Certain contexts such as shell middens, rockshelters and caves create conditions more amenable to the preservation of organic remains. More abundant radiocarbon dates for a given time period may therefore result not from increased human populations, but from increased dating of abundant sites with a high degree of organic preservation. Of these three site types, shell middens are particularly abundant around the time of initial domestication in Eastern North America, particularly in the Southeast.
At least in certain parts of the region though, shellfish exploitation actually peaked several millennia after initial domestication occurred [ 67 ]. The greater degree of organic preservation afforded by shell middens is therefore not likely to substantially affect our results. Error in interpretation may also result from the way population proxies are analysed. Here, we undertook a novel approach using GAMs to fit diachronic population trends, and the first derivatives of these fits to evaluate significant periods of change.
This method is useful in that it balances parsimony and goodness of fit, but there is a possibility that these models could identify spurious periods of change that result not from differences in human populations, but from differences in the sample of radiocarbon dates [ 33 ]. While simulations show that this method can produce spurious results using site density, false positives are extremely rare using histograms of counts see the electronic supplementary material, text S1.
Given that our empirical results are consistent across both the KDE method examining date density and the histogram method examining site counts, we find the conclusions to be real and robust. An additional problem with the use of radiocarbon dates as a proxy for human population levels may be found in the form of researcher bias [ 23 ].
Certain time periods, geographical regions or site types may be preferentially investigated by archaeologists, leading to differential dating through time and space that reflects the actions of scientists, not past human population levels. This problem continues to plague radiocarbon-based population measures.
While some solutions have been proposed to control for researcher bias in geographical sampling intensity [ 68 ], a way to control for temporal biases in sampling has yet to be proposed. We therefore acknowledge that it is possible that our results may be impacted by differential investigation of various time periods, geographical regions, site types and archaeological contexts.
With radiocarbon dates from a large region and lengthy time span, we expect such biases to be minimal, but cannot rule them out. Finally, these methods may also be capturing false periods of change that result from peaks or plateaus in the calibration curve [ 69 ]. If periods of inferred population change covary with periods of time in the calibration curve, models may show inaccurate fluctuations or periods of stasis that result from the calibration curve, not from past human population change or stability.
This suggests that imbalance between populations and resources, manifested as low foraging efficiency, drove the shift towards domestication. It is possible that wild foods were not able to sustain high human populations in the Middle and Late Holocene, making domestication an optimal response to low foraging profitability.
It is also possible that increasing populations led to decreased territory size [ 43 ], excluding foragers from profitable patches and leading to low foraging efficiency. Within this framework, declining foraging returns are likely to be the most immediate motivation to initiate the intensification processes that precedes domestication. Regardless of whether populations increase or resource availability decreases, such deficits should either unintentionally alter the selective pressures humans place on wild plants or encourage the intentional manipulation of plants in order to reduce processing time and increase yields.
In Eastern North America, it is the case that populations increased prior to domestication, but we do not expect this to be a universal trend. What we do predict to be universal, however, is the disequilibrium between supply and demand.
Humans are unlikely to put significant enough pressure on wild resources unless there is a need to do so. Profitable wild foods must be scarce enough relative to human population levels before it would be worthwhile for individuals to pay the costs of investing time and energy into the process of domestication without guaranteed future rewards. Further investigation of these patterns in line with predictions derived from foraging theory and niche construction theory is needed, and this work should integrate both perspectives within BE.
We expect that such investigations will find a general pattern of population—resource imbalance preceding domestication that can help explain the timing and process of domestication wherever it happened in the world. We are also grateful for access to the Canadian Archaeological Radiocarbon Database, which made this research possible. Both authors gave final approval for publication. National Center for Biotechnology Information , U. R Soc Open Sci.
Published online Aug 3. Elic M. Weitzel and Brian F. Author information Article notes Copyright and License information Disclaimer. Author for correspondence: Elic M. Weitzel e-mail: ude. Received May 10; Accepted Jun This article has been cited by other articles in PMC. Associated Data Supplementary Materials Text S1: A series of simulations using idealized population data that was randomly sampled to represent radiocarbon dates in order to validate the methodological approach used in this paper.
Dataset S1. Data of corrected and observed summed probability distributions SPDs , site counts, and generalized additive model GAM fits per year intervals from 0—15, cal BP. Figure S1. Figure S2. Unadjusted residuals for each year interval showing the difference between values of the summed probability distributions SPDs and the fitted values of the generalized additive models GAMs for observed and taphonomically corrected radiocarbon dates.
