| SongEvo {SongEvo} | R Documentation |
Model bird song evolution
Description
This function simulates bird song evolution. Submodels are performed once per time step, and include fledging from the nest, song learning, ageing and death, dispersal, competition for territories, mate attraction, and reproduction.
Usage
SongEvo(init.inds, iteration, steps, timestep, terr.turnover, mate.comp,
learning.method, integrate.dist, learning.error.d, learning.error.sd,
mortality.a, mortality.j, lifespan, phys.lim.min, phys.lim.max,
male.fledge.n.mean, male.fledge.n.sd, male.fledge.n, disp.age,
disp.distance.mean, disp.distance.sd, n.territories, prin, all)
Arguments
init.inds |
Initial population data. A data frame that includes columns for “id,” “age,” “trait,” “x1” (longitude) and “y1” (latitude). |
iteration |
The number of iterations that the model will run. |
steps |
The number of steps per iteration. |
timestep |
The length of time that passes in each step. For annually breeding species, timestep = 1 year. |
terr.turnover |
The proportion of territories that change ownership during a step. |
mate.comp |
Female preference for mates. Options are TRUE or FALSE. |
learning.method |
If an individual learns from their (“father”) or all males within a specified radius (“integrate”). |
integrate.dist |
Distance over which song learning is integrated. |
learning.error.d |
Direction of learning error. |
learning.error.sd |
The standard deviation of imitation error. |
mortality.a |
Annual mortality of adults (after the first time step). |
mortality.j |
Annual mortality of juvenile birds (in the first time step). |
lifespan |
Maximum age for individuals; any number is accepted. “NA” causes SongEvo to disregard lifespan and sets population size based on mortality rates alone. |
phys.lim.min |
The minimum physical limit of trait production. |
phys.lim.max |
The maximum physical limit of trait production. |
male.fledge.n.mean |
The mean number of offspring produced per time step per individual breeding male. Includes only offspring raised in that breeding male’s nest (i.e. it does not account for extra-pair offspring in other nests). |
male.fledge.n.sd |
Standard deviation of the number of male fledglings. |
male.fledge.n |
A vector of the number of offspring for the initial population, optionally calculated with male.fledge.n.mean and male.fledge.n.sd |
disp.age |
The age at which individual males disperse from their birth location. |
disp.distance.mean |
The distance that individual males disperse (meters). |
disp.distance.sd |
The standard deviation of dispersal distance. |
n.territories |
The number of territories in the population. This number is fixed for all iterations. |
prin |
Print summary values after each timestep has completed? Options are TRUE or FALSE. |
all |
Save data for all individuals? Options are TRUE or FALSE. |
Value
three objects. First, currently alive individuals are stored in a data frame called “inds.” Values within “inds” are updated throughout each of the iterations of the model, and “inds” can be viewed after the model is completed. Second, an array (i.e. a multi-dimensional table) entitled “summary.results” includes population summary values for each time step (dimension 1) in each iteration (dimension 2) of the model. Population summary values are contained in five additional dimensions: population size for each time step of each iteration (“sample.n”), the population mean and variance of the song feature studied (“trait.pop.mean” and “trait.pop.variance”), with associated lower (“lci”) and upper (“uci”) confidence intervals. Third, individual values may optionally be concatenated and saved to one data frame entitled “all.inds.” all.inds can become quite large, and is therefore only recommended if additional data analyses are desired.
See Also
par.sens, par.opt, mod.val, h.test
Examples
### See vignette for an example that uses all functions in SongEvo.
### Load the example data: song.data
# To explore the SongEvo package, we will use a database of songs from Nuttall’s
# white-crowned sparrow (*Zonotrichia leucophrys nuttalli*) recorded at three
# locations in 1969 and 2005.
data("song.data")
# Examine global parameters.Global parameters describe our understanding of the
# system and may be measured or hypothesized. They are called "global" because
# they are used by many many functions and subroutines within functions. For
# descriptions of all adjustable parameters, see `?WCSP` or Danner et al. (year)
str(glo.parms)
# Share global parameters with the global environment. We make these parameters
# available in the global environment so that we can access them with minimal
# code.
list2env(glo.parms, globalenv())
#### Examine song data
# Data include the population name (Bear Valley, PRBO, or Schooner), year of
# song recording (1969 or 2005), and the frequency bandwidth of the trill.
str(song.data)
