Overview
Forest ecosystem services
and climate change
in mountain regions
•  Forest ecosystem services – a European perspective?
•  Tools to assess future forest dynamics
•  Uncertainties
•  Two case studies with three models and four scenarios
•  Adaptive forest management
Harald Bugmann, Christof Bigler, Sebastian Leuzinger,
Annett Wolf, Che Elkin, Alvaro Gutierrez, Christian Temperli,
Corina Manusch & Livia Rasche
Forest Ecology, ETH Zürich, Switzerland
•  Conclusions
2
Ecosystem services framework
Forest ecosystem services
Resources
3
Millennium Ecosystem Assessment (2005)
4
Conservation
Water
Forest ecosystem services
Natural hazards
Carbon storage
Overview
(Tourism)
•  Forest ecosystem services – a European perspective?
•  Tools to assess future forest dynamics
•  Uncertainties
•  Two case studies with three models and four scenarios
•  Adaptive forest management
•  Conclusions
5
6
Forest succession models: approach
Forests don’t fit into greenhouses
•  Quantitative description of tree
population dynamics:
–  Establishment
–  Growth
–  Mortality
Images by speedtree
Spatial scale (m)!
•  Concept of small-scale mosaic of
successional patches (Gleason,
Botkin, Shugart):
so-called „Gap models“
•  Sensitive to climatic factors
7
Bugmann et al. (2000), Clim Change
8
•  Concept underlying most current
dynamic models of (potentially)
uneven-aged stands
Bugmann (2001), Clim Change
1. FORCLIM model
2. LPJ-GUESS model
•  Volume change of a tree:
Fagus
40
silvatica
T
Hm
30
H (m)
dV/dt = r · L – m · V
20
CO2
H 2O
measurement
10
•  Allometric relationships
Eq. 1 (parabolic)
Eq. 2 (asymptotic)
(D = tree diameter at breast height):
L = ƒ1(D)
V = ƒ2(H,D)
H = ƒ3(D)
0
0
10
20
30
D (cm)
40
50
• Carbon & water balance
• Vegetation distribution
at several spatial &
temporal scales:
“from leaf to ecosystem”
Dm
60
CO2
H 2O
H 2O
H 2O
CO2
CO2
•  ...from which follows (after some math):
dD = g · D · (1 – H ) · 1 · ƒ(e)
dt
Hmax
b(D)
exponential
asymptote
Soil processes
allometry environment
Moore (1989), Ecol Modelling
9
3. LandClim model
LandClim
Overview
Climate
•  Forest ecosystem services – a European perspective?
Fire
Vegetation (Gap model)
Recruitment
Growth
Mortality
•  Tools to assess future forest dynamics
Bark beetles
•  Uncertainties
Windthrow
Cell
Management
Dispersal
•  Two case studies with three models and four scenarios
Landscape
•  Adaptive forest management
Spatially explicit input
•
•
•
•
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Smith et al. (2001), Glob Ecol Biogeogr
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Elevation
Aspect
Slope
Soil AWC
•
•
•
•
•  Conclusions
Monthly climate
Browsing mask
Management masks
Land type mask
Schumacher et al. (2006), Landsc Ecol!
12
Types of uncertainty
Climate scenarios
•  Scenarios of climate change
•  Understanding of forest ecosystems (model formulations)
•  Derivation of indicators of ecosystem goods and services
•  Future management approaches (adaptive?)
… and others (with which I do not deal today)
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14
“Grow fast, die young”
•
Study of growth rate
vs. longevity:
– 3 species
– 2 continents
•
Data on maximum growth rate (at young age) and
maximum longevity of 141 temperate & boreal species
72 species, eastern North America
Maximum life expectancy (yrs)
•
Generalizing the finding
Negative exponential
relationships:
slope α in range
[-0.35…-0.64]
Slope A of the
relationship
600
y = 499.7e-0.0048x
500
R2 = 0.70
400
300
200
100
0
•
15
Implications?
e.g. CO2 fertilization,
long-term forest
dynamics & biomass?
