Distance to source populations and/or environmental gradients?
First of all, species richness is most likely the dependent variable (the one you want to explain), while distance to the source pool and pH are the independent variables (the ones you assume to have an effect on species richness).
The problem you are describing is termed multicollinearity and a common problem in regression analyses. The good news is that it does not affect the prediction of your model as a whole. The bad news is that the estimates for you individual predictors become increasingly erratic the higher their correlation is. You can estimate the severeness of multicollinearity in your model with the Variance inflation factor (VIF). Wikipedia explains nicely how to interpret the VIF:

The square root of the variance inflation factor tells you how much larger the standard error is, compared with what it would be if that variable were uncorrelated with the other predictor variables in the model.
Example
If the variance inflation factor of a predictor variable were 5.27 (√5.27 = 2.3) this means that the standard error for the coefficient of that predictor variable is 2.3 times as large as it would be if that predictor variable were uncorrelated with the other predictor variables.

You should calculate the VIF for your model. Many sources state that a VIF &lt; 10 is still acceptable, but since there is no rule of thumb, I suggest to do some further reading on the topic if you get some critical value (just google &quot;dealing with collinearity&quot; and you'll find tons of material).