02715nas a2200229   4500008004100000245006800041210006600109520178800175653002401963653002301987653002402010653002302034653002202057653002002079100003102099700002702130700003002157700001802187700002502205700002102230856023402251       2019                        eng d00aA roadmap for survey designs in terrestrial acoustic monitoring0 aroadmap for survey designs in terrestrial acoustic monitoring3 a<p>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.</p>
10aacoustic monitoring10aacoustic recorders10arecording schedules10arecording settings10atemporal sampling10awildlife survey1 aSugai, Larissa, Sayuri Mor1 aDesjonquères, Camille1 aSilva, Thiago, Sanna Frei1 aLlusia, Diego1 aPettorelli, Nathalie1 aLecours, Vincent  uhttps://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/rse2.13102184nas a2200169   4500008004100000245009200041210006900133520157800202100002201780700002601802700002201828700002701850700003001877700002501907700002101932856006101953       2019                        eng d00aAutomated identification of avian vocalizations with deep convolutional neural networks0 aAutomated identification of avian vocalizations with deep convol3 a<p>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.</p>
1 aRuff, Zachary, J.1 aLesmeister, Damon, B.1 aDuchac, Leila, S.1 aPadmaraju, Bharath, K.1 aSullivan, Christopher, M.1 aPettorelli, Nathalie1 aLecours, Vincent  uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.125