Spatial distribution of six managed tree species is influenced by topography conditions in the Central Amazon.

In tropical forests, the spatial distribution of trees may present random, uniform, or grouped patterns that can simultaneously be affected by site and species characteristics. In Central Amazon, topographic gradients and soil water levels drive differences in tree species distribution and in forest dynamics at local scales. Knowing this kind of information can be useful for a forest manager to plan harvesting operations considering the microhabitat preference of merchantable species to reduce the disturbances caused by logging activities. Thus, the spatial variation of tree species is an important information to be considered to support the planning process of forest logging. The present study aims to evaluate the spatial distribution pattern of six species and analyze the relationship between the topography and the population densities and stem size of those species. The study was carried out in a forest production compartment managed by a private company located in the municipality of Silves, state of Amazonas, Brazil. The spatial pattern of the six species was characterized by Ripley's K function. Spatial distribution of diameter at breast height (DBH) and tree density based on kernel incidence calculation were evaluated for topographic classes of slope, elevation, and distance from streams, which were mapped using geographic information systems (GIS). The means of DBH and density of each species were compared among topographic classes by ANOVA and Tukey's test. The results demonstrated the predominance of the aggregate distribution pattern for the six species up to 1105 m (p < 0.01). The tree species Minquartia guianensis Aubl., Protium puncticulatum J.F.Macbr, Manilkara elata (Allemão ex Miq.) Monach, and Caryocar glabrum Aubl. Pers showed an increase in the tree density as the distance from the streams and elevation increased, standing spatially incident on plateaus. Kernel densities of Dinizia excelsa Ducke and Goupia glabra Aubl. were higher closer to streams. The DBH averages followed similar trends of population density for M. guianensis, M. elata, and C. glabrum, and the opposite pattern for D. excelsa, which presented larger individuals in less densely populated areas. P. puncticulatum and G. glabra mean DBH distribution was not affected by the topographic variables analyzed. Topography-related variables showed effects on variations of density and tree size, suggesting that species may be spatially sensitive to the habitat variability available in the study area. In view of logging planning, spatial distribution must be considered in decisions related to cutting down trees and maintenance of remaining trees, especially because some species are more aggregated in smaller scales. Moreover, as topographic variations affect the spatial distribution of size and density, the timber yield will vary spatially in the area, bringing implications for planning logging intensities, roads, skid trails and forest operations. Finally, the procedures and information generated in this study can be reproduced and applied to other species and managed areas to support the planning toward minimizing impacts on the spatial structure of commercial species, as well as to increase the chances of future stock recovery of managed forests in the Amazon.

Litter and soil biogeochemical parameters as indicators of sustainable logging in Central Amazonia.

One-fourth of Brazilian Amazonia is managed for timber production, but only a small portion of active logging sites follow sustainable forest management plans (SFMPs). Amazon forests without SFMPs are susceptible to deforestation because such plans integrate the use of forest products and conservation goals by allowing selective wood extraction following regulations aimed at reducing the long-term impact of logging. However, it remains uncertain whether reduced-impact selective logging typical of SFMPs (17-20 m3 ha-1 yr-1 of 38-70 species) changes forest regeneration, carbon (C) stocks, and nutrient cycling. Here, we tested the hypothesis that litter and soil biogeochemical parameters serve as indicators of sustainable logging as forest regeneration, C stocks, and C-to-nutrient ratios in soil and litter become progressively similar to those of primary forests as time elapses after logging. We used a chronosequence spanning nine years since logging to relate litter and soil (at 0-10, 10-30, 30-50 cm depth) C stocks and 12 and 15 biogeochemical parameters, respectively, as well as canopy cover and tree seedling density (10-150 cm tall) in upland evergreen Amazon forests. In one unlogged and four logged stands sampled three, five, seven, and nine years after logging, we compared 15 permanent plots (three replicated 0.5 ha plots per time-since-logging category). We found that five parameters explained >80% of the variation in soil and litter properties among logged and unlogged stands. Litter parameters were more sensitive to logging than soil parameters, as litter C stocks and C-to-nutrient ratios increased systematically after logging. Canopy cover decreased over time and was ~14% lower nine years after logging. Total seedling density did not change consistently over time but was ~54% higher seven years after logging. Our data suggest that the SFMP guidelines have served the purpose of maintaining soil quality and forest regeneration. Litter and soil parameters can be useful indicators of sustainable forest management in upland evergreen forests in Central Amazonia.

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics.

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.