17 research outputs found

    Influ锚ncia dos fatores hidro-ed谩ficos na diversidade, composi莽茫o flor铆stica e estrutura da comunidade arb贸rea de igap贸 no Parque Nacional do Ja煤, Amaz么nia 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 v谩rzea floodplain. However, few similar studies were conducted in igap贸 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 igap贸 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 igap贸 of the Ja煤 National Park. All palms and trees with DBH 鈮 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 伪) 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 igap贸.Diversos estudos t锚m descrito a clara influ锚ncia do pulso de inunda莽茫o na distribui莽茫o diferencial de esp茅cies e a exist锚ncia de fitofisionomias distintas ao longo do gradiente topogr谩fico na v谩rzea. Entretanto, poucos estudos similares foram realizados no igap贸, sendo que os resultados obtidos at茅 agora j谩 permitem verificar que existe certa varia莽茫o na composi莽茫o de esp茅cies segundo a dura莽茫o da inunda莽茫o; por茅m at茅 o momento n茫o foi descrita a exist锚ncia de zona莽茫o de esp茅cies ao longo do gradiente. O presente estudo objetivou verificar a influ锚ncia dos fatores hidro-ed谩ficos sobre a composi莽茫o flor铆stica, riqueza, diversidade, 谩rea basal e densidade de indiv铆duos na floresta de igap贸, bem como identificar a exist锚ncia de zona莽茫o de esp茅cies ao longo dos gradientes de inunda莽茫o e ed谩fico. Para isso, foram estabelecidas 10 parcelas de 1 ha no igap贸 do Parque Nacional do Ja煤. Foram inventariados todos os indiv铆duos de 谩rvores e palmeiras com DAP 鈮 10 cm. Foram realizadas 4 coletas de solo por parcela, sendo consideradas 16 vari谩veis ed谩ficas para as an谩lises. Registraram-se 193 esp茅cies, 115 g锚neros e 41 fam铆lias. A diversidade encontrada (伪 de Fisher) foi 39,34. A dura莽茫o da inunda莽茫o demonstrou-se correlacionada 脿 varia莽茫o da composi莽茫o flor铆stica, 脿 diversidade e 脿 estrutura, com exce莽茫o da vari谩vel densidade de indiv铆duos. O gradiente ed谩fico demonstrou-se correlacionado 脿 varia莽茫o da composi莽茫o flor铆stica, por茅m n茫o correlacionado 脿 diversidade e estrutura. Apenas uma esp茅cie apresentou-se como praticamente restrita a locais altamente inundados, permitindo a identifica莽茫o de uma fitofisionomia distinta nessas 谩reas. As demais esp茅cies tiveram concentra莽茫o de abund芒ncia diferenciada ao longo do gradiente de inunda莽茫o. Verificou-se pouca estrutura莽茫o da comunidade segundo o gradiente ed谩fico, demonstrando que as vari谩veis do solo s茫o menos importantes do que a inunda莽茫o para a composi莽茫o flor铆stica no igap贸

    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鈥7 vast areas of the tropics remain understudied.8鈥11 In the American tropics, Amazonia stands out as the world鈥檚 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鈥15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon鈥檚 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鈥檚 vulnerability to environmental change. 15%鈥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鈥夆墺鈥10鈥塩m 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%鈥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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