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Related Experiment Video

Updated: May 23, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Real time forecasting of near-future evolution.

Philip J Gerrish¹, Paul D Sniegowski

¹Center for Evolutionary and Theoretical Immunology, Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA. pgerrish@unm.edu

Journal of the Royal Society, Interface

|April 20, 2012

Summary

This study introduces a new statistical model for adaptive evolution. It predicts future fitness and phenotype changes using real-time data, bypassing the need for a priori knowledge of the fitness landscape.

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Last Updated: May 23, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Evolutionary biology
Genetics
Statistical modeling

Background:

The 'mountain climbing' metaphor, representing genotype changes and fitness gains, is central to evolutionary theory.
Predicting evolutionary trajectories requires detailed knowledge of the 'fitness landscape,' which is often unavailable or dynamic.
Existing models struggle with the lack of a priori information on genotype-fitness relationships.

Purpose of the Study:

To develop a novel statistical model for adaptive evolution.
To predict future fitness and phenotype evolution without prior knowledge of the fitness landscape.
To provide a dynamical theory with long-term predictive power using real-time data.

Main Methods:

Utilizing a general statistical model building on classical evolutionary theory.
Employing real-time fitness or phenotype-fitness data.
Requiring no a priori information about the fitness landscape.

Main Results:

The model provides reasonable predictions of fitness evolution over many generations.
The model accurately forecasts phenotype evolution into the future.
The approach bypasses the limitations of traditional fitness landscape mapping.

Conclusions:

A new statistical framework enables robust predictions of adaptive evolution.
Real-time data and a novel model overcome the need for a priori fitness landscape information.
This approach offers a powerful tool for understanding and predicting evolutionary dynamics.