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Combining historic growth and climate data to predict growth response to climate change in balsam fir in the Acadian Forest region
Elizabeth McGarriglePh.D. Candidate University of New Brunswick
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
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
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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
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
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
First Stages
•Initial screening of climate variables•Needed:
▫Growth summaries Limited to only plot intervals that are
aggrading▫Climate variable summaries
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)
Climate Data Summaries
•For each climate variable:▫Calculate mean periodic value for each plot▫Calculate 30 year climatic norms by plot
(1970-2000)
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 variables2. With only periodic climate variables
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
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
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