02715nas a2200229 4500008004100000245006800041210006600109520178800175653002401963653002301987653002402010653002302034653002202057653002002079100003102099700002702130700003002157700001802187700002502205700002102230856023402251 2019 eng d00aA roadmap for survey designs in terrestrial acoustic monitoring0 aroadmap for survey designs in terrestrial acoustic monitoring3 a

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.

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/rse2.13101750nas a2200157 4500008004100000022001400041245006900055210006800124260001600192520118800208100003101396700003001427700002701457700001801484856009001502 2018 eng d a0006-356800aTerrestrial Passive Acoustic Monitoring: Review and Perspectives0 aTerrestrial Passive Acoustic Monitoring Review and Perspectives cMay-11-20203 a

Passive acoustic monitoring (PAM) is quickly gaining ground in ecological research, following global trends toward automated data collection and big data. Using unattended sound recording, PAM provides tools for long-term and cost-effective biodiversity monitoring. Still, the extent of the potential of this emerging method in terrestrial ecology is unknown. To quantify its application and guide future studies, we conducted a systematic review of terrestrial PAM, covering 460 articles published in 122 journals (1992–2018). During this period, PAM-related studies showed above a fifteenfold rise in publication and covered three developing phases: establishment, expansion, and consolidation. Overall, the research was mostly focused on bats (50%), occurred in northern temperate regions (65%), addressed activity patterns (25%), recorded at night (37%), used nonprogrammable recorders (61%), and performed manual acoustic analysis (58%), although their applications continue to diversify. The future agenda should include addressing the development of standardized procedures, automated analysis, and global initiatives to expand PAM to multiple taxa and regions.

1 aSugai, Larissa, Sayuri Mor1 aSilva, Thiago, Sanna Frei1 aRibeiro, José, Wagner1 aLlusia, Diego uhttps://academic.oup.com/bioscience/advance-article/doi/10.1093/biosci/biy147/519350603179nas a2200253 4500008004100000022001300041245011000054210006900164260001600233300001400249490000700263520236200270653002302632653002702655653002502682653003402707653001302741100002702754700001902781700001802800700002302818700001802841856006602859 2018 eng d a1470160X00aEstimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis0 aEstimating animal acoustic diversity in tropical environments us cJan-07-2018 a346 - 3550 v903 a

Ecoacoustic monitoring has proved to be a viable approach to capture ecological data related to animal communities. While experts can manually annotate audio samples, the analysis of large datasets can be significantly facilitated by automatic pattern recognition methods. Unsupervised learning methods, which do not require labelled data, are particularly well suited to analyse poorly documented habitats, such as tropical environments. Here we propose a new method, named Multiresolution Analysis of Acoustic Diversity (MAAD), to automate the detection of relevant structure in audio data. MAAD was designed to decompose the acoustic community into few elementary components (soundtypes) based on their time–frequency attributes. First, we used the short-time Fourier transform to detect regions of interest (ROIs) in the time–frequency domain. Then, we characterised these ROIs by (1) estimating the median frequency and (2) by running a 2D wavelet analysis at multiple scales and angles. Finally, we grouped the ROIs using a model-based subspace clustering technique so that ROIs were automatically annotated and clustered into soundtypes. To test the performance of the automatic method, we applied MAAD to two distinct tropical environments in French Guiana, a lowland high rainforest and a rock savanna, and we compared manual and automatic annotations using the adjusted Rand index. The similarity between the manual and automated partitions was high and consistent, indicating that the clusters found are intelligible and can be used for further analysis. Moreover, the weight of the features estimated by the clustering process revealed important information about the structure of the acoustic communities. In particular, the median frequency had the strongest effect on modelling the clusters and on classification performance, suggesting a role in community organisation. The number of clusters found in MAAD can be regarded as an estimation of the soundtype richness in a given environment. MAAD is a comprehensive and promising method to automatically analyse passive acoustic recordings. Combining MAAD and manual analysis would maximally exploit the strengths of both human reasoning and computer algorithms. Thereby, the composition of the acoustic community could be estimated accurately, quickly and at large scale.

10aAcoustic community10aEcoacoustic monitoring10aNocturnal soundscape10aUnsupervised machine learning10aWavelets1 aUlloa, Juan, Sebastian1 aAubin, Thierry1 aLlusia, Diego1 aBouveyron, Charles1 aSueur, Jerome uhttp://linkinghub.elsevier.com/retrieve/pii/S1470160X1830181X