Gingerich (1993) introduced a method that plots on log-log scale, the rate and interval for each pair of samples in an evolutionary sequence. On this plot, the slope is interpreted as an indicator of evolutionary mode (-1 for stasis, 0.5 for random walk, 0 for directional), and the intercept is interpreted as a measure of the rate of evolution over one generation.
LRI(y, gen.per.t = 1e+06, draw = TRUE)
the number of generations per unit time
logical, if TRUE, a plot is produced
Following Gingerich (1993), a robust line is fit through the
points by minimizing the sum of absolute deviations. If generations are one
year long and time is measured in Myr,
A named vector with three elements:
This method was important in early attempts to disentangle evolutionary tempo and mode. I view likelihood-based methods as more informative, and in particular the estimation of 'Generational Rates' using LRI is compromised by sampling error; see Hunt (2012) and the example below.
Gingerich, P.D. 1993. Quantification and comparison of
evolutionary rates. American Journal of Science 293-A:453–478.
Hunt, G. 2012. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38:351–373.
set.seed(1) xFast <- sim.GRW(ns = 20, ms = 0.5, vs = 0.2) # fast evolution xSlow <- sim.Stasis(ns = 20, omega = 0) # strict stasis (zero rates) lri.Fast <- LRI(xFast, draw = FALSE) lri.Slow <- LRI(xSlow, draw = FALSE) print(lri.Fast, 4) print(lri.Slow, 4) # LRI thinks strict stasis rates are faster!
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.