1. Welcome to the Training Workshop

2. What is ParFish? • An approach to stock assessment • Involve fishers and other stakeholders • Suitable for small-scale fisheries • Rapid assessment • Appropriate for data-poor situations

3. ParFish Process 5. Initiate management planning 4. Interpret results and give feedback 6. Evaluate ParFish process • 3. Undertake ParFishstock assessment 1. Understand the context 2. Agree objectives with stakeholders

4. ParFish Toolkit • Guidelines: guidance for carrying out the process, data collection, assessment and management planning • Software for carrying out the stock assessment and Software Manual

5. Stock Assessment Interviews Fisher preferences Fishing Experiments Catch-effort Overview of the Assessment ParFish software Recommended levels of control State of the fishery resource

6. Learning Objectives • By the end of today, you will: • Have been introduced to the 6 stages of ParFish and how to implement them; • Be more familiar with the ParFish Software and analysis; • Be introduced to various participatory techniques.

7. Characteristics of a suitable fishery • Sedentary local species (not highly migratory e.g. tuna) • Fishers responsible for the majority of fishing mortality can be identified • One or more fishing villages involved (depending on resources) • Co-management situation or wishing to develop co-management

8. Next: Paul

9. Stock Assessment A brief introduction to principles and methods

10. Principles • Identify measurable indicators related to policy • Identify state of exploited populations (reference points) • Identify controls on fishing • Identify and deal with uncertainty • Provide relevant advice accounting for the above

11. Types of Indicators • Stock size, SSB • Catch / Landings • Effort / vessels / days-at-sea • Fishing mortality • Employment • Profit / economic rent • Non-target catch • Interactions / illegal activity • etc

12. Data Variables must be: • measurable • relevant • Convert from data to indicator • possible to collect • Fit in with data collection system • Low costs

13. Example • Policy statement: “…sustainable utilisation maximising economic benefits.” • Interpret: “…maintain stock size above MSY point, balancing employment and economic rent.” • Indicators and reference points: • Stock size and MSY point • Total employment and current employment • Vessel profits (catch rates) and break-even point

14. Data and Analysis • Stock size • Total Catch • Stock size index • Employment • Number of people employed by sector • Vessel profits • Economic inputs • Vessel, gear, fuel costs • Economic outputs • Landings, prices

15. Reducing costs • Using Proxies • Catch rates: • Population size index • Profitability • Number of registered / licensed vessels • Employment • Sampling • Allow for error: sufficient sampling • Sampling design • Co-management

16. Reference Points • Impact of fishing on populations • Link fishing activity to depletion • Link stock size to productivity • Use models to interpret data • Simple indicators • Complex reference points

17. Uncertainty • Sources of uncertainty • Observation error (sampling) • Process error (time series) • Structural error (models) • Presentation of uncertainty • Making decisions under uncertainty

18. Summary • Interpret independent information in relation to policy aims • E.g. Indicators and reference points reduction • Address uncertainty • Provide simple understandable advice • Promote management action

19. ParFish I • Design incorporates all other methods • Robust • Explicitly deals with uncertainty • Involves fishers: promotes management action • Simple advice?

20. ParFish II • Target simulation model • Build probability density functions of parameters (encapsulates uncertainty) • Apply stochastic projections to simulation model under possible actions • Identifies management actions best for fishers • Generates standard indicators / reference points

21. Next: PM/SW concepts

22. Bayesian Approach A brief introduction

23. Summary • Introduction to probability • Likelihood • Bayes rule • Decision theory and utility • A practical application: ParFish

24. Mathematical Probability • Probabilities are between 0 and 1.0 • 0 = impossible • 1.0 = certainty • Probabilities often defined as sets of possible events or outcomes • A set of exclusive events, one of which must occur, sum to one

25. Subjective Probability • People assess a risk even without direct observations • Some events we may wish to estimate we do not wish to observe, such as nuclear war or overfishing.

26. Discrete → Continuous

27. Example Probability Density

28. Likelihood • Probability when p is known: • Pr(H) = p • Pr(T) = 1-p • Likelihood when H/T is known • Pr(p ¦ H) = p • Pr(p ¦ T) = 1-p

29. Binomial Likelihood • nCr is the number of ways (combinations) r heads could occur in n trials.

30. Likelihood: 8 Heads 2 Tails

31. Fishing Experiment • Population size on day 0 = n • We catch C0 fish on day 0 • Population size on day 1 = n - C0 • We catch C1 fish on day 1 • Population size on day 2 = n - C0 – C1 • Population size on day t = n - tCi

32. Fishing Experiment

33. Lake Fishing Likelihood

34. Bayes Rule • Posterior  Prior * Likelihood • Pr(p, n  Data)  Pr(p, n) * L(Data  p,n)

35. Updating Using Bayes Pr(p, n  Data)  Pr(p, n) * L(Data1  p,n)* L(Data2  p,n) Which gives Pr(p, n  Data)  Pr(p, n  Data1) * L(Data2  p,n)

36. Lake Fishing Experiment

37. Lake Fishing Experiment

38. Lake Fishing Likelihood

39. Lake Fishing Posterior

40. Utility • Score cost / benefits of outcomes in one dimension • Not monetary • Used in economics to manage risk • Explains why people enter games where they expect to lose money

41. Example Utility Curves

42. Decision Theory Combines probability and utility • Bayes action: • Choose the action which will maximise the expected (average) utility

43. Next: PM/SW

44. Software Structure Simulation Model Projected catch - effort time series Control Preference Posterior Parameter PDF PDF 1 Source Model 1 Probability Modelling Source Model 2 PDF 2 PDF N Source Model N

45. Generating Parameter Probabilities Observations • ParFish software takes frequency observations, and estimates the underlying probability distribution from which they were drawn Estimate PDF

47. Probability density functions from various data sources can be combined into a single ‘posterior’ PDF Combine Posterior

48. Conventional and New Information Sources • Current version uses logistic (Schaefer) as simulation model: r, Bcur, Binf and qj • Various data types and sources can be combined e.g. • Long term catch-effort data models • Interviews • Fishing experiments • Biological parameters • Others?

49. Software – probability models

50. Fishing Experiments • Estimate population size and catchability • Fishers concentrate their fishing effort in a specific area, catches and effort are recorded • Complemented by underwater visual surveys of fish population