Fall 2026 Missouri Data Science and Informatics Symposium

Callery pear was detected throughout Columbia, MO using high resolution (3m) imagery and machine learning. A new model was then developed to further distinguish between planted individuals and escaped populations with a high rate of accuracy (AUC > 0.9). Using these detections, we trained an XGboost model to identify socio-economic and environmental factors associated with each population type (AUC > 0.8). Divergent sets of predictors were influential for planted and escaped Callery pear tree populations. While both populations were influenced by environmental variables (e.g., fractional imperviousness, soil pH, flooding frequency), planted populations were also shaped by socio-economic variables (e.g., population density, vacancy rate, median household income) while socio-economic associations with escaped populations were not as strong. To predict areas vulnerable to invasion in the future, we modeled urbanization changes to the year 2050 assuming slow, average, or fast rates of urbanization (AUC > 0.85). Using our previously trained XGBoost model, we then predicted areas that would be most vulnerable to invasion based on the urban modeling for each scenario. These findings demonstrate the value of integrating spatial analysis and machine learning to examine invasion dynamics and support proactive management of urban invasive species.