Predators affect a plant virus through direct and trait-mediated indirect effects on vectors

Arthropods that vector plant pathogens often interact with predators within food webs. Predators affect vectors by eating them (consumptive effects) and by inducing antipredator behaviors (non-consumptive effects), and these interactions may affect transmission of vector-borne pathogens. However, it has proven difficult to experimentally tease apart the effects of predators on vector fitness and behavior as they are often correlated. We addressed this problem by assessing how both aphids and an aphid-borne pathogen were affected by variable predation risk. Specifically, we experimentally manipulated ladybeetle predators’ mouthparts to isolate consumptive, and non-consumptive, effects of predators on aphid fitness, movement, and virus transmission. We show that although lethal predators decreased aphid vector abundance, they increased pathogen transmission by increasing aphid movement among hosts. Moreover, aphids responded to risk of predation by moving to younger plant tissue that was more susceptible to the pathogen. Aphids also responded to predator risk through compensatory reproduction, which offset direct consumptive effects. Our results support predictions of disease models showing alterations of vector movement due to predators can have greater effects on transmission of pathogens than vector consumption. Broadly, our study shows isolating direct and indirect predation effects can reveal novel pathways by which predators affect vector-borne pathogens.

Fitness trade-off in peach-potato aphids (Myzus persicae) between insecticide resistance and vulnerability to parasitoid attack at several spatial scales

Insecticide-resistant clones of the peach-potato aphid, Myzus persicae (Sulzer), have previously been shown to have a reduced response to aphid alarm pheromone compared to susceptible ones. The resulting vulnerability of susceptible and resistant aphids to attack by the primary endoparasitoid, Diaeretiella rapae (McIntosh), was investigated across three spatial scales. These scales ranged from aphids confined on individual leaves exposed to single female parasitoids, to aphids on groups of whole plants exposed to several parasitoids. In all experiments, significantly fewer aphids from insecticide-susceptible clones became parasitised compared to insecticide-resistant aphids. Investigations of aphid movement showed at the largest spatial scale that more susceptible aphids than resistant aphids moved from their inoculation leaves to other leaves on the same plant after exposure to parasitoids. The findings imply that parasitoids, and possibly other natural enemies, can influence the evolution and dynamics of insecticide resistance through pleiotropic effects of resistance genes on important behavioural traits.

Evidence for intermittency and a truncated power law from highly resolved aphid movement data

Power laws are increasingly used to describe animal movement. Despite this, the use of power laws has been criticized on both empirical and theoretical grounds, and alternative models based on extensions of conventional random walk theory (Brownian motion) have been suggested. In this paper, we analyse a large volume of data of aphid walking behaviour (65 068 data points), which provides a highly resolved dataset to investigate the pattern of movement. We show that aphid movement is intermittent—with alternations of a slow movement with frequent change of direction and a fast, relatively directed movement—and that the fast movement consists of two phases—a strongly directed phase that gradually changes into an uncorrelated random walk. By measuring the mean-squared displacement and the duration of non-stop movement episodes we found that both spatial and temporal aspects of aphid movement are best described using a truncated power law approach. We suggest that the observed spatial pattern arises from the duration of non-stop movement phases rather than from correlations in turning angles. We discuss the implications of these findings for interpreting movement data, such as distinguishing between movement and non-movement, and the effect of the range of data used in the analysis on the conclusions.