Allele Retention, Inbreeding, and Demography
Simulate the effect of management or demography on allele
retention and inbreeding accumulation in bottlenecked
populations of animals with overlapping generations.

Simulate the effect of management or demography on allele retention and inbreeding accumulation in bottlenecked populations of animals with overlapping generations.

Predict and Visualize Population-Level Changes in Allele
Frequencies in Response to Climate Change
Roeland Kindt [cre, aut] (<https://orcid.org/0000-0002-7672-0712>)
c(person(given = "Roeland",
family = "Kindt",
role = c("cre", "aut"),
email = "R.KINDT@CGIAR.ORG",
comment = c(ORCID = "0000-0002-7672-0712")))
Methods (Kindt, R. 2021. <doi:10.1101/2021.01.15.426775>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.
vegan (>= 2.5-6), BiodiversityR (>= 2.12-3), adegenet
poppr, mgcv, dplyr, ggplot2, GGally, ggforce, ggrepel, ggsci,
gggibbous, gganimate, patchwork

Methods (Kindt, R. 2021. <doi:10.7717/peerj.11534>) are provided of calibrating and predicting shifts in allele frequencies through redundancy analysis ('vegan::rda()') and generalized additive models ('mgcv::gam()'). Visualization functions for predicted changes in allele frequencies include 'shift.dot.ggplot()', 'shift.pie.ggplot()', 'shift.moon.ggplot()', 'shift.waffle.ggplot()' and 'shift.surf.ggplot()' that are made with input data sets that are prepared by helper functions for each visualization method. Examples in the documentation show how to prepare animated climate change graphics through a time series with the 'gganimate' package. Function 'amova.rda()' shows how Analysis of Molecular Variance can be directly conducted with the results from redundancy analysis.