Amazon forest structure generates diurnal and seasonal variability in light utilization.

The complex three-dimensional (3-D) structure of tropical forests generates a diversity of light environments for canopy and understory trees. Understanding diurnal and seasonal changes in light availability is critical for interpreting measurements of net ecosystem exchange and improving ecosystem models. Here, we used the Discrete Anisotropic Radiative Transfer (DART) model to simulate leaf absorption of photosynthetically active radiation (lAPAR) for an Amazon forest. The 3-D model scene was developed from airborne lidar data, and local measurements of leaf reflectance, aerosols, and PAR were used to model lAPAR under direct and diffuse illumination conditions. Simulated lAPAR under clear-sky and cloudy conditions was corrected for light saturation effects to estimate light utilization, the fraction of lAPAR available for photosynthesis. Although the fraction of incoming PAR absorbed by leaves was consistent throughout the year (0.80?0.82), light utilization varied seasonally (0.67?0.74), with minimum values during the Amazon dry season. Shadowing and light saturation effects moderated potential gains in forest productivity from increasing PAR during dry-season months when the diffuse fraction from clouds and aerosols was low. Comparisons between DART and other models highlighted the role of 3-D forest structure to account for seasonal changes in light utilization. Our findings highlight how directional illumination and forest 3-D structure combine to influence diurnal and seasonal variability in light utilization, independent of further changes in leaf area, leaf age, or environmental controls on canopy photosynthesis. Changing illumination geometry constitutes an alternative biophysical explanation for observed seasonality in Amazon forest productivity without changes in canopy phenology.

Soil organic carbon, carbon stock and their relationships to physical attributes under forest soils in Central Amazonia.

The soil carbon under Amazonian forests has an important roles in global changing, making information on the soil content and depths of these stocks are considerable interest in efforts to quantify soil carbon emissions to the atmosphere.This study quantified the content and soil organic carbon stock under primary forest up to 2 m depth, at different topographic positions, at Cuieiras Biological Reserve, Manaus/ ZF2, km 34, in the Central Amazon, evaluating the soil attributes that may influence the permanence of soil carbon. Soil samples were collected along a transect of 850 m on topographic gradient Oxisol (plateau), Ultisol (slope) and Spodosol (valley). The stocks of soil carbon were obtained by multiplying the carbon content, soil bulk density and trickiness of soil layers. The watershed was delimited by using STRM and IKONOS images and the carbon contend obtained in the transects was extrapolated as a way to evaluate the potential for carbon stocks in an area of 2678.68 ha. The total SOC was greater in Oxisol followed by Spodosol and Ultisol. It was found direct correlations between the SOC and soil physical attributes. Among the clay soils (Oxisol and Ultisol), the largest stocks of carbon were observed in Oxisol at both the transect (90 to 175.5 Mg C ha-1) as the level of watershed (100.2 to 195.2 Mg C ha-1). The carbon stocks under sandy soil (Spodosol) was greater to clay soils along the transect (160-241 Mg C ha-1) and near them in the Watershed (96.90 to 146.01 Mg C ha-1).

Biomarker analysis in soils of the Amazon rainforest after vegetation fire.

The objective of this study was to determine the origin of organic matter incorporated in Amazon forest soils subjected to vegetation fire by analyzing the aliphatic biomarkers (n-alkanes) present in lipid extracts of soil samples.

Agriculture and forest: A sustainable strategy in the Brazilian Amazon.

