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Drastic interventions in an ecosystem, like deciding whether to eradicate an unwanted species, can have unforeseen, and sometimes even undesirable, consequences on the rest of the species present in that ecosystem.
Just published research shows that, even if there is limited knowledge of these ecosystems, modelers should not wait any longer to start developing ecological forecasts that managers could potentially use to decide whether to implement such interventions.
Research - Ecology - Letters - Researchers - ARC
The research, published in Ecology Letters, and led by researchers with the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), shows that despite their uncertainty, mathematical models can still be useful in informing management decisions.
"This is about moving the goalposts," said lead author Dr. Matthew Adams, a Research Fellow with ACEMS at The University of Queensland (UQ).
Ecology - Concern - Models - Prediction - Analysis
"In ecology, there has been this real concern that we can't use models for prediction until they are really, really good. But our analysis is saying to start using them for future predictions now. You might already get useful information out of them anyway. And they will only improve with time."
Dr. Adams compares the uncertainty in modeling ecosystems to how weather forecasts developed. People still relied on these weather forecasts, despite their uncertainty. They have continued to improve with time and more data, and ecological forecasts don't necessarily need to be as precise as weather forecasts.
Aim - Models - Ecosystem - Response - Scenarios
The aim of using mathematical models is to predict what will happen to an ecosystem in response to many different scenarios.
In their study, the researchers looked at thousands of virtual ecosystems where everything about them was known. Using statistical analysis, they were then able to quantify the accuracy of their dynamic models fitted to datasets that might be obtainable in these ecosystems, and also provide a level of uncertainty in the models' predictions.
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