Some perspectives from outside the EFIMAS tent Paul Connolly

1. EFIMAS Conference Brussels 11th 12th March 2008 Some perspectives from outside the EFIMAS tent Paul Connolly Director, Fisheries Science Services, The Marine Institute, Galway, Ireland

2. Linkage

3. Content • Where am I coming from ? • Approach – crafting my perspectives • 3 Understanding EFIMAS • 4 Key issues - Ireland Focus Groups • 5 The misuse of models • 6 It’s the Ecosystem stupid ! • 7 Some take home perspectives

4. Section 1 Where am I coming from ?

5. Which boxes do I tick ?

6. Section 2 Approach - Crafting my perspectives

7. Crafting my perspectives Take Home Perspectives IRL - The Ecosystem Misuse of Models IRL - Focus Groups The Foundation - My Experience

8. Section 3 Understanding EFIMAS

9. Understanding EFIMAS What does it say on the tin ? To facilitate the development of better fisheries management regimes Develop new tools to simulateand evaluate the biological, social and economical consequences of a range of fishery management options and objectives within different management regimes

10. Project strategy Develop computer based models to run simulations incorporating data from selected EU fisheries taking into account the impact of the environment on fisheries and vice versa Compare a range of management options for test stocks and fisheries Compare the performance of a range of management options under alternative management systems and objectives

11. EFIMAS - key areas The evolving FLR framework Modelling simulaion ‘ Crafting ’ of management strategies Case studies ICES, ICCAT, NAFO Interaction with stakeholders Economic modelling Modelling human behaviour ? Yes EFIMAS is published Is it communicated ?

12. The model “ a model is a peculiar blend of fact and fantasy, of truths, half truths and falsehood. In some ways a model, may be reliable, in other ways only helpful and at times and in some respects thoroughly misleading ” (Skellam, 1973)

13. Simulation A deceiving by words is called a lye……. and a deceiving by actions, gestures or behaviour is called a simulation…. (Robert South 1643-1716) Simulation - when a man industriously and expressly feigns and preteds to be, what he is not (Sir Francis Bacon, 1645)

14. Section 4 Key issues from Ireland’s Focus Groups What Stakeholders Say/Think Key Perceptions

15. Ireland’s Focus Groups The five themes 1 Science in General 2 Fisheries Science 3 FS in Management 4 Models in General 5 EFIMAS Framework

16. The resulting issues 1 Science in General Objectivity Independence Scepticism towards validity of science 2 Fisheries Science Scepticism and Fear Technical Failures, Political Bias, Arrogant Academic Attitude Trust in Science (who funds) Stakeholder Involvement

17. The resulting issues 3 FS in Management Should be backed by science Crucial for fishers accepance/compliance Need for ecologically sound FM • Models in General • Useful predictive abilities • Scepticism about validity and use • Misuse of Models • Only as good as data inputs • Do not allow knowledge input from fishers • Different Models – Different outputs • Political use of models

18. The resulting issues • EFIMAS Framework • Uncertainty what framework is about • Make modelling inclusive process • Include fishers knowledge • Make model accessible • What are basic assumptions of models ?

19. Some messages Arragont academic attitude Misuse of models Make modelling inclusive process Stakeholder involvement Communications Build trust

20. Key perception 1 B E S

21. Key perception 2 46.8886

22. Key perception 3

23. Section 5 The misuse of models on the ‘Back Foot’ Complexity and simplicity Sceptics Bad press - example Ireland - management plans

24. Simplicity and complexity Occams Razor “ … entities shoud not be multiplied beyond necessity ” Albert Einstein “ Everything should be made as simple as possible, but not simpler “ Woodword “ When you hear hoofbeats, think horses not zebras “ KISS - Keep it Simple Stupid The System - The Tools Simple Relationships – Complicated Models Complex Relationships – Simple Models

25. Sceptics (around for a long time) “ Today’s scientists have substituted mathematics for experiments, and they wander off through equation after equation and eventually build a structure which has no relation to reality “ (Nicola Tesla, 1934)

26. Sceptics (they are still around) Useless Arithmetic : Why environmental scientists can’t predict the future “ Predictions from any computer simulations of any complex reiterative dynamic process are not worth the binary code thet were written in, nor the supercomputer they were run on “ (Pilkey and Pilkey-Jarvis 2007)

27. Modelling in decision Support “ carnage by computer “ UK Foot and Mouth Disease (FMD) 2001 FMD epidemic in UK 10 million animals culled Public outrage Political Resolve – Never Again Culling policy was driven by unvalidated predictive models Models abused in the interests of scientific opportunism (Kitching et al. 2006)

