The Future of Artificial Intelligence in Ecosystem Modeling

Abstract

Developing ecosystem models has traditionally been limited to a small global community of experts because of the complex skills and resources required. However, the emergence of user-friendly artificial intelligence (AI) tools with powerful generative capabilities could democratize ecosystem modeling, enabling both experts and nonspecialists to build models. We explore a speculative future where AI enables automated end-to-end model development and application. Although such tools could accelerate and enhance modeling tasks, their widespread adoption raises concerns about data integrity, bias, interpretation reliability, and the potential erosion of human expertise. We argue that regardless of AI's technical advancement, human engagement and control remain essential. The global community must respond by identifying key factors that distinguish desirable outcomes and developing infrastructure, standards, and ethical guidelines to ensure AI use in ecosystem modeling remains scientifically robust while supporting sustainable and equitable outcomes.

Keywords: artificial intelligence; decision-making; human–AI collaboration; risk; socioecological models.

Graphical Abstract

Figure 1.

Agent-based model simulation of sheep–wolf ecosystem dynamics. The left panel shows the spatial distribution of sheep (dots without bars) and wolves (dots with bars) with green bars indicating wolf energy levels. The right panel displays a time series plot of sheep and wolf population sizes over the simulation period.

Figure 2.

The present and future of AI ecosystem modeling. (a) Present: the stylized role and capability (the distance from the origin) for humans to undertake modeling tasks, including innovation (the radial arrows), with limited support from AI. For a human (or AI) modeler, these tasks include the abilities to sift through research to pinpoint impactful study areas and objectives, identify human and computer resources needed, generate and refine ecosystem conceptual models, streamline data collection and synthesize domain insights, select algorithms and implement complex ecosystem frameworks, efficiently run simulations to optimize the model’s performance, conduct automated validation against real-world data, visualize and summarize findings for reporting, and consult with stakeholders throughout the modeling process to solicit feedback and field queries. (b) Future: as AI capability improves, it will be integrated across the modeling workflow. (i) It may become technically possible to automate existing ecosystem modeling workflows with AI, including having AI innovate on current modeling capabilities, however this may present unacceptable risks (ii) The division of roles that maximizes positive outcomes may involve specific roles for AI, humans, and their collaborations – with one or the other entity being excluded from some aspects of the work.