The reverse migration decision and occupational outcomes: A self-determination theory perspective

Return migration has a great impact for all parties involved, considering that motivation greatly shapes the migration and indeed the return migration experience it is beneficial to explore the motivation behind a migration decision and understand how this shapes the processes that follow. This study extends current research in migration by considering the motivation behind the decision in greater depth through the Self-Determination Theory. Semi-structured interview were used to explore motivation in this context and consider how this shaped behavioural and occupational outcomes. Overall, we found that employment opportunities were a greater motivator, and this greatly influenced the preparation undertaken following the migration decision. We also found that while social motivations were not considered when making the decision, the social network was used as a tool for research following the return migration decision.A migração de retorno tem um grande impacto para todas as partes envolvidas. Considerando que a motivação molda grandemente a migração e, de fato, a experiência de migração de retorno; explorar a motivação por trás de uma decisão de migração e observar como isso molda os processos que se seguem ia beneficiar o nosso entendemento nesta area. Este estudo amplia as pesquisas atuais em migração, considerando a motivação por trás da decisão em maior profundidade através da Teoria da Autodeterminação. Entrevista semi-estruturada foi usada para explorar a motivação neste contexto e considerar como isso moldou os resultados comportamentais e ocupacionais. No geral, descobrimos que as oportunidades de emprego era o motivador maior, e isso influenciou muito a preparação realizada após a decisão de migração. Também descobrimos que, embora as motivações sociais não tenham sido consideradas na tomada de decisão, a rede social foi usada como ferramenta de pesquisa após a decisão de migração de retorno

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

Núcleos de Ensino da Unesp: artigos 2007

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq