The authors wish to note: "We took the population growth rates derived from the summed probability distributions (SPDs) of radiocarbon dates (26-28) at face value. However, Shennan et al. (28) do not apply a correction for taphonomic loss. After correcting the Shennan et al. SPD for taphonomic loss using Eq. 3 (see Materials and Methods), the annual long-term growth rate for transitioning farmers in Europe decreases from 0.037% to 0.022%. The data indicate that transitioning farmers in Europe were growing more slowly than the huntergatherers of Wyoming and Colorado. Accounting for taphonomic loss, the hunter-gatherer growth rate we measure for Wyoming and Colorado is a factor of 1.9 larger than transitioning farmers in Europe. If no taphonomic correction is applied to either SPD, the difference in growth rate is a factor of 1.4. The smaller growth rate measured from the Shennan et al. SPD strengthens our primary conclusion that agriculture did not accelerate population growth. The difference in the long-term growth rate between hunter-gatherers and transitioning farmers is an order of magnitude smaller than the short-term growth rate fluctuations observed in the SPD. Thus, our conclusion that long-term growth rates measured globally are similar remains valid." The corrected Fig. 3 and its legend appear below.

No abstract

(2014). Effect of a tart cherry juice supplement on arterial stiffness and inflammation in healthy adults: a randomised controlled trial. Plant Foods for Human Nutrition, 69 (2), 122-127. Copyright and re-use policy

No abstract

ABSTRACT. Over the last decade, archaeologists have turned to large radiocarbon ( 14 C) data sets to infer prehistoric population size and change. An outstanding question concerns just how direct of an estimate 14 C dates are for human populations. In this paper we propose that 14 C dates are a better estimate of energy consumption, rather than an unmediated, proportional estimate of population size. We use a parametric model to describe the relationship between population size, economic complexity and energy consumption in human societies, and then parametrize the model using data from modern contexts. Our results suggest that energy consumption scales sub-linearly with population size, which means that the analysis of a large 14 C time-series has the potential to misestimate rates of population change and absolute population size. Energy consumption is also an exponential function of economic complexity. Thus, the 14 C record could change semi-independent of population as complexity grows or declines. Scaling models are an important tool for stimulating future research to tease apart the different effects of population and social complexity on energy consumption, and explain variation in the forms of 14 C date time-series in different regions.