Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected to match meadow data and added to the GIS project window along with the meadow polygons. For context, aerial photography from ESRI’s basemap streaming services were also added to the ArcMap project. MODIS pixels that were at least half-covered by meadow polygon area were used in further ndvi analysis. Meadows that did not cover at least half of one MODIS pixel were eliminated from the analysis. In total, 17 Gallatin meadow sites (M1= 0; M2= 0; M3= 4; M4= 4; M5= 4; M6=5), covering at least half of 39 MODIS pixels (M1= 0; M2= 0; M3= 12; M4= 4; M5= 6; M6= 17), were used in further analysis and 16 Teton meadow sites (M1=3; M2=1; M3=4; M5=5; M6=3) covering at least half of 1252 MODIS pixels (M1= 105; M2= 1; M3= 25; M4=0 ; M5= 19; M6=1102), were used in further analysis.List of site names that were located, but not used in the NDVI analysis b/c they were too small: Gallatin – Porcupine Exclosure; Twin Cabin Willows; Figure 8; Taylor Fork; Teepee Sage; Daly North; Wapiti (Taylor Fork); Specimen Creek; Bacon Rind M1; Bacon Rind M4, Teepee wet; Daly SouthTeton – Cygnet Pond; Christian Pond; Willow Flats North; Willow Flats South; Sound of MusicMODIS preprocessing methods: MODIS MOD13Q1 representing observations of normalized difference vegetation index (NDVI) from March 2000 through December 2012 were downloaded from the USGS Land Processes Distributed Area Archive Center (LPDAAC) during the spring of 2013. Also downloaded at the same time were grids that described the estimated reliability of NDVI observations and the actual day of the year for each NDVI observation used in maximum compositing routines by the MODIS program. All MODIS data layers were reprojected to match meadow data layers.

Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected to match meadow data and added to the GIS project window along with the meadow polygons. For context, aerial photography from ESRI’s basemap streaming services were also added to the ArcMap project. MODIS pixels that were at least half-covered by meadow polygon area were used in further ndvi analysis. Meadows that did not cover at least half of one MODIS pixel were eliminated from the analysis. In total, 17 Gallatin meadow sites (M1= 0; M2= 0; M3= 4; M4= 4; M5= 4; M6=5), covering at least half of 39 MODIS pixels (M1= 0; M2= 0; M3= 12; M4= 4; M5= 6; M6= 17), were used in further analysis and 16 Teton meadow sites (M1=3; M2=1; M3=4; M5=5; M6=3) covering at least half of 1252 MODIS pixels (M1= 105; M2= 1; M3= 25; M4=0 ; M5= 19; M6=1102), were used in further analysis.List of site names that were located, but not used in the NDVI analysis b/c they were too small: Gallatin – Porcupine Exclosure; Twin Cabin Willows; Figure 8; Taylor Fork; Teepee Sage; Daly North; Wapiti (Taylor Fork); Specimen Creek; Bacon Rind M1; Bacon Rind M4, Teepee wet; Daly SouthTeton – Cygnet Pond; Christian Pond; Willow Flats North; Willow Flats South; Sound of MusicMODIS preprocessing methods: MODIS MOD13Q1 representing observations of normalized difference vegetation index (NDVI) from March 2000 through December 2012 were downloaded from the USGS Land Processes Distributed Area Archive Center (LPDAAC) during the spring of 2013. Also downloaded at the same time were grids that described the estimated reliability of NDVI observations and the actual day of the year for each NDVI observation used in maximum compositing routines by the MODIS program. All MODIS data layers were reprojected to match meadow data layers.All negative NDVI values which are thought to correspond to standing water, partial snow-cover or wet bare soil were set to ‘NA’values (Huete, Justice and van Leeuwen 1999)The following steps were used to remove any conifer/evergreen signal from NDVI data and are based on an understanding that each pixel has a different “background”(i.e. no-growth) greenness against which any seasonal change must be compared (Beever et al. 2013; Piekielek and Hansen 2013). These methods also help to eliminate long gaps in data that can allow smoothing algorithms to interpolate beyond the valid range of data (in the case of NDVI from 0 –1):Annual minimum NDVI values that were labeled as high-quality were identified in the 13 year time-series.The bottom first percentileof a distribution of minimum values was used as the “background”value to fill-in missing values when the target was identified as being under snow cover.All NDVI values identified by pixel-reliability grids as being of high or marginal quality werepreserved and snow-covered pixels and dates were filled in with each pixels “background”value.Composite day of year grids were used to identify the actual date from which the 16-day maximum composite NDVI value came.Each pixel’s entire time-series (2000 –2012) was smoothed in a weighted regression framework against time using smoothing splines (Chambers and Hastie 1992). NDVI data of marginal quality and snow-covered background values contributed half the weight to final smoothed values as did high-quality values. The final smoothed values were used to interpolate the time-series to a daily time-step and to record annual NDVI amplitudes. Land surface phenology metrics were calculated as follows:Start of season (SOS) –the first annual day of year when smoothed NDVI crosses half of its annual amplitude (White et al. 2009).End of Season (EOS) –the last day of year when smoothed NDVI crosses half of its annual amplitude.Maximum annual NDVI (MAX) –the highest annual smoothed NDVITiming of annual maximum (DOYMAX) –the smoothed day of year when NDVI reaches its maximum valueEstimated annual productivity (INDVI) –the integrated area under the growing season (SOS to EOS) NDVI curve (Goward et al 1985).

