EPSCoR Year 3 Progress Report: Theme Three Ecological Vulnerability
Katie Ireland, Andy Hansen, Ben Poulter, Kristen Emmett, Mary Frances Ambrose
Montana State University
Foreword
In this report, we summarize progress in Year 3 of our EPSCoR funding. The targets identified
at the start of the funding period are presented below. This is an interim report of progress and
the work will continue into Year 4, consistent with EPSCoR’s decision to extend the Year 3
funds to Year 4.
Goal:
Evaluate potential response of vegetation in the Montana Rockies to future climate change using
models that incorporate varying levels of ecological realism including consideration of plant
demography, CO2 enhancement, interactions among species, and succession.
Proposed Work:

Compile results from a workshop on vegetation modeling in the region in order to: review
strengths and weakness of the different forest modeling approaches currently in use, identify
new approaches/applications that could complement existing approaches; and develop
increased synergies among existing projects.
Adapt the model LPJ-GUESS to the Montana Rockies.
 Validate the model by comparing its predictions for the current time period with independent
data sources and against paleo data sets for past periods.

Use the model to simulate vegetation response to alternative future climate scenarios for the
region as an input to climate adaptation planning in the region.
Introduction
Widespread tree mortality, insect outbreaks, and alteration of disturbance regimes
indicate that forested ecosystems in western North America are vulnerable to climate change.
Increasing temperatures and reduced water availability have led to extensive tree mortality both
globally (Allen et al. 2010) and across the western United States (van Mantgem et al. 2009). The
influence of changing climates on disturbance processes is apparent in the recent increase in
mountain pine beetle infestations and associated large-scale forest die-off in the US Rocky
Mountains and western Canada (Bentz et al. 2010). In addition, wildfire activity has increased
since the mid-1980s in response to earlier snowmelt dates and longer fire seasons (Westerling et
al. 2006). These large-scale mortality events and altered disturbance regimes have the potential
to shift species distribution patterns and alter the composition of ecological communities (Allen
and Breshears 1998; Allen et al. 2010; Anderegg et al. 2012). Further, such broad-scale shifts in
ecological communities will have implications for ecosystem function, such as changes in
nutrient cycling or reductions in carbon storage (Anderegg et al. 2012). The broad extent of
changes to forest ecosystems emphasizes the need for science and management that matches the
scale and complexity of ecosystem processes.
To explore the potential impacts of climate change at ecosystem scales, models capable
of simulating establishment, growth, and mortality from disturbance at large spatial scales are
needed. However, most projections of climate change impacts on species or disturbance regimes
have used statistical approaches based on past conditions or focused on scales either much larger
(e.g., global dynamic vegetation models) or much smaller (e.g., landscape succession models)
than the scale at which ecosystems function. Although useful for identifying areas of potentially
suitable future climate conditions, statistical approaches are now recognized as limited in scope
by the assumption that vegetation or fire regimes are in equilibrium with climate, their lack of
biotic interactions, and their inability to account for dispersal (Gustafson 2013; Morin and
Thuiller 2009; Pearson and Dawson 2003). Mechanistic models, such as dynamic global
vegetation models (DVGMs), simulate the physiological response of plants to climatic factors,
light, and nutrients. Because the physical processes responsible for vegetation responses to
climate change are explicitly included, DVGMs are capable of simulating plant responses to
novel climate conditions and CO2 concentrations. However, DVGMs are often too coarse in
spatial scale and overly simplistic in their representation of vegetation types to be useful at the
regional scales at which land management decisions are made.
After reviewing some of the current modeling approaches that have been applied to
ecosystems in western North America, we decided that the LPJ-GUESS model (Box 1; Smith et
al. 2001) would be suitable for simulating individual species responses to climate change at
ecosystem scales. A distinct advantage of LPJ-GUESS over global DVGMS is that it can be
parameterized for individual species. As opposed to statistical approaches, species interactions
are dynamic and disturbances are included. LPJ-GUESS includes many of the same
biogeochemical processes as other models in use in the Northern Rockies, such as BIOME-BGC
(Thornton et al. 2002) or FireBGCv2 (Keane et al. 2011), but with fewer parameters. Thus, it is
more easily applied to individual species at large spatial scales.
The aim of this study is to adapt LPJ-GUESS for application at ecosystem scales in western
North America and use the model to simulate ecosystem and vegetation dynamics under
projected climate change in the Greater Yellowstone Ecosystem (GYE). The LPJ-GUESS model
produces spatial output of vegetation distributions and density and summaries of carbon storage,
water balance variables, and fire return intervals (Table 1). The model will allow us to examine a
range of ecological responses to climate and land use change. Our first applications will focus on
the response of whitebark pine (Pinus albicaulis) to climate change. We plan to proceed from the
simplest applications to more complex ones. For example, we will first use LPJ-GUESS to
investigate the response of whitebark pine to climate change in the absence of competition. Next,
we plan to add in competitor species, such subalpine fir (Abies lasiocarpa), Engelmann spruce
(Picea engelmannii), and lodgepole pine (Pinus contorta). Further additions include simulating
whitebark pine and associated species with fire, and then with simulated management treatments.
Examples of additional questions to be addressed with LPJ-GUESS include:


Lifeform response to climate change and fire across GYE
Dominant tree species response to climate change and fire across GYE



Changes in fire regimes with climate change and dynamic vegetation
Hydrologic response to changes in veg under climate change
Carbon consequences of the above
Through a collaboration between the Institute on Ecosystems, Andrew Hansen’s lab group,
and Ben Poulter’s lab group, we are taking a step-by-step approach to adapting the LPJ-GUESS
model for application in the GYE. These steps include:
1. Calibrate LPJ-GUESS to correctly predict the current vegetation distribution and
structure under historical climate conditions and CO2 concentrations.
2. Validate the model across the GYE by comparing predicted vegetation patterns to an
existing map of current vegetation,
3. Validate the modelled tree density and annual net primary productivity (ANPP) against
Forest Inventory and Analysis (FIA) data,
4. Apply the model under future climate scenarios, and
5. Analyze changes to vegetation patterns and fire regimes under future climate
scenarios.
For year one, our objectives focused primarily on calibrating LPJ-GUESS for application
in the GYE (step 1). Although the implementation of LPJ-GUESS is through a collaboration of
the Hansen and Poulter lab groups, the focus of this report is on the use of EPSCoR Year 3 funds
awarded to Andrew Hansen. Here, we report on progress on the objectives of the Hansen lab
group in the context of the broader project being done in the Poulter lab. Year one objectives
for the overall project were:
1.
2.
3.
4.
5.
Review modeling approaches
Select test sites for model calibration
Review/revise model code for application in GYE
Evaluate climate input data requirements and develop appropriate data sets
Parameterize model for tree species
The Hansen lab took the lead on #1, and 2, and collaborated on #3 and 5.
Box 1. Description of the LPJ-GUESS Model
LPJ-GUESS (Smith et al. 2001) is an ecosystem model which combines the LPJ
(Lund-Potsdam-Jena) dynamic global vegetation model (Sitch et al. 2003) with a
forest gap model GUESS (General Ecosystem Simulator) to mechanistically simulate
vegetation dynamics. Ecological processes are simulated at two different time-steps,
daily and annual (Sitch et al. 2003). Daily processes include photosynthesis and
respiration, soil hydrology, and decomposition while carbon allocation, plant growth,
population dynamics, and disturbance are implemented on an annual basis.
The model can be run in two different vegetation “modes” (Smith et al. 2001).
Population mode is the implementation of the dynamic global vegetation model LPJDVM, is less computationally intensive, and represents vegetation in a general sense,
as PFTs but with no age structure. In cohort mode, individuals in smaller (0.1 ha)
patches belong to different age cohorts and are simulated using the GUESS model.
Cohort mode is similar to a forest gap model where age cohorts of vegetation compete
for light and water allowing the model to simulate vertical stand structure, the
interaction between shade-tolerant and shade-intolerant vegetation, and capture
successional dynamics more realistically.
Fire will be implemented in LPJ-GUESS by feeding information on vegetation and
fuels from LPJ-GUESS to the process-based fire regime model, SPITFIRE (Spread
and InTensity of FIRE; Thonicke et al. 2010). Ignitions are determined by a lightning
climatology and from human population density, but are only successful in causing
fires if there is enough fuel and the fuel is sufficiently dry. Fire spread is simulated
using the Rothermel models (i.e., elliptical fire front) and depends upon wind-speed
and the amounts and moisture content of live fuels and different size classes of dead
fuels. The modeled fire effects include fire-induced mortality as a function of tree
height and bark thickness, CO2 and other trace gas emissions, and fuel consumption.
