Distribution, threats and conservation of the White-collared Kite (Leptodon forbesi, Accipitridae), the most threatened raptor in the Neotropics

The White-collared Kite (Leptodon forbesi) is an endemic and threatened raptor of the Brazilian Atlantic Forest. Here we present the known records of the species, describe the vegetation types where it was found and show Ecological Niche Models generated using Maxent algorithm. Most of the presence data were recorded in open ombrophilous forest and seasonal semideciduous forest in the states of Alagoas and Pernambuco. Maxent model had a good performance (AUC = 0.982 ± 0.004 SD), showing higher suitability for the species from Paraíba to Alagoas states. Maxent average model revealed a distribution range of 20,344 km² and an area of occupancy of 1,636.89 km². The most suitable areas for the species are those near watercourses and streams. We suggest the creation of protected areas, including private ones, and possible restoration actions to connect the most suitable forest fragments, along with the captive breeding, as the most appropriate strategies for the conservation of the White-collared Kite.O gavião-de-pescoço-branco (Leptodon forbesi) é uma ave de rapina endêmica e ameaçada de extinção da Mata Atlântica Brasileira. Apresentamos aqui uma compilação dos registros sobre esta espécie, bem como o tipo de vegetação onde ele ocorre. Foram gerados modelos de nicho ecológico com o algoritmo Maxent, sendo estimada a área potencial com adequabilidade para o táxon através da medida da extensão da ocorrência e da área de ocupação. A maioria dos registros da espécie está concentrada nos estados de Alagoas e Pernambuco, principalmente na floresta ombrófila aberta (15 registros) e na floresta estacional semidecidual (12 registros). O modelo Maxent apresentou um bom desempenho (AUC = 0,982 ± 0,004 DP), mostrando alta probabilidade de ocorrência nos estados da Paraíba a Alagoas. Com base no modelo, a área de distribuição geográfica foi estimada em 20.344 km2 e a área de ocupação em 1.636,89 km2. As áreas com maior adequabilidade para a espécie estão próximas aos cursos d’água e córregos, sendo favorecidas principalmente pelas variáveis relacionadas à pluviometria. Como estratégia de conservação nós recomendamos a criação de novas Unidades de Conservação, principalmente RPPNs

Análise da eficácia da Tirzepatida como agente terapêutico para perda de peso em pacientes com Obesidade

A obesidade e o diabetes são doenças crônicas que afetam milhões de pessoas em todo o mundo, sendo consideradas epidemias crescentes. O tratamento da obesidade envolve uma abordagem multifacetada, incluindo mudanças no estilo de vida e intervenções farmacológicas. Nesse contexto, a tirzepatida, uma terapia combinada de dois medicamentos que atuam em diferentes vias metabólicas para reduzir o apetite e promover a perda de peso em pacientes com obesidade, tem se destacado como uma opção terapêutica promissora. O objetivo deste estudo foi avaliar a eficácia e segurança da tirzepatida como agente terapêutico para perda de peso em pacientes com obesidade. Para isso, foram selecionados quatro artigos que avaliaram o uso da tirzepatida em pacientes com obesidade, publicados entre 2018 e 2023, nas bases de dados PubMed (Medline), Scientific Electronic Library Online (SciELO) e Cochrane Library. Os resultados indicam que a tirzepatida é uma terapia promissora e segura para perda de peso em pacientes com obesidade. Todos os estudos relataram perda de peso significativa em pacientes tratados com essa terapia, variando de 8,6% a 16,0% do peso corporal inicial. Além disso, a tirzepatida também apresentou efeitos benéficos em outros parâmetros metabólicos, como redução da glicemia e melhora da função hepática. Efeitos adversos foram relatados em menor frequência e gravidade em comparação com outras terapias para perda de peso. Em resumo, a tirzepatida é uma terapia combinada de dois medicamentos que tem demonstrado eficácia e segurança para a perda de peso em pacientes com obesidade, de acordo com os resultados de quatro estudos avaliados nesta pesquisa. Essa terapia pode ser uma opção terapêutica válida para pacientes com obesidade. No entanto, é importante destacar a necessidade de mais pesquisas para avaliar sua eficácia e segurança a longo prazo e sua aplicabilidade em diferentes populações. Portanto, é fundamental que o tratamento seja realizado com acompanhamento médico e que cada caso seja avaliado individualmente

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

Perception and use of biodiversity in the vicinity of an urban conservation area, North eastern Brazil

44-50The Buraquinho Forest (Mata do Buraquinho) is a remnant of the Atlantic Forest located in the urban area of the city of João Pessoa, PB, Brazil. In this paper, we investigated aspects of environmental perception of the human population living in the surroundings, in order to contribute to future management plans and conservation of this area. A total of 43 questionnaires were applied to residents of neighborhoods adjacent to Buraquinho Forest, the questions sought to assess the social profile, environmental perception, habits, preferences and suggestions regarding natural features and services related to this protected area. Respondents recognize the role and importance of the forest to improve the quality of life as well as point out several aspects which may adversely affect the environment. Urbanization-related problems, such as the dumping of sewage and household garbage in the woods, and the lack of infrastructure, were mentioned by respondents. Data from ecological awareness indicated that respondents recognize the occurrence of 96 species of animals and 63 plants in this forest. Medicinal, edible and ornamental uses were identified in relation to the aforementioned species

Evaluation of two cowpea (Vigna unguiculata (l.) walp.) genotypes under rainfed farming with low rainfall

The sustainable agriculture in drylands depends on stored soil water, efficient use of water, prevention of soil degradation, and the use of crops that have drought tolerance. Dryland regions in the world are vulnerable ecosystems, for that reason, productive crops associated with erratic and low rainfall, and different land-use practices are fundamentals for dryland cropping systems. Cowpea (Vigna unguiculata (L.) Walp.) is an excellent crop for research in dryland regions due to high genetic variability, excellent nutritional value, wide adaptive capacity, high productive potential, and strategic values. We tested the productive performance of two cowpea genotypes in rainfed planting, during low rainfall (< 200mm), and on two different land-use conditions. Our results show that there are variations among the characters between Roxinho and Corujinha genotypes evaluated. Roxinho had more yield than Corujinha in rainfed farming with low rainfall. Besides that, Roxinho genotype shows a stable yield regardless of the environmental conditions evaluated. Yield and total dry mass show major contributions to genetic divergence and major heritability values. Therefore, phenotypic selection can be applied based on yield and using the number of vargem per plant as an indirect variable due to correlation among them.   DOI: https://doi.org/10.5281/zenodo.357540