Data Infrastructure and Local Stakeholder Engagement with Biodiversity Conservation Research

Biodiversity research that informs conservation action is increasingly data intensive. Cutting-edge projects at large institutions use massive aggregated datasets to build dynamic models and conduct novel analyses of natural systems. Most of these research institutions are geographically distant from the highest-priority conservation areas, which are found in South America, Africa, and Southeast Asia. There, data is typically collected by or with the help of local residents hired as field assistants. These field assistants have few meaningful opportunities to participate in biodiversity research and conservation beyond data logging. The literature indicates the data revolution has increased demand for impersonal and integrated large-scale systems that aggregate biodiversity data across sources with minimal friction. In this study, interviews were conducted with six active conservation workers to identify elements of these data systems that create barriers to field assistants’ engagement with the projects they make possible. As both creators and consumers of data, all six relayed frustration with various aspects of their data workflows. Regarding field assistant interaction with digital data systems, they observed that their field assistants engaged only at the initial point of data entry or not at all. Some suggested mobile apps as a good solution for field data collection. However, some also expressed doubt that their local assistants had the necessary knowledge background to navigate digital systems or understand scientific methodologies. These results suggest that trying to mold field assistants to fit existing data infrastructure and adapting purpose-built data systems to nontechnical users are both sub-optimal solutions. A human-mediated capacity building paradigm, which requires embedding people who are both culturally literate and data literate alongside field assistants, is explored as an alternative path to making data meaningful. Improving the accessibility of data this way can empower local communities to share ownership in biodiversity conservation.