strengthening the links between climate and succession in forest landscape models eric j. gustafson...
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Strengthening the links between climate and succession in forest
landscape models
Eric J. GustafsonUSDA Forest Service
Northern Research StationRhinelander, Wisconsin
Rhinelander, Wisconsin
Acknowledgments
Brian R. SturtevantUSDA Forest Service
Northern Research StationRhinelander, Wisconsin
Arjan M.G. De BruijnPurdue University &
Northern Research StationRhinelander, Wisconsin
The Forest Manager’s Challenge
• Predict the long-term effects of management actions at landscape scale– Accounting for complex interactions among multiple disturbances,
management actions and global changes– Accounting for spatial interactions and interactions among ecological
processes– Objectively comparing proposed alternative management strategies
over large temporal and spatial scales
• Yet much of the empirical research we now use to make management decisions was conducted under environmental conditions that no longer exist
• Many of the models used to support management decisions have functions based on past system behavior
Illustration: Predicting global change effects in Siberia
• Example of the use of a FLM to answer questions about global change and forest dynamics
• Show elements of FLM in action
Brief background
• Boreal forest - only 7 tree species• Fires are not suppressed• Timber harvest has only recently occurred
in the southern part of the study area• There is little insect-caused mortality of
trees• Climate has already warmed by about 1.5o
C since 1960, and is expected to warm by an additional 5o C by 2100
Initial conditions
Dominance determined by relative age (age/longevity)
Dominance determined by relative age (age/longevity)
Dominance determined by relative age (age/longevity)
What is wrong with this approach?
• Although useful, this study is unsatisfying because most of the links between climate and ecosystem response are general and approximate
• Will cause-effect relationships discovered under past conditions still hold in the future?
A new, emerging problem• For example, forest growth
and yield models are based on empirical relationships measured nearly a century ago when CO2 and temperatures were lower– The Aspen-FACE
experiment suggests forest growth in the Midwest may be up to 20% higher in the current century due to CO2 fertilization
Can the past still be used to predict the future?
• Will successional pathways (sequence, timing and likelihood) remain the same under climate change?
• Will community assemblages stay the same or will they re-assemble?
• How will species competition be affected? (winners and losers)
• How will disturbance regimes (rotation intervals, event size and severity) change?
• Will the effect of increased stressors (e.g., moisture stress, ozone pollution) be linear or non-linear or synergistic?
• Will the interactions among ecological processes change?
Illustration (#1) of problem with empirical approach
• Gustafson and Sturtevant
(2013) modeled drought-
induced tree mortality on
landscapes using statistical
relationships between length of
droughts and proportion of
cohort biomass lost to mortality
• Empirical models were
estimated using Midwest
climate and FIA data from the
1960s-2010
Drought index
Gustafson, E.J., B.R. Sturtevant. 2013. Modeling forest mortality caused by drought stress: implications for climate change. Ecosystems 16:60-74.
Illustration (#1) of problem with empirical approach
• How confident can we be that
those statistical relationships
between precipitation and
mortality will hold into the
future?
• Precipitation is not projected to
change dramatically in the
upper Midwest, but
temperatures are expected to
rise – More evapotranspiration will result
in considerably more moisture stress
under the same precipitation regime
Illustration (#2) of problem
• Most FLMs model succession using probabilities of transition
from one forest type to another– e.g., RMLANDS, LANDSUM, SIMPPLLE, VDDT/TELSA, BFOLDS
• How can we know what these probabilities will be under climate
change? – Will existing forest types persist, or will community assemblages
evolve?
– Will the timing and ultimate outcomes of successional trajectories
change?
• These questions are very difficult to answer without explicitly
considering the mechanisms by which succession plays out over
time
Can the past still be used to predict the future?
• THESIS: because all these features of forested ecosystems are likely to change, relationships derived empirically in the past will be of limited use for predicting the future
• However, our knowledge of processes with fundamental drivers (weather, atmospheric composition, competition for light and water, etc.) allows us to model the processes as a function of the drivers
Can the past still be used to predict the future?
• Therefore, FLMs should evolve to – reduce the use of relationships derived
empirically in the past where feasible– increase the use of mechanistic methods that
rely on fundamental drivers (first principles)• Caveat: there is a perennial tension for
modelers between simplifying assumptions to allow broad scales to be modeled and mechanistic detail
– this remains an issue to be managed
What might this new approach look like?
1. Ideally, it should explicitly link all aspects of system behavior to variation in the fundamental drivers that are changing (e.g., temperature, moisture, light, CO2 concentration, limiting factors such as pollutants and invaders)
– direct links can simulate processes based on “first principles” of physiology and biophysics
– indirect links can simplify simulation of a process or use a defensible extrapolation of empirical relationships
What might this new approach look like?
