Rapid identification of invasive species is crucial for deploying management strategies to prevent establishment. Recent Helicoverpa armigera (Hübner) invasions and subsequent establishment in South America has increased the risk of this species invading North America. Morphological similarities make differentiation of H. armigera from the native Helicoverpa zea (Boddie) difficult. Characteristics of adult male genitalia and nucleotide sequence differences in mitochondrial DNA are two of the currently available methods to differentiate these two species. However, current methods are likely too slow to be employed as rapid detection methods. In this study, conserved differences in the internal transcribed spacer 1 (ITS1) of the ribosomal RNA genes were used to develop species-specific oligonucleotide primers that amplified ITS1 fragments of 147 and 334 bp from H. armigera and H. zea, respectively. An amplicon (83 bp) from a conserved region of 18S ribosomal RNA subunit served as a positive control. Melting temperature differences in ITS1 amplicons yielded species-specific dissociation curves that could be used in high resolution melt analysis to differentiate the two Helicoverpa species. In addition, a rapid and inexpensive procedure for obtaining amplifiable genomic DNA from a small amount of tissue was identified. Under optimal conditions, the process was able to detect DNA from one H. armigera leg in a pool of 25 legs. The high resolution melt analysis combined with rapid DNA extraction could be used as an inexpensive method to genetically differentiate large numbers of H. armigera and H. zea using readily available reagents.

Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance usingHydrilla verticillataas a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence ofH. verticillatain North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well‐documented species likeH. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under‐reported and unexplored native ranges.