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

Triatoma maculata colonises urban domicilies in Boa Vista, Roraima, Brazil

Submitted by Sandra Infurna ([email protected]) on 2017-03-23T15:39:05Z No. of bitstreams: 1 alice_silva_etal_IOC_2016.pdf: 924847 bytes, checksum: 55121fdccc97e954f5e35dec32aaa266 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2017-03-23T15:50:06Z (GMT) No. of bitstreams: 1 alice_silva_etal_IOC_2016.pdf: 924847 bytes, checksum: 55121fdccc97e954f5e35dec32aaa266 (MD5)Made available in DSpace on 2017-03-23T15:50:06Z (GMT). No. of bitstreams: 1 alice_silva_etal_IOC_2016.pdf: 924847 bytes, checksum: 55121fdccc97e954f5e35dec32aaa266 (MD5) Previous issue date: 2016Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório Interdisciplinar de Vigilância Entomológica em Diptera e Hemiptera. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório Interdisciplinar de Vigilância Entomológica em Diptera e Hemiptera. Rio de Janeiro, RJ, Brasil.Universidade Federal de Roraima. Núcleo Observatório de Saúde de Roraima-ObservaRR., Boa Vista, RR, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório Interdisciplinar de Vigilância Entomológica em Diptera e Hemiptera. Rio de Janeiro, RJ, Brasil.Núcleo de Entomologia do Distrito Sanitário Especial Indígena Yanomami. Boa Vista, RR, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório Interdisciplinar de Vigilância Entomológica em Diptera e Hemiptera. Rio de Janeiro, RJ, Brasil.Secretaria de Saúde. Núcleo Estadual de Entomologia. Boa Vista, RR, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Mosquitos Transmissores de Hematozoários. Rio de Janeiro, RJ, Brasil.During a medical entomology course in Boa Vista, Roraima, colonies of Triatoma maculata closely associated with pigeon nests were observed in concrete air-conditioner box located on the external plastered and cemented walls of a modern brick-built apartment block. In only one eight-hole ceramic brick, located inside one air-conditioner box, 127 specimens of T. maculata were collected. T. maculata is a recognised vector of Trypanosoma cruzi in the surrounding area and its domiciliation increases the risk of Chagas disease transmission

Triatoma maculata colonises urban domicilies in Boa Vista, Roraima, Brazil

During a medical entomology course in Boa Vista, Roraima, colonies of Triatoma maculata closely associated with pigeon nests were observed in concrete air-conditioner box located on the external plastered and cemented walls of a modern brick-built apartment block. In only one eight-hole ceramic brick, located inside one air-conditioner box, 127 specimens of T. maculata were collected. T. maculata is a recognised vector of Trypanosoma cruzi in the surrounding area and its domiciliation increases the risk of Chagas disease transmission