Overview | 1. Shared setup | Species parameters — Chinook salmon | Scalar extraction helper | Derived temperature coefficients | Simulation inputs | 2. FB4-Shiny reference implementation | 3. fb4package object | 4. Comparison helper | 5. Five simulation modes | Method 1 — Fixed p-value (direct) | Method 2 — Final weight target (binary search) | Method 3 — Total consumption target (binary search) | Method 4 — Fixed ration as % body weight (direct) | Method 5 — Fixed ration in g/day (direct) | 6. Results summary | Weight trajectory — Methods 1 and 2 | Daily weight residuals | Note on Energy_density | 7. Nutrient sub-model validation | 8. Contaminant sub-model validation | 9. Conclusion | References

Overview | 1. Species parameters | 2. Environmental data | 3. Diet composition | 4. Building the Bioenergetic object | Setup visualisation | 5. Estimating consumption — binary search | Growth trajectory | Energy budget | Full dashboard | 6. Consumption with a fixed feeding level | 7. Bootstrap uncertainty estimation | 8. Result analysis | 9. Ecological interpretation | References

Overview | 1. Loading and inspecting the database | 2. Database structure | 3. Searching the database | By scientific name (partial match) | By common name | 4. Listing available life stages | 5. Comparing consumption parameters across species | Temperature optima distribution | 6. Predator energy density equations | 7. Building a model directly from the database | 8. Adding a custom species | References

Overview | 1. Species parameters | 2. Environmental and diet data | 3. Building a Bioenergetic object | 4. Running simulations | 4a. Direct simulation — fixed feeding level | 4b. Binary search — fit to a target final weight | 4c. MLE — uncertainty on p from observed weights | 4d. Bootstrap — non-parametric uncertainty | 5. Comparing strategies | 6. Analysing results | 7. Visualising results | 8. Temperature × p sensitivity | 9. Experimental features | Contaminant bioaccumulation | Nutrient regeneration | Mortality rates | References

Overview | Shared setup | Strategy 1 — Binary search | Strategy 2 — Maximum Likelihood Estimation | Strategy 3 — Bootstrap | Strategy 4 — Hierarchical model (mark-recapture) | Biological context | Simulating a mark-recapture data set | Running the hierarchical model | Assumptions and limitations | Comparing strategies | Strategies 1–3: single-population estimates | Strategy 4: population-level distribution | References

Overview | 1. Base model — juvenile Chinook salmon | 2. Manual sensitivity analysis across temperature offsets | 3. Built-in sensitivity plot | 4. Climate change scenarios — projected growth | 5. Thermal performance window | 6. Management implications | References

Overview | Basic Usage | Processing Fishing Data | Coordinate Conversion | Distance Calculations | Juvenile Analysis | Data Requirements | Built-in Data | References

Juvenile Fish Analysis | Basic Juvenile Analysis | Length-Weight Relationships | Juvenile Percentage Calculation | Convert Numbers to Weight | Visualization | Length-Weight Parameters | Parallel Processing | Analysis Tips | References

Spatial Analysis | Distance to Coast | Coordinate Conversion | Land vs Sea Classification | Built-in Coastline Data | Distance Categories | Parallel Processing | Fishing Zone Analysis | Tips for Spatial Analysis | References

Data Processing Workflow | Step 1: Process Individual Datasets | Step 2: Data Validation | Step 3: Merge Datasets | Step 4: Add Derived Variables | Coordinate Processing | Error Handling | Column Detection | Tips

Prerequisites | Basic Setup | Download Example