AlleleShift (version 1.1-3)

population.shift: Shifts of Populations in Environmental Space as Response to Climate Change

Description

The function plots the locations of each population in baseline and future climates. Arrows indicate the shifts in positions of the populations.

Usage

population.shift(baseline.env.data, future.env.data,
	option=c("PCA", "RDA"), vector.multiply=1)
environmental.novel(baseline.env.data, future.env.data)

Value

The main function generates an ordination diagram that depicts changes between baseline and future/past conditions for the populations.

Arguments

baseline.env.data

Baseline (bio-)climatic conditions for the populations.

future.env.data

Changed (bio-)climatic conditions in future/past for the populations.

option

Should an explanatory variable corresponding to the climate period be used by rda.

vector.multiply

Multiplier for vector scores in the ordination diagrams.

Author

Roeland Kindt (World Agroforestry, CIFOR-ICRAF)

Details

See Kindt (2020) for alternative methods of generating ordination diagrams via vegan, BiodiversityR and ggplot2.

Function environmental.novel identifies populations with future (or past) environmental conditions that are outside the baseline range. The function further calculates the probability of observing the future condition via pnorm with the mean and standard deviation from the baseline conditions. Where one or several variables are outside the baseline range, data are provided for the variable with the smallest probability.

References

Kindt R. 2020. Ordination graphs with vegan, BiodiversityR and ggplot2. https://rpubs.com/Roeland-KINDT

Examples

# \donttest{

data(Poptri.baseline.env)
data(Poptri.future.env)

environmental.novel(Poptri.baseline.env, Poptri.future.env)
# as if for past climates
environmental.novel(Poptri.future.env, Poptri.baseline.env)

VIF.select <- VIF.subset(Poptri.baseline.env,
                         keep=c("MAT", "CMI"),
                         cor.plot=FALSE)

VIF.select$vars.included

baseline.env <- Poptri.baseline.env[, VIF.select$vars.included]
future.env <- Poptri.future.env[, VIF.select$vars.included]

environmental.novel(baseline.env, future.env)

plotA <- population.shift(baseline.env,
                 future.env,
                 option="PCA")
plotA

plotB <- population.shift(baseline.env,
                 future.env,
                 option="RDA")
plotB

# }