Progress in Year One
1. Review of modeling approaches (K. Ireland and A. Hansen)
Our first step was to determine the most appropriate modeling approach for simulating
vegetation dynamics at the individual species level and across ecosystem scales. We conducted a
literature review to explore different modeling approaches. In particular, we reviewed species
distribution modeling approaches, BIOME-BGC, the landscape-fire-succession model
FireBGCv2, and the LPJ-GUESS model. Our criteria for selecting a modeling approach were
that it (1) was capable of simulating establishment, growth, and mortality of individual species,
(2) included dynamic species interactions (i.e., competition for light, resources), (3) incorporated
disturbance, (4) mechanistically linked climate to plant growth, establishment, and mortality, and
(5) could be applied at large spatial scales. LPJ-GUESS met all of these requirements, so we
determined that it would be a good candidate model. Since LPJ-GUESS produces output
summarizing changes in stand structure, carbon balance, water balance, and fire return intervals
(Table 1), it can be applied to a variety of ecological questions.
To further explore the most appropriate modeling approach, we organized a vegetation
modeling workshop in Bozeman in September, 2013. We invited scientists and natural resource
managers from Montana State University, the University of Montana, the National Park Service,
and the Forest Service to review major methods of modeling vegetation dynamics and solicit
feedback on recommended approaches to study vegetation response to climate change across
ecosystem scales. Andrew Hansen provided an overview and discussed species distribution
modeling approaches. Robert Keane from the Missoula Fire Sciences Laboratory discussed his
landscape succession model, FireBGCv2, and Steve Running from the University of Montana
provided an overview of BIOME-BGC. Kathryn Ireland and Ben Poulter discussed the LPJGUESS model. Together, workshop participants discussed the temporal and spatial scales at
which different modeling approaches were appropriate for different questions (Fig. 1).
As a result of both the literature review and the workshop, we determined LPJ-GUESS to be
a good candidate model because it is (1) capable of simulating demographic processes at the
species-level, (2) captures biotic interactions and physiological processes, and (3) is capable of
simulating vegetation dynamics at ecosystem spatial scales. Of all the approaches discussed at
the workshop, only LPJ-GUESS was both mechanistic and applicable at ecosystem or regional
spatial scales (Fig. 1).
However, one limitation of LPJ-GUESS involves the current method of fire simulation.
Currently, fire can be modeled by coupling LPJ-GUESS with a process-based fire regime model,
SPITFIRE (Spread and InTensity of FIRE; Thonicke et al. 2010). The SPITFIRE model was
originally developed for global applications and works with broadly defined plant functional
types (PFTs), rather than individual tree species. This led Kathryn Ireland to submit a proposal,
with Ben Poulter as mentor, to the Agriculture and Food Research Initiative (AFRI) National
Institute of Food and Agriculture (NIFA) Fellowships Grant Program in February, 2014. The
proposed work would be to develop a new fire module specifically designed to be suitable for
mechanistically simulating fire and individual tree species dynamics in western North American
ecosystems. We would consult closely with scientists at the Missoula Fire Sciences Laboratory
with experience in fire and landscape modeling to develop a fire model suited for forested
ecosystems in western North America.
Table 1. LPJ-GUESS model output variables.
Category
Description
Variable Name
Units
Productivity Variables
Annual
anpp
Annual net primary productivity (NPP)
kgC m-2
cflux
Ecosystem carbon fluxes
kgC m-2 yr-1
cmass
Annual carbon biomass
kgC m-2
cpool
dens
LAI
Soil carbon
Tree density
Leaf area index
kgC m-2
stems ha-1
-
mgpp
mlai
Monthly gross primary productivity (GPP) - leaf respriration
Monthly LAI
kgC m-2
-
mnee
Monthly net ecosystem exchange
kgC m-2
mnpp
Monthly NPP
kgC m-2
mra
Monthly autotrophic respiration
kgC m-2
mrh
Monthly heterotrophic respiration
kgC m-2
monthly actual evapotranspiration (AET)
monthly evaporation
monthly interception water loss
monthly potential evapotranspiration (PET)
monthly runoff
mm
mm
mm
mm
mm
monthly soil water content, 50-150 cm soil depth
monthly soil water content, 0-50 cm soil depth
% AWCb
% AWC
Fire return interval
years
Monthly
Water Balance Variables
Monthly maet
mevap
mintercep
mpet
mrunoff
mwcont_lower
mwcont_upper
Disturbance Variables a
Annual firert
a
Additional disturbance variables will be available once LPJ-GUESS is coupled with SPITFIRE.
AWC: available water holding capacity
b
Millenial
Paleoecological Studies
Temporal Scale
Presence/abundance of different species
C. Whitlock
Bioclimatic envelope
G. Rehfeldt
LCCVP,
EPSCoR Focus 3
A. Hansen
Century
Whitebark Pine Treatments
50
Decadal
R. Keane
Mountain
Pine Beetle
J. Hicke
LPJ-GUESS
B. Poulter, A. Hansen
Management
Needs
Yearly
5
Stand
T. Oliff
Watershed
Landscape
Ecosystem/region
Continental
Spatial Scale
Figure 1. Differences in the spatial and temporal scales of current modeling approaches and management needs. This was created
during the Vegetation Modeling Workshop to summarize the scales at which various questions can be addressed using current
modeling approaches. LCCVP refers to the Landscape Climate Change Vulnerability Project
(http://www.montana.edu/lccvp/index.html).
2. Selection of test sites for model calibration (K. Ireland)
Stochastic processes for establishment and mortality, requiring 20-100 simulations per grid
cell make LPJ-GUESS computationally expensive to run. Therefore, to more efficiently test the
model and adapt it for the GYE, we selected 46 sites to perform test runs of the model. To be
sure the model would perform well across forested vegetation types, we selected sites to
represent gradients in environmental conditions and capture the dominant forest types of the
GYE. We randomly selected one test site from each stratum, representing combinations of
elevation zones, precipitation classes, and vegetation types.
Elevation and annual precipitation were used to capture gradients in environmental
conditions. We divided the study area into five equal interval elevation zones (660 m each; Fig.
2). Within each elevation zone, we calculated variability in precipitation and created zones
representing low, medium, and high average annual precipitation, defined as areas below one
standard deviation (S.D.) from the mean precipitation, areas ± 1 S.D., and areas > 1 S.D. above
the mean, respectively (Table 2, Fig. 3).
Figure 2. The Greater Yellowstone Ecosystem was divided into equal-interval elevation zones
and sites were randomly sampled within these elevation zones for model calibration and testing.
Figure 3. Model test sites were selected in areas of low, medium, and high precipitation within
each elevation zone. Precipitation classes were defined as low precipitation: areas < 1 standard
deviation (S.D.) below the mean; medium precipitation: areas ± 1 S.D.; and high precipitation:
areas > 1 S.D. Because precipitation classes were defined by the variability within each elevation
zone, the classes differ between elevation zones. Precipitation classes for elevation zone 2 (15642224 m) are shown, as an example.
Table 2. Model test sites were selected within areas of low, medium, and high precipitation within each elevation zone. Precipitation
classes were defined as follows: low precipitation: minimum to lower standard deviation (S.D.); medium precipitation: ± 1 S.D.; and
high precipitation: upper S.D. to maximum.
Precipitation (cm)
Elevation Range
(m)
Min
Lower SD
Mean
Upper SD
Max
1
902-1563
10.3
19.8
26.8
33.9
50.0
2
1564-2224
13.2
23.0
37.7
52.4
117.9
3
2225-2885
16.5
40.7
61.5
82.3
176.8
4
2886-3546
39.7
55.5
74.5
93.4
189.4
5
3547-4206
51.5
64.2
76.8
89.4
181.2
Elevation Class
We examined possible data sources to characterize the dominant forest types in the GYE
(Jin et al. 2013; Kuchler 1964; Parmenter et al. 2003; Rollins 2009). Our goal was to capture
natural, woody vegetation at the level of detail that would permit us to benchmark model results
against Forest Inventory and Analysis (FIA; Smith 2002) data. We selected the LANDFIRE
Existing Vegetation Type (EVT) layer which is based upon NatureServe’s Ecological Systems
classification (Comer et al. 2003; Rollins 2009). The ecological systems are defined as plant
associations that occur in areas with similar physical environments and disturbance processes
and offer the mid-scale vegetation classification most useful for our purposes (Comer et al. 2003;
Rollins 2009). However, the EVT layer still included more detailed division of plant
communities than we required, so we excluded non-forested vegetation and reclassified it into
four major vegetation types (Fig. 4):
1. Lower Treeline: juniper (Junipurus spp.), sagebrush (Artemisia spp.), and limber pine
(Pinus flexilis) dominated groups
2. Woody Deciduous Forest: aspen (Populus tremuloides), cottonwood (Populus spp.),
willow (Salix spp.), maple (Acer spp.), and woody riparian dominated groups
3. Montane Forest: Douglas-fir (Pseudotsuga menziesii), lodgepole pine, and mixed
conifer/aspen dominated groups
4. Subalpine Forest: Engelmann spruce, subalpine fir, and whitebark pine dominated groups
No forested vegetation types occurred in the highest elevation zone (elevation class 5), so no
sites from this elevation zone were selected. Not all vegetation types occurred in all elevation
and precipitation zones, resulting in a total of 46 test sites (Fig. 4; Table 3).
