RESUMO
Peru ranks among the three countries with the highest bird species diversity globally and a majority of those species are found in the Peruvian Amazon. However, birds in this area are currently facing serious anthropogenic threats. Genetic and genomic methods are becoming important tools for avian biodiversity monitoring and conservation planning. Comprehensive molecular libraries that are publicly available are key to the effective deployment of these tools. We analyze the information gaps for four molecular markers in the most important genetic sequence databases, Barcode of Life Data Systems (BOLD) and NCBI GenBank, for bird species of the Peruvian Amazonia. We found that 64% of Peruvian Amazonian bird species have gene sequences for COI, 59.5% have CYTB sequences, 16.4% have 12S sequences, and only 0.6% have 18S sequences. However, these numbers decrease drastically to 4.3% for COI sequences when we only consider specimens sampled in Peru. Our data also showed that 43.8% of Peruvian Amazonian endemic species (n = 32) are missing sequences of any screened marker uploaded to GenBank or BOLD. Our results will encourage and guide efforts of the scientific community to complete reference libraries for Peruvian avian species that will be useful for future DNA-based monitoring projects that include birds.
Assuntos
Biodiversidade , Aves , Animais , Peru , Aves/genética , BrasilRESUMO
Innovative techniques, such as environmental DNA (eDNA) metabarcoding, are now promoting broader biodiversity monitoring at unprecedented scales, because of the reduction in time, presumably lower cost, and methodological efficiency. Our goal was to assess the efficiency of established inventory techniques (live-trapping grids, pitfall traps, camera trapping, mist netting) as well as eDNA for detecting Amazonian mammals. For terrestrial small mammals, we used 32 live-trapping grids based on Sherman and Tomahawk traps (total effort of 10,368 trap-nights); in addition to 16 pitfall traps (1,408 trap-nights). For bats, we used mist nets at 8 sites (4,800 net hours). For medium and large mammals, we used 72 camera trap stations (5,208 camera-days). We identified vertebrate and mammal taxa based on eDNA analysis (12S region, with V05 and Mamm01 markers) from water samples, including a total of 11 3-km transects for stagnant water sampling and seven small streams for running water sampling. A total of 106 mammal species were recorded. Building on sample-based rarefaction and extrapolation curves, both trapping grids and pitfall were successful, recording 91.16% and 82.1% of the expected species for these techniques (~22 and ~9 species), and 16.98% and 6.60% of the total recorded mammal species, respectively. Mist nets recorded 83.2% of the expected bat species (~48), and 34.91% of the total recorded species. Camera trapping recorded 99.2% of the predicted large- and medium-sized species (~31), and 33.02% of the total recorded species. eDNA recorded 75.4% of the expected mammal species for this technique (~68), and 47.0% of the total recorded species. eDNA resulted in a useful tool that saves on effort and reduces sampling costs. This study is among the first to show the large potential of eDNA metabarcoding for assessing Amazonian mammal communities, providing, in combination with conventional techniques, a rapid overview of mammal diversity with broad applications to monitoring, management and conservation. By including appropriate genetic markers and updated reference databases, eDNA metabarcoding method can be extended to the whole vertebrate community.