@article{Albert-2019-Cryptic,
title = "Cryptic phenology in plants: Case studies, implications, and recommendations",
author = "Albert, Loren P. and
Restrepo‐Coup{\'e}, Natalia and
Smith, Marielle N. and
Wu, Jin and
Chavana‐Bryant, Cecilia and
Prohaska, Neill and
Taylor, Tyeen and
Martins, Giordane and
Ciais, Philippe and
Mao, Jiafu and
Arain, M. Altaf and
Li, Wei and
Shi, Xiaoying and
Ricciuto, D. M. and
Huxman, Travis E. and
McMahon, Sean M. and
Saleska, S. R.",
journal = "Global Change Biology, Volume 25, Issue 11",
volume = "25",
number = "11",
year = "2019",
publisher = "Wiley",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G19-21001",
doi = "10.1111/gcb.14759",
pages = "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.",
}
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<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>
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%0 Journal Article
%T Cryptic phenology in plants: Case studies, implications, and recommendations
%A Albert, Loren P.
%A Restrepo‐Coupé, Natalia
%A Smith, Marielle N.
%A Wu, Jin
%A Chavana‐Bryant, Cecilia
%A Prohaska, Neill
%A Taylor, Tyeen
%A Martins, Giordane
%A Ciais, Philippe
%A Mao, Jiafu
%A Arain, M. Altaf
%A Li, Wei
%A Shi, Xiaoying
%A Ricciuto, D. M.
%A Huxman, Travis E.
%A McMahon, Sean M.
%A Saleska, S. R.
%J Global Change Biology, Volume 25, Issue 11
%D 2019
%V 25
%N 11
%I Wiley
%F Albert-2019-Cryptic
%X 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.
%R 10.1111/gcb.14759
%U https://gwf-uwaterloo.github.io/gwf-publications/G19-21001
%U https://doi.org/10.1111/gcb.14759
%P 3591-3608
Markdown (Informal)
[Cryptic phenology in plants: Case studies, implications, and recommendations](https://gwf-uwaterloo.github.io/gwf-publications/G19-21001) (Albert et al., GWF 2019)
ACL
- Loren P. Albert, Natalia Restrepo‐Coupé, Marielle N. Smith, Jin Wu, Cecilia Chavana‐Bryant, Neill Prohaska, Tyeen Taylor, Giordane Martins, Philippe Ciais, Jiafu Mao, M. Altaf Arain, Wei Li, Xiaoying Shi, D. M. Ricciuto, Travis E. Huxman, Sean M. McMahon, and S. R. Saleska. 2019. Cryptic phenology in plants: Case studies, implications, and recommendations. Global Change Biology, Volume 25, Issue 11, 25(11):3591–3608.
