First the link to this week’s complete list as HTML and as PDF.
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There is one inconsistency Leppard fails to explain. Seeing how little we find in regions, we consider to have been continuously inhabited for hundreds of thousands of years, how is it that artefacts left by single individuals in single lifetimes are found at all in the frequencies they obviously are on islands all over?
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The role of cooking for the development of fully modern humans can’t be denied and Hardy et al.’s hypothesis and timing has a lot going for itself. That said, they quote Perry et al. 2007 (Nature Genetics 39, 1256–1260) several times without addressing their finding of large differences in the copy number of the amylase gene correlating to starch consumption in late prehistory and early history. If strong amylase expression really was ancient, it ought to be for more evenly distributed.
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Mutation used to be seen as a purely random process proceeding at a constant average rate. This has always been at variance with the spikes of speciation frequently observed in the fossil records. After Sharp 2012 (list of 2012-05-05) now Singh et al. too demonstrate higher diversification rates under stress, this time for a completely natural stressor. I still see this as the better explanation for the difference between currently observed mutation rates in humans and probably apes and their long-term averages contrary to published hypotheses of deceleration (Fu 2013, list of 2013-03-25; Harris 2015, 2015-03-20; Scally 2012, 2012-10-14).
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We are just seeing the first subsidised power to gas plant in Germany. I was surprised to find an efficiency of at least 34 % for the full cycle back to electricity in Sterner et al., my own estimate had been 20–25 %. I remain skeptical about the current tendency of wasting the natural gas reserves for all kinds of uses instead of using clean coal fired power plants for electricity. My main worry is that this will lead to a renaissance of coal liquefaction in the long run.
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Edinborough et al. strongly reminds me of many studies in the economic sciences. A highly derived mathematical edifice is built upon the most shaky foundations and those are neither discussed, nor justified, nor any alternatives considered. In this case everything hinges on the similarity or dissimilarity of artefacts and assemblages. Edinborough et al. do tell us the attributes they used but nothing about how they aggregated them into types and how they arrived at their measure of dissimilarity. Mathematical modelling based on an arbitrary and possibly meaningless dataset won’t ever yield a meaningful result. They completely fail to follow Binford in differentiating between culturally derived traits and purely technical ones enforced by use and purpose. Where they deny previous claims of cultural change it remains quite possible that the previous intuitive sense of a break between assemblages subjectively perceived as strongly dissimilar captures the essence far better than their unstated and undefined numerical value.
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McDonnell is sound career advice. Less so if you mainly study for the fun of it and your role models are the old polymaths. I’ve always struck out for pastures new the moment I was beginning to get a real grasp on a particular subject.