How do I interpret the results from a basic interaction plot from the R effects package? Using the method in this post, I have made a plot to visualize the interaction between two predictor variables using the effects package in r, but I'm not really sure what I am looking at. 
Tide heights and rain averages are continuous. 8 bins were the maximum the function would allow me to use. The following is the call to effect producing this plot:
 R > plot(effect(term="rain.avg2:tide.avg",mod=bkrain9.lm,default.levels=8),
          main="", xlab="Precipitation - 24hr average (cm)",
          ylab=expression("TCB Concentration - CFU*100m"*L^-1),multiline=TRUE)

Plot updated. Could somebody explain the purpose of this plotting feature in context to predictor interactions?

Background:
For a class project, I have created a linear regression model to evaluate the effects of the interaction between two predictor variables (tide height and precipitation) on bacteria concentrations.
Thermo-tolerant coliform bacteria concentrations were sampled at 5 sites in a day, where a sample time was recorded at sampling completion. I took an average of these for roughly 20 days, and calculated an associated 24hr average of precipitation before sampling completion, and a 50 minute (the sampling duration) average of tide height before sampling completion.
 A: Without knowing more about the specifics of the dataset, I can't be especially helpful.  However, in general an interaction plot shows the effects of a focal predictor on the dependent variable at specific values of the nonfocal predictor.  In your case, your plot is showing the effects of "tide.avg" on "tcb.avg" when "rain.avg" is 0, .22, .44, etc.
A: @PatrickS.Forscher is giving you the right answer here.  Having an interaction means that the relationship between level of precipitation and the bacterial concentration depends on the height of the tide.  When the tide is 27.4 (cm?), the level of precipitation has essentially no effect on bacterial concentrations, but the effect of precipitation becomes increasingly steeper as the tide goes up, such that even small changes in precipitation can be associated with big changes in the concentration of the bacteria.  For example, when the tide height reaches 141.8, a .1 increase in precipitation is associated with an increase in the bacterial concentration of about 200 units.  
