Root behavior of savanna species in Brazil's Pantanal wetland.

The objective of this study was to determine the maximum depth, structure, diameter and biomass of the roots of common woody species in two savanna physiognomies (savanna woodland and open woody savanna) in Brazil's Pantanal wetland. The root systems of 37 trees and 34 shrubs of 15 savanna species were excavated to measure their length and depth and estimate the total root biomass through allometric relationships with stem diameter at ground level. In general, statistical regression models between root weight and stem diameter at ground level showed a significance of P < 0.05 and R2 values close to or above 0.8. The average depths of the root system in wetland savanna woodland and open woody savanna are 0.8 ± 0.3 m and 0.7 ± 0.2 m, respectively, and differ from the root systems of savanna woody species in non-flooding areas, whose depth usually ranges from 3 to 19 m.Weattribute this difference to the adaptation of woody plant to the shallow water table, particularly during the wet season. This singularity of woody species in wetland savannas is important when considering biomass and carbon stocks for national and global carbon inventories.

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.