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

The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges

Made available in DSpace on 2019-10-06T16:42:11Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-11-15Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)The present study was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350–2500 nm range of Brazilian soils. The Brazilian Soil Spectral Library (BSSL) began in 1995, creating a protocol to gather soil samples from different locations in Brazil. The BSSL reached 39,284 soil samples from 65 contributors representing 41 institutions from all 26 states. Through the BSSL spectra database, it was possible to estimate important soil attributes, such as clay, sand, soil organic carbon, cation exchange capacity, pH and base saturation, resulting in differences among the multi-scale models taking Brazil (overall), regional and state scale. In general, spectral descriptive and quantitative behavior indicated important relationship with physical, chemical and mineralogical properties. Statistical analyses showed that six basic patterns of spectral signatures represent the Brazilian soils types and that environmental conditions explain the differences in spectra. This study demonstrates that spectroscopy analyses along with the establishment of soil spectral libraries are a powerful technique for providing information on a national and regional levels. We also developed an interactive online platform showing soil sample locations and their contributors. As soil spectroscopy is considered a fast, simple, accurate and nondestructive analytical procedure, its application may be integrated with wet analysis as an alternative to support the sustainable management of soils.Department of Soil Science Luiz de Queiroz College of Agriculture (ESALQ) University of São Paulo (USP), Ave. Pádua Dias 11, Cx. Postal 9Department of Soil Federal University of Santa Maria, Av. Roraima 1000Geographical Sciences Department Federal University of Pernambuco, Av. Ac. Hélio Ramos, s/nDepartment of Agronomy State University of Maringá, Av. Colombo 5790Department of Agriculture Biodiversity and Forestry Federal University of Santa Catarina, Rodovia Ulysses Gaboardi 3000 - Km 3Federal Rural University of Amazon, Ave. Presidente Tancredo Neves 2501Faculty of Agronomy and Veterinary Medicine University of BrasíliaEMBRAPA - Solos, R. Antônio Falcão, 402, Boa ViagemCenter of Nuclear Energy in Agriculture (CENA) USP, Av. Centenário 303CDRS/Secretary of Agriculture of São Paulo State, R. Campos Salles 507Department of Soils Federal University of Viçosa, Ave. Peter Henry Rolfs s/nEMBRAPA – Informática Agropecuária, Ave. André Tosello, 209Department of Nuclear Energy Federal University of Pernambuco, Av. Prof. Luis Freire 1000Department of Geography Federal University of Rio Grande do Norte, R. Joaquim Gregório s/nAgronomic Institute of Campinas (IAC), Ave. Barão de Itapura 1481Institute of Agricultural Sciences Federal Rural University of Amazônia, Ave. Presidente Tancredo Neves 2501, 66.077-830Department of Soil Science Federal University of LavrasFederal University of Mato Grosso, Cuiabá, Av. Fernando Corrêa da Costa 2367Department of Soils Federal Rural University of Rio de Janeiro, Rodovia BR 465, Km 07 s/nSoil and Water Sciences Department University of Florida, 2181 McCarty Hallr, PO Box 110290EMBRAPA - Solos, R. Jardim Botânico, 1024Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/nFederal University of Sergipe, Av. Marechal Rondon s/nGraduate Program in Earth Sciences (Geochemistry) Department of Geochemistry Federal Fluminense University, Outeiro São João Batista, s/nFederal Institute of the Southeast of Minas Gerais, R. Monsenhor José Augusto 204Federal University of Rio Grande do Norte, R. Joaquim Gregório s/nFederal University of PiauíEMBRAPA Milho e Sorgo, Rod MG 424 Km 45Institute of Agricultural Sciences Federal University of Jequitinhonha e Mucuri Valleys, Ave. Ver. João Narciso 1380Department of Biosystems Engineering ESALQ USP, Ave. Pádua Dias 11, Cx. Postal 9Federal University of Acre, Rodovia BR 364 Km 04Federal University of Amazonas, Av. General Rodrigo O. J. Ramos 1200EMBRAPA Clima Temperado, BR-392, km 78Department of Agronomy Federal Rural University of Pernambuco, R. Manuel de Medeiros s/nEMBRAPA Cocais, Quadra 11, Av. São Luís Rei de França 4Paraense Emílio Goeldi Museum, Av. Gov. Magalhães Barata 376Exata Laboratory, Rua Silvestre Carvalho Q 11Federal University of Rondônia, BR 364, Km 9.5Nacional Institute for Amazonian Research, Ave. André Araújo 2936Department of Forestry Sciences ESALQ-USP, Ave. Pádua Dias 11, Cx. Postal 9Department of Soils and Fertilizers School of Agricultural and Veterinary Studies São Paulo State University (FCAV-UNESP), Via de Acesso Prof. Paulo Donato Castellane s/nFAPESP: 2014/22262-0FAPESP: 2016/26176-6FAPESP: 2017/03207-