["American Journal of Biological Anthropology, Volume 190, Issue 2, June 2026. ", "\nABSTRACT\n\nObjectives\nThis study examines the spatiotemporal ecology of probable malaria mortality in 19th‐century southern Ontario to evaluate how settlement expansion, landscape transformation, and infrastructure development structured transmission risk in a temperate, settler‐colonial context.\n\n\nMaterials and Methods\nProbable malaria‐attributed deaths recorded in Ontario death certificates (1869–1900; n = 2683) were geocoded and analyzed using Geographic Information Systems (GIS). Mortality locations were examined in relation to historical settlements, railways, and wetlands derived from 19th‐century spatial datasets. Spatial clustering was assessed using Average Nearest Neighbor analysis and kernel density estimation. Temporal changes in proximity to landscape features were tested using nonparametric statistics, while malaria mortality density was modeled using negative binomial regression and generalized additive models.\n\n\nResults\nMalaria mortality was significantly clustered across all decades but became progressively less spatially concentrated through time. Mortality locations shifted farther from mapped wetlands as drainage intensified, while proximity to settlements consistently decreased. Regression models identified settlement proximity as the strongest and only significant predictor of malaria mortality density, whereas distances to wetlands were not independently associated once settlement effects were accounted for.\n\n\nDiscussion\nThese results indicate a reorganization of malaria risk from environmentally constrained ecologies toward anthropogenic landscapes shaped by settlement, agriculture, and infrastructure. Malaria transmission in southern Ontario was embedded within everyday settlement practices rather than static environmental features, demonstrating how colonial landscape transformation structured disease risk in a temperate region. This study highlights the value of spatiotemporal GIS approaches for interpreting past disease ecologies within biological anthropology and paleopathology.\n\n"]