Correct way to account for repeated measures and spatial autocorrelation in GAMM (R)

Question

I am using the gamm function in the mgcv package in R to specify a model that predicts abundance with respect to elevation and year based on repeated measures from several sites. My overarching question is how abundance changes over elevation, knowing and accounting for the fact that it also changes over time. GAMM is used because I do not want to restrict the relationship of either elevation or year with abundance to be linear. I expect the data to be spatially autocorrelated within years (sites closer together will have more similar values than sites further apart within each year) and would like to account for this.

I'm having trouble figuring out the proper full formulation of the GAMM.

The variables are: 1) abundance (continuous response) 2) elevation (numeric, continuous predictor) 3) year (numeric, continuous predictor as we expect a gradual change in abundance over time) 4) spatial autocorrelation term (based on the lat/long of the sites where data were collected) 5) site (random factor)

The model formulation I have come up with is:

model <- gamm(abundance ~ s(elevation, bs = "tp")+s(year, bs = "tp"), 
              correlation=corSpher(form = ~ y+x|year),
              random=list(site=~1), data=input.data)

My questions are:

Is the repeated measures nature of the data adequately accounted for here or do I also need to include year in my random effects (nest site in year)?

Have I properly taken year into account in my spatial autocorrelation term?

Finally, when running this model, I get an error and a warning:

Error in corFactor.corSpatial(object):  
Na/NaN/Inf in foreign function call (arg 1) 
In addition: Warning message: 
In min(unlist(attr(object,"covariate"))): 
no non-missing arguments to min, returning Inf

Not sure why this is happening. If I run the GAMM without the random effect of site, it does not return the error...

Many thanks in advance for any help!