03949nas a2200253 4500008004100000245008100041210006900122260004000191520313300231653002003364653002103384653001703405653002803422653002603450653001503476653001403491653001703505653003103522653001503553653001303568653002403581100002203605856006803627 2018 eng d00aSMART SAMPLING OF ENVIRONMENTAL AUDIO RECORDINGS FOR BIODIVERSITY MONITORING0 aSMART SAMPLING OF ENVIRONMENTAL AUDIO RECORDINGS FOR BIODIVERSIT bQueensland University of Technology3 a

In order to safeguard our environment, we must first have tools that give us information about its health. Biodiversity monitoring is crucial for providing this information. One form of this is bird species richness surveys, which have traditionally been conducted by having an experienced bird-watcher travel to a location to make observations. This is a time-consuming and expensive task.
An emerging alternative approach is to record audio from an ecologically significant location and then listen to it at a later stage for species identification. Unlike on-site surveys, using audio recordings allows the capture of long periods of audio that can then be listened to non-sequentially. Samples can be chosen from throughout the long recording and presented to an ecologist in any order. By selecting this set of samples intelligently, the listener will be able to observe more species in less overall time, increasing the efficiency of their labour.
This thesis investigates methods to make this selection of samples from a long recording in order to maximise the number of bird species contained in a given number of selected samples. The task of species identification remains with the human listener, but the process of selecting the rich subset of the recording is performed by a machine.
We first lay out the framework whereby the problem is decomposed into two parts: how to estimate the internal variety of call types within each candidate sample, and how to estimate the inter-sample dissimilarity. The former allows us to find samples with a high overall number of species and the latter to avoid selecting samples whose species overlap heavily with previously selected samples.
The primary way that these objectives are achieved, and the main contribution of this thesis, is through clustering short segments of audio and using the clusters contained within each sample to estimate the internal variety of call types as well as comparing clusters between samples to estimate their dissimilarity. Two methods of segmentation are used, and, for each one, an appropriate set of preprocessing steps and feature representation was developed. Arising from this pipeline of computational steps is also a novel visualisation tool that can allow a user to rapidly scan long-duration recordings by filtering out parts with low activity and repetition of the same call types.

An algorithm was developed that scores samples based on a combination of their cluster content with other estimates of the internal variety and intra-sample dissimilarity. These other estimates are based on prior knowledge about the daily patterns of vocal activity and durations that species call for at a given time, as well the overall acoustic activity of each sample. The best combination of methods outperforms existing methods, such as randomly sampling from the dawn chorus.
Together, these contributions help to advance the field of ecoacoustics for bird species monitoring and offer a way forward towards developing valuable tools for the exploration of ecological audio recordings.

10aacoustic sensin10aavian monitoring10abioacoustics10aBiodiversity monitoring10abird species richness10aClustering10ae-science10aecoacoustics10ag environmental monitoring10aR Language10asampling10ascientific workflow1 aEichinski, Philip uhttps://eprints.qut.edu.au/123022/1/Philip_Eichinski_Thesis.pdf02461nas a2200289 4500008004100000245010600041210006900147260001600216520156100232653002401793653002101817653001401838653002801852653002501880653001701905653001501922653001801937100003001955700002101985700001802006700002202024700001702046700002302063700002502086700001902111856004102130 2018 eng d00aOptimization of sensor deployment for acoustic detection and localization in terrestrial environments0 aOptimization of sensor deployment for acoustic detection and loc cJun-10-20193 a

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.

10aacoustic monitoring10aacoustic sensors10aAudioMoth10aBiodiversity monitoring10aecosystem management10aoptimisation10aSoundscape10asubmodularity1 aPiña-Covarrubias, Evelyn1 aHill, Andrew, P.1 aPrince, Peter1 aSnaddon, Jake, L.1 aRogers, Alex1 aDoncaster, Patrick1 aPettorelli, Nathalie1 aGuillard, Jean uhttp://doi.wiley.com/10.1002/rse2.9702615nas a2200301 4500008004100000245008200041210006900123260001600192300000900208490000600217520172400223653001501947653001201962653002801974653001002002653002202012653001202034653003202046653002202078653001502100653002102115100001902136700002902155700003002184700002802214700002702242856004402269 2017 eng d00aSpecies Richness (of Insects) Drives the Use of Acoustic Space in the Tropics0 aSpecies Richness of Insects Drives the Use of Acoustic Space in  cJan-11-2017 a10960 v93 a

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (<22 kHz) can easily be visualized and ASU determined by aggregating recordings collected over relatively short periods (4–13 days). We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites. In addition, insects used a broad range of frequencies, including high frequencies (>8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology.

10aamphibians10aARBIMON10aBiodiversity monitoring10abirds10acommunity ecology10ainsects10apassive acoustic monitoring10arapid assessments10aSoundscape10aspecies richness1 aAide, Mitchell1 aHernández-Serna, Andres1 aCampos-Cerqueira, Marconi1 aAcevedo-Charry, Orlando1 aDeichmann, Jessica, L. uhttp://www.mdpi.com/2072-4292/9/11/109602616nas a2200301 4500008004100000245008200041210006900123260001600192300000900208490000600217520172400223653001501947653001201962653002801974653001002002653002202012653001202034653003202046653002202078653001602100653002102116100001902137700002902156700003002185700002802215700002702243856004402270 2017 eng d00aSpecies Richness (of Insects) Drives the Use of Acoustic Space in the Tropics0 aSpecies Richness of Insects Drives the Use of Acoustic Space in  cJan-11-2017 a10960 v93 a

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (<22 kHz) can easily be visualized and ASU determined by aggregating recordings collected over relatively short periods (4–13 days). We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites. In addition, insects used a broad range of frequencies, including high frequencies (>8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology.

