Study: New Statistical Model Outperforms Other Models In Predicting Biological Impact of Climate Change

Read the full article from Environmental Protection.

A recently developed statistical model that has been useful in such tasks as biomedical research or identifying credit card fraud has shown to be extraordinarily accurate in predicting the biological impacts of climate changes, according to a study announced on Aug. 15.

These “random forest” models — a data analysis technique that has nothing to do with forestry — greatly outperformed five other types of statistical or machine learning models that can evaluate how changes in climate may affect species distributions, Oregon State University (OSU) researchers concluded.

The study could help improve the accuracy of projections about the impact of climate change, and the way temperature or precipitation changes will force shifts in where species can live and survive, researchers said.

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