Transforming data prior to CCA

Hi everyone, I´m a student and relatively new to R so apologies in advance if this question seems stupid or obvious to you. I have collected a dataset with about 60 species of diatoms (count data from 19 different sample sites) and environmental variables for each site (salinity, pH, etc.). The long-term plan is to perform a canonical correspondence analysis (CCA in the vegan package) on it but the data obviously has to conform to some standarts first. Ideally, any two variables should be in a linear relationship and multivariate normality should be given as well as homoscedasticity (I haven´t tested for this one yet, that´ll be another adventure).  Now my data – surprise – does not conform to a normal distribution nor do the relationships seem linear so I need to transform it (but which parts?). The usual log transformation doesn’t change anything so I found this one (the poisson generalized linear model) 

glm(formula, family=poisson(link=log), data=envir) 

It doesn´t work because I dont know what formula to put in, does anyone know how to use it or what other transformations I could try?Any kind of help would be greatly appreciated, I am so lost… Thanks in advance, SRuhl

On a side-note: the CCA runs on my data already but what good is that when the data is not in the right format? It may look completely different when the data fits all the requirements.