Decomposição Da Serapilheira Foliar De Floresta Nativa E Plantios De Pterogyne Nitens E Eucalyptus Urophylla No Sudoeste Da Bahia

The decomposition process regulates the accumulation of litter and nutrient cycling in forest ecosystems, being central to its maintenance. The objective of this study was to evaluate the dynamics of leaf litter decomposition in three forest ecosystems (semideciduous forest Montana and homogeneous stands of Pterogyne nitens Tul. and Eucalyptus urophylla ST Blake), located in Vitória da Conquista, Bahia, Brazil. To evaluate the decomposition, newly fallen leaves on the ground of trees and shrubs in each of the studied toppings were collected. The sheets were dried at 65 °C, and thereafter, 10 g portions were weighed and placed in litter bags, which were randomly distributed on the surface of the forest floor in each of the areas studied. Five litter bags at random were collected after 30, 60, 90, 120, 150 and 180 days of installation. After collection, the material contained in each litter bag was subjected to drying in an oven at 65 °C and weighed. Based on the obtained masses were estimated the percentage of the remaining mass, the rate of decomposition (k) and half-life time of the litter (t1/2). For chemical characterization three subsamples were separated from the dried leaf litter, which were ground and analyzed by determination of the total nitrogen, carbon, polyphenols, lignin and cellulose. The decomposition was related to environmental variables (precipitation, temperature and humidity) and the microenvironment (temperature and soil moisture).The total accumulated litter varied significantly between the settlements studied, the highest value was observed in the area of Eucalyptus urophylla (12,7 Mg ha-1), followed by native forest (6,9 Mg ha-1) and Pterogyne nitens (1,1 Mg ha-1). At the end of the six months of the experiment, Eucalyptus urophylla showed the greatest remaining mass (73,6%), followed by native forest (67,8%) and Pterogyne nitens (46,3%). The decomposition constant (k) was higher in litter of Pterogyne nitens (0,0054 g g-1 day), with lower values for native forest (0,0016 g g-1 day) and Eucalyptus urophylla (0,0015 g g-1day). The rate of decomposition of leaf litter of the peopling of Pterogyne nitens is in a superior position in relation to rates of native forest and stand of Eucalyptus urophylla, which provides the largest species ability to recycle organic matter and nutrients. The decomposition process in the studied ecosystems is influenced not only by the quality of the litter but also by the quality of their microenvironment. © 2016, Universidade Federal de Santa Maria. All rights reserved.2641141115

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

The Caatinga biome in Brazil comprises the largest and most continuous expanse of the seasonally dry tropical forest (SDTF) worldwide; nevertheless, it is among the most threatened and least studied, despite its ecological and biogeographical importance. The spatial distribution of volumetric wood stocks in the Caatinga and the relationship with environmental factors remain unknown. Therefore, this study intends to quantify and analyze the spatial distribution of wood volume as a function of environmental variables in Caatinga vegetation in Bahia State, Brazil. Volumetric estimates were obtained at the plot and fragment level. The multiple linear regression techniques were adopted, using environmental variables in the area as predictors. Spatial modeling was performed using the geostatistical kriging approach with the model residuals. The model developed presented a reasonable fit for the volume m3 ha with r2 of 0.54 and Root Mean Square Error (RMSE) of 10.9 m3 ha–1. The kriging of ordinary residuals suggested low error estimates in unsampled locations and balance in the under and overestimates of the model. The regression kriging approach provided greater detailing of the global wood volume stock map, yielding volume estimates that ranged from 0.01 to 109 m3 ha–1. Elevation, mean annual temperature, and precipitation of the driest month are strong environmental predictors for volume estimation. This information is necessary to development action plans for sustainable management and use of the Caatinga SDTF in Bahia State, Brazil

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Climate change is being intensified by anthropogenic emission of greenhouse gasses, highlighting the value of forests for carbon dioxide storing carbon in their biomass. Seasonally dry tropical forests are a neglected, threatened, but potentially critical biome for helping mitigate climate change. In South America, knowing the amount and distribution of carbon in Caatinga seasonally dry vegetation is essential to understand its contribution to the global carbon cycle and subsequently design a strategic plan for its conservation. The present study aimed to model and map the spatial distribution of the potential forest biomass stock across 32 forest fragments of Caatinga, in the state of Bahia, Brazil, using regression kriging and Inverse Square of Distance techniques, building from point measurements of vegetation biomass made on-the-ground in ecological plots. First, a model for estimating biomass was fitted as a function of environmental variables to apply regression kriging, and then applied to the maps of the selected components. Elevation, temperature, and precipitation explained 46% of the biomass variations in the Caatinga. The model residuals showed strong spatial dependence and were mapped based on geostatistical criteria, selecting the spherical semivariogram model for interpolation by ordinary kriging. Biomass was also mapped by the Inverse Square of Distance approach. The quality of the regression model suggests that there is good potential for estimating biomass here from environmental variables. The regression kriging showed greater detail in the spatial distribution and revealed a spatial trend of increasing biomass from the north to south of the domain. Additional studies with greater sampling intensity and the use of other explanatory variables are suggested to improve the model, as well as to maximize the technique’s ability to capture the actual biomass behavior in this newly studied seasonally dry ecosystem