In the wind: Invasive species travel along predictable atmospheric pathways

Abstract

Invasive species such as insects, pathogens, and weeds reaching new environments by traveling with the wind, represent unquantified and difficultto-manage biosecurity threats to human, animal, and plant health in managed and natural ecosystems. Despite the importance of these invasion events, their complexity is reflected by the lack of tools to predict them. Here, we provide the first known evidence showing that the long-distance aerial dispersal of invasive insects and wildfire smoke, a potential carrier of invasive species, is driven by atmospheric pathways known as Lagrangian coherent structures (LCS). An aerobiological modeling system combining LCS modeling with species biology and atmospheric survival has the potential to transform the understanding and prediction of atmospheric invasions. The proposed modeling system run in forecast or hindcast modes can inform high-risk invasion events and invasion source locations, making it possible to locate them early, improving the chances of eradication success.

Computing Geographical Networks Generated by Air‐Mass Movement

“…The computation of spatial connectivities generated by air-mass movement and the resulting geographical networks offers new possibilities to test and explore the links between the atmosphere and biological systems with components that are disseminated via the air (Choufany, Martinetti, Senoussi, et al, 2021;Leyronas et al, 2018;Pretorius et al, 2023;Radici et al, 2022;M. Wang et al, 2021;J.…”

“…The computation of spatial connectivities generated by air‐mass movement and the resulting geographical networks offers new possibilities to test and explore the links between the atmosphere and biological systems with components that are disseminated via the air (Choufany, Martinetti, Senoussi, et al., 2021; Choufany, Martinetti, Soubeyrand, & Morris, 2021; Leyronas et al., 2018; Pretorius et al., 2023; Radici et al., 2022; M. Wang et al., 2021; J. Wang et al., 2023). The tropolink web application precisely contributes to this objective: it facilitates the computation of air mass trajectories and the resulting connectivity matrix for a network of sites, which relies on relatively complex software and massive data for non‐specialist scientists.…”

“…However, as illustrated by some of the aforementioned references, they can be spotted in the atmosphere at some specific locations and times, or their origin and flow can be somewhat inferred using genetic data or isotopic profiles. Such information can then be associated to air‐mass trajectories reconstructed with existing software, for example, the HYSPLIT atmospheric transport and dispersion model (Stein et al., 2015), the TAPPAS living organism dispersal model, which is based on HYSPLIT (Durr et al., 2017), and other models providing complementary analysis capabilities (Pretorius et al., 2023; Schmale & Ross, 2015). These software are useful for punctual estimations of dispersal over several days.…”

“…They can also be used to assess long‐term connectivity between distal areas as recently demonstrated by Pretorius et al. (2023), who identified attracting Lagrangian coherent structures (LCS) for invasive species and bushfire smoke between Australia and New Zealand (LCS resulting in predictable atmospheric pathways for such entities); see also Radici et al. (2022) for an illustration of long‐term and large scale connectivity assessment with HYSPLIT for a fungal plant pathogen.…”

“…However, these software do not facilitate (for users with moderate programming skills) the analysis of cumulative patterns of recurring atmospheric movements for large spatial networks over extended periods. Estimating long‐term connectivity (e.g., over the entire cultural season of a given crop, or over several years) between many sites is key to understanding the spatial dynamics of populations over large scales (Leyronas et al., 2018 ), determining the likelihood of an air migration route (Pretorius et al., 2023 ; Radici et al., 2022 ; Zadoks, 1967 ), assessing the capacity of an epidemic to invade a fragmented space (Strona et al., 2017 ), or identifying sentinel points to be favored within the framework of epidemiological surveillance (Martinetti & Soubeyrand, 2019 ).…”