Hybrid machine learning approaches outperform mechanistic models of bloom timing in Eastern Redbud, Cercis canadensis.

Predicting the timing of flowering has utility both in climate change planning and practical applications. Eastern Redbud (Cercis canadensis), a charismatic spring-flowering tree found predominantly in the eastern USA, has long captured public attention, yet robust models of its bloom timing are lacking. Here, we aim to understand flowering onset dynamics of C. canadensis, a challenge given potential for local adaptation and plasticity across its range. We utilized a hybrid phenology modeling framework that integrates a mechanistic chill–heat framework with a machine-learning correction layer. We expected that this integrative model would better capture non-linear dynamics in flowering onset across its range. Using observations of bloom onset maintained by the USA National Phenology Network collected across the tree’s range, we parameterized a process-based model incorporating chilling, forcing, and photoperiod cues, adjusted for latitude. This model predicted bloom dates with a mean absolute error of 7.3 days. Incorporating a machine-learning correction layer yielded a cross-validated mean error of 6.1 days, with improved representation of local anomalies and interannual variability. Out-of-sample validation using observations from herbarium and iNaturalist indicated the hybrid model retained predictive skill across contemporary and historical contexts. Our results also revealed that trees at higher latitudes require greater chilling and less forcing to flower than southern individuals. These results highlight the value of hybrid approaches that combine known extrinsic drivers linked to physiology with more flexible machine learning approaches, providing improved species-wide generalization. These analyses showcase both integrative modeling and integrative validation approaches to advance phenological forecasting.