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The authors link the frequency of convective storms in the Amazon basin to the density of large forest mortality events (windthrows) and project an increase in forest disturbance from these dynamics due to climate warming over this century.

Abstract This study identifies 38 cases of windthrows in the Amazonia to explore the relationship between windthrows and the characteristics (storm passing time, cloud top temperature, and maximum precipitation) of mesoscale convective systems (MCSs) that produced them. Most of windthrow cases in this study occurred in August and September. The storm passing time is positively correlated with the size of windthrows. MCSs with colder cloud top temperature (with a mean at 206 K)—indicating deeper convection—resulted in large windthrows, while those with warm cloud top (with a mean above 230 K) resulted in relatively small windthrows except for windthrows in the western Amazonia. No significant relationship is found between maximum precipitation intensity and the area of windthrows.

With current observations and future projections of more intense and frequent droughts in the tropics, understanding the impact that extensive dry periods may have on tree and ecosystem-level transpiration and concurrent carbon uptake has become increasingly important. Here, we investigate paired soil and tree water extraction dynamics in an old-growth upland forest in central Amazonia during the 2018 dry season. Tree water use was assessed via radial patterns of sap flow in eight dominant canopy trees, each a different species with a range in diameter, height, and wood density. Paired multi-sensor soil moisture probes used to quantify volumetric water content dynamics and soil water extraction within the upper 100 cm were installed adjacent to six of those trees. To link depth-specific water extraction patterns to root distribution, fine root biomass was assessed through the soil profile to 235 cm. To scale tree water use to the plot level (stand transpiration), basal area was measured for all trees within a 5 m radius around each soil moisture probe. The sensitivity of tree transpiration to reduced precipitation varied by tree, with some increasing and some decreasing in water use during the dry period. Tree-level water use scaled with sapwood area, from 11 to 190 L per day. Stand level water use, based on multiple plots encompassing sap flow and adjacent trees, varied from ∼1.7 to 3.3 mm per day, increasing linearly with plot basal area. Soil water extraction was dependent on root biomass, which was dense at the surface (i.e., 45% in the upper 5 cm) and declined dramatically with depth. As the dry season progressed and the upper soil dried, soil water extraction shifted to deeper levels and model projections suggest that much of the water used during the month-long dry-down could be extracted from the upper 2–3 m. Results indicate variation in rates of soil water extraction across the research area and, temporally, through the soil profile. These results provide key information on whole-tree contributions to transpiration by canopy trees as water availability changes. In addition, information on simultaneous stand level dynamics of soil water extraction that can inform mechanistic models that project tropical forest response to drought.

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

The dynamics of forest recovery after windthrows (i.e., broken or uprooted trees by wind) are poorly understood in tropical forests. The Northwestern Amazon (NWA) is characterized by a higher occurrence of windthrows, greater rainfall, and higher annual tree mortality rates (~2%) than the Central Amazon (CA). We combined forest inventory data from three sites in the Iquitos region of Peru, with recovery periods spanning 2, 12, and 22 years following windthrow events. Study sites and sampling areas were selected by assessing the windthrow severity using remote sensing. At each site, we recorded all trees with a diameter at breast height (DBH) ≥ 10 cm along transects, capturing the range of windthrow severity from old-growth to highly disturbed (mortality > 60%) forest. Across all damage classes, tree density and basal area recovered to >90% of the old-growth values after 20 years. Aboveground biomass (AGB) in old-growth forest was 380 (±156) Mg ha−1. In extremely disturbed areas, AGB was still reduced to 163 (±68) Mg ha−1 after 2 years and 323 (± 139) Mg ha−1 after 12 years. This recovery rate is ~50% faster than that reported for Central Amazon forests. The faster recovery of forest structure in our study region may be a function of its higher productivity and adaptability to more frequent and severe windthrows. These varying rates of recovery highlight the importance of extreme wind and rainfall on shaping gradients of forest structure in the Amazon, and the different vulnerabilities of these forests to natural disturbances whose severity and frequency are being altered by climate change.

