U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Variations of Leaf Longevity in Tropical Moist Forests Predicted by a Trait-driven Carbon Optimality Model
Published: July 04, 2017
Posted: September 06, 2017

Here we develop a new model to capture large intraspecific variability in leaf longevity of 105 tropical tree species within two tropical moist forests in Panama.

The Science
Leaf longevity (LL), how long a leaf lives, is closely linked to plant resource use, carbon uptake, and growth strategy. In tropical forests, there is remarkable diversity in LL across species, ranging from several weeks to 6 years or more. However, it remains unclear how to capture such large variation using predictive models. Here, we present a meta-analysis of 49 species across temperate and tropical biomes. Our results show that the leaf ageing rate is positively correlated with the mass-based carbon uptake rate of mature leaves. We further developed a LL model to capture leaf aging rate and evaluated it with LL data for 105 species measured in two tropical forests in Panama. Our results show that the new model explains over 40% of the cross-species variation in LL, including those species sampled from both canopy and understory. Collectively, our results reveal how variation in LL is constrained by both leaf structural traits and the growth environment.

The Impact
Leaf longevity has been recognized as critical for understanding tropical seasonality and carbon dynamics. Our proposed leaf longevity model can be used in next generation Earth System Models (ESMs) to improve projections of carbon dynamics and potential climate feedbacks in the tropics.

Summary
We use a trait-based carbon optimality approach to model leaf longevity (LL, in days), and assess the model performance with in-situ LL data for 105 species in two tropical forests in Panama. More specifically, we examine the relative impact of leaf ageing rate (i.e. the rate at which leaf photosynthetic capacity declines with age) and within-canopy variation in light environment on the modeled LL. We first assumed that all species have the same leaf ageing rate (i.e. the community average value) and receive the same light condition (i.e. canopy-level light), and the results are shown in panel a, with a correlation coefficient r=0.08 which is not significant. Then we performed the analysis with species-specific leaf ageing rates, while assuming that all species receive the same light condition (i.e. canopy-level light), and the results are shown in panel b, with r=0.53 and p-value<<0.001. We lastly performed the analysis with species-specific leaf ageing rate and light environment, and the results are shown in panel c, with r=0.66 and p-value <<0.001. Our results thus suggest that both leaf aging rate and within-canopy variation in light environment are essential for modeling LL in the tropics, and the best model can capture over 40% of interspecific variability in LL, including those species from canopy and understory.  

Contacts
(BER PM)

Daniel Stover
SC-23.1
Daniel.Stover@science.doe.gov (301-903-0289)

Dorothy Koch
SC-23.1
Dorothy.koch@science.doe.gov (301-903-0105)

(PI Contact)
Lead author contact information
Jin Wu
Brookhaven National Laboratory 
jinwu@bnl.gov 

Institutional contact
Alistair Rogers
Brookhaven National Laboratory
arogers@bnl.gov

Funding
J. Wu was supported by the Next-Generation Ecosystem Experiment (NGEE-Tropics) project. The NGEE-Tropics project is supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science.

Publications
Xu X, Medvigy D, Wright SJ, Kitajima K, Wu J, Albert LP, Martins GA, Saleska SR, Pacala SW. Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model. Ecology Letters, 2017.[doi:10.1111/ele.12804] (Reference link)

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Terrestrial Ecosystem Science

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)