@article{worm_saffer_takeuchi_walden-schreiner_jones_meentemeyer_2024, title={Border Interceptions Reveal Variable Bridgehead Use in the Global Dispersal of Insects}, volume={10}, ISSN={["1466-8238"]}, DOI={10.1111/geb.13924}, abstractNote={ABSTRACT Aim The global, human‐mediated dispersal of invasive insects is a major driver of ecosystem change, biodiversity loss, crop damage and other effects. Trade flows and invasive species propagule pressure are correlated, and their relationship is essential for predicting and managing future invasions. Invaders do not disperse exclusively from the species' native range. Instead, the bridgehead effect, where established, non‐native populations act as secondary sources of propagule, is recognised as a major driver of global invasion. The resulting pattern of global spread arises from a mixture of global interactions between invasive species, their vectors and, their invaded ranges, which has yet to be fully characterised. Location Global. Time Period 1997–2020. Major Taxa Studied Insects. Methods We analysed 319,283 border interception records of 514 insect species from a broad range of taxa from four national‐level phytosanitary organisations. We classified interceptions as coming from species native range or from bridgehead countries and examined taxonomic autocorrelation of global movement patterns between species. Results While 65% of interceptions originated from bridgehead countries, highlighting the importance of the bridgehead effect across taxa, patterns among individual species were highly variable and taxonomically correlated. Forty per cent of species originated almost exclusively from their native range, 28% almost exclusively from their non‐native range and 32% from a mix of source locations. These patterns of global dispersal were geographically widespread, temporally consistent, and taxonomically correlated. Conclusions Dispersal exclusively from bridgeheads represents an unrecognised pattern of global insect movement; these patterns emphasise the importance of the bridgehead effect and suggest that bridgeheads provide unique local conditions that allow invaders to proliferate differently than in their native range. We connect these patterns of global dispersal to the conditions during the human driven global dispersal of insects and provide recommendations for modellers and policymakers wishing to control the spread of future invasions.}, journal={GLOBAL ECOLOGY AND BIOGEOGRAPHY}, author={Worm, Thom and Saffer, Ariel and Takeuchi, Yu and Walden-Schreiner, Chelsey and Jones, Chris and Meentemeyer, Ross}, year={2024}, month={Oct} } @article{saffer_worm_takeuchi_meentemeyer_2024, title={GIATAR: a Spatio-temporal Dataset of Global Invasive and Alien Species and their Traits}, volume={11}, ISSN={["2052-4463"]}, DOI={10.1038/s41597-024-03824-w}, abstractNote={Monitoring and managing the global spread of invasive and alien species requires accurate spatiotemporal records of species presence and information about the biological characteristics of species of interest including life cycle information, biotic and abiotic constraints and pathways of spread. The Global Invasive and Alien Traits And Records (GIATAR) dataset provides consolidated dated records of invasive and alien presence at the country-scale combined with a suite of biological information about pests of interest in a standardized, machine-readable format. We provide dated presence records for 46,666 alien taxa in 249 countries constituting 827,300 country-taxon pairs in locations where the taxon's invasive status is either alien, invasive, or unknown, joined with additional biological information for thousands of taxa. GIATAR is designed to be quickly updateable with future data and easy to integrate into ongoing research on global patterns of alien species movement using scripts provided to query and analyze data. GIATAR provides crucial data needed for researchers and policymakers to compare global invasion trends across a wide range of taxa.}, number={1}, journal={SCIENTIFIC DATA}, author={Saffer, Ariel and Worm, Thom and Takeuchi, Yu and Meentemeyer, Ross}, year={2024}, month={Sep} } @article{saffer_tateosian_saville_yang_ristaino_2024, title={Reconstructing historic and modern potato late blight outbreaks using text analytics}, volume={14}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-024-52870-2}, abstractNote={AbstractIn 1843, a hitherto unknown plant pathogen entered the US and spread to potato fields in the northeast. By 1845, the pathogen had reached Ireland leading to devastating famine. Questions arose immediately about the source of the outbreaks and how the disease should be managed. The pathogen, now known as Phytophthora infestans, still continues to threaten food security globally. A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843–1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. The tools described will help researchers explore and map unstructured text to track and visualize pandemics.