This study uses macroecological approaches to test the impact of climate, social environment, inter-group borrowing and cultural history on vernacular house architecture among 1140 societies. The authors suggest that certain features will be influenced: wall materials, ground plan, roof shape, and floor placement. They use mixed binary and multinominal regressions models to test these predictions. The results strongly support that climatic drivers, cultural continuity, and inter-group borrowing predict three out of the four features: wall materials, roof shape, and floor placement. Social drivers are a strong predictor of every feature tested.

This study uses macroecological approaches to test the impact of climate, social environment, inter-group borrowing and cultural history on vernacular house architecture among 1140 societies. The authors suggest that certain features will be influenced: wall materials, ground plan, roof shape, and floor placement. They use mixed binary and multinominal regressions models to test these predictions. The results strongly support that climatic drivers, cultural continuity, and inter-group borrowing predict three out of the four features: wall materials, roof shape, and floor placement. Social drivers are a strong predictor of every feature tested.

This study uses macroecological approaches to test the impact of climate, social environment, inter-group borrowing and cultural history on vernacular house architecture among 1140 societies. The authors suggest that certain features will be influenced: wall materials, ground plan, roof shape, and floor placement. They use mixed binary and multinominal regressions models to test these predictions. The results strongly support that climatic drivers, cultural continuity, and inter-group borrowing predict three out of the four features: wall materials, roof shape, and floor placement. Social drivers are a strong predictor of every feature tested.

This study uses macroecological approaches to test the impact of climate, social environment, inter-group borrowing and cultural history on vernacular house architecture among 1140 societies. The authors suggest that certain features will be influenced: wall materials, ground plan, roof shape, and floor placement. They use mixed binary and multinominal regressions models to test these predictions. The results strongly support that climatic drivers, cultural continuity, and inter-group borrowing predict three out of the four features: wall materials, roof shape, and floor placement. Social drivers are a strong predictor of every feature tested.

Researchers examined both why so many language are spoken today, and why they are so unevenly distributed geographically. Instead of looking at correlative tests, this study uses a process-based simulation model that attempted to predict both the number or precolonial languages in Australia as well as the number of languages per unit of land. The model was based upon three basic assumptions: 1) humans fill unoccupied spaces; 2) rainfall limits population density; 3) groups divide after reaching a maximum population. While researchers used the model strictly on the Australia continent, it was able to correctly explain 56% of spatial variation in language richness, and predict the total number of languages across the continent. The accuracy of this model concludes that climatic conditions and changes in group size are important factors in shaping language diversity patterns and therefore global human cultural diversity.

Researchers examined both why so many language are spoken today, and why they are so unevenly distributed geographically. Instead of looking at correlative tests, this study uses a process-based simulation model that attempted to predict both the number or precolonial languages in Australia as well as the number of languages per unit of land. The model was based upon three basic assumptions: 1) humans fill unoccupied spaces; 2) rainfall limits population density; 3) groups divide after reaching a maximum population. While researchers used the model strictly on the Australia continent, it was able to correctly explain 56% of spatial variation in language richness, and predict the total number of languages across the continent. The accuracy of this model concludes that climatic conditions and changes in group size are important factors in shaping language diversity patterns and therefore global human cultural diversity.