Transcript Dynamics and Management of Alaska Boreal Forest

Dynamics of Alaska Boreal Forest
under Climate Change
Jingjing Liang a,
Mo Zhou a, Dave L. Verbyla b, Lianjun Zhang c,
Anna L. Springsteen d, Thomas Malone b
a Division of Forestry and Natural Resources, West Virginia University
b School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks,
c Department of Forest and Natural Resources Management, SUNY-ESF
d Scenarios Network for Alaska and Arctic Planning, University of Alaska Fairbanks
Alaska Boreal Forest
• Largest forest component in the
U.S. (500,000km2)
• A biome characterized by
coniferous forests
• Grow under the most severe
climate conditions in the world
• Forest industry is scarce, but with
great potentials
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Alaska Boreal Forest
4 Major species:
• Picea glauca (white spruce)
• Picea mariana (black spruce)
• Betula neoalaskana (Alaska birch)
• Populus tremuloides (quaking aspen)
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An Inconvenient Situation
• Global climate change is strengthened by human induced
greenhouse gas emissions (e.g. IPCC, 2007)
• Climate change is affecting forests around the world,
especially in the northern high latitudes (e.g. Serreze et al., 2000)
• Studies on the dynamics of Alaska boreal forest are
sporadic and rare (Wurtz et al., 2006 )
• Forest management in the region has been conducted in
the absence of a useful growth model (Alaska DNR, 2001)
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Objectives
• Develop a spatial-explicit and climate-sensitive matrix
model for ABF
• Verify model accuracy and compare it with other existing
models for the region
• Apply the model to map forest dynamics under three
IPCC climate change scenarios across the region
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Forest Inventory Data
Cooperative Alaska Forest Inventory (CAFI)
(Malone, Liang, and Packee, 2009)
• 1st inventory started in 1994
•
New plots added on an
annual basis
•
Established plots remeasured
with a 5-year interval
•
More than 100,000 tree
records from over 600 plots
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Forest Inventory Data
Tree-level variables
D
Diameter at breast height (cm) of a live tree
g
Diameter increment (cm)
m
Mortality rate of a live tree in a given period
Plot-level variables
R
Recruitment
N
Total number of trees per hectare
B
Stand basal area (m2ha-1)
z
Plot elevation (103m)
l
Plot slope (%)
a
Plot aspect
T
Mean growing season temperature (°C)
P
Total growth year precipitation (100mm)
λ
WGS84 Longitude (°)
φ
WGS84 Latitude (°)
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Climate Projection of ABF
(IPCC, 2001)
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• A2: High emission. Independently operating
and self-reliant nations, continuously
increasing populations, and regionally
oriented economic development
Temperature (°C)
Three IPCC Scenarios
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alternative energy technologies
B1
13
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• A1B: Medium emission. A more integrated
2020
2030
2040
2050
2060
2070
2080
2090
2100
2020
2030
2040
2050
2060
2070
2080
2090
2100
7.5
Precipitation (100mm)
• B1: Low Emission. Rapid adaptation of
A1B
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11
2010
world with rapid economic growth and a
balanced technological emphasis across all
sources
A2
7
6.5
6
5.5
5
2010
Year
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Methods: Climate-Sensitive Matrix Model
T: mean summer temperature
P: growth-year precipitation
y t 1 = G(Tt , Pt )  y t  R(Tt , Pt )
Tree growth depends largely on
temperature and soil water conditions of
the current year (Barnes et al. 1998. Forest Ecology
(4th ed.) )
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Methods: Mapping Forest Dynamics
• For each pixel s:
y t 1 (s)  G(s)y t (s)  R(s)  
• Initial stand conditions are
obtained from USGS DEM
and 2001 NLCD
• Projected dynamics of each
pixel is then aggregated to
map the entire region
(Liang and Zhou, 2010)
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Results: Climate-Induced Changes
Stem Density
% changes from the constant climate of the 397 sample plots under 3 IPCC emission scenarios
A2
A1B
A
B1
0
-5
-10
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Basal Area
B
0.0
-0.5
-1.0
-1.5
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
C
Diversity
5
3
1
-1
2000
2010
2020
2030
2040
2050
Year
2060
2070
2080
2090
2100
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Predicted 2100 ABF Vegetation under Climate Change
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Predicted ABF Basal Area Change 2000-2100
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Predicted ABF Diversity Change 2000-2100
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Results: Model Accuracy
1.5
1.5
RMSE(A)=1.42
RMSE(B)=1.47
RMSE(C)=1.66
birch
1
0.5
0.5
RMSE(A)=1.36
RMSE(B)=1.78
RMSE(C)=1.82
m2 / ha
1
aspen
0
0
0
1.5
5
10
15
20
25
w hite spruce
30
35
40
0
0.2
RMSE(A)=2.48
RMSE(B)=2.16
RMSE(C)=2.72
5
10
15
20
25
black spruce
30
35
40
RMSE(A)=0.33
RMSE(B)=0.42
RMSE(C)=0.33
m2 / ha
1
Observed
CSMatrix (A)
CTS
(B)
Conv.
(C)
0.1
0.5
0
0
0
5
10
15
20
Diameter (cm)
25
30
35
40
0
5
10
15
20
25
30
35
40
Diameter (cm)
Ref: CTS Model-Liang and Zhou, 2010; Conv. Model- Liang, 2010)
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Conclusion: Key Findings
• Basal area of ABF could continue to increase due to
natural succession
• Temperature-induced drought stress would hinder
the increase of basal area across the region,
especially in dry upland areas
• Climate change would boost stand diversity across
the region through transient species redistribution
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Conclusion: Model Limitations
• Sample range: 7-14°C. Extrapolation may be subject
to bias.
• Lack of control for major disturbances
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Acknowledgement
We thank Dr. Tara M. Barrett, Dr. Joseph Buongiorno, and Dr. David Valentine for their
helpful comments on this manuscript. The spatial analysis was assisted by the
Scenarios Network for Arctic Planning of the University of Alaska Fairbanks.
Contact Information:
Jingjing Liang
West Virginia University
http://jingjing.liang.forestry.wvu.edu/
Tel: 304-293-1577, Email: [email protected]
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Background: Forest Dynamics Modelling
Forest Matrix Model
•Superior Accuracy (Liang et al., 2005)
•Superior Applicability (Liang et al., 2006)
•Matrix model is constantly evolving
- Diversity effects (Liang et al., 2007)
- Geospatial trend (Liang and Zhou, 2010)
- Climatic effects (Liang et al., in review)
Ref: Liang et al., 2005. Canadian Journal of Forest Research 35: 2369-2382.
Liang et al., 2006. Forest Science 52(5): 579-594.
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Methods: Climate-Sensitive Matrix (CSMatrix) Model
Components
• Upgrowth:
g ij (s)  f1 ( Dij , B(s),  (T , P, s), (l ,  , z, s))
• Mortality:
mij (s)  f 2 ( Dij , B(s),  (T , P, s), (l ,  , z, s))
• Recruitment:
Ri (s)  f 3 ( Ni , B(s),  (T , P, s), (l ,  , z, s))
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Methods: Model Selection Criteria
• expected biological responses
• statistical significance
• contribution to the model goodness-of-fit (hierarchical
partitioning, see Chevan and Sutherland, 1991)
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