Figures
Computational complexity in a random ecosystem
Ecosystems consist of diverse mixtures of cooperating and competing species, which cause these systems to take a long time to reach stable equilibrium states. Here, we borrow tools from computational complexity theory to frame complex ecosystems as optimization problems, and we derive scaling laws for the time that it takes ecosystems to equilibrate. This image shows transient chaos in a model ecosystem, in which the equilibration time strongly varies as the initial species densities change. Gilpin et al. 2025
Image Credit: William Gilpin
Citation: (2025) PLoS Computational Biology Issue Image | Vol. 21(5) June 2025. PLoS Comput Biol 21(5): ev21.i05. https://doi.org/10.1371/image.pcbi.v21.i05
Published: June 10, 2025
Copyright: © 2025 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Ecosystems consist of diverse mixtures of cooperating and competing species, which cause these systems to take a long time to reach stable equilibrium states. Here, we borrow tools from computational complexity theory to frame complex ecosystems as optimization problems, and we derive scaling laws for the time that it takes ecosystems to equilibrate. This image shows transient chaos in a model ecosystem, in which the equilibration time strongly varies as the initial species densities change. Gilpin et al. 2025
Image Credit: William Gilpin