Camera trapping with photos and videos: implications for ecology and citizen science

Camera traps are increasingly used in wildlife monitoring and citizen science to address an array of ecological questions on a wide variety of species. However, despite the ability of modern camera traps to capture high-quality video, the majority of studies collect still images, in part because of concerns with video performance. We conducted a camera trap survey of a forested landscape in the UK, using a grid of paired camera traps, to quantify the impact of using video compared to photos on the outcomes of ecological research and for participation and engagement of citizen scientists. Ecological outputs showed no difference between photo and video datasets, but comparison between expert and citizen science classifications showed citizen scientists were able to classify videos more accurately (average accuracy of 95% for video, 86% for photo). Furthermore, citizen scientists were more likely to volunteer additional information on age (provided for 61% videos and 30% photos) and sex (provided for 63% videos and 45% photos) of animals in video footage. Concerns over slow trigger speeds for videos did not appear to affect our datasets or the inferences gained. When combined with citizen science, video datasets are likely to be of higher quality due to increased classification accuracy. Consequently, we encourage researchers to consider the use of video for future camera-trapping projects.

Automated summary

Camera traps are commonly used in wildlife monitoring and citizen science, but concerns over video performance have led to the majority of studies collecting still images. However, a study conducted in the UK found no difference in ecological outputs between video and photo datasets. Additionally, citizen scientists were able to classify videos more accurately and provide more additional information compared to experts. This suggests that using video in camera-trapping projects, especially when combined with citizen science, can yield higher quality data.