Physiological and biochemical responses of Eucalyptus seedlings to hypoxia

International audienceAbstractKey messageHypoxia promoted distinct changes in the levels of hormones, amino acids and organic acids in the roots and shoots of a seedling from 2Eucalyptusclones. These results indicate that modulation of hormone production, as well as specific chemical constituents associated with primary metabolism, contributes to the regulation of growth ofEucalyptusseedlings under hypoxic conditions.ContextAlthough floods in areas under Eucalyptus cultivation in Brazil negatively affect plant growth, chemical markers and/or indicators of hypoxia contributes to the regulation.sAimsThis study aimed to evaluate the hormonal and metabolic alterations induced by hypoxia on seedling growth.MethodsSeedlings of Eucalyptus urograndis clones VCC 975 and 1004 were grown in liquid solution and submitted to bubbling with air or with nitrogen. Levels of indol-3-acetic acid (IAA), abscisic acid (ABA), ethylene, 1-aminocyclopropane-1-carboxylic acid (ACC), primary metabolite profile and photosynthetic parameters were evaluated after fourteen days.ResultsHypoxia did not affect shoot dry mass of the seedlings. However, it decreased stomatal conductance and photosynthetic CO2 assimilation rate, and increased levels of ABA in the shoot. Hypoxia greatly reduced the dry mass and volume of roots, concomitantly with higher ACC and ethylene production. Moreover, hypoxia promoted distinct changes in IAA levels, and in amino acid and organic acid metabolism in roots and shoots.ConclusionThe biosynthesis of ABA, ethylene and IAA and its quantity in root tissues indicates the regulation of metabolism in response to hypoxia in Eucalyptus clones

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

Broad range flavonoid profiling by LC/MS of soybean genotypes contrasting for resistance to Anticarsia gemmatalis (Lepidoptera: Noctuidae)

Attack by herbivores is a major biotic stress limiting the soybean crop production. Plant defenses against caterpillars include the production of secondary metabolites such as flavonoids, which constitute a diverse group of plant secondary metabolites. Thus, a more discriminate metabolic profiling between genotypes are important for a more comprehensive and reliable characterization of soybean resistance. Therefore, in this study a non-targeted LC/MS-based for analysis of flavonoid profiles of soybean genotypes contrasting to the resistance to A. gemmatalis was applied. Clustering analysis revealed profiles highly distinct between the susceptible UFV 105 AP and the resistant IAC 17 genotypes. This comparative approach enables to identify directly from leaf extract some new compounds related to resistance, some of which were present in higher abundance specifically in the IAC 17 genotype: four Quercetin conjugates, Rutin (Quercetin 3-O-Rutinoside), Quercetin-3,7-O- di-glucoside, Quercetin-3-O-rhamnosylglycoside-7-O-glucoside and Quercetin-3-O-rhamnopyranosyl-glucopyranoside-rhamnopyranoside; two Genistein conjugates, Genistein-7-O-diglucoside-dimalonylated and Genistein-7-O-6 O-malonylglucoside; and one Daidzein conjugate, Daidzein-7-O-Glucoside-malonate. The most abundant flavonoid glycoconjugates in soybean leaves belongs to Quercetin and Kaempferol classes. However, only one from the identified compounds was classified as a Kaempferol. The Kaempferol-3-O-L-rhamnopyranosyl-glucopyranoside showed high abundance in the resistant genotype IAC 17. The metabolic profiles generated by LC/MS allowed the reconstruction of the flavonoid biosynthetic pathways, which revealed a constitutive character for herbivory resistance in the resistant genotype IAC-17 and a metabolic regulation for the rechanneling of Quercetin, Kaempferol and Genistein conjugates in soybean. Highest relative abundances were detected for glyconjugates, such as Rutin, Quercetin 3-O-rhamnosylglycoside-7-O-glucoside and Quercitin-3-O-rhamnopyranosyl-glucopyranoside-rhamnopyranoside in the leaves of the resistant genotype