00774nas a2200121 4500008004100000245004600041210004400087520039600131100001800527700002300545700001700568856006700585 2019 eng d00aClimate Change Is Breaking Earth’s Beat0 aClimate Change Is Breaking Earth s Beat3 a

Forests, deserts, rivers, and oceans are filled with animal vocalizations and geological sounds. We postu- late that climate change is changing the Earth’s natural acoustic fabric. In particular, we identify shifts in acoustic structure that all sound- sensitive organisms, marine and terrestrial, may experience. Only upstream solutions might mitigate these acoustic changes.

1 aSueur, Jerome1 aKrause, Bernie, L.1 aFarina, Almo uhttps://linkinghub.elsevier.com/retrieve/pii/S016953471930226503022nas a2200217 4500008004100000022001400041245010500055210006900160260001600229520213400245653002202379653003402401653001702435653002602452653001402478653001702492653001302509100002302522700002102545856023802566 2019 eng d a2045-775800aTemporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species0 aTemporally adaptive acoustic sampling to maximize detection acro cOct-08-20203 a

1. Acoustic recordings of the environment can produce species presence–absence data for characterizing populations of sound‐producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the species is absent (“false negative”). The risk of false negatives is compounded when audio devices have sampling constraints, do not record continuously, and must be manually scheduled to operate at pre‐selected times of day, particularly when research programs target multiple species with acoustic availability that varies across temporal conditions.

2. We developed a temporally adaptive acoustic sampling algorithm to maximize de‐ tection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user‐supplied species vocalization models with site‐specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to com‐ pare the probability of acoustic detection by a fixed (stationary) schedule versus a temporally adaptive optimized schedule under several sampling efforts and moni‐ toring durations.

3. We found that over the course of a study season, the probability of acoustically capturing a focal species (given presence) at least once via automated acous‐ tic monitoring was greater (and acoustic capture occurred earlier in the season) when using the temporally adaptive optimized schedule as compared to a fixed schedule.

4. The advantages of a temporally adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or spe‐ cies acoustic availability is minimal. This methodology presents the opportunity to maximize acoustic monitoring sampling efforts amid constraints.

10aadaptive sampling10aautomated acoustic monitoring10abioacoustics10adetection probability10aoccupancy10aoptimization10awildlife1 aBalantic, Cathleen1 aDonovan, Therese uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.5579https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.5579https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.5579https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.557900505nas a2200169 4500008004100000020001800041245002200059210002100081260005900102100002100161700002200182700001900204700001900223700002400242700002100266856004800287 2018 eng d a978145036188000aEcosound-explorer0 aEcosoundexplorer aMelbourne, AustraliaNew York, New York, USAbACM Press1 aBuchanan, George1 aStevenson, Duncan1 aRowe, Benjamin1 aZhang, Jinglan1 aTowsey, Michael, W.1 aBrereton, Margot uhttp://dl.acm.org/citation.cfm?doid=329214702237nas a2200157 4500008004100000245007400041210006900115260002000184300001100204490000600215520166900221653009001890100002601980700002702006856004602033 2018 eng d00aHot spots of ecoacoustics in Greece and the issue of background noise0 aHot spots of ecoacoustics in Greece and the issue of background  cSep-24-20182172 aU3XBIY0 v23 a

Biodiversity monitoring and assessment across a variety of gradients, could be achieved with the aid of the ecoacoustics discipline. Acoustic monitoring approaches can provide results regarding the species richness of birds, bats, frogs and insects including cicadas (Cicadoidea) and katydids (Tettigoniidae) with results similar to the ones provided by classical ecological methods (e.g. visual point count methods). The risk of extinction of several species has led to the creation of the Natura 2000 Network in the European Union’s territory. Greece provides a number of 202 Special Protection Areas (SPA’s) and 241 Sites of Community Importance (SCI), 239 of which are considered as Special Areas of Conservation (SAC). The specific areas provide both, an opportunity for ecoacoustics practice and an opportunity for ecoacoustic research. Even though the specific field of ecology has proven to be a valuable biodiversity assessment tool, areas that provide a variety of ecoacoustic events are yet to be documented. The goal of the specific article is to highlight these special conservation areas and propose a monitoring network using the non-invasive approach of ecoacoustics. For the specific research, the Greek protected areas were visualized in order to highlight sonotopes and soundtopes worthy of future research. Finally, in order to highlight the neglected issue of background noise regarding conservation efforts, the Kalloni’s salt pans were selected as a case study area. Noise measurements and sound recordings were conducted. Furthermore, noise and sound maps were created, in order to visualize the effects of noise.

10aNatura 2000; ecoacoustics; monitoring network; noise map; isobel map; Kalloni’s Bay1 aMatsinos, Yiannis, G.1 aTsaligopoulos, Aggelos uhttps://bio.acousti.ca/zh-hans/node/5343501998nam a2200253 4500008004100000020002200041022001400063245006900077210006900146260004400215300001400259490001000273520123900283653001701522653001701539653002301556100002001579700001401599700001701613700001901630700001801649700002201667856005501689 2018 eng d a978-3-030-03990-5 a0302-974300aInvestigation of Unsupervised Models for Biodiversity Assessment0 aInvestigation of Unsupervised Models for Biodiversity Assessment aChambSpringer International Publishing a160 - 1710 v113203 a

Significant animal species loss has been observed in recent decades due to habitat destruction, which puts at risk environmental integrity and biodiversity. Traditional ways of assessing biodiversity are limited in terms of both time and space, and have high cost. Since the presence of animals can be indicated by sound, recently acoustic recordings have been used to estimate species richness. Bioacoustic sounds are typically recorded in habitats for several weeks, so contain a large collection of different sounds. Birds are of particular interest due to their distinctive calls and because they are useful ecological indicators. To assess biodiversity, the task of manually determining how many different types of birds are present in such a lengthy audio is really cumbersome. Towards providing an automated support to this issue, in this paper we investigate and propose a clustering based approach to assist in automated assessment of biodiversity. Our approach first estimates the number of different species and their volumes which are used for deriving a biodiversity index. Experimental results with real data indicates that our proposed approach estimates the biodiversity index value close to the ground truth.

10abioacoustics10abiodiversity10aUnsupervised model1 aMitrovic, Tanja1 aXue, Bing1 aLi, Xiaodong1 aRao, KVSN Rama1 aGarg, Saurabh1 aMontgomery, James uhttp://link.springer.com/10.1007/978-3-030-03991-203179nas 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