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

Functional Ecology of Campinaranas

A ecologia funcional tem sido usada como base para planejar e responder questões sobre manejo, conservação e restauração de ecossistemas. Reconhecer grupos funcionais de plantas em Campinaranas é importante para entender como a diversidade e a morfologia dessa vegetação responde aos fatores ambientais e como esses traços podem ser usados na conservação e manejo desses ecossistemas. Nosso estudo foi conduzido em uma Campinarana da Amazônia Central sobre Espodossolo e o objetivo foi detectar grupos funcionais entre as espécies nas diferentes fitofisionomias desse ecossistema. Por meio de um modelo linear generalizado (GLM) confirmamos a hipótese de que a maioria das espécies de plantas de Campinarana pertence a grupos funcionais definidos por formas de vida que respondem à proporção de areia fina no solo (proxy do status de água). Confirmou-se que o status de água, cuja proxy é a proporção de areia fina sobre areia grossa (AF/AG), influencia a distribuição espacial de grupos funcionais no gradiente, influenciando a diversidade e a fisionomia da vegetação. O funcionamento nas Campinaranas é determinado pelo estresse, com o grupo funcional mésico beneficiado nos locais de menor estresse e o grupo funcional tolerante ao estresse por alagamento beneficiado pelo maior estresse. Espécies mésicas são fanerófitas e lianas enquanto as espécies tolerantes ao estresse por alagamento são as caméfitas, hemicriptófitas e terófitas. A hipótese de que a Campinarana estudada tem funcionamento semelhante ao de Mussunungas não foi confirmada. O estresse em Campinarana é de alagamento nos solos com maiores valores de AF/AG. O inverso ocorre em Mussununga com estresse de seca nos solos de menores valores de AF/AG. Os padrões observados neste estudo são importantes para fortalecer a inserção das Campinaranas dentro do conceito de Áreas Úmidas do Brasil. Isso beneficiaria esse ecossistema, uma vez que, receberia um tratamento específico na forma da lei, permitindo a conservação e o uso adequado desses ambientes.Functional ecology has long been used as a basis to plan and answer questions about management, conservation and restoration of ecosystems. Recognize functional groups of plants in Campinaranas is important to understand how the diversity and morphology of this vegetation responds to environmental factors and how these traits can be used in the conservation and management of these ecosystems. This study was conducted at white Campinarana vegetation in the Central Amazon. The aim of this study was to assess the proportion of fine to coarse sand (FS/CS) in different physiognomies of Campinarana to detect functional groups. By a generalized linear model (GLM) we confirmed the hypothesis that most plant species from the Campinarana ecosystem belong to functional groups defined by life forms that respond to the fine sand proportion in the soil (a proxy of water status). It was confirmed that FS/CS affects the spatial distribution of functional groups in the gradient and influences the diversity and morphological complexity of this vegetation. However, the hypothesis that the functioning of Campinaranas is similar to the Mussunungas was not confirmed. The patterns observed in this study are important to achieve inclusion of Campinaranas as wetlands within the concept of Brazilian wetlands. As such, these endangered ecosystems would receive special treatment by law, allowing the preservation and proper use of these environments.Fundação de Amparo a Pesquisa do Estado do Amazona