Transcript Combining historic growth and climate data to predict

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
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
T a (°C )
T a (°C )
00
-1
-1
GP
mo
PP
on
P (m
n th
(m o
o ll m
m -2 m
G
th ))
-5 0
200
-1 0 0
2004
2006
2007
2008
2009
-115500
-2 0 0
100
-2
-2-2
-1-1
GRPeP(m
(mo o
l m mmo o
nn
lm
thth ) )
2 500
-2 5 0
50
-3 0 0
-3 5 00
2-1
5 00
5 000
71
50
T a(m
(°C
In co m in g P A R
o l) m
1 02000
-2
-1
-10000
-1
-15500
-2
-20000
-2
-25500
-3
-30000
-3
-35500
0 .0
-250
1 23500
-1
0 .1
-100
0 .105
0 .2
10
0 .2
25
0
3
0 .3
30
-3
T are(°C
)
S o il m o istu
(cm
cm )
m o n th )
1 2 5 00
-1
m o n th )
0
-5 0
1000
-1 0 0
-2
750
-1 5 0
G P P (m o l m
-2
-1
In co m
m-1o)n th )
g P
(m-2o m
l mo n th
G in
PP
(mAoRl m
00
-2
-500
-5
-2
5 0000
-2 5 0
250
-3 0 0
-3 500
-200
-5 0
-1 0 0
-1 5 0
-2 0 0
-2 5 0
-3 0 0
-3 5 0
-1
2 500
500 0
15
00
7
12000 0
13205 0
(°Co)l m -2 m o n th -1 )
In co m in g P ATRa (m
0 .0 5
0 .1 0
0 .1 5
0 .2 0
0 .2 5
3
-3
S o il m o istu re (cm cm )
0 .3 0
20
20
2006
10
10
0
0
-1 0
-1 0
-2 0
-2 0
-3 0
-3 0
N E E (µ m o l m
-2
-1
s )
2004
1 /1
1 /3
1 /5
1 /7
1 /9
1 /1 1
1 /1
1 /1
1 /3
D a y o f ye a r
1 /9
1 /1 1
1 /1
1 /1 1
1 /1
20
2007
2008
10
10
0
0
-1 0
-1 0
-2 0
-2 0
-3 0
-3 0
N E E (µ m o l m
-2
-1
1 /7
D a y o f ye a r
20
s )
1 /5
1 /1
1 /3
1 /5
1 /7
1 /9
D a y o f ye a r
1 /1 1
1 /1
1 /1
1 /3
1 /5
1 /7
1 /9
D a y o f ye a r
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 (19702000)
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 variables
2. 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
Questions or Comments?
Funded by:
Natural Sciences and Engineering
Research Council of Canada
&
Canadian Forest Service