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

Area of Science:

• Ecology
• Entomology
• Mathematical Biology

Background:

• Dispersal of small insects, crucial for biological invasions and pest control, is poorly understood due to tracking difficulties.
• Parasitic hymenoptera (wasps) used in biocontrol offer a unique opportunity to study insect spread from known release points.
• Small insect size (<1 mm) and challenging individual tracking necessitate novel modeling approaches.

Purpose of the Study:

• To develop and validate a novel mathematical model for parasitoid wasp dispersal from point releases.
• To analyze the multi-scale wind-borne dispersal patterns of *Eretmocerus hayati*.
• To identify key factors influencing insect dispersal, particularly the role of wind.

Main Methods:

• Development of a deterministic mathematical model based on stochastic processes with an analytical solution.
• Application of a Bayesian framework to fit the model to empirical data.
• Analysis of Australian dataset on the dispersal of *Eretmocerus hayati*.

Main Results:

• Both local movements and long-distance wind dispersal are significant factors in parasitoid spread.
• Low-velocity winds are identified as primary indicators of field-scale dispersal direction shortly after release.
• Average wind data may be insufficient for predicting long-distance movement due to atmospheric flow complexities.

Conclusions:

• The developed model provides a robust method for analyzing insect dispersal from point sources.
• Understanding wind dynamics, especially low-velocity winds, is critical for predicting the spread of tiny organisms like parasitoids.
• Accurate wind data collection is essential for effective biological control programs and invasion ecology studies.