Tracing the Distribution of European Lactase Persistence Genotypes Along the Americas

In adulthood, the ability to digest lactose, the main sugar present in milk of mammals, is a phenotype (lactase persistence) observed in historically herder populations, mainly Northern Europeans, Eastern Africans, and Middle Eastern nomads. As the –13910∗T allele in the MCM6 gene is the most well-characterized allele responsible for the lactase persistence phenotype, the –13910C > T (rs4988235) polymorphism is commonly evaluated in lactase persistence studies. Lactase non-persistent adults may develop symptoms of lactose intolerance when consuming dairy products. In the Americas, there is no evidence of the consumption of these products until the arrival of Europeans. However, several American countries’ dietary guidelines recommend consuming dairy for adequate human nutrition and health promotion. Considering the extensive use of dairy and the complex ancestry of Pan-American admixed populations, we studied the distribution of –13910C > T lactase persistence genotypes and its flanking haplotypes of European origin in 7,428 individuals from several Pan-American admixed populations. We found that the –13910∗T allele frequency in Pan-American admixed populations is directly correlated with allele frequency of the European sources. Moreover, we did not observe any overrepresentation of European haplotypes in the –13910C > T flanking region, suggesting no selective pressure after admixture in the Americas. Finally, considering the dominant effect of the –13910∗T allele, our results indicate that Pan-American admixed populations are likely to have higher frequency of lactose intolerance, suggesting that general dietary guidelines deserve further evaluation across the continent

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