Figure S3. Relative population density from a calibrated but not taphonomically corrected 52 summed probability distribution of radiocarbon dates through time fit with a generalized additive model GAM. Figure S4. Relative population density from a calibrated and taphonomically corrected 52 summed probability distribution SPD of site counts through time fit with a generalized additive model GAM. Figure S5. Relative population density from a calibrated but not taphonomically corrected 52 summed probability distribution SPD of site counts through time fit with a generalized additive model GAM.
Abstract The transition to agriculture is one of the most significant events in human prehistory; yet, explaining why people initially domesticated plants and animals remains a contentious research problem in archaeology. Keywords: origins of agriculture, niche construction, behavioural ecology, optimal foraging theory, dates as data.
Introduction Explaining domestication remains one of the most important and contentious research problems in archaeology [ 1 — 3 ]. Open in a separate window. Figure 1. Material and methods To generate and evaluate the population proxies used in this paper, we undertook a seven-step process wherein we: 1 defined the study area, 2 queried the radiocarbon record and removed spurious dates, 3 calibrated the dates, 4 described the temporal distribution of dates following two approaches, 5 corrected each distribution for taphonomic bias, 6 fitted the resulting data with a statistical model and 7 evaluated the rate of change in the model fit to identify significant periods of population growth or decline.
Querying and cleaning the radiocarbon data With our study area defined in this manner, we queried the Canadian Archaeological Radiocarbon Database CARD [ 18 ] on 25 November to obtain radiocarbon dates within the bounds of our study area. Calibration The radiocarbon dates resulting from our standard deviational ellipse query and chronometric hygiene decisions were then calibrated using O x C al v.
Summarizing chronological distributions The first approach describes the distribution of dates through kernel density estimation KDE. Taphonomic correction To account for the greater probability that older material will be lost, we applied the taphonomic correction developed by Surovell et al. Statistical modelling To examine whether the variation in these four proxies varies significantly through time, we fit each as a function of time using generalized linear GLM and generalized additive models GAM; figure 2.
Figure 2. Diachronic trends in site counts Examining the distribution of dated site counts shows that these trends are robust across alternative methods of evaluation figure 2 b. Discussion 4. Population—resource imbalance as a global driver of domestication While our analysis reveals significant population increases in Eastern North America that may have led to declines in per capita foraging return rates through increased competition [ 41 ] or resource depression [ 47 ], it is important to note that population growth is a sufficient, but not a necessary condition for disequilibrium between populations and resources that may precipitate initial domestication.
Integrating behavioural ecological models and niche construction For simplicity, we refer to the two competing explanations of initial domestication in this paper as the NC and BE hypotheses, but for the sake of clarity it is important to note that the specific predictions tested here are two of many that could potentially be drawn from these larger bodies of theory to explain domestication.
Potential sources of error Given the potential risk of drawing spurious conclusions from this dataset, an exploration of possible confounding factors is necessary. Supplementary Material Text S1: A series of simulations using idealized population data that was randomly sampled to represent radiocarbon dates in order to validate the methodological approach used in this paper.
Click here to view. Supplementary Material Dataset S1. Supplementary Material Figure S1. Supplementary Material Figure S2. Supplementary Material Figure S3. Supplementary Material Figure S4. Supplementary Material Figure S5. Data accessibility All data from the models presented in this paper are available as the electronic supplementary material dataset S1. Authors' contributions E. Competing interests We have no competing interests. Funding No funding has been received for this article. References 1.
Particularism and the retreat from theory in the archaeology of agricultural origins. Natl Acad. USA , — Smith BD. A comparison of niche construction theory and diet breadth models as explanatory frameworks for the initial domestication of plants and animals.
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Contreras DA, Meadows J. Summed radiocarbon calibrations as a population proxy: a critical evaluation using a realistic simulation approach. R Core Team. A language and environment for statistical computing. Bettinger RL. Prehistoric hunter—gatherer population growth rates rival those of agriculturalists. Agriculture, population growth, and statistical analysis of the radiocarbon record.
Winterhalder B, Goland C. On population, foraging efficiency, and plant domestication. The population ecology of hunter-gatherers and their prey. Anderson DG. Approaches to modeling regional settlement in the archaic period Southeast. Miller DS. From colonization to domestication: a historical ecological analysis of Paleoindian and Archaic subsistence and landscape use in central Tennessee. PhD thesis, The University of Arizona. On territorial behavior and other factors influencing habitat distribution in birds.
Acta Biotheor. Mid-Holocene faunal exploitation in the Southeastern United States. Carmody SB. Knoxville, TN: University of Tennessee. Soil fertility and slope processes in the Western Cumberland Escarpment of Kentucky: influences on the development of horticulture in the Eastern Woodlands. Niche construction and the behavioral context of plant and animal domestication. Cedar County. Chase County. Cherry County. Cheyenne County.
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