Figure 4. Our model calibration test sites were distributed across elevational and precipitation
gradients and placed to sample the major vegetation types of the Greater Yellowstone
Ecosystem.
Table 3. Site characteristics of test sites for model calibration.
Site
Longitude
Latitude
Elevation
Class
Elevation
Precipitation
Class
Vegetation Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
-112.33
-112.31
-112.22
-108.50
-108.57
-109.63
-111.59
-109.10
-108.29
-111.33
-111.02
-109.35
-109.11
-109.53
-108.33
-111.44
-111.26
-111.56
-111.76
-111.24
-110.82
-111.28
-111.62
-109.52
-109.53
-110.42
-110.30
-111.33
-110.41
-109.58
-110.26
-111.14
-110.94
-111.03
-110.82
-109.33
-109.17
-109.07
-109.57
43.77
45.65
45.72
42.97
45.83
45.76
46.08
45.44
45.33
45.99
45.80
45.30
43.38
43.64
42.66
44.12
45.20
42.88
44.66
43.31
45.84
44.51
44.43
43.57
43.67
43.00
43.03
43.64
45.35
43.77
43.91
44.95
43.97
44.31
43.93
44.21
43.64
43.89
43.26
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
1465
1389
1361
1538
1107
1315
1277
1257
1548
1542
1504
1547
1937
2194
1690
1644
2050
1870
2022
2111
1809
1950
1940
2326
2648
2521
2361
2218
2456
2745
2525
2894
2334
2341
2695
3066
3060
2843
3026
Low
Low
Low
Low
Medium
Medium
Medium
Medium
High
High
High
High
Low
Low
Low
Medium
Medium
Medium
Medium
High
High
High
High
Low
Low
Low
Low
Medium
Medium
Medium
Medium
High
High
High
High
Low
Low
Low
Low
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Site
40
41
42
43
44
45
46
Longitude
-109.38
-109.39
-109.09
-109.64
-111.91
-111.96
-111.90
Latitude
43.74
43.80
42.78
43.42
45.47
45.50
44.56
Elevation
Class
4
4
4
4
4
4
4
Elevation
3130
3076
2897
3167
3067
2963
2962
Precipitation
Class
Medium
Medium
Medium
Medium
High
High
High
Vegetation Type
Lower treeline
Montane forest
Subalpine forest
Woody deciduous forest
Lower treeline
Montane forest
Subalpine forest
3. Review/revise model code for application in GYE (K. Ireland and B. Poulter)
Application of LPJ-GUESS required an iterative set of test runs, troubleshooting to identify
glitches, and revision of code. We first describe the structure of the model and then summarize
our steps for revising the model code for our application.
LPJ-GUESS is organized into eight modules which either contain related sets of ecosystem
processes or perform technical functions, such as reading input data into the model (Fig. 5). The
input/output module first initializes the model with classes containing species or PFTs, climate,
or soils information and functions and global model variables (e.g., number of years and patches
to simulate, patch size). Next, the input/output module reads in climate and soil input data,
simulation settings, and PFT parameters. It also preprocesses climate data, such as interpolating
monthly climate data to daily values. After climate data have been read, stands are initialized.
The stand represents a model location or grid cell and is initialized with characteristics such as its
initial climate, soil type, number of patches within the stand, and a list of species (or PFTs).
Daily climate and soils data are then calculated for each stand. Leaf phenology, photosynthesis,
evapotranspiration, and respiration are implemented on a daily basis. At the end of the year, the
daily net primary productivity (NPP) is summed and individual tree growth is calculated by
allocating annual NPP to leaves, sapwood, heartwood, and roots based upon allometric
equations. Establishment, mortality, decomposition, and disturbance are also implemented on an
annual time step.
The input/output module is comprised of a source code file (guessio.cpp) and a header file
(guessio.cp), and is where most of our efforts on revising the model code for application to the
GYE have been focused to date. Climate inputs required by the model are monthly temperature,
precipitation, radiation, and wet day frequency (Fig. 5; Table 4). Radiation data can take the
form of either incoming shortwave radiation or percent cloud cover. We made several revisions
to functions within the input/output module to account for differences in the format and
organization of the DAYMET climate data we are using (as described below) compared to the
global climate dataset (Cramer and Leemans Unpublished data) included with the demonstration
version of LPJ-GUESS. We made changes primarily to the readfor and readenv functions (Box
2), which are both called in the input/output module (guessio.cpp).
Figure 5. LPJ-GUESS model flow.
Table 4. Climate and soil input variables for running the LPJ-GUESS model.
Category
Variable Name
Units
Source
Citation
Temperature
Precipitation
o
DAYMET v2
DAYMET v3
Thornton et al. 1997; Thornton et al. 2014
Thornton et al. 1997; Thornton et al. 2014
Frequency of Wet Days
days month-1
DAYMET v4
Thornton et al. 1997; Thornton et al. 2014
Thornton et al. 1997; Thornton et al. 1999; Thornton et al. 2014
Default setting - LPJ-GUESS demonstration version
Climate Variables
C
mm
-2
Incoming Shortwave Radiation
Atmospheric CO2 concentration
Wm
ppm
DAYMET v5
set to 340
Percolation rate field capacity
mm d-1
CONUS-SOIL Miller and White 1998
Soil Variables
Soil Thermal Diffusivity, 0%
Soil Thermal Diffusivity, 15%
Soil Thermal Diffusivity, 100%
Texture
2 -1
CONUS-SOIL Miller and White 1998
2 -1
CONUS-SOIL Miller and White 1998
2 -1
CONUS-SOIL Miller and White 1998
CONUS-SOIL Miller and White 1998
mm s
mm s
mm s
class
Box 2. Code Modifications
Function descriptions:
1. readfor
a. read in and allow users to specify the data
format of ASCII text data.
b. used in the input/output module to search
for climate and soil data for each grid cell
c. called by readenv to read in climate and
soils data
2. readenv
a. used in the input/output module to search
for climate and soil data for each grid cell
b. in original code, called once in getstand to
get the environmental data for the current
grid cell.
3. getstand
a. used to obtain latitude, longitude, soil, and
climate data for each simulated stand
b. calls readenv to get stand’s climate/soil data
c. calls getclimate to get stand’s interpolated
climate data
4. getclimate
a. interpolates the monthly climate data to
daily values.
b. accesses the daily climate data for each
stand.
Code Modifications:
1. readfor
a. minor changes to properly read in the
DAYMET data; accounting for difference
in the number of significant digits and
spacing our climate data files and the
Leemans and Cramer global climate
dataset.
2. readenv
a. for each climate dataset, added a loop
through all the years so that the function
would read in all of the years in our input
climate files
3. getclimate
a. added a call to readenv during each day of
simulation so the model would cycle
through all the days and years of climate
data, for each stand
We ran a test of the model on our test sites
using European tree species and their default
parameters with the DAYMET climate data for
the GYE, but found that the model would only
read in the first year of our climate data. Since
the Leemans and Cramer global climate
dataset included with the model contained only
one year of data for each grid cell, the
demonstration version of the model code was
designed to only read in one year of climate
data.
Therefore, the next change we had to make
was to get the model to cycle through multiple
years of climate data. When the readenv
function was first called by the input/output
module, it was only reading in one line of data
from each of the input climate files
(temperature, precipitation, and radiation).
After adding a loop through all the years so
that the function would read in all of the years
in our input climate files, we were able to get
the model to read in all the years of climate
data. However, the output files contained only
one year of simulation.
During a simulation, LPJ-GUESS accesses
the climate data in two different functions:
getstand and getclimate (Box 2), so we looked
at these two functions to determine why only
one year of climate data was being used. As
the model simulation progresses, the model
loops through stands first (calling getstand)
and then through days and years (calling
getclimate). In the demonstration model code,
the readenv function was only being called by
getstand, so climate data for each stand were
being used for the simulation, but only once
each year. By adding a call to the getclimate
function for each day of simulation, we were
able to get the model to cycle through all the
days and years of climate data.
