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Despite experimental and observational studies demonstrating that biodiversity enhances primary productivity, the best metric for predicting productivity at broad geographic extents—functional trait diversity, phylogenetic diversity, or species richness—remains unknown. Using >1.8 million tree measurements from across eastern US forests, we quantified relationships among functional trait diversity, phylogenetic diversity, species richness, and productivity. Surprisingly, functional trait and phylogenetic diversity explained little variation in productivity that could not be explained by tree species richness. This result was consistent across the entire eastern United States, within ecoprovinces, and within data subsets that controlled for biomass or stand age. Metrics of functional trait and phylogenetic diversity that were independent of species richness were negatively correlated with productivity. This last result suggests that processes that determine species sorting and packing are likely important for the relationships between productivity and biodiversity. This result also demonstrates the potential confusion that can arise when interdependencies among different diversity metrics are ignored. Our findings show the value of species richness as a predictive tool and highlight gaps in knowledge about linkages between functional diversity and ecosystem functioning. 

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage—e.g., CO₂fertilization—and negative drivers that decrease it—e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance–regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO₂fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress. 

Climate change is intensifying the fire regime across Siberia, with the potential to alter carbon combustion and post‐fire carbon re‐accumulation trajectories. Few field‐based estimates of fire severity (e.g., carbon combustion and tree mortality) exist in Siberian larch forests (Larixspp.), which limits our ability to project how an intensified fire regime will affect regional and global climate feedbacks. Here, we present field‐based estimates of fire‐induced tree mortality and carbon loss in eastern Siberian larch forests. Our results suggest that fires in this region result in high tree mortality (means of 83% and 76% at Arctic and subarctic sites, respectively). In both absolute and relative terms, aboveground carbon loss following fire is higher in Siberian larch forests than in North America, but belowground carbon loss is considerably lower. This suggests fundamental differences in wildfire behavior and carbon dynamics between dominant vegetation types across the boreal biome. 

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. 

Understanding tropical forest dynamics and planning for their sustainable management require efficient, yet accurate, predictions of the joint dynamics of hundreds of tree species. With increasing information on tropical tree life histories, our predictive understanding is no longer limited by species data but by the ability of existing models to make use of it. Using a demographic forest model, we show that the basal area and compositional changes during forest succession in a neotropical forest can be accurately predicted by representing tropical tree diversity (hundreds of species) with only five functional groups spanning two essential trade-offs—the growth-survival and stature-recruitment trade-offs. This data-driven modeling framework substantially improves our ability to predict consequences of anthropogenic impacts on tropical forests. 

Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.