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.

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 5 . Though distribution data for crop pests and pathogens are noisy and incomplete 4 , similar changes have been detected for hundreds a species of pests and pathogens over recent decades 6 .…”

“…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 3 . Modelling the pattern and process of future changes in pest and pathogen burdens is therefore a key component in maintaining future food security 4 .…”

“…Like other species distributions models, we predicted areas of suitability and therefore potential distributions of species, and did not attempt to reconstruct observed distributions. Pathogens are spreading globally 17 , observational records suffer from under-reporting 4 and dispersal limitation prevents species from occupying all possible suitable environments 18 . These factors all likely contributed to the high false positive rates (0.47, IQR 0.37 -0.57) of our model.…”

Host phylogenetic diversity predicts the global extent and composition of tree pests

“…High saturation in Eurasia is likely due, in part, to the unique biogeography of the region (i.e., a large, continuous landmass with an east‐west orientation and a high number of shared tree taxa), but also the presence of well‐developed systems to identify and document tree pests. In contrast, low saturation in Oceanic Islands likely reflects both the unique biogeography of this realm (i.e., small, distantly spaced land masses, with unique tree compositions) and perhaps reduced documentation in the Western literature (as has been found for some larger countries; Bebber et al, 2019). Our results suggesting substantial undocumented pest diversity imply that targeted, systematic pest sampling would reveal a considerable number of new pest occurrences (such as those documented by Graziosi et al, 2020).…”

Weather does influence fungal and oomycete crop disease outbreaks, but ProMED‐mail reports don’t prove it

“…The spread of these organisms has been facilitated by international trade and transport, but there is growing concern that climate change could also increase disease risk and pest impacts (Deutsch et al, 2018;Chaloner et al, 2021). Observational data of pest and pathogen impacts are sparse, leaving researchers to infer climate change effects from biased and incomplete data sets which have often been collected for other purposes (Bebber et al, 2019). One example is an influential paper published in 2004, which extracted information on the factors causing plant diseases from reports on the ProMED-mail online service (Anderson et al, 2004).…”