous recording of specific diel periods was the main recording procedure used. When recording schedules were applied, a larger number of recordings per hour was generally associated with a smaller recording length. For PAM survey design, we proposed to (i) estimate mem- ory/battery autonomy and associated costs, (ii) assess signal detectability to optimize recording schedules in order to recover maximum biological infor- mation and (iii) evaluate cost-benefit scenarios between sampling effort and budget to address potential biases from a given PAM survey design. Establish- ing standards for PAM data collection will improve the quality of inferences over the broad scope of PAM research and promote essential standardization for cross-scale research to understand long-term biodiversity trends in a changing world.

}, keywords = {acoustic monitoring, acoustic recorders, recording schedules, recording settings, temporal sampling, wildlife survey}, doi = {10.1002/rse2.131}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.131https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.131https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.131https://onlinelibrary.wiley.com/doi/full-xml/10.1002/rse2.131}, author = {Sugai, Larissa Sayuri Moreira and Desjonqu{\`e}res, Camille and Silva, Thiago Sanna Freire and Llusia, Diego}, editor = {Pettorelli, Nathalie and Lecours, Vincent} } @article {57648, title = {Using acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean}, year = {2019}, abstract = {

Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid biodiversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on-shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness\–SR\–and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 different AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spectrum, achieving correct classifications for SR levels of 58\% and 92\% for pelagic and on-shelf sites respectively and \> 88\% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diversity, acoustic communities and underwater habitat quality.

}, keywords = {Acoustic metrics, Antarctic, community composition, marine acoustic environments, passive acoustic monitoring, species diversity}, doi = {10.1002/rse2.129}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.129}, author = {Roca, Irene T. and Van Opzeeland, Ilse}, editor = {Pettorelli, Nathalie and Quick, Nicola} } @article {57604, title = {Using acoustic metrics to characterize underwater acoustic biodiversity in the Southern Ocean}, year = {2019}, abstract = {

Acoustic metrics (AM) assist our interpretation of acoustic environments by aggregating a complex signal into a unique number. Numerous AM have been developed for terrestrial ecosystems, with applications ranging from rapid bio- diversity assessments to characterizing habitat quality. However, there has been comparatively little research aimed at understanding how these metrics perform to characterize the acoustic features of marine habitats and their relation with ecosystem biodiversity. Our objectives were to 1) assess whether AM are able to capture the spectral and temporal differences between two distinct Antarctic marine acoustic environment types (i.e., pelagic vs. on-shelf), 2) evaluate the performance of a combination of AM compared to the signal full frequency spectrum to characterize marine mammals acoustic assemblages (i.e., species richness\–SR\–and species identity) and 3) estimate the contribution of SR to the local marine acoustic heterogeneity measured by single AM. We used 23 differ- ent AM to develop a supervised machine learning approach to discriminate between acoustic environments. AM performance was similar to the full spec- trum, achieving correct classifications for SR levels of 58\% and 92\% for pelagic and on-shelf sites respectively and \> 88\% for species identities. Our analyses show that a combination of AM is a promising approach to characterize marine acoustic communities. It allows an intuitive ecological interpretation of passive acoustic data, which in the light of ongoing environmental changes, supports the holistic approach needed to detect and understand trends in species diver- sity, acoustic communities and underwater habitat quality.

}, keywords = {Acoustic metrics, Antarctic, community composition, marine acoustic environments, passive acoustic monitoring, species diversity}, doi = {10.1002/rse2.129}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.129}, author = {Roca, Irene T. and Van Opzeeland, Ilse}, editor = {Pettorelli, Nathalie and Quick, Nicola} } @article {57374, title = {Automated identification of avian vocalizations with deep convolutional neural networks}, year = {2019}, abstract = {

Passive acoustic monitoring is an emerging approach to wildlife monitoring that leverages recent improvements in automated recording units and other technolo- gies. A central challenge of this approach is the task of locating and identifying target species vocalizations in large volumes of audio data. To address this issue, we developed an efficient data processing pipeline using a deep convolutional neural network (CNN) to automate the detection of owl vocalizations in spec- trograms generated from unprocessed field recordings. While the project was ini- tially focused on spotted and barred owls, we also trained the network to recognize northern saw-whet owl, great horned owl, northern pygmy-owl, and western screech-owl. Although classification performance varies across species, initial results are promising. Recall, or the proportion of calls in the dataset that are detected and correctly identified, ranged from 63.1\% for barred owl to 91.5\% for spotted owl based on raw network output. Precision, the rate of true positives among apparent detections, ranged from 0.4\% for spotted owl to 77.1\% for northern saw-whet owl based on raw output. In limited tests, the CNN performed as well as or better than human technicians at detecting owl calls. Our model output is suitable for developing species encounter histories for occupancy models and other analyses. We believe our approach is sufficiently general to support long-term, large-scale monitoring of a broad range of species beyond our target species list, including birds, mammals, and others.

