MACROECOLOGIA, BIOGEOGRAFIA E ÁREAS PRIORITÁRIAS PARA CONSERVAÇÃO NO CERRADO

revista vol 13 nº 3.indd Há consenso entre os cientistas de que a há atualmente uma “crise da biodiversidade”, resultado da constante e intensa perda de habitat natural causada pela expansão da ocupação. Como a biologia da conservação tem sido muitas vezes reconhecida como uma ciência da crise, ela deve fornecer informações capazes de mediar, de forma mais científica possível, as tomadas de decisão que são necessárias. Dentre estas, uma das mais importantes é indicar regiões prioritárias para a conservação, já que por motivos óbvios não é possível preservar todos os ecossistemas por inteiro. Nesse contexto, recentemente sugeriu-se que a aplicação de princípios, teorias e análises provenientes da biogeografia e da macroecologia seriam importantes na Biologia da Conservação, formalizando uma abordagem que tem sido denominada “Biogeografia da Conservação”. Assim, o objetivo deste artigo é discutir e revisar esses componentes da biogeografia da conservação, utilizando uma abordagem macroecológica para desenvolver e aplicar métodos de planejamento sistemático em conservação, utilizando o bioma Cerrado como um modelo de estudo. Foram discutidos inicialmente os padrões de riqueza e diversidade beta e, em um segundo momento, como esses padrões podem ser correlacionados à ocupação humana do Bioma. Essa relação é fundamental para subsidiar a aplicação de modelos de planejamento sistemático de conservação em escala regional (análises de insubstituibilidade, complementaridade e de lacunas). É preciso considerar também que há sérias falhas de conhecimento sobre os padrões de biodiversidade na região e que a escolha de grupos indicadores pode ser importante para minimizar problemas gerados pela falta de conhecimento. Assim, essa abordagem é interessante em um cenário de grandes incertezas (ausência de dados detalhados) e de rápida transformação da paisagem, possibilitando a otimização de estudos em grandes escalas e depois transferir os resultados para escalas espaciais mais locais e realmente relevantes para a conservação. Nessas regiões, podem ser realizados, em um segundo momento, estudos mais detalhados a fim de avaliar padrões de viabilidade populacional, fragmentação de habitat e regiões potenciais de manutenção da diversidade genética

Rarity of monodominance in hyperdiverse Amazonian forests.

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

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

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