Congratulations to our resident theoretical biologist of high renown, Joe Felsenstein, on his presentation, yesterday, of the 37th Fisher Memorial Lecture. [ETA: I’ll post a separate announcement of the video, when it is released.] Following are the details provided by the Fisher Memorial Trust (with a link added by me).

**Title: Is there a more fundamental theorem of natural selection?**

**Abstract.** R.A. Fisher’s Fundamental Theorem of Natural Selection has intrigued evolutionary biologists, who wondered whether it could be the basis of a general maximum principle for mean fitness of the population. Subsequent work by Warren Ewens, Anthony Edwards, and George Price showed that a reasonable version of the FTNS is true, but only if the quantity being increased by natural selection is not the mean fitness of the population but a more indirectly defined quantity. That leaves us in an unsatisfactory state. In spite of Fisher’s assertion that the theorem “hold[s] the supreme position among the biological sciences”, the Fundamental Theorem is, alas, not-so-fundamental. There is also the problem that the additive genetic variances involved do not change in an easily predictable way. Nevertheless, the FTNS is an early, and imaginative, attempt at formulating macro-scale laws from population-genetic principles. I will not attempt to revive the FTNS, but instead am trying to extend a 1978 model of mine, put forth in what may be my least-cited paper. This attempts to make a “toy” model of an evolving population in which we can bookkeep energy flows through an evolving population, and derive a long-term prediction for change of the energy content of the system. It may be possible to connect these predictions to the rate of increase of the adaptive information (the “specified information”) embodied in the genetic information in the organisms. The models are somewhat absurdly oversimple, but I argue that models like this at least can give us some results, which decades of more handwavy papers on the general connection between evolution, entropy, and information have not.