The capacity for cumulative cultural evolution has often been invoked to explain the great diversity of habitats occupied by humans. This theory of cultural evolution emphasizes the gradual accumulation of technologies and cultural practices over many generations, and predicts that larger populations will generate more complex cultural adaptations than smaller, isolated ones. Here, the authors investigate the marine foraging tool repertoires of 10 Oceanic societies to determine whether population size and intergroup contact affect the cultural processes by which tool kits evolve.

The capacity for cumulative cultural evolution has often been invoked to explain the great diversity of habitats occupied by humans. This theory of cultural evolution emphasizes the gradual accumulation of technologies and cultural practices over many generations, and predicts that larger populations will generate more complex cultural adaptations than smaller, isolated ones. Here, the authors investigate the marine foraging tool repertoires of 10 Oceanic societies to determine whether population size and intergroup contact affect the cultural processes by which tool kits evolve.

This article presents a model of cultural evolution simulating the accumulation of tools in specialized and non-specialized populations under different demographic and environmental scenarios. The model predicts that the relationship between population size and repertoire size is nonlinear and can differ between non-specialized and specialized populations. For small population sizes, the non-specialized populations maintain knowledge better and therefore reach higher average repertoire sizes. In large populations, specialized populations can reach higher average repertoire sizes. This is because non-specialized population's total repertoire size is limited by the capacity of individuals to accumulate knowledge of different skills, while in specialized populations, each individual needs to know only a fraction of the population's repertoire. However, the model also predicts that specialized populations are more susceptible to information loss due to their subdivision of knowledge, and this can be amplified by demographic and environmental factors. The authors also use ethnographic data to analyze the relationship between population size and degree of craft specialization of societies, and how this may be influenced by ecological factors.

The researchers test the relationship between toolkit complexity and diversity as defined by Oswalt (1973) and environmental and demographic variables. Neither population size nor risk of resource failure predict toolkit characteristics among all groups in the sample. However, population size is significantly positively correlated with toolkit diversity and complexity among food-producers, whereas environmental factors indicating risk of resource failure are significantly positively correlated among hunter-gatherers. This leads the researchers to suggest that food-producers' effectiveness at niche construction is a result of their large population size, which thus has a larger effect on toolkit composition than does environmental risk relative to hunter-gatherers.

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.

This paper builds off previous research into the effect of population size and resource risk on complexity of subsistence technology by investigating the relationship between these independent variables and total number of material items and techniques used by various western North American hunter-gatherer groups. This tally of total technological complexity is found to be insignificantly related to population size or residential mobility; however, there is a significant correlation in the expected direction between technological complexity and one measure of resource risk (mean annual temperature during driest month). Tying this finding to previous analyses of subsistence technologies, the authors theorize that environmental risk is a pervasive driver of technological ingenuity and cultural evolution.

Previous studies have yielded contradictory results on the relationship between population size and cultural evolution. Focusing on tool complexity these authors introduce the risk of resource failure as a possible confounding variable. They conclude that population does not predict tool kit complexity when controlling on other factors. There were significant correlations between tool kit complexity and some of the resource measures.

Using a representative sample of 339 hunter-gatherer societies, researchers examine the relationship between hunter-gatherer use of space, size of population and supply of resources to see if they are similar to other organisms. By combining all factors into a single model, the authors claim to explain 86% of the variation in home range. Hunters have greater resource distribution than gatherers but both more so than aquatic foragers. Lastly, terrestrial foragers have more extensive home ranges than aquatic foragers.

In this paper, the authors analyzed data on 20 hunter-gatherer groups in order to understand the factors that influence the diversity and elaborateness of their tool assemblages. They used data collected by a variety of ethnographers to draw inferences about the complexity of implement assemblages and how it is affected by ecological constraints, modes of resource procurement, group movement, and population size. Regression analysis showed that the two strongest predictors of implement complexity were growth season (GS) (as a proxy for risk) and the number of annual residential moves (NMV). With the understanding that NMV and GS are likely not independent, the authors created addition and interaction models to understand how these variables may work in tandem to influence implement diversity and elaborateness. The results show that a shorter growing season (higher risk) and a lower number of moves are correlated with greater implement complexity. This analysis also divided the hunter-gatherers into two subgroups: a subgroup characterized by higher diversity of complex implements and more elaborate individual implements than predicted by the model, and a subgroup characterized by lower diversity and less elaborateness than predicted. These subgroups were found to correspond with the distinction between foragers (groups that move more-or-less as a unit while gathering) and collectors (groups that gather (logistically from a more-or-less fixed settlement), with the higher diversity subgroup being made up mostly of collectors and the lower diversity subgroup being made up mostly of foragers. Finally, the authors suggest that under conditions where population growth leads to increased density, foraging strategies will tend to shift to collector strategies in conjunction with increased elaborateness of implements to exploit resources with greater intensity.

This article explores the sexual division of labor among foragers, focusing on resource availability and constraints on women’s foraging activities. The authors conclude that “there is a greater division of foraging labor in more seasonal habitats where less gathering is possible and more extractive, tool-based foraging is required” (191).