#### Step-by-Step Guide to Community Occupancy Modeling

1. Species Selection
   - First, select which species to include in your community model
   - Review the species table showing detections and number of sites for each species
   - Use the filtering options to select species based on minimum number of detections or sites
   - You can manually select/deselect individual species by clicking the rows
   - Consider removing rare species with too few detections for reliable parameter estimation
2. Model Configuration
   - Choose model type (Occupancy or Royle-Nichols model)
   - Set occasion length (in days) to create detection histories
   - Configure detection and occupancy covariates:
     - Fixed Effects: Effect is same for all species
     - Species Random Effects: Species-specific effects with shared variance
     - Independent Effects: Completely independent effects for each species
   - Configure how species intercepts are modeled (fixed, random, or independent)
   - Optional & recommended: Include survey effort as detection covariate
   - Optional: Add species-site random effects
3. Model Fitting
   - Set MCMC parameters (chains, iterations, burn-in, thinning)
   - Run model
   - Monitor progress (currently R console output, not in the dashboard)
4. Results Inspection
   - Review parameter estimates and their uncertainties
   - Check convergence diagnostics (Gelman-Rubin statistics)
   - Examine trace plots for key parameters
   - Optional: Run Goodness-of-Fit tests (not yet implemented)
5. Effect Visualization
   - View response curves for covariates
   - Compare effect sizes across species
   - You can:
     - Select subsets of species to display
     - Order effects by size
     - Adjust confidence intervals
     - Scale plot sizes for better visibility
6. Spatial Predictions
   - Generate species-specific occupancy/abundance maps
   - Create species richness predictions
   - Calculate percentage of area occupied
   - Maps include uncertainty estimates

#### Important Notes:

- More species and covariates increase computation time
- Check convergence diagnostics before interpreting results
- Covariates are scaled to mean = 0 and standard deviation = 1 automatically.
- Prediction rasters shuold be provided in original scale. They are scaled automatically to match model covariatesy
- Species with very few detections may have unreliable estimates
- Save your model objects to avoid rerunning long computations

#### Tips for Model Convergence:

- Start with simpler models and gradually add complexity
- Covariates are standardized automatically
- Increase iterations if chains haven't converged
- Check for highly correlated covariates
- Consider removing rare species or combining similar species