This dataset represents the area in the Greater Yellowstone Ecosystem prioritized for different whitebark pine(Pinus albicaulis) management activities, summarized by climate suitability zones. This data was developed for use in a landscape simulation modeling study aimed at evaluating how well alternative management strategies maintain whitebark pine populations under historical climate and future climate conditions. For the study, we developed three spatial management alternatives for whitebark pine in the Greater Yellowstone Ecosystem representing no active management, current management, and climate-informed management. These management alternatives were implemented in the simulaton model FireBGCv2 under historical climate and three future climate change scenarios - the HadGEM-ES, CESM1-CAM5, and CNRM-CM5 Global Circulation Models under the RCP 8.5 emissions scenario. We worked with the Greater Yellowstone Coordinating Committee's (GYCC) Whitebark Pine Subcommittee to develop this spatial representation of their current management strategy. The treatments mapped represent a set of the treatments recommended in the GYCC Whitebark Pine 2011 Strategy document and include planting blister-rust resistant whitebark pine seedlings, competition removal thinning, wildland fire use and prescribed fire, and protection from mountain pine beetles using verbenone and carbaryl. We used historical and future projections of climate suitability based on species distribution models for whitebark pine (Chang et al. 2014) to map zones of core, deteriorating, and future whitebark pine habitat. Core zones were those areas that are currently suitable for whitebark and remain suitable in the future. Deteriorating zones were where the climatic conditions for whitebark pine are expected to decline. Future zones were areas that are projected to become newly suitable for whitebark pine. We then overlaid our climate zones for whitebark pine with similar projections of future climate suitability for all of whitebark pine’s competitors - Engelmann spruce, subalpine fir, lodgepole pine, and Douglas-fir (Piekielek et al. 2015. We discussed the different combinations of climate suitability zones (core, deteriorating, future) and potential future level of competition (low or high) from other species with the GYCC Whitebark Pine Subcommittee to determine which management activities should be prioritized within each management zone. The result is a map of management zones where different activities are prioritized to meet the goal of maintaining whitebark pine populations. This was used to determine which treatments would be implemented spatially during the simulation modeling, dependent upon additional criteria related to simulated stand-level conditions. In this dataset, we used the resulting map of spatially prioritized management activities to summarize the area prioritized for each management activity that fell within Core, Deteriorating, and Future climate suitability zones

