Advertising Behavior by Males Cormorants

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)

Source

McRae, M. (2015). Spatial, Habitat and Frequency Changes in Double-crested Cormorant Advertising Display in a Tree-nesting Colony. Unpublished MA project, Environmental Studies, York University.

Examples

data(Cormorants)
str(Cormorants)
#> 'data.frame':	343 obs. of  8 variables:
#>  $ category   : Ord.factor w/ 3 levels "Pre"<"Incubation"<..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ week       : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ station    : Factor w/ 6 levels "B1","B2","C1",..: 3 3 3 3 3 3 3 4 4 4 ...
#>  $ nest       : Ord.factor w/ 3 levels "no"<"partial"<..: 1 1 2 2 2 3 3 1 1 1 ...
#>  $ height     : Ord.factor w/ 3 levels "low"<"mid"<"high": 2 3 2 3 3 2 3 2 2 3 ...
#>  $ density    : Ord.factor w/ 4 levels "zero"<"few"<"moderate"<..: 2 2 2 2 2 2 2 2 2 2 ...
#>  $ tree_health: Factor w/ 2 levels "dead","healthy": 2 2 2 2 1 2 2 2 1 2 ...
#>  $ count      : int  3 2 2 2 1 3 3 1 8 3 ...

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)))
}
#> Loading required package: ggplot2

#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> `geom_smooth()` using formula = 'y ~ x'


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

if (requireNamespace("car"))
  car::Anova(fit1)
#> Analysis of Deviance Table (Type II tests)
#> 
#> Response: count
#>             LR Chisq Df Pr(>Chisq)    
#> week          62.791  1  2.298e-15 ***
#> station       14.068  5    0.01518 *  
#> nest         137.812  2  < 2.2e-16 ***
#> height        47.988  2  3.797e-11 ***
#> density       47.266  2  5.450e-11 ***
#> tree_health    0.070  1    0.79199    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  
# plot fitted effects
if (requireNamespace("effects"))
  plot(effects::allEffects(fit1))