@article {58468, title = {Spatial dynamics of soundscapes and biodiversity in a semi-arid landscape}, journal = {Landscape Ecology}, year = {2022}, abstract = {

Context
Semi-arid landscapes are naturally hetero- geneous with several factors influencing this varia- tion. Fauna responses and adaptations vary in xeric environments, and the scale of observation is impor- tant. Biodiversity monitoring at several scales can be challenging, and acoustics are an alternative to this issue.

Objectives
We investigated how audible biodiver- sity is influenced by environmental factors (e.g.: veg- etation metrics, climatic variables, etc.) across a fine spatial scale, aiming to provide a better understand- ing of the variation in audible species across record- ing locations placed close together. These results will improve the current knowledge on ecoacoustics as a tool for measuring ecological processes in this biome, and better inform conservation plans.

Methods
We collected data in the semi-arid region in Queensland, Australia placing 24 recorders 200 m apart for 48 h. We also sampled environmental attributes (e.g.: temperature and vegetation structure metrics) and used acoustic indices in a time-series algorithm to categorise sound into classes. Bird spe- cies and feeding guilds were also identified.

Results We found significant differences between proximate sensors, demonstrating that soundscape differences occur across fine spatial scales. Birds and insects were the predominant biophonic sound observed and both groups were associated with shrub cover and subcanopy height. Environments with higher shrub and subcanopy cover had a higher per- centage of all birds\’ feeding guilds and insects. Sixty- three bird species were identified, including a threat- ened bird species in Queensland.

Conclusion
We show biodiversity is influenced by vegetation heterogeneity across fine spatial scales in semi-arid regions, identifying which attributes sus- tain higher levels of biodiversity activity. Our study reveals the practicality of acoustic surveys for this biodiversity monitoring by covering a large area in 48 h. However, we caution that scale is an important consideration when designing surveys.

}, keywords = {acoustic indices, biodiversity, Shrub cover, Spatial scale, Subcanopy cover, Vegetation mosaics}, author = {Marina D. A. Scarpelli and David Tucker and Brendan Doohan and Paul Roe and Susan Fuller} } @article {57645, title = {Using Passive Acoustic Recording and Automated Call Identification to Survey Koalas in the Southern Forests of New South Wales}, journal = {Australian Zoologist}, year = {2019}, month = {Jul-10-2021}, abstract = {

Cost-effective surveys of low density koala populations are challenging, but technological developments in the acoustics field offer great potential for landscape-scale surveys and monitoring. We assessed passive acoustic recording coupled with automated call identification as a survey method for koalas Phascolarctos cinereus. Surveys targeted areas of previously known koala activity based on scat surveys in southern forests of New South Wales where a low density of koalas is suspected. We set 24 Song Meters to record at night over a two week period (~3,696 hours) in the koala breeding season (October/November) in Murrah Flora Reserve. Recordings were scanned by a koala call recogniser and \“matches\” were manually verified. Across the 24 sites, 522 validated koala bellows were recorded at 21 sites (87.5 \% detection rate). Three environmental variables had most influence on detection probability of koalas, including nightly rainfall (-ve), nightly temperature (-ve) and topographic position (lower on ridges). Calling activity peaked at midnight. Sustained site occupancy, at least in the short-term, was apparent as under optimal conditions (no rain) koalas were recorded, on average, for \> 50 \% of survey nights rather than for just a few nights. We suggest that only a modest survey effort (4\–5 nights) in the breeding season, on nights with \< 3 mm of rain, is required to achieve 90\–95 \% probability of koala call detection in an area of low koala density. Comparison with scat surveys at the same sites revealed that detection rates were more than three times greater with acoustic surveys. Technological advances will continue to provide improvements for wildlife survey, and perhaps most importantly, for collecting much needed long-term data to assess trends in occupancy or other population attributes over time.

}, keywords = {bellows, detectability, monitoring, Song Meters, Wildlife surveys}, issn = {0067-2238}, doi = {10.7882/AZ.2019.033}, url = {http://publications.rzsnsw.org.au/doi/10.7882/AZ.2019.033}, author = {Law, B. and Gonsalves, L. and Bilney, R. and Peterie, J. and Pietsch, R. and Paul Roe and Truskinger, A.} } @article {52748, title = {Revealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation}, journal = {PLOS ONE}, volume = {13}, year = {2018}, month = {Jan-03-2018}, pages = {e0193345}, abstract = {

Audio recordings of the environment are an increasingly important technique to monitor biodiversity and ecosystem function. While the acquisition of long-duration recordings is becoming easier and cheaper, the analysis and interpretation of that audio remains a significant research area. The issue addressed in this paper is the automated reduction of environmental audio data to facilitate ecological investigations. We describe a method that first reduces environmental audio to vectors of acoustic indices, which are then clustered. This can reduce the audio data by six to eight orders of magnitude yet retain useful ecological information. We describe techniques to visualise sequences of cluster occurrence (using for example, diel plots, rose plots) that assist interpretation of environmental audio. Colour coding acoustic clusters allows months and years of audio data to be visualised in a single image. These techniques are useful in identifying and indexing the contents of long-duration audio recordings. They could also play an important role in monitoring long-term changes in species abundance brought about by habitat degradation and/or restoration.

}, doi = {10.1371/journal.pone.0193345}, url = {http://dx.plos.org/10.1371/journal.pone.0193345}, author = {Yvonne F. Phillips and Paul Roe} } @article {11296, title = {Decision support for the efficient annotation of bioacoustic events}, journal = {Ecological Informatics}, volume = {25}, year = {2014}, pages = {14-21}, chapter = {14}, abstract = {

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories.

We describe two algorithms: (1) a {\textquotedblleft}naìˆve{\textquotedblright} decision support tool (16\%{\textendash}64\% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48\% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacous- tics projects.

}, keywords = {annotations, bioacoustics, decision support, Faunal vocalisation, semi-automated, Similarity search}, doi = {10.1016/j.ecoinf.2014.10.001}, author = {Anthony Truskinger and Paul Roe} }