Towards transparent and replicable flower selection in agricultural flower strips

Abstract Flower strips are increasingly employed as ecologically sustainable methods for attracting beneficial insects to agricultural landscapes. Since flowers differ substantially in an array of characteristics including morphology, nutritional content, and phenology, selecting “the right flowers for the right job” requires the designer to make careful decisions about which flowers to include. These decisions ultimately determine the success of flower strips. Given the importance of flower selection, how do researchers choose which flowers to plant? We conducted a quantitative content analysis to examine how researchers make flower selection decisions in studies aimed at supporting or attracting natural enemies or pollinators to target crops. We collected data on the criteria used to justify selection decisions and we recorded the type of evidence (experiential evidence such as “expert recommendations” or “personal communication” vs. peer‐reviewed evidence) used to support selection decisions. We found that the majority of studies relied on experiential data to inform their flower choices. Specific selection criteria varied, but “attractiveness” and “native to region” were most frequently cited for both natural enemies and pollinators. Overall, we found that the prevalent use of experiential evidence for flower selection may limit the replicability and transparency of studies. To address this concern, we introduce a flexible framework aimed at ensuring the clear communication of the flower selection process. Our framework emphasizes evidence‐based practices to enhance both replicability and transparency in flower strip design, offering a path toward more rigorous and effective ecological interventions.