Citizen Science for Individual Identification of Pine Martens Martes martes From Camera Trap Footage

Abstract

Camera traps have become a popular tool for monitoring a wide range of species, but determining the animals present in the footage can be incredibly time-consuming and remains a significant challenge for many projects. Crowdsourcing this task through the engagement of members of the public as citizen scientists can be a solution to this problem. There is good evidence that high confidence in classification accuracy can be attained from citizen science species classifications, but whether citizen scientists can also perform more difficult classification tasks, such as identification of individuals within a species, has received far less attention. Here, we used European pine marten Martes martes in the Forest of Dean, UK, as a case study to assess citizen scientist ability to identify different individuals from photo and video camera trap footage. Agreement between expert and citizen scientist individual ID classifications was variable, but there is potential for improving citizen science classification quality through optimising study design. Citizen scientists were more likely to classify pine marten footage correctly to species level and to attempt an individual ID when shown video footage rather than photos. Individual ID accuracy was also improved by the proximity of bait to a camera trap. Reassuringly, citizen scientists' assessment of their own confidence was positively associated with the accuracy of individual ID classification. This study highlights the potential for citizen science to contribute to more than just species classification in camera trapping projects but also emphasises the need for appropriate study design to ensure data quality.