@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{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{yoshizumi_coffer_collins_gaines_gao_jones_mcgregor_mcquillan_perin_tomkins_et al._2020, title={A Review of Geospatial Content in IEEE Visualization Publications}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85100716572&partnerID=MN8TOARS}, DOI={10.1109/VIS47514.2020.00017}, abstractNote={Geospatial analysis is crucial for addressing many of the world’s most pressing challenges. Given this, there is immense value in improving and expanding the visualization techniques used to communicate geospatial data. In this work, we explore this important intersection – between geospatial analytics and visualization – by examining a set of recent IEEE VIS Conference papers (a selection from 2017-2019) to assess the inclusion of geospatial data and geospatial analyses within these papers. After removing the papers with no geospatial data, we organize the remaining literature into geospatial data domain categories and provide insight into how these categories relate to VIS Conference paper types. We also contextualize our results by investigating the use of geospatial terms in IEEE Visualization publications over the last 30 years. Our work provides an understanding of the quantity and role of geospatial subject matter in recent IEEE VIS publications and supplies a foundation for future meta-analytical work around geospatial analytics and geovisualization that may shed light on opportunities for innovation.}, journal={2020 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS 2020)}, author={Yoshizumi, Alexander and Coffer, Megan M. and Collins, Elyssa L. and Gaines, Mollie D. and Gao, Xiaojie and Jones, Kate and McGregor, Ian R. and McQuillan, Katie A. and Perin, Vinicius and Tomkins, Laura M. and et al.}, year={2020}, pages={51–55} }