Amazon rainforest

Summary

The Amazon rainforest, also called Amazon jungle or Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome that covers most of the Amazon basin of South America. This basin encompasses , of which are covered by the rainforest. This region includes territory belonging to nine nations and 3,344 formally acknowledged indigenous territories. The majority of the forest, 60%, is in Brazil, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Bolivia, Ecuador, French Guiana, Guyana, Suriname, and Venezuela. Four nations have "Amazonas" as the name of one of their first-level administrative regions, and France uses the name "Guiana Amazonian Park" for French Guiana's protected rainforest area. The Amazon represents over half of Earth's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees in about 16,000 species. More than 30 million people of 350 dif

Official source

https://en.wikipedia.org/wiki/Amazon_rainforest

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Climate change is altering the natural equilibrium of the Amazon ecosystem, which is in turn deeply connected to the Earth’s biogeochemical cycles. Understanding this relationship aids optimal rainforests management in the future. Previous research has investigated the role of environmental variables and climatic drivers, neglecting the influence of forest height and age. This study uses remote sensing observations of precipitation, vapour-pressure deficit and canopy height along with model estimates of tree age and aboveground biomass to show that tall and old Amazonian trees are three times less sensitive to precipitation variability than younger and smaller forests. Canopy size is found to be equally important to mean precipitation in controlling the response to interannual climate variability. Our results align with those of in-situ experiments. These findings could help better predict the response of the Amazon forest to future droughts and water stress.

2017

Tropical forest conversion to agricultural land leads to a strong decrease of soil organic carbon (SOC) stocks. While the decrease of the soil C sequestration function is easy to measure, the impacts of SOC losses on soil fertility remain unclear. Especially the assessment of the sensitivity of other fertility indicators as related to ecosystem services suffers from a lack of clear methodology. We developed a new approach to assess the sensitivity of soil fertility indicators and tested it on biological and chemical soil properties affected by rainforest conversion to plantations. The approach is based on (non-)linear regressions between SOC losses and fertility indicators normalized to their level in a natural ecosystem. Biotic indicators (basal respiration, microbial biomass, acid phosphatase), labile SOC pools (dissolved organic carbon and light fraction) and nutrients (total N and available P) were measured in Ah horizons from rainforests, jungle rubber, rubber (Hevea brasiliensis) and oil palm (Elaeis guineensis) plantations located on Sumatra. The negative impact of land-use changes on all measured indicators increased in the following sequence: forest < jungle rubber < rubber < oil palm. The basal respiration, microbial biomass and nutrients were resistant to SOC losses, whereas the light fraction was lost stronger than SOC. Microbial C use efficiency was independent on land use. The resistance of C availability for microorganisms to SOC losses suggests that a decrease of SOC quality was partly compensated by litter input and a relative enrichment by nutrients. However, the relationship between the basal respiration and SOC was non-linear; i.e. negative impact on microbial activity strongly increased with SOC losses. Therefore, a small decrease of C content under oil palm compared to rubber plantations yielded a strong drop in microbial activity. Consequently, management practices mitigating SOC losses in oil palm plantations would strongly increase soil fertility and ecosystem stability. We conclude that the new approach enables quantitatively assessing the sensitivity and resistance of diverse soil functions to land-use changes and can thus be used to assess resilience of agroecosystems with various use intensities. (C) 2016 Elsevier Ltd. All rights reserved.

Elsevier Science Bv2016

Progressive conversion of tropical rainforests to agricultural monocultures in South East Asia increasingly affects landscape types such as riparian areas. The impacts of conversions on soil organic matter (SOM) vary with changing landforms. However, this was often not accounted for in previous studies where SOM in soils in riparian areas was combined with SOM from well-drained adjacent slopes. Because riparian areas have a high carbon (C) storage potential, our objectives were i) to assess their C stocks after conversion to rubber and oil palm plantations in Sumatra (Indonesia) and ii) to compare the impacts of land use conversion on C stocks between riparian and well-drained areas. Average soil C stock losses from the top 30 cm were about 14% and 4% following conversion of riparian forest to rubber and oil palm plantations, respectively, indicating a high resistance of C to mineralization. C losses from well-drained areas were twice as high as from riparian areas after the respective conversion. delta C-13 values from riparian areas showed clear heterogeneity down soil profiles that was explained i) by alternating oxic and anoxic conditions, leading to reduced SOM and litter decomposition in riparian areas and ii) by mineral sediments and organic matter deposition and accumulation by erosion from adjacent slopes covered by plantations. We conclude that riparian areas are more resilient in terms of soil C storage towards land-use change than well-drained areas because of sediment deposition and reduced oxygen availability. On this basis, we developed a conceptual model of the effects of land-use change and various ecotone characteristics on SOM mineralization in the top- and subsoil of riparian areas.

2021

Elsevier Science Bv2016