Reaction of peach tree genotypes to bacterial leaf spot caused by Xanthomonas arboricola pv. prunis

Bacterial leaf spot (BLS), caused by Xanthomonas arboricola pv. pruni, is one of the most important diseases in Brazilian peach [Prunus persica (L.) Batsch] orchards and all over the world. The main objective of this study was to evaluate for BLS sensitivity of peach genotypes. Evaluations of thirty genotypes were carried out during the onset of the disease, for incidence, severity and defoliation, in field conditions. Pearson's correlations between the percentage of defoliation and leaf severity rating were performed. Genotypes 'Conserva 985', 'Conserva 871', 'Conserva 1129', and 'Tropic Snow', as resistance sources, and 'Conserva 1153', 'Bonão', 'Conserva 1125', and 'Atenas', as susceptible to BLS, were submitted to detached-leaf bioassay and greenhouse evaluation. The peach genotypes showed different reactions to the BLS, and none was immune to the pathogen. 'Conserva 985' and 'Conserva 1129' confirmed resistance responsiveness while 'Conserva 1153', 'Conserva 1125' and 'Atenas' were found susceptible for the detached-leaf bioassay

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