Current climate change scenarios indicate warmer temperatures and the potential for more extreme droughts in the tropics, such that a mechanistic understanding of the water cycle from individual trees to landscapes is needed to adequately predict future changes in forest structure and function. In this study, we contrasted physiological responses of tropical trees during a normal dry season with the extreme dry season due to the 2015–2016 El Niño-Southern Oscillation (ENSO) event. We quantified high resolution temporal dynamics of sap velocity (Vs), stomatal conductance (gs) and leaf water potential (ΨL) of multiple canopy trees, and their correlations with leaf temperature (Tleaf) and environmental conditions [direct solar radiation, air temperature (Tair) and vapor pressure deficit (VPD)]. The experiment leveraged canopy access towers to measure adjacent trees at the ZF2 and Tapajós tropical forest research (near the cities of Manaus and Santarém). The temporal difference between the peak of gs (late morning) and the peak of VPD (early afternoon) is one of the major regulators of sap velocity hysteresis patterns. Sap velocity displayed species-specific diurnal hysteresis patterns reflected by changes in Tleaf. In the morning, Tleaf and sap velocity displayed a sigmoidal relationship. In the afternoon, stomatal conductance declined as Tleaf approached a daily peak, allowing ΨL to begin recovery, while sap velocity declined with an exponential relationship with Tleaf. In Manaus, hysteresis indices of the variables Tleaf-Tair and ΨL-Tleaf were calculated for different species and a significant difference (p < 0.01, α = 0.05) was observed when the 2015 dry season (ENSO period) was compared with the 2017 dry season (“control scenario”). In some days during the 2015 ENSO event, Tleaf approached 40°C for all studied species and the differences between Tleaf and Tair reached as high at 8°C (average difference: 1.65 ± 1.07°C). Generally, Tleaf was higher than Tair during the middle morning to early afternoon, and lower than Tair during the early morning, late afternoon and night. Our results support the hypothesis that partial stomatal closure allows for a recovery in ΨL during the afternoon period giving an observed counterclockwise hysteresis pattern between ΨL and Tleaf.

Amazonian swamp forests remove large amounts of carbon dioxide (CO2) but produce methane (CH4). Both are important greenhouse gases (GHG). Drought and cultivation cut the CH4 emissions but may release CO2. Varying oxygen content in nitrogen-rich soil produces nitrous oxide (N2O), which is the third most important GHG. Despite the potentially tremendous changes, GHG emissions from wetland soils under different land uses and environmental conditions have rarely been compared in the Amazon. We measured environmental characteristics, and CO2, CH4 and N2O emissions from the soil surface with manual opaque chambers in three sites near Iquitos, Peru from September 2019 to March 2020: a pristine peat swamp forest, a young forest and a slash-and-burn manioc field. The manioc field showed moderate soil respiration and N2O emission. The peat swamp forests under slight water table drawdown emitted large amounts of CO2 and CH4. A heavy post-drought shower created a hot moment of N2O in the pristine swamp forest, likely produced by nitrifiers. All in all, even small changes in soil moisture can create hot moments of GHG emissions from Amazonian wetland soils, and should therefore be carefully monitored.

We investigated the influence of seasonality and proximity to the forest canopy on nocturnal turbulence regimes in the roughness sublayer of a Central Amazon forest. Since convective systems of different scales are common in this region, we also analyzed the effect of extreme wind gusts (propagated from convective downdrafts) on the organization of the turbulence regimes, and their potential to cause the mortality of canopy trees. Our data include high-frequency winds, temperature and ozone concentration at different heights during the dry and wet seasons of 2014. In addition, we used critical wind-speed data derived from a tree-winching experiment and a modeling study conducted in the same study site. Two different turbulence regimes were identified at three heights above the canopy: a weakly stable (WS) and a very stable regime (VS). The threshold wind speeds that mark the transition between turbulence regimes were larger during the dry season and increased as a function of the height above the canopy. The turbulent fluxes of sensible heat and momentum during the WS accounted for 88% of the entire nighttime flux. Downdrafts occurred only in the WS and favored a fully coupled state of wind flow along the canopy profile. The destructive potential of winds was four times higher than on nights without downdrafts.

