vcdExtra (version 0.8-5)

Cormorants: Advertising Behavior by Males Cormorants

Description

Male double-crested cormorants use advertising behavior to attract females for breeding. In this study by Meagan McRae (2015), cormorants were observed two or three times a week at six stations in a tree-nesting colony for an entire season, April 10, 2014-July 10, 2014. The number of advertising birds was counted and these observations were classified by characteristics of the trees and nests.

The goal is to determine how this behavior varies temporally over the season and spatially, as well as with characteristics of nesting sites.

Usage

data("Cormorants")

Format

A data frame with 343 observations on the following 8 variables.

category

Time of season, divided into 3 categories based on breeding chronology, an ordered factor with levels Pre < Incubation < Chicks Present

week

Week of the season

station

Station of observations on two different peninsulas in a park, a factor with levels B1 B2 C1 C2 C3 C4

nest

Type of nest, an ordered factor with levels no < partial < full

height

Relative height of bird in the tree, an ordered factor with levels low < mid < high

density

Number of other nests in the tree, an ordered factor with levels zero < few < moderate < high

tree_health

Health of the tree the bird is advertising in, a factor with levels dead healthy

count

Number of birds advertising, a numeric vector

Details

Observations were made on only 2 days in weeks 3 and 4, but 3 days in all other weeks. One should use log(days) as an offset, so that the response measures rate.

Cormorants$days <- ifelse(Cormorants$week %in% 3:4, 2, 3)

Examples

data(Cormorants)
str(Cormorants)

if (require("ggplot2")) {
  print(ggplot(Cormorants, aes(count)) + 
    geom_histogram(binwidth=0.5) + 
	  labs(x="Number of birds advertising"))

# Quick look at the data, on the log scale, for plots of `count ~ week`, 
#   stratified by something else.

  print(ggplot(Cormorants, aes(week, count, color=height)) + 
    geom_jitter() +
	  stat_smooth(method="loess", size=2) + 
	  scale_y_log10(breaks=c(1,2,5,10)) +
	  geom_vline(xintercept=c(4.5, 9.5)))
}

# ### models using week 
fit1 <-glm(count ~ week + station + nest + height + density + tree_health, 
           data=Cormorants,
           family =  poisson)

if (requireNamespace("car"))
  car::Anova(fit1)
  
# plot fitted effects
if (requireNamespace("effects"))
  plot(effects::allEffects(fit1))