The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

The authors examine multiple ecological variables as possible predictors of language diversity in North America using path analysis, mechanistic simulation modelling, and geographically weighted regression. They conclude that many of the variables do not predict language diversity, but rather are mediated by population density. The authors also find that the variables' ability to predict is not universal across the continent, but rather more regional.

Researchers investigated further into why and how humans speak so many languages across the globe, and why they are spread out unevenly. Using two different path analyses, a Stationary Path analysis and a GWPath, researchers tested the effect of eight different factors on language diversity. Out of the eight variables (river density, topographic complexity, ecoregion richness, temperature and precipitation constancy, climate change velocity, population density, and carrying capacity with group size limits), population density, carrying capacity with group size limit, and ecoregion richness had the strongest direct effects. Overall, the study revealed the role of multiple different mechanisms in shaping language richness patterns. The GWPath showed that not only does the most important predictor of language diversity vary over space, but predictors can also vary in the direction of their effects in different regions. They conclude that there is no universal predictor of language richness.

Researchers investigated further into why and how humans speak so many languages across the globe, and why they are spread out unevenly. Using two different path analyses, a Stationary Path analysis and a GWPath, researchers tested the effect of eight different factors on language diversity. Out of the eight variables (river density, topographic complexity, ecoregion richness, temperature and precipitation constancy, climate change velocity, population density, and carrying capacity with group size limits), population density, carrying capacity with group size limit, and ecoregion richness had the strongest direct effects. Overall, the study revealed the role of multiple different mechanisms in shaping language richness patterns. The GWPath showed that not only does the most important predictor of language diversity vary over space, but predictors can also vary in the direction of their effects in different regions. They conclude that there is no universal predictor of language richness.

Researchers investigated further into why and how humans speak so many languages across the globe, and why they are spread out unevenly. Using two different path analyses, a Stationary Path analysis and a GWPath, researchers tested the effect of eight different factors on language diversity. Out of the eight variables (river density, topographic complexity, ecoregion richness, temperature and precipitation constancy, climate change velocity, population density, and carrying capacity with group size limits), population density, carrying capacity with group size limit, and ecoregion richness had the strongest direct effects. Overall, the study revealed the role of multiple different mechanisms in shaping language richness patterns. The GWPath showed that not only does the most important predictor of language diversity vary over space, but predictors can also vary in the direction of their effects in different regions. They conclude that there is no universal predictor of language richness.