Cryptic phenology in plants: Case studies, implications, and recommendations. (22nd August 2019) - Albert, Loren P.; Restrepo‐Coupe, Natalia; Smith, Marielle N.; Wu, Jin; Chavana‐Bryant, Cecilia; Prohaska, Neill; Taylor, Tyeen C.; Martins, Giordane A.; Ciais, Philippe; Mao, Jiafu; Arain, M. Altaf; Li, Wei; et al.
Abstract: Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant‐mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season‐initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models. Abstract : Plant phenology offers insight into plant biology and plant‐mediated ecosystem processes. However, many plant phenological processes are "cryptic" in that they are hidden (e.g., root production) or undistinguishable based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). Here, we offer a framework for cryptic phenology and synthesize literature with a focus on whether terrestrial biosphere models currently capture cryptic phenology. We illustrate how capturing cryptic phenological processes can advance scientific understanding and vegetation modeling with two case studies: wood phenology in a temperate deciduous forest and leaf phenology in tropical evergreen forests of Amazonia.