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

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

Cumulative Volume Killed in All 'Pine' Units year timber volume ( 's of m^3) Projected Kill Observed Kill Millions of m 3 ~ 80% Dendroctonus ponderosae Cumulative Volume Killed on the Timber Harvesting Landbase Source: BC MoF, 2005

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

Candidate Growth Models: 1) SORTIE-ND Forest Ecology Model 2) Prognosis BC Forest Management Tool

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

Project Specific Prognosis BC Advantages 1) Calibrated using local data 2) Designed for complex, mixed stands 3) Includes Site factors – transportable 4) Government supported model

PrognosisBC Project Disadvantages 1) Poor results with Regeneration Submodel 2) No Post-MPB specific Mortality Submodel 3) Not Spatially Explicit (i.e., Clumped vs. Even distribution)

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

SORTIE Project Specific Advantages: 1. Episodic Mortality Behaviour 2. One year cycles for simulated runs 3. Post-MPB specific snag fall down function 4. Light mediated model

Project Specific Disadvantages: 1.Has not been calibrated for study area 2.Less precision in G & Y estimates 3.Over-simplified crown allometry 4. Used Less (?)

Hybrid Model (SORTIE + Prognosis BC ) Advantages of Hybrid Approach: 1) Natural Regen Following MPB – Dynamic - Process-based Model 2) Tree Growth through Empirical Model 3) Uses Existing Models

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

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

Ht Class = cm Observed Stems Per Hectare Predicted SPH Lodgepole Pine Other Conifers Deciduous trees. a) Ht Class = Observed Stems Per Hectare Predicted SPH c) Lodgepole Pine Other Conifers Deciduous trees.

Modifications to SORTIE-ND 1. Bath seed rain function 2. Height/DBH allometry 3. Light-dependent mortality 4. Crown allometry

Crown allometry Crown Ratio (CR): X e a RC    1 ˆ

Crown Allometry Results ElevationgSlopefSPHeCCFd HtcDHb CR          ln / 

Next Steps for the Hybrid Model 1. Crown Width Model 2. Other SORTIE-ND parameter adjustments Using new dataset 3. Identification of ‘Hand-off’ point 4. Efficient Linkage (SORTIE to Prognosis)

Outstanding Questions 1. How to determine hand-off point between SORTIE-ND and PrognosisBC? 2. Does the Hybrid Model improve upon MSN results? 3. Does the Hybrid Model improve upon SORTIE alone, Prognosis alone? How to test this?

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 ScottDonald Robinson, ESSA