Lark Sparrow
An example of Multiple Scale Occupancy model for some lark sparrow data that was contributed by David Pavlacky at Rocky Mountain bird observatory. The study design was a GRTS selection of paired "Deferred" and "Grazed" pastures. The point count locations within each pasture were a random selection of systematic point count locations separated by 250 m. Each point count had a radius of 125m. A removal design was used to the set the data to missing after the first detection. The point count data were set to missing when fewer than nine points were surveyed.
A data frame with 52 observations on the following 20 variables.
a character vector containing the encounter history
a factor with two levels "Deferred" and "Grazed" corresponding to a rest rotation grazing system with pastures either rested (Deferred) or grazed (Grazed) during the spring bird breeding season.
a continuous covariate for primary occasion x, representing an ocular estimate of the proportion of area covered by crested wheatgrass in a 50-m radius around the point count location.
a continuous covariate for primary occasion x, representing the starting time (h) of each 6-min point count survey measured on the ratio scale (1.5 h = 1 h 30 min).
# This example is excluded from testing to reduce package check time
# Create dataframe
data(LASP)
mscale=LASP
# Process data with MultScalOcc model and use group variables
mscale.proc=process.data(mscale,model="MultScalOcc",groups=c("ceap"),begin.time=1,mixtures=3)
# Create design data
ddl=make.design.data(mscale.proc)
# Create function to build models
do.Species=function()
{
p.1=list(formula=~1)
p.2=list(formula=~ceap)
p.3=list(formula=~td)
Theta.1=list(formula=~1)
Theta.2=list(formula=~ceap)
Theta.3=list(formula=~cw)
Psi.1=list(formula=~1)
Psi.2=list(formula=~ceap)
cml=create.model.list("MultScalOcc")
return(mark.wrapper(cml,data=mscale.proc,ddl=ddl,adjust=FALSE,realvcv=TRUE))
}
# Run function to get results
Species.results=do.Species()
# Output model table and estimates
Species.results$model.table
Species.results[[as.numeric(rownames(Species.results$model.table[1,]))]]$results$real
Species.results[[as.numeric(rownames(Species.results$model.table[1,]))]]$results$beta
write.csv(Species.results$model.table,file="lasp_model_selection.csv",row.names=FALSE)
write.csv(Species.results[[as.numeric(rownames(Species.results$model.table[1,]))]]$results$real,
file="lasp_m1_real.csv")
write.csv(Species.results[[as.numeric(rownames(Species.results$model.table[1,]))]]$results$beta,
file="lasp_m1_beta.csv")
# Covariate prediction and model averaging
# p(time of day)
mintd <- min(mscale[,12:20])
maxtd <- max(mscale[,12:20])
td.values <- mintd+(0:100)*(maxtd-mintd)/100
PIMS(Species.results[[1]],"p",simplified=FALSE)
td <- covariate.predictions(Species.results,data=data.frame(td1=td.values),indices=c(21))
write.table(td$estimates,file="lasp_cov_pred_p_td.csv",sep=",",col.names=TRUE,row.names=FALSE)
# Theta(crested wheatgrass cover)
mincw <- min(mscale[,3:11])
maxcw <- max(mscale[,3:11])
cw.values <- mincw+(0:100)*(maxcw-mincw)/100
PIMS(Species.results[[1]],"Theta",simplified=FALSE)
cw <- covariate.predictions(Species.results,data=data.frame(cw1=cw.values),indices=c(3))
write.table(cw$estimates,file="lasp_cov_pred_theta_cw.csv",sep=",",col.names=TRUE,row.names=FALSE)
# Psi(ceap grazing for wildlife practice)
ceap.values <- as.data.frame(matrix(c(1,2),ncol=1))
names(ceap.values) <- c("index")
PIMS(Species.results[[1]],"Psi",simplified=FALSE)
ceap <- covariate.predictions(Species.results,data=data.frame(ceap=ceap.values))
write.table(ceap$estimates,file="lasp_cov_pred_psi_ceap.csv",sep=",",col.names=TRUE,row.names=FALSE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.