4.8 Review

Cryptic phenology in plants: Case studies, implications, and recommendations

期刊

GLOBAL CHANGE BIOLOGY
卷 25, 期 11, 页码 3591-3608

出版社

WILEY
DOI: 10.1111/gcb.14759

关键词

climate change; dynamic global vegetation models; plant ecology; plant physiology; seasonality; terrestrial biosphere models; whole plant biology

资金

  1. Oak Ridge National Laboratory [DE-AC05-00OR22725]
  2. National Science Foundation [EF1137366, MSB ENSA 1638490]
  3. U.S. Department of Energy Biological and Environmental Research Program
  4. NASA ROSES [NNX17AF65G]
  5. U.S. Department of Energy [DE-SC0008383]
  6. Brookhaven National Laboratory [DE-SC0012704]
  7. Gordon and Betty Moore Foundation
  8. GhG Europe FP7
  9. U.S. Department of Energy (DOE) [DE-SC0008383] Funding Source: U.S. Department of Energy (DOE)

向作者/读者索取更多资源

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.

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