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Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

ABSTRACT: Emerging plant pathogens are a significant problem for conservation and food security. Surveillance is often instigated in an attempt to detect an invading epidemic before it gets out of control. Yet in practice many epidemics are not discovered until already at a high prevalence, partly due to a lack of quantitative understanding of how surveillance effort and the dynamics of an invading epidemic relate. We test a simple rule of thumb to determine, for a surveillance programme taking a fixed number of samples at regular intervals, the distribution of the prevalence an epidemic will have reached on first discovery (discovery-prevalence) and its expectation E(q*). We show that E(q*) = r/(N/?), i.e. simply the rate of epidemic growth divided by the rate of sampling; where r is the epidemic growth rate, N is the sample size and ? is the time between sampling rounds. We demonstrate the robustness of this rule of thumb using spatio-temporal epidemic models as well as data from real epidemics. Our work supports the view that, for the purposes of early detection surveillance, simple models can provide useful insights in apparently complex systems. The insight can inform decisions on surveillance resource allocation in plant health and has potential applicability to invasive species generally.

SUBMITTER: Parnell S

PROVIDER: S-EPMC4571706 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

Parnell S S Gottwald T R TR Cunniffe N J NJ Alonso Chavez V V van den Bosch F F

Proceedings. Biological sciences 20150901 1814

Emerging plant pathogens are a significant problem for conservation and food security. Surveillance is often instigated in an attempt to detect an invading epidemic before it gets out of control. Yet in practice many epidemics are not discovered until already at a high prevalence, partly due to a lack of quantitative understanding of how surveillance effort and the dynamics of an invading epidemic relate. We test a simple rule of thumb to determine, for a surveillance programme taking a fixed nu ...[more]

PMID: 26336177

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Project description:The application of high-density polymorphic single-nucleotide polymorphisms (SNP) markers derived from high-throughput sequencing methods has heralded plenty of biological questions about the linkages of processes operating at micro- and macroevolutionary scales. However, the effects of SNP filtering practices on population genetic inference have received much less attention. By performing sensitivity analyses, we empirically investigated how decisions about the percentage of missing data (MD) and the minor allele frequency (MAF) set in bioinformatic processing of genomic data affect direct (i.e., parentage analysis) and indirect (i.e., fine-scale spatial genetic structure - SGS) gene flow estimates. We focus specifically on these manifestations in small plant populations, and particularly, in the rare tropical plant species Dinizia jueirana-facao, where assumptions implicit to analytical procedures for accurate estimates of gene flow may not hold. Avoiding biases in dispersal estimates are essential given this species is facing extinction risks due to habitat loss, and so we also investigate the effects of forest fragmentation on the accuracy of dispersal estimates under different filtering criteria by testing for recent decrease in the scale of gene flow. Our sensitivity analyses demonstrate that gene flow estimates are robust to different setting of MAF (0.05-0.35) and MD (0-20%). Comparing the direct and indirect estimates of dispersal, we find that contemporary estimates of gene dispersal distance (σ r t = 41.8 m) was ∼ fourfold smaller than the historical estimates, supporting the hypothesis of a temporal shift in the scale of gene flow in D. jueirana-facao, which is consistent with predictions based on recent, dramatic forest fragmentation process. While we identified settings for filtering genomic data to avoid biases in gene flow estimates, we stress that there is no 'rule of thumb' for bioinformatic filtering and that relying on default program settings is not advisable. Instead, we suggest that the approach implemented here be applied independently in each separate empirical study to confirm appropriate settings to obtain unbiased population genetics estimates.

Project description:Backgroundglobal trade in living plants and plant material has significantly increased the geographic distribution of many plant pathogens. As a consequence, several pathogens have been first found and described in their introduced range where they may cause severe damage on naïve host species. Knowing the center of origin and the pathways of spread of a pathogen is of importance for several reasons, including identifying natural enemies and reducing further spread. Several Phytophthora species are well-known invasive pathogens of natural ecosystems, including Phytophthora multivora. Following the description of P. multivora from dying native vegetation in Australia in 2009, the species was subsequently found to be common in South Africa where it does not cause any remarkable disease. There are now reports of P. multivora from many other countries worldwide, but not as a commonly encountered species in natural environments.Resultsa global collection of 335 isolates from North America, Europe, Africa, Australia, the Canary Islands, and New Zealand was used to unravel the worldwide invasion history of P. multivora, using 10 microsatellite markers for all isolates and sequence data from five loci from 94 representative isolates. Our population genetic analysis revealed an extremely low heterozygosity, significant non-random association of loci and substantial genotypic diversity suggesting the spread of P. multivora readily by both asexual and sexual propagules. The P. multivora populations in South Africa, Australia, and New Zealand show the most complex genetic structure, are well established and evolutionary older than those in Europe, North America and the Canary Islands.Conclusionsaccording to the conducted analyses, the world invasion of P. multivora most likely commenced from South Africa, which can be considered the center of origin of the species. The pathogen was then introduced to Australia, which acted as bridgehead population for Europe and North America. Our study highlights a complex global invasion pattern of P. multivora, including both direct introductions from the native population and secondary spread/introductions from bridgehead populations.

Dataset Information

Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

Publications

Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

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