I have count data measuring the wildlife (animals) crossing a passage. A modification was made to the connecting passage. I want to measure whether this affected the rate of wildlife crossings.
The event data are taken daily over the course of 36 months: 18 months before the modifications until 18-months after the modifications. The species of the animal is measured as well. The frequencies are non-normal. The same animal may cross more than once. Due to the distribution of these data and the possibility for dependence, I don't think the T-test is an appropriate method to compare pre- and post- wildlife crossing rates.
Am I correct in using a Wilcoxon-Signed rank test procedure, matching the number of daily crossings before and after, to compare before and after?
I want to perform an aggregate analysis as well as a species stratified analysis. When stratifying by species, there are many 0-crossing days, so lots of days with zeros by species which result in a large number of ties. Does a Pratt correction fix this problem?