环境DNA
生物
生物多样性
洞穴
生态学
分类单元
作者
Enrico Lunghi,Barbara Valle,Alessia Guerrieri,Aurélie Bonin,Fabio Cianferoni,Raoul Manenti,Gentile Francesco Ficetola
标识
DOI:10.1016/j.scitotenv.2022.154022
摘要
Subterranean environments host a substantial amount of biodiversity, however assessing the distribution of species living underground is still extremely challenging. Environmental DNA (eDNA) metabarcoding is a powerful tool to estimate biodiversity in poorly known environments and has excellent performance for soil organisms. Here, we tested 1) whether eDNA metabarcoding from cave soils/sediments allows to successfully detect springtails (Hexapoda: Collembola) and insects (Hexapoda: Insecta); 2) whether eDNA mostly represents autochthonous (cave-dwelling) organisms or it also incorporates information from species living in surface environments; 3) whether eDNA detection probability changes across taxa with different ecology. Environmental DNA metabarcoding analyses detected a large number of Molecular Operational Taxonomic Units (MOTUs) for both insects and springtails. For springtails, detection probability was high, with a substantial proportion of hypogean species, suggesting that eDNA provides good information on the distribution of these organisms in caves. Conversely, for insects most of MOTUs represented taxa living outside caves, and the majority of them represented taxa/organisms living in freshwater environments (Ephemeroptera, Plecoptera and Trichoptera). The eDNA of freshwater insects was particularly abundant in deep sectors of caves, far from the entrance. Furthermore, average detection probability of insects was significantly lower than the one of springtails. This suggests that cave soils/sediments act as "conveyer belts of biodiversity information", possibly because percolating water lead to the accumulation of eDNA of organisms living in nearby areas. Cave soils hold a complex mix of autochthonous and allochthonous eDNA. eDNA provided unprecedented information on the understudied subterranean cave organisms; analyses of detection probability and occupancy can help teasing apart local eDNA from the eDNA representing spatially-integrated biodiversity for whole landscape.
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