Once we had LPJ-GUESS running
successfully with the climate data for the
GYE, we ran the model on our test sites using
the default species parameters for European
species to see whether anything could grow.
Although we were using default parameters, only a cold-adapted pine species (Pinus
sylvestris), a boreal evergreen shrub plant functional type, and C3 grasses were able to grow on
most of our sites. To determine which of the GYE climate variables might be causing the
problem, we sequentially substituted GYE climate data for European climate data and ran the
model with default species parameters. For example, we ran the model using precipitation and
radiation data from the demonstration Cramers and Leeman dataset but temperature data from
the GYE, through every combination of climate data inputs. To do this, we had to go back to the
original model code for the Cramers and Leeman source data but keep the code changes for the
GYE source data. Only when using radiation data from the GYE did the species fail to grow. The
radiation data we were using from DAYMET were in the form of daily shortwave radiation
while the demonstration data were in the form of percent cloudiness. Looking back at the model
code, we found that the radiation input type had to be specified in the getstand function. After
switching the radiation type, the European species were able to grow with their default
parameters under GYE climate.
4. Evaluate climate and soil input data requirements and develop appropriate data sets
(B. Poulter)
The LPJ-GUESS model requires monthly temperature, precipitation, radiation (or
cloudiness) and wet day frequency as climate inputs (Table 4). We derived the required climate
inputs for LPJ-GUESS from the DAYMET v2 climate database (Thornton et al. 2000; Thornton
and Running 1999; Thornton et al. 1997; Thornton et al. 2014). The DAYMET data are daily
gridded weather data including: minimum and maximum temperature, precipitation, incident
shortwave radiation, water vapor pressure, snow water equivalent, and day-length. The data are
available as 2o x 2o tiles at 1-km resolution. We downloaded the continental mosaic for all the
daily weather variables. We used the NCAR Command Language (NCL; The NCAR Command
Language 2014) and the Climate Data Operators language (CDO;
https://code.zmaw.de/projects/cdo/) to:
1.
2.
3.
4.
5.
Download the continental mosaic for all the DAYMET weather variables
Clip the DAYMET dataset to the Northern Rocky Mountains
Calculate monthly sums and means for all the variables
Merge the variables into single netCDF files for all years
Calculate mean temperature (Tmean) as the average of minimum (Tmin) and maximum
(Tmax) temperature
6. Calculate wetdays as the number of wet days per month
7. Reproject the DAYMET data from a curvilinear to rectangular projection
Soil parameters required by LPJ-GUESS include the percolation rate field capacity (kperc),
soil thermal diffusivities at 0%, 15%, and 100% soil water contents (k0, k15, and k100), soil
texture, and the volumetric water holding capacity (WHC) at field capacity minus WHC at the
wilting point (Sitch et al. 2003). We derived these soil parameters from the 1-km gridded soil
characteristics dataset developed by Miller & White (1998), which is based on the U.S.
Department of Agriculture’s State Soil Geographic Database (Table 4).
5. Parameterize model for tree species(B. Poulter, K. Ireland, M. F. Ambrose, K. Emmett)
In LPJ-GUESS, a set of parameters describing plant physiology, morphology, allometric
relationships, bioclimatic limits, and phenology govern each species’ growth and survival
(Appendix A). Several sources were investigated for developing new parameters, including:
1. Default parameters for European woody vegetation from the demonstration version of
LPJ-GUESS
2. Max Planck Institute for Biogeochemistry Plant Trait Database (TRY)
3. Published literature and related mechanistic model parameters (i.e., BIOME-BGC,
FireBGCv2 parameters)
4. Overlay of species distribution from FIA data with DAYMET climate data to
estimate bioclimatic limits
5. Field sampling campaign to develop fire traits and fuel loads for SPITFIRE modeling
We took an incremental approach to testing and developing new parameters for the 16
dominant tree species in the GYE. First, we compiled a list of potential parameter values from
default values used for European vegetation, the Max Plank Institute for Biogeochemistry TRY
database, previous modeling efforts, and the published literature. Next, we analyzed species
distribution data from FIA data and DAYMET climate data to develop species bioclimatic limits
for the model. Currently, we are testing parameter values by running LPJ-GUESS under
historical climate conditions with default values and the bioclimatic limits we developed for the
GYE. Finally, we measured tree characteristics and fuel loads in burned and unburned forests to
develop fire traits and fuel loads which will be used to incorporate SPITFIRE into the LPJGUESS model.
5a. Compiling potential parameter values
i.
Default parameters (K. Ireland and M. F. Ambrose)
Our first step was to assign default parameter values to GYE tree species. We compiled all
the parameters used for European vegetation. For the GYE species, we then took the average
parameter values for European species of the same genus. Where species of the same genus
were unavailable in the European data, we used European species from closely related genera or
similar functional types.
ii.
TRY Plant Traits Database proposal (B. Poulter and M. F. Ambrose)
We submitted a proposal to the Max Planck Institute for Biogeochemistry to obtain
additional plant traits and structural data from the TRY plant traits database (Kattge et al. 2011).
Examples of the variables requested include the maximum rate of carboxylation (Vcmax), specific
leaf area (SLA), and growth-temperature limits for tree and shrub species in Montana, Wyoming
and Idaho.
iii.
Modeling and literature review (K. Ireland)
We also looked to other modeling efforts in the western United States and the published
literature to develop a list of potential parameter values. The FireBGCv2 model (Keane et al.
2011) and the BIOME-BGC biogeochemical model (Running and Coughlan 1988; Running and
Gower 1991; Thornton 1998; Thornton et al. 2002) are mechanistic models that have been run in
the north-western United States and share some required parameters with LPJ-GUESS.
Examples of parameters shared by LPJ-GUESS and FireBGCv2 or BIOME-BGC include tissue
carbon to nitrogen ratios, specific leaf area, longevity, and minimum conductance rates. We
compiled species parameters used for running FireBGCv2 in Glacier National Park (Keane et al.
2011) and Yellowstone National Park (Loehman, R. personal communication). Additional
sources of some parameters included a review of BIOME-BGC parameters by White and others
(2000) and the Ecophysiological Parameterization Database for Pacific Northwest Conifers
(Hessl et al. 2004). However, there are many parameters in LPJ-GUESS that were not available
through the previous modeling efforts or the database. Tang and others (2012) parameterized
LPJ-GUESS for PFTs in the north-eastern United States. Following their methods, we derived
potential parameters for drought-tolerance and fire tolerance from the United States Department
of Agriculture (USDA) Conservation Plant Characteristic (CPC) database
(http://plants.usda.gov/about_characteristics.html). Specifically, we assigned values of 0.1, 0.2,
0.3, and 0.4 to the USDA ranks of none, low, medium, and high drought tolerance, respectively.
For fire resistance, USDA ranks of none, low, medium, and high fire tolerance were assigned the
values of 0.7, 0.10, 0.13, and 0.16, respectively (Tang et al. 2012, see Appendix S1 in
Supplementary Material). Finally, we reviewed published studies of individual species for
possible parameter values.
5b. Analyzing species distribution and climate data for bioclimatic limits
(B. Poulter and K. Ireland)
Bioclimatic limits are used in LPJ-GUESS to determine whether each species can survive or
establish under the prevailing climatic conditions in a particular grid cell at a particular year in
the simulation. Four bioclimatic limits are currently defined in the LPJ-GUESS code:
1.
2.
3.
4.
5.
Tc, min surv = minimum coldest-month temperature for survival
Tc, min est = minimum coldest month temperature for establishment
Tc, max est = maximum coldest-month temperature for establishment
Tw-c, min = minimum warmest minus coldest month temperature range
GDDmin = minimum growing degree-days (5oC base)
We compared tree species distribution data from FIA plots against the DAYMET climate
data to establish bioclimatic limits for all the tree species found in Montana, Idaho, and
Wyoming. For this report, we summarize the bioclimatic limits used for the 16 dominant tree
species in the Greater Yellowstone Ecosystem (Table 5). Ben Poulter analyzed the FIA species
distribution data to develop an initial set of bioclimatic limit parameters based upon the means of
the distributions of the climate data by species. The steps in this process were to:
1. Create a spatial data layer of the FIA plots representing which tree species were recorded
at each plot
2. Calculate each of the bioclimatic limit variables from the DAYMET climate data for all
three states
3. Intersect the bioclimatic limit datasets with the distribution of each tree species
4. Take the mean of the distribution of each bioclimatic limit across all the plots where a
given species occurred. This mean value was used as our initial bioclimatic limit for each
species.
