Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

Using multiple logistic regression, the researchers compare the relative strength of predictors of land ownership across 102 societies. The analysis finds significant predictive power in factors such as neighbors' property system, population density, and geography.

This study asks the following research questions: What are the dynamics of land ownership norms over time? Do these changes follow predetermined trajectories? The authors utilize biogeographical and evolutionary analyses to explore temporal and spatial patterns in land ownership norms within a sample of 73 Bantu societies. For the first component of the research, they test three prominent hypotheses regarding evolutionary trajectories: rectilinear, unilinear, and shift without restriction. For the second component, they use a multi-model inference approach to evaluate three hypotheses regarding the possible spatial patterns of land ownership. The results show evidence for the unilinear trajectory in type, but not with a consistent decrease in group ownership in size. Land ownership is more likely in areas with neighboring landowners and predictable resource productivity, while subsistence type is not significantly correlated with type of land ownership.