In this article, the authors seek to investigate why some societies reject agriculture despite its many benefits. By modeling data regarding ecological fitness and cultural transmission, the authors found predictors for the degree to which a society relies on agriculture. The authors conclude that the degree of fitness a local environment had for early domesticates as well as the degree of contact with neighboring societies strongly predicts levels of dependence on agriculture.

In this article, the authors seek to investigate why some societies reject agriculture despite its many benefits. By modeling data regarding ecological fitness and cultural transmission, the authors found predictors for the degree to which a society relies on agriculture. The authors conclude that the degree of fitness a local environment had for early domesticates as well as the degree of contact with neighboring societies strongly predicts levels of dependence on agriculture.

In this article, the authors seek to investigate why some societies reject agriculture despite its many benefits. By modeling data regarding ecological fitness and cultural transmission, the authors found predictors for the degree to which a society relies on agriculture. The authors conclude that the degree of fitness a local environment had for early domesticates as well as the degree of contact with neighboring societies strongly predicts levels of dependence on agriculture.

In this article, the authors seek to investigate why some societies reject agriculture despite its many benefits. By modeling data regarding ecological fitness and cultural transmission, the authors found predictors for the degree to which a society relies on agriculture. The authors conclude that the degree of fitness a local environment had for early domesticates as well as the degree of contact with neighboring societies strongly predicts levels of dependence on agriculture.

The researchers, using principal component analysis, generalized additive models across 12 agriculture origin locations, and a model predicting hunter-gatherer population density, evaluate hindcasted population density trends to suggest predictors of the development of agriculture. Using domestication as an indicator of agriculture, they test 3 competing hypotheses regarding agriculture development. Their results are consistent with the "surplus" hypothesis, indicating that agriculture arose as population densities increased along with environmental capabilities.

The researchers, using principal component analysis, generalized additive models across 12 agriculture origin locations, and a model predicting hunter-gatherer population density, evaluate hindcasted population density trends to suggest predictors of the development of agriculture. Using domestication as an indicator of agriculture, they test 3 competing hypotheses regarding agriculture development. Their results are consistent with the "surplus" hypothesis, indicating that agriculture arose as population densities increased along with environmental capabilities.

The researchers, using principal component analysis, generalized additive models across 12 agriculture origin locations, and a model predicting hunter-gatherer population density, evaluate hindcasted population density trends to suggest predictors of the development of agriculture. Using domestication as an indicator of agriculture, they test 3 competing hypotheses regarding agriculture development. Their results are consistent with the "surplus" hypothesis, indicating that agriculture arose as population densities increased along with environmental capabilities.

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.