Variation and genetic structure of Melipona quadrifasciata Lepeletier (Hymenoptera, Apidae) populations based on ISSR pattern

For a study of diversity and genetic structuring in Melipona quadrifasciata, 61 colonies were collected in eight locations in the state of Minas Gerais, Brazil. By means of PCR analysis, 119 ISSR bands were obtained, 80 (68%) being polymorphic. He and H B were 0.20 and 0.16, respectively. Two large groups were obtained by the UPGMA method, one formed by individuals from Januária, Urucuia, Rio Vermelho and Caeté and the other by individuals from São João Del Rei, Barbacena, Ressaquinha and Cristiano Otoni. The Φst and θB values were 0.65 and 0.58, respectively, thereby indicating high population structuring. UPGMA grouping did not reveal genetic structuring of M. quadrifasciata in function of the tergite stripe pattern. The significant correlation between dissimilarity values and geographic distances (r = 0.3998; p < 0.05) implies possible geographic isolation. The genetic differentiation in population grouping was probably the result of an interruption in gene flow, brought about by geographic barriers between mutually close geographical locations. Our results also demonstrate the potential of ISSR markers in the study of Melipona quadrifasciata population structuring, possibly applicable to the studies of other bee species

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