Detecting tipping points

Ecosystems worldwide are increasingly vulnerable to environmental degradation.

Current trends of climate change, land use change, resourse use, or pollution are pushing ecosystems towards undesirable and simplified configurations.

Examples include the collapse of coral reefs, the loss of Arctic summer sea ice, or the potential shift from forest to savannahs.

These ecological reconfigurations or regime shifts can reduce the benefits society gets from nature, and can be very difficult or impossible to reverse.

This project aims at detecting tipping points in social-ecological systems and answer the question of where regime shifts are likely to occur?

The project will use known early warning signals of proximity to tipping points in time and space, and it will develop new methods for situations where current techniques fail.

We will use harmonized satellite observation data and machine learning techniques to detect where regime shifts will occur. We will also develop machine learning algorithms to detect the symptoms of regime shifts in social media.

The results of this project will be relevant for ecosystem managers in need of ecological forecasts, as well as policy-makers interested in achieving sustainable development goals related to the conservation of healthy ecosystems in land and sea (SDG 14,15).

Knowing where regime shifts will occur and their potential tipping points can help society to prepare for these transitions, or take opportune actions to avert them.