The role of AI in ecology’s computational carbon footprint
| Authors: | Kari Norman, Carl Boettiger, Timothée Poisot, Gavin M. Jones |
| Year: | 2025 |
| Type: | Scientific Journal |
| Station: | Rocky Mountain Research Station |
| DOI: | https://doi.org/10.1002/fee.70021 |
| Source: | Frontiers in Ecology and the Environment |
Abstract
Ecologists increasingly recognize that novel computational approaches are critical for effectively addressing the ongoing climate and biodiversity crises. Some of the century’s most substantive methodological developments are in artificial intelligence (AI), including generative AI (GenAI) as well as classical AI and machine learning (ML) approaches, which collectively have spurred advances across all fields of science. In ecology, AI has already been leveraged for diverse applications, ranging from large-scale image recognition for monitoring to conservation decision-making in complex systems. Recently, multiple calls for its application to address the biodiversity crisis (e.g., Pollock et al. 2025) outline past successes and avenues for its continued adoption.