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Using ‘sentinel’ plants to improve early detection of invasive plant pathogens
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Lovell-Read, Francesca A., Parnell, Stephen, Cunniffe, Nik J. and Thompson, Robin N. (2023) Using ‘sentinel’ plants to improve early detection of invasive plant pathogens. PLoS Computational Biology, 19 (2). e1010884. doi:10.1371/journal.pcbi.1010884 ISSN 1553-7358.
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Official URL: https://doi.org/10.1371/journal.pcbi.1010884
Abstract
Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. ‘Sentinel’ plants–alternative susceptible host species that display visible symptoms of infection more rapidly–could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.
| Item Type: | Journal Article | ||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| Journal or Publication Title: | PLoS Computational Biology | ||||||
| Publisher: | Public Library of Science | ||||||
| ISSN: | 1553-7358 | ||||||
| Book Title: | Using ‘sentinel’ plants to improve early detection of invasive plant pathogens | ||||||
| Official Date: | 2 February 2023 | ||||||
| Dates: |
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| Volume: | 19 | ||||||
| Number: | 2 | ||||||
| Article Number: | e1010884 | ||||||
| DOI: | 10.1371/journal.pcbi.1010884 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 23 January 2023 | ||||||
| Date of first compliant Open Access: | 8 March 2023 | ||||||
| Open Access Version: |
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