1. Combining historic growth and climate data to predict growth response to climate change in balsam fir in the Acadian Forest region Elizabeth McGarrigle Ph.D. Candidate University of New Brunswick

2. Acadian Forest Region Multi-species Complex stand structures Mixture of Northern hardwood species and boreal species Long history of selective cutting Because of species mixture and history of human disturbance, it is thought to be more sensitive to predicted climate change

3. Why balsam fir? Subject to cyclical catastrophic mortality due to spruce budworm Species at southern limit of range ▫Should be sensitive to climatic changes in the region ▫Predicted to be one of the most heavily impacted species in the Acadian Forest Fluxnet data shows a sensitivity to temperature

6. Project Overview Climatic variables predicted to change How to assess potential influence on future growth? Has climate influenced growth in the past? Identify climatic variables that influence growth Explore the changes of those climate variables in process-based model to create a growth surface Incorporate the growth surface into empirical growth and yield model

7. Sample Plot Locations

8. Permanent Sample Plots Network of plots across Nova Scotia (NS), New Brunswick (NB) and Newfoundland (NF) Earliest plots in NS – measurements dating back to 1965 3-5 year remeasurement periods Plots with greater than 75% basal area in balsam fir

9. Climate Data BIOCLIM/ANUCLIM – bioclimatic prediction system ▫Uses SEEDGROW to produce growing season information Inputs: Latitude/Longitude and digital elevation model for the region Outputs: ▫ Annual and monthly mean temperatures, precipitation. ▫Growing Season length and average temperatures

10. First Stages Initial screening of climate variables Needed: ▫Growth summaries  Limited to only plot intervals that are aggrading ▫Climate variable summaries

11. Growth Data Summaries Calculate basal area survivor growth for each tree ▫Sum by plot ▫Growth of surviving trees + ingrowth Calculate Leaf Area Index (LAI) Calculate growth efficiency (Survival growth/Leaf area) Other stand-level variables (initial basal area, average heights of tallest trees)

12. Range of Growth Efficiency & Survival Growth

13. Climate Data Summaries For each climate variable: ▫Calculate mean periodic value for each plot ▫Calculate 30 year climatic norms by plot (1970- 2000)

14. Range of Periodic and Climatic Normal Annual Temperatures

15. Screening Climate Variables Boosted regression used to identify variables with high relative influence on growth efficiency Two boosted regressions : 1.With both periodic and climate variables 2.With only periodic climate variables

16. Influential Variables

19. Points of Interest Yearly growth efficiency influenced more by climatic normals then periodic averages Growth efficiency levels off at higher temperatures ▫Decline eventually? What about variables that can be modeled directly by the process-based model? ▫Second boosted regression

20. Influential Variables

23. What Next? Second boosted regression gives variables that can be changed in a process-based model. Process-based model calibrated using: ▫Historical climate variables ▫Historical growth Change climate variables and record changes in growth from process-based model Forms a growth surface

24. After the Process-Based Model? Examine outputs on short and long term scales Incorporate growth surfaces into empirical model Repeat process for other commercial species and puckerbrush

25. Funded by: Natural Sciences and Engineering Research Council of Canada & Canadian Forest Service Questions or Comments?