Comparative landscape genomics reveals species-specific spatial patterns and suggests human-aided dispersal in a global hotspot for biological invasions

Biological invasions are a growing threat to native ecosystems, and genomic studies have become an increasingly useful tool for invasive species management by providing the ability to identify spatial population structure in the invaded range. In this study, we compared the spatial genetic structure of two ecologically distinct non-native lizard species both established and widespread throughout South Florida, a global hotspot for reptile invasions. We used an individual-based sampling approach to collect genetic samples of Agama picticauda, a rock specialist native to West Africa, and Basiliscus vittatus, a riparian specialist from Central and South America. We collected specimens across Miami-Dade County (the original site of introduction) and then genotyped ~4,000 single nucleotide polymorphisms (SNPs) for each species. Both species exhibited fine-scale population structure at distances <5 km for A. picticauda and <10 km for B. vitattus, but at the county scale B. vittatus showed much stronger spatial structure compared to A. picticauda. Landscape genomic analysis revealed no significant landscape influence on A. picticauda genetic differentiation, while for B. vittatus low canopy cover was the best predictor of genetic connectivity. The genetic structure of both species may suggest human-aided dispersal is driving long distance movements, and A. picticauda appear more susceptible to these events likely due to their affinity for highly urbanized areas. By identifying variable dispersal patterns among two ecologically distinct species, we hope that this study will help combat the spread of these or similar species as they continue to arrive at urban centers across the globe. 

Methods
Genomic DNA was extracted from tissues (liver or muscle) using Qiagen’s DNeasy Blood and Tissue Extraction kit (Qiagen, Hilden, Germany), following the standard extraction protocol. DNA quality was checked by running 100 ng of each sample on a 1% agarose gel to confirm intact, unfragmented DNA. DNA samples were sent to University of Wisconsin-Madison’s Biotechnology Center DNA Sequencing Facility for genotyping-by-sequencing using the restriction enzyme ApeKI for both species (following enzyme optimization). SNPs were called using the non-reference genome version of the Universal Network Enabled Analysis Kit (UNEAK) pipeline (Lu et al. 2013) implemented in Tassel 3.0 (Bradbury et al. 2007). This pipeline trims reads to 64 bp, merges identical reads into tags within each barcoded individual, and uses pairwise alignment to identify tag pairs with 1 bp mismatch (while probabilistically correcting sequencing errors using the error tolerance rate parameter set to 0.05). This resulted in 388,535 SNPs for A. picticauda and 497,346 SNPs for B. vittatus. These candidate SNPs were further filtered to only those present in >90% of individuals and those with >5% minor allele frequency, which left a final dataset of 4,446 and 3,952 SNPs for A. picticauda and B. vittatus, respectively, that was used for all analyses.