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

Abstract

Biological invasions have movement at the core of their success. However, due to difficulties in collecting data, medium- and long-distance dispersal of small insects has long been poorly understood and likely to be underestimated. The agricultural release of parasitic hymenoptera, a group of wasps that are critical for biological pest control, represents a rare opportunity to study the spread of insects on multiple spatial scales. As these insects are typically less than 1 mm in size and are challenging to track individually, a first-time biocontrol release will provide a known spatial position and time of initial release for all individuals that are subsequently collected. In this paper, we develop and validate a new mathematical model for parasitoid wasp dispersal from point release, as in the case of biocontrol. The model is derived from underlying stochastic processes but is fully deterministic and admits an analytical solution. Using a Bayesian framework, we then fit the model to an Australian dataset describing the multi-scale wind-borne dispersal pattern of Zolnerowich& Rose (Hymenoptera: Aphelinidae). Our results confirm that both local movements and long-distance wind dispersal are significant to the movement of parasitoids. The model results also suggest that low velocity winds are the primary indicator of dispersal direction on the field scale shortly after release, and that average wind data may be insufficient to resolve long-distance movement given inherent nonlinearities and heterogeneities in atmospheric flows. The results highlight the importance of collecting wind data when developing models to predict the spread of parasitoids and other tiny organisms.

To what extent can the tiny parasitoid wasps,Eretmocerus mundus, fly upwind?

“…The negligible success of landings in strong winds in our simulations is consistent with experiments showing take‐off suppression of parasitoids at wind speed above ~2 m/s or even lower (Messing et al., 1997; Strickland et al., 2017). Another interesting prediction stemming from our simulations is that in higher wind speeds, attracting tiny insects to visual targets can be used to manipulate the drifting of these insects even if they do not actually reach the target (Figure 4f,h‐i).…”

“…It shows that wasps fly differently in the presence and absence of wind and that their active flight can manipulate flight trajectory relative to the ground, even while drifting in winds that substantially exceed their maximum flight speed. Theoretical models of miniature insect dispersal and host‐parasitoid spatial ecology tend not to include aspects of dispersal in directions that are not in the predominant wind direction (Strickland et al., 2017). The insights from the current study can be used to improve such models by including the active flight of the insects (Cronin & Reeve, 2005).…”

Flow Structure and Force Generation on Flapping Wings at Low Reynolds Numbers Relevant to the Flight of Tiny Insects

“…These insects are of significant agricultural, ecological, and economic importance. Thrips are a common agricultural pest [2,3], and parasitoid wasps have the potential for use in biological control of agricultural pests [4,5]. For example, a recent study demonstrated that the successful control of thrips using a small predatory insect resulted in an economic benefit of nearly $60 million in California [6].…”

Dispersal of Optimal Foragers in a Patchy Environment: Simulations with a Mathematical Model and Tests of Predictions in Field Experiments

“…If there is as much variability in 348 dispersal distance in these species as in those in experiments using replicated releases, the results 349 based on few observations must be treated with caution. Furthermore, stratified dispersal means 350 that the dispersal estimated from both replicated experimental releases and spread of species 351 introduced for biological control may give different results depending on the spatial scale of 352 sampling (Kristensen et al, 2013;Strickland et al, 2017). 353…”