Correlações do índice de área do cladódio com características morfogênicas e produtivas da palma forrageira

O objetivo deste trabalho foi avaliar as correlações do índice de área do cladódio com características morfogênicas e produtivas da palma forrageira (Nopalea sp. e Opuntia sp.). Foram avaliados três clones de palma forrageira, em condições de sequeiro: IPA Sertânia, Miúda e Orelha de Elefante Mexicana. Dados morfológicos dos cladódios e da planta, índice de área do cladódio e biomassa acumulada foram obtidos na ocasião da colheita. A relação entre os dados coletados foi avaliada por meio da análise de trilha, após a aplicação da matriz de correlação de Pearson e do teste de multicolinearidade. Foram observadas maiores correlações das características morfogênicas com o rendimento da cultura do que com o índice de área do cladódio, com R2 entre 0,5930 e 0,9502. As variáveis altura x largura e número total de cladódios foram as que melhor explicaram a variação do índice de área do cladódio. O número total de cladódios, o índice de área do cladódio e a morfologia dos cladódios de quarta ordem são as variáveis que melhor explicam a variabilidade do rendimento dos clones de palma forrageira avaliados em ambiente semiárido

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

Evaluation of the cell population of the seminiferous epithelium and spermatic indexes of the bat Sturnira lilium (Chiroptera: Phyllostomidae).

Due to the scarcity of information about patterns of spermatogenesis in bats, this study aimed to provide information on the testicular activity of the bat Sturnira lilium along the annual seasons. Thus, a series of morphometrical and stereological analyses were made using the testes of adult S. lilium in order to achieve a better understanding of the sperm production dynamics. Light and transmission electron microscopy analyses were performed in testicular fragments of animals captured during dry and rainy seasons. The testes followed the pattern of organization described for other mammals, and there were no morphological differences between organs collected either in dry or in rainy seasons. Each tubular cross-section in stage 1 was made of 0.5 type-A spermatogonia, 4.4 primary spermatocytes in preleptotene/leptotene, 3.7 in zygotene, 11.9 in pachytene, 35.6 round spermatids and 8.5 Sertoli cells. The mitotic and meiotic indexes were 15.4 and 2.9 cells, respectively, while the spermatogenesis yield was 68.7 cells. The testicular sperm reserves was 37.61×10(6) cells, and daily sperm production per gram of testis averaged 209.68×10(6) cells, both highest averages occurring in the rainy season. S. lilium male bats have a continuous reproductive pattern, high spermatogenesis yield and low support capacity by the Sertoli cells