rjags Index expression evaluates to non-integer value

Question

I have a model that I am trying to add covariates into. The model runs when I don't add "gamma". When I add "gamma", I get the following error:

Error in checkForRemoteErrors(val) : 
  3 nodes produced errors; first error: Error in node -1.8306
Index expression evaluates to non-integer value

I have tried to make all of the parts of the data list into numeric, I still have the same error. I am not sure how else to resolve this and I don't understand how a node could be negative. The augtemp data are normally distributed, I have z-scored the data. I have tried different prior distributions and that has not resolved the problem. If you are able to help, what does this error mean, where is a negative node, and how could I resolve this?

Thank you. The model and code are below. (cross-posted on: https://sourceforge.net/p/mcmc-jags/discussion/610037/thread/3634179a0d/?limit=25#85db)

# compile data into a list for JAGS
jags.data.augtemp <- list(Ndata = nrow(dat),
                  SS=dat$Redds.10000m2,
                  lRR=log(dat$EST.1.10000m2),
                  RR=dat$EST.1.10000m2,
                  stream = dat$StreamNum, 
                  Nstream = max(dat$StreamNum),
                  augtemp = c(as.numeric(dat$Z_stream_augtemp_yr)),
                  #drainage=DrainageHeirarchy$DrainageNum, 
                  #Ndrainage=max(DrainageHeirarchy$DrainageNum),
                  Year=as.numeric(as.factor(dat$Year)), 
                  Nyear = max(as.numeric(as.factor(dat$Year)))
                  #MSF = BLT_SR$Z_MSF
                  )
 str(jags.data.augtemp)
List of 9
 $ Ndata  : int 246
 $ SS     : num [1:246] 3.444 3.587 4.592 2.296 0.287 ...
 $ lRR    : num [1:246] 6.08 5.56 5.23 5.03 4.56 ...
 $ RR     : num [1:246] 437.6 259.4 186.6 153.6 95.4 ...
 $ stream : num [1:246] 1 1 1 1 1 1 1 1 1 1 ...
 $ Nstream: num 11
 $ augtemp: num [1:246] -1.831 -0.074 0.694 -0.626 -2.066 ...
 $ Year   : num [1:246] 5 6 7 8 9 10 11 12 13 14 ...
 $ Nyear  : num 38


 ##### STEP 2: SPECIFY JAGS MODEL CODE #####
modelScript.name <- "ricker_augtemp.txt"
jagsscript <- cat("
model {

###priors####

    alpha.m ~ dunif(1,100);
    beta.m ~ dunif(0,1);
    gamma ~ dunif(-5,5); # Prior for the coefficient of the august temp covariate
    #FlowEff ~ dnorm(0,3);

    s.a.sd ~ dunif(0,3);
    s.b.sd ~ dunif(0,3);
    #d.a.sd ~ dunif(0,3);
    #y.a.sd ~ dunif(0,3);

    sigmaObs ~ dunif(0,2);

####
####
####define ricker model###
####
####
    for(i in 1:Ndata){
    ##alpha = recruits per stock unit at small stock size
    ##SS= stock, in this case the Redds/km2
    ##beta =  b to density dependence

    lRR[i] ~ dnorm(log(pRR[i]) -
    (pow(sigmaObs,2)/2),pow(sigmaObs,-2)); #log recruits        defined as a normal distribution where the mean is the      log of the output from the ricker model and the sd is       made into a precision parameter by taking the SD to the     power of -2

    pRR[i] <- alpha[i] * SS[i] * exp(-1*beta[i]*SS[i]);         ##ricker model



      lalpha[i] <- log(alpha.m) + s.a[stream[i]];# + y.a[Year[i]]; #taking into account the random effect of stream on alpha
      lbeta[i] <- log(beta.m) + s.b[stream[i]] + gamma[augtemp[i]];#taking into account the random effect of stream on beta & covariate effect of mean august temp


      alpha[i] <- exp(lalpha[i]); #exp() so no longer on log scale
      beta[i] <- exp(lbeta[i]);

    }

####
####
######this is defining things for drainage, but I don't have drainage in the data as of 8/11/23####
####
####
    #for(DD in 1:Ndrainage){
      #d.a[DD] ~ dnorm(0,pow(d.a.sd,-2));
      #s.a.sd[DD] ~ dunif(0,3);
    #}

####
####
####defining the distributions for the random effects of stream####
####
####
    for(SS in 1:Nstream){
      #s.a[SS] ~ dnorm(d.a[drainage[SS]],pow(s.a.sd[drainage[SS]],-2));
      s.a[SS] ~ dnorm(0,pow(s.a.sd,-2));
      s.b[SS] ~ dnorm(0,pow(s.b.sd,-2));
    }

####
####
####defining the distributions for the random effects of year####
####
####   
     #for(YY in 1:Nyear){
       #y.a[YY] ~ dnorm(0,pow(y.a.sd,-2));
     #}

####
####
####defining management quantities####
####
####
    for(SS in 1:Nstream){
      Smsy[SS] <- ((log(alpha.m) + s.a[stream[SS]]) * (0.5 - 0.07*(log(alpha.m) + s.a[stream[SS]]))) / exp(log(beta.m) + s.b[stream[SS]])
    }


}  
", 
file = here(modelScript.name))


##### SET NODES TO MONITOR #####
jags.params.temp <- c('beta.m','alpha.m','gamma','s.a','s.b','sigmaObs','Smsy')



mod_lm_augtemp <- R2jags::jags.parallel(jags.data.augtemp, 
                                parameters.to.save = jags.params.temp,
                                model.file = here(modelScript.name), 
                                n.chains = 3, 
                                n.burnin = 30000/2, # number of post-burn-in samples per chain
                                n.thin = 10, # thinning rate
                                n.iter = 30000)