As an example, the minimum coldest month temperature for survival (Tc, min surv) was
calculated by finding the mean of the minimum annual temperature for all the years of the
DAYMET data. These values were then intersected with the plots where a species was present.
This resulted in a distribution of minimum temperatures across the species range and we used the
mean of this distribution as our initial value of Tc, min surv for each species.
To clarify, Tc, min est was calculated slightly differently than Tc, min surv, resulting in warmer
limits for Tc, min est. Rather than using minimum annual temperature, we found the coldest month
for each year, calculated the minimum temperature of that coldest month, and averaged these
values for all the years of the DAYMET data.
5c. Testing parameter values (K. Ireland)
For our first tests of the species parameters, we ran the model for GYE tree species with the
default parameters from the European species (averaged by genus, as described above). The only
change we made to the default parameters was to substitute the bioclimatic limits we calculated
for GYE species. However, we found that in most of our sites, none of the GYE tree species
would grow.
To determine which of the bioclimatic limit parameters was restricting growth, we tried
single-species test runs and sequentially changed one bioclimatic limit at a time. By testing one
species at a time, we removed the effects of any competition between species. We selected
lodgepole pine as our test species and set all of the parameters to be the same as Pinus sylvestris,
the cold-adapted pine species in the European demonstration dataset for LPJ-GUESS. Then we
changed the bioclimatic limits, one at a time, from the P. sylvestris values to the bioclimatic
limits we had calculated for lodgepole pine. We found that lodgepole pine could grow in all of
our test sites when all of the parameters were the same as P. sylvestris. But, when the values we
calculated for lodgepole pine for either Tc, min est or GDDmin were used, but all others set to those
of P. sylvestris, lodgepole pine did not grow in many of our sites. The bioclimatic limit for Tc, min
est was too warm, so that when temperatures got too cold lodgepole pine could not establish. For
GDDmin the values was much too high and the growing season length was too short for lodgepole
pine to establish and grow.
Since the bioclimatic limits we were using were limiting growth, we re-examined our
analysis of bioclimatic limits. Kathryn Ireland used the methods described above (section 5b) to
reanalyze species distributions against the DAYMET climate data. However, instead of using the
mean of the distribution of each bioclimatic limit across all the plots where a given species
occurred, we calculated the standard deviation of the distribution. Then, we set the new
bioclimatic limits to be twice the standard deviation from the mean (Table 5; Appendix B). For
bioclimatic limits representing minimum values (Tc, min surv,Tc, min est, Tw-c, min, GDDmin) we
subtracted two standard deviations from the mean as the parameter value; for Tc, max est, we added
two standard deviations to the mean. We are currently testing these new bioclimatic limits for
each of the GYE species individually. Tests completed for lodgepole pine indicate that basing
the bioclimatic limits on twice the standard deviation allows lodgepole pine to grow on all but
the highest, upper treeline sites.
Table 5. Bioclimatic limits for tree species found in the Greater Yellowstone Ecosystem: Tc, min surv = minimum coldest-month
temperature for survival, Tc, min est = minimum coldest month temperature for establishment, Tc, max est = maximum coldest-month
temperature for establishment, Tw-c, min = minimum warmest minus coldest month temperature range, GDDmin = minimum growing
degree-days (5oC base). Values shown are mean (two standard deviations from the mean) of the distribution of each value across all
FIA plots in Montana, Idaho, and Wyoming with the species present.
Species
Scientific Name
Abies lasciocarpa
Acer glabrum
Acer grandidentatum
Cercocarpus ledifolius
Juniperus osteosperma
Juniperus scopulorum
Picea engelmannii
Pinus albicaulis
Pinus contorta
Pinus flexilis
Pinus ponderosa
Populus angustifolia
Populus balsamifera ssp. trichocarpa
Populus spp.
Populus tremuloides
Pseudotsuga menziesii
a
Common Name
Subalpine fir
Rocky Mountain maple
Bigtooth maple
Curlleaf mountain-mahogany
Utah juniper
Rocky Mountain juniper
Engelmann spruce
Whitebark pine
Lodgepole pine
Limber pine
Ponderosa pine
Narrowleaf cottonwood
Black cottonwood
Cottonwood and poplar spp.
Quaking aspen
Douglas-fir
Bioclimatic Limits
Number of
Plots a
4682
217
68
150
259
1036
3659
1548
5165
854
2999
36
164
13
1051
7033
Tc,min surv
-14.8 (-19)
-10.3 (-14.3)
-11.6 (-13.3)
-12.3 (-16.1)
-11.7 (-15.7)
-14.4 (-18.2)
-14.6 (-19.4)
-16.5 (-19.9)
-14.3 (-19.2)
-14.9 (-18.6)
-12.9 (-18.4)
-12.6 (-16.3)
-12.2 (-16.8)
-13 (-18.1)
-13.2 (-17.2)
-12.9 (-18.2)
Tc, min est
-8.4 (-12)
-5 (-8.1)
-6.5 (-8.7)
-6.7 (-10.6)
-5.7 (-8)
-5.7 (-8.5)
-8.1 (-12.1)
-9.8 (-12.5)
-7.8 (-11.8)
-7.9 (-11.5)
-5.3 (-7.9)
-5.7 (-9.7)
-5.4 (-8.3)
-5.5 (-9.7)
-7.3 (-10.5)
-6.6 (-10.7)
Tc, max est
-4.2 (-0.6)
-1.4 (1.5)
-2.8 (0.4)
-2.8 (1.3)
-1.4 (1.1)
-0.8 (2.5)
-3.8 (0)
-5.3 (-2.1)
-3.6 (0.2)
-3.1 (1.1)
-1.1 (2)
-0.8 (4)
-1.5 (1.1)
-1.2 (3.3)
-3.1 (0.7)
-2.7 (1)
Tw-c, min
8.9 (5.3)
11.9 (8.6)
12.7 (10.1)
11.9 (7.8)
15 (11.9)
14.9 (9.2)
9.1 (5.2)
7.4 (4.8)
9.7 (5.9)
10.5 (5.5)
14.2 (9.1)
14.5 (9.7)
12.5 (9)
15 (7.4)
11.5 (7.9)
11 (6.9)
GDDmin
1121.8 (316.5)
1859.2 (1099.5)
1911.7 (1377.9)
1732.2 (936.1)
2231.9 (1617.6)
2212.4 (1207.8)
1179.2 (266.6)
778.4 (182.9)
1298.2 (410.2)
1334.5 (403.5)
2160.1 (1309.5)
2176.8 (1258.6)
1973.3 (1225)
2249.5 (1063.9)
1590.6 (857.3)
1599.7 (649.8)
Number of plots refers to the total number of FIA plots with the species present in Montana, Idaho, and Wyoming, not just the GYE.
5d. Field campaign for plant traits and fuel loads for fire modeling
(K. Emmett and M. F. Ambrose)
In order to mechanistically model shifts in wildfire regimes, we are coupling the SPITFIRE
fire model with LPJ-GUESS. Since the SPITFIRE model requires parameters on species
response to fire and characteristic fuel loads, we collected information on the necessary
parameters in burned and unburned forests around the GYE. We sampled 21 burned and 27
plots, which were selected based on the USGS/USFS burn severity database(Eidenshink et al.
2007), LANDFIRE Existing Vegetation Type (Rollins 2009), and the USGS NLCD2011 dataset
(Jin et al. 2013). Survey sites were selected based on elevation, fire severity, and vegetation
community type. In burned areas, data were collected on tree species, diameter at breast height
(DBH, 1.37 m), scorch height, crown length, and bark thickness. Fuel loads were measured on
unburned sites to characterize the loading of 1-hr, 10-hr, and 100-hr fuels by vegetation type,
using the planar transect method (Brown 1974).
The plant traits data are being analyzed for correlations between tree diameter and other traits
(e.g., bark thickness), by species (Fig. 6). This will allow us to estimate plant traits needed for
SPITFIRE from stand structural data. Similarly, the fuel loading data are being used to calculate
fire vulnerability for a given area and vegetation type. Additional field sampling is planned for
next summer and the SPITFIRE model will be incorporated into LPJ-GUESS and these
preliminary data used to calibrate the model this fall and spring.
Figure 6. Fire traits are being analyzed to allow for coupling of SPITFIRE and LPJ-GUESS.
Species-specific correlations between fire traits and structural characteristics will enable more
efficient collection of the parameters required for SPITFIRE. Shown as an example are the
relationships between bark thickness and diameter at breast height (DBH) for Pseudotsuga
menziessii (PSME) and Pinus contorta (PICO).
