jay: European Jay data from the Swiss Breeding Bird Survey 2002

Description Usage Format Note Source References Examples

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

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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

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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) )

unmarked documentation built on May 27, 2021, 5:07 p.m.