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