How can genomic data inform biological invasions?
Permanent link to Research Commons versionhttps://hdl.handle.net/10289/15477
Rates of biological invasions are increasing, with global trade and climate change causing significant damage to biodiversity, human well-being, primary industries, and economies around the world. However, our ability to predict and prevent future invasions is limited by significant gaps in our mechanistic understanding of the invasion process. Advances in next generation sequencing technologies and bioinformatics make it possible to investigate potential genomic factors that drive invasion success with much higher resolution and accuracy than prior research based on a small number of genetic loci. My thesis argues for the value of population genomic data in invasion biology, first examining the uptake of genomics in invasion research and then providing a case study for using genomic data to understand invasion patterns of pink bollworm (Pectinophora gossypiella). The first analysis (Chapter 2) compares the extent to which population genetic data versus population genomic data, including reference genomes, have been used or are publicly available to study globally invasive species from the International Union for Conservation of Nature (IUCN) “100 of the World’s Worst Invasive Alien Species” (WAS) list. In this chapter, I demonstrate that ‘invasion genomics’ is still in its infancy with respect to research uptake: while 82% of species on the WAS list have been studied using some form of population genetic data, just 32% have been studied using population genomic data. Further, 55% of the WAS list species lack a reference genome, however 18% of these were sequenced in the last three years, indicating a growing investment in genomic resources that looks promising for future invasion genomics research. The second analysis (Chapter 3) showcases population genomic data being used as a tool to inform a biological invasion. Pink bollworm is one of the most destructive global pests of cotton, costing farmers millions of dollars each year in productivity losses and management efforts. A small population of pink bollworm is currently established in North West Australia, where it poses a significant threat to the expanding cotton industry there. In this chapter, I analysed genomic data in the form of single nucleotide polymorphisms (SNPs) – obtained through a reduced representation, genotyping-by- sequencing technique (DArTseq) – for global populations of pink bollworm to elucidate the population structure and connectivity patterns of the pest. My results show that pink bollworm populations in my dataset have low genetic diversity and strong differentiation between populations from different continents. Importantly, the high genetic differentiation between Australia and other continents reduces concerns about gene flow to North West Australia, particularly from populations in India and Pakistan that have evolved resistance to transgenic insecticidal cotton. As species continue to move globally beyond their natural ranges, understanding how genome-driven processes facilitate invasion is critical. Genomic data can enhance our mechanistic understanding of the invasion process and inform proactive management of invasive species. Yet, despite progress in this space, there remain limitations to the breadth and depth of such studies that are highlighted throughout my thesis. These represent valuable research opportunities. With the cost of generating genomic data constantly decreasing and long-read sequencing bridging the gap for many taxon-specific challenges, genomic data is starting to address many previously intractable research questions and has the potential to improve overall biosecurity outcomes worldwide.
The University of Waikato
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- Masters Degree Theses