0
50
100
150
200
250
300
350
400
450
500
Maximum growth rate (cm/yr)
•
Bigler & Veblen (2009), Oikos
16
Slope α (scaled to Bigler & Veblen units):
[-0.31...-0.61]
Bugmann & Bigler (2011), Oecologia
Forest succession models: mortality
Exploring the effect using FORCLIM
•  Combination of
•
•
–  “background“ mortality that is constant across tree life, tied to maximum tree age kAm):
small fraction of trees survives to kAm (“age-independent” mortality = AIM)
–  growth-related mortality (“stress-related” mortality = SM)
Net effect of growth stimulation vs. reduced longevity unknown
Simulation study at 6 sites along climate gradient
Davos
Change
in
total
biomass
(%)
Davos, change in total
aboveground
biomass
Mortality probability (yr-1)
!
•  Overall effect:
A005
A004
A003
G+10
•  CO2 fertilization:
G+20
slope of growth-age
relationship
A006
A000
G+30
growth
rate
–  Higher AIM (reduced longevity)
–  Reduced SM (higher growth rate)
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Bugmann (2001), Clim Change
Bugmann & Bigler (2011), Oecologia
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Exploring the effect using FORCLIM
21.0%-24.0%
18.0%-21.0%
15.0%-18.0%
12.0%-15.0%
9.0%-12.0%
6.0%-9.0%
3.0%-6.0%
0.0%-3.0%
-3.0%-0.0%
-6.0%--3.0%
-9.0%--6.0%
-12.0%--9.0%
-15.0%--12.0%
-18.0%--15.0%
-21.0%--18.0%
-24.0%--21.0%
Taken together...
•  Lack of growth-longevity relationship
& emphasis on “source limitation” (not shown here) …
…explain strong CO2 effect in “mechanistic” Dynamic Global
Vegetation Models (DGVMs)
•  Results averaged over all sites (multi-species case):
All sites, multi-species, Δbiomass!
most probable
value of slope
19
Bugmann & Bigler (2011), Oecologia
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(Short term) reality – (long-term) artefact?
Cramer et al. (2001), GCB
Indicators of ecosystem goods & services
•  Forest diversity
Shannon’s tree
species diversity
Example: Protection from rockfall
•  Forest protection
against gravitational
hazards
Shannon’s
structural
diversity
•
•
•
•
General protection
Avalanche
Rockfall
Landslip
Stand
Maturity
index
Habitat quality
(Umbrella species)
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22
Overview
Case study areas
Saas Valley
•  Forest ecosystem services – a European perspective?
•  Tools to assess future forest dynamics
•  Uncertainties
Dischma Valley
•  Two case studies with three models and four scenarios
•  Adaptive forest management
•  Conclusions
23
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Forest models and management scenarios
Forest ecosystem services
•  Forest models:
–  FORCLIM: forest stand dynamics
•
•
•
•
(e.g., Rasche et al. 2011, J Appl Ecol)
–  LPJ-GUESS: biogeochemistry
(e.g., Wolf et al. 2012, Clim Change)
–  LANDCLIM: landscape dynamics
•  Net Ecosystem Exchange (NEE) and Net Primary Productivity (NPP)
•  Ecosystem carbon storage
•  Runoff (pixel-based, no explicit water routing)
(e.g., Temperli et al. 2012, Ecol Appl)
•  Wildfire risk and impacts
•  Protection from rockfall and avalanches
•  Landscape diversity
•  Management scenarios:
–  Continuation of current management
(past-WWII)
–  Management parameters calibrated
across models to yield comparable results
Rasche et al. (2011), J Appl Ecol
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26
Climate scenarios
27
Forest biomass
Forest composition
Diversity (species, structure)
Harvestable timber
Results: Forest biomass
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LPJ-GUESS
Results: Forest composition
Results: Soil carbon
FORCLIM
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Results: Rockfall protection
ACQWA
LPJ-GUESS
30
Results: Harvestable biomass
+2°
A2
Saas valley,
Year 2100
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Strong decrease
Strong increase
LANDCLIM
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FORCLIM
Case study northern Black Forest
Stem number >16 cm DBH /ha
0
100
200
Overview
•  Forest ecosystem services – a European perspective?
