ABSTRACT: As multiple species of bats are currently experiencing dramatic declines in populations due to white-nose syndrome (WNS) and other factors, conservation managers have an urgent need for data on the ecology and overall status of populations of once-common bat species. Standard approaches to obtain data on bat populations often involve capture and handling, requiring extensive expertise and unavoidably resulting in stress to the bats. New methods to rapidly obtain critical data are needed that minimize both the stress on bats and the spread of WNS. Guano provides a noninvasive source of DNA that includes information from the bat, but also dietary items, parasites, and pathogens. DNA metabarcoding is a high-throughput, DNA-based identification technique to assess the biodiversity of environmental or fecal samples. We investigated the use of multifaceted DNA metabarcoding (MDM), a technique combining next-generation DNA sequencing (NGS), DNA barcodes, and bioinformatic analysis, to simultaneously collect data on multiple parameters of interest (bat species composition, individual genotype, sex ratios, diet, parasites, and presence of WNS) from fecal samples using a single NGS run. We tested the accuracy of each MDM assay using samples in which these parameters were previously determined using conventional approaches. We found that assays for bat species identification, insect diet, parasite diversity, and genotype were both sensitive and accurate, the assay to detect WNS was highly sensitive but requires careful sample processing steps to ensure the reliability of results, while assays for nectivorous diet and sex showed lower sensitivity. MDM was able to quantify multiple data classes from fecal samples simultaneously, and results were consistent whether we included assays for a single data class or multiple data classes. Overall, MDM is a useful approach that employs noninvasive sampling and a customizable suite of assays to gain important and largely accurate information on bat ecology and population dynamics.