28. One of the tools Mathematical models should be one of the tools available to policy makers. They are not a substitute for experience and expertise in the control of FMD. All theoretical models are only one aspect to providing good scientific advice (Kitching et al. 2006)

29. Management plans Some examples involving Ireland

30. The elephant in the room

31. Section 6 It’s the ecosystem - stupid ! “ Motherhood and apple pie “ Ireland – approach to EAF Complexcity and simplicity Modelling

32. “ Motherhood and Apple Pie “

33. Ireland – Crafting EAF - the Beaufort award Series of government awards Stimulate innovation and capacity building Areas of Socio - Economics, EAF, Biodiscovery, Genetics, Sensor Technology EAF project Linking expertise - third level + Marine Institute € 4 million over 7 Years (PI, Researchers, PhD) Use existing data expertise – link – “ joined up thinking “ Craft pragmatic management plans – evaluate performance Study areas – complex to simple Adaptive management Stakeholder participation Incentives – brand the seafood product Healthy Ecosystem - Healthy Resource - Healthy Profits

34. Complexity to simplicity (Beaufort, 2008)

35. (Beaufort, 2008)

36. F-PRESS (FAO, 2003)

37. Section 7 Some take home perspectives ISFMS – looming issues ICES Research Science – advice axis Its about managing people Silos, Cows and Communications

38. ICES Symposium on Fisheries Management Strategies Looming issue 1 Many case histories focus on stocks in or near crisis. “ Healthy stocks status - through sheer luck rather than because science and management strategies had forseen all the risks and planned successfully to avoid them.” “ We have no time to waste learning how to design, test, and implement robust and reliable management strategies for all fisheries and perhaps we should not concentrate on those few that keep grabbig the headlines “ (Rice and Connolly 2007)

39. ICES Symposium on Fisheries Management Strategies Looming issue 2 “ The global biodiversity – conservation agenda is moving swiftly into marine ecosystems. If we don’t fix the current problems of unsustainable fisheries relative to target species, the larger ecosystem, and dependent economies and communities quickly, new players with different perspectives and goals may seize control of much of the public dialogue and possibly the decision making “ (Rice and Connolly 2007)

40. ICES Symposium on Fisheries Management Strategies Looming issue 3 “ Yes we have collaboration between fisheries biologists and social scientists examining cases where management was failing. Cases with contested scientific advice, lack of commitment by fishers to measures imposed on them, and inedaquate communications among players. “ “ Social justice is a key determinant of government choices. Yet we do not know how to measure social justice in currencies that will fit comfortably into our modelling of management strategies.” (Rice and Connolly 2007)

41. Slow progress ? “ Successful fisheries management required many more jobs to be done than fisheries science had thought before, and all involved should go back to their laboratories and offices and get on with doing them “. (ICES Symposium, 1999) “ Successful fisheries management required many more jobs to be done than fisheries science has assumed, but that the proper fora needs to be created for all of us to get on with those together “. (ICES Symposium, 2007) (Rice and Connolly 2007)

42. Research results “ The details of research programmes often seem unattainable for the professional sector, being too complicated – too long and distanced from the problems of everyday work. “ “The driving force of European RTD is a policy led demand which has to be matched by the ability of the RTD Stakeholders to respond. More importantly, it is the adoption of the results – in the field – which has to be encouraged and this cannot be achieved without effective and efficient communications. “ (PROFET Workshop 2007)

43. ICES - reform of advice “ At lot done, a lot more to do “ Break down the ICES Silos Integrated Advice Stakeholders - Observers to Participators Communication of Advice Science - Advice Links Sociology ? Economics ?

44. For the future Managing Fisheries is Managing People “ We need to have more explicit social objectives to guide us in the trade offs between yield, employment and maintenance of pristine ecosystems. We need to devote much more effort to evaluating the performance of existing fisheries and their institutions.” (Hilborn, 2007)

45. For the future “ We need to move beyond the bad old days where we sit around the campfire and tell stories about how bad fisheries are and decide what we realy want from fisheries and see how these objectives have been obtained in current fisheries. “ (Hilborn, 2007)

46. Break down the silos • Build trust (The Speed of Trust, Covey 2006)

47. Remember the cows • Models help craft management plans

48. (3) Communications is key Is the message getting across ?

49. And finally - the model “ a model is a peculiar blend of fact and fantasy, of truths, half truths and falsehood. In some ways a model, may be reliable, in other ways only helpful and at times and in some respects thoroughly misleadinmg.” “ It is indeed, just as great a mistake to take the imperfections of our models too seriously as it is to ignore them altogether “… (Skellam, 1973)

50. The unknown As we know, There are known knowns. There are things we know we know. We also know There are known unknowns. That is to say We know there are some things We do not know. But there are also unknown unknowns, The ones we don't know We don't know. Rumsfeld, 2002