Phenotypic diversity and symbiotic efficiency of Bradyrhizobium spp. strains from Amazonian soils

Este trabalho teve como objetivo avaliar a diversidade fenotípica e a eficiência simbiótica de estirpes de Bradyrhizobium, isoladas de solos da Amazônia, sob diferentes sistemas de uso da terra (monocultura, capoeira, pastagem, floresta e sistema agroflorestal). A análise dos perfis de proteína total de 46 estirpes, obtidos por eletroforese em gel de poliacrilamida (SDS-PAGE), mostrou grande diversidade, tendo formado 11 grupos com similaridade acima de 80%. Apenas um dos grupos continha a estirpe referência de B. elkanii: BR29, recomendada como inoculante para soja. Vinte e duas estirpes testadas em vasos de Leonard, com caupi (Vigna unguiculata (L.) Walp.), induziram à produção de matéria seca e ao acúmulo de nitrogênio, na parte aérea da planta, e à eficiência relativa superiores aos da testemunha (sem N e sem inoculação). Entre as estirpes testadas, 13 induziram à produção de matéria seca e à eficiência relativa similares às da testemunha nitrogenada (com N, sem inoculação); cinco estirpes induziram a acúmulo de N superior ao da testemunha nitrogenada. Essas populações nativas são constituídas por grande diversidade de estirpes, com eficiência simbiótica variável, algumas das quais podem ser recomendadas para testes de eficiência agronômica.This work aimed to evaluate the phenotypic diversity and symbiotic efficiency of Bradyrhizobium strains isolated from Amazonian soils, under different land use systems (crop, fallow, pasture, forest and agroforestry system). Total protein profiles obtained by polyacrylamide gel electrophoresis (SDS-PAGE) of 46 strains showed great diversity, while 11 groups presented similarity above 80%. One of these groups contained the reference strain of B. elkanii: BR29, recommended as soybean inoculant. Twenty-two strains, tested in Leonard jars for symbiotic efficiency with Vigna unguiculata (L.) Walp. (cowpea) produced shoot dry matter, N-content, and relative efficiency higher than the control (without N, with inoculation). Production of shoot dry matter and relative efficiency were induced by 13 strains in a way similar to the N-control (with N, without inoculation); five strains induced higher nitrogen content than N-control. Native populations comprise high diversity of strains with variable symbiotic efficiency, and some of them could be recommended for agronomic efficiency assays

New rhizobia strains isolated from the Amazon region fix atmospheric nitrogen in symbiosis with cowpea and increase its yield

Studies in the Amazon indicate a wide diversity of rhizobia with the ability for biological nitrogen fixation (BNF), which could expand the number of strains approved for cowpea. Thus, the aim of this field study was to evaluate the agronomic performance in cowpea of the several strains isolated from the soils of the Brazilian states Acre and Rondônia, and to compare them withstrains approved by the Ministry of Agriculture (MAPA) and withnon-inoculated controls (without and with mineral nitrogen fertilizer). The inoculants performed well. Though less effective than the other strains, the UFLA 03-36 strain also was prominent with respect to grain yield. Because of the positive response of the UFLA 03-129 strain, which led to yield increases greater than the obtained from the control without inoculation plus mineral-N, it can be recommended as an inoculum for cowpea. Further investigations should be carried out to obtain MAPA’s approval for their use. Other experiments involving this strain and several cultivars are being carried out on other types of soil and environmental conditions of the state of Minas Gerais

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost