Ecology Careers in the AI Era: Early-Career Data Literacy Must Be Protected

The rapid adoption of artificial intelligence (AI) in ecology signals a shift in how organizations structure entry-level positions and the role of young ecologists in data processing tasks. Implementing AI to automate data processing could mean that stepping-stone positions provide less training of key skills needed for career growth. To aid educators, employers, and early-career ecologists in navigating the adoption of AI into ecology and conservation workflows, we outline two plausible paths for the structure of entry-level positions: one where data processing and management are streamlined by AI and the entry-level ecologist focuses almost exclusively on hands-on fieldwork, and a second path where, in addition to fieldwork, entry-level ecologists participate in the implementation of AI-assisted data processing tasks. We argue for the second path, the analyst-technician model, and recommend that employers reserve workday time to involve mentees in data processing pipelines using novel technologies to protect training, broaden participation, improve career persistence, and keep early-career ecologists competitive for the next steps in their careers.