Wild dogs at stake: deforestation threatens the only Amazon endemic canid, the short-eared dog (Atelocynus microtis)

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species

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

Screening of febrile patients with suspected malaria from the Brazilian Amazon for virus infection

Sao Paulo Research Foundation (FAPESP), Sao Paulo, Brazil, Grant no. 2017/09194-3 and no. 2015/04882-3. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)— Finance Code 001. Brazilian National Council for Scientific and Technological Development—Brazil (CNPq)—PQ fellowship (Grant no. 306471/2017-5).University of Sao Paulo. School of Pharmaceutical Sciences of Ribeirao Preto. Department of Clinical Analyses, Toxicology and Food Sciences. Laboratory of Virology. Ribeirao Preto, SP, Brazil.The University of Tennessee Health Science Center. Department of Microbiology, Immunology, and Biochemistry. Memphis, TN, USA.The University of Tennessee Health Science Center. Department of Microbiology, Immunology, and Biochemistry. Memphis, TN, USA.University of Sao Paulo. School of Pharmaceutical Sciences of Ribeirao Preto. Department of Clinical Analyses, Toxicology and Food Sciences. Laboratory of Virology. Ribeirao Preto, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.University of Sao Paulo. Ribeirao Preto Medical School. Virology Research Center. Ribeirao Preto, SP, Brazil.Fundação de Medicina Tropical Dr Heitor Vieira Dourado. Manaus, AM, Brazil.Fundação de Medicina Tropical Dr Heitor Vieira Dourado. Manaus, AM, Brazil / Universidade do Estado do Amazonas. Manaus, AM, Brazil.Fundação de Medicina Tropical Dr Heitor Vieira Dourado. Manaus, AM, Brazil / Fundação Oswaldo Cruz. Instituto Leônidas and Maria Deane. Manaus, AM, Brazil.University of Sao Paulo. School of Pharmaceutical Sciences of Ribeirao Preto. Department of Clinical Analyses, Toxicology and Food Sciences. Laboratory of Virology. Ribeirao Preto, SP, Brazil.Arthropod-borne viruses (arboviruses) are a significant public health threat, especially in tropical and subtropical regions. More than 150 arboviruses can cause febrile illness following infection in humans. The Brazilian Amazon region has the highest number of arboviruses detected worldwide. In addition to arboviruses, malaria, caused by Plasmodium vivax, is endemic in the Amazon. Patients with malaria and arboviral disease frequently show similar clinical presentation and laboratory findings, making the diagnosis of the cause of the infection challenging. The aim of this study was to evaluate the potential for viral infections in patients with suspected malaria but without Plasmodium infection in the Brazilian Amazon. We recruited 200 subjects with suspected malaria in Manaus, Brazil. First, we tested for arboviruses in serum samples from 124 of the 200 participants using an arbovirus DNA microarray platform, which did not detect any virus. Then, we mixed the serum samples of the other 76 participants in 10 pools and subjected them to next-generation sequencing. Analysis of the sequencing data revealed the presence of only one arbovirus (Zika virus) in one sample pool. This analysis also detected the presence of primate erythroparvovirus 1 and pegivirus C. These results suggest that arboviruses are not the most frequent viral infections in patients with suspected malaria but without Plasmodium infection in the metropolitan region of Manaus. Implementation of specific viral surveillance tests will help in the early detection of viruses with epidemic potential

Variabilidade espacial de atributos químicos do solo sob diferentes usos e manejos Spatial variability of chemical attributes of a soil under different uses and managements

O uso e manejo do solo e da cultura são importantes condicionadores da variabilidade de atributos do solo. O trabalho foi desenvolvido em Selvíria (MS), com o objetivo de avaliar a variabilidade espacial do pH, potássio (K), cálcio (Ca), magnésio (Mg) e saturação por bases (V) em Latossolo Vermelho sob diferentes usos e manejos. Os solos foram amostrados em malha, com intervalos regulares de 2 m, perfazendo o total de 64 pontos, nas profundidades de 0,0-0,1 e 0,1-0,2 m, nas seguintes áreas: vegetação natural (Cerrado), plantio direto, plantio convencional e pastagem. As maiores variabilidades, medidas por meio do coeficiente de variação, foram observadas para K, Mg e Ca; o pH apresentou o menor coeficiente de variação nos diferentes usos e manejo do solo, e o atributo V, coeficiente de variação médio. Os sistemas preparo convencional e pastagem apresentaram os menores alcances quando comparado aos sistemas Cerrado e plantio direto.<br>The use and management of soil and crop condition the variability of soil attributes directly. This study was conducted in Selvíria-MS, Brazil with the objective of evaluating the spatial variability of pH, potassium (K), calcium (Ca), magnesium (Mg) and base saturation (% BS) in an Oxisol under different use and management conditions. Soil samples were collected in a grid, in regular 2 m intervals, at 64 grid points, at depths of 0.0-0.1 m and 0.1-0.2 m, from areas of: native cerrado vegetation (savannah), annual crops under no-tillage, annual crops under conventional tillage, and pasture. The highest variabilities, as determined by the coefficient of variation, were observed for K, Mg and Ca, while the lowest coefficient of variation was found for pH in the different uses and management conditions. A medium coefficient of variation was observed for the %BS. The ranges of soils under conventional tillage and pasture systems were smaller than those under native vegetation and no-tillage