Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences

Generative artificial intelligence (GenAI) tools are increasingly incorporated into ecological workflows at every stage of the scientific process, from hypothesis generation and data collection to synthesis, analysis, manuscript preparation, and peer review. This rapid integration presents new opportunities for discovery but also poses fundamental challenges to the epistemological foundations of the field. We discuss how the uncritical adoption of GenAI risks eroding core principles of ecological inquiry, including idea generation, reproducibility, transparency, and engagement with natural systems. By prioritizing scale, speed, and statistical pattern detection over rigorous theory development and testing, GenAI may shift research away from theory-driven questions and toward large-scale data-mining approaches. GenAI tools offer clear benefits when used thoughtfully; these include expanded access to information and the ability to synthesize across otherwise disparate disciplines and datasets. But overemphasis on these approaches could have adverse impacts on the field, including reducing reproducibility and ecological inference and reinforcing existing inequities in knowledge production. Here, we identify key challenges associated with GenAI integration in the field of ecology and provide seven actionable guidelines to support its responsible and effective use. We call for a concerted effort to engage in reflective and intentional use of GenAI in ecological research, resisting pressures to prioritize efficiency over the rigor, creativity, inclusivity, and societal relevance of our field.