Evaluation of Progress in Year One
Lessons learned

Developing test sites: Test sites have been critical to discovering problems with the
model code and our parameter values across a range of environmental gradients and
vegetation types. However, we spent more time than necessary selecting the major
vegetation types to guide our site selection. At first, we tried to consider both forest
and non-forest vegetation in order to expand the application of LPJ-GUESS. But,
since the primary focus for this project is on forested vegetation we ended by
simplifying an existing classification of forested vegetation.

Revising model code: The LPJ-GUESS model is delivered as a demonstration
version for use with a specific set of input data. It took longer than expected to
familiarize ourselves with the model code, investigate potential problems, and revise
the model code to get the model to correctly read in the climate and soils data used
by the model.

Parameter testing: It is critical to get the parameters right for a new suite of species
that have not been tested in LPJ-GUESS yet. We spent time investigating other
modeling studies and the published literature, as well as putting together a proposal
to get parameters from the TRY database. However, we had to step back and start
from the simplest set of parameters, those that have been developed for European
species, before testing new parameters. We tried to change all of the bioclimatic
limits at once and found that nothing would grow. So, we learned that an incremental
approach is needed to test each bioclimatic limit individually and reassess the values
we were using to develop these parameters.
Implications for effectively pulling off next steps
We have learned from Year One on the project that an incremental approach is most likely
to yield results. We plan to proceed first with a focused application of the model. Our goal in
year 2 of the project is to simplify our approach by focusing on some specific applications of the
model to whitebark pine. We plan to:
1. Use LPJ-GUESS to investigate the potential response of whitebark pine to climate alone,
without the influence of competition or disturbance (fire)
2. Examine differences in establishment, growth, and survival of whitebark pine under
future climate
3. Incrementally add competition to the whitebark pine model by including associated
species. First we would add whitebark pine’s main competitor, subalpine fir. Then, we
would incrementally add in other associated species, such as Engelmann spruce,
lodgepole pine, and Douglas-fir.
4. Implement management treatments, such as planting or thinning into the model
Products and Outcomes
Publications
Chang, T. A.J. Hansen, N. Piekielek. In Press. Patterns and variability of projected bioclimate
habitat for Pinus albicaulis in the Greater Yellowstone Ecosystem. PLOS One.
Stine, P., P. Hessburg, T. Spies, M. Kramer, C. Fettis, A. Hansen, J. Lehmkuhl, K. O'Hara, K.
Polivka, P. Singleton, S. Charnley, and A. Merschel. 2014. The Ecology and management
of Moist Mixed-conifer forests in Eastern Oregon and Washington: a synthesis of the
relevant biophysical science and implications for future land management. USDA Forest
Service PNW – GTR XXXX. In Press.
Hansen, A.J., L.B. Phillips, R. Dubayah, S. Goetz, and M. Hofton, 2014. Regional-scale
application of Lidar: Variation in forest canopy structure across the southeastern US,
Forest Ecology and Management 329 (2014) 214–226.
Goetz, S. J., Sun, M., Zolkos, S., Hansen, A., & Dubayah, R. (2014). The relative importance of
climate and vegetation properties on patterns of North American breeding bird species
richness. Environmental Research Letters, 9(3), 034013. doi:10.1088/17489326/9/3/034013.
Hansen, A.J., Piekielek, N., Davis, C., Haas, J., Theobald, D., Gross, J., Monahan, W., Olliff, T.,
Running, S., 2014. Exposure of U.S. National Parks to land use and climate change 19002100, Ecological Applications, 24(3), pp. 484-502.
Powell SL, Hansen AJ, Rodhouse TJ, Garrett LK, Betancourt JL, et al. (2013) Woodland
Dynamics at the Northern Range Periphery: A Challenge for Protected Area Management
in a Changing World. PLoS ONE 8(7): e70454. doi:10.1371/journal.pone.0070454
Unpublished reports
Ireland, K., Hansen, A. J., and Poulter, B. A comparison of modeling approaches of vegetation
dynamics under climate change: potential for ecosystem scale applications. Landscape
Biodiversity Lab, Montana State University, Bozeman. Available at:
Proposals
Funded
Ambrose, M. F., Poulter, B. Collecting plant trait data for fire modeling in the Greater
Yellowstone Ecosystem. Institute on Ecosystems, summer undergraduate research
program.
Hansen, A. (Principal), “Project /Proposal Title: Informing implementation of the Greater
Yellowstone Coordinating Committee’s Whitebark Pine Strategy”, North Central
Climate. $378,000 over 3 years.
Whitlock, C. (Principal), Hansen, A. (Co-Principal), "NC CSC Activity 2", Sponsored by
Colorado State University (COLSTA), University.$198,787.00
Hansen, A. , Garroutte, E. L. , "Using field data to validate satellite models of elk forage in the
Upper Yellowstone River Basin", Sponsored by University of Wyoming (WYOUNI),
University. $5,000.00. Hansen, A.J. and T. Chang. Physical disturbance model integration
with bioclimatic envelope modeling for conservation management under climate change.
NASA Earth and Space Science
Hansen, A.J. NC CSC Foundational Science: Impacts and Vulnerability. North Central Climate
Sciences Center. $491,000 for three years.
Fellowship 2014. $30,000 for one year.
Pending
Ireland, K. B., Poulter, B. Using simulation modeling to investigate vegetation response to
climate change at ecosystem scales. Proposal submitted 2/20/2014 to the USDA Agricultural
and Food Research Initiative (AFRI), National Institute of Food and Agriculture (NIFA)
Postdoctoral Fellowship Program. $149,928 for two years.
Hansen, A.J., D. Theobald, K. Mullan, S. Powell. Downscaling IPCC land use scenarios for
global change adaptation planning in mountainous environments. NASA Land Cover Land
Use Change Program. $760,000 for three years.
Submitted, not funded
Hansen, A. B. Poulter. Incorporating Climate Change and the Human Footprint into Wolverine
Connectivity Efforts in the Northern Rockies. Great Northern Landscape Conservation
Cooperative. $150,000 for one year.
Hansen, A. Collaborative Research EaSM-3: Determining the Potential Predictability of
Interannual-to-Decadal Regional Climate Impacts. National Science Foundation. $209,000
for three years.
Hansen, A.J. and E. Garroutte. Using field data to validate the relationship between MODISderived vegetation metrics and grassland phenology, biomass, and forage quality to improve
prediction under climate and land-use change. NASA Earth and Space Science Fellowship
2014. $30,000 for one year.
Presentations
Ireland, K. B., Emmett, K., Ambrose, M. F., Hansen, A. J., and Poulter, B. 2014. Calibrating a
dynamic vegetation model to simulate climate change impacts in Greater Yellowstone. To be
presented at: The 12th Biennial Scientific Conference on the Greater Yellowstone Ecosystem,
October 6-8, 2014.
Ireland, K. B., Hansen, A. J., Poulter, B. 2014. Modeling vegetation dynamics with LPJ-GUESS.
Presented at:


Workshop: “How can Vegetation Dynamics under Climate Change Best be Modeled at
Greater Ecosystem Scales?” Sept 23, 2013; Bozeman, MT
Workshop: Landscape Climate Change Vulnerability Project (LCCVP), whitebark team
meeting. Nov 25, 2014; Missoula, MT
Ambrose, M. F., Emmett, K., Poulter, B. 2014. Collecting plant trait data for fire modeling in the
Greater Yellowstone Ecosystem. Presented at: Institute on Ecosystems MSU Summer
Research Symposium. Aug 6, 2014; Bozeman, MT.
Chang, T., Hansen, A. J., Piekielek, N., and Olliff, T. 2013. Whitebark pine distribution models
under projected future climates in the GYA. Talk presented at Whitebark Pine Ecosystem
Foundation Annual Science Meeting, Bozeman MT.
Chang, T. and Hansen, A. J. 2013. A bioclimatic habitat suitability model of Pinus albicaulis in
the Greater Yellowstone Ecosystem. Poster presented at Institute of Ecosystem annual
summit, Helena MT.
Hansen, A.J., N. Piekielek, C. Davis, J. Haas, D. Theobald, J. Gross, W. Monahan, S. Running.
Exposure of US National Parks to Land Use and Climate Change 1900-2100. Society for
Conservation Biology Annual Meeting. Baltimore, WA. July 2013.
Hansen, A.J., S.W. Running. Focus 3: Understanding impacts of climate change through
ecosystem modeling and vulnerability assessment. Montana Institute on Ecosystems 2013
Science Summit, Helena, MT. Aug 2013.