Topography affects the patterns of forest disturbance produced by tropical cyclones. It determines the degree of exposure of a surface and can alter wind characteristics. Whether multispectral remote sensing data can sense the effect of topography on disturbance is a question that deserves attention given the multi-scale spatial coverage of these data and the projected increase in intensity of the strongest cyclones. Here, multispectral satellite data, topographic maps and cyclone surface wind data were used to study the patterns of disturbance in an Australian rainforest with complex mountainous terrain produced by tropical cyclone Yasi (2011). The cyclone surface wind data (H*wind) was produced by the Hurricane Research Division of the National Oceanic and Atmospheric Administration (HRD/NOAA), and this was the first time that this data was produced for a cyclone outside of United States territory. A disturbance map was obtained by applying spectral mixture analyses on satellite data and presented a significant correlation with field-measured tree mortality. Our results showed that, consistent with cyclones in the southern hemisphere, multispectral data revealed that forest disturbance was higher on the left side of the cyclone track. The highest level of forest disturbance occurred in forests along the path of the cyclone track (±30°). Levels of forest disturbance decreased with decreasing slope and with an aspect facing off the track of the cyclone or away from the dominant surface winds. An increase in disturbance with surface elevation was also observed. However, areas affected by the same wind intensity presented increased levels of disturbance with increasing elevation suggesting that complex terrain interactions act to speed up wind at higher elevations. Yasi produced an important offset to Australia’s forest carbon sink in 2010. We concluded that multispectral data was sensitive to the main effects of complex topography on disturbance patterns. High resolution cyclone wind surface data are needed in order to quantify the effects of topographic accelerations on cyclone related forest disturbances.

This study identifies 38 cases of windthrows in the Amazonia to explore the relationship between windthrows and the characteristics (storm passing time, cloud top temperature, and maximum precipitation) of mesoscale convective systems (MCSs) that produced them. Most of windthrow cases in this study occurred in August and September. The storm passing time is positively correlated with the size of windthrows. MCSs with colder cloud top temperature (with a mean at 206 K)—indicating deeper convection—resulted in large windthrows, while those with warm cloud top (with a mean above 230 K) resulted in relatively small windthrows except for windthrows in the western Amazonia. No significant relationship is found between maximum precipitation intensity and the area of windthrows.

Tree mortality is a key driver of forest community composition and carbon dynamics. Strong winds associated with severe convective storms are dominant natural drivers of tree mortality in the Amazon. Why forests vary with respect to their vulnerability to wind events and how the predicted increase in storm events might affect forest ecosystems within the Amazon are not well understood. We found that windthrows are common in the Amazon region extending from northwest (Peru, Colombia, Venezuela, and west Brazil) to central Brazil, with the highest occurrence of windthrows in the northwest Amazon. More frequent winds, produced by more frequent severe convective systems, in combination with well-known processes that limit the anchoring of trees in the soil, help to explain the higher vulnerability of the northwest Amazon forests to winds. Projected increases in the frequency and intensity of convective storms in the Amazon have the potential to increase wind-related tree mortality. A forest demographic model calibrated for the northwestern and the central Amazon showed that northwestern forests are more resilient to increased wind-related tree mortality than forests in the central Amazon. Our study emphasizes the importance of including wind-related tree mortality in model simulations for reliable predictions of the future of tropical forests and their effects on the Earth’ system.

Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, the linkage between windthrows at the surface and convective storms in the atmosphere remains unclear. In addition, the current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. Here we find an empirical relationship that maps convective available potential energy, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, our model projects a 51 ± 20% increase in the area favorable to extreme storms, and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon cycle dynamics under climate change.

