Genomic data is missing for many highly invasive species, restricting our preparedness for escalating incursion rates.

Biological invasions drive environmental change, potentially threatening native biodiversity, human health, and global economies. Population genomics is an increasingly popular tool in invasion biology, improving accuracy and providing new insights into the genetic factors that underpin invasion success compared to research based on a small number of genetic loci. We examine the extent to which population genomic resources, including reference genomes, have been used or are available for invasive species research. We find that 82% of species on the International Union for Conservation of Nature "100 Worst Invasive Alien Species" list have been studied using some form of population genetic data, but just 32% of these species have been studied using population genomic data. Further, 55% of the list's species lack a reference genome. With incursion rates escalating globally, understanding how genome-driven processes facilitate invasion is critical, but despite a promising trend of increasing uptake, "invasion genomics" is still in its infancy. We discuss how population genomic data can enhance our understanding of biological invasion and inform proactive detection and management of invasive species, and we call for more research that specifically targets this area

How can genomic data inform biological invasions?

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

Genomic Tools in Biological Invasions: Current State and Future Frontiers

Human activities are accelerating rates of biological invasions and climate-driven range expansions globally, yet we understand little of how genomic processes facilitate the invasion process. Although most of the literature has focused on underlying phenotypic correlates of invasiveness, advances in genomic technologies are showing a strong link between genomic variation and invasion success. Here, we consider the ability of genomic tools and technologies to (i) inform mechanistic understanding of biological invasions and (ii) solve real-world issues in predicting and managing biological invasions. For both, we examine the current state of the field and discuss how genomics can be leveraged in the future. In addition, we make recommendations pertinent to broader research issues, such as data sovereignty, metadata standards, collaboration, and science communication best practices that will require concerted efforts from the global invasion genomics community

How Might Climate Change Affect Adaptive Responses of Polar Arthropods?

Climate change is expected to impact the global distribution and diversity of arthropods, with warmer temperatures forcing species to relocate, acclimate, adapt, or go extinct. The Arctic and Antarctic regions are extremely sensitive to climate change and have displayed profound and variable changes over recent decades, including decreases in sea ice extent, greening of tundra, and changes to hydrological and biogeochemical cycles. It is unclear how polar-adapted arthropods will respond to such changes, though many are expected to be at great risk of extinction. Here, we review the adaptive mechanisms that allow polar arthropods to persist in extreme environments and discuss how the effects of climate change at the poles will likely favour non-native species or those with the ability to rapidly evolve and/or acclimate. We find that physiological, behavioural, plastic, and genetic data are limited in scope for polar arthropods and research on adaptive responses to change is scarce. This restricts our ability to predict how they may respond to a warming climate. We call for a greater investment in research that specifically targets the ecology and evolution of these taxa, including genomic and transcriptomic approaches that can evaluate the potential for plastic and evolved environmental responses

PBW_SNP_DArTseq.csv

This dataset contains genome-wide SNPs for pink bollworm (Pectinophora gossypiella) samples from Australia, India, Pakistan, and U.S.. SNP data was generated by Diversity Arrays Technology, Canberra.</p

Quantifying Geographic Variation in Health Care Outcomes in the United States before and after Risk-Adjustment

Background: Despite numerous studies of geographic variation in healthcare cost and utilization at the local, regional, and state levels across the U.S., a comprehensive characterization of geographic variation in outcomes has not been published. Our objective was to quantify variation in US health outcomes in an all-payer population before and after risk-adjustment. Methods and Findings: We used information from 16 independent data sources, including 22 million all-payer inpatient admissions from the Healthcare Cost and Utilization Project (which covers regions where 50% of the U.S. population lives) to analyze 24 inpatient mortality, inpatient safety, and prevention outcomes. We compared outcome variation at state, hospital referral region, hospital service area, county, and hospital levels. Risk-adjusted outcomes were calculated after adjusting for population factors, co-morbidities, and health system factors. Even after risk-adjustment, there exists large geographical variation in outcomes. The variation in healthcare outcomes exceeds the well publicized variation in US healthcare costs. On average, we observed a 2.1-fold difference in risk-adjusted mortality outcomes between top- and bottom-decile hospitals. For example, we observed a 2.3-fold difference for risk-adjusted acute myocardial infarction inpatient mortality. On average a 10.2-fold difference in risk-adjusted patient safety outcomes exists between top and bottom-decile hospitals, including an 18.3-fold difference for risk-adjusted Central Venous Catheter Bloodstream Infection rates. A 3.0-fold difference in prevention outcomes exists between top- and bottom-decile counties on average; including a 2.2-fold difference for risk-adjusted congestive heart failure admission rates. The population, co-morbidity, and health system factors accounted for a range of R2 between 18–64% of variability in mortality outcomes, 3–39% of variability in patient safety outcomes, and 22–70% of variability in prevention outcomes. Conclusion: The amount of variability in health outcomes in the U.S. is large even after accounting for differences in population, co-morbidities, and health system factors. These findings suggest that: 1) additional examination of regional and local variation in risk-adjusted outcomes should be a priority; 2) assumptions of uniform hospital quality that underpin rationale for policy choices (such as narrow insurance networks or antitrust enforcement) should be challenged; and 3) there exists substantial opportunity for outcomes improvement in the US healthcare system

Fully risk-adjusted geographic distributions of select outcomes (IQI 15, PSI 07, PQI 08).

<p>Large variation in outcomes is present both between and within US states. Substantially different performances highlight the variation in outcomes across the US. This variation is observed across all outcomes plotted. (A) IQI 15 Acute Myocardial Infarction (AMI) Mortality Rate and PSI 07 Central Venous Catheter-Related Blood Stream Infection Rate are adjusted for low-volume noise using a Bayesian shrinkage methodology and are adjusted for population, co-morbidities, and health system factors. After risk-adjustment, there is 2.1-fold variation in IQI 15 between the top and bottom decile HSAs. After risk-adjustment, there is 12.6-fold variation in PSI 07 between the top and bottom decile HSAs. (B) PQI 08 Heart Failure Admission Rate data has been adjusted for population, co-morbidities, and health system factors. After risk-adjustment, there is 2.2-fold variation in PQI 08 between the top and bottom decile counties. Areas shown in white are due to HCUP not making geographically identifiable data on hospital or county performance available.</p

Correlations among outcomes after adjustment for population factors and co-morbidities and system factors.

<p>Inpatient mortality measures are weakly correlated with each other. Inpatient safety measures show little to no correlation with each other. Prevention quality measures show little to no correlation with each other. Correlation categorization after Dancey and Reidy (2004), analysis following low denominator number outlier removal and risk-adjustment based on the identified population factors, co-morbidities and system factors.</p