Organização de comunidades de besouros rola-bosta (Coleoptera, Scarabaeidae) em áreas de re-estabelecimento de vegetação em Feira de Santana, Bahia, Brasil

The present work refers to the study of colonization of four vegetation physiognomies considered as succession stages by Scarabaeidae beetles in Feira de Santana, Brazil. The four environments present structures of 1) herbs, 2) herbs and bushes, 3) bushes, and 4) bushes and trees. The last one has similar vegetation elements to the original vegetation of the area (Caatinga, in transition with deciduous forest). Beetles’ richness was not statistically different in the three structurally simpler habitats, the composition, as well as the abundance structure of the communities of the four sites were very similar. Guild structure changed in number and proportion of specimens, with an increase in the number of tunnelers as the vegetation grew in complexity. The positive/negatives effects on the composition and beetles’s community structure were related to the interference with the dissemination of odor plumes or maintenance of sources of resources (vertebrates). The organization of the functional structure in the beetles’ community should be related to the time needed for the establishment of complex ecological connections.O presente trabalho refere-se ao estudo de colonização de quatro fisionomias de vegetação consideradas como estágios sucessionais, em Feira de Santana, Brasil, por besouros Scarabaeidae. Os quatro ambientes possuem estruturas: 1) herbácea, 2) herbácea/arbustiva, 3) arbustivo/arbórea e 4) arbórea. Esta última com elementos de vegetação similares à vegetação original da área (Caatinga, em transição com floresta decídua). A riqueza de besouros não foi estatisticamente diferente nos três ambientes estruturalmente mais simples, e tanto a composição quanto a estrutura de abundância das comunidades dos quatro sítios foram muito semelhantes. A estrutura de guildas mudou em número e proporção de espécies, com aumento no número de “escavadores” com o aumento na complexidade estrutural da vegetação, cujos efeitos positivos / negativos sobre a composição e estrutura da comunidade de besouros foi relacionado à interferência sobre a disseminação de plumas de odor ou manutenção de fontes de recursos (vertebrados). A organização da estrutura funcional da comunidade de besouros deve ser relacionada ao tempo necessário para o estabelecimento de conexões ecológicas complexas.O presente trabalho refere-se ao estudo de colonização de quatro fisionomias de vegetação consideradas como estágios sucessionais, em Feira de Santana, Brasil, por besouros Scarabaeidae. Os quatro ambientes possuem estruturas: 1) herbácea, 2) herbácea/arbustiva, 3) arbustivo/arbórea e 4) arbórea. Esta última com elementos de vegetação similares à vegetação original da área (Caatinga, em transição com floresta decídua). A riqueza de besouros não foi estatisticamente diferente nos três ambientes estruturalmente mais simples, e tanto a composição quanto a estrutura de abundância das comunidades dos quatro sítios foram muito semelhantes. A estrutura de guildas mudou em número e proporção de espécies, com aumento no número de “escavadores” com o aumento na complexidade estrutural da vegetação, cujos efeitos positivos / negativos sobre a composição e estrutura da comunidade de besouros foi relacionado à interferência sobre a disseminação de plumas de odor ou manutenção de fontes de recursos (vertebrados). A organização da estrutura funcional da comunidade de besouros deve ser relacionada ao tempo necessário para o estabelecimento de conexões ecológicas complexas

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

Virome analyses of Hevea brasiliensis using small RNA deep sequencing and PCR techniques reveal the presence of a potential new virus

Abstract Background Hevea brasiliensis is an important commercial crop due to the high quality of the latex it produces; however, little is known about viral infections in this plant. The only virus described to infect H. brasiliensis until now is a Carlavirus, which was described more than 30 years ago. Virus-derived small interfering RNA (vsiRNAs) are the product of the plant’s antiviral defense triggered by dsRNA viral intermediates generated, during the replication cycle. These vsiRNAs are complementar to viral genomes and have been widely used to identify and characterize viruses in plants. Methods In the present study, we investigated the virome of leaf and sapwood samples from native H. brasiliensis trees collected in two geographic areas in the Brazilian Amazon. Small RNA (sRNA) deep sequencing and bioinformatic tools were used to assembly, identify and characterize viral contigs. Subsequently, PCR amplification techniques were performed to experimentally verify the presence of the viral sequences. Finally, the phylogenetic relationship of the putative new virus with related viral genomes was analyzed. Results Our strategy allowed the identification of 32 contigs with high similarity to viral reference genomes, from which 23 exhibited homology to viruses of the Tymoviridae family. The reads showed a predominant size distribution at 21 nt derived from both strands, which was consistent with the vsiRNAs profile. The presence and genome position of the viral contigs were experimentally confirmed using droplet digital PCR amplifications. A 1913 aa long fragment was obtained and used to infer the phylogenetic relationship of the putative new virus, which indicated that it is taxonomically related to the Grapevine fleck virus, genus Maculavirus. The putative new virus was named Hevea brasiliensis virus (HBrV) in reference to its host. Conclusion The methodological strategy applied here proved to be efficient in detecting and confirming the presence of new viral sequences on a ‘very difficult to manage’ sample. This is the second time that viral sequences, that could be ascribed as a putative novel virus, associated to the rubber tree has been identified