How to compare variability within and between groups?

Question

I re-edited this question for clarity:

1. I have coordinates obtained by tracking birds with GPS (all points in plot).
2. I've used these points to perform kernel density estimation to reveal the areas with most dense bird activity (areas "1" and "2" in plot).
3. I also know coordinates of nesting trees where each nest was protected by female and male (big blue points with labels ("A" ..."J").
4. On each nest were done nest-defense recordings (10 min each recording, on three various intruders, twice per the same intruder)= 60 min total for each nest.
5. Each bird (female and male) exhibit 4 behaviours: "attack", "threat", "jump","check". I have all these values (duration).

Thanks to David Robinson for his time. I've done mistake in refer! I want to refer to Table 2. in this article:

onlinelibrary.wiley.com/doi/10.1111/jbi.12048/full

In article is used analysis of molecular variance (AMOVA). May I use something similar to my data in order to explain: "source of variation"

• among areas
• among nesting trees within clusters
• among individuals (female vs. male) within nesting tree

Is it a problem that this AMOVA is (probably) "designed" on genetic data? Which procedure in appropriate for my data? Or should I use nested ANOVA? How?

Thanks.

Answer 1

You can do this with an ANOVA analysis:

my.locs$cluster = factor(rep(c(1, 2), each=5))
anova(lm(attribute ~ cluster, my.locs))

# Analysis of Variance Table
# 
# Response: attribute
#           Df Sum Sq Mean Sq F value Pr(>F)
# cluster    1   62.5   62.50  0.1109 0.7477
# Residuals  8 4510.0  563.75               

This finds the variance within and between groups, and uses an F-test to determine a p-value. For the simulated data, the p-value is 0.7477, indicating there is not a significant difference between the clusters of nests (not surprising, since the data was randomly generated without distinguishing between clusters).