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Training Workshop

Welcome to the. Training Workshop. 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. ParFish Process. 5. Initiate management planning.

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Training Workshop

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  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

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

  47. 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?

  48. Software – probability models

  49. 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

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