How to test whether the variance of two distributions is different if the distributions are not normal I'm studying two geographically-isolated populations of the same species. Inspecting the distributions, I see that both are bimodal (there's some seasonality to their occurrence), but the peaks in one population are much higher and much narrower (i.e., the variance of the local peaks is smaller).
What sort of statistical test would be appropriate to determine whether these differences are significant?
To clarify, my y-axis is the number of individuals identified in a trap on a particular day, and the x-axis is Julian day.
 A: Are these distributions of something over time? Counts, perhaps? (If so then you might need something quite different from the discussions here so far)
What you describe doesn't sound like it would be very well picked up as a difference in variance of the distributions.
It sounds like you're describing something vaguely like this (ignore the numbers on the axes, it's just to give a sense of the general kind of pattern you seem to be describing):

If that's right, then consider:
While the width of each peak about the local centers is narrower for the blue curve, the variance of the red and blue distributions overall hardly differs.
If you identify the modes and antimodes beforehand, you could then measure the local variability.
A: First of all, I think that you should look at the seasonal distributions separately, since the bimodal distribution is likely to be the outcome of two fairly separate processes. The two distributions might be controlled by different mechanisms, so that e.g. winter distributions could be more sensitive to yearly climate. If you want to look at population differences and reasons for these I think it is therefore more useful to study the seasonal distributions separately.
As for a test, you could try Levine's test (basically a test of homoscedasticity), which is used to compare variances between groups. Bartlett's test is an alternative, but Levene's test is supposed to be more robust to non-normality (especially when using the median for testing). In R the Levene's and Bartlett's tests are found in library(car).
