I'm analysing a dataset and comparing three zones at two points in time. The data in question is:
Hourly sightings of wild fish (CPUE)
In one of three adjacent zones (A, B, and C)
During the Spring of 2009 and Spring of 2017
I'm trying to see if there was any significant change in CPUE between 2009 and 2017, for each zone, as well as for the entire region (A+B+C). Null hypothesis is that there was no observed change in CPUE over time. I'm not necessarily interested in finding differences between the zones, just the same zone across the two time periods. Simplys put i'm comparing CPUE between:
A2009 vs A2017
B2009 vs B2017
C2009 vs C2017
(A+B+C)2009 vs (A+B+C)2017
The data is not normally distributed, includes many 0s, and not all zones have the same amount of data.
So far I have conducted Mann-Whitney U tests comparing each zone between years, as well as for the entire region in both years. Is the Mann-Whitney U the right choice? I've looked into kruskall wallace and similar tests, but I'm ony interested in comparisons between time period, not zone. Is there a more appropriate test? Would a linear / time series model be more appropriate? I've attached an analogue of my data for clarity.