Population genetic analysis of brazilian peach breeding germplasm.

ABSTRACT Peach has great economic and social importance in Brazil. Diverse sources of germplasm were used to introduce desirable traits in the Brazilian peach breeding pool, composed mainly by local selections and accessions selected from populations developed by the national breeding programs, adapted to subtropical climate, with low chill requirement, as well as accessions introduced from several countries. In this research, we used SSR markers, selected by their high level of polymorphism, to access genetic diversity and population structure of a set composed by 204 peach selected genotypes, based on contrasting phenotypes for valuable traits in peach breeding. A total of 80 alleles were obtained, giving an average of eight alleles per locus. In general, the average value of observed heterozygosity (0.46) was lower than the expected heterozygosity (0.63). STRUCTURE analysis assigned 162 accessions splitted into two subpopulations based mainly on their flesh type: melting (96) and non-melting (66) flesh cultivars. The remaining accessions (42) could not be assigned under the 80% membership coefficient criteria. Genetic variability was greater in melting subpopulation compared to non-melting. Additionally, 55% of the alleles present in the breeding varieties were also present in the founder varieties, indicating that founding clones are well represented in current peach cultivars and advanced selections developed. Overall, this study gives a first insight of the peach genetic variability available and evidence for population differentiation (structure) in this peach panel to be exploited and provides the basis for genome-wide association studies

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