A social biogeography of homicide: Multilevel and sequential canonical examinations of intragroup unlawful killings.

Abstract

A considerable number of publications have examined the effect of various geographical, life history, social, economic and political factors on homicide. However, few studies were interested in examining the effect of these forces in an integrated social biogeography of homicide. This study collected data for 172 nation-states from various publications and databases. Standardized Studentized residuals were extracted from a multilevel model examining the effects of geographical adjacency upon homicide rates. A general linear model was used, with the residuals, to observe the effects of physical, community, social, cultural, and cognitive ecology upon homicide. Two sequential canonical analyses (SEQCA) were conducted to determine the mediating effects among the ecological indicators with respect to homicide. In the SEQCA, we hypothesized physical ecology would lead to communal ecology, in turn leading to social ecology, subsequently leading to cognitive ecology, and ultimately to homicide. A parsimony test concluded that economic growth and inequality fully mediated the relationship between cognitive ecology and homicide residuals. Similarly, the effects of life history upon homicide were fully mediated by social ecology. This study suggests several social ecology factors appear to directly affect homicide; however, other aspects of ecology indirectly affected homicide through influences on social ecology. The effect of indicators of social ecology such as income inequality and the operational sex ratio indicate competition for resources is a significant force generating differences in homicide rates across populations. In conclusion, a suite of evolutionary pressures seems to influence homicide rates, but mainly in a sequential nature rather than simultaneously.

What causes the anti-Flynn effect? A data synthesis and analysis of predictors.

“…To determine the effect of autocorrelated residuals in the present sample, we calculated a multilevel model (MLM) with AR(1) covariance parameter estimation and restricted maximum likelihood between decadal loss and a variable indicating geographical contiguity (this method assigns each country a rank based on its immediate geographical adjacency with other countries; Peñaherrera-Aguirre, Hertler, Figueredo, Fernandes, & Cabeza de Baca, 2017). The results from this analysis indicated a covariance of zero and a nonsignificant effect of geographical/spatial contiguity.…”

The biogeography of human diversity in life history strategy.

“…This cosmopolitan status exposed human communities to an array of unique environmental pressures leading to trait variation (Cochran & Harpending, 2009). Social biogeography, a branch of human sociobiology, emerged as a discipline destined to examine the influence of physical and community ecology (e.g., mean annual temperature, mean annual precipitation, altitude, latitude and longitude, parasite burden) on interpopulation differences in life history as well as in social, political, and economic outcomes (e.g., Figueredo et al, 2017; Peñaherrera-Aguirre et al, 2019). Since biogeographic and zoogeographic classifications are predicted to capture differences in physical ecology, these regions should also adequately detect variations between human populations.…”

“…Moreover, it is worth noting that while behaviorally modern humans are characterized for their reliance on cultural niche construction (Odling-Smee, Laland, & Feldman, 2013), several aspects of physical ecology are less susceptible to human interference, such as altitude, temperature, and precipitation, except for the construction of shelters. Current biogeographic examinations have demonstrated the additive and nonadditive influence of various dimensions of physical, community, and cognitive ecology on human variation (e.g., Figueredo et al, 2017; Peñaherrera-Aguirre et al, 2019).…”

Homicide, Inequality, and Climate: Untangling the Relationships

“…For instance, Barlett et al ( 2020 ) provide a path model based on country-level correlations from 1961 to 2015 that connects global warming to extreme weather events that threaten clean water supplies, which they infer creates resource stresses that motivate homicide. Similarly, Peñaherrera-Aguirre et al ( 2019 ) conducted a 25-year moving average study demonstrating that climate change exacerbates resource competition and inequality, which is mediated through proposed evolutionary influences based on latitude, cultural norms, and climate change. Furthermore, some studies find mixed or no association between temperature and homicide.…”