Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/16605
Title: Cryptic phenology in plants: Case studies, implications, and recommendations
Authors: Albert, Loren P.
Restrepo-Coupé, Natalia
Smith, Marielle N.
Wu, Jin
Chavana-Bryant, Cecilia
Prohaska, Neill
Taylor, Tyeen C.
Martins, Giordane Augusto
Ciais, Philippe
Mao, Jiafu
Arain, Muhammad Altaf
Li, Wei
Shi, Xiaoying
Ricciuto, Daniel M.
Huxman, Travis E.
McMahon, Sean M.
Saleska, Scott Reid
Keywords: Biosphere
Climate Change
Ecological Modeling
Evergreen Forest
Phenology
Physiology
Plant
Seasonality
Vegetation
Amazonia
Climate Change
Ecosystem
Forest
Season
Climate Change
Ecosystem
Forests
Seasons
Issue Date: 2019
metadata.dc.publisher.journal: Global Change Biology
metadata.dc.relation.ispartof: Volume 25, Número 11, Pags. 3591-3608
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. © 2019 John Wiley & Sons Ltd
metadata.dc.identifier.doi: 10.1111/gcb.14759
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