Let’s go fishing: A quantitative analysis of subsistence choices with a special focus on mixed economies among small-scale societies

Abstract

The transition to agriculture is regarded as a major turning point in human history. In the present contribution we propose to look at it through the lens of ethnographic data by means of a machine learning approach. More specifically, we analyse both the subsistence economies and the socioecological context of 1290 societies documented in the Ethnographic Atlas with a threefold purpose: (i) to better understand the variability and success of human economic choices; (ii) to assess the role of environmental settings in the configuration of the different subsistence economies; and (iii) to examine the relevance of fishing in the development of viable alternatives to cultivation. All data were extracted from the publicly available cross-cultural database D-PLACE. Our results suggest that not all subsistence combinations are viable, existing just a subset of successful economic choices that appear recurrently in specific ecological systems. The subsistence economies identified are classified as either primary or mixed economies in accordance with an information-entropy-based quantitative criterion that determines their degree of diversification. Remarkably, according to our results, mixed economies are not a marginal choice, as they constitute 25% of the cases in our data sample. In addition, fishing seems to be a key element in the configuration of mixed economies, as it is present across all of them.

Human-Plant Coevolution: A modelling framework for theory-building on the origins of agriculture

“…Within this context of change, intensive gathering and cultivation have been considered economic practices within a continuum, where some plant species are gathered opportunistically and others systematically exploited. At the beginning of every transition to agriculture, predatory strategies (fishing, hunting, and gathering) were central to human subsistence, while mutualism (plant tending and animal husbandry), if any, were complementary [ 32 ].…”

“…Broad contextual analyses of the archaeobotanical record within macroevolutionary theory [18] and singlecrop approaches [30] started to bring new light on the process of domestication based on a fast-growing body of archaeological evidence. The analysis of this massive and relatively recent volume of data makes clear that it is now necessary to return to theory by revisiting the mechanisms allegedly involved in domestication, disentangling their connection to a diversity of trajectories [31,32], being those protracted or sudden, and identifying the weight of the social and ecological parameters. Approaches developed within human behavioural ecology [7,[33][34][35][36][37][38], such as niche construction or cultural niche construction theories, have gained momentum in this effort.…”

Human-Plant Coevolution: A modelling framework for theory-building on the origins of agriculture

“…Within this context of change, intensive gathering and cultivation have been considered economic practices within a continuum, where some plant species are gathered opportunistically and others systematically exploited. At the beginning of every transition to agriculture, predatory strategies (fishing, hunting, and gathering) were central to human subsistence, while mutualism (plant tending and animal husbandry), if any, were complementary [30].…”

“…Broad contextual analyses of the archaeobotanical record within macroevolutionary theory [18] and single-crop approaches [29] started to bring new light on the process of domestication based on a fast-growing body of archaeological evidence. The analysis of this massive and relatively recent volume of data makes clear that it is now necessary to return to theory by revisiting the mechanisms allegedly involved in domestication, disentangling their connection to a diversity of trajectories [30], being those protracted or sudden, and identifying the weight of the social and ecological parameters.…”

Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach

“…Many different Machine Learning methods have been employed to build socio-ecological systems, centred on understanding how people managed their environment [ 18 , 19 , 34 , 53 ]. Notwithstanding, the number of archaeological studies that use the Machine Learning methodology is still a minority compared to those that use other quantitative and/or qualitative methods.…”