What is causing a syntax error on last line of JAGS model?

Question

I have created a model to investigate the effects of covariates on occupancy of different bird species at different points across different properties. However, when I try to run the model using rjags, I get this error: Error parsing model file: syntax error on line 115 near "#"

Here is my full model:

model{
#-----------------------------------------------------------------------------------------------------
# 1) Priors
#-----------------------------------------------------------------------------------------------------
  
  ######## Abundance ########
  mean_alpha0.psi ~ dnorm(0, 0.01)   # Mean intercept for occupancy probability for all species
  sd_alpha0 ~ dunif(0, 10)      # Standard deviation on mean abundance for all species
  prec_alpha0 <- 1 / (sd_alpha0 ^ 2)
  
  for (h in 1:nspecies) {
     alpha0.psi[h] ~ dnorm(mean_alpha0.psi, prec_alpha0) # Mean log abundance per point for species h
    
    # Uninformative priors on covariates
    alpha1[h] ~ dnorm(mu_alpha1, tau_alpha1) # Effect of fire frequency for each species
    alpha2[h] ~ dnorm(mu_alpha2, tau_alpha2) # Effect of ground cover for each species
    alpha3[h] ~ dnorm(mu_alpha3, tau_alpha3) # Effect of vegetation height for each species
    alpha4[h] ~ dnorm(mu_alpha4, tau_alpha4) # Effect of total basal area for each species
    alpha5[h] ~ dnorm(mu_alpha5, tau_alpha5) # Effect of % deciduous area for each species
  } # close h
  
  sd_eps ~ dexp(1) # Standard deviation for random property effect
  prec_eps <- 1 / (sd_eps ^ 2) # Precision for random property effect
  
  for (i in 1:nprops) {
    eps[i] ~ dnorm(0, prec_eps) # Random property effect
  } # close i
  
  mu_alpha1 ~ dnorm(0, 0.001)
  tau_alpha1 ~ dgamma(0.01, 0.01)
  
  mu_alpha2 ~ dnorm(0, 0.001)
  tau_alpha2 ~ dgamma(0.01, 0.01)
  
  mu_alpha3 ~ dnorm(0, 0.001)
  tau_alpha3 ~ dgamma(0.01, 0.01)
  
  mu_alpha4 ~ dnorm(0, 0.001)
  tau_alpha4 ~ dgamma(0.01, 0.01)
  
  mu_alpha5 ~ dnorm(0, 0.001)
  tau_alpha5 ~ dgamma(0.01, 0.01)
  
  ######## Availability #########
  for (h in 1:nspecies) {
    mean_phi0[h] ~ dnorm(0, 0.001)    # Mean availability for each species
    sd_phi0[h] ~ dexp(1)              # Standard deviation of availability for each species
    prec_phi0[h] <- 1 / (sd_phi0[h] ^ 2)
    
    for (i in 1:nprops) {
      for (j in 1:npoints_site[i]) {
        phi0[h, i, j] ~ dnorm(mean_phi0[h], prec_phi0[h]) # Mean availability for species h at property i and point j
      } # close j
    } # close i
  } # close h
  
  phi1 ~ dnorm(0, 0.001) # coefficient of day of year in availiabity linear model
  phi2 ~ dnorm(0, 0.001) # coefficient of day of year^2 in availiabity linear model
  phi3 ~ dnorm(0, 0.001) # coefficient of time after sunrise in availiabity linear model
  phi4 ~ dnorm(0, 0.001) # coefficient of temp in availiabity linear model
  phi5 ~ dnorm(0, 0.001) # coefficient of wind in availiabity linear model
  
  
  
#-----------------------------------------------------------------------------------------------------
# 2) Ecological process model
#-----------------------------------------------------------------------------------------------------

  for (h in 1:nspecies) {
    for (i in 1:nprops) {
      for (j in 1:npoints_site[i]) {
        z[h,i,j] ~ dbern(psi[h,i,j]) # True occupancy based on occupancy probability
        psi[h,i,j] <- 1 / (1 + exp(-lpsi.lim[h,i,j]))
        lpsi.lim[i] <- min(999, max(-999, lpsi[i]))
        lpsi[i] <- alpha0.psi[h] + alpha1[h] * fire[i, j] + alpha2[h] * ground[i, j] + alpha3[h] *
          height[i, j] + alpha4[h] * tba[i, j] + alpha5[h] * decid[i, j] + eps[i]
       
        
        
        
#-----------------------------------------------------------------------------------------------------
# 3) Observational process model
#-----------------------------------------------------------------------------------------------------

        ###### Availability with time removal ######
        for (v in 1:vh[i, j]) {
          # loop over each visit (v) to each site
          
          # Availability is a function of intercept + covariates
          
          y[h,i,j,v] ~ dbern(mu.p[h,i,j,v]) # Detection/non-detection
          mu.p[h,i,j,v] <- z[h,i,j] * pa[h,i,j,v]
          
          logit(p[h,i,j,v]) <- phi0[h, i, j] + phi1 * obs_date[i, j, v] + phi2 * obs_date[i, j, v] * obs_date[i, j, v] + phi3 * obs_time[i, j, v] + phi4 * obs_temp[i, j, v] + phi5 * obs_wind[i, j, v]
          
          for (z in 1:ntbins) {
            # Probability of being available in each time bin
            # pi_pa in time bin >1 conditional on not being detected before
            pi_pa[h, i, j, z, v] <-
              p[h, i, j, v] * pow(1 - p[h, i, j, v], (z - 1))
            pi_pa_normalized[h, i, j, z, v] <-
              pi_pa[h, i, j, z, v] / pa[h, i, j, v]
          } # close z
          
          # Probability ever available
          pa[h, i, j, v] <- sum(pi_pa[h, i, j, 1:ntbins, v])
          
          # Time data - when were detections mentally removed? NOT SURE ABOUT THIS LINE
          ytb[h, i, j, 1:ntbins, v] ~ dbern(pi_pa_normalized[h, i, j, 1:ntbins, v])
          
        } # close v
      } # close j
    } # close i
  } # close h
} # end model

Line 115 of the model is the very last line and clearly there is nothing on that line that is wrong. I'm guessing there is some missing parenthesis or bracket or something. Could anybody help me figure out what is causing the issue here?

I looked through all the brackets to make sure there were the correct pairs of open and closed brackets, but I couldn't find anything wrong.