1. Goreaud F. 1, Lhuillier C. 1, de Coligny F. 2 CAPSIS Meeting, 28/06/2006 - Montpellier Simulating mixed virtual stands with the "spatial" library in CAPSIS : Application to Samsara module. 1 CEMAGREF - LISC, Clermont Ferrand. 2 INRA - AMAP, Montpellier.

2. 1. Why a spatial library in CAPSIS ? 2. pure and regular stands. 3. mixed and uneven-aged stands. 4. Application to Samsara. 5. Next steps ? Simulating mixed virtual stands with the "spatial" library in CAPSIS : Application to Samsara module. CAPSIS Meeting, 28/06/2006 - Montpellier

3. Attention Ce diaporama est un support de cours prévu pour être accompagné d'explications orales. Si vous n'avez pas assisté à l'exposé, la lecture des diapositives seules peut vous amener à faire des contresens.

4. 1. Why a spatial library ?

5. To manage more complex stands... 11. Tree level models.... we need new models.

6. modelling the growth of each tree : (x,y) 11. Tree level models.

7. take spatial structure into account –location of each individuals (x,y) 11. Tree level models.

8. (x,y) take spatial structure into account –location of each individuals –local neighbourhood & dynamics –relevant for complex systems 11. Tree level models.

9. (x,y) Structure characterises neighbourhood : Spatial Structure Size Structure 12. Spatial structure & Dynamics.

10. Spatial Structure Size Structure neighbourhood 12. Spatial structure & Dynamics. Structure characterises neighbourhood :

11. Neighbourhood influences dynamics : Spatial Structure Size Structure neighbourhood Natural processes (growth, regeneration, death) or anthropic actions individual growth Competition index 12. Spatial structure & Dynamics.

12. Dynamics modifies structure : Spatial Structure Size Structure neighbourhood Natural processes (growth, regeneration, death) or anthropic actions individual growth Competition index 12. Spatial structure & Dynamics.

13. 13. Initial state of the simulations. IBMs require detailed initial states.. –model the evolution of each individual IBM (tree level) initial state individual data results of the model time

14. Individual data are not always available –managers only have aggregated data IBM (tree level) initial state individual data Aggregated data (N, G, V,...) 13. Initial state of the simulations.

15. IBMs can not be used with aggregated data Aggregated data (N, G, V,...) IBM (tree level) 13. Initial state of the simulations.

16. We need to simulate detailed initial state –Concept of Virtual Stand Aggregated data (N, G, V,...) IBM (tree level) simulated initial structure (virtual stand) results of the model time 13. Initial state of the simulations.

17. Some crucial issues : –how to describe the structure of a stand ? –how to simulate it, esp. initial states ? –how does it influence the dynamics ? –how to take it into account in the models ? 14. Spatial structure & Forest modelling.

18. The needs for spatial.lib in CAPSIS? –characterise the spatial structure of a stand at a step through time 14. Spatial structure & Forest modelling.

19. The needs for spatial.lib in CAPSIS? –characterise the spatial structure of a stand at a step through time –simulate initial states of various structure to replace missing data to solve scale incompatibilities for sensitivity analysis 14. Spatial structure & Forest modelling.

20. 2. Pure & Regular stands.

21. From forest stand to point pattern –each tree = one point 21. General principle. location of trees point pattern

22. From forest stand to point pattern –each tree = one point –only one population = set of points 21. General principle. location of trees point pattern

23. only one population = set of points There exist many methods... 2 are implemented in spatial.lib : –Clark & Evans index –Ripley's L(r) function 22. characterise the spatial structure.

24. Clark & Evans index –distance to nearest neighbour. 22. characterise the spatial structure.

25. Clark & Evans index Regular Random Clumped CE=1.44 CE=1.05 CE=0.57 22. characterise the spatial structure.

26. Clark & Evans index –evolution through time... 22. characterise the spatial structure.

27. Ripley's L(r) function –number of neighbours at distance <r. 22. characterise the spatial structure.

28. Ripley's L(r) function 0 100 0 Aléatoire Régulière Agrégée -4 -3 -2 0 1 2 3 4 1020304050 L(r) distance d'analyse r 22. characterise the spatial structure.