10aamphibians10aARBIMON10aBiodiversity monitoring10abirds10acommunity ecology10ainsects10apassive acoustic monitoring10arapid assessments10asoundscapes10aspecies richness1 aAide, Mitchell1 aHernández-Serna, Andres1 aCampos-Cerqueira, Marconi1 aAcevedo-Charry, Orlando1 aDeichmann, Jessica, L. uhttp://www.mdpi.com/2072-4292/9/11/109602310nas a2200217 4500008004100000022001400041245009700055210006900152260001600221490000700237520161500244653002101859653002801880653002501908653003201933653001701965653002401982100002102006700002002027856004502047 2017 eng d a1712-656800aAutonomous recording units in avian ecological research: current use and future applications0 aAutonomous recording units in avian ecological research current  cJan-01-20170 v123 aAcoustic surveys are a widely used sampling tool in ecological research and monitoring. They are used to monitor populations and ecosystems and to study various aspects of animal behavior. Autonomous recording units (ARUs) can record sound in most environments and are increasingly used by researchers to conduct acoustic surveys for birds. In this review, we summarize the use of ARUs in avian ecological research and synthesize current knowledge of the benefits and drawbacks of this technology. ARUs enable researchers to do more repeat visits with less time spent in the field, with the added benefits of a permanent record of the data collected and reduced observer bias. They are useful in remote locations and for targeting rare species. ARUs are mostly comparable to human observers in terms of species richness, but in some cases, they detect fewer species and at shorter distances. Drawbacks of ARUs include the cost of equipment, storage of recordings, loss of data if units fail, and potential sampling trade-offs in spatial vs. temporal coverage. ARUs generate large data sets of audio recordings, but advances in automated species recognition and acoustic processing techniques are contributing to make the processing time manageable. Future applications of ARUs include biodiversity monitoring and studying habitat use, animal movement, and various behavioral ecology questions based on vocalization activity. ARUs have the potential to make significant advances in avian ecological research and to be used in more innovative ways than simply as a substitute for a human observer in the field. 10aacoustic surveys10aBiodiversity monitoring10anoninvasive sampling10apassive acoustic monitoring10apoint counts10avocal communication1 aShonfield, Julia1 aBayne, Erin, M. uhttp://www.ace-eco.org/vol12/iss1/art14/02999nas a2200241 4500008004100000022001400041245011700055210006900172260001600241300001400257490000700271520223100278653002602509653002802535653001502563653001002578653002302588100002702611700002502638700002202663700001702685856005502702 2014 eng d a1366-638X00aBaseline data for automated acoustic monitoring of Orthoptera in a Mediterranean landscape, the Hymettos, Greece0 aBaseline data for automated acoustic monitoring of Orthoptera in cJan-10-2014 a909 - 9250 v183 a

Acoustic emissions of animals serve communi- cative purposes and most often contain species-specific and individual information exploitable to listeners, rendering bioacoustics a valuable tool for biodiversity monitoring. Recording bioacoustic signals allows reproducible species identification. There is a great need for increased use and further development of automated animal sound recording and identification to improve monitoring efficiency and accuracy for the benefit of conservation. Greece, with its high number of endemic species, represents a hotspot for European Biodiversity, including Orthopteran insects. Songs of many Orthoptera might be employed for the inventorying and monitoring of individual species and communities. We assessed the regional spatio-temporal composition of Orthoptera species at the Hymettos near Athens, which is a Natura 2000 site under constant threat due to the surrounding megacity. Within the framework of the EU Life Plus funded AmiBio project, we documented the Orthopteran species’ habitat characteristics, their co-occurrence and phenology. We found, in total, 20 species with seven to ten Orthoptera at locations characterised by diverse vegetation patterns of perennial herbs and bushes. For the purposes of implemen- tation of an automated remote monitoring scheme, we identified sampling sites with high Orthopteran diversity, allowing the monitoring of all singing Orthoptera within single localities. By analysing sound depositories and adding recordings from new sample individuals, we established a song library as prerequisites for future automatic song detection. Based on our results, acoustic recording units have been placed at remote sites at the Hymettos. We discuss recommendations for further studies to fully employ the potential of automated acoustic monitoring of Orthoptera. A reliable assessment of singing Orthoptera needs recording units covering ultrasound. Due to high attenuation and absorbance by the vegetation, particularly of the high fre- quencies characterising Orthopteran songs, positioning of microphones at sites is critical: the microphone sensor net- work has to be an order of magnitude denser than for mon- itoring birds,

10aAutonomous monitoring10aBiodiversity monitoring10aOrthoptera10asound10awildlife recording1 aLehmann, Gerlind, U.C.1 aFrommolt, Karl-Heinz1 aLehmann, Arne, W.1 aRiede, Klaus uhttp://link.springer.com/10.1007/s10841-014-9700-2