02807nas a2200217 4500008004100000245008000041210007100121520213700192653002102329653001702350653001602367653001802383653002002401653002302421100002902444700001802473700002002491700001402511700001802525856004602543 2022 eng d00aSpatial dynamics of soundscapes and biodiversity in a semi‐arid landscape0 aSpatial dynamics of soundscapes and biodiversity in a semi‐arid 3 a

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.

10aacoustic indices10abiodiversity10aShrub cover10aSpatial scale10aSubcanopy cover10aVegetation mosaics1 aScarpelli, Marina, D. A.1 aTucker, David1 aDoohan, Brendan1 aRoe, Paul1 aFuller, Susan uhttps://bio.acousti.ca/zh-hans/node/5846802656nas a2200253 4500008004100000022001400041245013100055210006900186260001600255520187700271653001202148653001802160653001502178653001602193653002102209100001202230700001802242700001502260700001602275700001602291700001402307700001902321856006202340 2019 eng d a0067-223800aUsing Passive Acoustic Recording and Automated Call Identification to Survey Koalas in the Southern Forests of New South Wales0 aUsing Passive Acoustic Recording and Automated Call Identificati cJul-10-20213 a

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.

10abellows10adetectability10amonitoring10aSong Meters10aWildlife surveys1 aLaw, B.1 aGonsalves, L.1 aBilney, R.1 aPeterie, J.1 aPietsch, R.1 aRoe, Paul1 aTruskinger, A. uhttp://publications.rzsnsw.org.au/doi/10.7882/AZ.2019.03301691nas a2200145 4500008004100000245012700041210006900168260001600237300001300253490000700266520118100273100002501454700001401479856005201493 2018 eng d00aRevealing the ecological content of long-duration audio-recordings of the environment through clustering and visualisation0 aRevealing the ecological content of longduration audiorecordings cJan-03-2018 ae01933450 v133 a

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.

1 aPhillips, Yvonne, F.1 aRoe, Paul uhttp://dx.plos.org/10.1371/journal.pone.019334502058nas a2200205 4500008004100000245007200041210006900113300001000182490000700192520139600199653001601595653001701611653002101628653002401649653001901673653002201692100002401714700001401738856010001752 2014 eng d00aDecision support for the efficient annotation of bioacoustic events0 aDecision support for the efficient annotation of bioacoustic eve a14-210 v253 a

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 “naïve” decision support tool (16%–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.

10aannotations10abioacoustics10adecision support10aFaunal vocalisation10asemi-automated10aSimilarity search1 aTruskinger, Anthony1 aRoe, Paul uhttps://bio.acousti.ca/zh-hans/content/decision-support-efficient-annotation-bioacoustic-events