Human-centric skills are essential for the responsible and rigorous application of AI in ecology

Jessica Marielle Kendall-Bar , Allison Payne, Max Czapanskiy, Nathan Fox, Jessica Alice Leivesley, Michaela Alksne, Beverly French, Alice Malivert, Gerard O'Leary, Avik Biswas, Isaac Nealey, Rachel Parkinson, Christopher Lowrie, Ismael Perez, Eliot Miller, Ian Costello, Timothy Sainburg, Dominique Rissolo, Lauren Severance, Ritika Mukherji, Felipe Montealegre-Mora, Kevin Huynh Nguyen, Carla Calvo Tusell, William K Oestreich , Michael Sorenson, Sergey Gurvich, Joseph Walker, Allison Kellum, Tanya Berger-Wolf, İlkay Altıntaş, Stuart Sandin, Nicolas Lecomte

Artificial Intelligence (AI) can rapidly process large ecological datasets, uncover patterns, and inform conservation decisions, but responsible adoption depends as much on human-centric skills as on technical methods. Ecologists face steep learning curves, an overwhelming and fast-evolving model landscape, uneven access to data and computing, and a growing transparency deficit. These challenges require human-centric skills like time management, critical thinking, collaboration, communication, creativity, and project management to select, implement, interpret, and responsibly translate AI outputs into ecological insight and action. We led a workshop, EcoViz+AI: Visualization and AI for Ecology, that brought together 35 experts to synthesize practical guidance for navigating these challenges across the AI pipeline. Using workshop discussions and experiences as a foundation, this position paper proposes practical solutions and complementary human-centric skill development to address these challenges: (1) educational resources that support opportunity-cost reasoning (time management) and methodological judgment (critical thinking), (2) communities of practice that build inclusive shared expertise (collaboration and mentorship), (3) effective visualizations that improve interpretability and strengthen transparency of model behavior and uncertainty (creativity and communication), and (4) computational resources that reduce implementation burden through shared data, extensible code, and accessible infrastructure (project management and problem-solving). Our workshop compiled resources, including science communication videos for five AI use cases and repositories for ecology-related AI models and communities of practice. Emphasizing human-centric skills and working in tandem with efforts to promote open science and computational literacy can make AI in ecology more rigorous, equitable, and ecologically relevant, advancing research and conservation.