Regeneração natural em fragmento de Floresta Ombrófila Semidecidual em Sergipe, Brasil

The objective of this study was to evaluate floristic, phytosociology and floristic similarity between the regenerative stratum of two areas of a semideciduous ombrophilous forest fragment, in different successional stages (initial and advanced), in Sergipe state. For the evaluation of the natural regeneration of the tree species, 10 plots of 10 m2 (2 m x 5 m) were systematically allocated in each area. All regenerating individuals were inventoried at an inclusion level of less than or equal to 15 cm. The parameters of the horizontal structure, Shannon diversity, Pielou equability and floristic similarity between the studied areas were calculated. The advanced successional forest presented more species, of climax ecological group, however the species with greater number of individuals are pioneers in the two areas. The lower incidence of light in the natural regeneration of the advanced successional forest resulted in a higher density of regeneration species. Both areas had floristic and diversity similarity.O trabalho foi realizado com o objetivo de avaliar a florística, fitossociologia e similaridade florística entre o estrato regenerativo de duas áreas de um fragmento de Floresta Ombrófila Semidecidual, em diferentes estágios sucessionais (inicial e avançado), no município de São Cristóvão, SE. Para a avaliação da regeneração natural das espécies arbóreas foram alocadas de forma sistemática 10 parcelas de 10 m2 (2 m x 5 m) em cada área. Organizou-se a inventariação de todos os indivíduos regenerantes, em um nível de inclusão menor ou igual a 15 cm. Calcularam-se os parâmetros da estrutura horizontal, diversidade de Shannon, equabilidade de Pielou e similaridade florística entre as áreas estudadas. A floresta em estágio avançado de sucessão tem maior número de espécies, pertencentes ao grupo clímax na sucessão, porém as espécies com maior número de indivíduos são pioneiras nas duas áreas. A menor incidência de luz na regeneração natural da floresta em estágio avançado de sucessão resultou em uma maior densidade de espécies da regeneração. As duas áreas tiveram similaridade florística e de diversidade

Aporte de serapilheira em reflorestamento misto

Ações de monitoramento são necessárias para a avaliação de indicadores que revelem a evolução da restauração ecológica. O aporte da serapilheira pode ser indicador da sustentabilidade de uma floresta plantada ou reflorestamento misto com espécies nativas. Este trabalho visou avaliar a funcionalidade de um reflorestamento em Laranjeiras (SE) utilizando o aporte de serapilheira como indicador. Realizou-se o reflorestamento em novembro de 2005, com 31 espécies florestais nativas. O aporte de serapilheira foi coletado mensalmente, de setembro de 2014 a agosto de 2015. Analisaram-se quimicamente nitrogênio (N), fósforo (P) e potássio (K), transformados para conteúdo de nutrientes (kg ha ano-1). O aporte anual de serapilheira foi estimado em 3,85 Mg ha-1 ano-1, com média mensal de 0,33 Mg ha-1. O baixo aporte de serapilheira pode advir do maior espaçamento e pelo fato de 26 das 31 espécies usadas para plantio serem secundárias tardias ou clímax, produzindo menos serapilheira. O aporte de serapilheira apresentou padrão sazonal (aumento da produção no início da estação seca e redução na estação chuvosa). O aporte anual de nutrientes, via serapilheira, foi: nitrogênio (N) >potássio (K) >fósforo (P). O padrão de aporte de serapilheira no reflorestamento misto está funcionalmente semelhante ao de ecossistemas florestais naturais de florestas estacionais semideciduais

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