Dragonfly Wings are Naturally Designed for Efficient Flight, Grass Fellow Finds

Dragonflies are some of the best flyers in the insect world. Bending and twisting their flexible wings, they maneuver their bodies through the air with speed and agility. How do they accomplish such complex flying behaviors? Despite a relatively simple neural architecture, new findings suggest that they’ve evolved efficient solutions to maintain stability and control during flight.

Dragonflies monitor their flight by sensing environmental cues. Apart from vision, they receive information from the multiple mechanosensors across their bodies and along the surface of their veiny wings. While flying, the dragonfly’s wings constantly bend and deform, changing shape as they move and in response to airflow.

“[The wing] is an extremely complicated structure that can bend in many ways,” said Alexandra Yarger of Imperial College London, a 2025 Grass Fellow at the Marine Biological Laboratory. “How do dragonflies keep track of what their wings are doing [while flying]?”

Yarger and her collaborators investigated that question in a recent paper, published in Proceedings of the National Academy of Sciences. Combining experimental work conducted at the MBL and Imperial College London, computational modelling, and high-speed videography, researchers analyzed how the wing’s unique architecture and placement of mechanosensors align for efficient flight control.

“The wings are structured in a way that limits how it bends,” Yarger said. Of the thousands of wing configurations that take shape during flight, but just three dominant features can explain how the wing deforms: bend, twist, and camber. These three fundamental components serve as a baseline that describe typical flight patterns. In fact, analyses revealed that 99 percent of wing shapes during flight can be described with just those three variables.

Researchers identified the three deformation patterns – bend, twist, and camber – in modeling the wing architecture. They found that the areas of the wing that experience the highest strain during these deformations are also the locations with the most strain sensors. Of the hundreds of mechanosensors scattered around the wings, only a few strategically placed sensors in these locations are actively recruited during normal flight. However, if conditions change, additional sensors activate to capture the more complex configurations that occur during an atypical flying maneuver.

This close alignment between sensor clustering and wing morphology helps the body detect only the most important information during flight, filtering out what is not relevant. This simplifies the heavy computational load that the brain would otherwise have to perform.

“Dragonfly sensors are perfectly matched to what they need,” said Yarger.

Rather than processing all available information, dragonfly wings are naturally designed for efficient flight.

Citation: Alexandra M. Yarger et al. (2025) Structural dynamics and neural representation of wing deformation. Proc. Natl. Acad. Sci., DOI: 10.1073/pnas.2518032122.