Prolonged drought stress combined with high leaf temperatures can induce programmed leaf senescence involving lipid peroxidation, and the loss of net carbon assimilation during early stages of tree mortality. Periodic droughts are known to induce widespread tree mortality in the Amazon rainforest, but little is known about the role of lipid peroxidation during drought-induced leaf senescence. In this study, we present observations of green leaf volatile (GLV) emissions during membrane peroxidation processes associated with the combined effects of high leaf temperatures and drought-induced leaf senescence from individual detached leaves and a rainforest ecosystem in the central Amazon. Temperature-dependent leaf emissions of volatile terpenoids were observed during the morning, and together with transpiration and net photosynthesis, showed a post-midday depression. This post-midday depression was associated with a stimulation of C₅ and C₆ GLV emissions, which continued to increase throughout the late afternoon in a temperature-independent fashion. During the 2010 drought in the Amazon Basin, which resulted in widespread tree mortality, green leaf volatile emissions (C₆ GLVs) were observed to build up within the forest canopy atmosphere, likely associated with high leaf temperatures and enhanced drought-induced leaf senescence processes. The results suggest that observations of GLVs in the tropical boundary layer could be used as a chemical sensor of reduced ecosystem productivity associated with drought stress.

This study aimed to test taper functions and artificial intelligence (AI) models in order to estimate merchantable volumes of Japanese cedar (Cryptomeria japonica) trees in a homogenous plantation in southern Brazil. A total of 30 individuals were rigorously scaled and their total volumes were calculated, including those of the following log assortments: veneer, sawn, pulp and energy. Three AI models, i.e. two variants of k-nearest neighbours (KNN) instance-based classification (one and three nearest neighbours) and an artificial neural network (ANN) approach, were compared with three traditional taper models: fifth-order polynomial, fractional powers and the Garay model. The estimated volumes were compared with the actual volumes by means of the standard error (Syx), bias, precision and accuracy. Total volume estimates proved to be unbiased (maximum bias 5.42%), precise (maximum precision 9.28%) and accurate (maximum accuracy 10.79%) with all of the investigated models. The tested models tended to give l...

Biomass models are useful for several purposes, especially for quantifying carbon stocks and dynamics in forests. Selecting appropriate equations from a fitted model is a process which can involves several criteria, some widely used and others used to a lesser extent. This study analyzes six selection criteria for models fitted to six sets of individual biomass collected from woody indigenous species of the Tropical Atlantic Rain Forest in Brazil. Six models were examined and the respective fitted equations evaluated by the residual sum of squares, adjusted coefficient of determination, absolute and relative estimates of the standard error of estimate, and Akaike and Schwartz (Bayesian) information criteria. The aim of this study was to analyze the numeric behavior of these model selection criteria and discuss the ease of interpretation of them. The importance of residual analysis in model selection is stressed. The adjusted coefficient of determination ( $$ R^{2}_{adj.} $$ R a d j . 2 ) and the standard error of estimate in percentage (Syx%) are relative model selection criteria and are not affected by sample size and scale of the response variable. The sum of squared residuals (SSR), the absolute standard error of estimate (Syx), the Akaike information criterion and the Schwartz information criterion, in turn, depend on these quantities. The best fit model was always the same within a given data set regardless the model selection criteria considered (except for SSR in two cases), indicating they tend to converge to a common result. However, such criteria are not always closely related across different data sets. General model selection criteria are indicative of the average goodness of fit, but do not capture bias and outlier effects. Graphical residual analysis is a useful tool to this detection and must always be used in model selection. It is concluded that the criteria for model selection tend to lead to a common result, regardless their mathematical formulation and statistical significance. Relative measures of goodness of fitting are easier to interpret than the absolute ones. Careful graphical residual analysis must always be used to confirm the performance of the models.

Volatile terpenoid resins represent a diverse group of plant defense chemicals involved in defense against herbivory, abiotic stress, and communication. However, their composition in tropical forests remains poorly characterized. As a part of tree identification, the 'smell' of damaged trunks is widely used, but is highly subjective. Here, we analyzed trunk volatile monoterpene emissions from 15 species of the genus Protium in the central Amazon. By normalizing the abundances of 28 monoterpenes, 9 monoterpene 'fingerprint' patterns emerged, characterized by a distinct dominant monoterpene. While 4 of the 'fingerprint' patterns were composed of multiple species, 5 were composed of a single species. Moreover, among individuals of the same species, 6 species had a single 'fingerprint' pattern, while 9 species had two or more 'fingerprint' patterns among individuals. A comparison of 'fingerprints' between 2015 and 2017 from 15 individuals generally showed excellent agreement, demonstrating a strong dependence on species identity, but not time of collection. The results are consistent with a previous study that found multiple divergent copies of monoterpene synthase enzymes in Protium. We conclude that the monoterpene 'fingerprint' database has important implications for constraining Protium species identification and phylogenetic relationships and enhancing understanding of physiological and ecological functions of resins and their potential commercial applications.

Warming surface temperatures and increasing frequency and duration of widespread droughts threaten the health of natural forests and agricultural crops. High temperatures (HT) and intense droughts can lead to the excessive plant water loss and the accumulation of reactive oxygen species (ROS) resulting in extensive physical and oxidative damage to sensitive plant components including photosynthetic membranes. ROS signaling is tightly integrated with signaling mechanisms of the potent phytohormone abscisic acid (ABA), which stimulates stomatal closure leading to a reduction in transpiration and net photosynthesis, alters hydraulic conductivities, and activates defense gene expression including antioxidant systems. While generally assumed to be produced in roots and transported to shoots following drought stress, recent evidence suggests that a large fraction of plant ABA is produced in leaves via the isoprenoid pathway. Thus, through stomatal regulation and stress signaling which alters water and carbon fluxes, we highlight the fact that ABA lies at the heart of the Carbon-Water-ROS Nexus of plant response to HT and drought stress. We discuss the current state of knowledge of ABA biosynthesis, transport, and degradation and the role of ABA and other isoprenoids in the oxidative stress response. We discuss potential variations in ABA production and stomatal sensitivity among different plant functional types including isohydric/anisohydric and pioneer/climax tree species. We describe experiments that would demonstrate the possibility of a direct energetic and carbon link between leaf ABA biosynthesis and photosynthesis, and discuss the potential for a positive feedback between leaf warming and enhanced ABA production together with reduced stomatal conductance and transpiration. Finally, we propose a new modeling framework to capture these interactions. We conclude by discussing the importance of ABA in diverse tropical ecosystems through increases in the thermotolerance of photosynthesis to drought and heat stress, and the global importance of these mechanisms to carbon and water cycling under climate change scenarios.