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

Loren P. Albert, Natalia Restrepo-Coupe, Marielle N. Smith, Jin Wu, Cecilia Chavana-Bryant, Neill Prohaska, Tyeen C. Taylor, Giordane A. Martins, Philippe Ciais, Jiafu Mao, M. Altaf Arain, Wei Li, Xiaoying Shi, Daniel M. Ricciuto, Travis E. Huxman, Sean M. McMahon, Scott R. Saleska

Research output: Contribution to journalReview articlepeer-review

30 Scopus citations

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 languageEnglish
Pages (from-to)3591-3608
Number of pages18
JournalGlobal Change Biology
Volume25
Issue number11
DOIs
StatePublished - 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.

FundersFunder number
Marshall Foundation of Arizona
National Science FoundationEF1137366, MSB ENSA 1638490
U.S. Department of EnergyDE-SC0008383, DE-SC0012704
National Aeronautics and Space AdministrationNNX17AF65G
Gordon and Betty Moore Foundation
Office of ScienceDE-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

    Fingerprint

    Dive into the research topics of 'Cryptic phenology in plants: Case studies, implications, and recommendations'. Together they form a unique fingerprint.

    Cite this