Windthrows are a recurrent disturbance in Amazonia and are an important driver of forest dynamics and carbon storage. In this study, we present for the first time the seasonal and interannual variability of windthrows, focusing on Central Amazonia, and discuss the potential meteorological factors associated with this variability. Landsat images over the 1998–2010 time period were used to detect the occurrence of windthrows, which were identified based on their spectral characteristics and shape. Here, we found that windthrows occurred every year but were more frequent between September and February. Organized convective activity associated with multicell storms embedded in mesoscale convective systems, such as northerly squall lines (that move from northeast to southwest) and southerly squall lines (that move from southwest to northeast) can cause windthrows. We also found that southerly squall lines occurred more frequently than their previously reported ~50 year interval. At the interannual scale, we did not find an association between El Niño-Southern Oscillation (ENSO) and windthrows.

Canopy gaps created by wind-throw events, or blowdowns, create a complex mosaic of forest patches varying in disturbance intensity and recovery in the Central Amazon. Using field and remote sensing data, we investigated the short-term (four-year) effects of large (>2000 m(2)) blowdown gaps created during a single storm event in January 2005 near Manaus, Brazil, to study (i) how forest structure and composition vary with disturbance gradients and (ii) whether tree diversity is promoted by niche differentiation related to wind-throw events at the landscape scale. In the forest area affected by the blowdown, tree mortality ranged from 0 to 70%, and was highest on plateaus and slopes. Less impacted areas in the region affected by the blowdown had overlapping characteristics with a nearby unaffected forest in tree density (583 ± 46 trees ha(-1)) (mean ± 99% Confidence Interval) and basal area (26.7 ± 2.4 m(2) ha(-1)). Highly impacted areas had tree density and basal area as low as 120 trees ha(-1) and 14.9 m(2) ha(-1), respectively. In general, these structural measures correlated negatively with an index of tree mortality intensity derived from satellite imagery. Four years after the blowdown event, differences in size-distribution, fraction of resprouters, floristic composition and species diversity still correlated with disturbance measures such as tree mortality and gap size. Our results suggest that the gradients of wind disturbance intensity encompassed in large blowdown gaps (>2000 m(2)) promote tree diversity. Specialists for particular disturbance intensities existed along the entire gradient. The existence of species or genera taking an intermediate position between undisturbed and gap specialists led to a peak of rarefied richness and diversity at intermediate disturbance levels. A diverse set of species differing widely in requirements and recruitment strategies forms the initial post-disturbance cohort, thus lending a high resilience towards wind disturbances at the community level.

Earthquakes can produce significant tree mortality, and consequently affect regional carbon dynamics. Unfortunately, detailed studies quantifying the influence of earthquake on forest mortality are currently rare. The committed forest biomass carbon loss associated with the 2008 Wenchuan earthquake in China is assessed by a synthetic approach in this study that integrated field investigation, remote sensing analysis, empirical models and Monte Carlo simulation. The newly developed approach significantly improved the forest disturbance evaluation by quantitatively defining the earthquake impact boundary and detailed field survey to validate the mortality models. Based on our approach, a total biomass carbon of 10.9 Tg∙C was lost in Wenchuan earthquake, which offset 0.23% of the living biomass carbon stock in Chinese forests. Tree mortality was highly clustered at epicenter, and declined rapidly with distance away from the fault zone. It is suggested that earthquakes represent a significant driver to forest carbon dynamics, and the earthquake-induced biomass carbon loss should be included in estimating forest carbon budgets.

A significant fraction of anthropogenic CO _2 emissions is assimilated by tropical forests and stored as biomass, slowing the accumulation of CO _2 in the atmosphere. Because different plant tissues have different functional roles and turnover times, predictions of carbon balance of tropical forests depend on how earth system models (ESMs) represent the dynamic allocation of productivity to different tree compartments. This study shows that observed allocation of productivity, biomass, and turnover times of main tree compartments (leaves, wood, and roots) are not accurately represented in Coupled Model Intercomparison Project Phase 5 ESMs. In particular, observations indicate that biomass saturates with increasing productivity. In contrast, most models predict continuous increases in biomass with increases in productivity. This bias may lead to an over-prediction of carbon uptake in response to CO _2 or climate-driven changes in productivity. Compartment-specific productivity and biomass are useful benchmarks to assess terrestrial ecosystem model performance. Improvements in the predicted allocation patterns and turnover times by ESMs will reduce uncertainties in climate predictions.

