Many unreported crop pests and pathogens are probably already present

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. K E Y W O R D S agriculture, biogeography, food security, invasive species, observational bias, pest risk analysis, species distribution model

Australia: A Continent Without Native Powdery Mildews? The First Comprehensive Catalog Indicates Recent Introductions and Multiple Host Range Expansion Events, and Leads to the Re-discovery of Salmonomyces as a New Lineage of the Erysiphales

“…The introduction of E. necator from North America to Europe with infected grapevines is a textbook example (Brewer and Milgroom, 2010 ; Gadoury et al, 2012 ). Amongst all crop pathogenic fungi, a number of powdery mildews appear to be spreading most rapidly on a global scale, and are predicted to reach all available hosts by 2050 in many countries of the world (Bebber et al, 2014 , 2019 ; Bebber and Gurr, 2015 ). Most probably, their introduction and spread in Australia has happened as part of the fundamental changes in the entire vegetation and landscape since 1788 (Kirkpatrick, 1999 ; Cook and Dias, 2006 ; Fensham and Laffineur, 2019 ).…”

Plant pathogen infection risk tracks global crop yields under climate change

“…Spatial movements in geographical distributions and temporal shifts in phenologies of wild populations are among the clearest signs of anthropogenic global warming 6 . Though distribution data for crop pests and pathogens are noisy and incomplete 5 , similar changes have been detected for hundreds a species of pests and pathogens over recent decades 7 . Increasing burdens of insect pests at high latitudes, and decreasing burdens at low latitudes, have been projected using ecological niche models (ENM) 8 .…”

“…As climate changes, suitable conditions for pest outbreaks shift in time and space, altering the threats that farmers face and the management regimes required for their control 4 . Modelling the pattern and process of future changes in pest and pathogen burdens is therefore a key component in maintaining future food security 5 .…”

“…High false positives rates were more likely in countries with low per capita GDP (Fig. S9), indicating an under-reporting bias in developing countries 5 .…”

Plant pest surveillance: from satellites to molecules

“…Climate change, global trade (including internet sales) of plants and plant parts, movement of people, and changes to agricultural systems are major drivers for plant disease emergence [5][6][7][8][9]. New and emerging diseases are frequently highly significant as management measures are often not in place, resulting in devastating consequences for food security and rural economies.…”