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Many unreported crop pests and pathogens are probably already present
Bebber, Daniel P.1; Field, Elsa2; Gui, Heng3; Mortimer, Peter3; Holmes, Timothy4; Gurr, Sarah J.1
Corresponding AuthorBebber, Daniel P.(d.bebber@exeter.ac.uk)
2019-08-01
Source PublicationGLOBAL CHANGE BIOLOGY
ISSN1354-1013
Volume25Issue:8Pages:2703-2713
AbstractInvasive 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.
Keywordagriculture biogeography food security invasive species observational bias pest risk analysis species distribution model
DOI10.1111/gcb.14698
Indexed BySCI ; SCI
Language英语
WOS IDWOS:000497502700016
Citation statistics
Document Type期刊论文
Identifierhttp://ir.kib.ac.cn/handle/151853/70970
Collection资源植物与生物技术所级重点实验室
Corresponding AuthorBebber, Daniel P.
Affiliation1.Univ Exeter, Dept Biosci, Stocker Rd, Exeter EX4 4QD, Devon, England
2.Univ Oxford, Dept Plant Sci, Oxford, England
3.Chinese Acad Sci, Kunming Inst Bot, Kunming, Yunnan, Peoples R China
4.CABI, Wallingford, Oxon, England
Recommended Citation
GB/T 7714
Bebber, Daniel P.,Field, Elsa,Gui, Heng,et al. Many unreported crop pests and pathogens are probably already present[J]. GLOBAL CHANGE BIOLOGY,2019,25(8):2703-2713.
APA Bebber, Daniel P.,Field, Elsa,Gui, Heng,Mortimer, Peter,Holmes, Timothy,&Gurr, Sarah J..(2019).Many unreported crop pests and pathogens are probably already present.GLOBAL CHANGE BIOLOGY,25(8),2703-2713.
MLA Bebber, Daniel P.,et al."Many unreported crop pests and pathogens are probably already present".GLOBAL CHANGE BIOLOGY 25.8(2019):2703-2713.
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