eDNA metabarcoding: a promising method for anuran surveys in highly diverse tropical forests - Lopes, Carla M. [UNESP]; Sasso, Thais; Valentini, Alice; Dejean, Tony; Martins, Marcio; Zamudio, Kelly R.; Haddad, Celio F. B. [UNESP] (Wiley-Blackwell, 2017)
Understanding the geographical distribution and community composition of species is crucial to monitor species persistence and define effective conservation strategies. Environmental DNA (eDNA) has emerged as a powerful noninvasive tool for species detection. However, most eDNA survey methods have been developed and applied in temperate zones. We tested the feasibility of using eDNA to survey anurans in tropical streams in the Brazilian Atlantic forest and compared the results with short-term visual and audio surveys. We detected all nine species known to inhabit our focal streams with one single visit for eDNA sampling. We found a higher proportion of sequence reads and larger number of positive PCR replicates for more common species and for those with life cycles closely associated with the streams, factors that may contribute to increased release of DNA in the water. However, less common species were also detected in eDNA samples, demonstrating the detection power of this method. Filtering larger volumes of water resulted in a higher probability of detection. Our data also show it is important to sample multiple sites along streams, particularly for detection of target species with lower population densities. For the three focal species in our study, the eDNA metabarcoding method had a greater capacity of detection per sampling event than our rapid field surveys, and thus, has the potential to circumvent some of the challenges associated with traditional approaches. Our results underscore the utility of eDNA metabarcoding as an efficient method to survey anuran species in tropical streams of the highly biodiverse Brazilian Atlantic forest.
Keywords: 12S mitochondrial gene; amphibians; biodiversity; monitoring methods; next-generation sequencing; site occupancy-detection modelling