This dataset represents the area in the Greater Yellowstone Ecosystem prioritized for different whitebark pine(Pinus albicaulis) management activities, summarized by land classes. This data was developed for use in a landscape simulation modeling study aimed at evaluating how well alternative management strategies maintain whitebark pine populations under historical climate and future climate conditions. For the study, we developed three spatial management alternatives for whitebark pine in the Greater Yellowstone Ecosystem representing no active management, current management, and climate-informed management. These management alternatives were implemented in the simulaton model FireBGCv2 under historical climate and three future climate change scenarios - the HadGEM-ES, CESM1-CAM5, and CNRM-CM5 Global Circulation Models under the RCP 8.5 emissions scenario. We worked with the Greater Yellowstone Coordinating Committee's (GYCC) Whitebark Pine Subcommittee to develop this spatial representation of their current management strategy. The treatments mapped represent a set of the treatments recommended in the GYCC Whitebark Pine 2011 Strategy document and include planting blister-rust resistant whitebark pine seedlings, competition removal thinning, wildland fire use and prescribed fire, and protection from mountain pine beetles using verbenone and carbaryl. We used historical and future projections of climate suitability based on species distribution models for whitebark pine (Chang et al. 2014) to map zones of core, deteriorating, and future whitebark pine habitat. Core zones were those areas that are currently suitable for whitebark and remain suitable in the future. Deteriorating zones were where the climatic conditions for whitebark pine are expected to decline. Future zones were areas that are projected to become newly suitable for whitebark pine. We then overlaid our climate zones for whitebark pine with similar projections of future climate suitability for all of whitebark pine’s competitors - Engelmann spruce, subalpine fir, lodgepole pine, and Douglas-fir (Piekielek et al. 2015. We discussed the different combinations of climate suitability zones (core, deteriorating, future) and potential future level of competition (low or high) from other species with the GYCC Whitebark Pine Subcommittee to determine which management activities should be prioritized within each management zone. The result is a map of management zones where different activities are prioritized to meet the goal of maintaining whitebark pine populations. This was used to determine which treatments would be implemented spatially during the simulation modeling, dependent upon additional criteria related to simulated stand-level conditions. In this dataset, we used the resulting map of spatially prioritized management activities to summarize the area prioritized for each management activity that fell within different land classifications (mutliple use forests, National Park Service lands, Wilderness lands, and non-federal lands).

Natural resource managers face the need to develop strategies to adapt to projected future climates. Few existing climate adaptation frameworks prescribe where to place management actions to be most effective under anticipated future climate conditions.  We developed an approach to spatially allocate climate adaptation actions and applied the method to whitebark pine (WBP; Pinus albicaulis) in the Greater Yellowstone Ecosystem (GYE).  WBP is expected to be vulnerable to climate-mediated shifts in suitable habitat, pests, pathogens, and fire. We worked with a team of biologists and managers to identify management actions aimed at mitigating climate impacts to WBP. Identified actions were spatially allocated across the GYE under two management strategies: (1) current management and (2) climate-informed management which used projected climate suitability for WBP and competing tree species to place management actions.  The current management strategy reflected current legal, policy and access contraints, such as restricting active management in Wilderness and remote locations, while the climate-informed management strategy was designed to maximize preservation of WBP forests regardless of such constraints. Thus, the climate-informed strategy highlighted how the spatial location of management actions might need to shift to most effectively maintain WBP forests under future climate conditions. The spatial distribution of actions and area treated differed among the current and climate-informed management strategies, with 33-60% more wilderness area prioritized for action under climate-informed management. High priority areas for implementing management actions include the 1-8% of the GYE where current and climate-informed management agreed, since this is where actions are most likely to be successful in the long-term and where current management permits implementation. Areas where climate-informed strategies agreed with one another but not with current management (6-22% of the GYE) are potential locations for experimental testing and monitoring of management actions. Our method for prioritizing locations for climate-adaptation actions is applicable to any species for which information regarding climate vulnerability and climate-mediated risk factors is available.

Sagebrush steppe is one of the most widely distributed ecosystems in North America. Found in eleven western states, this important yet fragile ecosystem is dominated by sagebrush, but also contains a diversity of native shrubs, grasses, and flowering plants. It provides critical habitat for wildlife like pronghorn and threatened species such as the greater sage-grouse, and is grazed by livestock on public and private lands. However, this landscape is increasingly threatened by shifts in wildfire patterns, the spread of invasive grasses, and changing climate conditions. While sagebrush is slow to recover after fires, non-native grasses such as cheatgrass thrive in post-fire conditions and the spread of these species can increase the frequency and intensity of wildfires. These changes to the sagebrush ecosystems have implications for big game, threatened wildlife, and ranching. To address this growing concern, resource managers will often try to limit the spread of exotic grasses after fire events by applying herbicides, or will help native species recover through seeding or planting. However, these treatments have mixed results, and poor success is often attributed to droughts, which make it more difficult for seeds and native plants to survive; to the limited amount of time in which these treatments can be applied (usually in the first year after a fire); or because the seeds or plants used aren’t adapted to the environmental conditions of the location where they’re applied. The goal of this project is to improve our understanding of the factors that affect post-fire treatment success. Researchers will use data collected from more than 300 fires over the last 40 years, after which treatments were applied. They will identify the impacts of drought on those treatments, how incorporating information on drought forecasts or extending the period over which treatments are applied could have altered the outcomes, and how managers can better select plant material that will be more adaptable to the conditions of planting locations. Addressing this knowledge gap has been identified as a high priority in the DOI Integrated Rangeland Fire Management Strategy, by the BLM Emergency Stabilization and Rehabilitation Program, and by state management agencies in the West. The results of this project will support adaptive management of sagebrush ecosystems, which will be critical if these ecologically and economically important landscapes are to be maintained into the future. This project was jointly funded by the Southwest, Northwest, and North Central CASCs.