### Simulate bird song evolution with `SongEvo()`
#### Define initial individuals
# In this example, we use songs from individual birds recorded in one population
# (PRBO) in the year 1969, which we will call `starting.trait`.
starting.trait <- subset(song.data, Population=="PRBO" & Year==1969)$Trill.FBW
# We want a starting population of 40 individuals, so we generate additional
# trait values to complement those from the existing 30 individuals. Then we
# create a data frame that includes a row for each individual; we add
# identification numbers, ages, and geographical coordinates for each
# individual.
starting.trait2 <- c(starting.trait, rnorm(n.territories-length(starting.trait),
mean=mean(starting.trait),
sd=sd(starting.trait)))
init.inds <- data.frame(id = seq(1:n.territories), age = 2, trait = starting.trait2)
init.inds$x1 <- round(runif(n.territories, min=-122.481858, max=-122.447270), digits=8)
init.inds$y1 <- round(runif(n.territories, min=37.787768, max=37.805645), digits=8)
#### Specify and call the SongEvo model
# SongEvo() includes several settings, which we specify before running the
# model. For this example, we run the model for 10 iterations, over 36 years
# (i.e. 1969--2005). When conducting research with `SongEvo()`, users will want
# to increase the number iterations (e.g. to 100 or 1000). Each timestep is one
# year in this model (i.e. individuals complete all components of the model in 1
# year). We specify territory turnover rate here as an example of how to adjust
# parameter values. We could adjust any other parameter value here also. The
# learning method specifies that individuals integrate songs heard from adults
# within the specified integration distance (intigrate.dist, in kilometers). In
# this example, we do not includ a lifespan, so we assign it NA. In this
# example, we do not model competition for mates, so specify it as FALSE. Last,
# specify all as TRUE in order to save data for every single simulated
# individual because we will use those data later for mapping. If we do not need
# data for each individual, we set all to FALSE because the all.inds data.frame
# becomes very large!
iteration <- 5
years <- 36
timestep <- 1
terr.turnover <- 0.5
learning.method <- "integrate"
integrate.dist <- 0.1
lifespan <- NA
mate.comp <- FALSE
prin <- FALSE
all <- TRUE
# Now we call SongEvo with our specifications and save it in an object called
# SongEvo1.
SongEvo1 <- SongEvo(init.inds = init.inds,
iteration = iteration,
steps = years,
timestep = timestep,
n.territories = n.territories,
terr.turnover = terr.turnover,
learning.method = learning.method,
integrate.dist = integrate.dist,
learning.error.d = learning.error.d,
learning.error.sd = learning.error.sd,
mortality.a = mortality.a,
mortality.j = mortality.j,
lifespan = lifespan,
phys.lim.min = phys.lim.min,
phys.lim.max = phys.lim.max,
male.fledge.n.mean = male.fledge.n.mean,
male.fledge.n.sd = male.fledge.n.sd,
male.fledge.n = male.fledge.n,
disp.age = disp.age,
disp.distance.mean = disp.distance.mean,
disp.distance.sd = disp.distance.sd,
mate.comp = mate.comp,
prin = prin,
all)
#### Examine results from SongEvo model
# The model required the following time to run on your computer:
SongEvo1$time
# Three main objects hold data regarding the SongEvo model. Additional objects
# are used temporarily within modules of the model.
# First, currently alive individuals are stored in a data frame called “inds.”
# Values within “inds” are updated throughout each of the iterations of the
# model, and “inds” can be viewed after the model is completed.
head(SongEvo1$inds, 5)
# Second, an array (i.e. a multi-dimensional table) entitled “summary.results”
# includes population summary values for each time step (dimension 1) in each
# iteration (dimension 2) of the model. Population summary values are contained
# in five additional dimensions: population size for each time step of each
# iteration (“sample.n”), the population mean and variance of the song feature
# studied (“trait.pop.mean” and “trait.pop.variance”), with associated lower
# (“lci”) and upper (“uci”) confidence intervals.
dimnames(SongEvo1$summary.results)
# Third, individual values may optionally be concatenated and saved to one data
# frame entitled “all.inds.” all.inds can become quite large, and is therefore
# only recommended if additional data analyses are desired.
head(SongEvo1$all.inds, 5)
#### Simulated population size
# We see that the simulated population size remains relatively stable over the
# course of 36 years. This code uses the summary.results array.
plot(SongEvo1$summary.results[1, , "sample.n"],
xlab="Year", ylab="Abundance",
type="n", xaxt="n",
ylim=c(0, max(SongEvo1$summary.results[, , "sample.n"], na.rm=TRUE)))
axis(side=1, at=seq(0, 40, by=5), labels=seq(1970, 2010, by=5))
for(p in 1:iteration){
lines(SongEvo1$summary.results[p, , "sample.n"], col="light gray")
}
n.mean <- apply(SongEvo1$summary.results[, , "sample.n"], 2, mean, na.rm=TRUE)
lines(n.mean, col="red")
#Plot 95% quantiles
quant.means <- apply(SongEvo1$summary.results[, , "sample.n"],
MARGIN=2,
quantile, probs=c(0.975, 0.025), R=600, na.rm=TRUE)
lines(quant.means[1,], col="red", lty=2)
lines(quant.means[2,], col="red", lty=2)
# Load Hmisc package for plotting functions.
library("Hmisc")
