simulate strict phenotypic stasis (specimen level)

simulates strict stasis on the population level (Hunt et al. 2015). This means each population has the same mean trait value, and all deviations are due to the fact that specimens traits differ from this value due to randomness.

Usage

strict_stasis_sl(t, mean = 0, intrapop_var = 1, n_per_sample = 10)

Arguments

t

times at which the traits are determined

mean

mean trait value

intrapop_var

intrapopulation variance, determines how much specimens from the same population vary

n_per_sample

integer, number of specimens sampled per population/sampling locality/time

Value

an object of S3 class pre_paleoTS, inherits from timelist and list. The list has two elements: t, containing a vector of times of sampling, and vals, a list of trait values of the same length as t, with element containing trait values of individual specimens. This object can be transformed using apply_taphonomy, apply_niche or time_to_strat, and then reduced to a paleoTS object using reduce_to_paleoTS. This can then be used to test for different modes of evolution.

References

• Hunt, Gene, Melanie J. Hopkins, and Scott Lidgard. 2015. “Simple versus Complex Models of Trait Evolution and Stasis as a Response to Environmental Change.” Proceedings of the National Academy of Sciences of the United States of America 112 (16): 4885–90. https://doi.org/10.1073/pnas.1403662111.

See also

• stasis_sl() for the (non-strict) equivalent

• reduce_to_paleoTS() to transform outputs into paleoTS format

• random_walk_sl() and ornstein_uhlenbeck_sl() for other modes of evolution

Examples

library("paleoTS")
x = strict_stasis_sl(1:5, mean = 2, intrapop_var = 2) # simulate strict stasis
y = reduce_to_paleoTS(x)   # transform into paloeTS format
plot(y) # plot using paleoTS package


# see also
#vignette("paleoTS_functionality")
#for details and advanced usage