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

Elucidating the etiology of onion bacterial scale rot in the semi-arid region of Northeastern Brazil

The etiology of onion scale rot caused by bacteria in the semi-arid region of northeastern Brazil is unclear, which complicates disease control. Forty-five bacterial strains collected from the main onion producing regions in the states of Pernambuco and Bahia were identified by sequencing and phylogenetic analysis of the 16S rRNA gene and were characterized by rep-PCR and pathological behaviour on onion. The strains were grouped into three clades: 29 strains in clade I formed by the Burkholderia cepacia complex, 10 strains in clade II formed by Burkholderia gladioli, and six strains in clade III formed by Pseudomonas aeruginosa. Rep-PCR analysis grouped the strains in 31 clusters at 70% similarity. However, it was not possible to identify the three clades by rep-PCR analysis. Inoculation of wounded onion scales showed that the strains from the B. cepacia complex and B. gladioli were more aggressive on onion scales than those from P. aeruginosa. Based on the phylogenetic identification performed in this study, we conclude that scale rot of onion bulb in the semi-arid region of northeastern Brazil is caused by bacteria from the B. cepacia complex, B. gladioli, and P. aeruginosa. In addition, more than one species of B. cepacia complex may be associated with the disease in this region.Fil: Oliveira, Willams J.. Universidad Federal Rural Pernambuco; BrasilFil: Souza, Elineide B.. Universidad Federal Rural Pernambuco; BrasilFil: Silva, Adriano M. F.. Universidad Federal Rural Pernambuco; Brasil. Universidade Federal de Alagoas; BrasilFil: Bernardi Lima, Nelson. Instituto Nacional de Tecnologia Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatologia y Modelizacion Agricola. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Unidad de Fitopatologia y Modelizacion Agricola.; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Instituto de Patología Vegetal; ArgentinaFil: Leal, Carla M.. Universidad Federal Rural Pernambuco; BrasilFil: Candeia, Jonas A.. Instituto Agronômico de Pernambuco; BrasilFil: Gama, Marco A. S.. Universidad Federal Rural Pernambuco; Brasi

Complete genome sequence of Xanthomonas campestris pv. viticola strain CCRMXCV 80 from Brazil

Here, we report the complete 5.3-Mb genome sequence of Xanthomonas campestris pv. viticola (CCRMXCV 80), which causes grapevine (Vitis vinifera L.) bacterial canker. Genome data will improve our understanding of the strain's comparative genomics and epidemiology, and help to further define plant protection and quarantine procedures.Fil: Bernardi Lima, Nelson. Universidade Federal de Pernambuco; Brasil. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Instituto de Patología Vegetal; ArgentinaFil: Gama, Marco A. S.. Universidad Federal Rural Pernambuco; BrasilFil: Mariano, Rosa L. R.. Universidad Federal Rural Pernambuco; BrasilFil: Silva, Wilson J.. Universidad Federal Rural Pernambuco; BrasilFil: Farias, Antônio R. G.. Universidad Federal Rural Pernambuco; BrasilFil: Falcão, Raul M.. Universidad Federal Rural Pernambuco; BrasilFil: Sousa-Paula, Lucas C.. Universidad Federal Rural Pernambuco; BrasilFil: Benko-Iseppon, Ana M.. Universidad Federal Rural Pernambuco; BrasilFil: Paiva, Sérgio S. L.. Universidad Federal Rural Pernambuco; BrasilFil: Balbino, Valdir Q.. Universidad Federal Rural Pernambuco; Brasil. Universidad Federal Rural Pernambuco; BrasilFil: Souza, Elineide B.. Universidad Federal Rural Pernambuco; Brasi