These data were used to estimate models relating climate and land cover to wetland densities and develop projections under climate and land use change. Data for model estimation were derived from historical climate data, estimates of hydrological processes based on the Variable Infiltration Capacity model, National Wetlands Inventory, and the National Land Cover Database. Wetland densities were based on observations from the Waterfowl Breeding Population and Habitat Survey. Projected climate conditions were derived from ten Global Climate Models, and projected changes in land use were based on an economic model of the effects of climate on land use transitions. These data support the following publication: Sofaer, H. R., Skagen, S. K., Barsugli, J. J., Rashford, B. S., Reese, G. C., Hoeting, J. A., Wood, A. W. and Noon, B. R. (2016), Projected wetland densities under climate change: habitat loss but little geographic shift in conservation strategy. Ecol Appl. Accepted Author Manuscript. doi:10.1890/15-0750.1.

This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 75.5%. A series of post-modeling steps brought the final number of land cover classes to 28.

This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 72.7%. A series of post-modeling steps brought the final number of land cover classes to 28.

This study had two objectives: first, to generate a landcover map for the Charles M. Russell Wildlife Refuge (CMR) emphasizing the distribution of land cover types in relation to greater sage grouse ( Centrocercus urophasianus) habitat needs, and second, to provide data that would allow a determination of whether results were better with SPOT imagery or Landsat 8 imagery. SPOT imagery is provided at a 10m pixel resolution, while Landsat 8 is at 30m. Results from this classification will allow managers to determine which resolution provides the accuracy needed for habitat planning and management.

Training points collected in the field between 2012 and 2013 were grouped into 18 classes: Forested Burn (66), Foothill Woodland Steppe Transition (73), Greasewood Flat (73), Greasewood Steppe (239), Greasewood Sage Steppe (277), Great Plains Badlands (166), Great Plains Riparian (255), Low Density Sage Steppe (776), Medium Density Sage Steppe (783), Mixed Grass Prairie (555), Mixed Grass Prairie Burned (278), Ponderosa Pine Woodland and Shrubland (512), Riparian Floodplain (223), Semi-Desert Grassland (103), Sparsely Vegetated Mixed Shrub (252), Silver Sage Flat (70) , Silver Sage Steppe (64), and Water (246). When insufficient field data were available for a class, we augmented it through photointerpretation of 15 cm aerial imagery, using expert knowledge and field experience to guide us. The final dataset had 5,011 training points.

We assessed the vulnerability of ecological processes and vegetation to climate change in the US Northern Rocky Mountains with a focus on the Greater Yellowstone Ecosystem. We found that climate has warmed substantially since 1900 while precipitation has increased. An index of aridity decreased until about 1980 and then increased slightly. Projected future climate indicates warming of about 3-7 degrees C by 2100 and a substantial increase in aridity, depending on climate scenario. Snow pack, soil moisture, runoff, and primary productivity are projected to decrease dramatically in summer under future climate scenarios, with snow pack and runoff declining annually. Habitat suitability for the four subalpine tree species is projected to contract dramatically while mid elevation tree species are projected to expand in area of suitable habitat. Across Greater Yellowstone, sagebrush communities are projected to expand and total forest cover is projected to decrease. The most vulnerable tree species are Whitebark pine and Mountain hemlock (found on the west-slope of the Rockies), both of which are projected to have 0-10% of current area of suitable habitat by 2100. These results represent the first comprehensive climate vulnerability assessment for the Northern Rockies and provide critical information for guiding the development and evaluation of climate adaptation strategies.

Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.

This data set provides the abiotic water balance variables used for species distribution modelings for Pinus albicaulis within the Greater Yellowstone Ecosystem