Big sagebrush plant communities are important and widespread in western North America and are crucial for meeting long-term conservation goals for greater sage-grouse and other wildlife of conservation concern. Yet wildfire is increasing in the West, turning biodiverse, shrub-based ecosystems dominated by sagebrush into grasslands containing invasive species such as cheatgrass and less overall plant and animal diversity. These transformations negatively impact people and ecosystems by reducing habitat quality for wildlife and the aesthetic value of the landscape.   Understanding how sagebrush communities are already responding and will continue to respond to changes in wildfire, invasive species, and climate is a priority for managers in the West. However, we currently know very little about how invasive grasses and fire will affect big sagebrush rangelands in the future and whether all big sagebrush ecosystems in the western U.S. will be negatively affected. In collaboration with the U.S. Fish and Wildlife Service, this project aims to fill this gap by assessing the vulnerability of sagebrush plant communities to future changes in climate, wildfire, and invasive grasses. To do this, researchers will predict sagebrush plant community responses to climate variability, wildfire-driven increases in invasive grasses,and grazing pressure at 200 sites across the West that are particularly important for the greater sage-grouse. They will then produce maps of what future sagebrush plant communities could look like by mid- and late-century for local and regional land and wildlife managers. Additionally, a web interface will be made available for managers to view this information, allowing them to access the data.   This work will provide resource and land managers with maps of what future plant communities will look like and will focus on aspects of the plant community that are most relevant for range-wide management priorities. A better understanding of the effects that climate, wildfire, and invasive grasses could have on sagebrush habitats will help managers more efficiently target their conservation efforts on areas that are projected to be the least vulnerable to these threats.

Pinyon pine woodlands are among the most widespread and iconic vegetation types in the western United States and support recreation, resource extraction, grazing, and cultural enrichment. However, severe drought conditions have recently caused dramatic mortality of pinyon pines, creating concern about the long-term impact of increasing aridity on the viability of pinyon woodlands. Ecological transformations, or regime shifts, are rapid reorganizations of an ecosystem’s species composition, governing processes, and functions.   The goal of this project is to investigate ecological transformation across the Western U.S, characterize the environmental drivers of these changes in vegetation, and apply those insights to map contemporary transformation risk – across pinyon pine woodlands and other vegetation types in the U.S. West. Researchers will do this by employing data from existing paleorecords and statistical analysis of recent observations to understand what controls ecological transformations and assess the potential for 21st century transformation in pinyon pine woodlands.   The primary product will be maps depicting the risk of existing pinyon and other systems transforming to other vegetation types. These maps will help resource managers understand the potential for important change in pinyon resources, helping them maximize the long-term effectiveness of their conservation and restoration management strategies.

Fossil fuel and agriculture have increased atmospheric concentrations of the greenhouse gases carbon dioxide and methane, which have caused global air temperature to increase by almost 1- degree Celsius. In the absence of climate mitigation, over the next century human-driven climate change is expected to increase temperatures from pre-industrial levels by more than 2-degrees. Understanding the consequences of climate change on ecosystems and the services they provide are critical for guiding land management activities that aim to improve resiliency and to prevent species losses. Here we evaluated how sagebrush ecosystems in the Western United States respond to climate change by using multiple climate projections and ecosystem modeling approaches to assess uncertainty and to identify future areas of field and experimental research. We find that in the absence of changes in fire, invasive species, and habitat loss, that sagebrush is tolerant of both low moisture levels and high air temperatures, and that climate change will impact the southern extent of its range most significantly. Process-based models, which consider the effects of carbon dioxide on leaf photosynthesis and water exchange show potential increases in the growth of sagebrush into the 21st century. Compared to field observations, there is a need to further constrain how sagebrush allocates carbon to roots, stems and foliage, and how these processes respond to water limitation. Agreement between modeling approaches that sagebrush is tolerant to higher air temperatures suggests that land managers should consider enhancing resilience of these systems through fire and invasive species management strategies.