Categorization of species as native or nonnative using DNA sequence signatures without a complete reference library.

Area of Science:

• Biodiversity research
• Genetics
• Biosecurity

Background:

• DNA barcoding is crucial for biodiversity and biosecurity but limited by incomplete reference libraries, especially in species-rich, underexplored tropical regions.
• Accurate species identification is vital for understanding ecosystems and preventing invasive species spread.

Purpose of the Study:

• To develop and validate a novel statistical approach for categorizing unidentified species as native or nonnative using nucleotide diversity.
• To enhance the classification of species in biodiversity-rich regions with limited taxonomic expertise and DNA reference data.

Main Methods:

• A generalized linear mixed model (GLMM) was employed to analyze nucleotide diversity in DNA sequences.
• A training dataset of known endemic and introduced species was used to build the classification model.
• The approach was applied to a large sample of terrestrial insects and spiders from the Moorea BioCode project.

Main Results:

• The novel method successfully categorized unidentified species, increasing categorized specimens by 4.4x and species by 4.8x.
• Achieved 77.6% accuracy in distinguishing between native and nonnative species.
• Identified distinct phylogenetic signatures for native and nonnative species.

Conclusions:

• This statistical approach effectively categorizes species' native/nonnative status based on DNA diversity, overcoming limitations of traditional DNA barcoding.
• The method significantly improves biodiversity documentation and biosecurity monitoring in data-deficient regions.
• Applications include enhanced biodiversity inventories and early detection of invasive species.