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ABSTRACT Lightning frequency in tropical forests has been increasing for decades and lightning is a major agent of forest biomass mortality, but the implications of increased lightning frequency are unclear. Here, we provide a species‐ and spatially explicit implementation of lightning in a mechanistic forest dynamics model. We evaluated the model's ability to reproduce current‐day observations in a Panamanian tropical forest, and the sensitivity of model outputs to plausible changes in lightning frequency. The lightning‐enabled model simulated aboveground biomass (AGB), carbon flux, and stem densities that were consistent with observations. As expected, AGB declined with increasing lightning frequency. However, the magnitude of AGB decline was greatly reduced when trees were assigned empirically derived, species‐specific lightning tolerances. Changes in species composition weakened the sensitivity of AGB to increasing lightning: the AGB of a small number of large‐statured, lightning‐tolerant species increased with increasing lightning frequency. In addition, the effect of lightning on AGB tended to saturate at high lightning frequencies because of the combined effect of changes in size structure and composition. Specifically, the number of large, lightning‐susceptible trees was relatively small at high lightning frequencies. Overall, this study shows that an empirically informed representation of lightning captures the contemporary effects of lightning on forests, indicates that changes in lightning frequency will change forest AGB, species composition, and size structure, and shows that forests can partially acclimate to higher lightning frequency through changes in composition. Thus, more widespread inclusion of the lightning into global ecosystem models would be an important step toward improving simulations of forest responses to global change. 

Abstract Lianas, or woody vines, and trees dominate the canopy of tropical forests and comprise the majority of tropical aboveground carbon storage. These growth forms respond differently to contemporary variation in climate and resource availability, but their responses to future climate change are poorly understood because there are very few predictive ecosystem models representing lianas. We compile a database of liana functional traits (846 species) and use it to parameterize a mechanistic model of liana-tree competition. The substantial difference between liana and tree hydraulic conductivity represents a critical source of inter-growth form variation. Here, we show that lianas are many times more sensitive to drying atmospheric conditions than trees as a result of this trait difference. Further, we use our competition model and projections of tropical hydroclimate based on Representative Concentration Pathway 4.5 to show that lianas are more susceptible to reaching a hydraulic threshold for viability by 2100. 

Abstract. Climatic extreme events are expected to occur more frequently in the future, increasing the likelihood of unprecedented climate extremes (UCEs) or record-breaking events. UCEs, such as extreme heatwaves and droughts, substantially affect ecosystem stability and carbon cycling by increasing plant mortality and delaying ecosystem recovery. Quantitative knowledge of such effects is limited due to the paucity of experiments focusing on extreme climatic events beyond the range of historical experience. Here, we present a road map of how dynamic vegetation demographic models (VDMs) can be used to investigate hypotheses surrounding ecosystem responses to one type of UCE: unprecedented droughts. As a result of nonlinear ecosystem responses to UCEs that are qualitatively different from responses to milder extremes, we consider both biomass loss and recovery rates over time by reporting a time-integrated carbon loss as a result of UCE, relative to the absence of drought. Additionally, we explore how unprecedented droughts in combination with increasing atmospheric CO2 and/or temperature may affect ecosystem stability and carbon cycling. We explored these questions using simulations of pre-drought and post-drought conditions at well-studied forest sites using well-tested models (ED2 and LPJ-GUESS). The severity and patterns of biomass losses differed substantially between models. For example, biomass loss could be sensitive to either drought duration or drought intensity depending on the model approach. This is due to the models having different, but also plausible, representations of processes and interactions, highlighting the complicated variability of UCE impacts that still need to be narrowed down in models. Elevated atmospheric CO2 concentrations (eCO2) alone did not completely buffer the ecosystems from carbon losses during UCEs in the majority of our simulations. Our findings highlight the consequences of differences in process formulations and uncertainties in models, most notably related to availability in plant carbohydrate storage and the diversity of plant hydraulic schemes, in projecting potential ecosystem responses to UCEs. We provide a summary of the current state and role of many model processes that give way to different underlying hypotheses of plant responses to UCEs, reflecting knowledge gaps which in future studies could be tested with targeted field experiments and an iterative modeling–experimental conceptual framework. 

Abstract The strength and persistence of the tropical carbon sink hinges on the long‐term responses of woody growth to climatic variations and increasing CO₂. However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2‐hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual‐level woody growth responses to historical climate variability and increases in atmospheric CO₂(C_a). When forced with historical C_a, ED2.2‐hydro reproduced the magnitude of increases in intercellular CO₂concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing C_abased on model‐data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi‐year mature‐forest CO₂fertilization experiment. In addition, we found that ED2.2‐hydro generally overestimated climatic sensitivity of woody growth, especially for late‐successional plant functional types. The model‐data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree‐level growth sensitivity to C_aand climate against tropical tree‐ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing C_a. More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by C_arise predicted by biosphere models. 

Abstract Lateral carbon transport (LCT), the flux of terrestrial C transported to aquatic ecosystems, displaces carbon (C) across the terrestrial‐aquatic continuum and is on the same order of magnitude as terrestrial net ecosystem production. However, few continental scale C models include LCT or the C‐hydrology linkages necessary for modeling LCT. Those that do exist, borrow processes and conceptual understanding from watershed scale models, assuming that large‐scale and small‐scale drivers of LCT are the same. We develop a conceptual framework of LCT, which focuses on lateral dissolved organic carbon (DOC) transport (LCT‐DOC), and operationalize it with a coupled terrestrial‐aquatic C and hydrology model. After comparing our model LCT‐DOC to previous estimates derived from a summation of landscape scale fluxes for the Contiguous U.S., we use model experiments to partition the importance of LCT‐DOC drivers including total annual precipitation, air temperature, and plant traits, which interact across regional and local scales. We find that climate is the strongest driver of LCT‐DOC, where LCT‐DOC is positively related to precipitation but inversely related to temperature at continental scales. However, the net effect of climate on LCT‐DOC is the product of cross‐scale interactions between climate and vegetation. Plant traits also interact strongly with climate and have a measurable influence on LCT‐DOC, with water use efficiency as the most influential plant trait because it couples terrestrial water and C cycling. We demonstrate that our conceptual framework and relatively simple linked C‐hydrology process model of LCT‐DOC can inform hypotheses and predict LCT‐DOC.