In order to combine data from surveys conducted in different seasons--which have important differences in methods (temperature/timing) and results (e.g., for spiny dogfish, different patterns in distribution, abundance, etc.)--we need some statistical method to account for this source of variation... an observation model?
I could see a hierarchy of sophistication, starting with simplest:
Just use one survey
Use an annual average of the two surveys
Use a statistical model that includes julian day of the tow to reconstruct an average spatial pattern per year
Build a seasonal observation model into the process-based model. Maybe it's just a seasonal offset that gets added to the observations for one of the seasons (could depend on latitude, so would estimate two parameters: an intercept and slope)
In order to combine data from surveys conducted in different seasons--which have important differences in methods (temperature/timing) and results (e.g., for spiny dogfish, different patterns in distribution, abundance, etc.)--we need some statistical method to account for this source of variation... an observation model?