1. Load and inspect a system | 2. The status quo | 3. Equilibrium scenarios | 4. Yield curves | 5. Safeguarded MMSY | 6. Harvest strategies | 7. Performance statistics | 8. Projections | 9. Tuning triggers | 10. The app

Part A: the Atlantic example | A1. Stocks | A2. Metiers and catchability: from a habitat landscape | A3. Fleet segments | A4. Tuning the status quo | Part B: the Gulf of Thailand draft | B1. The four tables | Species | Metiers and segments | B2. Catchability | Route A: an expert overlap matrix | Route B: a habitat landscape | B3. Fleet behaviour | B4. Calibrating the starting point | Checking the result | Part C: checklist for your own system | Next steps

Example data | Step 1. Format the data | Step 2. Explore the fleet | Step 3. Choose the species set | Step 4. Nominal CPUE and catch composition | Step 5. Habitat-based subsetting (optional) | Step 6. Targeting covariates from the catch composition | How many PCs? | What do the PCs and clusters represent? | Removing irrelevant tactics (optional) | Step 7. Fit the standardisation models | Step 8. Diagnostics | Model comparison | Indices and steps | Influence | Residuals, effects and vessels | Predictive check of the error distribution | Step 9. The final index | Processing log | Reporting checklist | References

1. Settings | 2. Environment | 3. Species and habitats | 4. Tactics | 5. Fleet: specialists, mixed vessels and loyalty | 6. Prices, closure and fleet changes | 7. Reporting: from sets to a logbook | 8. Basic mechanics: what the PCs and clusters capture | PCs against tactic and habitat | PCs against depth and the true position | Mixed trips lie between the clusters | Clusters against the true tactic | Targeting over time | Vessel profiles against the true vessel type | Things to try | References

The logbook | Part 1. Preparing the logbook | Step 1. Format the data | Step 2. Explore the fleet | Step 3. Choose the species set | Step 4. Nominal CPUE and catch composition | Step 6. Targeting covariates for the whole fleet | Part 2. Worked example: blackspot seabream | Step 5. Remove the trips that could not catch seabream | Step 6. Targeting covariates of the demersal trips | Step 7. Fit the models | Step 8. Diagnostics | Model comparison, indices and steps | Influence | Residuals | Predictive check of the error distribution | Effects and vessels | Sensitivity to the data selection | Step 9. The index | Part 3. Worked example: dolphinfish | Step 5. Keep the pelagic trips | Step 6. Targeting covariates of the pelagic trips | Influence and residuals | Predictive check by month and by year | Error distribution: Tweedie or delta-lognormal? | Sensitivity | Processing logs | Reporting checklist | Things to try | References

The simulated fishery | Standardisation as in the guidelines | What do the PCs capture? | What do the clusters capture? | Replicates | References

The operating model in brief | How to control the scenario | Targeting through prices | Species habitat | Longline gear | Environment | Fleet and data | Step 1: define the scenarios | Step 2: simulate | Step 3: the environment | Step 4: why the thermocline matters for HBF | Step 5: how the fishery responds | Gear use | Vertical availability by HBF | Nominal CPUE versus true biomass | Step 6: standardise | Step 7: compare the indices with the truth | Indices | Accuracy | What to look for | Step 8: what do the principal components capture? | Replicates: is the ranking robust? | Optional: with or without space, and at what resolution | Optional: interactions with year | A note on habitat-based standardisation | References

Settings | Nominal CPUE versus the truth | Standardised indices | What do the PCs capture? | What do the clusters capture? | Replicates: flat versus contrasting trends | What to take from this