Advertising Behavior by Males Cormorants
Male double-crested cormorants use advertising behavior to attract females for breeding. In this study by Meagan Mc Rae (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.
data("Cormorants")
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
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)
Mc Rae, 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 Unpublished MA project, Environmental Studies, York University.
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))
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