Effective wildlife monitoring is essential for biodiversity conservation and sustainable management, particularly in the face of rapid environmental changes and human-wildlife interactions. Advances in camera trap technology and citizen science, here used to denote non-professional involvement in scientific research, irrespective of citizenship status, have revolutionized ecological data collection, providing scalable and non-invasive methods for tracking species distribution, abundance and behaviour across large spatial and temporal scales. However, challenges in managing the vast datasets generated, ensuring user engagement and addressing privacy concerns persist. To address these issues, we introduce Trapper Citizen Science (Trapper CS), an open-source platform combining artificial intelligence-based data processing pipelines with citizen science to enhance wildlife monitoring efforts. Trapper CS supports automated data processing, provides user-friendly interfaces and real-time species identification, while promoting collaboration and data sharing through standardized protocols and data formats (Camtrap DP). With applications spanning research, management and citizen engagement, Trapper CS exemplifies a novel approach to integrate technology and public participation for addressing global wildlife challenges. This paper discusses the platform's architecture, functionality and applications, highlighting its potential to contribute to more effective wildlife monitoring and management.
Frauendorf et al. (Mon,) studied this question.