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

Rendimento e correlações da mamoneira consorciada com feijão-caupi e gergelim no semiárido paraibano

Cultivos consorciados representam benefícios econômicos e ambientais, por diversificar a produção na propriedade rural. Propôs-se, então, avaliar o rendimento e o grau de relacionamento entre características agronômicas da mamoneira em cultivo isolado e consorciado com feijão-caupi e gergelim. O experimento foi desenvolvido no período de março a novembro de 2011 em condições de campo, em Pombal, PB. Foram utilizados o delineamento em blocos casualizados em esquema fatorial 2 x 3 + 2, sendo 2 cultivares de mamona (IAC 2028 e BRS Nordestina), 3 sistemas de cultivo (monocultivo e consórcio com feijão-caupi ou gergelim) mais 2 tratamentos adicionais (feijão-caupi e gergelim em monocultivo) com 4 repetições. Avaliaram-se, também, as seguintes características: produção de grãos por planta (g), rendimento relativo (%), contribuição relativa das ordens dos racemos na produtividade total e estudo de correlações. Ocorreu em média maior contribuição de racemos secundários na produtividade total. O comprimento do racemo e o número de frutos por racemo são características altamente correlacionadas; dentre as culturas avaliadas a melhor opção de cultura companheira para o cultivo consorciado da mamoneira na região é o feijão-caupi

Quantum efficiency of photosystem II and production of orange under salt stress and nitrogen fertilization

ABSTRACT This study aimed to evaluate the salt tolerance of 'Mimo do Céu' orange grafted onto three rootstocks using physiological parameters such as maximum quantum efficiency of photosystem II (PSII) and the production of fruits. It also evaluated the effectiveness of increased nitrogen (N) fertilization in reducing the effects of salt stress. Two concomitant experiments were carried out under controlled conditions, using drainage lysimeters. The Experiment I evaluated the effects of the application of five levels of saline water on three combinations of scion-rootstocks grafted with 'Mimo de Ceu' orange, in a randomized block design with three replicates in a 5 x 3 factorial scheme. The Experiment II evaluated the application of two N levels (100 and 200% of recommendation) in three scion-rootstock combinations irrigated with water of electrical conductivity of 3.0 dS m-1, in a 3 x 2 factorial scheme, with three replicates. The maximum quantum efficiency of PSII was inhibited in citrus plants under salt stress. 'Mimo do Ceu' orange grafted onto 'Common' Rangpur lime has higher yield potential. The increase in N dose did not reduce the deleterious effects of water salinity on fruit production