Maximizing inference from distributed experimental networks via “add-on” studies

Distributed experimental networks have emerged as a powerful approach in field ecology, enabling experimental replication across global gradients. These networks use standardized treatments at dispersed sites to identify factors like climate or soil that shape biotic responses. Reserving space for future “add-on” work fosters discovery by transforming distributed networks into distributed experimental infrastructure. However, challenges include balancing feasibility, plot impacts, and demands on site scientists. Using the Disturbance and Recovery Across Grasslands Network (DRAGNet) as a case study informed by lessons learned in the Nutrient Network (NutNet), we outline effective practices for designing add-on work to retain the original experiment’s integrity while effectively using the resources of the network participants. By following guidelines for hypothesis-driven, inclusive research that engages contributors intellectually, minimizes plot impacts using field-tested protocols, and maximizes scientific impact and inclusion, distributed networks can become valuable infrastructure for advancing ecological understanding.