Statistical Analysis of the Combined Effects of Land Use Change and Climate Change on Manoomin/Psiŋ (Wild Rice) Abundance in the Upper Great Lakes Region

Manoomin (Ojibwemowen)/Psiŋ (Dakota language), also called Zizania palustris (scientific name) or Wild Rice, is culturally significant to the Anishinaabe and Dakota peoples, who have harvested and stewarded the plant for thousands of years. Euro-American colonization of the upper Great Lakes region disrupted the watersheds of natural Wild Rice water bodies through changing land use, water quality and quantity, and climate. In response to concerns expressed by tribal and inter-tribal organizations about increasing declines of Wild Rice abundance in the upper Great Lakes region in recent years, the Kawe Gidaa-naanaagadawendaamin Manoomin (First We Must Consider Manoomin/Psiŋ/Wild Rice) research collaborative formed to deepen our understandings of the layered impacts of human-driven environmental changes on Wild Rice. Kawe Gidaa-naanaagadawendaamin Manoomin/Psiŋ research collaborative is a Tribal-University of Minnesota partnership established in 2017 that centers tribal research priorities and respects tribal sovereignty. In this study, we used generalized additive models in R to statistically analyze the relationships among changing climate, land use, and Wild Rice abundance data from the Great Lakes Indian Fish and Wildlife Commission in 30 lakes across present-day Wisconsin from 2001 to 2013. Of the climate variables considered, early season (floating leaf stage) precipitation by watershed size and total winter snowfall by watershed size were statistically significant. The interactions between snowfall and lake area, and between snowfall and open water percentage were also significant. Precipitation and snowfall directly affect water levels and mixing of lake sediments, which in turn affect Wild Rice abundance. We also tested land use variables' relationship with Wild Rice abundance, specifically the proportions of forested, agricultural, and developed land area, based on the classifications used by the USGS National Land Cover Database. We aim to gain an understanding of how climate change interacts with land use to affect Wild Rice abundance by jointly testing the significance of both types of variables in the model.