Many unreported crop pests and pathogens are probably already present. (24th June 2019)

• Record Type: Journal Article
• Authors: Bebber, Daniel P.; Field, Elsa; Gui, Heng; Mortimer, Peter; Holmes, Timothy; Gurr, Sarah J.
• Is Part Of: Global change biology. Volume 25:Number 8(2019)
• Page Start: 2703
• Page End: 2713
• Publication Date: 2019-06-24
• Subjects: agriculture -- biogeography -- food security -- invasive species -- observational bias -- pest risk analysis -- species distribution model; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.14698
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1, 739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75–0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling. Abstract : Invasive species threaten global biodiversity, agriculture, food security and ecosystem function. Pest risk analysis is key to biosecurity efforts, but is hampered by incomplete knowledge of invasive species distributions. We use statistical species distribution models to estimate presence probabilities for 1, 739 crop pests and pathogens globally, and test model predictions for unobserved occurrences in China against observations abstracted from the Chinese literature. We show that large numbers of currently unobserved invasive species of agriculture are probably already present around the world, particularly in China, India and the former USSR.