Hansen, A.J., H. Naughton, E. Shanahan, N. Piekielek, T. Chang, T. Olliff. Informing
implementation of the Greater Yellowstone Coordinating Committee’s Whitebark Pine
Strategy based on climate sciences. Challenges of Whitebark Pine Restoration Meeting.
Whitebark Pine Foundation. Bozeman, MT. Sept 2013.
Hansen, A.J., Foundational Science: Ecological Vulnerability. North Central Climate Sciences
Center Program Review. Oct 2013.
Nelson, R., A.J. Hansen, H. Naughton, E. Shanahan, N. Piekielek, T. Chang, T. Olliff. Informing
implementation of the Greater Yellowstone Coordinating Committee’s Whitebark Pine
Strategy based on climate sciences. Poster. Montana Institute on Ecosystems 2013 Science
Summit, Helena, MT. Aug 2013.
Piekielek, NB, and AJ Hansen. 2013. Climate and land use change modify the patch dynamics of
green forage in the Upper Yellowstone River Basin. Montana NSF EPSCoR Summit.
Helena, MT.
Chang, T., A. Hansen, N. Piekielek. Estimating future suitable bioclimatic habitats for whitebark
pine in the Greater Yellowstone under projected climates. Society for Conservation Biology
North American Congress for Conservation Biology July 13-16, University of Montana,
Missoula, Montana
Hansen, A.J. Assessing ecological vulnerability to climate change across the Great Northern
LCC. Society for Conservation Biology North American Congress for Conservation Biology
July 13-16, University of Montana, Missoula, Montana.
Hansen, A.J. Landscape Climate Change Vulnerability Project. Greater Yellowstone
Coordinating Committee. Jackson, WY. March 2014.
Hansen, A.J. Landscape Climate Change Vulnerability Project. NASA Ecological Forecasting
annual meeting. Washington D.C. April 2014.
Hansen, A.J. Which tree species are most vulnerable to climate change in the Northern Rockies?
Climate Change Adaptation Regional Tribal Conference, Bozeman, MT. August 2014.
Symposia/Workshops/meetings
Wildland Ecosystems Under Climate Change: Pioneering Approaches to Science and
Management in the US Northern Rockies and Appalachians. Symposium at Society for
Conservation Biology North American Congress for Conservation Biology July 13-16,
University of Montana, Missoula, Montana.
Hansen, A. J. Landscape Climate Change Vulnerability Project (LCCVP), whitebark team
meeting. Nov 25, 2014; Missoula, MT. Meeting notes and presentations available at:
http://www.montana.edu/lccvp/pages/meetings.html
 Included presentation on and discussion of LPJ-GUESS work
Hansen, A. J. Landscape Climate Change Vulnerability Project (LCCVP) team meeting. July 15,
2014; Missoula, MT. Meeting notes and presentations available at:
http://www.montana.edu/lccvp/pages/meetings.html.
 Included discussion of LPJ-GUESS work
Hansen, A. J., Ireland, K. B. “How can Vegetation Dynamics under Climate Change Best be
Modeled at Greater Ecosystem Scales?” Sept 23, 2013; Bozeman, MT. Meeting notes and
presentations available at: http://www.montana.edu/lccvp/pages/meetings.html
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Appendix A. Species parameters used in initial test runs of LPJ-GUESS for the 16 dominant tree species in the Greater Yellowstone
Ecosystem. Values shown for the bioclimatic limits (tcmin_surv, tcmin_est, tcmax_est, twmin_est, and gdd5min_est) are two standard
deviations from the mean of each species’ distribution along climate gradient, as described in section 5b.
Definition
whether to include PFT in model run
PFT lifeform
biochemical pathway
optimal intercellular (Ci ) to ambient (Ca) CO2
ratio
Abies
Cercocarpus
lasiocarpa Acer glabrum
ledifolius
1
0
1
"tree"
"tree"
"tree"
"c3"
"c3"
"c3"
Juniperus
osteosperma
1
"tree"
"c3"
Juniperus
scopulorum
1
"tree"
"c3"
Picea
engelmannii
1
"tree"
"c3"
Pinus
albicaulis
1
"tree"
"c3"
Pinus
contorta
1
"tree"
"c3"
Parameter Name
include
lifeform
pathway
Units
0 or 1
"tree","grass"
"c3","c4"
lambda_max
fraction (-)
emax
mm yr-1
reprfrac
fraction (-)
wscal_min
fraction (-)
maximum rate of transpiration per year
fraction of annual net primary production used
for fruits, seeds, flowers
minimum soil moisture fraction before plant
responds (i.e. leaf shedding)
crownarea_max
m2 per
individual
maximum crown area
40
15
40
10
10
40
40
40
turnover_root
year-1
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
ltor_max
k_allom2
k_allom3
k_rp
fraction (-)
unitless (-)
unitless (-)
unitless (-)
rate at which individual replaces fine roots
leaf to root ratio under non-water stressed
conditions
constant in allometry equations
constant in allometry equations
constant in allometry equations
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
wooddens
cton_leaf
cton_root
cton_sap
kest_repr
kest_bg
kest_pres
litterme
rootdist
longevity
g cm-2
fraction (-)
fraction (-)
fraction (-)
unitless (-)
unitless (-)
unitless (-)
unitless (-)
fraction (-)
years
200
29
29
330
200
0.1
1
0.3
0.6 0.4
350
200
29
29
330
200
0.1
1
0.3
0.6 0.4
100
200
29
29
330
200
0.1
1
0.3
0.5 0.5
350
200
29
29
330
200
0.1
1
0.3
0.5 0.5
200
200
29
29
330
200
0.1
1
0.3
0.5 0.5
200
200
29
29
330
200
0.1
1
0.3
0.8 0.2
600
200
29
29
330
200
0.1
1
0.3
0.6 0.4
350
200
29
29
330
200
0.1
1
0.3
0.6 0.4
350
k_allom1
k_latosa
leaflong
unitless (-)
unitless (-)
years
wood density
carbon to nitrogen ratio in leaf
carbon to nitrogen ratio in leaf
carbon to nitrogen ratio in leaf
constant in establishment equations
constant in establishment equations
constant in establishment equations
moisture of extinction used in the fire model
fraction of roots in upper and lower soil layer
maximum tree age
allometry parameter that determines relationship
of stem diameter and crown area
leaf area to sapwood area ratio
leaf longevity
150
4000
3
200
4000
0.5
200
3000
0.5
150
1500
1.5
150
1500
1.5
150
4000
3.5
150
3000
2
150
3000
2
turnover_leaf
respcoeff
year-1
0-1
rate that individual replaces leaves
respiration coefficient
0.33
1
1
1
1
1
0.6667
1
0.6667
1
0.29
1
0.5
1
0.5
1
est_max
saplings yr-1
0.05
0.15
0.15
0.2
0.2
0.1
0.175
0.175
parff_min
W m-2
maximum establishment rate for seedlings
minimum PAR/light at forest floor for
establishment
350000
2000000
2000000
2500000
2500000
1175000
2250000
2250000
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
5
5
5
5
5
5
5
5
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.35
0.35
0.35
0.35
0.35
0.35
0.35
0.35
Parameter Name
alphar
Units
unitless (-)
Definition
Fulton (1991) recruitment shape parameter
greff_min
turnover_sap
gC m-2 yr-1
fraction (-)
growth efficiency parameter
sapwood to heartwood turnover rate
pstemp_min
o
C
pstemp_low
o
C
pstemp_high
o
pstemp_max
o
sla
Abies
Cercocarpus
lasiocarpa Acer glabrum
ledifolius
3
7
7
Juniperus
osteosperma
10
Juniperus
scopulorum
10
Picea
engelmannii
5
Pinus
albicaulis
8.5
Pinus
contorta
8.5
0.06
0.075
0.08
0.1
0.08
0.1
0.05
0.0625
0.07
0.0875
0.07
0.0875
0.04
0.05
0.06
0.075
minumum temperature for photosynthesis
23
23
-4
23
23
-4
9.5
9.5
low temperature for photosynthesis
15
15
10
15
15
10
12.5
12.5
C
high temperature for photosynthesis
25
25
25
25
25
25
25
25
C
maximum temperature for photosynthesis
38
38
35
38
38
35
36.5
36.5
m2 kgC-1
specific leaf area
9.3
12
9.3
10
10
9.3
9.3
9.3
gmin
mm s -1
0.3
0.5
0.5
0.5
0.5
0.3
0.3
0.3
phengdd5ramp
o
C-days
0
200
0
0
0
0
0
0
tcmin_surv
o
C
-14.8
-14.3
-16.1
-15.7
-18.2
-19.4
-19.9
-19.2
tcmin_est
o
C
-12.0
-8.1
-10.6
-8.0
-8.5
-12.1
-12.5
-11.8
tcmax_est
o
C
minimum conductance rate
phenological growing degree day sum on 5 deg
C base
minimum coldest month temperature for the last
20 years
minimum coldest month mean temperature for
the last 20 years
maximum coldest month mean temperature for
the last 20 years
-0.6
1.5
1.3
1.1
2.5
0.0
-2.1
0.2
twmin_est
o
C
5.3
8.6
7.8
11.9
9.2
5.2
4.8
5.9
gdd5min_est
o
C-days
316.5
1099.5
936.1
1617.6
1207.8
266.6
182.9
410.2
k_chilla
unitless (-)
0
0
0
0
0
0
0
0
k_chillb
unitless (-)
100
350
100
100
100
100
100
100
k_chillk
fireresist
intc
unitless (-)
fraction (-)
unitless (-)
0.05
0.1
0.06
0.05
0.1
0.02
0.05
0.3
0.02
0.05
0.4
0.02
0.05
0.4
0.02
0.05
0.1
0.06
0.05
0.2
0.06
0.05
0.2
0.06
drought_tolerance
fraction (-)
0.35
0.3
0.1
0.01
0.01
0.465
0.15
0.15
Phenology
unitless (-)
evergreen
summergreen
evergreen
evergreen
evergreen
evergreen
evergreen
evergreen
minimum warmest month mean temperature
minimum growing degree day sum on 5 deg C
base
constant in equation for budburst chilling
requirement
coefficient in equation for budburst chilling
requirement
exponent in equation for budburst chilling
requirement
fraction of individuals surviving fire
interception coefficient
minimum growing season fraction of available
soil water holding capacity in the first layer
summergreen, evergreen, or raingreen
phenology
Parameter Name
include
lifeform
pathway
Units
0 or 1
"tree","grass"
"c3","c4"
lambda_max
fraction (-)
-1
Definition
whether to include PFT in model run
PFT lifeform
biochemical pathway
optimal intercellular (Ci ) to ambient (Ca) CO2
ratio
Populus
balsamifera
ssp.
