I have position data for fish in a river relative to a point-source pollutant coming from upstream. When pollution levels are high, I expect the fish to either move downstream, deeper, or both. My response variable is therefore a two dimensional lattice and I would like to model this as a function of the pollution levels.. conceptually a generalized additive model such as

y ~ s(effect)

where y is a two dimensional array (latitude, depth),

so (latitude, depth) ~ s(pollution)

which ultimately will be extended to

(latitude, depth) ~ s(pollution) + s(hour) + 
                    s(yday) + s(ID, bs="re")

I have looked at models in which the response variable is concatenated using cbind (or mvbind in brms), but these do not seem to provide the correct response.

I would then be able to draw predictions about the animal distributions as a function of time of year and pollution levels in the system.

Is what I am describing possible? Or am I thinking about the question incorrectly?


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