Bacterial community composition and diversity of two different forms of an organic residue of bioenergy crop

The use of residue of sugarcane ethanol industry named vinasse in fertirrigation is an established and widespread practice in Brazil. Both non-concentrated vinasse (NCV) and concentrated vinasse (CV) are used in fertirrigation, particularly to replace the potassium fertilizer. Although studies on the chemical and organic composition of vinasse and their impact on nitrous oxide emissions when applied in soil have been carried out, no studies have evaluated the microbial community composition and diversity in different forms of vinasse. We assessed the bacterial community composition of NCV and CV by non-culturable and culturable approaches. The non-culturable bacterial community was assessed by next generation sequencing of the 16S rRNA gene and culturable community by isolation of bacterial strains and molecular and biochemical characterization. Additionally, we assessed in the bacterial strains the presence of genes of nitrogen cycle nitrification and denitrification pathways. The microbial community based on 16S rRNA sequences of NCV was overrepresented by Bacilli and Negativicutes while CV was mainly represented by Bacilli class. The isolated strains from the two types of vinasse belong to class Bacilli, similar to Lysinibacillus, encode for nirK gene related to denitrification pathway. This study highlights the bacterial microbial composition particularly in CV what residue is currently recycled and recommended as a sustainable practice in sugarcane cultivation in the tropics

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

Genome-resolved metagenomics of sugarcane vinasse bacteria

Abstract Background The production of 1 L of ethanol from sugarcane generates up to 12 L of vinasse, which is a liquid waste containing an as-yet uncharacterized microbial assemblage. Most vinasse is destined for use as a fertilizer on the sugarcane fields because of the high organic and K content; however, increased N2O emissions have been observed when vinasse is co-applied with inorganic N fertilizers. Here we aimed to characterize the microbial assemblage of vinasse to determine the gene potential of vinasse microbes for contributing to negative environmental effects during fertirrigation and/or to the obstruction of bioethanol fermentation. Results We measured chemical characteristics and extracted total DNA from six vinasse batches taken over 1.5 years from a bioethanol and sugar mill in Sao Paulo State. The vinasse microbial assemblage was characterized by low alpha diversity with 5–15 species across the six vinasses. The core genus was Lactobacillus. The top six represented bacterial genera across the samples were Lactobacillus, Megasphaera and Mitsuokella (Phylum Firmicutes, 35–97% of sample reads); Arcobacter and Alcaligenes (Phylum Proteobacteria, 0–40%); Dysgonomonas (Phylum Bacteroidetes, 0–53%); and Bifidobacterium (Phylum Actinobacteria, 0–18%). Potential genes for denitrification but not nitrification were identified in the vinasse metagenomes, with putative nirK and nosZ genes the most represented. Binning resulted in 38 large bins with between 36.0 and 99.3% completeness, and five small mobile element bins. Of the large bins, 53% could be classified at the phylum level as Firmicutes, 15% as Proteobacteria, 13% as unknown phyla, 13% as Bacteroidetes and 6% as Actinobacteria. The large bins spanned a range of potential denitrifiers; moreover, the genetic repertoires of all the large bins included the presence of genes involved in acetate, CO2, ethanol, H2O2, and lactose metabolism; for many of the large bins, genes related to the metabolism of mannitol, xylose, butyric acid, cellulose, sucrose, “3-hydroxy” fatty acids and antibiotic resistance were present based on the annotations. In total, 21 vinasse bacterial draft genomes were submitted to the genome repository. Conclusions Identification of the gene repertoires of vinasse bacteria and assemblages supported the idea that organic carbon and nitrogen present in vinasse together with microbiological variation of vinasse might lead to varying patterns of N2O emissions during fertirrigation. Furthermore, we uncovered draft genomes of novel strains of known bioethanol contaminants, as well as draft genomes unknown at the phylum level. This study will aid efforts to improve bioethanol production efficiency and sugarcane agriculture sustainability

Correction to: Genome-resolved metagenomics of sugarcane vinasse bacteria

The original version of the article contained a mistake. The accession number has been incorrectly published in the Availability of data and materials section