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Modelling Natural Regeneration in Mountain Pine Beetle Affected Stands A Hybrid Model Approach

This study explores a hybrid model approach combining SORTIE-ND and PrognosisBC models to assess natural regeneration in mountain pine beetle-affected stands. The preliminary results show promising improvements compared to existing models.

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Modelling Natural Regeneration in Mountain Pine Beetle Affected Stands A Hybrid Model Approach

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  1. Modelling Natural Regeneration in Mountain Pine Beetle Affected StandsA Hybrid Model Approach Derek Sattler, M.Sc. Candidate Faculty of Forestry. University of British Columbia, Vancouver, Canada.

  2. Mountain Pine Beetle (MPB) Epidemic Lodgepole Pine (Pinus contorta var. latifolia)

  3. Cumulative Volume Killed in All 'Pine' Units 1000 750 timber volume (1000000's of m^3) 500 250 0 2000 2005 2010 2015 2020 2025 year Cumulative Volume Killed on the Timber Harvesting Landbase ~ 80% Millions of m3 Projected Kill Dendroctonus ponderosae Observed Kill Source: BC MoF, 2005

  4. Stand Dynamics Post-MPB Attack • Highly variable snag fall rates (5 – 15 years) • Expect to see small tree release • Changing light dynamics • 15-20 year regeneration delay Challenge to model regeneration • Uncertainty in Yield Projections

  5. Candidate Growth Models: 1) SORTIE-ND • Forest Ecology Model 2) PrognosisBC • Forest Management Tool

  6. PROGNOSISBC Model Flow Input data: tree list, site info smoothing Small trees Ht then DBH growth Large Trees DBH then Ht growth Change in Crown Mortality Competition, dbh, etc Thinning Regeneration results

  7. Project Specific PrognosisBC Advantages • Calibrated using local data • Designed for complex, mixed stands • Includes Site factors – transportable • Government supported model

  8. PrognosisBC Project Disadvantages • Poor results with Regeneration Submodel • No Post-MPB specific Mortality Submodel • Not Spatially Explicit (i.e., Clumped vs. Even distribution)

  9. SORTIEND Model Flow Input data: tree list, location Stem Map Light Seedling/Saplings Diameter then Ht Large Trees DBH then Ht growth Change crown size Thinning Mortality Regeneration results

  10. SORTIE Project Specific Advantages: • Episodic Mortality Behaviour • One year cycles for simulated runs • Post-MPB specific snag fall down function • Light mediated model

  11. Project Specific Disadvantages: • Has not been calibrated for study area • Less precision in G & Y estimates • Over-simplified crown allometry 4. Used Less (?)

  12. Hybrid Model (SORTIE + PrognosisBC) Advantages of Hybrid Approach: • Natural Regen Following MPB – Dynamic - Process-based Model • Tree Growth through Empirical Model • Uses Existing Models

  13. Hybrid Model Flow Sortie-ND New O/S + U/S tree list following simulation New Seedlings Sortie-ND O/S + U/S tree list (from field data) - Defined by ? Time 2 (Post MPB attack) Time 3 Time 1 (MPB attack) PrognosisBC O/S + U/S tree list (from field data) PrognosisBC New O/S+ U/S tree list following projection Imputation from SORTIE PrognosisBC O/S + U/S + New Seedlings projected in Prognosis Regeneration submodel ‘off’

  14. Preliminary Results • Tested SORTIE-ND using CFS data (R. Scott) (1987, 2001) • SORTIE behaviour selection: O/S + U/S + Initial Mortality + Subsequent Mortality • Non-spatial Seed dispersal • Number of Seeds = f (Basal Area parent trees) • Proportional Seedling Establishment • Light dependent mortality

  15. a) Ht Class = 0.1-0.5cm 8000 6000 Predicted SPH Lodgepole Pine 4000 Other Conifers Deciduous trees. 2000 0 0 2000 4000 6000 8000 Observed Stems Per Hectare c) Ht Class = 1.0-1.5 1500 Predicted SPH 1000 Lodgepole Pine Other Conifers 500 Deciduous trees. 0 0 500 1000 1500 Observed Stems Per Hectare

  16. Modifications to SORTIE-ND • Bath seed rain function • Height/DBH allometry • Light-dependent mortality • Crown allometry

  17. a ˆ = C R - b + X 1 e Crown allometry Crown Ratio (CR):

  18. æ ö 1 - = b + ´ + ´ + ç ÷ ln 1 b H / D c Ht CR 0 è ø ´ + ´ + ´ + ´ d ln CCF e SPH f Slope g Elevation Crown Allometry Results

  19. Next Steps for the Hybrid Model • Crown Width Model • Other SORTIE-ND parameter adjustments • Using new dataset • Identification of ‘Hand-off’ point • Efficient Linkage (SORTIE to Prognosis)

  20. Outstanding Questions • How to determine hand-off point between SORTIE-ND and PrognosisBC? • Does the Hybrid Model improve upon MSN results? • Does the Hybrid Model improve upon SORTIE alone, Prognosis alone? • How to test this?

  21. Acknowledgments Data For Preliminary Analyses: Natural Resource Canada (Brad Hawkes) - MBPI Funding: British Columbia Forest Science Program Supervisor: Dr. Valerie LeMay Committee Members: Peter Marshall, Bruce Larson, Dave Coates Preliminary Analysis:Prognosis Technical Support: Robyn Scott Donald Robinson, ESSA

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