This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers at UC Santa Barbara's National Center for Ecological Analysis and Synthesis (NCEAS) are blazing a trail through the Wild West of AI-assisted science, with a field guide aimed at environmental data scientists. "It started as something we needed for ourselves," said ecologist and data scientist Rachel King.
"We kept repeating the same conversation, project after project: whether to trust a suggestion, how much access to give an AI agent, what to do when a long chat session forgets a decision it made an hour earlier. At some point, it made more sense to work it out together, once, than have every team rediscover it independently." The researchers' work is published in PLOS Computational Biology. When a six-person team set out to build the Wildfire Resilience Index, an open-access tool for measuring how prepared communities and landscapes are for wildfire, they knew the science would be hard: stitching together satellite imagery, land-cover data and socioeconomic variables with R and Python pipelines across two countries and 13 unique jurisdictions.
What they didn't expect was how fast the ground would move beneath them. The project began in 2023, when AI was just emerging as a transformative tool, and by the time the project was released, an entirely new generation of AI coding tools had arrived—and lessons the team had learned six months earlier no longer applied. That whiplash wasn't unique to the WRI team.
At NCEAS, a kind of think tank for environmental science, dozens of research teams work side by side on everything from wildfire to biodiversity to climate change. Up and down the building, the same conversations kept surfacing: junior researchers leaning on AI they didn't fully understand, veteran scientists doubting it could be trusted at all, and everyone in between quietly inventing their own rules for what counted as responsible use. WRI wasn't an isolated case—it was a case study of a reckoning already underway across the center.
Rather than let every project work it out alone, NCEAS brought its community together, spanning researchers, software developers, data analysts and professors, to hash it out directly. What began as internal guidance didn't stay internal for long. The result, refined through literature review and months of co-writing among 22 researchers, developers and data analysts, is "Ten simple rules for effective use of generative AI for code development in environmental science"—offered not just throughout NCEAS, but to the entire field.
"What started as guidance for our own community ended up filling a gap nobody else had addressed," said senior author Cat Fong, a researcher at NCEAS. "Existing advice was written for software engineers, or for science in the abstract. Almost none of it accounted for what our field actually looks like—messy, multi-source data, small teams, wildly different levels of coding experience in the same room.
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