Artificial Intelligence Tracks The Migration of Birds

Each species of birds has a completely different and unique migration pattern. The majority of these birds fly north in the spring to breed in the temperature climates, or return in the autumn to wintering grounds in the south. However, it’s not as clear cut as a pattern might indicate. With the heavy variety in bird species and the inevitable discovery of new ones as existing species crossbreed, migration patterns are expected to heavily change in the next decade. This is why artificial intelligence is becoming a powerful tool in recognizing these migration patterns - with its abilities to recognize the unique patterns of each species without falling ill to general conclusions.

While many are in awe by artificial intelligence’s vast capability in this sense, as it is able to store the migration information of each bird species successfully, these same individuals wonder on the significance of this knowledge. The knowledge is significant for the advancement of biological research. Knowing the patterns of animals and what conditions they thrive in aids humanity in adapting its needs to alter the environment to meet safe standards for these animals, directly supporting the chain of life in an ecosystem. As more discoveries are found in biology, technology, specifically artificial intelligence, must meet this demand by tagging along its growth.

This artificial intelligence program stems based off of machine learning. This program analyzes weather radar images and is able to differentiate migrating birds from any precipitation. Furthermore, it works like neural networks in our brain - storing different bird species in different neural categories. This program has already been used to survey century long radar data sets, revealing a variety of seasonal and content-wide migration patterns.

Its name is MistNet, and its capabilities were mentioned in the prestigious Methods in Ecology and Evolution journal.

"This is a really important advance. Our results are excellent compared with humans working by hand," Amherst artificial intelligence researcher Dan Sheldon said in a news release. "It allows us to go from limited 20th-century insights to 21st-century knowledge and conservation action." He and co-authors point out, "Deep learning has revolutionized the ability of computers to mimic humans in solving similar recognition tasks for images, video and audio."

Scientists employed the system in a test process to verify both its validity and efficiency. They wanted to know if using AI in this process would be a more effective tool than researchers spending time recording the information of each bird species manually. Scientists found that MistNet has very positive uses for employment that offer more benefits than manual labor. With MistNet, the chance of incorrect calculations is eliminated entirely. Similarly, there is no chance that MistNet may mix between bird species due to the sophisticated technology that differs birds based on an approval and verification process.

The new technology may also be incorporated with programs such as eBird or other animal tracking devices and earth observation instruments, to further bolster its capabilities. This combination could help scientists not only identify birds migration patterns, but other species including land and marine animals. In addition, scientists could further identify similarities between migration patterns of different bird species. Any mutual landing zones, mutual taste in environments or mutual migrating preferences could be beneficial to biology in the vast future.

Yousef Khan