With current observations and future projections of more intense and frequent droughts in the tropics, understanding the impact that extensive dry periods may have on tree and ecosystem-level transpiration and concurrent carbon uptake has become increasingly important. Here, we investigate paired soil and tree water extraction dynamics in an old-growth upland forest in central Amazonia during the 2018 dry season. Tree water use was assessed via radial patterns of sap flow in eight dominant canopy trees, each a different species with a range in diameter, height, and wood density. Paired multi-sensor soil moisture probes used to quantify volumetric water content dynamics and soil water extraction within the upper 100 cm were installed adjacent to six of those trees. To link depth-specific water extraction patterns to root distribution, fine root biomass was assessed through the soil profile to 235 cm. To scale tree water use to the plot level (stand transpiration), basal area was measured for all trees within a 5 m radius around each soil moisture probe. The sensitivity of tree transpiration to reduced precipitation varied by tree, with some increasing and some decreasing in water use during the dry period. Tree-level water use scaled with sapwood area, from 11 to 190 L per day. Stand level water use, based on multiple plots encompassing sap flow and adjacent trees, varied from ∼1.7 to 3.3 mm per day, increasing linearly with plot basal area. Soil water extraction was dependent on root biomass, which was dense at the surface (i.e., 45% in the upper 5 cm) and declined dramatically with depth. As the dry season progressed and the upper soil dried, soil water extraction shifted to deeper levels and model projections suggest that much of the water used during the month-long dry-down could be extracted from the upper 2-3 m. Results indicate variation in rates of soil water extraction across the research area and, temporally, through the soil profile. These results provide key information on whole-tree contributions to transpiration by canopy trees as water availability changes. In addition, information on simultaneous stand level dynamics of soil water extraction that can inform mechanistic models that project tropical forest response to drought.

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

Debate continues over the adequacy of existing field plots to sufficiently capture Amazon forest dynamics to estimate regional forest carbon balance. Tree mortality dynamics are particularly uncertain due to the difficulty of observing large, infrequent disturbances. A recent paper (Chambers et al 2013 Proc. Natl Acad. Sci. 110 3949–54) reported that Central Amazon plots missed 9–17% of tree mortality, and here we address ‘why’ by elucidating two distinct mortality components: (1) variation in annual landscape-scale average mortality and (2) the frequency distribution of the size of clustered mortality events. Using a stochastic-empirical tree growth model we show that a power law distribution of event size (based on merged plot and satellite data) is required to generate spatial clustering of mortality that is consistent with forest gap observations. We conclude that existing plots do not sufficiently capture losses because their placement, size, and longevity assume spatially random mortality, while mortality is actually distributed among differently sized events (clusters of dead trees) that determine the spatial structure of forest canopies.

The dynamics of forest recovery after windthrows (i.e., broken or uprooted trees by wind) are poorly understood in tropical forests. The Northwestern Amazon (NWA) is characterized by a higher occurrence of windthrows, greater rainfall, and higher annual tree mortality rates (~2%) than the Central Amazon (CA). We combined forest inventory data from three sites in the Iquitos region of Peru, with recovery periods spanning 2, 12, and 22 years following windthrow events. Study sites and sampling areas were selected by assessing the windthrow severity using remote sensing. At each site, we recorded all trees with a diameter at breast height (DBH) ≥ 10 cm along transects, capturing the range of windthrow severity from old-growth to highly disturbed (mortality > 60%) forest. Across all damage classes, tree density and basal area recovered to >90% of the old-growth values after 20 years. Aboveground biomass (AGB) in old-growth forest was 380 (±156) Mg ha−1 . In extremely disturbed areas, AGB was still reduced to 163 (±68) Mg ha−1 after 2 years and 323 (± 139) Mg ha−1 after 12 years. This recovery rate is ~50% faster than that reported for Central Amazon forests. The faster recovery of forest structure in our study region may be a function of its higher productivity and adaptability to more frequent and severe windthrows. These varying rates of recovery highlight the importance of extreme wind and rainfall on shaping gradients of forest structure in the Amazon, and the different vulnerabilities of these forests to natural disturbances whose severity and frequency are being altered by climate change.

