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

Relationship between raindrops and ultrasonic energy on the disruption of a Haplic Cambisol Relação entre energia de gotas de chuva e energia ultra-sônica na desagregação de um Cambissolo Háplico

The aim of this work was to compare aggregate disruption of undisturbed soil samples by ultrasonic energy to aggregate disruption caused by the energy of simulated raindrops, to provide equations that can describe this relationship, and to evaluate whether aggregate stability, expressed by sonication method, may be used to estimate the effect that raindrops have on undisturbed soil samples. Undisturbed soil samples from A, Bi and C horizons of a Cambisol were submitted to different levels of ultrasonic energy and simulated raindrops. Sieved samples (aggregates) were also submitted to different levels of ultrasonic energy so that both disturbed and undisturbed conditions of samples could be compared. The results showed that the method using ultrasonic energy on undisturbed soil samples can simulate the amount of aggregate disruption of soil due to raindrop impact. Dispersion curves of disturbed samples may not be used to estimate the effect of raindrops on undisturbed soil samples.<br>Objetivou-se neste trabalho comparar a desagregação de amostras de solo indeformadas pela energia ultra-sônica com a desagregação causada pela energia cinética de gotas de chuva simulada, estabelecer equações para descrever essa relação e avaliar se a estabilidade de agregados determinada pelas curvas de desagregação por ultra-som pode ser usada para estimar o efeito que as gotas de chuva têm sobre amostras indeformadas. Amostras indeformadas dos horizontes A, Bi e C de um Cambissolo Háplico foram submetidas a diferentes níveis de energia ultra-sônica e gotas de chuva simulada. Amostras peneiradas (agregados) também foram submetidas a diferentes níveis de energia ultra-sônica para obtenção de curvas de desagregação. Os resultados mostraram que a quantidade de solo desagregado pelo impacto das gotas de chuva pode ser simulada pela aplicação de energia ultra-sônica sobre amostras indeformadas de solo. Curvas de desagregação de amostras deformadas não podem ser usadas para estimar o efeito das gotas de chuva sobre amostras indeformadas de solo

Acurácia e calibração de sonda de capacitância em Latossolo Vermelho cultivado com cafeeiro

O objetivo deste trabalho foi determinar a acurácia da sonda de multisensores de capacitância "Delta-T Profile probe PR2/6", na avaliação do conteúdo de água do solo com uso de calibrações padrão do fabricante, realizar a calibração para condições específicas de locais e profundidades de amostragem do solo e obter coeficientes de calibração para medições acuradas em tempo real. Em janeiro de 2010, foram coletadas amostras de solo com estrutura preservada a diferentes profundidades, nas linhas de plantio do cafeeiro e nas entrelinhas. As análises foram realizadas em laboratório, com o sensor ML2x Theta probe. Após a obtenção das leituras do sensor, o teor de água foi determinado por meio do método gravimétrico. Foram utilizadas amostras de Latossolo Vermelho distrófico muito argiloso. As calibrações padrão do fabricante (mineral e orgânica) não não se mostraram adequadas para emprego nas condições de manejo (locais e profundidades de amostragem) avaliadas. Na impossibilidade de averiguar a acurácia obtida pelo método recomendado pelo fabricante, o uso de ajustes de regressão linear ou da ferramenta Solver mostrou-se útil no processo de calibração. São necessárias apenas duas equações de calibração para avaliação do teor de água das situações contrastantes de manejo