}, doi = {10.1002/rse2.125}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.125}, author = {Ruff, Zachary J. and Lesmeister, Damon B. and Duchac, Leila S. and Padmaraju, Bharath K. and Sullivan, Christopher M.}, editor = {Pettorelli, Nathalie and Lecours, Vincent} } @article {53255, title = {Optimization of sensor deployment for acoustic detection and localization in terrestrial environments}, journal = {Remote Sensing in Ecology and Conservation}, year = {2018}, month = {Jun-10-2019}, abstract = {

The rapid evolution in miniaturization, power efficiency and affordability of acoustic sensors, combined with new innovations in smart capability, are vastly expanding opportunities in ground-level monitoring for wildlife conservation at a regional scale using massive sensor grids. Optimal placement of environmen- tal sensors and probabilistic localization of sources have previously been consid- ered only in theory, and not tested for terrestrial acoustic sensors. Conservation applications conventionally model detection as a function of distance. We developed probabilistic algorithms for near-optimal placement of sensors, and for localization of the sound source as a function of spatial variation in sound pressure. We employed a principled-GIS tool for mapping soundscapes to test the methods on a tropical-forest case study using gunshot sensors. On hilly ter- rain, near-optimal placement halved the required number of sensors compared to a square grid. A test deployment of acoustic devices matched the predicted success in detecting gunshots, and traced them to their local area. The methods are applicable to a broad range of target sounds. They require only an empirical estimate of sound-detection probability in response to noise level, and a sound- scape simulated from a topographic habitat map. These methods allow conser- vation biologists to plan cost-effective deployments for measuring target sounds, and to evaluate the impacts of sub-optimal sensor placements imposed by access or cost constraints, or multipurpose uses.

}, keywords = {acoustic monitoring, acoustic sensors, AudioMoth, Biodiversity monitoring, ecosystem management, optimisation, Soundscape, submodularity}, doi = {10.1002/rse2.97}, url = {http://doi.wiley.com/10.1002/rse2.97}, author = {Pi{\~n}a-Covarrubias, Evelyn and Hill, Andrew P. and Prince, Peter and Snaddon, Jake L. and Rogers, Alex and Doncaster, C. Patrick}, editor = {Pettorelli, Nathalie and Guillard, Jean} } @article {52613, title = {Catching insects while recording bats: impacts of light trapping on acoustic sampling}, journal = {Remote Sensing in Ecology and Conservation}, year = {2018}, month = {Apr-01-2020}, abstract = {

Collecting information on bat prey availability usually involves the use of light traps to capture moths and flies that constitute the main prey items of most insectivorous bats. However, despite the recent awareness on the adverse effects of light on bats, little is known regarding the potential impacts of light trapping on the bat sampling outcomes when passive acoustic sampling and light trap- ping are implemented simultaneously. Using a before\–after experimental design that involved the installation of a 6 W actinic light trap 1 m away from the bat detector, we tested the predictions that (1) slow-flying bat species will be less active when the light trap is present, while the opposite will be true for fast-fly- ing species; and (2) bat species richness will be lower at lit conditions compared to dark ones. Our results suggest that the use of light traps in combination with bat detectors may considerably influence the outcomes of acoustic sampling. Although the activity of fast-flying bat species did not differ between the two treatments, we found that the activity of slow-flying ones such as Rhinolophus ferrumequinum and Rhinolophus hipposideros decreased significantly at lit condi- tions. Furthermore, we recorded fewer bat species when the light trap was deployed. To overcome this issue, we strongly recommend either (1) placing light traps at a considerable distance from bat detectors; or (2) using light traps during the night that follows the bat sampling if sampling needs to be at the same position; or (3) deploying non-attractant insect traps such as Malaise traps if Lepidoptera is not the main order targeted.

}, doi = {10.1002/rse2.71}, url = {http://doi.wiley.com/10.1002/rse2.71}, author = {Froidevaux, J{\'e}r{\'e}my S. P. and Fialas, Penelope C. and Jones, Gareth}, editor = {Pettorelli, Nathalie and Merchant, Nathan} }