A mechanistic understanding of how tropical-tree mortality responds to climate variation is urgently needed to predict how tropical-forest carbon pools will respond to anthropogenic global change, which is altering the frequency and intensity of storms, droughts, and other climate extremes in tropical forests. We used 5 years of approximately monthly drone-acquired RGB (red-green-blue) imagery for 50ĝ€¯ha of mature tropical forest on Barro Colorado Island, Panama, to quantify spatial structure; temporal variation; and climate correlates of canopy disturbances, i.e., sudden and major drops in canopy height due to treefalls, branchfalls, or the collapse of standing dead trees. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though wind speeds were lower in the wet season. The strongest correlate of monthly variation in canopy disturbance rates was the frequency of extreme rainfall events. The size distribution of canopy disturbances was best fit by a Weibull function and was close to a power function for sizes above 25 m2. Treefalls accounted for 74 % of the total area and 52 % of the total number of canopy disturbances in treefalls and branchfalls combined. We hypothesize that extremely high rainfall is a good predictor because it is an indicator of storms having high wind speeds, as well as saturated soils that increase uprooting risk. These results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions over large areas, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers. Further insights could be gained by integrating these canopy observations with high-frequency measurements of wind speed and soil moisture in mechanistic models to better evaluate proximate drivers and with focal tree observations to quantify the links to tree mortality and woody turnover.

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. We used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

There is large uncertainty whether Amazon forests will remain a carbon sink as atmospheric CO2 increases. Hence, we simulated an old-growth tropical forest using six versions of four terrestrial models differing in scale of vegetation structure and representation of biogeochemical (BGC) cycling, all driven with CO2 forcing from the preindustrial period to 2100. The models were benchmarked against tree inventory and eddy covariance data from a Brazilian site for present-day predictions. All models predicted positive vegetation growth that outpaced mortality, leading to continual increases in present-day biomass accumulation. Notably, the two vegetation demographic models (VDMs) (ED2 and ELM-FATES) always predicted positive stem diameter growth in all size classes. The field data, however, indicated that a quarter of canopy trees didn't grow over the 15-year period, and while high interannual variation existed, biomass change was near neutral. With a doubling of CO2, three of the four models predicted an appreciable biomass sink (0.77 to 1.24 Mg ha−1 year−1). ELMv1-ECA, the only model used here that includes phosphorus constraints, predicted the lowest biomass sink relative to initial biomass stocks (+21%), lower than the other BGC model, CLM5 (+48%). Models projections differed primarily through variations in nutrient constraints, then carbon allocation, initial biomass, and density-dependent mortality. The VDM's performance was similar or better than the BGC models run in carbon-only mode, suggesting that nutrient competition in VDMs will improve predictions. We demonstrate that VDMs are comparable to nondemographic (i.e., “big-leaf”) models but also include finer scale demography and competition that can be evaluated against field observations.

Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

Current climate change scenarios indicate warmer temperatures and the potential for more extreme droughts in the tropics, such that a mechanistic understanding of the water cycle from individual trees to landscapes is needed to adequately predict future changes in forest structure and function. In this study, we contrasted physiological responses of tropical trees during a normal dry season with the extreme dry season due to the 2015-2016 El Niño-Southern Oscillation (ENSO) event. We quantified high resolution temporal dynamics of sap velocity (Vs), stomatal conductance (gs) and leaf water potential (ΨL) of multiple canopy trees, and their correlations with leaf temperature (Tleaf) and environmental conditions [direct solar radiation, air temperature (Tair) and vapor pressure deficit (VPD)]. The experiment leveraged canopy access towers to measure adjacent trees at the ZF2 and Tapajós tropical forest research (near the cities of Manaus and Santarém). The temporal difference between the peak of gs (late morning) and the peak of VPD (early afternoon) is one of the major regulators of sap velocity hysteresis patterns. Sap velocity displayed species-specific diurnal hysteresis patterns reflected by changes in Tleaf. In the morning, Tleaf and sap velocity displayed a sigmoidal relationship. In the afternoon, stomatal conductance declined as Tleaf approached a daily peak, allowing ΨL to begin recovery, while sap velocity declined with an exponential relationship with Tleaf. In Manaus, hysteresis indices of the variables Tleaf-Tair and ΨL-Tleaf were calculated for different species and a significant difference (p < 0.01, α = 0.05) was observed when the 2015 dry season (ENSO period) was compared with the 2017 dry season ("control scenario"). In some days during the 2015 ENSO event, Tleaf approached 40°C for all studied species and the differences between Tleaf and Tair reached as high at 8°C (average difference: 1.65 ± 1.07°C). Generally, Tleaf was higher than Tair during the middle morning to early afternoon, and lower than Tair during the early morning, late afternoon and night. Our results support the hypothesis that partial stomatal closure allows for a recovery in ΨL during the afternoon period giving an observed counterclockwise hysteresis pattern between ΨL and Tleaf.