trichocarpa Populus spp.
1
1
"tree"
"tree"
"c3"
"c3"
Pinus flexilis
1
"tree"
"c3"
Pinus
ponderosa
1
"tree"
"c3"
Populus
angustifolia
1
"tree"
"c3"
Populus Pseudotsuga
tremuloides
menziesii
1
1
"tree"
"tree"
"c3"
"c3"
0.8
0.8
0.8
0.8
0.8
0.8
0.8
5
5
5
5
5
5
5
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.35
0.35
0.35
0.35
0.35
0.35
0.35
emax
mm yr
reprfrac
fraction (-)
wscal_min
fraction (-)
maximum rate of transpiration per year
fraction of annual net primary production used
for fruits, seeds, flowers
minimum soil moisture fraction before plant
responds (i.e. leaf shedding)
crownarea_max
m2 per
individual
maximum crown area
40
40
40
40
40
40
40
turnover_root
year-1
0.7
0.7
0.7
0.7
0.7
0.7
0.7
ltor_max
k_allom2
k_allom3
k_rp
fraction (-)
unitless (-)
unitless (-)
unitless (-)
rate at which individual replaces fine roots
leaf to root ratio under non-water stressed
conditions
constant in allometry equations
constant in allometry equations
constant in allometry equations
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
1
40
0.67
1.6
wooddens
cton_leaf
cton_root
cton_sap
kest_repr
kest_bg
kest_pres
litterme
rootdist
longevity
g cm-2
fraction (-)
fraction (-)
fraction (-)
unitless (-)
unitless (-)
unitless (-)
unitless (-)
fraction (-)
years
200
29
29
330
200
0.1
1
0.3
0.6 0.4
350
200
29
29
330
200
0.1
1
0.3
0.6 0.4
350
200
29
29
330
200
0.1
1
0.3
0.7 0.3
160
200
29
29
330
200
0.1
1
0.3
0.7 0.3
160
200
29
29
330
200
0.1
1
0.3
0.7 0.3
160
200
29
29
330
200
0.1
1
0.3
0.7 0.3
160
200
29
29
330
200
0.1
1
0.3
0.6 0.4
350
k_allom1
k_latosa
leaflong
unitless (-)
unitless (-)
years
wood density
carbon to nitrogen ratio in leaf
carbon to nitrogen ratio in leaf
carbon to nitrogen ratio in leaf
constant in establishment equations
constant in establishment equations
constant in establishment equations
moisture of extinction used in the fire model
fraction of roots in upper and lower soil layer
maximum tree age
allometry parameter that determines relationship
of stem diameter and crown area
leaf area to sapwood area ratio
leaf longevity
150
3000
2
150
3000
2
200
5000
0.5
200
5000
0.5
200
5000
0.5
200
5000
0.5
150
4000
3
turnover_leaf
respcoeff
year-1
0-1
rate that individual replaces leaves
respiration coefficient
0.5
1
0.5
1
1
1
1
1
1
1
1
1
0.33
1
est_max
saplings yr-1
0.175
0.175
0.2
0.2
0.2
0.2
0.05
parff_min
W m-2
maximum establishment rate for seedlings
minimum PAR/light at forest floor for
establishment
2250000
2250000
2500000
2500000
2500000
2500000
350000
Parameter Name
alphar
Units
unitless (-)
Definition
Fulton (1991) recruitment shape parameter
greff_min
turnover_sap
gC m-2 yr-1
fraction (-)
growth efficiency parameter
sapwood to heartwood turnover rate
pstemp_min
o
C
minumum temperature for photosynthesis
pstemp_low
o
C
low temperature for photosynthesis
pstemp_high
o
C
high temperature for photosynthesis
pstemp_max
o
C
maximum temperature for photosynthesis
sla
m2 kgC-1
gmin
mm s -1
phengdd5ramp
o
C-days
tcmin_surv
o
C
tcmin_est
o
C
tcmax_est
o
C
twmin_est
o
C
gdd5min_est
o
C-days
k_chilla
unitless (-)
k_chillb
unitless (-)
k_chillk
fireresist
intc
unitless (-)
fraction (-)
unitless (-)
drought_tolerance
fraction (-)
Phenology
unitless (-)
Populus
balsamifera
ssp.
trichocarpa Populus spp.
10
10
Pinus flexilis
8.5
Pinus
ponderosa
8.5
Populus
angustifolia
10
Populus Pseudotsuga
tremuloides
menziesii
10
3
0.07
0.0875
0.07
0.0875
0.08
0.1
0.08
0.1
0.08
0.1
0.08
0.1
0.04
0.05
9.5
9.5
23
23
23
23
23
12.5
12.5
15
15
15
15
15
25
25
25
25
25
25
25
36.5
36.5
38
38
38
38
38
specific leaf area
9.3
9.3
24.3
24.3
24.3
24.3
9.3
minimum conductance rate
phenological growing degree day sum on 5 deg
C base
minimum coldest month temperature for the last
20 years
minimum coldest month mean temperature for
the last 20 years
maximum coldest month mean temperature for
the last 20 years
0.3
0.3
0.5
0.5
0.5
0.5
0.3
0
0
200
200
200
200
0
-18.6
-18.4
-16.3
-16.8
-18.1
-17.2
-18.2
-11.5
-7.9
-9.7
-8.3
-9.7
-10.5
-10.7
1.1
2.0
4.0
1.1
3.3
0.7
1.0
5.5
9.1
9.7
9.0
7.4
7.9
6.9
403.5
1309.5
1258.6
1225.0
1063.9
857.3
649.8
0
0
0
0
0
0
0
100
100
350
350
350
350
100
0.05
0.2
0.06
0.05
0.2
0.06
0.05
0.2
0.02
0.05
0.2
0.02
0.05
0.2
0.02
0.05
0.2
0.02
0.05
0.1
0.06
0.15
0.15
0.4
0.4
0.4
0.4
0.35
evergreen
evergreen
summergreen
summergreen
summergreen
summergreen
evergreen
minimum warmest month mean temperature
minimum growing degree day sum on 5 deg C
base
constant in equation for budburst chilling
requirement
coefficient in equation for budburst chilling
requirement
exponent in equation for budburst chilling
requirement
fraction of individuals surviving fire
interception coefficient
minimum growing season fraction of available
soil water holding capacity in the first layer
summergreen, evergreen, or raingreen
phenology
Appendix B. Histograms of the distribution of the 16 dominant tree species in GYE by each of the bioclimatic limits used in LPJGUESS. We first tried using the mean of the distribution, but are currently testing using twice the standard deviation from the mean.