Predicting species invasiveness with genomic data: is Genomic Offset related to establishment probability?

Abstract

Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species, but have still rarely been used in this context. Here, we evaluated the relationship between GO and the estab-lishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which the-oretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public real data from the crop pest speciesBactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between “source” populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the estab-lishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g. propagule pressure or admixture) will need further investigation.

Chromosome structural rearrangements in invasive haplodiploid ambrosia beetles revealed by the genomes ofEuwallacea fornicatusandEuwallacea similis(Coleoptera, Curculionidae, Scolytinae)

“…Access to high quality genomes has led to advancements in many fields, including conservation management (Formenti et al, 2022; Hogg, 2023; Theissinger et al, 2023), biodiversity genomics and monitoring (Breed et al, 2019; Cordier et al, 2021; Mc Cartney et al, 2023; The Darwin Tree of Life Project Consortium et al, 2022), and species invasion research and management (McGaughran et al, 2021, 2024; North et al, 2021). In the management of invasive species, genomic resources are facilitating accurate and time-efficient diagnostics and surveillance (Chua et al, 2023; Frey et al, 2022; Gardy & Loman, 2018; Matheson & McGaughran, 2022; McGaughran et al, 2024), understanding the origin of invasive species, invasion pathways (Atsawawaranunt et al, 2023; Liu et al, 2022; Rane et al, 2023; Zhang et al, 2023), identifying genetic (Jin et al, 2023; Parvizi et al, 2023; Yin et al, 2021) and genome structural (Stapley et al, 2015; Tepolt et al, 2022) variation related to invasion potential (Camus et al, 2024; McGaughran et al, 2024; Sillero et al, 2020). These resources are also accelerating the ways in which invasive species are controlled, such as through chemical management (Hopkinson et al, 2023; Tay et al, 2022; Thia et al, 2023), biocontrol agents (Morrow & Riegler, 2021; Nikolouli et al, 2018; Ross et al, 2022), genome-informed population control (Burgess et al, 2022; Kennedy et al, 2018; Maselko et al, 2017), or gene-editing and gene drive (Devos et al, 2022; Hammond et al, 2016, 2021).…”