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

Abstract

1. 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.2. 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.3. 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.4. Synthesis and applications. Our application of camera trap distance sampling (CTDS) to a diverse vertebrate community demonstrates the enormous potential of this This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

A field test of unconventional camera trap distance sampling to estimate abundance of marmot populations

“…The performance of CTDS, however, remains to be tested in various field situations (Moeller et al 2018, but see Bessone et al 2020). Furthermore, although the use of videos or long 'bursts' of images is recommended to align observations to predetermined snapshot moments at specific times of day (Howe et al 2017), researchers and wildlife managers might find this set-up inconvenient and may prefer to use single still images recorded when the camera is triggered (e.g.…”

Distance sampling of duikers in the rainforest: Dealing with transect avoidance

“…Interestingly, Howe et al [57] used distance sampling on camera trapping data, by placing distance markers in the field of view of the cameras. They and others report a behavioural response to the cameras similar to the one we describe [56,57]. This strongly suggests that the 2D methodology is necessary to apply distance sampling to camera trapping data.…”

“…The frequency of animal pictures taken by automatic "camera traps" has also been proposed as an index of abundance [55], including in a context very similar to ours [56,57]. However, the camera-trapping approach may be jeopardized by camera failures and high maintenance costs in the rainforest environment, by poachers, and remains relatively imprecise in the absence of information about the individual identity of the photographed animals, and their local fine-scale microhabitat selection and daily range [40,41,55].…”

High performance machine learning models can fully automate labeling of camera trap images for ecological analyses

“…As a case study, the model was developed for classifying terrestrial forest mammals and birds in Central Africa (see Table S1 for further details on species and groups), where camera traps are now frequently deployed over large spatial scales to survey secretive birds and mammals in remote and inaccessible landscapes (11)(12)(13). Training data were obtained from multiple countries and sources (c.1.6 million images; reduced to n = 347120 images after data processing; Table 1).…”

“…High performance machine learning models can fully automate labeling of camera trap images for ecological analyses Robin Whytock 1,2 * †, Jędrzej Świeżewski 3 †, Joeri A. Zwerts 4 , Tadeusz Bara-Słupski 4 , Aurélie Flore Koumba Pambo 2 , Marek Rogala 3 , Laila Bahaa-el-din 5 , Kelly Boekee 6,7 , Stephanie Brittain 8,9 , Anabelle W. Cardoso 10 , Philipp Henschel 11,12 , David Lehmann 2,1 , Brice Momboua 2 , Cisquet Hector Ror Kiebou Opepa 13 , Christopher Orbell 11,1 , Ross T. Pitman 11 , Hugh S. Robinson 11,14 , Katharine A. Abernethy 1,12 † These authors contributed equally to the manuscript. *Corresponding author: Robin C. Whytock, robbie.whytock1@stir.ac.uk…”