Wind-Borne Dispersal of a Parasitoid: the Process, the Model, and its Validation

Abstract

The aphelinid parasitoid Eretmocerus hayati Zolnerowich & Rose (Hymenoptera: Aphelinidae) was recently released in Australia as a biocontrol agent against the crop pest Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae). It was found that the parasitoid can spread over several kilometers in a single generation and continue laying eggs for over a fortnight. A simple wind-advection model was fitted to emergence data from a first release between Fassifern and Kalbar, Queensland, and its predictive ability was tested against the second release near Carnarvon, Western Australia. The fitting of the model was used to develop several hypotheses about the dispersal of E. hayati, which were validated by the second release: E. hayati flies in the same direction as the wind to a distance proportional to the wind speed; this wind-borne flight takes place at any time during daylight hours; a flight is attempted every day after emergence unless there are high wind conditions during that day; and the high wind condition that will delay flight is wind speeds in excess of ≍2 m/s. This model of E. hayati dispersal may be contrasted with previous models fitted for Eretmocerus species, for which dispersal was dominated by diffusion processes, and parasitoid spread was constrained to the scales of tens and hundreds of meters.

Field dispersal of the parasitoid wasp Habrobracon hebetor (Hymenoptera: Braconidae) following augmentative release against the millet head miner Heliocheilus albipunctella (Lepidoptera: Noctuidae) in the Sahel

“…This could be due to differences in methodology as Adashkevich et al (1986) investigated H. hebetor dispersal after only 6 days of observations while in the present study H. hebetor dispersal was monitored for 5 weeks. Moreover, as reported in other settings, parasitoid species dispersal distance could be influenced by weather, especially temperature (Fahrner, Lelito, & Aukema, 2015;Yu et al, 2009), wind (Desouhant, Driessen, Lapchin, Wielaard, & Bernstein, 2003;Kristensen, Schellhorn, Hulthen, Howie, & Barro, 2013;Messing, Klungness, & Purcell, 1994) and landscape (Josso et al, 2013).…”

Inferring stratified parasitoid dispersal mechanisms and parameters from coarse data using mathematical and Bayesian methods

“…[28]). In the previous study [25], a parameter specifying the maximum wind speed at which flights occurred was found to be a key parameter determining fit between the model and data.…”

“…To model the number of parasitized hosts present in each field at collection time, we assumed that time from oviposition to emergence was 19 -25 days, distributed approximately according to a truncated normal distribution with variance of 2 days. Although the literature on laboratory observed times often suggests a shorter period [30,31], the field data observed by Kristensen et al necessitated the use of this longer time distribution [7,25] based on the number of emergences observed at longer times from collection. The mean number of collected parasitized hosts could then be calculated based on the population density history and the probability of collection in each field.…”

“…Our study builds upon previous work that used a simple model to identify wind advection as a necessary process to explain the dispersal of an introduced E. eretmocerus population [25]. Three shortcomings of this earlier study were that (i) the diffusion processes occurring during wind-borne flight were not modelled explicitly, but rather an approximation was made by assuming that spread occurred over the entire spatial grid cell; (ii) model fitting was performed based upon a match between the presence/absence in the model and the data, rather than between inferred and predicted densities; and (iii) active flight behaviour was not considered in a probabilistic sense, but operated as a global switch mechanism based on changing environmental conditions.…”

Cassava whitefly,Bemisia tabaci(Gennadius) (Hemiptera: Aleyrodidae) in East African farming landscapes: a review of the factors determining abundance

“…The model simulations indicated that peak densities of MEAM1 B. tabaci were higher for low or non-suitable crops than for crops with a medium suitability. This counter-intuitive result was explained by the fact that medium suitability winter crops supported high parasitoid ( Eretmocerus hayati ) populations, which can suppress B. tabaci populations in summer crops (De Barro, 2012; Kristensen et al , 2013). Therefore, both the surrounding landscape and crop rotation choices had a significant effect on simulated B. tabaci population dynamics.…”