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Title: Assessing the Spatiotemporal Variability of Leaf Functional Traits and Their Drivers Across Multiple Amazon Evergreen Forest Sites: A Stochastic Parameterization Approach With Land‐Surface Modeling

Most earth system models fail to capture the seasonality of carbon fluxes in radiation‐limited tropical evergreen forests (TEF) in the Amazon. Kim et al. (2012, first statistically incorporated a light‐controlled phenology module into an ecosystem model to improve carbon flux simulations at one TEF site. However, it is not clear how their approach can be extended to other TEF sites with different climatic conditions. Here we evaluated temporal variability in plant functional traits at three different TEF sites using a data‐conditioned stochastic parameterization method. We showed that previously studied links—between seasonal photosynthetically active radiation (PAR) and the traitsVcmax25and leaf longevity—occur across sites. We further determined that seasonal PAR could similarly drive variations in the stomatal conductance slope parameter. Differences found in temporal trait estimates among sites indicate that dynamic trait parameters cannot be applied uniformly over space, but it may be possible to extrapolate them based on climatic factors. Motivated by recent observations that physiological capacity develops as leaves mature, we built new regression models for predicting traits that not only include PAR but also an autoregressive lag term to capture observed physiological delays behind PAR‐driven phenology shifts. With our stochastic parameterization, we predicted the three sites to be carbon neutral or carbon sinks under the RCP 8.5 future climate scenario. In contrast, projections using standard static trait parameters show most of the Amazonian TEF region becoming a carbon source. We further approximated that variable traits may allow at least a third of the radiation‐limited TEF region in the Amazon to serve as a future net carbon sink.

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DOI PREFIX: 10.1029
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Journal of Geophysical Research: Biogeosciences
Medium: X
Sponsoring Org:
National Science Foundation
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