jay {unmarked} | R Documentation

## European Jay data from the Swiss Breeding Bird Survey 2002

### Description

The Swiss breeding bird survey ("Monitoring Haufige Brutvogel" MHB) has monitored the populations of 150 common species since 1999. The MHB sample consists of 267 1-km squares that are laid out as a grid across Switzerland. Fieldwork is conducted by about 200 skilled birdwatchers, most of them volunteers. Avian populations are monitored using a simplified territory mapping protocol, where each square is surveyed up to three times during the breeding season (only twice above the tree line). Surveys are conducted along a transect that does not change over the years.

The list `jay` has the data for European Jay territories for 238 sites surveyed in 2002.

### Usage

    data("jay")

### Format

`jay` is a list with 3 elements:

caphist

  a data frame with rows for 238 sites and columns for each of the observable detection histories. For the sites visited 3 times, these are `"100", "010", "001", "110", "101", "011", "111"`. Sites visited twice have `"10x", "01x", "11x"`.

  Each row gives the number of territories with the corresponding detection history, with NA for the detection histories not applicable: sites visited 3 times have NAs in the last 3 columns while those visited twice have NAs in the first 7 columns.

sitescovs

  a data frame with rows for 238 sites, and the following columns:

  1. elev : the mean elevation of the quadrat, m.

  2. length : the length of the route walked in the quadrat, km.

  3. forest : percentage forest cover.

covinfo

  a data frame with rows for 238 sites, and the following columns:

  1. x, y : the coordinates of the site.

  2. date1, date2, date3 : the Julian date of the visit, with 1 April = 1. Sites visited twice have NA in the 3rd column.

  3. dur1, dur2, dur3 : the duration of the survey, mins. For 10 visits the duration is not available, so there are additional NAs in these columns.

### Note

In previous versions, `jay` had additional information not required for the analysis, and a data frame with essentially the same information as the `Switzerland` data set.

### Source

Swiss Ornithological Institute

### References

Royle, J.A., Kery, M., Gauthier, R., Schmid, H. (2007) Hierarchical spatial models of abundance and occurrence from imperfect survey data. Ecological Monographs, 77, 465-481.

Kery & Royle (2016) Applied Hierarachical Modeling in Ecology Section 7.9

### Examples

    data(jay)
    str(jay)

    # Carry out a simple analysis, without covariates:
    # Create a customised piFun (see ?piFun for details)
    crPiFun <- function(p) {
       p1 <- p[,1] # Extract the columns of the p matrix, one for
       p2 <- p[,2] #   each of J = 3 sample occasions
       p3 <- p[,3]
       cbind(      # define multinomial cell probabilities:
          "100" = p1 * (1-p2) * (1-p3),
          "010" = (1-p1) * p2 * (1-p3),
          "001" = (1-p1) * (1-p2) * p3,
          "110" = p1 * p2 * (1-p3),
          "101" = p1 * (1-p2) * p3,
          "011" = (1-p1) * p2 * p3,
          "111" = p1 * p2 * p3,
          "10x" = p1*(1-p2),
          "01x" = (1-p1)*p2,
          "11x" = p1*p2)
    }
    # Build the unmarkedFrame object
    mhb.umf <- unmarkedFrameMPois(y=as.matrix(jay$caphist),
      obsToY=matrix(1, 3, 10), piFun="crPiFun")
    # Fit a model
    ( fm1 <- multinomPois(~1 ~1, mhb.umf) )

[Package unmarked version 0.12-3 Index]