I have a dataset which consists of fish density/biomass from two sampling periods (1 and 2) in two locations (A and B). In sampling period 1 locations A and B are both clear, while in sampling period 2 A is clear but B is experiencing very high turbidity. I want to assess whether the increased turbidity has caused a change in how the fish are using the habitat-- i.e. if they are preferentially choosing the clear "A" location at sampling period 2, compared to sampling period 1.
I was thinking I could assess this with a chi-square test, but my measures of biomass and density are continuous, not discrete-- would rounding up to the nearest whole number be valid?/Any other suggestions on how to analyze this? I don't think an ANOVA will work because I really only have one measure of density/biomass per group/time.
*** EDIT ***
I actually have 100's of measures of density and biomass in each sampling of the 4 sampling areas/periods-- density and biomass were obtained using a sonar-like device that pings the fish every minute or so. But these measures obviously won't be independent of each other, so I was planning on just averaging across the sampling period.