Estimating animal abundance and effort–precision relationship with camera trap distance sampling

Abstract

Effective monitoring methods are needed for assessing the state of biodiversity and detecting population trends. The popularity of camera trapping in wildlife surveys continues to increase as they are able to detect species in remote and difficult-to-access areas. As a result, several statistical estimators of the abundance of unmarked animal populations have been developed, but none have been widely tested. Even where the potential for accurate estimation has been demonstrated, whether these methods estimators can yield estimates of sufficient precision to detect trends and inform conservation action remains questionable. Here, we assess the effort-precision relationship of camera trap distance sampling (CTDS) in order to help researchers design efficient surveys. A total of 200 cameras were deployed for 10 months across 200 km 2 in the Taï National Park, Côte d'Ivoire. We estimated abundance of Maxwell's duikers, western chimpanzees, leopards, and forest elephants that are challenging to enumerate due to rarity or semi-arboreality. To test the effects of spatial and temporal survey effort on the precision of CTDS estimates, we calculated coefficient of variation (CV) of the encounter rate from subsets of our complete data sets. Estimated abundance of leopard and Maxwell's duiker density (20% < CV < 30% and CV = 11%, respectively) were similar to prior estimates from the same area. Abundances of chimpanzees (20% < CV < 30%) were underestimated, but the quality of inference was similar to that reported after labor-intensive line transect surveys to nests. Estimates for the rare forest elephants were potentially unreliable since they were too imprecise (60% < CV < 200%). Generalized linear models coefficients indicated that for relatively common, ground-dwelling species, CVs between 10% and 20% are achievable from a variety of survey designs, including long-term (6+ months) surveys at few locations (50), or short term (2-week to 2-month) surveys at 100-150 locations. We conclude that CTDS can efficiently provide estimates of abundance of multiple species of sufficient quality and precision to inform conservation decisions. However, estimates for the rarest species will be imprecise even from ambitious surveys and may be biased for species that exhibit strong reactions to cameras.

Does the Presence of Shade Trees and Distance to the Forest Affect Detection Rates of Terrestrial Vertebrates in Coffee Home Gardens?

“…We were, however, not able to identify the individuals or to obtain enough information on animal movements of the different species object to this study to analyze the data in a more robust way. We did not attempt the camera trap distance sampling method (CTDS; [58]) as with our reduced sample size in terms of locations of camera traps the coefficient of variation would have been too high [59]. Additionally, one of the main limitations in the use of CTDS can be the delay between the time the sensor is activated and the time the first image is recorded [58].…”

“…We did not attempt the camera trap distance sampling method (CTDS; [58]) as with our reduced sample size in terms of locations of camera traps the coefficient of variation would have been too high [59]. Additionally, one of the main limitations in the use of CTDS can be the delay between the time the sensor is activated and the time the first image is recorded [58]. Since we set the cameras to record at an automatic sensor level, we believe that this would have biased the estimates via CTDS as the trigger speed in automatic mode varies depending on the weather.…”

Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors

“…Distance sampling therefore requires accurate measurements of these distances. Camera trap data have also been successfully used as point transects (Bessone et al, 2020;Cappelle et al, 2019Cappelle et al, , 2020Howe et al, 2017), which requires the recording of distances at which recorded animals pass in front of the camera. Numbers of replicates (points) and detections (distance measurements) required for robust estimation are comparable to those required on line and point transects by human observers (Bessone et al, 2020;Cappelle et al, 2020).…”

Assessing the camera trap methodologies used to estimate density of unmarked populations

“…Bessone et al (2020) obtained an average CV of 0.37 in spite of sampling 750 locations. In this respect,Cappelle et al (2021) concluded that a wide variety of survey designs can be applied to achieve CV between 0.10 and 0.20 with CT-DS, for instance, with at least 100 sampling days at as few 50 camera trap placements. Future field studies are needed to evaluate the improvement in precision regarding the number of sampled locations.…”