Modelagem da proteção do solo por plantas de cobertura no sul de Minas Gerais.

A cobertura do solo é o fator de maior importância relativa no controle da erosão hídrica. Assim, objetivou-se no presente estudo elaborar a modelagem da cobertura vegetal de vinte e quatro plantas de cobertura, em diversos sistemas de plantio e históricos de uso, com potencial para cultivo no Sul de Minas Gerais. Para avaliação da cobertura vegetal foram realizadas avaliações no campo utilizando uma régua de classificação da cobertura vegetal, sendo o delineamento experimental inteiramente casualizado, com três repetições, utilizado neste experimento. As plantas cultivadas sobre a palhada de feijão irrigado apresentaram alto índice de cobertura do solo, o que pode estar relacionado à maior disponibilidade de nutrientes deixado por esta cultura na palhada e a maior reserva de água no solo, promovido pela irrigação do feijão. O milheto cultivado em nível e sobre a palhada de milheto e feijão-de-porco apresentou o menor índice de cobertura entre as plantas testadas. Na região sul de Minas Gerais os padrões de chuvas ocorrem em maior quantidade nos períodos de outubro a março, com elevação em dezembro e janeiro. Neste período o solo deve estar protegido do impacto da gota de chuva, pois o risco de erosão hídrica é maior. Assim, a utilização das plantas de cobertura é de grande importância, pois estas protegem o solo do impacto direto das gotas de chuvas e diminuem os picos de temperatura do solo, sendo que estas devem ser cultivadas, preferencialmente, sobre a palhada de feijão

Soil cover plants on water erosion control in the South of Minas Gerais

Water erosion is responsible for soil, water, carbon and nutrient losses, turning into the most important type of degradation of Brazilian soils. This study aimed to evaluate the influence of three cover plants under two tillage systems on water erosion control in an Argisol at south of Minas Gerais state, Brazil. The cover plants utilized in the study were pigeon pea, jack bean and millet, under contour seeding and downslope tillage. Experimental plots of 4 x 12 m, with 9% slope, under natural rainfall were used for the quantification of losses of soil, water, nutrients, and organic matter. One experimental plot was kept without plant cover (reference). Higher erosivity was observed in December and January, although a great quantity of erosive rainfall was detected during the whole raining period. Contour seeding provided a greater reduction of water erosion than downslope tillage, as expected. The jack bean under contour seeding revealed the lowest values of soil, water, nutrients and organic matter losses

A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area

Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states

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