Bacteria in Wisconsin's Lake Mendota are experiencing a repetitive evolutionary cycle, according to a study published in Nature Microbiology. Researchers observed that throughout the year, most bacterial species in the lake evolved rapidly, seemingly adapting to seasonal changes. Despite these changes, hundreds of species reverted almost entirely to their previous genetic states after thousands of generations.
Robin Rohwer, a postdoctoral researcher at The University of Texas at Austin and lead author of the study, noted, "I was surprised that such a large portion of the bacterial community was undergoing this type of change." Rohwer initially began this research as a doctoral student with Trina McMahon at the University of Wisconsin-Madison.
Lake Mendota undergoes significant seasonal transformations—covered in ice during winter and algae during summer. Within each bacterial species, strains adapted to specific conditions outcompete others seasonally. The researchers utilized an archive of 471 water samples collected over two decades from Lake Mendota by UW-Madison researchers for long-term monitoring projects funded by the National Science Foundation.
"This study is a total game changer in our understanding of how microbial communities change over time," said Brett Baker, co-author and researcher at UT Austin. The findings suggest that most bacteria evolved with seasonal changes but returned to similar states annually for 20 years.
In 2012, unusual weather conditions led to significant genetic shifts among many bacteria related to nitrogen metabolism due to scarce algae. Rohwer remarked on finding substantial sequence changes: "I thought, out of hundreds of bacteria, I might find one or two with a long-term shift."
The study also touches on climate change impacts. "Climate change is slowly shifting the seasons and average temperatures," Rohwer said. The team reconstructed bacterial genomes using supercomputing resources at the Texas Advanced Computing Center (TACC), which significantly expedited their work compared to conventional computing methods.
Other contributors include Mark Kirkpatrick from UT; Sarahi Garcia from Carl von Ossietzky University and Stockholm University; and Matthew Kellom from the U.S. Department of Energy’s Joint Genome Institute. This research received support from various organizations including the U.S. National Science Foundation and other scientific bodies.