Predicting Environmental Collapse
Predicting when an ecosystem is likely to collapse has benefits for foretelling crises in agriculture, fisheries and even social systems, and scientists from the University of Southampton in the UK are pioneering a new technique that may be able to do just that.
The research applies a mathematical model to a real world situation, in this case, the environmental collapse of Lake Erhai in the Yunnan province of China.
Their theory suggests that an ecosystem ‘flickers’ – or fluctuates dramatically between healthy and unhealthy states – prior to its eventual collapse.
“We wanted to prove that this ‘flickering’ occurs just ahead of a dramatic change in a system – be it a social, ecological or climatic one – and that this method could potentially be used to predict future critical changes in other impacted systems in the world around us,” said Head of Geography at Southampton, Professor John Dearing.
The team was led by Dr Rong Wang as they extracted core samples from sediment at the bottom of Lake Erhai, and charted the levels and variation of fossilised algae (diatoms) over a 125-year period.
They found that the algae communities remained relatively stable up until approximately 30 years before the lake’s eventual collapse. The final three decades, however, showed massive fluctuation, evidence of the ‘flickering’ before final collapse.
“By using the algae as a measure of the lake’s health, we have shown that its eco-system ‘wobbled’ before making a critical transition – in this instance, to a turbid state,” explained Rong Wang.
“Dramatic swings can be seen in other data, suggesting large external impacts on the lake over a long time period – for example, pollution from fertilisers, sewage from fields and changes in water levels – caused the system to switch back and forth rapidly between alternate states. Eventually, the lake’s ecosystem could no longer cope or recover – losing resilience and reaching what is called a ‘tipping point’ and collapsing altogether.”
The researchers hope that this method will work not only in their test case but across other regions and landscapes.
Co-author Dr Pete Langdon comments: “In this case, we used algae as a marker of how the lake’s ecosystem was holding-up against external impacts – but who’s to say we couldn’t use this method in other ways? For example, perhaps we should look for ‘flickering’ signals in climate data to try and foretell impending crises?”