Climate, mountain ecosystems, and
disturbance across scales:
challenges for the next century
Don McKenzie
Pacific Wildland Fire Sciences Lab
US Forest Service
with assistance and inspiration from
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Craig Allen
Sam Cushman
Ze’ev Gedalof
Paul Hessburg
Phil Higuera
Lara Kellogg
Jeremy Littell
Carol Miller
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Max Moritz
Bruce Milne
Phil Mote
Ron Neilson
Dave Peterson
Steve Running
Nate Stephenson
Tom Veblen
Challenges
• Biophysical controls on
species distribution and
abundance
• Fire regimes in context
of climatic variability
• Linking drivers and
responses across
scales
Species distribution and abundance
• Conifer tree species of
western mountains
• West-east gradients
across Cascade Range
• Energy and water as
canonical limiting factors
High
Water
Energy
Low
Energy and water limited
•
•
Summer drought
Temperature increases possibly
associated with increased growth
1.4
PIPO
PSME
TSME
ABLA2
ABGR
ABAM
0.8
0.6
0.4
0.2
0.0
Proportion of total
1.0
1.2
Relative abundance of 6 key
species along a west-east gradient
in Washington State
MBS
Grizzly
Wenatchee
Okanogan
Colville
Br. A. Brousseau – St. Mary’s College
C. Webber – California Academy of Sciences
Mountain hemlock (Tsuga mertensiana) GLM
AUC = 0.907
R2 = 0.396
McKenzie et al. (2003)
Peterson & Peterson (2001)
Observed: association
between environmental
factors and species
distributions in eastside
forests, but not in westside
forests.
Working hypotheses
• Water-limited systems and energylimited ecosystems display different
dynamics.
• Abiotic controls on species in waterlimited ecosystems; biotic controls in
energy-limited ecosystems.
CCA - species/environment biplot – Wenatchee NF
R2
1
Axis 1
Axis 2
Axis 3
Total
TSHE
THPL
ABAM
tdayann
soilDD
baseflow
pptann
0
CHNO
Axis 2
aspect
TSME
0.106
0.057
0.009
0.172
PSME
ABGR
LAOC
PIPO
soilW
-1
PIEN
PICO
sradann
ABLA2
-2
PIAL
-1.5
-1.0
-0.5
0.0
Axis 1
0.5
1.0
1.5
CCA - species/environment biplot – Mt. Baker/Snoqualmie NF
0.4
0.6
R2
aspect
0.2
baseflow
Axis 1
Axis 2
Axis 3
Total
0.174
0.016
0.010
0.200
tdayann
0.0
soilDD
ABAM
PICO
soilW
-0.2
pptann ABGR
PIEN
TSME
-0.4
PSME
ABLA2
sradann
-0.6
Axis 2
THPL
TSHE
-1.5
-1.0
-0.5
0.0
Axis 1
0.5
1.0
1.5
TSME
Multivariate regression
(composition)
TSME
Water-limited (Wenatchee)
ABLA2
ABAM
ABLA2
ABAM
TSME
TSME
Energy-limited (MBS)
ABLA2
ABAM
ABLA2
ABAM
TSME
Generalized linear models
(composition)
TSME
Water-limited (Wenatchee)
ABLA2
ABAM
ABLA2
TSME
ABAM
TSME
Energy-limited (MBS)
Mean R2 = 0.25
ABLA2
ABAM
ABLA2
ABAM
Wenatchee NF
Mt. Baker/Snoqualmie NF
AUC (classification accuracy)
0.8
1.0
Relative performance of models
Energy-limited?
Abiotic
Biotic
R2
R2
R2
0.0
0.2
0.4
0.6
Water-limited?
GLMs
CCA
GLMs
(p/a)
(p/a + composition)
(composition)
Changing relationship over time between precipitation and growth
Precipitation coefficient
Long-term trend (due to gradual warming)
0
Energy-limited?
Decoupling
High variability, sensitive
to e.g., excessive snowpack
Water-limited?
Time
Very sensitive to
e.g., drought
Recoupling
Challenge #1
Dynamics of energy-limited vs.
water-limited systems
• Coupling climate,
vegetation, and terrain
(Milne et al. 2001).
• The “ecotone” (if any) of
limiting factors vs.
biolcimatic envelopes.
• Simulation vs. empirical
approaches.
