Jiafu Mao


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Cryptic phenology in plants: Case studies, implications, and recommendations
Loren P. Albert, N. Restrepo-Coupé, Margaret Smith, Jin Wu, Cecilia Chavana‐Bryant, N. Prohaska, Tyeen Taylor, Giordane Martins, Philippe Ciais, Jiafu Mao, M. Altaf Arain, Wei Li, Xiaoying Shi, Daniel M. Ricciuto, Travis E. Huxman, Sean M. McMahon, S. R. Saleska
Global Change Biology, Volume 25, Issue 11

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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Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region
Erqian Cui, Kun Huang, M. Altaf Arain, Joshua B. Fisher, D. N. Huntzinger, Akihiko Ito, Yiqi Luo, Atul K. Jain, Jiafu Mao, A. M. Michalak, Shuli Niu, Nicholas C. Parazoo, Changhui Peng, Shushi Peng, Benjamin Poulter, Daniel M. Ricciuto, Kevin Schaefer, Christopher R. Schwalm, Xiaoying Shi, Hanqin Tian, Weile Wang, Jinsong Wang, Yaxing Wei, En‐Rong Yan, Liming Yan, Ning Zeng, Qiuan Zhu, Jianyang Xia
Global Biogeochemical Cycles, Volume 33, Issue 6

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.

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Global vegetation biomass production efficiency constrained by models and observations
Yue He, Shushi Peng, Yongwen Liu, Xiangyi Li, Kai Wang, Philippe Ciais, M. Altaf Arain, Yuanyuan Fang, Joshua B. Fisher, Daniel Goll, D. J. Hayes, D. N. Huntzinger, Akihiko Ito, Atul K. Jain, Ivan A. Janssens, Jiafu Mao, Matteo Campioli, A. M. Michalak, Changhui Peng, Josep Peñuelas, Benjamin Poulter, Dahe Qin, Daniel M. Ricciuto, Kevin Schaefer, Christopher R. Schwalm, Xiaoying Shi, Hanqin Tian, Sara Vicca, Yaxing Wei, Ning Zeng, Qiuan Zhu
Global Change Biology, Volume 26, Issue 3

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

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Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization
Yongwen Liu, Shilong Piao, Thomas Gasser, Philippe Ciais, Hui Yang, Han Wang, Trevor F. Keenan, Mengtian Huang, Shiqiang Wan, Jian Song, Kai Wang, Ivan A. Janssens, Josep Peñuelas, Chris Huntingford, Xuhui Wang, M. Altaf Arain, Yuanyuan Fang, Joshua B. Fisher, Maoyi Huang, D. N. Huntzinger, Akihiko Ito, Atul K. Jain, Jiafu Mao, A. M. Michalak, Changhui Peng, Benjamin Poulter, Christopher R. Schwalm, Xiaoying Shi, Hanqin Tian, Yaxing Wei, Ning Zeng, Qiuan Zhu, Tao Wang
Nature Geoscience, Volume 12, Issue 10

Clarifying how increased atmospheric CO2 concentration (eCO2) contributes to accelerated land carbon sequestration remains important since this process is the largest negative feedback in the coupled carbon–climate system. Here, we constrain the sensitivity of the terrestrial carbon sink to eCO2 over the temperate Northern Hemisphere for the past five decades, using 12 terrestrial ecosystem models and data from seven CO2 enrichment experiments. This constraint uses the heuristic finding that the northern temperate carbon sink sensitivity to eCO2 is linearly related to the site-scale sensitivity across the models. The emerging data-constrained eCO2 sensitivity is 0.64 ± 0.28 PgC yr−1 per hundred ppm of eCO2. Extrapolating worldwide, this northern temperate sensitivity projects the global terrestrial carbon sink to increase by 3.5 ± 1.9 PgC yr−1 for an increase in CO2 of 100 ppm. This value suggests that CO2 fertilization alone explains most of the observed increase in global land carbon sink since the 1960s. More CO2 enrichment experiments, particularly in boreal, arctic and tropical ecosystems, are required to explain further the responsible processes. The northern temperate carbon sink is estimated to increase by 0.64 PgC each year for each increase in atmospheric CO2 concentrations by 100 ppm, suggests an analysis of data from field experiments at 7 sites constraints.