by A groundbreaking study by scientists at the University of Birmingham and Goethe University in Frankfurt has successfully demonstrated the capabilities of a DNA “time machine” that sheds light on a hundred years of environmental change. By utilizing artificial intelligence (AI) in analyzing DNA-based biodiversity, climate variables, and pollution data, this innovative approach offers potential solutions for protecting and even improving existing biodiversity levels.
The researchers selected a lake in Denmark known for its well-documented shifts in water quality as a natural experiment for testing the biodiversity time machine. By examining the sediment from the lake’s bottom, they were able to reconstruct a biodiversity library spanning a century, along with chemical pollution and climate change levels.
Using environmental DNA, which refers to genetic material left behind by plants, animals, and bacteria, the scientists were able to create a comprehensive picture of the freshwater community. With the assistance of AI algorithms, they analyzed this information alongside climate and pollution data to investigate the factors contributing to the historic loss of species in the lake.
The study, published in eLife, revealed that pollutants, such as insecticides and fungicides, in combination with an increase in minimum temperature, caused significant damage to biodiversity levels. However, the DNA evidence also showed signs of recovery in the lake over the past two decades as water quality improved due to decreased agricultural land use in the surrounding area.
Nevertheless, while overall biodiversity increased, the communities that reestablished were not identical to those in the earlier, more pristine phase. The inability of certain species to return to the lake poses concerns as different species perform unique ecosystem services, and their absence may impede the restoration of specific services.
Lead author and Ph.D. student at the University of Birmingham, Niamh Eastwood, highlighted the irreversibility of biodiversity loss caused by pollution and warming water temperatures. It is clear that the species lost over a century will not be able to fully recover, emphasizing the importance of protecting biodiversity to avoid permanent damage.
Dr. Jiarui Zhou, co-lead author and Assistant Professor in Environmental Bioinformatics at the University of Birmingham, emphasized the value of AI models in understanding historic drivers of biodiversity loss. These holistic models can help predict future biodiversity loss under various pollution scenarios. As more data becomes available, more refined AI models can be developed to further improve predictions.
Building on this initial study, the researchers plan to expand their investigations to other lakes in England and Wales, evaluating the generalizability of their findings on pollution and climate change impacts on lake biodiversity.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
