How important is the assumption of the poisson distribution? I measured count data across different sites for multiple dependent variables. I encountered a problem with zero inflation, so I added a count of '1' to all my data points, converted all count data to relative proportions (densities), and arcsine transformed my entire data set. 
Graphically, it appeared that my data follows a poisson distribution. Consequently, I measured the mean and the standard deviation (not variance), and found that they are approximately equal for some dependent variables, but not all of them. To get an idea about how different the mean is from the standard deviation, I measured the ratio of mean: standard deviation, and the results ranged from 0.29 to 1.34, with an average of 0.81.
My question is: How important is the assumption of mean = variance for analyses of data based on poisson-distribution generating transformations?
 A: Having "a lot of zeroes" in a dataset does not mean that it is zero-inflated. If the Poisson rate parameter is 0, then 100% of observations are 0 but it is not zero-inflated. Zero-inflated Poisson processes are a special type of mixture distribution that will (usually) give rise to a bimodal frequency distribution.
If a distribution is in fact a zero-inflated Poisson distribution, then it is a corollary that the mean-variance relationship of a Poisson distribution will be violated. That is not the only problem to consider, however, whether doing either prediction or inference. On the other hand, the mean-variance assumption can be violated without zero-inflation. Examples of this are over/under dispersion or other Poisson mixture distributions.
The "importance" of the mean-variance assumption verges on being an opinion-based question. But I guess that is in and of itself an answer to your question. It could be unimportant: The variance might be "only kind of" different from the mean. In that case, any parametric prediction or inference using an incorrect Poisson assumption will mostly agree with the more sophisticated zero-inflated models. On the other hand, count data are often far from "only kind of" in terms of this discrepancy. 
Under- and over-dispersion of large magnitude can cause massive miscalibration of error estimates for prediction and confidence intervals (PIs and CIs respectively). In this case, quasipoisson or GEE models correct the CIs and negative binomial models correct the CIs and PIs. 
A: 
"converted all count data to relative proportions (densities), and arcsine transformed my entire data set"

This will transform the variance and mean by different ratio's and is a likely cause that you get such high numbers for the ratio 'mean:variance' as between 3.9 and 36 (unless you have good reasons that this high amount of under dispersion might occur, adding 1 extra counts to all of your data, for instance, doesn't do good either and is a source of under-dispersion)
