Often it is difficult to find dung and/or specify the correct species and some of these challenges emerged in our results. We found that dung counts and camera trapping estimated similar densities for moose, which were also comparable to independent estimates from local managers. This follows our expectations since moose dung is large and can be identified and detected with relative ease. In contrast, dung counts appeared to underestimate roe deer densities when compared to camera trapping. Dung of roe deer is relatively small which decreases detectability. The large overlap in dung morphology among roe deer, red deer and fallow deer may also influence density estimates from dung counts. This highlights the value of camera trapping in multi-species ungulate communities. Estimates of detection distance and angle from modelled versus raw camera data resulted in nearly identical outcomes. However, the telemetry-derived daily movement rate for moose and roe deer resulted in much higher density estimates than the camera trap-derived estimates.
The simplified use of a well-developed camera trapping method holds great potential for citizen science-based programs (e.g., involving hunters) that can track the rapidly changing European wildlife landscape. We suggest to include camera trapping into management programs, where data can be analysed via web-based applications.
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