Models and applications for risk assessment and prediction of Asian soybean rust epidemics

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhiziSyd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed

Influence of light and leaf epicuticular wax layer on Phakopsora pachyrhizi infection in soybean

A ferrugem asiática, causada pelo fungo Phakopsora pachyrhizi, apresenta-se como um dos mais graves problemas fitossanitários da cultura da soja no Brasil, principalmente por não existirem, até o presente momento, cultivares com níveis de resistência satisfatórios. Objetivou-se estudar a influência da luminosidade e da camada de cera das superfícies foliares na infecção de folhas de soja por P. pachyrhizi. A superfície adaxial ou abaxial de folíolos do primeiro trifólio de plantas da cultivar BRS 154, estádio fenológico V2, foi inoculada com suspensão de 10(5) urediniósporos/mL-1. As plantas foram mantidas por 24 horas em câmara úmida e temperatura de 23ºC, sob luz ou escuro, em delineamento fatorial. Posteriormente, permaneceram 14 dias em fotoperíodo de 12 horas, sendo em seguida avaliada a densidade de lesões e a severidade da doença. Em um segundo experimento, avaliou-se in vitro , no escuro e na luz, a porcentagem de germinação de urediniósporos e de formação de apressórios. As camadas de cera adaxial e abaxial dos folíolos foram analisadas quantitativamente (extrações com clorofórmio) e estruturalmente (microscopia eletrônica de varredura). A densidade de lesões e a severidade foram maiores quando se inoculou a superfície adaxial de plantas incubadas no escuro, sem interação significativa entre os fatores. A germinação dos esporos no escuro (40,7%) foi significativamente superior à germinação na luz (28,5%). O mesmo ocorreu para a formação de apressórios, no escuro (24,7%) e na luz (12,8%). A quantidade e a estrutura das ceras epicuticulares não apresentaram diferenças entre as duas superfícies.Asian rust, caused by the fungus Phakopsora pachyrhizi, is one of the most serious phytosanitary problems of soybean in Brazil, especially because no cultivars with satisfactory resistance levels as yet exist. The objective of this study was to evaluate the influence of luminosity and of leaf epicuticular wax on the infection of soybean by P. pachyrhizi. The adaxial and abaxial leaflet surfaces of the first trifoliate leaf from cultivar BRS 154, phenological stage V2, were inoculated with a suspension of 10(5) uredospores/mL. The plants were kept for 24 hours in a humid chamber at temperature of 23ºC, in light or dark conditions, using a factorial design. Subsequently, the plants were maintained for 14 days under a 12-hour photoperiod. The disease severity and density were evaluated. For in vitro experiments, in light or dark conditions, the evaluation was done in terms of uredospore germination and appressorium formation. The wax content of adaxial and abaxial leaflets was analyzed quantitatively using chloroform extraction and ultrastructurally using scanning electron microscope. Higher density and severity were observed when the adaxial surface was inoculated, with later incubation of the plants in the dark, with no significant interaction between these factors. Spore germination in the dark (40.7%) was statistically different from spore germination in the light (28.5%). The same effect was observed with appressorium formation, in the dark (24.7%) and in the light (12.8%). The quantity and the ultrastructural aspects of epicuticular wax content did not show differences between the adaxial and abaxial surfaces; nor did they show any effect on infection by Phakopsora pachyrhizi in the soybean cultivar studied

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