stake78 {mrds}

Wooden stake data from 1978 survey

Description

Multiple surveys by different observers of a single 1km transect containing 150 wooden stakes placed based on expected uniform distribution throughout a 40 m strip (20m on either side).

Format

A data frame with 150 observations on the following 13 variables.

StakeNo

unique number for each stake 1-150

PD

perpendicular distance at which the stake was placed from the line

Obs1

0/1 whether missed/seen by observer 1

Obs2

0/1 whether missed/seen by observer 2

Obs3

0/1 whether missed/seen by observer 3

Obs4

0/1 whether missed/seen by observer 4

Obs5

0/1 whether missed/seen by observer 5

Obs6

0/1 whether missed/seen by observer 6

Obs7

0/1 whether missed/seen by observer 7

Obs8

0/1 whether missed/seen by observer 8

Obs9

0/1 whether missed/seen by observer 9

Obs10

0/1 whether missed/seen by observer 10

Obs11

0/1 whether missed/seen by observer 11

Details

The 1997 survey was based on a single realization of a uniform distribution. Because it was a single transect and there was no randomization of the distances for each survey, we repeated the experiment and used distances that provided a uniform distribution but randomly sorted the positions along the line so there was no pattern obvious to the observer.

Source

Laake, J. 1978. Line transect estimators robust to animal movement. M.S. Thesis. Utah State University, Logan, Utah. 55p.

References

Burnham, K. P., D. R. Anderson, and J. L. Laake. 1980. Estimation of Density from Line Transect Sampling of Biological Populations. Wildlife Monographs:7-202.

Examples

data(stake78)
data(stake77)
# compare distribution of distances for all stakes
hist(stake77$PD)
hist(stake78$PD)
# Extract stake data and put in the mrds format for model fitting.
extract.stake <- function(stake,obs){
  extract.obs <- function(obs){
    example <- subset(stake,eval(parse(text=paste("Obs",obs,"==1",sep=""))),
                      select="PD")
    example$distance <- example$PD
    example$object <- 1:nrow(example)
    example$PD <- NULL
    return(example)
  }
  if(obs!="all"){
     return(extract.obs(obs=obs))
  }else{
    example <- NULL
    for(i in 1:(ncol(stake)-2)){
      df <- extract.obs(obs=i)
      df$person <- i
      example <- rbind(example,df)
    }
    example$person <- factor(example$person)
    example$object <- 1:nrow(example)
    return(example)
  }
}
extract.stake.pairs <- function(stake,obs1,obs2,removal=FALSE){
  obs1 <- paste("Obs",obs1,sep="")
  obs2 <- paste("Obs",obs2,sep="")
  example <- subset(stake,eval(parse(text=paste(obs1,"==1 |",obs2,"==1 ",
                                     sep=""))), select=c("PD",obs1,obs2))
  names(example) <- c("distance","obs1","obs2")
  detected <- c(example$obs1,example$obs2)
  example <- data.frame(object=rep(1:nrow(example),2),
                        distance=rep(example$distance,2),
                        detected = detected,
                        observer=c(rep(1,nrow(example)),
                                   rep(2,nrow(example))))
  if(removal) example$detected[example$observer==2] <- 1
  return(example)
}

# extract data for observer 10 and fit a single observer model
stakes <- extract.stake(stake78,10)
ds.model <- ddf(dsmodel = ~mcds(key = "hn", formula = ~1), data = stakes,
                method = "ds", meta.data = list(width = 20))
plot(ds.model,breaks=seq(0,20,2),showpoints=TRUE)
ddf.gof(ds.model)

# extract data from observers 5 and 7 and fit an io model
stkpairs <- extract.stake.pairs(stake78,5,7,removal=FALSE)
io.model <- ddf(dsmodel = ~mcds(key = "hn", formula=~1),
                mrmodel=~glm(formula=~distance),
                data = stkpairs, method = "io")
summary(io.model)
par(mfrow=c(3,2))
plot(io.model,breaks=seq(0,20,2),showpoints=TRUE,new=FALSE)
ddf.gof(io.model)