PCR amplification bias is a well-known problem in metagenomic analysis of arthropod communities. In contrast, variation of DNA degradation rates is a largely neglected source of bias. Differential degradation of DNA molecules could cause underrepresentation of taxa in a community sequencing sample. Arthropods are often collected by passive sampling devices, like malaise traps. Specimens in such a trap are exposed to varying periods of suboptimal storage and possibly different rates of DNA degradation. Degradation bias could thus be a significant issue, skewing diversity estimates. Here, we estimate the effect of differential DNA degradation on the recovery of community diversity of Hawaiian arthropods and their associated microbiota. We use a simple DNA size selection protocol to test for degradation bias in mock communities, as well as passively collected samples from actual Malaise traps. We compare the effect of DNA degradation to that of varying PCR conditions, including primer choice, annealing temperature and cycle number. Our results show that DNA degradation does indeed bias community analyses. However, the effect of this bias is of minor importance compared to that induced by changes in PCR conditions. Analyses of the macro and microbiome from passively collected arthropod samples are thus well worth pursuing.

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase the number of categorized specimens by a factor of 4.4 (from 794 to 3,497), and the number of categorized species by a factor of 4.8 from (147 to 707) at a rate much greater than chance (77.6% accuracy). The study identifies phylogenetic signatures of both native and nonnative species and suggests several practical applications for this approach including monitoring biodiversity and facilitating biosecurity.