Many unreported crop pests and pathogens are probably already present

Daniel P Bebber,Heng Gui,Sarah J Gurr,Peter Mortimer,Timothy Holmes,Elsa Field

doi:10.1111/gcb.14698

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.

Highlights

The spread of invasive species is homogenizing the biosphere, with wide-ranging implications for natural ecosystems (Baiser et al, 2012; Santini et al, 2013) and agriculture (Fisher et al, 2012; Bebber et al, 2014a; Bebber, 2015)
People’s Republic of China (PRC), against observations of these pests abstracted from the Chinese literature
Our results shows that Generalized Linear Models (GLM) can predict presences of pseudo-absent pests at sub-national resolution

Summary

Introduction

The spread of invasive species is homogenizing the biosphere, with wide-ranging implications for natural ecosystems (Baiser et al, 2012; Santini et al, 2013) and agriculture (Fisher et al, 2012; Bebber et al, 2014a; Bebber, 2015). The number of first observations of crop pests and pathogens has accelerated in recent years, driven primarily by global trade (Ding et al, 2008; Bacon et al, 2014), and potentially by climate change and our improving ability to monitor and identify threats (Bebber et al, 2014a; Bebber, 2015). Central America, Europe, East Africa and Australia have been identified as hotspots of new pest invasions, with maize, bananas, citrus and potato as the crops most likely to be affected (Bebber, 2015). A major outbreak of coffee leaf rust (Hemileia vastatrix) in Latin America, likely to have been triggered by a failure in disease management, is reported to have caused large-scale unemployment and social upheaval in recent years (Avelino et al, 2015)

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