Genetic diversity of Ralstonia solanacearum isolates and molecular characterization as to phylotypes and sequevars

O objetivo deste trabalho foi identificar isolados brasileiros de Ralstonia solanacearum quanto a filotipos e sequevares, determinar sua diversidade genética, realizar a associação da estrutura genética do patógeno com sua classificação e origem geográfica e identificar um marcador molecular para a diagnose do moko-da-bananeira. Um grupo de 33 isolados de R. solanacearum, da coleção da Embrapa Tabuleiros Costeiros, coletado de diversos hospedeiros, foi caracterizado por meio de PCR em sequência palindrômica extragênica repetitiva (rep‑PCR) e RAPD. Deste grupo, 19 perteciam à raça 2 do patógeno e 14 à raça 1, e 15 isolados eram associados à cultura da bananeira. Os filotipos e sequevares foram caracterizados e determinados por PCR Multiplex. Verificou‑se que 82% dos isolados pertencem ao filotipo II, e 12% ao filotipo III. Todos os isolados da bananeira pertencem ao filotipo II. A técnica de RAPD foi eficiente em agrupar os isolados de acordo com sua origem geográfica; entretanto, ela requer um número elevado de marcas moleculares. Foi possível relacionar os isolados pela análise rep‑PCR. O iniciador com sequências repetitivas enterobacterianas intergênicas de consenso (ERIC) possibilitou a separação dos isolados de acordo com a raça, e o iniciador REP permitiu a discriminação entre os filotipos. Estas duas análises foram as mais informativas.The objective of this work was to identify Brazilian isolates of Ralstonia solanacearum according to phylotypes and sequevars, to determine their genetic diversity, to associate the pathogen genetic structure with its taxonomy and geographical origin, and to identify a specific molecular marker to diagnose banana moko  disease. A  group  of  33  isolates  of  R. solanacearum, from  the  collection  of  Embrapa Tabuleiros Costeiros, collected from different plant hosts, was characterized using the repetitive extragenic palindromic sequence‑based PCR (rep‑PCR) and RAPD. From this group, 19 belonged to the pathogen race 2 and 14 to the race 1, and 15 isolates were associated with banana crop. Phylotypes and sequevars were characterized and determined by Multiplex PCR. It was verified that the isolates belonged to phylotypes II (82%) and III (12%). All isolates from banana plants belonged to phylotype II. The RAPD technique was efficient in grouping these isolates according to their geographical origin; however, it requires a large number of molecular markers. It was possible to establish the relationships among the isolates by rep‑PCR. The enterobacterial repetitive intergenic consensus primer (ERIC) made it possible to separate the isolates according to the race, and the REP primer allowed for the discrimination among phylotypes. These were the two most informative analyses

O CAJUEIRO E SUAS FITOBACTERIOSES: MANCHA ANGULAR E MANCHA DE XANTHOMONAS

No Brasil, a produção de amêndoa de castanha de caju (Anacardiumoccidentale L.) destina-se tradicionalmente ao mercado externo, movimentandoanualmente bilhões de dólares. No entanto, essa produção pode ser limitadadevido à ocorrência de doenças causadas por fitobactérias do gênero Xanthomonas. Nesta revisão, são abordados aspectos taxonômicos do agente causal da mancha angular e mancha de xanthomonas, assim como a sintomatologia, etiologia, epidemiologia e controle dessas fitobacterioses em Anacardiáceas, com ênfase no cajueiro

12,500+ and counting: biodiversity of the Brazilian Pampa

Knowledge on biodiversity is fundamental for conservation strategies. The Brazilian Pampa region, located in subtropical southern Brazil, is neglected in terms of conservation, and knowledge of its biodiversity is fragmented. We aim to answer the question: how many, and which, species occur in the Brazilian Pampa? In a collaborative effort, we built species lists for plants, animals, bacteria, and fungi that occur in the Brazilian Pampa. We included information on distribution patterns, main habitat types, and conservation status. Our study resulted in referenced lists totaling 12,503 species (12,854 taxa, when considering infraspecific taxonomic categories [or units]). Vascular plants amount to 3,642 species (including 165 Pteridophytes), while algae have 2,046 species (2,378 taxa) and bryophytes 316 species (318 taxa). Fungi (incl. lichenized fungi) contains 1,141 species (1,144 taxa). Animals total 5,358 species (5,372 taxa). Among the latter, vertebrates comprise 1,136 species, while invertebrates are represented by 4,222 species. Our data indicate that, according to current knowledge, the Pampa holds approximately 9% of the Brazilian biodiversity in an area of little more than 2% of Brazil’s total land The proportion of species restricted to the Brazilian Pampa is low (with few groups as exceptions), as it is part of a larger grassland ecoregion and in a transitional climatic setting. Our study yielded considerably higher species numbers than previously known for many species groups; for some, it provides the first published compilation. Further efforts are needed to increase knowledge in the Pampa and other regions of Brazil. Considering the strategic importance of biodiversity and its conservation, appropriate government policies are needed to fund studies on biodiversity, create accessible and constantly updated biodiversity databases, and consider biodiversity in school curricula and other outreach activitie

