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Goreaud F. 1 , Lhuillier C. 1 , de Coligny F. 2

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

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Goreaud F. 1 , Lhuillier C. 1 , de Coligny F. 2

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

  2. Simulating mixed virtual stands with the "spatial" library in CAPSIS : Application to Samsara module. 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 ? 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. 11. Tree level models. • To manage more complex stands... ... we need new models.

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

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

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

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

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

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

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

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

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

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

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

  17. 14. Spatial structure & Forest modelling. • 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 ?

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

  19. 14. Spatial structure & Forest modelling. • 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

  20. 2. Pure & Regular stands.

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

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

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

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

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

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

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

  28. 100 0 L(r) 0 100 Aléatoire 4 Régulière 3 Agrégée 2 1 0 distance d'analyse r -1 10 20 30 40 50 -2 -3 -4 22. characterise the spatial structure. • Ripley's L(r) function

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

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

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

  32. 23. simulate virtual stands. • Results of simulations

  33. 3. Mixed & uneven-aged stands.

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

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

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

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

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

  39. 80 80 60 60 40 40 20 20 0 0 20 40 60 80 100 0 0 20 40 60 80 100 32. characterise the spatial structure. • different populations • define the populations (species, diameter) • characterise the structure of each population

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

  41. 32. characterise the spatial structure. • 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 CE12 index • Intertype L12(r) function

  42. 32. characterise the spatial structure. • Inter population CE12 index independance repulsion attraction CE12=3.36 CE=0.65 CE=1.89

  43. 32. characterise the spatial structure. • Intertype function L12(r) • relative location of type 1 / type 2 points

  44. independence 4 repulsion 3 attraction 2 L1.2(r) 1 0 -1 -2 range r -3 10 20 30 40 50 -4 32. characterise the spatial structure. • Intertype function L12(r) Repulsion independence attraction

  45. 32. characterise the spatial structure. • Intertype function L12(r)

  46. 33. simulate mixed virtual stands. • 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

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

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

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

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

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