RMark (version 2.1.1)

larksparrow: Lark Sparrow

Description

An example of Multiple Scale Occupancy model for some lark sparrow data that was contributed by Davi Pavalacky 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 125 m. 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.

Examples

# 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=F,realvcv=T))
}

# 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=F)

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=F)

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=T,row.names=F)

# 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=F)

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=T,row.names=F)

# 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=F)

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=T,row.names=F)