Safeguarding the resilience of America’s vast natural resources depends on scientific knowledge to help streamline and direct monitoring efforts. Forecast models are critical for prioritizing resources in a fast-paced, changing world, but knowing what data is needed to accurately parameterize these models remains a challenge. For example, wild animal populations are made up of both males and females, and it is generally assumed that both are equally sensitive to environmental changes. However, new research is revealing that this may be an oversimplification. Knowing when and where such vulnerabilities are most likely to arise will be key to increasing the efficiency and efficacy of monitoring operations. To validate findings, the project will leverage machine learning to analyze model outputs, while also applying cutting-edge biotechnology to track the small-bodied amphibians in their natural environment. These efforts will contribute to the generation of large, publicly available ecological monitoring datasets, ideal for AI model training. The project will also advance the education and training of the nation’s future STEM workforce with a new, hands-on research course at Virginia Tech, with datasets also implemented as modules in classes and summer data camps for undergraduates and high schoolers. In each case, students will be trained in the use of Artificial Intelligence to automate, debug, and translate code, enhancing data literacy and teaching foundational concepts in