shift.surf.ggplot {AlleleShift}

Shifts of Allele Frequencies as Response to Climate Change

Description

The function shows changes in allele frequencies between the baseline and future/past climate.

Usage

shift.surf.ggplot(freq.future,
	Allele.focus=unique(freq.future$Allele)[1],
	freq.focus="Allele.freq",
	xcoord="LON", ycoord="LAT",
	mean.change = FALSE, change.FUN = stats::median,
	manual.colour.values = c("firebrick3", "chartreuse4"),
	...)

Arguments

freq.future

Result from AlleleShift::predFreq.

freq.focus

Selection of the Allele.

Allele.focus

Selection of the frequency.

xcoord, ycoord

Geographical coordinates of the populations.

mean.change

Aggregate changes among alleles.

change.FUN

Function used the aggregate changes.

manual.colour.values

Colours to be used in the plot.

...

Options for ordisurf.

Details

Populations are plotted in geographical space via ordination plotting methods, which is suitable as fixed coordinate systems are recommended both in ordination diagrams and maps. See Kindt (2020) for alternative methods of generating ordination diagrams via vegan, BiodiversityR and ggplot2.

Kindt (2021) shows how a STAMEN baseline map can be used to produce high resolution images via the ggmap package.

Value

The function generates a ggplot that depicts changes between baseline and future/past allele frequencies of the populations.

Author(s)

Roeland Kindt (World Agroforestry, CIFOR-ICRAF)

References

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

Kindt, R. 2021. Plotting smoothed surface diagrams of allele frequencies obtained from AlleleShift on a baseline map via ggmap. https://rpubs.com/Roeland-KINDT

See Also

shift.dot.ggplot, shift.pie.ggplot, shift.moon.ggplot, shift.waffle.ggplot

Examples

# The data can be obtained via the count.model and freq.model calibrations.
# These procedures are not repeated here.
data(Poptri.freq.baseline)
data(Poptri.freq.future)

# Plots for the first allele
# Symbols and colours indicate future change (green, ^ = future increase)
# Symbol size reflects the frequency in the climate shown

# Baseline climate
plotA <- shift.surf.ggplot(Poptri.freq.future,
                  xcoord="Long", ycoord="Lat",
                  Allele.focus=unique(Poptri.freq.future$Allele)[1],
                  freq.focus="Allele.freq")
plotA

# Future/past climate
plotB <- shift.surf.ggplot(Poptri.freq.future,
                  xcoord="Long", ycoord="Lat",
                  Allele.focus=unique(Poptri.freq.future$Allele)[1],
                  freq.focus="Freq.e2")
plotB

# Plots for the fifth allele

# Baseline climate
plotC <- shift.surf.ggplot(Poptri.freq.future,
                  xcoord="Long", ycoord="Lat",
                  Allele.focus=unique(Poptri.freq.future$Allele)[5],
                  freq.focus="Allele.freq")
plotC

# Future climate
plotD <- shift.surf.ggplot(Poptri.freq.future,
                  xcoord="Long", ycoord="Lat",
                  Allele.focus=unique(Poptri.freq.future$Allele)[5],
                  freq.focus="Freq.e2")
plotD

# create an animation

## Not run: 

library(ggplot2)
library(ggforce)
library(gganimate)
library(gifski)
library(transformr)

# The data is an interpolation and extrapolation between the baseline and future climate.
# For actual application, interpolate between climate data from available sources

data(Poptri.1985to2085)

Poptri.1985to2085$xcoord <- Poptri.1985to2085$Long
Poptri.1985to2085$ycoord <- Poptri.1985to2085$Lat

alleles <- sort(unique(as.character(Poptri.1985to2085$Allele)))
future.data <- Poptri.1985to2085[Poptri.1985to2085$Allele == alleles[1], ]

decades <- sort(unique(future.data$Decade))

for (d in 1:length(decades)) {
  decade.focal <- decades[d]
  decade.data <- future.data[future.data$Decade == decade.focal, ]
  plotLONLAT <- vegan::ordiplot(decade.data[, c("xcoord", "ycoord")])
  surfAllele <- BiodiversityR::ordisurfgrid.long(vegan::ordisurf(plotLONLAT, y=decade.data$Freq.e2))
  decade.surf <- cbind(Decade=rep(decade.focal, nrow(surfAllele)), surfAllele)
  if (d == 1) {
    future.surfs <- decade.surf
  }else{
    future.surfs <- rbind(future.surfs, decade.surf)
  }
}

# The function above will not be able to predict far into the future.
# The results obtained (future.surfs) can still be used for plotting.

ggsurf.all <- ggplot(data=future.surfs, group=Decade) +
  geom_contour_filled(aes(x=x, y=y, z=z),
                      breaks=seq(from=0.0, to=1.05, by=0.05)) +
  geom_point(data=subset(future.data, Decade==Decade),
             aes(x=xcoord, y=ycoord, size=Freq.e2, shape=increasing),
             colour="red", alpha=0.8, stroke=1.5, show.legend=FALSE) +
  xlab(element_blank()) +
  ylab(element_blank()) +
  labs(fill=alleles[1]) +
  scale_fill_viridis_d() +
  scale_colour_manual(values=c("firebrick3", "chartreuse4"),
                      guide=FALSE) +
  scale_size_area(max_size=6) +
  scale_shape_manual(values=c(6, 2)) +
  theme(panel.grid = element_blank()) +
  theme(axis.text= element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(legend.title = element_text(size=9)) +
  theme(legend.text = element_text(size=8)) +
  coord_fixed()

ggsurf.all

ggsurf.anim <- ggsurf.all +
  transition_states(as.factor(Decade), transition_length = 10, state_length = 100) +
  labs(title = "Decade: {closest_state}s")

ggsurf.anim2 <- animate(ggsurf.anim, fps=5, width=1280, height=720)

getwd()
anim_save(filename="Allele shift surf animation.gif", animation=ggsurf.anim2)


## End(Not run)