Vulnerability of Amazon forests to storm-driven tree mortality

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

Large-scale wind disturbances promote tree diversity in a Central Amazon forest

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 m2) 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 m2 ha-1). Highly impacted areas had tree density and basal area as low as 120 trees ha-1 and 14.9 m2 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 m2) 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. © 2014 Marra et al

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

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

Turbulence regimes in the nocturnal roughness sublayer: Interaction with deep convection and tree mortality in the Amazon

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

Mechanical vulnerability and resistance to snapping and uprooting for Central Amazon tree species

High descending winds generated by convective storms are a frequent and a major source of tree mortality disturbance events in the Amazon, affecting forest structure and diversity across a variety of scales, and more frequently observed in western and central portions of the basin. Soil texture in the Central Amazon also varies significantly with elevation along a topographic gradient, with decreasing clay content on plateaus, slopes and valleys respectively. In this study we investigated the critical turning moments (Mcrit - rotational force at the moment of tree failure, an indicator of tree stability or wind resistance) of 60 trees, ranging from 19.0 to 41.1 cm in diameter at breast height (DBH) and located in different topographic positions, and for different species, using a cable-winch load-cell system. Our approach used torque as a measure of tree failure to the point of snapping or uprooting. This approach provides a better understanding of the mechanical forces required to topple trees in tropical forests, and will inform models of wind throw disturbance. Across the topographic positions, size controlled variation in Mcrit was quantified for cardeiro (Scleronema mincranthum (Ducke) Ducke), mata-matá (Eschweilera spp.), and a random selection of trees from 19 other species. Our analysis of Mcrit revealed that tree resistance to failure increased with size (DBH and ABG) and differed among species. No effects of topography or failure mode were found for the species either separately or pooled. For the random species, total variance in Mcrit explained by tree size metrics increased from an R2 of 0.49 for DBH alone, to 0.68 when both DBH and stem fresh wood density (SWD) were included in a multiple regression model. This mechanistic approach allows the comparison of tree vulnerability induced by wind damage across ecosystems, and facilitates the use of forest structural information in ecosystem models that include variable resistance of trees to mortality inducing factors. Our results indicate that observed topographic differences in windthrow vulnerability are likely due to elevational differences in wind velocities, rather than by differences in soil-related factors that might effect Mcrit

Predicting biomass of hyperdiverse and structurally complex Central Amazon forests – a virtual approach using extensive field data

Abstract. Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total AGB above-ground biomass in tropical forests and AGB estimation models are crucial for forest management and conservation. In the Central Amazon, predicting AGB at large spatial-scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this dataset we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape-level across successional gradients. We found that good individual tree model fits do not necessarily translate into good predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from −31 % (pantropical) to +39 %, with RMSE root-mean-square error values of up to 130 Mg ha−1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha−1) when applied over scenarios. Predicting biomass correctly at the landscape-level in complex tropical forests, especially allowing good performance at the margins of data availability for model parametrization, requires the inclusion of predictors related to species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin still depend on the collection of destructive allometry data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.</jats:p