Estimating and validating koala

Abstract

Context It is notoriously difficult to estimate the size of animal populations, especially for cryptic or threatened species that occur in low numbers. Recent advances with acoustic sensors make the detection of animal populations cost effective when coupled with software that can recognise species-specific calls. Aims We assess the potential for acoustic sensors to estimate koala, Phascolarctos cinereus, density, when individuals are not identified, using spatial count models. Sites were selected where previous independent estimates of density were available. Methods We established acoustic arrays at each of five sites representing different environments and densities of koalas in New South Wales. To assess reliability, we compared male koala density estimates derived from spatial count modelling to independently derived estimates for each site. Key results A total 11 312 koala bellows were verified across our five arrays. Koalas were detected at most of our sample locations (96–100% of sensors; n = 130), compared with low detection rates from rapid scat searches at trees near each sensor (scats at <2% of trees searched, n = 889, except one site where scats were present at 69% of trees, n = 129). Independent estimates of koala density at our study areas varied from a minimum of 0.02 male koalas ha−1 to 0.32 ha−1. Acoustic arrays and the spatial count method yielded plausible estimates of male koala density, which, when converted to total koalas (assuming 1:1 sex ratio), were mostly equivalent to independent estimates previously derived for each site. The greatest discrepancy occurred where the acoustic estimate was larger (although within the bounds of uncertainty) than the independent mark–recapture estimate at a fragmented, high koala-density site. Conclusions Spatial count modelling of acoustic data from arrays provides plausible and reliable estimates of koala density and, importantly, associated measures of uncertainty as well as an ability to model spatial variations in density across an array. Caution is needed when applying models to higher-density populations where home ranges overlap extensively and calls are evenly spread across the array. Implications The results add to the opportunities of acoustic methods for wildlife, especially where monitoring of density requires cost-effective repeat surveys.

Multiyear monitoring of threatened iconic arboreal mammals in a mid‐elevation conservation reserve in eastern Australia

“…Mean density is commonly reported to be >0.2 per ha in koalas (Dique et al, 2004;White & Kunst, 1990) and >0.8 per ha in greater gliders (Emerson et al, 2019). Further research in Richmond Range NP should be directed at estimating density to confirm or revise the values here, using distance sampling for the greater glider (Cripps et al, 2021;Emerson et al, 2019) and arrays of audio recorders for the koala (see Law et al, 2022).…”

“…We used an estimate of adult koala density based on Law et al ( 2022 ) who used song meter arrays to estimate a density of 0.07 adult males per ha (average of three estimates) in tall forest (Bongil National Park) 170 km south of our study area. The total number of adult koalas can be derived from this based on the adult sex ratio (age class 2 and above).…”

Broad‐scale acoustic monitoring of koala populations suggests metapopulation stability, but varying bellow rate, in the face of major disturbances and climate extremes

“…Previous analyses showed that this level of survey effort (at least seven nights) has little impact on detection probability because of the high detection rate per night. Call playback in forests at 75 dB has found that call amplitude in recordings attenuates to background noise levels within 100-150 m (Charlton et al, 2012;Hagens et al, 2018), although we expect under ideal conditions this could extend to ~300 m (Law et al, 2021).…”

“…Call playback in forests at 75 dB has found that call amplitude in recordings attenuates to background noise levels within 100–150 m (Charlton et al., 2012 ; Hagens et al., 2018 ), although we expect under ideal conditions this could extend to ~300 m (Law et al., 2021 ). Temperature could also affect bellowing frequency or sound propagation (Law et al., 2018 ), and we account for this by modelling month (related to temperature) as a covariate for detection probability (see below).…”

“…Such large changes between consecutive years are unlikely to be due to changes in koala abundance in the absence of a major disturbance, suggesting changes in bellowing behaviour may be more likely, although the trigger remains unknown at this point. More research is needed to better understand the relationship between bellow rate and abundance as it is correlated at some sites (Hagens et al., 2018 ), but the relationship is variable elsewhere (Law et al., 2021 ).…”