Predictive Species Distribution Modeling of Molluscan Agricultural Pests to Assess the Probability of Future Invasions in the United States



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As a result of the explosive increase in the globalization of the world’s economy, travel, and trade, the introduction of non-native invasive species, made either intentionally or accidentally, is a well-documented phenomenon. Industries such as the horticulture, pet, and live-food trades are all major culprits in the dispersal of non-native alien species around the globe. Invasive terrestrial gastropods pose a significant and understudied threat to United States agriculture, native biodiversity, and public health. Thus, the objective of this research is to make use of publicly available occurrence data sourced from the Global Biodiversity Information Facility (GBIF) to generate a suite of correlative and mechanistic predictive species distribution models including the General Additive Model (GAM), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT), CLIMEX, and weighted consensus ensemble forecasts to better identify areas of potentially suitable habitat for high-risk invasive terrestrial mollusk species in the continental US under both current and future climatic conditions. The results of this study have been used to generate a series of ranked species threat assessments which can ultimately be used to better inform domestic pest quarantine measures, long-term pest monitoring projects, and integrative pest-management strategies to further aid in the prevention and early detection of the successful establishment of high-risk invasive terrestrial gastropods here in the US as the global climate continues to change.



Biology, Ecology, Biology, General, Agriculture, General