Tropical forest structural variation across heterogeneous landscapes may control above‐ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) – remotely estimated from LiDAR – control variation in above‐ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one‐hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.

We review the consequences of environmental changes caused by human activities on forest products and forest-dependent communities in the Amazon region—the vast Amazonas River basin and the Guiana Shield in South America. We used the 2018 and 2021 Intergovernmental Panel on Climate Change reports and recent scientific studies to present evidence and hypotheses for changes in the ecosystem productivity and geographical distribution of plants species. We have identified species associated with highly employed forest products exhibiting reducing populations, mainly linked with deforestation and selective logging. Changes in species composition along with a decline of valuable species have been observed in the eastern, central, and southern regions of the Brazilian Amazon, suggesting accelerated biodiversity loss. Over 1 billion native trees and palms are being lost every two years, causing economic losses estimated between US$1–17 billion. A decrease in native plant species can be abrupt and both temporary or persistent for over 20 years, leading to reduced economic opportunities for forest-dependent communities. Science and technology investments are considered promising in implementing agroforestry systems recovering deforested and degraded lands, which could engage companies that use forest products due to supply chain advantages.

Biodiversity & Ecology (B&E) - University of Hamburg - Biocenter Klein Flottbek and Botanical Garden

Mobilizing funds is a major challenge to achieve scalable Forest Landscape Restoration projects. While pure ecological restoration may not be a feasible investment from the private perspective, combining native species with non-timber forest products (NTFP) species may be a solution for reaching large scale and financially sustainable forest restoration. This study addresses potential species combinations for 12 restoration models, three models being based in pure ecological restoration and nine models being based on agroforests with NTFP, their economic costs, and benefits in tropical forests in Brazil, Peru, Cambodia, and Indonesia. A total of 12 semi-structured interviews were conducted to capture the models’ productivity and prices. As for the prices that the producers did not know, specialized stores were consulted in the cities of the collection. The starting investment to restore 01 hectare (ha⁻¹) of tropical forest ranged between US $104 and $7736, with an average of $1963 ha⁻¹ and a standard deviation of $2196 ha⁻¹, considering the 12 cases evaluated in 2018 and 2019. From nine restoration models that had economic purposes, financial indicators showed a median net present value (NPV) of $1548 ha⁻¹, and a median internal rate of return of 22%, considering a discount rate of 10%. The NPV varied between $−685 ha⁻¹ and $55,531 ha⁻¹. Costs of pure ecological restoration were on average 42% lower than agroforestry systems, but did not produce direct income from NTFP, therefore yielding negative NPV. The study demonstrated the economic feasibility of seven of nine models that had economic objective, showing that there are promising business cases for private investment in tropical forest restoration.