Greetings,
I have a question regarding data analysis of habitat use of animals.
These animals were radio collared and tracked periodically throughout
the year. When they were sighted/detected, the habitat type was
marked. Our dataset recorded the sex of the animal, and we know the
data when the surveys were performed. The goal was to address the
questions: does habitat use differ between the sexes, and does habitat
use vary between seasons? Below is a summary table, ignoring seasons.
dattab <- matrix(c(190,87,206,170,103,23,66,72,53,22),nrow=5,byrow=T)
rownames(dattab) <- c("Rock","Burrow","Cactus","Brushpile","Other")
colnames(dattab) <- c("Female","Male")
dattab
Female Male
Rock 190 87
Burrow 206 170
Cactus 103 23
Brushpile 66 72
Other 53 22
We could perform a test of independence, but the problem lies with our
assumptions. Because individual animals were tracked through time, each
animal give a different number of datapoints (min=1, max=126), which
violates our assumption of independence. Thus, our sampling unit should
be at the level of the skunk and analysis should proceed from there.
I'm familiar (theory and practice) with linear mixed effect models, but
I believe that these data call for a mixed effects MANOVA. Is there
such a test in R? Or, would it be better to analyze the data using a
standard MANOVA where our y1, y2, ... are the percentage of data points
within that various habitats? My problem with this last analysis is that
each skunk will carry the same weight even though both could have a
large difference in the number of data points. Thanks...
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