Taking the bait: The influence of attractants and microhabitat on detections of fauna by remote‐sensing cameras. (16th January 2021) - Rendall, Anthony R.; White, John G.; Cooke, Raylene; Whisson, Desley A.; Schneider, Thomas; Beilharz, Lisa; Poelsma, Eleanor; Ryeland, Julia; Weston, Michael A.
Summary: Autonomously triggered cameras are a common wildlife survey technique. The use of attractants and surrounding microhabitats is 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 (38º50ʹS, 143º30ʹE). 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 with 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.