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

Sample Plot Locations

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)

Range of Growth Efficiency & Survival Growth

Climate Data Summaries

•For each climate variable:▫Calculate mean periodic value for each plot▫Calculate 30 year climatic norms by plot

(1970-2000)

Range of Periodic and Climatic Normal Annual Temperatures

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

Influential Variables

Influential Variables

Influential 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

Influential Variables

Influential Variables

Influential Variables

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

Funded by:

Natural Sciences and Engineering

Research Council of Canada

&

Canadian Forest Service

Questions or Comments?