I have a dataset where I counted the number of a species in different environments and grouped it into different categories ranging from 0 to 5. 0= no occurrence; 5= very high occurrence. All categories are well defined. I know that the environment has an important influence: however, there is a lot of variation for the different environments. To identify spots with high occurrences I wanted to standardize my data for every environment to be able to compare them and identify the samples with abnormal high/low occurrence.
E.g.: For environment A the occurrence varies from 0 to 5 with 95% in group 0 or 1. 5% are in group 5. For environment B 95% show occurrence 0 but 5% occurrence 2. As environment A is very favorable for the species I am observing and B is not, group B occurrence in group 2 is already quite high for my species in this environment. I am therefore looking for a standardization that for every environment transforms the values to have the same mean. I want to find spots that show above average occurrence for the environment they are in. I therefore need some kind of standardization/normalization for count data.
Can I use z-Score transformation ((x-mean)/stdDev) or are there more appropriate tests for this?