2. Interactions among ecological processes should rarely be specified based on expectations derived from the past
– the nature of interactions is usually a big unknown under any conditions• models should be designed so that processes can
interact based on system drivers and system state• the system outcome should be an emergent property
of interactions of independent process that are directly linked (often mechanistically) to the drivers
What might this new approach look like?
3. All the ecological processes necessary to ensure that model projections are realistic and reliable should be included
– Many FLMs were developed to answer focused research questions• specific processes of interest were included and all
the other processes were assumed to be held constant
• this is valid for research purposes, but can be problematic when such models are applied to make projections of actual future forest conditions (dynamics) to inform management decisions
Where do we stand?
• Currently, no FLM achieves these ideals– most FLMs are being updated to include
environmental drivers that are changing, but most such modifications are thus far crude and do not take the plunge into the “first principles” approach
– drivers such as temperature and precipitation are being added, but others such as relative humidity, PAR and CO2 and ozone concentrations are lagging
– Simulation of biological invaders that are competitors rather than disturbance agents is very rare
Where do we stand?
• Most FLMs do currently simulate forest dynamics as an emergent property of interacting independently modeled processes acting on various ecosystem variables (e.g., vegetation type, fuel class, habitat type)– However, the degree to which the processes can
produce novel system states and behavior varies considerably
– Model data structure and process design must have sufficient degrees of freedom to allow all plausible future system states to occur
LANDIS
• The LANDIS family of FLMs has some important advantages– Succession is not deterministic– Species assemblages are very dynamic– It is fundamentally process-based– Its design allows great flexibility to increase
mechanistic components– New landscape-structuring process
(disturbance) components can easily be added
Where do we stand?
• Potentially important landscape-structuring processes still in need of development include: – ungulate herbivory– beaver activity– exotic earthworms– competitive invaders – disruptive weather events
(e.g., early thaw or late frost)
Important Caveat
• A truly robust mechanistic approach is not possible at landscape scales because ultimate reductionism to the molecular level is intractable, uncertain and undesirable
• Furthermore, the idea that we might actually be able to correctly model multiple complex processes mechanistically and have their combined behavior reliably reflect a future reality without being able to test that assertion is ridiculous
• Therefore, compromises must be made– I believe that the best solution is to blend mechanistic and
phenomenological approaches in a way that maximizes the use of mechanisms (especially where novel driver conditions are expected) while achieving modeling objectives and keeping the model tractable
What is wrong with this approach?
Going back to the Siberia study, here are some important limitations of that study• Climate change only affected:
– Mean productivity rates of species – not dynamic
– Mean rate of fire spread and average length of fire season (+5%)
– Incidence of insect outbreak (yes or no)
• No response to extreme weather cycles, which can be extremely important for forest dynamics
• Feedbacks (interactions) among temperature, precipitation, growth, stress, disturbance regimes very weak
• No CO2 effects (+20%?)
New capabilities for the LANDIS Forest Landscape Model
• The PnET-Succession extension of LANDIS-II uses first principles to simulate growth and competition as a function of available light and water (using functions from PnET-II)– Simulates photosynthesis, which first depends on soil
water, which varies with monthly precipitation and consumption by cohorts
– When water is adequate, the rate of photosynthesis:• increases with light available to the cohort (dependent on
canopy position and leaf area), atmospheric CO2 concentration and foliar N
• decreases with age and departure from optimal temperature
PnET-Succession
• Cohort death is mechanistic rather than deterministic– Photosynthesis declines with age,
and respiration eventually exceeds productivity, resulting in death by senescence
– Death by competition occurs when productivity declines due to shading or inability to compete for water
