Advanced search
Start date
Betweenand

Comparative analysis of bacterial communities in phyllosphere, litter and rhizosphere of tree species in the Amazon rain forest

Grant number: 16/15932-4
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): November 01, 2016
Effective date (End): January 02, 2019
Field of knowledge:Agronomical Sciences - Agronomy
Principal Investigator:Marcio Rodrigues Lambais
Grantee:Julio Cezar Fornazier Moreira
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Associated scholarship(s):17/14698-0 - Comparative analysis of bacterial communities assemblages associated with the phyllosphere, leaf litter and rhizosphere of tree species of the Amazon Forest, BE.EP.DR

Abstract

The Amazon rainforest has the largest land area of rainforest in the world, playing a key role in the conservation of biodiversity, biogeochemical cycles and climate regulation. It has been observed in tree species of the Brazilian Atlantic rainforest, the bacterial community structures associated with phyllosphere, dermosfera and rhizosphere are unique and depend on the plant taxon, and the intra-taxon similarity is greater than the inter-taxon. Although the Amazon rainforest is functionally different from the Atlantic rainforest, it is possible that bacterial communities have similar structuring patterns. In order to determine whether the variation patterns of bacterial communities associated with phyllosphere, rhizosphere and litter of the Amazon rainforest are similar to those of the Atlantic rainforest, nine major tree species of the Amazon rainforest will be studied. Sampling shall performed at three different times, corresponding to the dry and rainy seasons in 6-14 individuals by species, totaling 90 trees. The bacterial community structures and diversity of bacteria will be determined based on the sequencing of the V4 region of the 16S rRNA gene, using the Illumina MiSeq platform. The bacterial community structures will be compared and related to environmental and phenological variables of plant species using multivariate analysis and the main factors of the structuring of bacterial communities will be determined using non-supervised artificial neural networks (KohonenSef-Organizing Maps) and supervised. In addition, will be also evaluated the networks of ecological associations between different groups of bacteria detected.