RMark (version 3.0.0)

larksparrow: Lark Sparrow

Description

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.

Format

A data frame with 52 observations on the following 20 variables.

ceap

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.

cwx

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.

tdx

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).

Examples

# \donttest{

# 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,delete=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)

# }