24 research outputs found
Uso da geotecnologia para caracterizar os cafezais no município de Londrina-PR, em relação à altimetria, declividade e tipo de solo
Caracterização de lavouras cafeeiras, utilizando técnicas de geoprocessamento e sensoriamento remoto, no município de Umuarama - PR
Relação entre a patogenicidade de Schistosoma mansoni em camundongos e a susceptibilidade do molusco vetor: I. Infecciosidade das cercárias e carga de vermes
Perdas de solo e água por erosão hídrica em sistemas florestais na região de Aracruz (ES)
Dinâmica físico-hídrica de uma toposseqüência de solos sob Savana Florestada (Cerradão) em Assis, SP
Gênese de horizonte coeso, fragipã e duripã em solos do tabuleiro costeiro do sul da Bahia
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