Choosing the best family and link function for a GLM I'm new to GLMs and I need to do several GLM using a kilometric abundance index (non integer, but calculated from a count) as the dependent variable and several habitat traits as the independent ones.
I was told to try to do it with family=poisson(link="log") (although its not an integer) and with family=gaussian(link="identity") by different people. I tried to do both, but now I don't know how to choose the best one for each case. I printed the QQPlots of the residuals and the residual vs fitted, and I can only see very small differences. Are there more effective (or less subjective) methods to determine which GLM fits better in each case?

You don't want to divide through to turn your abundance data into non-integer data.  Instead, you should use the denominator (e.g., time or area) as an offset (see my answer here: How to deal with "non-integer" warning from negative binomial GLM?).  If you prefer, you can also use the denominator as a covariate (see my answer here: In a Poisson model, what is the difference between using time as a covariate or an offset?).
Setting that aside, your base data are counts, so I would use a distribution for counts a-priori.  Using the Poisson distribution is fine, but it makes a very restrictive assumption that the conditional variance is the same as the conditional mean.  That's often a dicey assumption.  So it may be better to use the quasi-Poisson distribution, or to fit a negative binomial model, instead.