}, number={1}, journal={SCIENTIFIC REPORTS}, author={Saffer, Ariel and Tateosian, Laura and Saville, Amanda C. and Yang, Yi-Peng and Ristaino, Jean B.}, year={2024}, month={Feb} } @article{montgomery_walden-schreiner_saffer_jones_seliger_worm_tateosian_shukunobe_kumar_meentemeyer_2023, title={Forecasting global spread of invasive pests and pathogens through international trade}, volume={14}, ISSN={["2150-8925"]}, url={http://dx.doi.org/10.1002/ecs2.4740}, DOI={10.1002/ecs2.4740}, abstractNote={AbstractNon‐native plant pests and pathogens threaten biodiversity, ecosystem function, food security, and economic livelihoods. As new invasive populations establish, often as an unintended consequence of international trade, they can become additional sources of introductions, accelerating global spread through bridgehead effects. While the study of non‐native pest spread has used computational models to provide insights into drivers and dynamics of biological invasions and inform management, efforts have focused on local or regional scales and are challenged by complex transmission networks arising from bridgehead population establishment. This paper presents a flexible spatiotemporal stochastic network model called PoPS (Pest or Pathogen Spread) Global that couples international trade networks with core drivers of biological invasions—climate suitability, host availability, and propagule pressure—quantified through open, globally available databases to forecast the spread of non‐native plant pests. The modular design of the framework makes it adaptable for various pests capable of dispersing via human‐mediated pathways, supports proactive responses to emerging pests when limited data are available, and enables forecasts at different spatial and temporal resolutions. We demonstrate the framework using a case study of the invasive planthopper spotted lanternfly (Lycorma delicatula). The model was calibrated with historical, known spotted lanternfly introductions to identify potential bridgehead populations that may contribute to global spread. This global view of phytosanitary pandemics provides crucial information for anticipating biological invasions, quantifying transport pathways risk levels, and allocating resources to safeguard plant health, agriculture, and natural resources.}, number={12}, journal={ECOSPHERE}, author={Montgomery, Kellyn and Walden-Schreiner, Chelsey and Saffer, Ariel and Jones, Chris and Seliger, Benjamin J. and Worm, Thom and Tateosian, Laura and Shukunobe, Makiko and Kumar, Sunil and Meentemeyer, Ross K.}, year={2023}, month={Dec} } @article{tateosian_saffer_walden-schreiner_shukunobe_2023, title={Plant pest invasions, as seen through news and social media}, volume={100}, ISSN={["1873-7587"]}, DOI={10.1016/j.compenvurbsys.2022.101922}, abstractNote={Invasion by exotic pests into new geographic areas can cause major disturbances in forest and agricultural systems. Early response can greatly improve containment efforts, underscoring the importance of collecting up-to-date information about the locations where pest species are being observed. However, existing invasive species databases have limitations in both extent and rapidity. The spatial extent is limited by costs and there are delays between species establishment, official recording, and consolidation. Local online news outlets have the potential to provide supplemental spatial coverage worldwide and social media has the potential to provide direct observations and denser historical data for modeling. Gathering data from these online sources presents its own challenges and their potential contribution to historical tracking of pest invasions has not previously been tested. To this end, we examine the practical considerations for using three online aggregators, the Global Database of Events, Language and Tone (GDELT), Google News, and a commercial media listening platform, Brandwatch, to support pest biosurveillance. Using these tools, we investigate the presence and nature of cogent mentions of invasive species in these sources by conducting case studies of online news and Twitter excerpts regarding two invasive plant pests, Spotted Lanternfly and Tuta absoluta. Our results using past data demonstrate that online news and social media may provide valuable data streams to supplement official sources describing pest invasions.}, journal={COMPUTERS ENVIRONMENT AND URBAN SYSTEMS}, author={Tateosian, Laura G. and Saffer, Ariel and Walden-Schreiner, Chelsey and Shukunobe, Makiko}, year={2023}, month={Mar} }