In an unprecedented study, researchers have developed a “biodiversity time machine” to delve into the environmental history and biological changes of a freshwater lake over the past century.
This innovative approach could signal a leap forward in the way we understand and protect biodiversity amidst growing environmental concerns.
The collaborative research, conducted by experts at the University of Birmingham and Goethe University in Frankfurt, focused on sediment from a Danish lake known for its detailed water quality records.
The sediment provided a continuous record of the biological changes that occurred from the beginning of the industrial revolution to modern times.
The researchers used AI to analyze environmental DNA (eDNA) from the lake’s sediment. This eDNA is the genetic residue left by organisms such as plants, animals, and bacteria.
The experts compared the biological data with climate and pollution data to pinpoint the causes of the lake’s historical biodiversity loss.
Principal investigator Luisa Orsini is a professor of Evolutionary Systems Biology and Environmental Omics at the University of Birmingham and fellow of the Alan Turing Institute.
“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,” explained Professor Orsini.
“By analyzing biological data with climate change data and pollution levels we can identify the factors having the biggest impact on biodiversity.”
“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,” said Orsini.
“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 mean protecting these services.”
The team discovered that chemical pollutants, including insecticides and fungicides, coupled with a rise in minimum temperature (between 1.2 and 1.5 degrees Celsius), inflicted significant harm on the lake’s biodiversity.
Despite some recovery in the lake’s biodiversity in the last 20 years due to improved water quality and reduced agricultural activity nearby, the species composition was altered.
“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,” said study lead author and PhD student Niamh Eastwood.
“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.”
Dr. Jiarui Zhou, co-lead author, pointed out that their AI models offer valuable insights into the historic factors driving biodiversity loss and could improve predictions about future losses under various scenarios.
“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,” said Dr. Zhou.
“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 implications of this research extend far beyond a single lake. The team is expanding their study to include lakes in England and Wales, aiming to determine the broader applicability of their findings on pollution and climate change effects on lake biodiversity.
The study is published in the journal eLife.
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