Fire regimes in the context of climatic variability
“The historical dynamics of any real
landscape are one realization of a stochastic
process… we have access to only the single
series of fires that has actually occurred.”
Lertzman (1998)
Air quality in protected areas
Hai
Habitat
Fire-related issues
Exotics (e.g., Bromus tectorum)
Closed-canopy &
stable microclimate
Mixed-severity fire
Fire
Disturbance
synergy
Climate
Vegetation
25-100 yr
Climatic
change
100-500 yr
Habitat changes
Broad-scale homogeneity
Truncated succession
Loss of forest cover
Loss of refugia
Fire-adapted species
New fire regimes
More frequent fire
More extreme events
Greater area burned
Species responses
Fire-sensitive species
Annuals & weedy species
Specialists with restricted ranges
• Frequency
Mean
– Variance
– Synchrony
– Scale
–
• Severity
– Mean
– Variance
– Thresholds/extremes
Intuitively, there is an inverse relationship
• Extent
– Mean
– Variance
– Thresholds?
Intuitively, there is a
negative feedback
as extent reaches a
certain proportion
of available area.
N
Fire history sites in
eastern Washington
SOUTH DEEP
18 km
QUARTZITE
USFSService
Boundary
Forest
Boundary
Recorder Tree Locations
11 km
SWAUK
SWAUK
NILE CREEK
20 km
14 km
ENTIAT
ENTIAT
16 km
WMPI = 6.5
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Polygons in
Entiat watershed
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1700
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1750
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S22
S25
S23
R5
R7
R6
R24
R8
T2
S21
B6
T4
T3
S10
R21
R20
D2
B7
S11
S6
D4
B4
R2
S9
R3
B5
D1
S8
D6
T5
R4
S26
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1800
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1850
Composite
1900
Year
WMPI = 6.0
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Frequency
Synchrony
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1700
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1750
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1800
Year
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1850
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1900
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4-8
4-6
3A-3
KX3
4-7
4-5
4-15
3B-3
3A-1
2-2
4-17
4-11
3A-2
4-9
3D-2
1-3
4-10
4-2
4-12
4-3
3C-2
3C-1
3B-1
4-14
4-1
1-2
3D-1
2-4
2-3
3B-4
1-1
2-1
4-16
4-13
4-4
Composite
yg
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WMPI = 28.3
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Polygons in
South Deep
watershed
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1400
1500
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1700
d30
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a24
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a22
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a40
a27
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l8
k34
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a23
a41
a26
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1600
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r60
d23
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k35
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a21
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l6
l2
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1800
Composite
1900
ygYear
|
WMPI = 17.9
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Frequency
Synchrony
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1600
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r2
r3
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d1
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a11
k2
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a1
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d2
d11
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r5
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d5
k31
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d6
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k30
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d4
k1
k29
1700
1800
Year
1900
r15
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Composite
Vegetation transitions with increased mean fire frequency
Alder/ash
Aspen/birch
Spruce/hemlock
Cedar/hemlock/pine
Hemlock/Douglas-fir
Silver fir/Douglas-fir
Douglas-fir
Ponderosa pine
Mixed conifer
W. oakwoods
Aspen parkland
Redwood
Pinyon/juniper
Pine/cypress
Fir/spruce
Lodgepole pine
Alpine tundra
GrBasin pine
Short
grass
prairie
GrBasin shrub
Chaparral
Desert grass
Desert shrub
McKenzie et al. (1996)
Mesquite
Mixed
grass
prairie
McKenzie et al. (1996)
Pre-transition
Aggregated Küchler
Vegetation Types
Post-transition
Desert
Great Basin shrub
N. Floodplain
Shortgrass prairie
Desert grassland
Hemlock/Douglas-fir
Oak/juniper
Silver fir/Douglas-fir
Alder/ash
Desert shrub
Lodgepole pine
Pine/cypress
Spruce/hemlock
Alpine tundra
Douglas-fir
Mesquite savanna
Pinyon/juniper
Tallgrass prairie
Cedar/hemlock/pine
Grassland/wetland
Mixed conifer
Ponderosa pine
W. Fir/spruce
Chaparral
Great Basin pine
Mixed grass prairie
Redwood
W. Oakwoods
Projected increases in area burned -- Washington (McKenzie et al. 2004)
Projected increases in area burned -- Montana (McKenzie et al. 2004)
Climatic change and controls on fire
ENSO?
