New paper: Passive Acoustic Monitoring for detection the Yellow-bellied Glider

Passive acoustic monitoring (PAM) is increasingly being used for the survey of vocalising wildlife species that are otherwise cryptic and difficult to survey. Our study aimed to develop PAM guidelines for detecting the Yellow-bellied Glider, a highly vocal arboreal marsupial that occurs in native Eucalyptus forests in eastern and south-eastern Australia. To achieve this, we considered the influence of background noise, weather conditions, lunar illumination, time since sunset and season on the probability of detecting vocalisations. We deployed Autonomous Recording Units (ARUs) at 43 sites in the Central Highlands of Victoria during two periods: spring/summer (October 2018 to January 2019), and autumn/winter (May to August 2019). ARUs were programmed to record for 11 hours from sunset for 14 consecutive days during each period. Background noise resulted from inclement weather (wind and rain) and masked vocalisations in spectrograms of the recordings, thus having the greatest influence on detection probability. Vocalisations were most common in the four hours after sunset. Rainfall negatively influenced detection probability, especially during the autumn/winter sampling period. Detection of Yellow-bellied Gliders with PAM requires deploying ARUs programmed to record for four hours after sunset, for a minimum of six nights with minimal inclement weather (light or no wind or rain). The survey period should be extended to 12 nights when rain or wind are forecast. Because PAM is less labour intensive than active surveys (i.e., spotlighting and call playbacks with multiple observers and several nights’ survey per site), its use will facilitate broad-scale surveys for Yellow-bellied Gliders.

Where wildlife and traffic collide: drivers of roadkill rates change through time in a wildlife-tourism hotspot

Rendall, AR. Webb, V. Sutherland, DS. White, JG. Renwick, L. Cooke, R.

Understanding when and where roadkill is most likely to occur is vital to reducing wildlife-vehicle collisions. However, little is known about how roadkill rates change through time and whether or not the key influences on roadkill also change. Understanding changes in roadkill will facilitate the best implementation of mitigation measures. We aimed to determine how roadkill rates have changed between two distinct time periods and assess whether the spatial and temporal drivers of roadkill rates may have changed: with a view to informing species-specific mitigation strategies.

Roadkill hotspots in 1998-99 versus 2014 on Phillip Island between February and June. The size of dot represents the number of roadkill per segment per year (square root transformed). Yellow outlined circles represent roadkill locations only sampled within the 2014 data.
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Taking the bait: The influence of attractants and microhabitat on detection of fauna by remote-sensing cameras.

Rendall, AR., White, JG., Cooke, R., Whisson, DA., Schneider, T., Beilharz, L., Poelsma, E., Ryeland, J., Weston, MA.

Autonomously triggered cameras are a common wildlife survey technique. The use of attractants and surrounding microhabitats are likely to influence detection probabilities and survey outcomes, however, few studies consider these factors. We compared three attractants (peanut butter-based, tuna-based, and a control) in a Latin square design through a coastal shrubland with high microhabitat variability at Cape Otway, Victoria, Australia. Deployments involved 36 cameras for four days in each of five years. The percentage cover of each vegetation structural type (low [no or sparse cover], moderate [grass] or high [shrubs]) within 20 m of each camera was calculated and reduced to a single variable using PCA. Dynamic occupancy modelling, with lure type and vegetation structure as covariates of detection probability, found that peanut butter attracted the greatest diversity of species (24 of 35 species, 69%) and yielded the greatest number of detections (50% of 319) when compared to tuna oil (66% and 24% respectively) and the control (43% and 26% respectively). Peanut butter attracted more Macropodidae (wallabies) and Muridae (rats and mice); however, vegetation structural variables were the greatest influence on Corvidae/Artamidae (raven/currawong) detections with higher detectability in more open areas. Vegetation structure also influenced Muridae detections. This study reinforces the critical choice of appropriate attractants and camera placement when investigating vertebrate groups and highlights the role of microhabitat in the detection of small mammals and birds. We suggest future large-scale camera surveys consider different bait types and microhabitats in their designs, to control for any biases and enable future advice on ‘optimal’ methods.

Number of nights required to be 95% confidence of detecting Macropodidae species (top) and Muridae species (bottom) across three attractants: peanut butter (solid line), tuna oil (dashed line), and control (dot line). Red dash-dot line represents target 95% confidence in a site-specific absence.

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