Large-scale agriculture is increasing in anthropogenically modified areas in the Amazon Basin. Crops such as soybean, maize, oil palm, and others are being introduced to supply the world demand for food and energy. However, the current challenge is to enhance the sustainability of these areas by increasing efficiency of production chains and to improve environmental services. The Amazon Basin has experienced a paradigm shift away from the traditional slash-and-burn agricultural practices, which offers decision makers the opportunity to make innovative interventions to enhance the productivity in previously degraded areas by using trees to ecological advantage. This study describes a successful experiment integrating the production of soybean and paricá (Glycine max L. and Schizolobium amazonicum) based on previous research that indicated potential topoclimatic zones for planting paricá in the Brazilian state of Pará. This paper shows that a no-tillage system reduces the effects of drought compared to conventional tillage still used by many farmers in the region. The integrated system was implemented during the 2014/2015 season in 234.6 ha in the high-potential zone in the municipality of Ulianópolis, Pará. Both soybean and paricá were planted simultaneously. Paricá was planted in 5 m x 2 m inter-tree spacing totaling 228x103 trees per hectare and soybean, in 4 m x 100 m spacing, distributed in nine rows with a 0.45 m inter-row distance, occupying 80% of the area. The harvested soybean production was 3.4 t ha-1, higher than other soybean monocultures in eastern Pará. Paricá benefited from soybean fertilization in the first year: It exhibited rapid development in height (3.26 m) and average diameter (3.85 cm). Trees and crop rotation over the following years is six years for forest species and one year for each crop. Our results confirm there are alternatives to the current production systems able to diminish negative impacts resulting from monoculture. In addition, the system provided environmental services such as reduced soil erosion and increased carbon stock by soil cover with no-tillage soybean cultivation. The soybean cover contributes to increased paricá thermal regulation and lower forestry costs. We concluded that innovative interventions are important to show local farmers that it is possible to adapt an agroforest system to large-scale production, thus changing the Amazon.

Soil carbon stock in integrated crop-livestock-forest system in the Central Region of Minas Gerais.

2016

Sustainable forest management for smallholder farmers in the Brazilian Amazon.

The paper describes a forest management system to be applied on smallholder farms, particularly on settlement projects in the Brazilian Amazon. The proposed forest management system was designed to generate a new source of family income and to maintain forest structure and biodiversity. The system is new in three main characteristics: the use of short cycles in the management of tropical forests, the low harvesting intensity and environmental impact, and the direct involvement of the local population in ali forest management activities. It is based on a minimum felling cycle of ten years and an annual timber harvest of 5-10 m3 ha-1.

Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.

Next-generation transcriptome assembly of an Amazon palm (Euterpe precatoria).

2016

Regeneration in felling gaps after logging in Acre state, Western Amazon.

Regeneration of tree species in felling gaps were studied during the first two years following harvesting in a tropical forest in Acre state, Brazil. Felling gaps averaged 340 m2 in size, while canopy openings averaged 17%. Seedling mortality in adjacent undisturbed forest was 4.6% yr', and 59.6% yr·1 and 100% yr' in the crown and trunk zones respectively, two years after logging. Recruitment of new seedlings inthe undisturbed forest understorey averaged 462 plants ha' yr', two years after gap creation. Inthe crown zones of the gaps, recruitment of seedlings averaged 1350 ha' yr', and in the trunk zones 1392 ha' yr'. The entire seedling community in trunk zones after logging was composed of new recruits. lhere was a tendency for seedling growth rates to increase from the natural forest (0,21cm yr-1) to the crown zone (0.40cm yr'), Before gap creation, species richness and diversity and seedling density were quite similar. After gap creation a sharp decrease could be verified in the gap.zones, however the differences between gap and undisturbed forest decreased rapidly in the second year after gap creation. lhe regeneration of commercial species was not affected by gap creation apart from the increase in growth rates.

Análise do padrão de distribuição espacial de espécies florestais em 8 hectares na Amazônia Central.

Objetivou-se analisar a distribuição espacial explícita de espécies florestais de interesse comercial. A pesquisa foi desenvolvida em Silves, na Precious Woods Amazon Ltda. (PWA), em 8 ha contínuos.

Modeling nitrous oxide emissions in grass and grass-legume pastures in the western Brazilian Amazon.

Mineral nitrogen (N) dynamics in soil and the exchange of N gaseous in the interface soil-atmosphere are intimately associated with animal manure in pastures. According to soil inorganic-N pools and the site studied, forest or pasture, and pastures age the soil inorganic-N pools of ammonium and nitrate can be similar in the forest or ammonium dominated in the pasture. Also annual average net nitrification rates at soil surface in forest can be higher than in pasture suggesting a higher potential for nitrate-N losses either through leaching or gaseous emissions from intact forests compared with established pastures (NEILL et al., 1995).

Spatial scale effects of sampling on the interpolation of species distribution models in the southwestern Amazon.

Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.