Hi, The 'regular' Bonferroni is used when you wish to test an overall hypothesis by combining the results of testing individual hypotheses. So in the case of a single correlogram, the overall (null) hypothesis is 'no correlation at any distance scale', and the individual hypotheses are 'no correlation at distance scale 1,' no correlation at distance scale 2,' and so on.
In the case of your six correlograms, the answer simply depends on what your larger hypothesis is. If it is 'no correlation at ANY distance scale in ANY of the plots,' then it is conceptually appropriate to correct for 60 separate tests. In theory this will give you the same error rate as doing a Bonferroni correction for each correlogram separately, and then a correction for comparing those results across your six plots. Note though that with a large number of subtests, you will run into difficulties. Your corrected threshold for significance will be very high (alpha / 60, or about 0.0008 if alpha = 0.05). It's possible, for example, that the sample size for each subtest (one distance class in one plot) may not be sufficient to achive this level of significance, not matter how aggregated the canopy. Thus, you might never achieve a significant test. That's why in general, the aggregation of MANY small sub-tests into a larger test is not a good idea. It's better to find a statistic that allows an overall test, or perhaps lump your data together. So, what if you lump your five undamaged plots into one correlogram, and test just the ten distance classes (using the Bonferroni correction)? You might indeed find significance now, as your sample size per class will be five times bigger. For the single, damaged plot, you will have less data, and you may need to do the uneven distance classes. Even so, you can test that one with Bonferroni correction. All things being equal, you are less likely to find significance than with the other data because of your smaller sample size, but you still might. The bottom line is that the fact that you have more data for one test than the other reflects reality, and can't be corrected for except by collecting more. But all is not lost, because what's ECOLOGICALLY interesting is the effect size. Your undamaged plots may show statistically significant autocorrelation, but of very small magnitude, which makes it perhaps not interesting. Your damaged plot may have high autocorrelation values, but not be 'significant' merely because of low sample size. You should always report both the statistical tests and the effect sizes. Gareth Russell
