I apologize in advance if this question has been already solved elsewhere, I have search for it but I could not find a solution for my problem yet.
I want to assess the effect of forest cover on species presences and absences using logistic regressions (glm family=binomial). I have a large dataset (data compiled from different studies and datasets across different regions and countries) with presences and absences of different species but some of these species have many presences and few absences and vice-versa. My questions are:

* Is this per se a problem? I guess that with so unbalanced data I would not find effects of forest cover on my species and I would have to remove some of my presences or absences
* Is there any rule for binomial data such as having at least 25% of your data being presences or absences? And if not, how can I know how to choose it?
Example of the data set

|  | Presences | Absences |
| --- | --- | --- |
| Species 1 | 872 | 1 |
| Species 2 | 50 | 4 |
| Species 3 | 30 | 18 |
| Species 4 | 3 | 409 |