•  Tools to assess future forest dynamics
•  Uncertainties
•  Two case studies with three models and four scenarios
Mixed forest
(equilibrium)
Even-aged spruce forest
(age 100)
DBH classes [cm]
DBH classes [cm]
•  Adaptive forest management
2 km
•  Conclusions
Elevation: 250 – 1050 m a.s.l.
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Local stand data courtesy of FVA
German national forest inventory from Yousefpour et al. (2010), Env. Management
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Adaptive forest management
Adaptive forest management
Management types
1.  No-change management: Traditions
Management type
Even-aged Norway spruce
No-change
(Business-As-Usual scenario in mid and upper part)
Uneven-aged
Managament decisions based on traditions
Does not consider climate change or uncertainties in general
2.  Reactive management: Current knowledge
Considers observed forest state and current knowledge
3.  Trend-adaptive management: Expert predictions
Takes observed forest state and expert predictions of climate
change into account
3 subtypes: 1. Adapts at one decision point
2. Adapts at multiple decision points
3. Considers change in climate change prediction
Mixed forest
Reactive
(Business-As-Usual scenario in lower part)
Natural Vegetation
–
Douglas/silver fir
Simple trend-adaptive (Subtype 1)
Mixed oak forest
Simple trend-adaptive (Subtype 1)
Species adaptation
No species adaptation
4.  Forward-looking adaptive management: Inherent Uncertainty
Douglas-fir / silver fir
Even-aged spruce
Timber production
Solves a stochastic dynamic programming problem to identify the
optimal decision
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Management scenario
!
Thorsen et al. (2010), MOTIVE Working Paper
Mixed oak forest
Mixed forest
Natural veg.
Biodiversity promotion
Ministerium Ländlicher Raum Baden-Württemberg, 1999, Richtlinien landesweiter Waldentwicklungstypen, Stuttgart
Dunker et al. 2007, Definition of forest management alternatives, EFORWOOD D2.1.3, Albert-Ludwigs-Universität, Freiburg
36 Spiecker et al. 2004, Norway Spruce Conversion: Options and Consequences, Brill Academic Publishers, Köln
Climatic change
1
0
1
Ecosystem services
Harvested volume and biodiversity (normalized)
Forest state
Simulation results
0
Simulation results
Management intensity
Temperli et al. (2012), Ecol Appl
37
!
Overview
Utility function:
!
•  Forest ecosystem services – a European perspective?
UTot = wT·UT + wEco·(UH + USMI)/2
wT = 1 – wEco
•  Tools to assess future forest dynamics
•  Uncertainties
Utility
Timber production
Trade-offs over time
wT
Biodiversity promotion
EGS
39
!
wT
!
Temperli et al. (2012), Ecol Appl
38
•  Two case studies with three models and four scenarios
•  Adaptive forest management
!
wT
•  Conclusions
40
Conclusions
•  Dynamic models are essential tools for assessing future ecosystem
service provision from mountain forests, but large uncertainties remain
Forest ecosystem services
and climate change
in mountain regions
•  The issue may be less whether processes are formulated “statistically” or
“mechanistically”, but rather whether we consider all relevant processes
•  Ensemble climate scenarios may smooth out important ecosystem
responses, and imply a false sense of certainty for impact models
•  Collapsing ecosystem states into indicators of ecosystem service
provisions is necessary step, but a slippery slope
•  For some ecosystem services in some parts of some landscapes, strongly
negative effects under a 2° scenario – but: also positive effects of CC!
Thank you for your attention!
•  Not even a 2° world is generally “safe” for mountain forests and the
multiple services they provide
=> Worrying! High sensitivity of mountain forests to climate change
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http://www.fe.ethz.ch
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