Climate
Temperature increases
Cerro Grande
Topography
Management
AREA BURNED (ha)
Fuels
12000
Nahuel Huapi N.P., Patagonia
8000
4000
0
1940
1960
YEAR
1980
2000
Challenge #2
Fire severity as a random variable
• Effects on ecosystem of:
– Change in the mean (e.g., New Mexico & Cerro Grande)
– Change in the variance (Clark 1996)
– Change in the extremes
• Thresholds representing points of no return
–
–
–
–
–
Forced by climatic change?
Alternative stable states or devolution of ecosystems?
“No modern analogue”
Coincidence of extreme events and climatic change
Examples of cheatgrass and buffelgrass
Linking drivers and response across scales
“___ controls ___ at multiple spatial and
temporal scales.”
Anonymous (200x)
“At middle scales we have a problem…”
• “…intractable to extend fine-scale mechanistic relationships
between organisms and their environments across space and
time…”
• “…regional- and continental-scale relationships lose
explanatory power due to increasing spatial and temporal
variance.”
Cushman and Littell (2005)
N
Fire history sites in
eastern Washington
SOUTH DEEP
18 km
QUARTZITE
USFSService
Boundary
Forest
Boundary
Recorder Tree Locations
11 km
SWAUK
SWAUK
NILE CREEK
20 km
14 km
ENTIAT
ENTIAT
16 km
Historical fire and climate across eastern Washington (Hessl et al. 2004)
1
12
1684-1900
r = -0.577
P < 0.001
10
0.5
8
0
6
-0.5
4
-1
2
-1.5
1650
1700
1750
1800
1850
Year
1900
1950
0
2000
10-yr Mean Percent Scarred
10-yr Mean Reconstructed PDSI
1684-1978
r = -0.375
P < 0.001
Watershed scale
Arrows =
ranges of
anisotropic
variograms
SWAUK
14 km
14 km
Large cluster groups (~ fire synchrony) cluster in homogeneous areas
Kellogg (2004)
Swauk Creek
600
800
200
400
600
Search radius (m)
Search radius (m)
Quartzite
South Deep
800
1.28
1.20
1.24
Weibull shape parameter
2.4
2.2
Weibull shape parameter
2.0
1.8
400
600
Search radius (m)
800
400
500
600
700
Search radius (m)
1.70
200
400
600
800
Search radius (m)
N.S.
200
1.65
1.55
1.6
400
2.6
200
?
1.60
1.8
1.9
2.0
Weibull shape parameter
2.1
1.75
2.2
Entiat River
1.7
Weibull shape parameter
2.0
1.9
1.8
1.7
1.6
1.5
Weibull shape parameter
2.1
Nile Creek
Changes in the (fire)
hazard function
associated with different
spatial scales of
analysis. Vertical lines
approximate range of
fuel constraint on fire.
800
McKenzie et al. (in review)
Complexity across scales in fire regimes
Fuel consumption
YEARS
STAND
Fuel (type, amount)
Fuel moisture
Physics of heat transfer
Fire behavior
slope
effects
Shading,
live
moisture
stress
Ppt, T, RH,
solar rad.
Topography
Ppt, T,
growing
season
Biomass
production
CENTURIES
Vegetation
T, wind
Weather
Topography
----------Climate Drought,
fire season
frequency, severity,
season, size, variability
severity,
size
Fire regime
Mortality, scorch, N mineralization, seedbed prep.
REGION
Carol Miller (2004)
Mountain landscapes and the middle-number problem
Scale mismatch between
drivers and responses
Multiple stochastic elements
with no equilibrium
Neilson, Lenihan, Bachelet, et al.
Stands
?
Landscapes
?
Continents
Linking drivers and responses
across scales
Challenge #3
• Scaling laws or scale invariance
– Disturbance event/area relationships (Falk, Miller,
McKenzie, et al. in prep.).
– Self-affine properties of drivers, responses, or both.
• Transfer functions
– Hierarchical modeling? (Cushman and McGarigal 2003)
– “Minimum-variance” aggregation (Rastetter et al. 1992)
• Examples
– Climate anomalies and disturbance regimes (Hessburg et
al. 2005).
– Linking forcing functions to scaling relations rather than
single-scale responses.
Thanks!
Discussion?