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.
Original language | English |
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Pages (from-to) | 3591-3608 |
Number of pages | 18 |
Journal | Global Change Biology |
Volume | 25 |
Issue number | 11 |
DOIs | |
State | Published - Nov 1 2019 |
Bibliographical note
Publisher Copyright:© 2019 John Wiley & Sons Ltd
Funding
This work was supported in part by the U.S. Department of Energy (DOE; DE-SC0008383). LPA thanks the Marshall Foundation of Arizona for dissertation support and the Institute at Brown for Environment and Society for postdoctoral support. Amazon model–observation comparisons were supported in part by the Gordon and Betty Moore Foundation and NASA ROSES (Award Number: NNX17AF65G). SMM was supported by NSF grant EF1137366 and NSF MSB ENSA 1638490. Computational support for CLASS-CTEM-N+ was provided by SHARCNET at McMaster University, Hamilton, ON, Canada. J. Mao, X. Shi, and D. Ricciuto are supported by the Terrestrial Ecosystem Science Scientific Focus Area (TES SFA) project funded through the Terrestrial Ecosystem Science Program in the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the US Department of Energy (DOE) Office of Science. The simulation of CLM4.5 used the resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. ORCHIDEE is a global land surface model developed at the IPSL institute in France. The MSTMIP simulations were performed with the support of the GhG Europe FP7 grant with computing facilities provided by “LSCE” or “TGCC. JW was supported by the US DOE contract No. DE-SC0012704 to Brookhaven National Laboratory. Authors are grateful to David Orwig and Jay Aylward for the Harvard Forest stem map, and to Girardin et al. () for sharing LAI and litterfall data. Authors thank David D. Breshears, Russell K. Monson, and Greg Barron-Gafford for comments on drafts.
Funders | Funder number |
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Marshall Foundation of Arizona | |
National Science Foundation | EF1137366, MSB ENSA 1638490 |
U.S. Department of Energy | DE-SC0008383, DE-SC0012704 |
National Aeronautics and Space Administration | NNX17AF65G |
Gordon and Betty Moore Foundation | |
Office of Science | DE-AC05-00OR22725 |
Biological and Environmental Research | |
Seventh Framework Programme |
Keywords
- climate change
- dynamic global vegetation models
- plant ecology
- plant physiology
- seasonality
- terrestrial biosphere models
- whole plant biology