Pictures or pellets? Comparing camera trapping and dung counts as methods for estimating population densities of ungulates

• Main Authors: Pfeffer, Sabine, Spitzer, Robert, Allen, Andrew, Hofmeester, Tim, Ericsson, Göran, Widemo, Fredrik, Singh, Navinder, Cromsigt, Joris
• Format: Article in Journal/Newspaper
• Language: Swedish; English
• Published: 2018
• Subjects: Ecology; Remote Sensing; Alces alces; Northern Sweden
• Online Access: https://pub.epsilon.slu.se/15927/; https://pub.epsilon.slu.se/15927/1/pfeffer_et_al_190221.pdf

Across the northern hemisphere, land use changes and, possibly, warmer winters are leading to more abundant and diverse ungulate communities causing increased socioeconomic and ecological consequences. Reliable population estimates are crucial for sustainable management, but it is currently unclear which monitoring method is most suitable to track changes in multi-species assemblages. We compared dung counts and camera trapping as two non-invasive census methods to estimate population densities of moose Alces alces and roe deer Capreolus capreolus in Northern Sweden. For camera trapping, we tested the random encounter model (REM) which can estimate densities without the need to recognize individual animals. We evaluated different simplification options of the REM in terms of estimates of detection distance and angle (raw data vs. modelled) and of daily movement rate (camera trap based vs. telemetry based). In comparison to density estimates from camera traps, we found that, dung counts appeared to underestimate population density for roe deer, but not for moose. Estimates of detection distance and angle from modelled versus raw camera data resulted in nearly identical outcomes. The telemetry-derived daily movement rate for moose and roe deer resulted in much higher density estimates than the camera trap-derived estimates. We suggest that camera trap- ping may be a robust complement to dung counts when monitoring ungulate communities, particularly when similarities between dung pellets from sympatric deer species make unambiguous assignment difficult. Moreover, we show that a simplified use of the REM method holds great potential for large-scale citizen science-based programmes (e.g. involving hunters) that can track the rapidly changing European wildlife landscape. We suggest to include camera trapping in management programmes, where the analysis can be verified via web-based applications.