Professor of Ecology University of Notre Dame, United States
Background/Question/Methods Background/Question/
Methods:
Indigenous students pursuing degrees in natural resource management at tribal colleges need training in data science to meet management needs unique to reserved lands: Tribal managers must contend with a checkerboard of federal, tribal, and state land, managed by agencies often with conflicting goals. The federal agencies funding most tribal natural resources programs do not have a background or vocabulary in Traditional Ecological Knowledge (TEK) and therefore do not incorporate tribal values into funding metrics, monitoring strategies, or analysis of data. Finally, tribal natural resources programs often lack trained data scientists who know how to work with large ( >30 year) datasets designed to evaluate the quality of a Tribe’s water, air, soil, groundwater, forest health, or fisheries.
Salish Kootenai College (SKC) and Notre Dame faculty partnered to develop a new course at SKC: (a) emphasizing inclusion of TEK values in data analysis, and (b) increasing the capacity of tribal natural resource managers to analyze large datasets. SKC students represent 68 North American Tribes and most Wildlife and Fisheries graduates return to their home reservations to work within their tribes’ natural resource programs.
Results/Conclusions Results/
Conclusions:
Students learned to perform basic trends analysis of water quality data on a 10-year monitoring data set using R statistical software. Next, they learned to interpret trends in the data using a Western Science approach focused upon numeric criteria and water quality standards. Students were then challenged to evaluate the design of a water quality monitoring network on tribal land on a fictional landscape in the context of TEK priorities (e.g. language, medicine, food sovereignty, spiritual use, and aesthetics). Class discussions allowed students to express their own priorities and to contextualize these priorities with the values of students from different tribal backgrounds. Finally, students combine Western and TEK based analysis to create more robust tools for management of tribal resources. Compared to previous data-science curricula, the 10 week long focus on a culturally relevant data set and the incorporation of TEK into analysis engaged students and provided a tangible model for careers at the interface of physical science and cultural needs. The instructors have shared the class curriculum and R code with instructors at other colleges with high Native American enrollment, as part of a larger effort to develop effective curricula for data science for students from underrepresented groups.
