How humans obtain food has dramatically reshaped ecosystems and altered both the trajectory of human history and the characteristics of human societies. Our species’ subsistence varies widely, from predominantly foraging strategies, to plant-based agriculture and animal husbandry. The extent to which environmental, social, and historical factors have driven such variation is currently unclear. Prior attempts to resolve long-standing debates on this topic have been hampered by an over-reliance on narrative arguments, small and geographically-narrow samples, and by contradictory findings. Here we overcome these methodological limitations by applying multi-model inference tools developed in biogeography to a global data set (818 societies). Although some have argued that unique conditions and events determine each society’s particular subsistence strategy, we find strong support for a general global pattern in which a limited set of environmental, social, and historical factors predicts an essential characteristic of all human groups: how we obtain our food.
Data associated with Geography of Subistence. Gavin et al. ROS
We acquired all data from D-PLACE (www.d-place.org). We only used data collected in a relatively narrow time span (1860-1960) to avoid the effects of changing environmental and social conditions, including long-term transitions in subsistence strategies, as well as the possibility that over the course of human history multiple groups may have occupied a given location. Variables describing subsistence economy (EA001 to EA005) were used to determine the dominant subsistence strategy, which we defined as the strategy relied on for more than 56% of total subsistence. We summed the hunting (EA002), gathering (EA001) and fishing (EA003) categories to represent dependence on the foraging subsistence strategy. We omitted from our analyses societies for which multiple strategies (foraging, animal husbandry, or plant-based agriculture) contributed equally, with no one strategy contributing more than 56%. Climate data were from the baseline historical (1900-1949) CCSM ecoClimate model (www.ecoclimate.org). We derived topographic data (slope and elevation) from the Global Multi-resolution Terrain Elevation Data 2010 (see ref 60 in Gavin et al paper). We extracted all climatic and topographic variables for the localities of the societies in our sample based on global maps at a 0.5 by 0.5 degree resolution. To avoid multi-collinearity in our explanatory models, we reduced these often highly correlated environmental predictors to orthogonal components via principal components analysis (PCA). The PCA produced 3 main composite variables: (1) 'environmental stability' describes a gradient of increasing mean temperature, temperature predictability, mean precipitation, lower precipitation variance, and decreasing temperature variance; (2) 'topographic complexity' describes a gradient of increasing slope and elevation; and (3) 'environmental productivity' describes a gradient of increasing mean precipitation, lower precipitation variance, precipitation predictability, and net primary productivity. To capture the potential effects of horizontal transmission, we included as a predictor the proportion of the 10 nearest neighboring societies that share a society's subsistence strategy ('neighbor effect').
Gavin et al. RSOS2018.datafile.xlsx
