Windthrows control biomass patterns and functional composition of 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. ©2018 The Authors. Global Change Biology Published by John Wiley & Sons Lt

Detection of subpixel treefall gaps with Landsat imagery in Central Amazon forests

Treefall gaps play important roles in both forest dynamics and species diversity, but variability across the full range of gap sizes has not been reported at a regional scale due to the lack of a consistent methodology for their detection. Here we demonstrate the sensitivity of Landsat data for detecting gaps at the subpixel level in the Manaus region, Central Amazon. Spectral mixture analysis (SMA) on treefall gaps was used to map their occurrence across a 3.4×104km2 landscape using the annual change in non-photosynthetic vegetation (δNPV) as the change metric. Thirty randomly selected pixels with a spectral signature of a treefall event (i.e. high δNPV) were surveyed in the field. The most frequent single-pixel gap size detected using Landsat was ~360m2, and the severity of the disturbance (δNPV) exhibited a significant (r2=0.32, p=0.001) correlation with the number of dead trees (>10cm diameter at breast height), enabling quantification of the number of downed trees in each gap. To place the importance of these single-pixel disturbances into a broader context, the cumulative disturbance of these gaps was equivalent to 40% of the calculated deforestation across the Manaus region in 2008. Most detected single-pixel gaps consisted of six to eight downed trees covering an estimated area of 250-900m2. These results highlight the quantitative importance of small blowdowns that have been overlooked in previous satellite remote sensing studies. © 2011 Elsevier Inc

Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon

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. © 2016 by the Ecological Society of America