29. Ripley's L(r) function 22. characterise the spatial structure.

30. only one population = set of points using point processes implemented in spatial.lib : –interfaces to define parameters –various point processes : Poisson, Neyman-Scott, Gibbs 23. simulate virtual stands.

31. Interfaces to define parameters 23. simulate virtual stands.

32. Results of simulations 23. simulate virtual stands.

33. 3. Mixed & uneven-aged stands.

34. From forest stand to point pattern –each tree = one point 31. General principle. location of trees point pattern

35. From forest stand to point pattern –each tree = one point –different populations = different sets of points location of trees 31. General principle. marked point pattern

36. High variability of spatial structure : Independance Repulsion Attraction 31. General principle.

37. two strata Selection stand 31. General principle. High variability of spatial structure :

38. different populations –define the populations (species, diameter) 32. characterise the spatial structure.

39. different populations –define the populations (species, diameter) –characterise the structure of each population 32. characterise the spatial structure. 0 20 40 60 80 020406080100 0 20 40 60 80 020406080100

40. different populations –define the populations (species, diameter) –characterise the structure of each population –and the relative structure between populations 32. characterise the spatial structure.

41. different populations –define the populations (species, diameter) –characterise the structure of each population –and the relative structure between populations 2 additional methods in spatial.lib : –Inter population CE 12 index –Intertype L 12 (r) function 32. characterise the spatial structure.

42. Inter population CE 12 index attraction independance repulsion CE=0.65 CE=1.89 CE 12 =3.36 32. characterise the spatial structure.

43. Intertype function L 12 (r) –relative location of type 1 / type 2 points 32. characterise the spatial structure.

44. Repulsionindependenceattraction 32. characterise the spatial structure. Intertype function L 12 (r)

45. 32. characterise the spatial structure.

46. different populations = N sets of points using point processes implemented in spatial.lib : –interfaces to define parameters –various point processes : Poisson, Neyman-Scott, Gibbs Intertype Gibbs processes 33. simulate mixed virtual stands.

47. We have to simulate different sets of points –successively, from oldest to youngest 33. simulate mixed virtual stands.

48. Interfaces to define parameters 33. simulate mixed virtual stands. Number of populations

49. For each population : 33. simulate mixed virtual stands. species own independent spatial structure

50. For each population : 33. simulate mixed virtual stands. species dependent spatial structure

51. Simulation result : 33. simulate mixed virtual stands.

52. 4. Application to Samsara : Influence of intertype initial spatial structure on the dynamics of mixed Spruce Fir stands. Corentin Lhuillier 2006

53. Simulating Spruce-Fir mixed stands –with Samsara in CAPSIS (Courbaud B.) 41. General principle. InitialisationEvolution Silviculture Operation Loading stand Creating plot Creating rays Growth dea th regeneration recruitement lightning

54. Simulating Spruce-Fir mixed stands –with Samsara in CAPSIS (Courbaud B.) –there is a sensibility to initial spatial structure with pure regular stands (see 2005) –what about initial mixed stands ? 41. General principle.

55. Using a real 1ha mixed plot (Saisie) –375 spruce –53 fir 41. General principle.

56. Changing the spatial structure –of spruce –or of fir 41. General principle.

57. Using mixed virtual stands simulations 41. General principle.

58. Spruce regeneration 42. Results.

59. Fir regeneration 42. Results.

60. Basal area for Spruce 42. Results.

61. Basal area for Fir 42. Results.

62. 5. Next steps ?

63. improve spatial.lib –a new improved version –some options to add –easy to use interfaces ? –new tools could be developed 5. Next steps ?

64. integrate these tools in your modules –to simulate initial states –as with Samsara & Mountain –specific interfaces 5. Next steps ?

65. simulate REALISTIC virtual stands –similar to real stands ? –because models are sensitive to initial structure –unrealistic pattern => false results –Thesis of M.A. Ngo Bieng 5. Next steps ?

66. spatial.lib now has the tools for you to simulate complex initial stands ! Conclusion