An Interaction Model for Resource Implement Complexity Based on Risk and Number of Annual Moves

Note

Variables proxying risk: growing season (GS), natural log of effective temperature (LET), natural log of above ground productivity (LNAGP). GS is considered by the authors to be the most direct measure of the impact of annual climatic variation. Variables proxying implement diversity: total number of complex subsistants (CSTS), total number of complex subsistant types (NCT), total number of subsistants (STS). Out of four regression models run on each of the diversity variables (a total of twelve models), LET is included in the backward stepwise and best subset against CSTS, LNAGP is included in none of the models, and GS is included in every model. As GS is considered by the authors to be the best predictor variable, this suggests that risk is strongly associated with higher implement diversity.