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

VIVÊNCIAS NO ESTÁGIO SUPERVISIONADO EM SETOR CLÍNICO DE ENDOSCOPIA E COLONOSCOPIA – RELATO DE EXPERIÊNCIA.

Introduction: The study of upper digestive endoscopy (EDA) and colonoscopy are exams that have improved with advances in global technologies. They serve as a basis for diagnosing basic diseases of the digestive system, with high prevalence and mortality in the world. The non-mandatory internship chosen in this area of knowledge aims to learn and develop clinical skills with the aim of improving the medical academic curriculum and exposing the activity as an experience report to the scientific community. Method: descriptive, longitudinal study, carried out in the first half of 2023 in a private clinic in Belém do Pará, where the intern presents its advantages and difficulties when carrying out the procedure. Results: the confluence between the curriculum and practical internship activities proved to be productive, in accordance with the objectives of the study and a new clinical experience for the academic. Conclusion: the active methodology of the medical course was passed on to the scientific community, where placing students in direct contact with care in an uncontrolled environment is essential to their training.  Introdução: O estudo da endoscopia digestiva alta (EDA) e colonoscopia, são exames que se aprimoraram com os avanços das tecnologias mundiais. Servem de base ao diagnóstico de doenças bases do sistema digestório, de alta prevalência e mortalidade no mundo. O estágio não obrigatório escolhido nesta área do conhecimento visa aprender e desenvolver habilidades clínicas com o intuito melhor o currículo acadêmico médico e expor a atividade como relato de experiência a comunidade científica. Método: estudo descritivo, longitudinal, realizado no primeiro semestre de 2023 em uma clínica particular em Belém do Pará, onde o estagiário apresenta suas vantagens e dificuldades ao realiza-lo. Resultados: a confluência entre grade curricular e atividades prática em estágio mostraram-se produtiva, de acordo com os objetivos do estudo e uma nova experiência clínica ao acadêmico. Conclusão: repassado a comunidade científica a metodologia ativa do curso de medicina onde colocar os alunos em contato direto com o atendimento em um ambiente não controlado é essencial a sua formação.   &nbsp

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

Changes in soil pest populations caused by sugarcane straw removal in Brazil

The adoption of green cane harvesting system without burning has triggered the increase of pest infestations in sugarcane fields. This makes straw removal a promising alternative for pest control in sugarcane fields. However, further research is needed to understand the impacts of straw removal on pest infestation under field conditions. The objective of this study was to evaluate the incidence of root spittlebug (Mahanarva fimbriolata) and sphenophorus (Sphenophorus levis) under different rates of straw removal and contrasting edaphoclimatic conditions in the São Paulo state, southeastern Brazil. Eight field experiments were conducted throughout the sugarcane cropping cycle (two crop seasons) and were arranged in a randomized block design with four replicates. The experimental design included four straw removal rates: total removal, low removal, high removal, and no removal. The incidence of pests was evaluated during September and March (spring/summer seasons in the Southern Hemisphere) for root spittlebug and April to August (autumn/winter) for sphenophorus populations. Straw removal reduced root spittlebug infestation and this effect was more evident in clayey soils. The incidence of root spittlebug was three times higher in clayey soils compared to sandy soils. Straw removal did not influence sphenophorus population but reduced damages in sugarcane plants especially in clayey soils. The damage caused by sphenophorus was three times higher in clayey than in sandy soils. This study emphasizes that straw removal by itself is not a feasible strategy to control the root spittlebug and sphenophorus infestations in sugarcane cultivated areas of Brazil, thus an integrated management strategy should be a high priority124878887This research was supported by the Sugarcane Renewable Electricity project—SUCRE/PNUD (grant number BRA/10/G31). We would like to thank LNBR technicians’ group for all the support in the field and laboratory activities, and the Mills for providing the experimental sites and the logistical support during the fieldwor