Hi Stephanie,
First, I think you should try to simplify your model (unless you have
good reasons to keep the non-significant variables in your model), this
will likely influence the other estimates. Also, I wouldn't rely on the
p-values from the summary to assess the significance of your variables.
As for your question about back-transforming to interpret the effect of
a variable, I don't think you can just use exp(estimate)? as the
estimate is part of a model equation involving all the parameters (e.g.
intercept + beta1 * Grass + beta2 * AllFlowers ...) and the estimates
are also influenced by the scale of the response variable. I usually use
the predict function to graphically plot the effect of a given variable.
If you're interested in knowing if a given variable has a stronger
effect than another one, I'd recommend standardising your continuous
variables, so that they are all on the same scale.
A negative intercept would mean that the baseline level is somewhere
between 0 and 1 (but still positive!), as exp(-1) = 0.36.. for example.
Hope that helps,
Thomas