Amazon forests account for ~25% of global land biomass and tropical tree species. In these forests, windthrows (i.e., snapped and uprooted trees) are a major natural disturbance, but the rates and mechanisms of recovery are not known. To provide a predictive framework for understanding the effects of windthrows on forest structure and functional composition (DBH ≥10 cm), we quantified biomass recovery as a function of windthrow severity (i.e., fraction of windthrow tree mortality on Landsat pixels, ranging from 0%-70%) and time since disturbance for terra-firme forests in the Central Amazon. Forest monitoring allowed insights into the processes and mechanisms driving the net biomass change (i.e., increment minus loss) and shifts in functional composition. Windthrown areas recovering for between 4-27 years had biomass stocks as low as 65.2-91.7 Mg/ha or 23%-38% of those in nearby undisturbed forests (~255.6 Mg/ha, all sites). Even low windthrow severities (4%-20% tree mortality) caused decadal changes in biomass stocks and structure. While rates of biomass increment in recovering vegetation were nearly double (6.3 ± 1.4 Mg ha-1  year-1 ) those of undisturbed forests (~3.7 Mg ha-1  year-1 ), biomass loss due to post-windthrow mortality was high (up to -7.5 ± 8.7 Mg ha-1  year-1 , 8.5 years since disturbance) and unpredictable. Consequently, recovery to 90% of "pre-disturbance" biomass takes up to 40 years. Resprouting trees contributed little to biomass recovery. Instead, light-demanding, low-density genera (e.g., Cecropia, Inga, Miconia, Pourouma, Tachigali, and Tapirira) were favored, resulting in substantial post-windthrow species turnover. Shifts in functional composition demonstrate that windthrows affect the resilience of live tree biomass by favoring soft-wooded species with shorter life spans that are more vulnerable to future disturbances. As the time required for forests to recover biomass is likely similar to the recurrence interval of windthrows triggering succession, windthrows have the potential to control landscape biomass/carbon dynamics and functional composition in Amazon forests.

Amazon forests account for ~25% of global land biomass and tropical tree species. In these forests, windthrows (i.e., snapped and uprooted trees) are a major natural disturbance, but the rates and mechanisms of recovery are not known. To provide a predictive framework for understanding the effects of windthrows on forest structure and functional composition (DBH ≥10 cm), we quantified biomass recovery as a function of windthrow severity (i.e., fraction of windthrow tree mortality on Landsat pixels, ranging from 0%-70%) and time since disturbance for terra-firme forests in the Central Amazon. Forest monitoring allowed insights into the processes and mechanisms driving the net biomass change (i.e., increment minus loss) and shifts in functional composition. Windthrown areas recovering for between 4-27 years had biomass stocks as low as 65.2-91.7 Mg/ha or 23%-38% of those in nearby undisturbed forests (~255.6 Mg/ha, all sites). Even low windthrow severities (4%-20% tree mortality) caused decadal changes in biomass stocks and structure. While rates of biomass increment in recovering vegetation were nearly double (6.3 ± 1.4 Mg ha- 1  year- 1 ) those of undisturbed forests (~3.7 Mg ha- 1  year- 1 ), biomass loss due to post-windthrow mortality was high (up to -7.5 ± 8.7 Mg ha- 1  year- 1 , 8.5 years since disturbance) and unpredictable. Consequently, recovery to 90% of "pre-disturbance" biomass takes up to 40 years. Resprouting trees contributed little to biomass recovery. Instead, light-demanding, low-density genera (e.g., Cecropia, Inga, Miconia, Pourouma, Tachigali, and Tapirira) were favored, resulting in substantial post-windthrow species turnover. Shifts in functional composition demonstrate that windthrows affect the resilience of live tree biomass by favoring soft-wooded species with shorter life spans that are more vulnerable to future disturbances. As the time required for forests to recover biomass is likely similar to the recurrence interval of windthrows triggering succession, windthrows have the potential to control landscape biomass/carbon dynamics and functional composition in Amazon forests.

