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

Abstract

BACKGROUNDSugar 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. RESULTSSpatial synchrony, indicating the coincident change in counts across the region over time, 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 log 10 counts. A strongly negative correlation between forecasting error and the proportion of zeros was shown. CONCLUSIONField 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.

Predicting the seasonal flight activity of Myzus persicae, the main aphid vector of Virus Yellows in sugar beet

“…Abundance was the least accurately predicted feature of aphid flight, sometimes leading to notable discrepancies between the observed and predicted values. This seems to confirm the conclusions of Bell et al 65 who recently found a low to moderate intrinsic predictability of M. persicae aerial numbers using suction- trap data in England. They suggested that the difficulty in predicting yearly aerial aphid numbers could be due to the multiplicity of factors acting on flying individuals, rendering annual time series highly stochastic.…”

“…Abundance was the least accurately predicted feature of aphid flight, sometimes leading to notable discrepancies between the observed and predicted values. This seems to confirm the conclusions of Bell et al 65 . who recently found a low to moderate intrinsic predictability of M. persicae aerial numbers using suction‐trap data in England.…”

“…who recently found a low to moderate intrinsic predictability of M. persicae aerial numbers using suction‐trap data in England. They suggested that the difficulty in predicting yearly aerial aphid numbers could be due to the multiplicity of factors acting on flying individuals, rendering annual time series highly stochastic 65 . Besides, we showed that for a given flight feature, the choice of the proxy influences the quality of the prediction.…”

“…They suggested that the difficulty in predicting yearly aerial aphid numbers could be due to the multiplicity of factors acting on flying individuals, rendering annual time series highly stochastic. 65 Besides, we showed that for a given flight feature, the choice of the proxy influences the quality of the prediction. Indeed, the date of fifth capture (D5c) was systematically better predicted than the date of first capture (D1c).…”