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Water stress regulates land‐atmosphere carbon dioxide (CO₂) exchanges in the tropics; however, its role remains poorly characterized due to the confounding roles of radiation, temperature and canopy dynamics. In particular, uncertainty stems from the relative roles of plant‐available water (supply) and atmospheric water vapor deficit (demand) as mechanistic drivers of photosynthetic carbon (C) uptake variability. Using satellite measurements of gravity, CO₂and fluorescence to constrain a mechanistic carbon‐water cycle model from 2001 to 2018, we found that the interannual variability (IAV) of water stress on photosynthetic C uptake was 52% greater than the combined effects of other factors. Surprisingly, the dominance of water stress on C uptake IAV was greater in the wet tropics (94%) than in the dry tropics (26%). Plant‐available water supply and atmospheric demand both contributed to the IAV of water stress on photosynthetic C uptake across the tropics, but the IAV of demand effects was 21% greater than the IAV of supply effects (33% greater in the wet tropics and 6% greater in the dry tropics). We found that the IAV of water stress on C uptake was 24% greater than the IAV of the combination of other factors in the net land‐atmosphere C sink in the whole tropics, 26% greater in the wet tropics, and 7% greater in the dry tropics. Given the recent trends in tropical precipitation and atmospheric humidity, our findings indicate that water stress——from both supply and demand——will likely dominate the climate response of land C sink across tropical ecosystems in the coming decades.

 

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high-dimensionality of these data. 2. We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source R package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least seven years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g., canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community. 

Hurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. Our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late‐successional species, but increase the dominance of palm species. Warmer climates with increased CO₂would have little effect on the functional‐type composition but increase the aboveground biomass. However, the predicted climate and CO₂fertilization effects would not compensate for the biomass loss due to more frequent severe‐hurricane disturbances.

 

Understanding the effects of intensification of Amazon basin hydrological cycling—manifest as increasingly frequent floods and droughts—on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change, including forest “tipping points”. Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001–2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015–2016 El Niño drought and La Niña 2008–2009 wet events. We found that the forest responded strongly to El Niño‐Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration (ET) leading to large increases in sensible heat fluxes (H). PartitioningETby an approach that assumes transpiration (T) is proportional to photosynthesis, we found that water stress‐induced reductions in canopy conductance (G_s) droveTdeclines partly compensated by higher evaporation (E). By contrast, the abnormally wet La Niña period gave higherTand lowerE, with little change in seasonalET. Both El Niño‐Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet‐season leaf area index. However, only during El Niño 2015–2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown ofG_sand significant leaf shedding). Drought‐reducedTandG_s, higherHandE, amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post‐drought. Identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin‐scale threshold‐crossing changes in forest energy and water cycling, leading to slow‐down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.

 

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year −1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year −1 . Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement’s bold targets. 

In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size–frequency distributions over an old‐growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10 m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot‐ (97.4%; 3% bias) and tree‐height‐classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar‐derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot‐level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot‐ and tree‐height‐classes level (−5.2% and 2.1%). Our ITC method provides a powerful tool for tree‐ to landscape‐level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area‐based approaches that require local calibration using a large number of inventory plots.