I might be totally missing a key point here - or making a mountain out of a molehill, but I have been reading around and trying to find the answer to this question for a day or so now, so figured I might as well ask!
I have counts of birds per hour in a effort-based monitoring survey which took place each summer over a 4 year period. It looks like the average annual number of birds sighted per hour increased in the last year of the survey and all I am trying to do is show whether this 'increase' seems to be significant.
I have averaged all the hourly counts from each year to get a mean 'birds per hour' value for each year and then carried out an ANOVA, with year as the factor/grouping variable. The results show the there are differences between mean counts per year. Post-hocs indicate the last year of the survey is sig. diff. to the other 3 years. Ok - sounds fine........
My concern is that the data within each year group are autocorellated (I have tested this and found they are). BUT, I can't work out if this is a problem or not!
I know that IF the annual mean values were correlated (i.e. observations across groups are corellated), then this would definitely be a problem. But does it matter that the within year data is correlated? If it does matter, what is the solution??
I have investigated non-parametric tests for differences between means (Kruskal-Wallis for example). But it seems that these can get around issues of non-constant variance and non-normality, BUT can't deal with dependence.
I just can't work out if the within-year dependence in my data is an issue, or whether it doesn't matter because I am only comparing the mean values from each year with each other (and these are not corellated).
Thanks in advance for any help!