Tree mortality is a key driver of forest community composition and carbon dynamics. Strong winds associated with severe convective storms are dominant natural drivers of tree mortality in the Amazon. Why forests vary with respect to their vulnerability to wind events and how the predicted increase in storm events might affect forest ecosystems within the Amazon are not well understood. We found that windthrows are common in the Amazon region extending from northwest (Peru, Colombia, Venezuela, and west Brazil) to central Brazil, with the highest occurrence of windthrows in the northwest Amazon. More frequent winds, produced by more frequent severe convective systems, in combination with well-known processes that limit the anchoring of trees in the soil, help to explain the higher vulnerability of the northwest Amazon forests to winds. Projected increases in the frequency and intensity of convective storms in the Amazon have the potential to increase wind-related tree mortality. A forest demographic model calibrated for the northwestern and the central Amazon showed that northwestern forests are more resilient to increased wind-related tree mortality than forests in the central Amazon. Our study emphasizes the importance of including wind-related tree mortality in model simulations for reliable predictions of the future of tropical forests and their effects on the Earth' system.

Tropical forests absorb large amounts of atmospheric CO2 through photosynthesis but elevated temperatures suppress this absorption and promote monoterpene emissions. Using 13 CO2 labeling, here we show that monoterpene emissions from tropical leaves derive from recent photosynthesis and demonstrate distinct temperature optima for five groups (Groups 1-5), potentially corresponding to different enzymatic temperature-dependent reaction mechanisms within β-ocimene synthases. As diurnal and seasonal leaf temperatures increased during the Amazonian 2015 El Niño event, leaf and landscape monoterpene emissions showed strong linear enrichments of β-ocimenes (+4.4% °C-1 ) at the expense of other monoterpene isomers. The observed inverse temperature response of α-pinene (-0.8% °C-1 ), typically assumed to be the dominant monoterpene with moderate reactivity, was not accurately simulated by current global emission models. Given that β-ocimenes are highly reactive with respect to both atmospheric and biological oxidants, the results suggest that highly reactive β-ocimenes may play important roles in the thermotolerance of photosynthesis by functioning as effective antioxidants within plants and as efficient atmospheric precursors of secondary organic aerosols. Thus, monoterpene composition may represent a new sensitive 'thermometer' of leaf oxidative stress and atmospheric reactivity, and therefore a new tool in future studies of warming impacts on tropical biosphere-atmosphere carbon-cycle feedbacks.

Windthrows are a recurrent disturbance in Amazonia and are an important driver of forest dynamics and carbon storage. In this study, we present for the first time the seasonal and interannual variability of windthrows, focusing on Central Amazonia, and discuss the potential meteorological factors associated with this variability. Landsat images over the 1998-2010 time period were used to detect the occurrence of windthrows, which were identified based on their spectral characteristics and shape. Here, we found that windthrows occurred every year but were more frequent between September and February. Organized convective activity associated with multicell storms embedded in mesoscale convective systems, such as northerly squall lines (that move from northeast to southwest) and southerly squall lines (that move from southwest to northeast) can cause windthrows. We also found that southerly squall lines occurred more frequently than their previously reported ~50 year interval. At the interannual scale, we did not find an association between El Niño-Southern Oscillation (ENSO) and windthrows.

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.