Relation Between Sexual Partner's Income and Their Desire to Have Children Wanted by Homosexual and Heterosexual Men and Women

A eficácia do desempenho sexual, de um modo geral, depende de uma seleção rigorosa de parceirosfeita pelos membros de cada espécie. Entre os humanos, o critério financeiro é um dos aspectos quecaracterizam a diferença sexual na seleção de parceiros. Contudo, o comportamento sexual não podeser definido apenas pelos aspectos característicos de pessoas heterossexuais, pois também existemrelacionamentos homossexuais. A partir desta perspectiva, investigou-se, através de um website, aspreferências de homossexuais e heterossexuais por renda mensal e desejo de ter filhos no parceiro.Constatou-se que os heterossexuais desejam mais ter filhos do que os homossexuais. Além disso, paraambas as orientações, a “renda mensal” não demonstrou ser um critério seletivo na hora de escolherum parceiro.The effectiveness of sexual performance generally depends on a selection made by rigorous partnersmembers of each species. Among humans, the financial test is one of the aspects that characterize thesexual difference in the selection of partners. However, sexual behavior cannot be defined only by thecharacteristic aspects of heterosexual people, because there are also homosexual relationships. Fromthis perspective, it was investigated, through a website, the preferences of homosexual andheterosexual by monthly income and partner’s desire to have children. It was found thatheterosexual’s desire to have children is bigger than homosexuals. In addition, for both orientations,the “monthly income” not proved to be a selective criterion when choosing a partner

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

Relação entre a Renda Mensal e o Desejo de Ter Filhos Procurados no Parceiro Afetivo por Homens e Mulheres Homossexuais e Heterossexuais

A eficácia do desempenho sexual, de um modo geral, depende de uma seleção rigorosa de parceiros feita pelos membros de cada espécie. Entre os humanos, o critério financeiro é um dos aspectos que caracterizam a diferença sexual na seleção de parceiros. Contudo, o comportamento sexual não pode ser definido apenas pelos aspectos característicos de pessoas heterossexuais, pois também existem relacionamentos homossexuais. A partir desta perspectiva, investigou-se, através de um website, as preferências de homossexuais e heterossexuais por renda mensal e desejo de ter filhos no parceiro. Constatou-se que os heterossexuais desejam mais ter filhos do que os homossexuais. Além disso, para ambas as orientações, a “renda mensal” não demonstrou ser um critério seletivo na hora de escolher um parceiro