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

Levantamento da intensidade da podridão-mole da alface e couve-chinesa nas regiões da Mata e Agreste do Estado de Pernambuco e determinação do tamanho das amostras para avaliação da incidência da doença

Os cultivos de alface (Lactuca sativa L.) e couve-chinesa (Brassica pekinnensis L.) podem ter a produção reduzida, devido à ocorrência da podridão-mole causada por Pectobacterium carotovorum subsp. carotovorum. No período de janeiro a maio de 2004, foram realizados levantamentos da intensidade da podridão-mole em plantios de alface e couve-chinesa, nas mesorregiões da Zona da Mata e Agreste do estado de Pernambuco. A prevalência da doença foi de 45,2% em alface e de 100% em couve-chinesa, enquanto a incidência variou entre 0 a 22% na primeira cultura e 1 a 67% na segunda. Em alface, maior intensidade da podridão-mole foi constatada em áreas: com mais de 17 anos de plantio; plantadas com as cultivares Elba, Cacheada e Tainá; com solo argiloso; irrigadas pelo sistema de rega e com drenagem deficiente. Por outro lado, menor intensidade da doença foi observada emáreas: plantadas com as cultivares Verdinha e Salad Bowl; cultivadas anteriormente com coentro e onde foram plantadas mudas produzidas em bandejas. Em couve-chinesa, observou-se que a intensidade da podridão-mole foi maior em áreas: com mais de 10 anos de cultivo e em plantios com mais de 50 dias. A única subespécie encontrada causando podridão-mole em todas as áreas de cultivo de alface e couve-chinesa foi Pectobacterium carotovorum subsp. carotovorum. Para a estimativa do tamanho ideal das amostras para avaliação da incidência da podridão-mole em campo, foram conduzidas amostragens-piloto em oito áreas de plantio de alface e cinco de couve-chinesa, situadas nos principais municípios produtores do estado de Pernambuco. Considerando os resultados obtidos e um erro aceitável de 20%, emfuturos levantamentos da incidência da podridão-mole, recomenda-se a amostragemde 32 parcelas/ha e 20 plantas por parcela de 4,5m2 para a alface e de 21 parcelas/ha e 20 plantas por parcela de 10,5m2 para a couve-chinesa. Para as duas culturas, houve correlação significativa (P=0,05) entre a intensidade de agregação da doença e o tamanho da amostra, mas não entre os níveis de incidência da doença e os tamanhos das amostrasLettuce (Lactuca sativa L.) and Chinese cabbage (Brassica pekinnensis L.) may present yield reduction due to the occurrence of soft rot caused by Pectobacterium carotovorum subsp. carotovorum. Surveys of the intensity of soft rot in plantations of lettuce and Chinese cabbage were performed from January to May 2004 in mesoregions of the Zona da Mata and Agreste of the state of Pernambuco, Brazil. Disease prevalence of 45.2% was observed in lettuce and 100% in Chinese cabbage. The incidence of soft rot ranged from 0 to 22% in lettuce and 1 to 67% in Chinese cabbage. In lettuce higher intensity of soft rot was observed in areas: having more than 17 years of cultivation; planted with ‘Elba’, ‘Cacheada’ and ‘Tainá’; with clay soil type; irrigated by hosing and having poor drainage. Lower disease intensity was detected in areas: planted with ‘Verdinha’ and ‘Salad Bowl’; having coriander asprevious crop and when seedlings were produced in trays. In Chinese cabbage higher intensity of soft rot was found in areas having more than 10 years of cultivation, and in plantations with more than 50 days. The sole subspecies detected causing soft rot in all areas of lettuce and Chinese cabbage was Pectobacterium carotovorum subsp. carotovorum. To determine the ideal sample size for assessing incidence of soft rot in field, pilot-samples were conducted in eight lettuce planting areas and five Chinese cabbage planting areas, located in the main production municipalities in the state of Pernambuco. Based on our data and considering 20% of acceptable error, future surveys of the soft rot incidence should analyze 32 plots/ha and 20 plants/plot with 4.5m2 for lettuce and 21 plots/ha and 20 plants/plot with 10.5m2 for Chinese cabbage. For both crops there was significant correlation (P=0.05)between the intensity of disease aggregation and sample size but not between disease incidence levels and sample sizesCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPE