1 / 36

Modelling Mixedwoods at the Whole-Stand Level

Modelling Mixedwoods at the Whole-Stand Level. Oscar Garc í a University of Northern British Columbia. Outline. Why?? Spatial structure Initial state info Predictability How? Canopy driven Species layers, interaction Model structure. Why??!. Spatial structure.

michaelhamilton
Download Presentation

Modelling Mixedwoods at the Whole-Stand Level

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Modelling Mixedwoods at the Whole-Stand Level Oscar García University of Northern British Columbia

  2. Outline • Why?? • Spatial structure • Initial state info • Predictability • How? • Canopy driven • Species layers, interaction • Model structure

  3. Why??!

  4. Spatial structure Tree sizes not random on the ground: • Competition  neighbours more different • Micro-site  neighbours more similar  Size distribution properties change with area

  5. Spatial structure Expected dbh variance in a circular plot

  6. Initial state estimates Samples of 50

  7. Limits to predictability

  8. Limits to predictability

  9. Limits to predictability

  10. Limits to predictability

  11. Limits to predictability

  12. Limits to predictability

  13. Limits to predictability

  14. Aggregation PV = kT d2x / dt2 = F/m (Newton, 1687)

  15. Understanding, prediction Explain Predict?

  16. Simulation Prediction?

  17. Predictability

  18. Complex systems requiresimple models

  19. Mixedwoods

  20. Individual-tree? Tree-level model tree list tree list

  21. Individual-tree? Tree-level model tree list tree list (B,N,H) Inventory

  22. Individual-tree? Tree-level model tree list tree list (B,N,H) (B,N,H) Inventory Application

  23. Individual-tree? Tree-level model tree list tree list Stand-level model (B,N,H) (B,N,H) Inventory Application

  24. Whole-stand • Mix species, uneven-aged: Eg. Moser • What does the xylem have to do with it? • Allometry, or lack of it • “Top down”

  25. Aspen-spruce

  26. Aggregated (whole-stand) Aspen foliage Aspen wood Spruce foliage Spruce wood

  27. Mechanism Interceptance (%) Volume (m3/ha)

  28. Site quality

  29. Site quality

  30. Gross increment

  31. Resource capture • Closure  Amount of foliage, relative to maximum • Occupancy (R)  Interceptance, relative to maximum R Closure R = 1- (1- C)2.4

  32. Simplest models • First approximation: dV/dt = a (closed canopy, no mortality) • Including open, no mortality : dV/dt = a R dR/dt = b (1 – R) or b R(1 – R) • Mortality: dN/dt = - c RN dV/dt = a R - Vmort Vmort = (mean V of dead) (- dN/dt) = - k N-1/2 V/N dN/dt

  33. Conclusions • Predicting behaviour of individual trees may be hopeless • Not necessary • No dbh-driven modelling • More research is needed

More Related