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Tropical forest degradation (e.g., anthropogenic disturbances such as selective logging and fires) alters forest structure and function and influences the forest's carbon sink. In this study, we explored structure-function relationships across a variety of degradation levels in the southern Brazilian Amazon by 1) investigating how forest structural properties vary as a function of degradation history using airborne lidar data; 2) assessing the effects of degradation on solar-induced chlorophyll fluorescence (SIF) seasonality using TROPOMI data; and 3) quantifying the contribution of structural variables to SIF using multiple regression models with stepwise selection of lidar metrics. Forest degradation history was obtained through Landsat time-series classification. We found that fire, logging, and time since disturbance were major determinants of forest structure, and that forests affected by fires experienced larger variability in leaf area index (LAI), canopy height and vertical structure relative to logged and intact forests. Moreover, only recently burned forests showed significantly depressed SIF during the dry season compared to intact forests. Canopy height and the vertical distribution of foliage were the best predictors of SIF. Unexpectedly, we found that wet-season SIF was higher in active regenerating forests (~ 4 years after fires or logging) compared with intact forests, despite lower LAI. Our findings help to elucidate the mechanisms of carbon accumulation in anthropogenically disturbed tropical forests and indicate that they can capture large amounts of carbon while recovering.

Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is an essential enzyme that plays a vital role in plants by regulating carbon assimilation rate and the process of photorespiration. It is responsible for about 15% of soluble leaf proteins in C4 plants like maize and sorghum, while making up over 50% of total nitrogen in C3 plants such as wheat, tobacco, and alfalfa. RuBisCO has a molecular weight of approximately 560 kDa and consists of 8 large subunits and 8 small subunits. Its shape can be either spherical or cylindrical, and it can undergo subunit dissociation under specific conditions. However, the current methods for extracting RuBisCO have several issues, including high cost, low efficiency, and complex technology. To address these problems, it is important to ensure strict control over the quality of raw materials, develop extraction and purification methods that are both economically viable and of high quality, and explore the possibility of developing additional by-products. From a physicochemical perspective, RuBisCO exhibits excellent solubility, emulsifying capacity, foaming capacity, and can even form brittle gels allow concentrations, highlighting its significant application potential. In terms of nutritional and functional characteristics, RuBisCO is rich in essential amino acids, and its derived peptides have demonstrated health-promoting functions in laboratory studies. This review provides a comprehensive overview of the sources, structure, extraction process, physicochemical properties, nutritional and functional characteristics of RuBisCO. It also suggests future research directions and discusses the potential applications of RuBisCO. [ABSTRACT FROM AUTHOR]

Transpiration (T) represents plant water use, while sun-induced chlorophyll fluorescence (SIF) emitted during photosynthesis, relates well to gross primary production. SIF can be influenced by vegetation structure, while uncertainties remain on how this might impact the relationship between SIF and T, especially for open and sparse woodlands. In this study, a method was developed to map T in riverine floodplain open woodland environments using satellite data coupled with a radiative transfer model (RTM). Specifically, we used FluorFLiES, a three-dimensional SIF RTM, to simulate the full spectrum of SIF for three open woodland sites with varying fractional vegetation cover. Five specific SIF bands were selected to quantify their correlation with field measured T derived from sap flow sensors. The coefficient of determination of the simulated far-red SIF and field measured T at a monthly scale was 0.93. However, when comparing red SIF from leaf scale to canopy scale to predict T, performance declined by 24%. In addition, varying soil reflectance and understory leaf area index had little effect on the correlation between SIF and T. The method developed can be applied regionally to predict tree water use using remotely sensed SIF datasets in areas of low data availability or accessibility. [ABSTRACT FROM AUTHOR]

Summary: Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite‐observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy‐surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite‐observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance‐based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun–sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES‐simulated EVI seasonality, respectively. These factors also strongly influenced modeled near‐infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy‐scale biophysics rules out satellite artifacts as significant causes of satellite‐observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite‐detected Amazon phenology, and improves our use of satellite observations to study climate–phenology relationships in the tropics. [ABSTRACT FROM AUTHOR]