Mammal monitoring and public engagement with camera traps and citizen science

We are currently experiencing a biodiversity crisis, with many species facing extinction. To counter these threats, species monitoring across large spatial and temporal scales, as well as public support and engagement is needed. Citizen science has an important role to play in tackling biodiversity loss, as it can help with both requirements. To optimise the effectiveness of citizen science, two major challenges need addressing. The first is ensuring confidence in data quality, the second is engaging a large and diverse audience. In this thesis, I address aspects of both challenges, with a focus on camera trapping for mammal monitoring with the citizen science project ‘MammalWeb’. I first concentrate on data quality, particularly the use of video camera trapping with citizen science. I use paired camera traps, with one camera taking photos and the other videos, to monitor an assemblage of mammals in the Forest of Dean, Gloucestershire and then to focus on individual identification of pine martens. The aim was to understand whether there was any meaningful difference in the photo and video datasets in terms of ecological data and citizen scientist classifications. Photo and video settings produced the same results for a range of ecological analysis, but citizen scientists were able to classify species more accurately in videos compared to photographs. Furthermore, citizen scientists were more likely to provide information on age, sex, and individual IDs of animals in videos. In the second half of this thesis, I look at use of social media for engagement and recruitment. I examined the influence of media on engagement with social media posts and found that video had a positive effect on post engagement. I then looked at the relationship between social media engagement and recruitment to MammalWeb and found that social media can be an effective recruitment ground for citizen science. I follow this up by looking at participation and retention. Where people heard about MammalWeb, initial user experience, and participation type effected participation and retention. These findings indicate that there is great potential for expanding citizen science camera trapping partnerships with the use of video both to improve species classification accuracy, and go beyond species identification to answer a wider range of ecological questions. They also highlight the potential for use of social media in recruitment and the importance of understanding retention patterns. Together these findings can help inform the development of citizen science projects, such as MammalWeb, to aid in biodiversity monitoring efforts and ultimately tackle biodiversity loss.