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

Ensaios sobre diferencial de salários e estimação de demanda no Brasil

A presente tese engloba três artigos sobre diferencial de salários e estimação de demanda no Brasil. O primeiro artigo investiga o diferencial de salários entre os trabalhadores dos setores público e privado. A principal contribuição deste estudo é a estimação de um modelo de regressão com mudança endógena (endogenous switching regression model), que corrige o viés de seleção no processo de escolha setorial realizada pelos trabalhadores e permite a identificação de fatores determinantes na entrada do trabalhador no mercado de trabalho do setor público. O objetivo do segundo trabalho é calcular a elasticidade-preço e a elasticidade-despesa de 25 produtos alimentares das famílias residentes nas áreas rurais e urbanas do Brasil. Para tanto, foram estimados dois sistemas de equações de demanda por alimentos, um referente às famílias residentes nas áreas rurais do país e o outro sistema associado às famílias residentes nas áreas urbanas. O terceiro artigo busca testar a validade do modelo unitário para solteiros(as) e a validade do modelo de racionalidade coletiva de Browning e Chiappori (1998) para casais no Brasil. Para tanto, foi estimado um sistema de demanda do consumo brasileiro com base no modelo QUAIDS, que apresenta uma estrutura de preferências flexível o suficiente para permitir curvas de Engel quadráticas.This study investigates the wage gap between the public and private sectors in Brazil. The analysis is carried out with 2009 microdata from the Pesquisa Nacional por Amostra de Domicílios (PNAD) and it takes account its complex sample design. The main contribution of this study is the estimation of a switching endogenous regression model that corrects the selection bias in the choice of employment sector. This model allows for the identification of some factors that determine the entrance of the individual in the public sector labor market. The public-private wage gap is calculated by gender as labor supply varies significantly between women and men. The results show that public sector wages are higher than those in the private sector. In particular, the public-private wage gap for women is higher than for men