02184nas 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/rse2.12503987nas a2200109   4500008004100000245009500041210006900136260002600205520358400231100002103815856004103836       2018                        eng d00aUsing Bioacoustics to Examine the Effects of Industrial Disturbance on Owls and their Prey0 aUsing Bioacoustics to Examine the Effects of Industrial Disturba  bUniversity of Alberta3 a<p>Anthropogenic disturbance is known to have negative population consequences and alter animal behaviour, and a growing body of research on the effects of anthropogenic noise is finding similar negative impacts on wildlife. Noise in natural environments can mask important acoustic signals used for animal communication. Owls use vocal communication to attract mates and defend territories, and rely on acoustic cues to locate their prey. Industrial noise has been shown to negatively affect owl hunting success and reduce foraging efficiency by affecting their ability to detect prey, but whether this results in reduced habitat suitability for owls in areas near industrial noise sources is largely unknown. It is also not known if the abundance of small mammals, the primary prey of owls, is affected by industrial noise. Communication between small mammals is unlikely to be directly affected by noise because they primarily communicate using olfactory rather than vocal signals, but small mammals could be indirectly affected through altered predation dynamics. I sought to determine if owls avoid the areas surrounding chronic industrial noise sources, if prey availability is affected by chronic industrial noise, and the relative importance of noise compared to other types of disturbance resulting from industrial development on owl habitat use. I used autonomous recording units and automated recognizers to survey for owls and scan recordings for owl vocalizations, to assess the impacts of disturbance from industrial development on owls in northeastern Alberta, a region that has seen increased development in the oil and gas industries in recent years. I found that barred owls (Strix varia), great horned owls (Bubo virginianus), and boreal owls (Aegolius funereus) were equally likely to occupy noisy sites compared to sites with no noise, indicating that site-level occupancy (representing a home range scale) was unaffected by the presence of noise sources on the landscape. I found no difference in abundance or activity of red-backed voles (Myodes gapperi)&nbsp;and deer mice (Peromyscus maniculatus), indicating that these important prey species are not strongly affected by noise. Finally, I found each owl species responded differently to the different disturbance types. Barred owls were less likely to be present in areas with greater proportions of human footprint and roads, whereas great horned owls were more tolerant to disturbance and were more likely to be present in areas with greater proportions of soft linear features (e.g. seismic lines, pipelines), though they did avoid areas with large industrial facilities. Boreal owl presence was more strongly affected by forest composition than disturbance; they prefer more coniferous forests, and it was less clear if they were sensitive or relatively tolerant to disturbance. Assessing the relative impacts of multiple types of disturbance and how the accumulation of disturbances can impact wildlife is important in understanding species declines and can help focus conservation efforts. My thesis contributes to research on the impacts of anthropogenic disturbance and suggests the effect of noise on barred owls, great horned owls, and boreal owls is minimal at the spatial scales I looked at. Similarly, the effect of noise on abundance and activity of red-backed voles and deer mice was weak, suggesting similar prey availability for owls. For the owl species I studied, forest composition and other types of disturbance on the landscape are more likely to influence their habitat use.</p>
1 aShonfield, Julia  uhttps://bio.acousti.ca/sv/node/53116