Talk given at Joined-Up Ecology workshop, at Microsoft, Cambridge:

1. Glued Ecology Bob O'Hara University of Helsinki Finland

2. All models are wrong, but some are useful G.E.P. Box

3. Where Statistics Fits in Data Theory Scientific Knowledge

4. Where Statistics Fits In Data Theory Scientific Knowledge

5. Approaches to Statistical Inference

6. Graphics and Summary Stats

7. Traditional Statistical Models http://www.vias.org/science_cartoons/

8. Mechanistic Modelling http://www.vias.org/science_cartoons/

9. Which One To Use? http://www.vias.org/science_cartoons/

10. Whichever Works Best! http://www.vias.org/science_cartoons/

11. Graphics and Summary Stats Should be simple and easy to follow

12. Traditional Statistical Models Forces the analysis into (flexible) boxes Fitting and interpretation Well understood

13. Mechanistic Modelling Model behaviour can be difficult to understand Closer link to theoretical models

14. Community Dynamics Real work done by Crispin Mutshinda-Mwanza

15. All Trees Are Equal

16. ... but should some be more equal than others?

17. Use Real Time Series Data

18. Discrete Time Neutral Theory n i,t – number of individuals of species i at time t N t – total community size at time t p i,t = n i,t / N t So,

19. In Other Words N t t p t N t t p t

20. Sampling Model True N Observed N q

21. Moth Data

22. Fit The Model...

23. Community Size Sampling Rate Immigration Rate

24. Impossible Results = possible range (0 to 1)

25. Directly fitting the model to data showed that it is wrong

26. What Next?

27. What Next? Add more mecahnisms!

28. The Environment

29. Competition

30. Other Interactions Between Individuals

31. A Gompertz Model log Abundances Growth rates Density Effects Environmental shocks

32. A

33. A Density Dependence

34. A Between-species Competition Density Dependence

35. e ~ N(0, V e )

36. Decompose the variation Interspecific variation: off-diagonal Intraspecific variation: leading diagonal Environmental variation

37. Data

38. Proportions of Environmental Variance Sampling variation also estimated

39. No Interspecific Interactions Largest Bayes Factor 1.25 <1: evidence against an effect 1 - 3: “Not worth more than a bare mention”

40. Environmental Correlations

41. Summary None Some Lots

42. What This Means

43. Models and data are brought closer together

44. How the data were collected is important

45. There are usually more things affecting the data than are in the model

46. The models that are fitted usually contain some traditional statistical components

47. Thoughts on Bayes

48. He's getting popular Data from Web of Science Number of papers with ”Bayes*”/Number of papers with ”Statisti*”

49. He's Flexible

50. But he can be difficult

51. What is more exciting is the models we can fit, not the methods we use to fit them