Quantifying inherent predictability and spatial synchrony in the aphid vector Myzus persicae - field-scale patterns of abundance and regional forecasting error in the UK

A - Papers appearing in refereed journals

Bell, J. R., Clark, S. J., Stevens, M. and Mead, A. 2022. Quantifying inherent predictability and spatial synchrony in the aphid vector Myzus persicae - field-scale patterns of abundance and regional forecasting error in the UK. Pest Management Science. p. 7292. https://doi.org/10.1002/ps.7292

AuthorsBell, J. R., Clark, S. J., Stevens, M. and Mead, A.
Abstract

BACKGROUND
Sugar beet is threatened by virus yellows, a disease complex vectored by aphids that reduces sugar content. We present an analysis of Myzus persicae population dynamics with and without neonicotinoid seed treatment. We use six years’ yellow water trap and field-collected aphid data and two decades of 12.2 m suction-trap aphid migration data. We investigate both spatial synchrony and forecasting error to understand the structure and spatial scale of field counts and why forecasting aphid migrants lacks accuracy. Our aim is to derive statistical parameters to inform regionwide pest management strategies.

RESULTS
Spatial synchrony, indicating the coincident change in counts over time across the region, is rarely present and is best described as stochastic. Uniquely, early season field populations in 2019, did show spatial synchrony to 90 km compared to the overall average weekly correlation length of 23 km. However, 70% of the time series were spatially heterogenous, indicating patchy between-field dynamics. Field counts lacked the same seasonal trend and did not peak in the same week. Forecasts tended to under-predict mid-season log10 counts. A strongly negative correlation between forecasting error and the proportion of zeros was shown.

CONCLUSION
Field populations are unpredictable and stochastic, regardless of neonicotinoid seed treatment. This outcome presents a problem for decision-support that cannot usefully provide a single regionwide solution. Weighted permutation entropy inferred that M. persicae 12.2 m suction-trap time series had moderate to low intrinsic predictability. Early warning using a migration model tended to predict counts at lower levels than observed.

KeywordsBeta vulgaris; Sugar beet; Virus reservoirs; Yellow water traps; Spatial synchrony; Weighted permutation entropy
Year of Publication2022
JournalPest Management Science
Journal citationp. 7292
Digital Object Identifier (DOI)https://doi.org/10.1002/ps.7292
Open accessPublished as ‘gold’ (paid) open access
FunderBritish Beet Research Organisation
Biotechnology and Biological Sciences Research Council
Funder project or codeThe Rothamsted Insect Survey - National Capability [2017-2022]
The Rothamsted Long Term Experiments [2017-2022]
Accepted author manuscript
Output statusPublished
Publication dates
Online22 Nov 2022
PublisherWiley
ISSN1526-498X

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