Engineers from the University of Michigan are working with the Department of Ecology and Evolutionary Biology to learn more about monarch butterflies’ migratory patterns. To track the butterflies, the team are using the world’s smallest computers, the latest iteration of the Michigan Micro Mote. The tiny computers were created by a team from the University of Michigan and is a fully energy-autonomous computing system acting as a smart sensing system.
The tiny computers can be configured for a wide variety of applications. The Michigan Micro Mote computers are glued to the back of individual Monarch butterflies to track and monitor environmental conditions. The sensors monitor light and temperature, and they will eventually monitor air pressure encountered during migration.
The chip and system design uses an algorithm to analyze the captured data and reconstruct the specimen’s migratory path. The paths help biologists learn more about Monarch biology and apply it to conservation efforts. Monarch butterflies can travel as far as 3000 miles during migration, spending the summer in the northern parts of the US to reproduce and then spend the winter along the coast of California, Florida, and Mexico.
The tiny sensors glued to the robots have to be robust enough to survive the long trips and inclement weather while being lightweight enough to not interfere with the butterfly’s flight and motion. The tiny computers are around 50 milligrams in weight, ten times lighter than the lightest tracking devices so far. Researchers glued the tiny sensors to butterflies and monitored them in a greenhouse, noting that the sensors didn’t have a strong negative effect on the butterfly.
Traditionally, monitoring migratory patterns of butterflies required the attachment of a paper tag to an individual butterfly, and the butterflies had to be recovered at a known monarch destination. With the new sensors, researchers can track each individual’s complete path. While sensors like GPS are too bulky, researchers can infer location data indirectly using a data-driven framework and information gathered by volunteers along the migratory path.