M. Scheffer

Improved potential analysis for inadequate ecological data

This paper introduces an improved potential analysis method to better handle ecological data limitations and enhance predictions of ecosystem dynamics and resilience.

Stochastic regimes can hide the attractors in data, reconstruction algorithms can reveal them

The paper shows that stochastic regimes can obscure attractors in data, and that reconstruction algorithms can reveal them by analyzing the relationship between noise and system equilibria.

Monitoring Resilience in Bursts

This paper compares methods for detecting resilience changes in systems, showing that burst sampling outperforms continuous monitoring with the same data points, suggesting it can improve early warning signals for critical transitions.

Quantifying the Human Cost of Global Warming

This paper quantifies the human cost of global warming by estimating how many people will be outside the historical climate niche under different warming scenarios.

Deep learning for early warning signals of regime shifts

This paper develops a deep learning algorithm to detect early warning signals of regime shifts across diverse systems, improving sensitivity and specificity over generic methods.

Ecosystem tipping points in an evolving world

The paper examines how evolutionary changes in trait distributions affect ecosystem tipping points and their interaction with ecological shifts.