Morphological analysis and description of the ovaries of female silky sharks, Carcharhinus falciformis (Müller & Henle, 1839)

This work aims to study the female reproductive tract of silky sharks, Carcharhinus falciformis, captured in the South and Equatorial Atlantic Ocean. Samples were collected between January 2008 and March 2010 through oceanic commercial vessels that targeted tuna and swordfish, with a total of 17 females collected. The methodologies followed for analyzing the ovaries of those females included both macroscopic and histological analysis. Macroscopically, it was possible to determine that the ovaries on these sharks is suspended by mesenteries in the anterior section of the body cavity, heavily irrigated by blood vessels, and contains a wide range of oocytes. Ovaries were found in three distinct maturational stages: Stage I (Immature), Stage II (Maturing) and Stage III (Mature). Immature ovaries were small, with widths ranging from 1.0 to 3.1 cm, and had a gelatinous or granulose internal structure; maturing ovaries were slightly larger, ranging in width between 5.2 and 6.0 cm; mature ovaries ranged in width between 6.5 and 7.8 cm, and had a more rounded shape and the presence of large and well developed oocytes. Under microscopic examination, it was observed that the ovaries were covered with simple epithelial tissue during the early development stages and a simple cubic epithelium in the final stages of maturation. During the initial maturation stages the epigonal organ was not differentiated from the ovary. In mature specimens, the ovary showed a simple cubic epithelium and just below this epithelium there was a layer of dense connective tissue and muscle with the presence of vitellogenic oocytes and fat cells. A thin yolk membrane enclosing the oocytes was also evident. Finally, it was possible to distinguish a zona pellucida, separating the oocytes from the follicle wall and a basal lamina between the granular layers and the teak layer.info: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 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

Padrões de saúde e segurança no trabalho e extrativismo: o caso de comunidades rurais da Amazônia brasileira

Este artigo é resultado de pesquisa de campo realizada junto aos coletores de sementes e frutos oleaginosos - Produtos Florestais Não Madeireiros (PFNMs) - em áreas rurais de Salvaterra e Bragança, no estado do Pará. O objetivo é contribuir com os estudos empíricos sobre saúde e segurança ocupacional no extrativismo, ainda incipientes no Brasil, por falta de amparo técnico-científico relacionado à atividade extrativista e por falta de regulamentação específica para a área. A pesquisa, realizada a partir da coleta de dados primários e observacionais, aponta os riscos de saúde e segurança aos quais os coletores estão expostos e os métodos de prevenção de acidentes, a fim de identificar possíveis melhorias nas condições de trabalho em um contexto onde a regulamentação de padrões trabalhistas para autônomos, informais e extrativistas na Amazônia é praticamente inexistente. A metodologia se caracteriza como estudo de caso em profundidade com coleta de dados primários com adoção de métodos mistos para sua sistematização. Os resultados demonstram que novas práticas e normas precisam ser adotadas para que os riscos à saúde e à segurança dos coletores sejam minimizados, além de garantir fiscalização, incentivo e monitoramento de práticas de segurança adequadas à atividade e específicas para cada região.This article is the result of a field research conducted among collectors of oleaginous seeds and fruits - Non-Timber Forest Products (NTFP) - in the rural areas of Salvaterra and Bragança, in the state of Pará, Brazil. It aimed to contribute to empirical studies on health and occupational safety in extractivism, still incipient in Brazil due to the lack of technical-scientific support and the lack of specific regulation on the area. The research was conducted by the collection of primary and observational data that point out the risks to health and safety to which collectors are exposed to and the methods to prevent accidents in order to identify possible improvements to working conditions in a context in which labor regulation standards for autonomous, informal and extractive workers in the Amazon is practically inexistent. This paper’s methodology is characterized as an embedded case study along with the collection of quantitative and qualitative data with the adoption of mixed methods for their systematization. The results show that new practices and norms should be adopted so the risks to health and safety of collectors are minimized, besides ensuring the inspection, incentives and monitoring of safety practices that are adequate to the activity and specific to each region