Genetic Characterization of Melipona subnitida Stingless Bee in Brazilian Northeast
DOI:
https://doi.org/10.13102/sociobiology.v66i4.3408Keywords:
conservation, habitat, haplotypes, mitochondrial DNA, pollinatorsAbstract
The study of M. subnitida, stingless bee well adapted to extreme environmental conditions, is noteworthy once the Northeastern Brazil faces climate changing predictions in which the precipitation rates are expected to decrease, and the average of temperatures to increase. The well-studied populations are limited to the Caatinga biome, where the species was considered endemic. However, the occurrence of this species has been reported in contrasting environments from arid region, such as mangrove and sandbanks in Maranhão state. Our primary goal was to characterize samples from these different environments and compare them with previously studied populations. We identified a unique mitochondrial haplotype per region. The haplotype found in Lençóis Maranhenses National Park was exclusive from this location and differed regarding the amino acid sequence when compared to the literature presented haplotypes from Caatinga, which might be related with different evolutionary processes in the distinct environments, though further studies are needed to confirm. Melipona subnitida is highly adapted to extreme environmental conditions and can survive under long drought periods, since during water and resource scarcity, the bees reduce the colony size to a minimum number of brood and workers. Thus, the species maintains only essential tasks for the nest, being an important mechanism to save the resources stored during the bloom of rainy periods (Maia-Silva et al., 2014; 2015). Assuming the climate change predictions for Northeastern Brazil (Marengo et al., 2011) in the semi-arid region, where average reductions in precipitation rates of up to 40–50% are expected (PBMC, 2013), the study bees resistant to these conditions, such as M. subnitida, is noteworthy. Considering the importance of this species regarding resistance to extreme climatic conditions, distributional modeling
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