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We take an integrative approach in assessing how introgression and Pleistocene climate fluctuations have shaped the diversification of the coreLentagoclade ofViburnum, a group of five interfertile species with broad areas of sympatry. We specifically tested whether flowering time plays a role in maintaining species isolation.

RAD‐seq data for 103 individuals were used to infer the species relationships and the genetic structure within each species. Flowering times were compared among species on the basis of historical flowering dates documented by herbarium specimens.

Within each species, we found a strong relationship between flowering date and latitude, such that southern populations flower earlier than northern ones. In areas of sympatry, the species flower in sequence rather than simultaneously, with flowering dates offset by ≥9 d for all species pairs. In two cases it appears that the offset in flowering times is an incidental consequence of adaptation to differing climates, but in the recently diverged sister speciesV. prunifoliumandV. rufidulum, we find evidence that reinforcement led to reproductive character displacement. Long‐term trends suggest that the two northern‐most species are flowering earlier in response to recent climate change.

We argue that speciation in theLentagoclade has primarily occurred through ecological divergence of allopatric populations, but differences in flowering time were essential to maintain separation of incipient species when they came into secondary contact. This combination of factors may underlie diversification in many other plant clades.

Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study.

Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time‐consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras.

Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer‐scale information (84.3% for flower and 80.5% for fruit detection).

The method described has the potential to allow fine‐grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.