Detailed tutorials explaining the theory and how to use the models are here (thanks John Templeton Foundation, Cultural Evolution Society, and National Institute for Mathematical and Biological Synthesis!). If you’re lazy the tutorials are linked below:
The introduction discusses biological macroevolution and how it motivates cultural macroevolution; highlights questions approachable from a cultural macroevolutionary perspective; introduces macroevolutionary methods that will be applied in future modules; and lays out the structure of these tutorials.
In this tutorial we introduce users to metrics for diversity and diversification rates. We build a simulator to explore the diversification of lineages within a cultural form. Empirically, we contextualize these analyses within diversity of car models that make up American automobiles throughout the 20th century (Gjesfjeld et al. 2020).
In this tutorial we introduce the linear birth-death process as a statistical model for cutting through stochasticity in diversification rates. We also introduce LiteRate, an unsupervised machine-learning algorithm built on birth-death processes designed to identify statistically-signifcant shifts in diversification rates (Silvestro et al., 2019). Finally, we show users how to run LiteRate on their own data. Empirically, the module introduces the diversification of Metal bands active from 1968-2000 as a means to understand the history of the Metal music genre (Koch et al, nd).
This tutorial shows users how to check the convergence of LiteRate’s Markov chain Monte Carlo algorithms, as well as how to plot LiteRate results. Empirically, we show how shifts in diversification rates delineate a multi-stage trajectory for the evolution of Metal music over time (Koch et al, nd).
### 4. Modeling Evolutionary Mechanisms in Diversification Rates In this tutorial we expand our simulator to model evolutionary mechanisms within diversification rates like significant extinctions, key innovations, and competition. We describe how to translate simulations to statistical models, and apply a competition model to the Metal data.
This supplemental tutuorial examines the concepts and methods behind the use of phylogenetic approaches. It shows users the types of questions addressable using phylogenies, explains how phylogenies are constructed, and contrasts this approach to diversifcation rate analysis. Empirically, we demonstrate some phylogenetic analyses on a dataset of Austronesia languages (Gray, Drummond and Greenhill 2009).