Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling. (21st September 2017)

• Record Type: Journal Article
• Authors: Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha; Thompson, Jill; Zimmerman, Jess K.; Murphy, Lora
• Is Part Of: Global change biology. Volume 24:Number 1(2018)
• Page Start: e213
• Page End: e232
• Publication Date: 2017-09-21
• Subjects: carbon flux -- climate change -- ecosystem demography model -- GPP -- NPP -- sensitivity analysis -- tropical forest -- variance decomposition; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.13863
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species‐specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model‐data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate. Abstract : Tropical forests play a critical role in terrestrial global carbon cycle but whether they will be sources or sinks of carbon under a changing climate is unknown. Efforts to model the impacts of climate change on carbon fluxes in tropical forests have not reached a consensus. We predict carbon fluxes of a tropical forest in Puerto Rico under realistic climate change scenarios using the Ecosystem Demography model parameterized with species‐specific physiological tree data. Under a future warming and drying climate scenario, our model predicts reductions in carbon storage and tree growth rates together with large shifts in forest community and structure. Such rapid changes in climate lead the forest to transition from a carbon sink to a source. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change.