– Death by drought occurs when carbon reserves are depleted Breshears et al 2009
• Cohort establishment probability similarly depends on the light and water available during the time step
PnET-Succession
• This first-principles approach offers important advantages for global change research– self-thinning of cohorts automatically occurs when light or
water availability is too low– drought mortality is a consequence of exhaustion of carbon
reserves as photosynthesis becomes water limited
– CO2 effects on growth are included, including its moderation of drought effects (increased WUE)
– temperature increases evapotranspiration and respiration, and therefore moisture stress
– combined effects of weather on competition, mortality and establishment are simulated dynamically at each time step
– weather variability and extremes are easily included
Predicting drought effects • Field precipitation manipulation experiment in a
piñon-juniper ecosystem in New Mexico (McDowell, Pockman and others)
– we calibrated the model using measurements from the ambient (control) plots
– we tested model predictions under the drought and irrigation treatments against experiment measurements
Wm. Pockman
Calibration - Ambient
Year2008 2009 2010 2011 2012 2013
VP
D (kP
a)
0
1
2
3
4
5
MeasuredModeled
MeasuredModeled
Soil w
ater (mm
)
0
40
80
120
160
MeasuredModeled
Year
2010 2011 2012 2013
Year
2010 2011 2012 2013
Net photosynth. (g/m
2/mo)
0
20
40
60
Foliar resp. (g/m
2/mo)
0
6
12
18W
UE
(g/mm
)0
2
4
6
8Juniper Piñon
Calibration - Ambient
0
2
4
6
8
MeasuredModeled
Year
2010 2011 2012 2013
0
10
20
30
40
50
60
MeasuredModeled
Year
2010 2011 2012 2013
Ne
t ph
oto
synth
esis (g
/m2
/mo
)
0
10
20
30
40
50
60
0
3
6
9
12
15
18
Fo
liar re
spira
tion
(g/m
2/m
o)
0
3
6
9
12
15
18
MeasuredModeled
WU
E (g
/mm
)
0
2
4
6
8Juniper Piñon
Testing - treatmentsDrought
So
il wa
ter (m
m)
0
20
40
60
80
100
120
140
Measured
Modeled
Irrigated
0
20
40
60
80
100
120
140
Year
2008 2009 2010 2011 2012 2013
Fo
liar re
spira
tion
(gC
/m2
/mo
)
0
5
10
15
20
25
30
Year
2008 2009 2010 2011 2012 20130
5
10
15
20
25
30
Juniper
Testing - treatments
Ne
t ph
oto
synth
esis (g
C/m
2/m
o)
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
Year
2008 2009 2010 2011 2012 2013
NS
C (g
C/m
2)
0
100
200
300
400
500
Year
2008 2009 2010 2011 2012 20130
100
200
300
400
500
Measured
Modeled
Juniper
Drought Irrigated
Testing - treatments
Drought Irrigated
Drought
So
il wa
ter (m
m)
0
20
40
60
80
100
120
140
Measured
Modeled
Irrigated
0
20
40
60
80
100
120
140
Fo
liar re
spira
tion
(gC
/m2
/mo
)
0
1
2
3
4
5
0
1
2
3
4
5
Year
2008 2009 2010 2011 2012 2013
Ne
t ph
oto
synth
esis (g
C/m
2/m
o)
0
2
4
6
8
10
12
Year
2008 2009 2010 2011 2012 20130
2
4
6
8
10
12
Year
2008 2009 2010 2011 2012 2013N
SC
(gC
/m2
)0
20
40
60
80
100
120
140
160
Year
2008 2009 2010 2011 2012 20130
20
40
60
80
100
120
140
160
Piñon
Drought Irrigated
Piñon
Modeled carbon reserves as an index of likelihood of mortality
Minimum NSC (% of cohort biomass)
1.0 1.5 2.0 2.5 3.0 3.5 4.0
% dead individuals
0
20
40
60
80
100
Modeled
Observed
Virtual drought experiment in WI using PnET-Succession
NSC as a fraction of biomass
Drought Treatment -50% precip
Cohort death
Near death experience
Symptomof waterstress
Effect of Soil and drought length2-year droughts
NS
Cfrac (gN
SC
/gActive biom
ass)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
4-year droughts 8-year droughtsSAND
queralb
fraxame
acersac
popugra
Year2010 2020 2030 2040
NS
Cfrac (gN
SC
/gActive biom
ass)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
Year2010 2020 2030 2040
Year2010 2020 2030 2040
SILO
Interaction of drought & life history
NSC
frac (gNSC
/gActive biom
ass)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
betupaptsugcanquerrubpinures
Assemblage 3
Year
2010 2020 2030 2040
NS
Cfrac (gN
SC
/gActive biom
ass)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
Year
2010 2020 2030 2040
Year
2010 2020 2030 2040
Assemblage 4
fraxnigthujoccacerrubpinustr
Conclusions
• A “first-principles” ecological modeling approach provides the tightest link to fundamental drivers that in the future may range outside values studied in the past
• PnET-Succession shows promise for the study of novel ecological conditions in forested landscapes – Climate change (precipitation, temperature, cloudiness)– Effects of melting permafrost on soil hydrology– Elevated CO2
– Invasive or re-introduced species (e.g., chestnut) as competitors– Leaf defoliators and disturbances that reduce leaf area– Altered hydrology– Ozone pollution
Strengthening the links between climate and succession in forest
landscape models
Eric J. GustafsonUSDA Forest Service
Northern Research StationRhinelander, Wisconsin