Impacts of a severe storm on carbon accumulation in coarse woody debris within a secondary Atlantic Forest fragment in Brazil.

The alarming increase in extreme weather events, such as severe storms with torrential rain and strong winds, is a direct result of climate change. These events have led to discernible shifts in forest structure and the carbon cycle, primarily driven by a surge in tree mortality. However, the impacts caused by these severe storms on the production and carbon increment from coarse woody debris (CWD) are still poorly understood, especially in the Brazilian Atlantic Forest. Thus, the goal proposed by the study was to quantify the CWD volume, necromass, and carbon stock before and after the occurrence of a severe storm and to determine the importance of spatial, structural, and qualitative variables of trees in the CWD carbon increment. The increase in carbon by the storm was 2.01 MgC ha-1, with a higher concentration in the CWD less decomposed and smaller diameter class. The forest fragment plots showed distinct increments (0.05-0.35 MgC), being influenced by spatial (elevation, declivity, and slope angle) structural (basal area) and qualitative factors (trunk quality and tree health), intrinsic to the forest. Thus, it is concluded that severe storms cause a large increase in carbon in CWD, making it essential to understand the susceptibility of forests to the action of intense rains and strong winds to model and monitor the future impacts of these extreme weather events on Atlantic Forest and other tropical forests in the world.

Physical and chemical properties of Coarse Woody Debris submitted to the natural process of decomposition in a Secondary Atlantic Forest Fragment in Brazil.

Coarse Woody Debris (CWDs) are constantly exposed to the natural decomposition process of wood, which can lead to a change in its physical-chemical properties. However, these changes have not yet been fully elucidated, requiring further studies to help to understand the effect of this process on CWDs degradation. Thus, the objectives of this study were: (i) verify if the decomposition affects the physical-chemical properties of the CWDs; (ii) verify if the structural chemical composition of the CWDs is altered as a function of decomposition, using immediate chemical and thermogravimetric analysis. Wood samples were collected from the CWDs to carry out these analyses, considering pieces with diameters ≥ 5 cm separated into 4 decay classes. The results indicated that the average apparent density decreased as a function of the increase of CWDs decomposition (0.62-0.37 g cm-3). The averages contents of Carbon and Nitrogen suffered less impact with the increase of CWDs decompositions, ranging from 49.66 to 48.80% and 0.52 to 0.58%, respectively. Immediate chemical and thermogravimetric analysis indicated a loss of holocelluloses and extractives and an increase in the concentration of lignin and ash throughout the decomposition process. The weight loss analyzed by thermogravimetric analysis was greater for less decomposed CWDs and with larger diameters. The use of these analyzes removes the subjectivity of CWDs decay classes, reducing the number of tests to determine CWDs physical-chemical properties and increasing the studies accuracy focused on the carbon cycle of these materials.

Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil.

Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition index and climatic and categorical variables, to predict tree survival and mortality in Semideciduous Seasonal Forests in the Atlantic Forest biome. Numerical and categorical trees variables, in permanent plots, were used. The Agricultural Reference Index for Drought (ARID) and the distance-dependent competition index were the variables used. The overall efficiency of classification by ANNs was higher than 92% and 93% in the training and test, respectively. The accuracy for classification and number of surviving trees was above 99% in the test and in training for all ANNs. The classification accuracy of the number of dead trees was low. The mortality accuracy rate (10.96% for training and 13.76% for the test) was higher with the ANN 4, which considers the climatic variable and the competition index. The individual tree-level model integrates dendrometric and meteorological variables, representing a new step for modeling tree survival in the Atlantic Forest biome.