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

Gene Expression, Histology and Histochemistry in the Interaction between Musa sp. and Pseudocercospora fijiensis

Bananas are the main fruits responsible for feeding more than 500 million people in tropical and subtropical countries. Black Sigatoka, caused by the fungus Pseudocercospora fijiensis, is one of the most destructive disease for the crop. This fungus is mainly controlled with the use of fungicides; however, in addition to being harmful to human health, they are associated with a high cost. The development of resistant cultivars through crosses of susceptible commercial cultivars is one of the main focuses of banana breeding programs worldwide. Thus, the objective of the present study was to investigate the interaction between Musa sp. and P. fijiensis through the relative expression of candidate genes involved in the defence response to black Sigatoka in four contrasting genotypes (resistant: Calcutta 4 and Krasan Saichon; susceptible: Grand Naine and Akondro Mainty) using quantitative real-time PCR (RT–qPCR) in addition to histological and histochemical analyses to verify the defence mechanisms activated during the interaction. Differentially expressed genes (DEGs) related to the jasmonic acid and ethylene signalling pathway, GDSL-like lipases and pathogenesis-related proteins (PR-4), were identified. The number and distance between stomata were directly related to the resistance/susceptibility of each genotype. Histochemical tests showed the production of phenolic compounds and callosis as defence mechanisms activated by the resistant genotypes during the interaction process. Scanning electron microscopy (SEM) showed pathogenic structures on the leaf surface in addition to calcium oxalate crystals. The resistant genotype Krasan Saichon stood out in the analyses and has potential for use in breeding programs for resistance to black Sigatoka in banana and plantains

Synthesis and Antimicrobial Activities of 5-Arylidene-thiazolidine-2,4-dione Derivatives

Antibiotic resistance is considered one of the world's major public health concerns. The main cause of bacterial resistance is the improper and repeated use of antibiotics. To alleviate this problem, new chemical substances against microorganisms are being synthesized and tested. Thiazolidines are compounds having many pharmacological activities including antimicrobial activities. For this purpose some thiazolidine derivatives substituted at position 5 in the thiazolidine nucleus were synthesized and tested against several microorganisms. Using a disc diffusion method, antimicrobial activity was verified against Gram-positive, Gram-negative, and alcohol acid resistant bacteria and yeast. The minimum inhibition concentrations (MIC) and minimum bactericidal concentrations (MBC) were determined. All derivatives showed antimicrobial activity mainly against Gram-positive bacteria, with MIC values ranging from 2 to 16 µg/mL

Molecular, Histological and Histochemical Responses of Banana Cultivars Challenged with <i>Fusarium oxysporum</i> f. sp. <i>cubense</i> with Different Levels of Virulence

Fusarium wilt caused by Fusarium oxysporum f. sp. cubense (Foc) is the most limiting factor in the banana agribusiness worldwide. Therefore, studies regarding pathogen attack mechanisms, and especially host defense responses, in this pathosystem are of utmost importance for genetic breeding programs in the development of Foc-resistant banana cultivars. In this study, analysis at the molecular, histological and histochemical levels of the Musa spp. x Foc interaction was performed. Three Foc isolates representative of race 1 (R1), subtropical race 4 (ST4) and isolate 229A, which is a putative ST4, were inoculated in two Prata-type cultivars (Prata-Anã and BRS Platina) and one cultivar of the Cavendish type (Grand Naine). Of seven genes related to plant–pathogen interactions, five were overexpressed in ‘BRS Platina’ 12 h after inoculation (HAI) with Foc R1 and ST4 but had reduced or negative expression after inoculation with Foc 229A, according to RT–qPCR analyses. While hyphae, mycelia and spores of the Foc 229A isolate grow towards the central cylinder of the Grand Naine and Prata-Anã cultivars, culminating in the occlusion of the xylem vessels, the BRS Platina cultivar responds with increased presence of cellulose, phenolic compounds and calcium oxalate crystals, reducing colonization within 30 days after inoculation (DAI). In general, these data indicate that the cultivar BRS Platina has potential for use in banana-breeding programs focused on resistance to Foc tropical race 4 (TR4) and in aggregating information on the virulence relationships of the Foc pathogen and the defense responses of banana plants after infection

Phytoene Desaturase (PDS) Gene-Derived Markers Identify “A” and “B” Genomes in Banana (<i>Musa</i> spp.)

Phytoene desaturase (PDS) is a plant enzyme involved in carotenoid biosynthesis. The PDS gene has been used as a selective marker for genome editing in several plant species, including banana (Musa spp.). Its knockout promotes dwarfism and albinism, characteristics that are easily recognizable and highly favorable. In Musa spp., the A genome increases fruit production and quality, whereas the B genome is associated with tolerance to biotic and abiotic stresses. The objective of this study was to identify a molecular marker in the PDS gene to easily discriminate the A and B genomes of banana. A 2166 bp fragment for the “PDSMa” marker was identified as polymorphic for the A genome (identification accuracy of 99.33%), whereas ~332 and ~225 bp fragments were detected for the “PDSMb” marker with 100% accuracy using MedCalc software. In this study, we used genotypes with A and B genomes that are used in the genetic improvement of bananas and an accession with the BT genome. It was not possible to differentiate the accession with the BT genome from the others, suggesting that the markers do not have the capacity to separate the T genome from the A and B genomes. To the best of our knowledge, this is the first study to use the PDS gene to determine doses of the A genome and identify the B genome in Musa spp., which will aid in evaluating the genomic constitution of banana hybrids and accessions at the seedling stage and accelerating their classification in crop genetic improvement programs