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

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

O cultivo do algodão herbáceo no sistema de sequeiro no Nordeste do Brasil, no cenário de mudanças climática Cultivation of upland cotton in the rainfed system in Northeastern Brazil in the climate change scenario

O principal objetivo do estudo foi avaliar o impacto das mudanças climáticas no algodoeiro herbáceo (Gossypium hirsutum L. latifolium Hutch) cultivado no Nordeste do Brasil a partir de estimativas da disponibilidade de terras aptas para a atividade agrícola de sequeiro. Essas informações, baseadas em cenários de aumento de temperatura e variabilidade da precipitação pluvial do Painel Intergovernamental de Mudanças Climáticas (IPCC), alimentam um modelo inter-regional de balanço hídrico. Os dados utilizados no estudo foram séries climatológicas diárias de precipitação pluvial, maior que 30 anos, coeficientes da cultura, evapotranspiração potencial e a duração do ciclo. Os cenários denominados A, B e C correspondem, respectivamente, aos aumentos de temperatura média do ar em 1,5; 3,0 e 5,0 ºC associados com as oscilações percentuais de precipitação de ±10; ±25 e ±40%. O Índice de Satisfação das Necessidades de Água para a cultura (ISNA), definido como a relação entre a evapotranspiração real e a evapotranspiração máxima (ETr/ETm) foi utilizado como critério na definição das áreas favoráveis ao cultivo do algodoeiro. Os resultados obtidos sugerem que os cenários de mudanças climáticas podem provocar reduções de áreas favoráveis ao algodoeiro herbáceo em toda a região Nordeste do Brasil.<br>The main objective of the study was to analyse the impact of climate change on upland cotton (Gossypium hirsutum L. latifolium Hutch) grown in Northeastern Brazil from estimates of the availability of land suitable for rainfed agriculture. This information, based on scenarios of increased temperature and rainfall variability of the Intergovernmental Panel on Climate Change (IPCC), was used in a model of inter-regional water balance. The data series used in the study were climatological daily rainfall of more than 30 years, crop coefficients, evapotranspiration potential and cycle length. The scenarios named A, B and C were related to increases in average air temperature of 1.5, 3.0 and 5.0 °C, respectively. In addition, these scenarios were associated with the precipitation fluctuations of ± 10, ± 25 and ± 40%.The Index Satisfaction of Water Requirements for crop (ISNA), defined as the ratio between the maximum evapotranspiration and actual evapotranspiration (ETa/ETm), was used as a criterion in defining areas favorable for cotton cultivation. The results obtained suggest that the climate change scenarios should lead to reduction in areas favorable for upland cotton in the entire northeast region of Brazil