Combining data from different sources


I have data from two very different sources (bird counts from ships and bird counts from the shore) which aim to estimate the same population/outcome. Both data have different covariates/predictors and thus it is difficult to combine both data in the same regression model.

Does somebody know which methods exist to combine both data in order to estimate the same outcome? I know a little bit about model averaging / machine learning techniques, but are there alternatives? I would really like to use both data within the same model.



Less is more. Stay pure. Stay poor.
I think there are test to compare the outcomes, but combining them would leave a lot of missing data, right??
Yes, I mean assume that we have two different counting methods, both with outcome "N", and the variable "Year" in order to estimate the trend of "N". And now we have two distinctly different covariates, each covariate corrects for a certain bias produced with each counting method, let's say "X" and "Y". If I just combine all data in a dataframe, I have always one missing value in each row (either for X or for Y), so no data would be used within the regression. However, I would like to use the data from both sources simultaneously to have a more precise trend estimate and to increase the power of the regression analysis.