Relative importance of long-term changes in climate and land-use on the phenology and abundance of legume crop specialist and generalist aphids

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

BACKGROUND

Virus Yellows (VY), a disease caused by several aphid-borne viruses, is a major threat to the global sugar beet production. Following the ban of neonicotinoid-based seed treatments against aphids in Europe, increased efforts are needed to monitor and forecast aphid population spread during the sugar beet growing season. In particular, predicting aphid flight seasonal activity could allow anticipation of the timing and intensity of crop colonisation and contribute to the proper implementation of management methods. Forecasts should be made early enough to assess risk, but can be updated as the season progresses to refine management. Based on a long-term suction-trap dataset gathered between 1978 and 2014, we built and evaluated a set of models to predict the flight activity features of the main VY vector, Myzus persicae, at any location in the French sugar beet production area (c. 4 × 105  ha). Flight onset dates, length of flight period and cumulative abundance of flying aphids were predicted using climatic and land-use predictors as well as geographical position.

RESULTS

Our predictions outperformed current models published in the literature. The importance of the predictor variables varied according to the predicted flight feature but winter and early spring temperature always played a major role. Forecasts based on temperature were made more accurate by adding predictors related to aphid winter reservoirs. In addition, updating the model parameters to take advantage of new weather data acquired during the season improved the flight forecast.

CONCLUSION

Our models can be used as a tool for the mitigation in sugar beet crops. © 2023 Society of Chemical Industry.

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring

Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.

Precipitation change accentuates or reverses temperature effects on aphid dispersal

Global temperatures are generally increasing, and this is leading to a well documented advancement and extension of seasonal activity of many pest insects. Effects of changing precipitation have received less attention, but might be complex because rain and snow are increasing in some places but decreasing in others. This raises the possibility that altered precipitation could accentuate, or even reverse, the effects of rising temperatures on pest outbreaks. We used >592 K aphid suction-trap captures over 15 years, in the heavily farmed central USA, to examine how the activity of Aphis glycines (soybean aphid), Rhopalosiphum maidis (corn aphid), and Rhopalosiphum padi (bird cherry-oat aphid) changed with variation in both temperature and precipitation. Increasing precipitation caused late-season flight activity of A. glycines and early-season activity of R. padi to shift earlier, while increasing temperature did the same for early-season activity of A. glycines and R. maidis. In these cases, precipitation and temperature exhibited directionally similar, but independent, effects. However, precipitation sometimes mediated temperature effects in complex ways. At relatively low temperatures, greater precipitation generally caused late-season flights of R. maidis to occur earlier. However, this pattern was reversed at higher temperatures with precipitation delaying late-season activity. In contrast, greater precipitation delayed peak flights of R. padi at lower temperatures, but caused them to occur earlier at higher temperatures. So, in these two cases the interactive effects of precipitation on temperature were mirror images of one another. When projecting future aphid flight phenology, models that excluded precipitation covariates consistently underpredicted the degree of phenological advance for A. glycines and R. padi, and underpredicted the degree of phenological delay for R. maidis under expected future climates. Overall, we found broad evidence that changing patterns of aphid flight phenology could only be understood by considering both temperature and precipitation changes. In our study region, temperature and precipitation are expected to increase in tandem, but these correlations will be reversed elsewhere. This reinforces the need to include both main and interactive effects of precipitation and temperature when seeking to accurately predict how pest pressure will change with a changing climate.

The complexity of global change and its effects on insects

Global change includes multiple overlapping and interacting drivers: 1) climate change, 2) land use change, 3) novel chemicals, and 4) the increased global transport of organisms. Recent studies have documented the complex and counterintuitive effects of these drivers on the behavior, life histories, distributions, and abundances of insects. This complexity arises from the indeterminacy of indirect, non-additive and combined effects. While there is wide consensus that global change is reorganizing communities, the available data are limited. As the pace of anthropogenic changes outstrips our ability to document its impacts, ongoing change may lead to increasingly unpredictable outcomes. This complexity and uncertainty argue for renewed efforts to address the fundamental drivers of global change.