#### Simulated trait values
# We see that the mean trait values per iteration varied widely, though mean
# trait values over all iterations remained relatively stable. This code uses
# the summary.results array.
plot(SongEvo1$summary.results[1, , "trait.pop.mean"],
xlab="Year", ylab="Bandwidth (Hz)",
xaxt="n", type="n",
xlim=c(-0.5, 36), ylim=range(SongEvo1$summary.results[, , "trait.pop.mean"], na.rm=TRUE))
for(p in 1:iteration){
lines(SongEvo1$summary.results[p, , "trait.pop.mean"], col="light gray")
}
freq.mean <- apply(SongEvo1$summary.results[, , "trait.pop.mean"], 2, mean, na.rm=TRUE)
lines(freq.mean, col="blue")
axis(side=1, at=seq(0, 35, by=5), labels=seq(1970, 2005, by=5))#, tcl=-0.25, mgp=c(2,0.5,0))
#Plot 95% quantiles
quant.means <- apply(SongEvo1$summary.results[, , "trait.pop.mean"],
MARGIN=2,
quantile, probs=c(0.95, 0.05), R=600, na.rm=TRUE)
lines(quant.means[1,], col="blue", lty=2)
lines(quant.means[2,], col="blue", lty=2)
#plot mean and CI for historic songs.
#plot original song values
library("boot")
sample.mean <- function(d, x) {
mean(d[x])
}
boot_hist <- boot(starting.trait, statistic=sample.mean, R=100)#, strata=mn.res$iteration)
ci.hist <- boot.ci(boot_hist, conf=0.95, type="basic")
low <- ci.hist$basic[4]
high <- ci.hist$basic[5]
points(0, mean(starting.trait), pch=20, cex=0.6, col="black")
errbar(x=0, y=mean(starting.trait), high, low, add=TRUE)
#text and arrows
text(x=5, y=2720, labels="Historical songs", pos=1)
arrows(x0=5, y0=2750, x1=0.4, y1=mean(starting.trait), length=0.1)
#### Trait variance
# We see that variance for each iteration per year increased in the first few
# years and then stabilized. This code uses the summary.results array.
#plot variance for each iteration per year
plot(SongEvo1$summary.results[1, , "trait.pop.variance"],
xlab="Year", ylab="Bandwidth Variance (Hz)",
type="n", xaxt="n",
ylim=range(SongEvo1$summary.results[, , "trait.pop.variance"], na.rm=TRUE))
axis(side=1, at=seq(0, 40, by=5), labels=seq(1970, 2010, by=5))
for(p in 1:iteration){
lines(SongEvo1$summary.results[p, , "trait.pop.variance"], col="light gray")
}
n.mean <- apply(SongEvo1$summary.results[, , "trait.pop.variance"], 2, mean, na.rm=TRUE)
lines(n.mean, col="green")
#Plot 95% quantiles
quant.means <- apply(SongEvo1$summary.results[, , "trait.pop.variance"],
MARGIN=2,
quantile, probs=c(0.975, 0.025), R=600, na.rm=TRUE)
lines(quant.means[1,], col="green", lty=2)
lines(quant.means[2,], col="green", lty=2)
#### Maps
# The simulation results include geographical coordinates and are in a standard
# spatial data format, thus allowing calculation of a wide variety of spatial
# statistics.
# Load packages for making maps.
library("reshape2")
library("lattice")
library("sp")
# Convert data frame from long to wide format. This is necessary for making a
# multi-panel plot.
all.inds1 <- subset(SongEvo1$all.inds, iteration==1)
w <- dcast(as.data.frame(all.inds1), id ~ timestep, value.var="trait", fill=0)
all.inds1w <- merge(all.inds1, w, by="id")
names(all.inds1w) <- c(names(all.inds1), paste("Ts", seq(1:years), sep=""))
# Create a function to generate a continuous color palette--we will use the
# palette in the next call to make color ramp to represent the trait value.
rbPal <- colorRampPalette(c('blue','red'))
# Plot maps, including a separate panel for each timestep (each of 36 years).
# Our example shows that individuals move across the landscape and that regional
# dialects evolve and move. The x-axis is longitude, the y-axis is latitude, and
# the color ramp indicates trill bandwidth in Hz.
spplot(all.inds1w[,-c(1:ncol(all.inds1))],
as.table=TRUE,
cuts=c(0, seq(from=1500, to=4500, by=10)),
ylab="",
# cuts specifies that the first level (e.g. <1500) is transparent.
col.regions=c("transparent", rbPal(1000)),
colorkey=list(
right=list(
fun=draw.colorkey,
args=list(
key=list(
at=seq(1500, 4500, 10),
col=rbPal(1000),
labels=list(at=c(1500, 2000, 2500, 3000, 3500, 4000, 4500),
labels=c("1500", "2000", "2500", "3000", "3500", "4000", "4500")
)
)
)
)
)
)
# In addition, you can plot simpler multi-panel maps that do not take advantage
# of the spatial data class.
# Lattice plot (not as a spatial frame)
it1 <- subset(SongEvo1$all.inds, iteration==1)
# Create a function to generate a continuous color palette
rbPal <- colorRampPalette(c('blue','red'))
it1$Col <- rbPal(10)[as.numeric(cut(it1$trait, breaks = 10))]
xyplot(it1$y1~it1$x1 | it1$timestep,
groups=it1$trait, asp="iso", col=it1$Col,
xlab="Longitude", ylab="Latitude")