Estimating avian wingspan from wing length: an order-level regression approach

Wingspan is a key parameter in avian collision risk models but is rarely measured systematically, meaning for many species, collision risk remains largely unquantified due to a lack of morphological and flight data required to parameterise models. Folded wing length scales predictably with wingspan and is available for all extant bird species, yet no widely available method has been published for predicting wingspan from wing length; here we compiled a global wingspan dataset for 1,442 species across 25 orders and fitted order-level linear regression models predicting wingspan from wing length. Order-level models performed well, with 85% achieving R2 [≥] 0.95 (global model R2 0.96); cross-validation identified that model predictions generally fell within the intraspecific variation in wingspan, resulting in wingspan estimates for 9,194 species. Our models and accompanying wingspan data provide a transparent, reproducible, and validated method for generating species-level wingspan estimates from widely available wing length data, with direct applications to collision risk modelling, flight performance research, and macroecological analyses of avian morphology.