The study revealed that pollutants such as insecticides and fungicides, coupled with a rise in minimum temperature (a 1.2-1.5-degree increase), inflicted most harm on biodiversity levels.
Scientists have achieved the first proof of concept for their DNA-based “time machine” to unravel a century of environmental transformations in a freshwater lake, including rising temperatures and pollution, leading to potential irreversible biodiversity loss.
Collaborating with Goethe University in Frankfurt, researchers from the University of Birmingham utilized sediment from a Danish lake to construct a 100-year historical record of biodiversity, chemical contamination, and climate shifts. This lake, with well-documented shifts in water quality, provided an ideal natural setting for testing the DNA biodiversity time machine.
Using environmental DNA, which comprises genetic material left by plants, animals, and bacteria, the team created a comprehensive portrait of the entire freshwater ecosystem.
Augmented by AI, they scrutinized this information in tandem with climate and pollution data to discern the underlying factors contributing to the historical decline of lake species.
Principal investigator Professor Luisa Orsini, an expert in Evolutionary Systems Biology and Environmental Omics at the University of Birmingham, explained, “We took a sediment core from the bottom of the lake and used biological data within that sediment like a time machine—looking back in time to build a detailed picture of biodiversity over the last century at yearly resolution. By analyzing biological data with climate change data and pollution levels we can identify the factors having the biggest impact on biodiversity.”
Orsini added, “Protecting every species without impacting human production is unrealistic, but using AI we can prioritize the conservation of species that deliver ecosystem services. At the same time, we can identify the top pollutants, guiding regulation of chemical compounds with the most adverse effect. These actions can help us not only to preserve the biodiversity we have today, but potentially to improve biodiversity recovery. Biodiversity sustains many ecosystem services that we all benefit from. Protecting biodiversity means protecting these services.”
The study revealed that pollutants such as insecticides and fungicides, coupled with a rise in minimum temperature (a 1.2-1.5-degree increase), inflicted the most harm on biodiversity levels.
However, the DNA in the sediment also indicated that over the last two decades, the lake had begun to rebound. Water quality improved as agricultural land use reduced around the lake. Nonetheless, while overall biodiversity increased, the communities were not identical to those in the initial (semi)pristine phase. This is worrisome as distinct species offer different ecosystem services, and their failure to return to a specific site can hinder the reinstatement of specific services.
Lead author Niamh Eastwood, a Ph.D. student at the University of Birmingham, cautioned, “The biodiversity loss caused by this pollution and the warming water temperature is potentially irreversible. The species found in the lake 100 years ago that have been lost will not all be able to return. It is not possible to restore the lake to its original pristine state, even though the lake is recovering. This research shows that if we fail to protect biodiversity, much of it could be lost forever.”
Co-lead author Dr. Jiarui Zhou, an Assistant Professor in Environmental Bioinformatics at the University of Birmingham, added, “Learning from the past, our holistic models can help us to predict the likely loss of biodiversity under a ‘business as usual’ and other pollution scenarios. We have demonstrated the value of AI-based approaches for understanding historic drivers of biodiversity loss. As new data becomes available, more sophisticated AI models can be used to further improve our predictions of the causes of biodiversity loss.”
The researchers plan to expand their initial study from a single lake to lakes in England and Wales, aiming to determine how replicable the observed patterns are, and consequently, how they can generalize their findings on pollution and climate change impacts on lake biodiversity.