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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

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

Efeito de composto orgânico na produção da batata-doce (Ipomoea batatas (L.) Lam., na incidência de plantas daninhas e na eficiência do diuron

O experimento foi realizado em condições de campo na Fazenda Experimental da EPAMIG, em Ponte Nova-MG, no período de janeiro a julho de 1995, com o objetivo de avaliar o efeito de doses do composto orgânico produzido com dejeto de suínos na forma líquida e bagaço de canada-açúcar, na produção de batata-doce, cultivar Brazlândia Rosada, na incidência de plantas daninhas e na “eficiência do diuron. Utilizou-se o delineamento em blocos ao acaso, no esquema fatorial, com quatro repetições e quatro testemunhas capinadas, com O, 30, 60 e 90 um de composto orgânico. Avaliaram-se os efeitos das doses O, 30, 60 e 90 t/ha de composto orgânico, combinadas com 0, 800, 1.600 e 2.400 g/ha de diuron. Verificou-se incremento da biomassa fresca das plantas daninhas causado pelo aumento das doses de composto orgânico. A cultura de batata-doce respondeu positivamente ao "aumento das doses do composto orgânico com incremento na produção de raízes extra A, extra, total e comercial. O diuron foi seletivo para a cultura de batata-doce, com melhores resultados nas doses 2.400 g/ha, tanto para produção de raízes quanto para controle de plantas daninhas. Os tratamentos que receberam capina e os tratamentos que apresentaram melhor controle de plantas daninhas pelo diuron tiveram produção semelhante de raízes. Houve correlação negativa entre biomassa fresca das plantas daninhas e as principais características de produção avaliadas da batata-doce.This experiment was carried out under field conditions at the Experimental Farm located in Ponte Nova, state of Minas Gerais, Brazil, during January-July 1995, aiming to evaluate the eífects of doses of organic matter produced with liquid swine manure and crushed-sugar cane on the yield of sweet potato cv. Brazlândia Rosada, weed incidence and diuron eHiciency. A randomized block design in a factorial scheme with four replicates and four weeded controls, with 0, 30, 60 and 90 Um of the organic compound were used. The effects of doses of 0, 30, 60 and 90 t/ha organic matter combined/with 0, 800, l,600 and 2,400 g/ha diuron were evaluated. The weed fresh biomass increased as a Emotion of the increasing doses of organic matter. The sweet potato crop responded positively to the increase of organic matter doses with increment in extra A, extra, total and commercial roots. Diuron was selective for the sweet potato crop with beuer results in the 2,400 g/ha doses for both root yield and weed control. The root yield was similar in the treatments which received wwding and the ones which presented a better control of weeds by the diuron herbicide. There was a negative correlation between weeds and the main yield characteristics of the sweet potato crop