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 Units Description Variable Name 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 References: Allen CD, Breshears DD (1998) Drought-induced shift of a forest-woodland ecotone: rapid landscape response to climate variation. Proceeedings of the National Academies of Science 95:14839-14842 Allen CD et al. (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risk for forests. Forest Ecology and Management 259:660-684 Anderegg WRL, Kane JM, Anderegg LDL (2012) Consequences of widespread tree mortality triggered by drought and temperature stress. 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(2014) Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. Dataset. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Archive Center, Oak Ridge, Tennesse, USA. Date accessed: YYYY/MM/DD Temporal range: YYYY/MM/DD-YYYY/MM/DD. Spatial range: N=DD.DD, S=DD.DD, E=DDD.DD, W=DDD.DD. http://dx.doi.org/10.3334/ORNLDAAC/1219 van Mantgem PJ et al. (2009) Widespread increase of tree mortality rates in the western United States. Science 323:521-524 Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western U.S. forest wildfire activity. Science 313:940-943 White MA, Thornton PE, Running SW, Nemani RR (2000) Parameterization and sensitivity analysis of the Biome-BGC terrestrial ecosystem model: net primary production controls. Earth Interactions 4:1-85 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.