Drawn out of the shadows: Surveying secretive forest species with camera trap distance sampling

Abstract With animal species disappearing at unprecedented rates, we need an efficient monitoring method providing reliable estimates of population density and abundance, critical for the assessment of population status and trend. We deployed 160 camera traps (CTs) systematically over 743 locations covering 17,127 km 2 of evergreen lowland rainforest of Salonga National Park, block South, Democratic Republic of the Congo. We evaluated the applicability of CT distance sampling (CTDS) to species different in size and behaviour. To improve precision of estimates, we evaluated two methods estimating species' availability ( ‘A’ ) for detection by CTs. We recorded 16,700 video clips, revealing 43 different animal taxa. We estimated densities of 14 species differing in physical, behavioural and ecological traits, and extracted species‐specific availability from available video footage using two methods (a) ‘ACa’ (Cappelle et al. [2019] Am . J . Primatol ., 81, e22962) and (b) ‘ARo’ (Rowcliffe et al. [2014] Methods Ecol . Evol . 5, 1170). With sample sizes being large enough, we found minor differences between ACa and ARo in estimated densities. In contrast, low detectability and reactivity to the camera were main sources of bias. CTDS proved efficient for estimating density of homogenously rather than patchily distributed species. Synthesis and applications. Our application of camera trap distance sampling (CTDS) to a diverse vertebrate community demonstrates the enormous potential of this methodology for surveys of terrestrial wildlife, allowing rapid assessments of species' status and trends that can translate into effective conservation strategies. By providing the first estimates of understudied species such as the Congo peafowl, the giant ground pangolin and the cusimanses, CTDS may be used as a tool to revise these species' conservation status in the IUCN Red List of Threatened Species. Based on the constraints we encountered, we identify improvements to the current application, enhancing the general applicability of this method.