17 research outputs found

    Influência dos fatores hidro-edáficos na diversidade, composição florística e estrutura da comunidade arbórea de igapó no Parque Nacional do Jaú, Amazônia Central

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    Several studies have described the clear influence of the flood pulse in the differential distribution of species and the existence of different vegetation types along the topographic gradient in várzea floodplain. However, few similar studies were conducted in igapó floodplain, but the results obtained until now show that there is some variation in species composition according to flooding duration; but until now it has not been reported the existence of species zonation along the gradient. This study aimed to verify the influence of hydro-edaphic factors on the floristic composition, richness, diversity, basal area and density of individuals in igapó forest and identify the existence of species zonation over the flooding and edaphic gradients. For this, 10 plots of 1 ha were established in the igapó of the Jaú National Park. All palms and trees with DBH ≥ 10 cm were registered. Four soil samples per plot were collected, considering 16 soil variables for analysis. It was recorded 193 species, 115 genera and 41 families. The diversity obtained (Fisher α) was 39.34. The flooding duration was correlated to the variation of floristic composition, diversity and structure, except to density. The soil gradient was correlated to floristic composition, but not correlated to the diversity and structure. Only one species was virtually restricted to highly flooded sites, allowing the identification of a distinct vegetation type in these areas. The other species had differentiated concentration of abundance along the flooding gradient. There has been little structuring of the community according to the soil gradient, showing that soil variables are less important than flooding to the floristic composition in igapó.Diversos estudos têm descrito a clara influência do pulso de inundação na distribuição diferencial de espécies e a existência de fitofisionomias distintas ao longo do gradiente topográfico na várzea. Entretanto, poucos estudos similares foram realizados no igapó, sendo que os resultados obtidos até agora já permitem verificar que existe certa variação na composição de espécies segundo a duração da inundação; porém até o momento não foi descrita a existência de zonação de espécies ao longo do gradiente. O presente estudo objetivou verificar a influência dos fatores hidro-edáficos sobre a composição florística, riqueza, diversidade, área basal e densidade de indivíduos na floresta de igapó, bem como identificar a existência de zonação de espécies ao longo dos gradientes de inundação e edáfico. Para isso, foram estabelecidas 10 parcelas de 1 ha no igapó do Parque Nacional do Jaú. Foram inventariados todos os indivíduos de árvores e palmeiras com DAP ≥ 10 cm. Foram realizadas 4 coletas de solo por parcela, sendo consideradas 16 variáveis edáficas para as análises. Registraram-se 193 espécies, 115 gêneros e 41 famílias. A diversidade encontrada (α de Fisher) foi 39,34. A duração da inundação demonstrou-se correlacionada à variação da composição florística, à diversidade e à estrutura, com exceção da variável densidade de indivíduos. O gradiente edáfico demonstrou-se correlacionado à variação da composição florística, porém não correlacionado à diversidade e estrutura. Apenas uma espécie apresentou-se como praticamente restrita a locais altamente inundados, permitindo a identificação de uma fitofisionomia distinta nessas áreas. As demais espécies tiveram concentração de abundância diferenciada ao longo do gradiente de inundação. Verificou-se pouca estruturação da comunidade segundo o gradiente edáfico, demonstrando que as variáveis do solo são menos importantes do que a inundação para a composição florística no igapó

    Pervasive gaps in Amazonian ecological research

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    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

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    Rarity of monodominance in hyperdiverse Amazonian forests.

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    Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors

    Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

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    In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

    Pervasive gaps in Amazonian ecological research

    Get PDF
    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

    Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

    Get PDF
    In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics
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